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GNU GENERAL PUBLIC LICENSE |
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Version 3, 29 June 2007 |
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A contributor's "essential patent claims" are all patent claims |
||||
owned or controlled by the contributor, whether already acquired or |
||||
hereafter acquired, that would be infringed by some manner, permitted |
||||
by this License, of making, using, or selling its contributor version, |
||||
but do not include claims that would be infringed only as a |
||||
consequence of further modification of the contributor version. For |
||||
purposes of this definition, "control" includes the right to grant |
||||
patent sublicenses in a manner consistent with the requirements of |
||||
this License. |
||||
|
||||
Each contributor grants you a non-exclusive, worldwide, royalty-free |
||||
patent license under the contributor's essential patent claims, to |
||||
make, use, sell, offer for sale, import and otherwise run, modify and |
||||
propagate the contents of its contributor version. |
||||
|
||||
In the following three paragraphs, a "patent license" is any express |
||||
agreement or commitment, however denominated, not to enforce a patent |
||||
(such as an express permission to practice a patent or covenant not to |
||||
sue for patent infringement). To "grant" such a patent license to a |
||||
party means to make such an agreement or commitment not to enforce a |
||||
patent against the party. |
||||
|
||||
If you convey a covered work, knowingly relying on a patent license, |
||||
and the Corresponding Source of the work is not available for anyone |
||||
to copy, free of charge and under the terms of this License, through a |
||||
publicly available network server or other readily accessible means, |
||||
then you must either (1) cause the Corresponding Source to be so |
||||
available, or (2) arrange to deprive yourself of the benefit of the |
||||
patent license for this particular work, or (3) arrange, in a manner |
||||
consistent with the requirements of this License, to extend the patent |
||||
license to downstream recipients. "Knowingly relying" means you have |
||||
actual knowledge that, but for the patent license, your conveying the |
||||
covered work in a country, or your recipient's use of the covered work |
||||
in a country, would infringe one or more identifiable patents in that |
||||
country that you have reason to believe are valid. |
||||
|
||||
If, pursuant to or in connection with a single transaction or |
||||
arrangement, you convey, or propagate by procuring conveyance of, a |
||||
covered work, and grant a patent license to some of the parties |
||||
receiving the covered work authorizing them to use, propagate, modify |
||||
or convey a specific copy of the covered work, then the patent license |
||||
you grant is automatically extended to all recipients of the covered |
||||
work and works based on it. |
||||
|
||||
A patent license is "discriminatory" if it does not include within |
||||
the scope of its coverage, prohibits the exercise of, or is |
||||
conditioned on the non-exercise of one or more of the rights that are |
||||
specifically granted under this License. You may not convey a covered |
||||
work if you are a party to an arrangement with a third party that is |
||||
in the business of distributing software, under which you make payment |
||||
to the third party based on the extent of your activity of conveying |
||||
the work, and under which the third party grants, to any of the |
||||
parties who would receive the covered work from you, a discriminatory |
||||
patent license (a) in connection with copies of the covered work |
||||
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||||
for and in connection with specific products or compilations that |
||||
contain the covered work, unless you entered into that arrangement, |
||||
or that patent license was granted, prior to 28 March 2007. |
||||
|
||||
Nothing in this License shall be construed as excluding or limiting |
||||
any implied license or other defenses to infringement that may |
||||
otherwise be available to you under applicable patent law. |
||||
|
||||
12. No Surrender of Others' Freedom. |
||||
|
||||
If conditions are imposed on you (whether by court order, agreement or |
||||
otherwise) that contradict the conditions of this License, they do not |
||||
excuse you from the conditions of this License. If you cannot convey a |
||||
covered work so as to satisfy simultaneously your obligations under this |
||||
License and any other pertinent obligations, then as a consequence you may |
||||
not convey it at all. For example, if you agree to terms that obligate you |
||||
to collect a royalty for further conveying from those to whom you convey |
||||
the Program, the only way you could satisfy both those terms and this |
||||
License would be to refrain entirely from conveying the Program. |
||||
|
||||
13. Use with the GNU Affero General Public License. |
||||
|
||||
Notwithstanding any other provision of this License, you have |
||||
permission to link or combine any covered work with a work licensed |
||||
under version 3 of the GNU Affero General Public License into a single |
||||
combined work, and to convey the resulting work. The terms of this |
||||
License will continue to apply to the part which is the covered work, |
||||
but the special requirements of the GNU Affero General Public License, |
||||
section 13, concerning interaction through a network will apply to the |
||||
combination as such. |
||||
|
||||
14. Revised Versions of this License. |
||||
|
||||
The Free Software Foundation may publish revised and/or new versions of |
||||
the GNU General Public License from time to time. Such new versions will |
||||
be similar in spirit to the present version, but may differ in detail to |
||||
address new problems or concerns. |
||||
|
||||
Each version is given a distinguishing version number. If the |
||||
Program specifies that a certain numbered version of the GNU General |
||||
Public License "or any later version" applies to it, you have the |
||||
option of following the terms and conditions either of that numbered |
||||
version or of any later version published by the Free Software |
||||
Foundation. If the Program does not specify a version number of the |
||||
GNU General Public License, you may choose any version ever published |
||||
by the Free Software Foundation. |
||||
|
||||
If the Program specifies that a proxy can decide which future |
||||
versions of the GNU General Public License can be used, that proxy's |
||||
public statement of acceptance of a version permanently authorizes you |
||||
to choose that version for the Program. |
||||
|
||||
Later license versions may give you additional or different |
||||
permissions. However, no additional obligations are imposed on any |
||||
author or copyright holder as a result of your choosing to follow a |
||||
later version. |
||||
|
||||
15. Disclaimer of Warranty. |
||||
|
||||
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY |
||||
APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT |
||||
HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY |
||||
OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, |
||||
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR |
||||
PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM |
||||
IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF |
||||
ALL NECESSARY SERVICING, REPAIR OR CORRECTION. |
||||
|
||||
16. Limitation of Liability. |
||||
|
||||
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING |
||||
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS |
||||
THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY |
||||
GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE |
||||
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF |
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DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD |
||||
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), |
||||
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF |
||||
SUCH DAMAGES. |
||||
|
||||
17. Interpretation of Sections 15 and 16. |
||||
|
||||
If the disclaimer of warranty and limitation of liability provided |
||||
above cannot be given local legal effect according to their terms, |
||||
reviewing courts shall apply local law that most closely approximates |
||||
an absolute waiver of all civil liability in connection with the |
||||
Program, unless a warranty or assumption of liability accompanies a |
||||
copy of the Program in return for a fee. |
||||
|
||||
END OF TERMS AND CONDITIONS |
||||
|
||||
How to Apply These Terms to Your New Programs |
||||
|
||||
If you develop a new program, and you want it to be of the greatest |
||||
possible use to the public, the best way to achieve this is to make it |
||||
free software which everyone can redistribute and change under these terms. |
||||
|
||||
To do so, attach the following notices to the program. It is safest |
||||
to attach them to the start of each source file to most effectively |
||||
state the exclusion of warranty; and each file should have at least |
||||
the "copyright" line and a pointer to where the full notice is found. |
||||
|
||||
<one line to give the program's name and a brief idea of what it does.> |
||||
Copyright (C) <year> <name of author> |
||||
|
||||
This program is free software: you can redistribute it and/or modify |
||||
it under the terms of the GNU General Public License as published by |
||||
the Free Software Foundation, either version 3 of the License, or |
||||
(at your option) any later version. |
||||
|
||||
This program is distributed in the hope that it will be useful, |
||||
but WITHOUT ANY WARRANTY; without even the implied warranty of |
||||
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the |
||||
GNU General Public License for more details. |
||||
|
||||
You should have received a copy of the GNU General Public License |
||||
along with this program. If not, see <https://www.gnu.org/licenses/>. |
||||
|
||||
Also add information on how to contact you by electronic and paper mail. |
||||
|
||||
If the program does terminal interaction, make it output a short |
||||
notice like this when it starts in an interactive mode: |
||||
|
||||
<program> Copyright (C) <year> <name of author> |
||||
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. |
||||
This is free software, and you are welcome to redistribute it |
||||
under certain conditions; type `show c' for details. |
||||
|
||||
The hypothetical commands `show w' and `show c' should show the appropriate |
||||
parts of the General Public License. Of course, your program's commands |
||||
might be different; for a GUI interface, you would use an "about box". |
||||
|
||||
You should also get your employer (if you work as a programmer) or school, |
||||
if any, to sign a "copyright disclaimer" for the program, if necessary. |
||||
For more information on this, and how to apply and follow the GNU GPL, see |
||||
<https://www.gnu.org/licenses/>. |
||||
|
||||
The GNU General Public License does not permit incorporating your program |
||||
into proprietary programs. If your program is a subroutine library, you |
||||
may consider it more useful to permit linking proprietary applications with |
||||
the library. If this is what you want to do, use the GNU Lesser General |
||||
Public License instead of this License. But first, please read |
||||
<https://www.gnu.org/licenses/why-not-lgpl.html>. |
||||
@ -0,0 +1,72 @@
|
||||
ComfyUI |
||||
======= |
||||
A powerful and modular stable diffusion GUI. |
||||
----------- |
||||
 |
||||
|
||||
This ui will let you design and execute advanced stable diffusion pipelines using a graph/nodes/flowchart based interface. |
||||
|
||||
|
||||
# Installing |
||||
|
||||
Git clone this repo. |
||||
|
||||
Put your SD checkpoints (the huge ckpt/safetensors files) in: models/checkpoints |
||||
|
||||
Put your VAE in: models/vae |
||||
|
||||
At the time of writing this pytorch has issues with python versions higher than 3.10 so make sure your python/pip versions are 3.10. |
||||
|
||||
### AMD |
||||
AMD users can install rocm and pytorch with pip if you don't have it already installed: |
||||
|
||||
```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/rocm5.2"``` |
||||
|
||||
### NVIDIA |
||||
|
||||
Nvidia users should install Xformers. |
||||
|
||||
### Dependencies |
||||
|
||||
Install the dependencies: |
||||
|
||||
```pip install -r requirements.txt``` |
||||
|
||||
|
||||
|
||||
# Running |
||||
|
||||
```python main.py``` |
||||
|
||||
|
||||
# Notes |
||||
|
||||
Only parts of the graph that have an output with all the correct inputs will be executed. |
||||
|
||||
Only parts of the graph that change from each execution to the next will be executed, if you submit the same graph twice only the first will be executed. If you change the last part of the graph only the part you changed and the part that depends on it will be executed. |
||||
|
||||
Dragging a generated png on the webpage or loading one will give you the full workflow including seeds that were used to create it. |
||||
|
||||
You can use () to change emphasis of a word or phrase like: (good code:1.2) or (bad code:0.8). The default emphasis for () is 1.1. To use () characters in your actual prompt escape them like \\( or \\). |
||||
|
||||
### Fedora |
||||
|
||||
To get python 3.10 on fedora: |
||||
```dnf install python3.10``` |
||||
|
||||
Then you can: |
||||
|
||||
```python3.10 -m ensurepip``` |
||||
|
||||
This will let you use: pip3.10 to install all the dependencies. |
||||
|
||||
|
||||
# QA |
||||
|
||||
### Why did you make this? |
||||
|
||||
I wanted to learn how Stable Diffusion worked in detail. I also wanted something clean and powerful that would let me experiment with SD without restrictions. |
||||
|
||||
### Who is this for? |
||||
|
||||
This is for anyone that wants to make complex workflows with SD or that wants to learn more how SD works. The interface follows closely how SD works and the code should be much more simple to understand than other SD UIs. |
||||
@ -0,0 +1,105 @@
|
||||
from functools import reduce |
||||
import math |
||||
import operator |
||||
|
||||
import numpy as np |
||||
from skimage import transform |
||||
import torch |
||||
from torch import nn |
||||
|
||||
|
||||
def translate2d(tx, ty): |
||||
mat = [[1, 0, tx], |
||||
[0, 1, ty], |
||||
[0, 0, 1]] |
||||
return torch.tensor(mat, dtype=torch.float32) |
||||
|
||||
|
||||
def scale2d(sx, sy): |
||||
mat = [[sx, 0, 0], |
||||
[ 0, sy, 0], |
||||
[ 0, 0, 1]] |
||||
return torch.tensor(mat, dtype=torch.float32) |
||||
|
||||
|
||||
def rotate2d(theta): |
||||
mat = [[torch.cos(theta), torch.sin(-theta), 0], |
||||
[torch.sin(theta), torch.cos(theta), 0], |
||||
[ 0, 0, 1]] |
||||
return torch.tensor(mat, dtype=torch.float32) |
||||
|
||||
|
||||
class KarrasAugmentationPipeline: |
||||
def __init__(self, a_prob=0.12, a_scale=2**0.2, a_aniso=2**0.2, a_trans=1/8): |
||||
self.a_prob = a_prob |
||||
self.a_scale = a_scale |
||||
self.a_aniso = a_aniso |
||||
self.a_trans = a_trans |
||||
|
||||
def __call__(self, image): |
||||
h, w = image.size |
||||
mats = [translate2d(h / 2 - 0.5, w / 2 - 0.5)] |
||||
|
||||
# x-flip |
||||
a0 = torch.randint(2, []).float() |
||||
mats.append(scale2d(1 - 2 * a0, 1)) |
||||
# y-flip |
||||
do = (torch.rand([]) < self.a_prob).float() |
||||
a1 = torch.randint(2, []).float() * do |
||||
mats.append(scale2d(1, 1 - 2 * a1)) |
||||
# scaling |
||||
do = (torch.rand([]) < self.a_prob).float() |
||||
a2 = torch.randn([]) * do |
||||
mats.append(scale2d(self.a_scale ** a2, self.a_scale ** a2)) |
||||
# rotation |
||||
do = (torch.rand([]) < self.a_prob).float() |
||||
a3 = (torch.rand([]) * 2 * math.pi - math.pi) * do |
||||
mats.append(rotate2d(-a3)) |
||||
# anisotropy |
||||
do = (torch.rand([]) < self.a_prob).float() |
||||
a4 = (torch.rand([]) * 2 * math.pi - math.pi) * do |
||||
a5 = torch.randn([]) * do |
||||
mats.append(rotate2d(a4)) |
||||
mats.append(scale2d(self.a_aniso ** a5, self.a_aniso ** -a5)) |
||||
mats.append(rotate2d(-a4)) |
||||
# translation |
||||
do = (torch.rand([]) < self.a_prob).float() |
||||
a6 = torch.randn([]) * do |
||||
a7 = torch.randn([]) * do |
||||
mats.append(translate2d(self.a_trans * w * a6, self.a_trans * h * a7)) |
||||
|
||||
# form the transformation matrix and conditioning vector |
||||
mats.append(translate2d(-h / 2 + 0.5, -w / 2 + 0.5)) |
||||
mat = reduce(operator.matmul, mats) |
||||
cond = torch.stack([a0, a1, a2, a3.cos() - 1, a3.sin(), a5 * a4.cos(), a5 * a4.sin(), a6, a7]) |
||||
|
||||
# apply the transformation |
||||
image_orig = np.array(image, dtype=np.float32) / 255 |
||||
if image_orig.ndim == 2: |
||||
image_orig = image_orig[..., None] |
||||
tf = transform.AffineTransform(mat.numpy()) |
||||
image = transform.warp(image_orig, tf.inverse, order=3, mode='reflect', cval=0.5, clip=False, preserve_range=True) |
||||
image_orig = torch.as_tensor(image_orig).movedim(2, 0) * 2 - 1 |
||||
image = torch.as_tensor(image).movedim(2, 0) * 2 - 1 |
||||
return image, image_orig, cond |
||||
|
||||
|
||||
class KarrasAugmentWrapper(nn.Module): |
||||
def __init__(self, model): |
||||
super().__init__() |
||||
self.inner_model = model |
||||
|
||||
def forward(self, input, sigma, aug_cond=None, mapping_cond=None, **kwargs): |
||||
if aug_cond is None: |
||||
aug_cond = input.new_zeros([input.shape[0], 9]) |
||||
if mapping_cond is None: |
||||
mapping_cond = aug_cond |
||||
else: |
||||
mapping_cond = torch.cat([aug_cond, mapping_cond], dim=1) |
||||
return self.inner_model(input, sigma, mapping_cond=mapping_cond, **kwargs) |
||||
|
||||
def set_skip_stages(self, skip_stages): |
||||
return self.inner_model.set_skip_stages(skip_stages) |
||||
|
||||
def set_patch_size(self, patch_size): |
||||
return self.inner_model.set_patch_size(patch_size) |
||||
@ -0,0 +1,110 @@
|
||||
from functools import partial |
||||
import json |
||||
import math |
||||
import warnings |
||||
|
||||
from jsonmerge import merge |
||||
|
||||
from . import augmentation, layers, models, utils |
||||
|
||||
|
||||
def load_config(file): |
||||
defaults = { |
||||
'model': { |
||||
'sigma_data': 1., |
||||
'patch_size': 1, |
||||
'dropout_rate': 0., |
||||
'augment_wrapper': True, |
||||
'augment_prob': 0., |
||||
'mapping_cond_dim': 0, |
||||
'unet_cond_dim': 0, |
||||
'cross_cond_dim': 0, |
||||
'cross_attn_depths': None, |
||||
'skip_stages': 0, |
||||
'has_variance': False, |
||||
}, |
||||
'dataset': { |
||||
'type': 'imagefolder', |
||||
}, |
||||
'optimizer': { |
||||
'type': 'adamw', |
||||
'lr': 1e-4, |
||||
'betas': [0.95, 0.999], |
||||
'eps': 1e-6, |
||||
'weight_decay': 1e-3, |
||||
}, |
||||
'lr_sched': { |
||||
'type': 'inverse', |
||||
'inv_gamma': 20000., |
||||
'power': 1., |
||||
'warmup': 0.99, |
||||
}, |
||||
'ema_sched': { |
||||
'type': 'inverse', |
||||
'power': 0.6667, |
||||
'max_value': 0.9999 |
||||
}, |
||||
} |
||||
config = json.load(file) |
||||
return merge(defaults, config) |
||||
|
||||
|
||||
def make_model(config): |
||||
config = config['model'] |
||||
assert config['type'] == 'image_v1' |
||||
model = models.ImageDenoiserModelV1( |
||||
config['input_channels'], |
||||
config['mapping_out'], |
||||
config['depths'], |
||||
config['channels'], |
||||
config['self_attn_depths'], |
||||
config['cross_attn_depths'], |
||||
patch_size=config['patch_size'], |
||||
dropout_rate=config['dropout_rate'], |
||||
mapping_cond_dim=config['mapping_cond_dim'] + (9 if config['augment_wrapper'] else 0), |
||||
unet_cond_dim=config['unet_cond_dim'], |
||||
cross_cond_dim=config['cross_cond_dim'], |
||||
skip_stages=config['skip_stages'], |
||||
has_variance=config['has_variance'], |
||||
) |
||||
if config['augment_wrapper']: |
||||
model = augmentation.KarrasAugmentWrapper(model) |
||||
return model |
||||
|
||||
|
||||
def make_denoiser_wrapper(config): |
||||
config = config['model'] |
||||
sigma_data = config.get('sigma_data', 1.) |
||||
has_variance = config.get('has_variance', False) |
||||
if not has_variance: |
||||
return partial(layers.Denoiser, sigma_data=sigma_data) |
||||
return partial(layers.DenoiserWithVariance, sigma_data=sigma_data) |
||||
|
||||
|
||||
def make_sample_density(config): |
||||
sd_config = config['sigma_sample_density'] |
||||
sigma_data = config['sigma_data'] |
||||
if sd_config['type'] == 'lognormal': |
||||
loc = sd_config['mean'] if 'mean' in sd_config else sd_config['loc'] |
||||
scale = sd_config['std'] if 'std' in sd_config else sd_config['scale'] |
||||
return partial(utils.rand_log_normal, loc=loc, scale=scale) |
||||
if sd_config['type'] == 'loglogistic': |
||||
loc = sd_config['loc'] if 'loc' in sd_config else math.log(sigma_data) |
||||
scale = sd_config['scale'] if 'scale' in sd_config else 0.5 |
||||
min_value = sd_config['min_value'] if 'min_value' in sd_config else 0. |
||||
max_value = sd_config['max_value'] if 'max_value' in sd_config else float('inf') |
||||
return partial(utils.rand_log_logistic, loc=loc, scale=scale, min_value=min_value, max_value=max_value) |
||||
if sd_config['type'] == 'loguniform': |
||||
min_value = sd_config['min_value'] if 'min_value' in sd_config else config['sigma_min'] |
||||
max_value = sd_config['max_value'] if 'max_value' in sd_config else config['sigma_max'] |
||||
return partial(utils.rand_log_uniform, min_value=min_value, max_value=max_value) |
||||
if sd_config['type'] == 'v-diffusion': |
||||
min_value = sd_config['min_value'] if 'min_value' in sd_config else 0. |
||||
max_value = sd_config['max_value'] if 'max_value' in sd_config else float('inf') |
||||
return partial(utils.rand_v_diffusion, sigma_data=sigma_data, min_value=min_value, max_value=max_value) |
||||
if sd_config['type'] == 'split-lognormal': |
||||
loc = sd_config['mean'] if 'mean' in sd_config else sd_config['loc'] |
||||
scale_1 = sd_config['std_1'] if 'std_1' in sd_config else sd_config['scale_1'] |
||||
scale_2 = sd_config['std_2'] if 'std_2' in sd_config else sd_config['scale_2'] |
||||
return partial(utils.rand_split_log_normal, loc=loc, scale_1=scale_1, scale_2=scale_2) |
||||
raise ValueError('Unknown sample density type') |
||||
@ -0,0 +1,134 @@
|
||||
import math |
||||
import os |
||||
from pathlib import Path |
||||
|
||||
from cleanfid.inception_torchscript import InceptionV3W |
||||
import clip |
||||
from resize_right import resize |
||||
import torch |
||||
from torch import nn |
||||
from torch.nn import functional as F |
||||
from torchvision import transforms |
||||
from tqdm.auto import trange |
||||
|
||||
from . import utils |
||||
|
||||
|
||||
class InceptionV3FeatureExtractor(nn.Module): |
||||
def __init__(self, device='cpu'): |
||||
super().__init__() |
||||
path = Path(os.environ.get('XDG_CACHE_HOME', Path.home() / '.cache')) / 'k-diffusion' |
||||
url = 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/inception-2015-12-05.pt' |
||||
digest = 'f58cb9b6ec323ed63459aa4fb441fe750cfe39fafad6da5cb504a16f19e958f4' |
||||
utils.download_file(path / 'inception-2015-12-05.pt', url, digest) |
||||
self.model = InceptionV3W(str(path), resize_inside=False).to(device) |
||||
self.size = (299, 299) |
||||
|
||||
def forward(self, x): |
||||
if x.shape[2:4] != self.size: |
||||
x = resize(x, out_shape=self.size, pad_mode='reflect') |
||||
if x.shape[1] == 1: |
||||
x = torch.cat([x] * 3, dim=1) |
||||
x = (x * 127.5 + 127.5).clamp(0, 255) |
||||
return self.model(x) |
||||
|
||||
|
||||
class CLIPFeatureExtractor(nn.Module): |
||||
def __init__(self, name='ViT-L/14@336px', device='cpu'): |
||||
super().__init__() |
||||
self.model = clip.load(name, device=device)[0].eval().requires_grad_(False) |
||||
self.normalize = transforms.Normalize(mean=(0.48145466, 0.4578275, 0.40821073), |
||||
std=(0.26862954, 0.26130258, 0.27577711)) |
||||
self.size = (self.model.visual.input_resolution, self.model.visual.input_resolution) |
||||
|
||||
def forward(self, x): |
||||
if x.shape[2:4] != self.size: |
||||
x = resize(x.add(1).div(2), out_shape=self.size, pad_mode='reflect').clamp(0, 1) |
||||
x = self.normalize(x) |
||||
x = self.model.encode_image(x).float() |
||||
x = F.normalize(x) * x.shape[1] ** 0.5 |
||||
return x |
||||
|
||||
|
||||
def compute_features(accelerator, sample_fn, extractor_fn, n, batch_size): |
||||
n_per_proc = math.ceil(n / accelerator.num_processes) |
||||
feats_all = [] |
||||
try: |
||||
for i in trange(0, n_per_proc, batch_size, disable=not accelerator.is_main_process): |
||||
cur_batch_size = min(n - i, batch_size) |
||||
samples = sample_fn(cur_batch_size)[:cur_batch_size] |
||||
feats_all.append(accelerator.gather(extractor_fn(samples))) |
||||
except StopIteration: |
||||
pass |
||||
return torch.cat(feats_all)[:n] |
||||
|
||||
|
||||
def polynomial_kernel(x, y): |
||||
d = x.shape[-1] |
||||
dot = x @ y.transpose(-2, -1) |
||||
return (dot / d + 1) ** 3 |
||||
|
||||
|
||||
def squared_mmd(x, y, kernel=polynomial_kernel): |
||||
m = x.shape[-2] |
||||
n = y.shape[-2] |
||||
kxx = kernel(x, x) |
||||
kyy = kernel(y, y) |
||||
kxy = kernel(x, y) |
||||
kxx_sum = kxx.sum([-1, -2]) - kxx.diagonal(dim1=-1, dim2=-2).sum(-1) |
||||
kyy_sum = kyy.sum([-1, -2]) - kyy.diagonal(dim1=-1, dim2=-2).sum(-1) |
||||
kxy_sum = kxy.sum([-1, -2]) |
||||
term_1 = kxx_sum / m / (m - 1) |
||||
term_2 = kyy_sum / n / (n - 1) |
||||
term_3 = kxy_sum * 2 / m / n |
||||
return term_1 + term_2 - term_3 |
||||
|
||||
|
||||
@utils.tf32_mode(matmul=False) |
||||
def kid(x, y, max_size=5000): |
||||
x_size, y_size = x.shape[0], y.shape[0] |
||||
n_partitions = math.ceil(max(x_size / max_size, y_size / max_size)) |
||||
total_mmd = x.new_zeros([]) |
||||
for i in range(n_partitions): |
||||
cur_x = x[round(i * x_size / n_partitions):round((i + 1) * x_size / n_partitions)] |
||||
cur_y = y[round(i * y_size / n_partitions):round((i + 1) * y_size / n_partitions)] |
||||
total_mmd = total_mmd + squared_mmd(cur_x, cur_y) |
||||
return total_mmd / n_partitions |
||||
|
||||
|
||||
class _MatrixSquareRootEig(torch.autograd.Function): |
||||
@staticmethod |
||||
def forward(ctx, a): |
||||
vals, vecs = torch.linalg.eigh(a) |
||||
ctx.save_for_backward(vals, vecs) |
||||
return vecs @ vals.abs().sqrt().diag_embed() @ vecs.transpose(-2, -1) |
||||
|
||||
@staticmethod |
||||
def backward(ctx, grad_output): |
||||
vals, vecs = ctx.saved_tensors |
||||
d = vals.abs().sqrt().unsqueeze(-1).repeat_interleave(vals.shape[-1], -1) |
||||
vecs_t = vecs.transpose(-2, -1) |
||||
return vecs @ (vecs_t @ grad_output @ vecs / (d + d.transpose(-2, -1))) @ vecs_t |
||||
|
||||
|
||||
def sqrtm_eig(a): |
||||
if a.ndim < 2: |
||||
raise RuntimeError('tensor of matrices must have at least 2 dimensions') |
||||
if a.shape[-2] != a.shape[-1]: |
||||
raise RuntimeError('tensor must be batches of square matrices') |
||||
return _MatrixSquareRootEig.apply(a) |
||||
|
||||
|
||||
@utils.tf32_mode(matmul=False) |
||||
def fid(x, y, eps=1e-8): |
||||
x_mean = x.mean(dim=0) |
||||
y_mean = y.mean(dim=0) |
||||
mean_term = (x_mean - y_mean).pow(2).sum() |
||||
x_cov = torch.cov(x.T) |
||||
y_cov = torch.cov(y.T) |
||||
eps_eye = torch.eye(x_cov.shape[0], device=x_cov.device, dtype=x_cov.dtype) * eps |
||||
x_cov = x_cov + eps_eye |
||||
y_cov = y_cov + eps_eye |
||||
x_cov_sqrt = sqrtm_eig(x_cov) |
||||
cov_term = torch.trace(x_cov + y_cov - 2 * sqrtm_eig(x_cov_sqrt @ y_cov @ x_cov_sqrt)) |
||||
return mean_term + cov_term |
||||
@ -0,0 +1,179 @@
|
||||
import math |
||||
|
||||
import torch |
||||
from torch import nn |
||||
|
||||
from . import sampling, utils |
||||
|
||||
|
||||
class VDenoiser(nn.Module): |
||||
"""A v-diffusion-pytorch model wrapper for k-diffusion.""" |
||||
|
||||
def __init__(self, inner_model): |
||||
super().__init__() |
||||
self.inner_model = inner_model |
||||
self.sigma_data = 1. |
||||
|
||||
def get_scalings(self, sigma): |
||||
c_skip = self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) |
||||
c_out = -sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 |
||||
c_in = 1 / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 |
||||
return c_skip, c_out, c_in |
||||
|
||||
def sigma_to_t(self, sigma): |
||||
return sigma.atan() / math.pi * 2 |
||||
|
||||
def t_to_sigma(self, t): |
||||
return (t * math.pi / 2).tan() |
||||
|
||||
def loss(self, input, noise, sigma, **kwargs): |
||||
c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)] |
||||
noised_input = input + noise * utils.append_dims(sigma, input.ndim) |
||||
model_output = self.inner_model(noised_input * c_in, self.sigma_to_t(sigma), **kwargs) |
||||
target = (input - c_skip * noised_input) / c_out |
||||
return (model_output - target).pow(2).flatten(1).mean(1) |
||||
|
||||
def forward(self, input, sigma, **kwargs): |
||||
c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)] |
||||
return self.inner_model(input * c_in, self.sigma_to_t(sigma), **kwargs) * c_out + input * c_skip |
||||
|
||||
|
||||
class DiscreteSchedule(nn.Module): |
||||
"""A mapping between continuous noise levels (sigmas) and a list of discrete noise |
||||
levels.""" |
||||
|
||||
def __init__(self, sigmas, quantize): |
||||
super().__init__() |
||||
self.register_buffer('sigmas', sigmas) |
||||
self.register_buffer('log_sigmas', sigmas.log()) |
||||
self.quantize = quantize |
||||
|
||||
@property |
||||
def sigma_min(self): |
||||
return self.sigmas[0] |
||||
|
||||
@property |
||||
def sigma_max(self): |
||||
return self.sigmas[-1] |
||||
|
||||
def get_sigmas(self, n=None): |
||||
if n is None: |
||||
return sampling.append_zero(self.sigmas.flip(0)) |
||||
t_max = len(self.sigmas) - 1 |
||||
t = torch.linspace(t_max, 0, n, device=self.sigmas.device) |
||||
return sampling.append_zero(self.t_to_sigma(t)) |
||||
|
||||
def sigma_to_t(self, sigma, quantize=None): |
||||
quantize = self.quantize if quantize is None else quantize |
||||
log_sigma = sigma.log() |
||||
dists = log_sigma - self.log_sigmas[:, None] |
||||
if quantize: |
||||
return dists.abs().argmin(dim=0).view(sigma.shape) |
||||
low_idx = dists.ge(0).cumsum(dim=0).argmax(dim=0).clamp(max=self.log_sigmas.shape[0] - 2) |
||||
high_idx = low_idx + 1 |
||||
low, high = self.log_sigmas[low_idx], self.log_sigmas[high_idx] |
||||
w = (low - log_sigma) / (low - high) |
||||
w = w.clamp(0, 1) |
||||
t = (1 - w) * low_idx + w * high_idx |
||||
return t.view(sigma.shape) |
||||
|
||||
def t_to_sigma(self, t): |
||||
t = t.float() |
||||
low_idx = t.floor().long() |
||||
high_idx = t.ceil().long() |
||||
w = t-low_idx if t.device.type == 'mps' else t.frac() |
||||
log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx] |
||||
return log_sigma.exp() |
||||
|
||||
|
||||
class DiscreteEpsDDPMDenoiser(DiscreteSchedule): |
||||
"""A wrapper for discrete schedule DDPM models that output eps (the predicted |
||||
noise).""" |
||||
|
||||
def __init__(self, model, alphas_cumprod, quantize): |
||||
super().__init__(((1 - alphas_cumprod) / alphas_cumprod) ** 0.5, quantize) |
||||
self.inner_model = model |
||||
self.sigma_data = 1. |
||||
|
||||
def get_scalings(self, sigma): |
||||
c_out = -sigma |
||||
c_in = 1 / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 |
||||
return c_out, c_in |
||||
|
||||
def get_eps(self, *args, **kwargs): |
||||
return self.inner_model(*args, **kwargs) |
||||
|
||||
def loss(self, input, noise, sigma, **kwargs): |
||||
c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)] |
||||
noised_input = input + noise * utils.append_dims(sigma, input.ndim) |
||||
eps = self.get_eps(noised_input * c_in, self.sigma_to_t(sigma), **kwargs) |
||||
return (eps - noise).pow(2).flatten(1).mean(1) |
||||
|
||||
def forward(self, input, sigma, **kwargs): |
||||
c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)] |
||||
eps = self.get_eps(input * c_in, self.sigma_to_t(sigma), **kwargs) |
||||
return input + eps * c_out |
||||
|
||||
|
||||
class OpenAIDenoiser(DiscreteEpsDDPMDenoiser): |
||||
"""A wrapper for OpenAI diffusion models.""" |
||||
|
||||
def __init__(self, model, diffusion, quantize=False, has_learned_sigmas=True, device='cpu'): |
||||
alphas_cumprod = torch.tensor(diffusion.alphas_cumprod, device=device, dtype=torch.float32) |
||||
super().__init__(model, alphas_cumprod, quantize=quantize) |
||||
self.has_learned_sigmas = has_learned_sigmas |
||||
|
||||
def get_eps(self, *args, **kwargs): |
||||
model_output = self.inner_model(*args, **kwargs) |
||||
if self.has_learned_sigmas: |
||||
return model_output.chunk(2, dim=1)[0] |
||||
return model_output |
||||
|
||||
|
||||
class CompVisDenoiser(DiscreteEpsDDPMDenoiser): |
||||
"""A wrapper for CompVis diffusion models.""" |
||||
|
||||
def __init__(self, model, quantize=False, device='cpu'): |
||||
super().__init__(model, model.alphas_cumprod, quantize=quantize) |
||||
|
||||
def get_eps(self, *args, **kwargs): |
||||
return self.inner_model.apply_model(*args, **kwargs) |
||||
|
||||
|
||||
class DiscreteVDDPMDenoiser(DiscreteSchedule): |
||||
"""A wrapper for discrete schedule DDPM models that output v.""" |
||||
|
||||
def __init__(self, model, alphas_cumprod, quantize): |
||||
super().__init__(((1 - alphas_cumprod) / alphas_cumprod) ** 0.5, quantize) |
||||
self.inner_model = model |
||||
self.sigma_data = 1. |
||||
|
||||
def get_scalings(self, sigma): |
||||
c_skip = self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) |
||||
c_out = -sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 |
||||
c_in = 1 / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 |
||||
return c_skip, c_out, c_in |
||||
|
||||
def get_v(self, *args, **kwargs): |
||||
return self.inner_model(*args, **kwargs) |
||||
|
||||
def loss(self, input, noise, sigma, **kwargs): |
||||
c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)] |
||||
noised_input = input + noise * utils.append_dims(sigma, input.ndim) |
||||
model_output = self.get_v(noised_input * c_in, self.sigma_to_t(sigma), **kwargs) |
||||
target = (input - c_skip * noised_input) / c_out |
||||
return (model_output - target).pow(2).flatten(1).mean(1) |
||||
|
||||
def forward(self, input, sigma, **kwargs): |
||||
c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)] |
||||
return self.get_v(input * c_in, self.sigma_to_t(sigma), **kwargs) * c_out + input * c_skip |
||||
|
||||
|
||||
class CompVisVDenoiser(DiscreteVDDPMDenoiser): |
||||
"""A wrapper for CompVis diffusion models that output v.""" |
||||
|
||||
def __init__(self, model, quantize=False, device='cpu'): |
||||
super().__init__(model, model.alphas_cumprod, quantize=quantize) |
||||
|
||||
def get_v(self, x, t, cond, **kwargs): |
||||
return self.inner_model.apply_model(x, t, cond) |
||||
@ -0,0 +1,99 @@
|
||||
import torch |
||||
from torch import nn |
||||
|
||||
|
||||
class DDPGradientStatsHook: |
||||
def __init__(self, ddp_module): |
||||
try: |
||||
ddp_module.register_comm_hook(self, self._hook_fn) |
||||
except AttributeError: |
||||
raise ValueError('DDPGradientStatsHook does not support non-DDP wrapped modules') |
||||
self._clear_state() |
||||
|
||||
def _clear_state(self): |
||||
self.bucket_sq_norms_small_batch = [] |
||||
self.bucket_sq_norms_large_batch = [] |
||||
|
||||
@staticmethod |
||||
def _hook_fn(self, bucket): |
||||
buf = bucket.buffer() |
||||
self.bucket_sq_norms_small_batch.append(buf.pow(2).sum()) |
||||
fut = torch.distributed.all_reduce(buf, op=torch.distributed.ReduceOp.AVG, async_op=True).get_future() |
||||
def callback(fut): |
||||
buf = fut.value()[0] |
||||
self.bucket_sq_norms_large_batch.append(buf.pow(2).sum()) |
||||
return buf |
||||
return fut.then(callback) |
||||
|
||||
def get_stats(self): |
||||
sq_norm_small_batch = sum(self.bucket_sq_norms_small_batch) |
||||
sq_norm_large_batch = sum(self.bucket_sq_norms_large_batch) |
||||
self._clear_state() |
||||
stats = torch.stack([sq_norm_small_batch, sq_norm_large_batch]) |
||||
torch.distributed.all_reduce(stats, op=torch.distributed.ReduceOp.AVG) |
||||
return stats[0].item(), stats[1].item() |
||||
|
||||
|
||||
class GradientNoiseScale: |
||||
"""Calculates the gradient noise scale (1 / SNR), or critical batch size, |
||||
from _An Empirical Model of Large-Batch Training_, |
||||
https://arxiv.org/abs/1812.06162). |
||||
|
||||
Args: |
||||
beta (float): The decay factor for the exponential moving averages used to |
||||
calculate the gradient noise scale. |
||||
Default: 0.9998 |
||||
eps (float): Added for numerical stability. |
||||
Default: 1e-8 |
||||
""" |
||||
|
||||
def __init__(self, beta=0.9998, eps=1e-8): |
||||
self.beta = beta |
||||
self.eps = eps |
||||
self.ema_sq_norm = 0. |
||||
self.ema_var = 0. |
||||
self.beta_cumprod = 1. |
||||
self.gradient_noise_scale = float('nan') |
||||
|
||||
def state_dict(self): |
||||
"""Returns the state of the object as a :class:`dict`.""" |
||||
return dict(self.__dict__.items()) |
||||
|
||||
def load_state_dict(self, state_dict): |
||||
"""Loads the object's state. |
||||
Args: |
||||
state_dict (dict): object state. Should be an object returned |
||||
from a call to :meth:`state_dict`. |
||||
""" |
||||
self.__dict__.update(state_dict) |
||||
|
||||
def update(self, sq_norm_small_batch, sq_norm_large_batch, n_small_batch, n_large_batch): |
||||
"""Updates the state with a new batch's gradient statistics, and returns the |
||||
current gradient noise scale. |
||||
|
||||
Args: |
||||
sq_norm_small_batch (float): The mean of the squared 2-norms of microbatch or |
||||
per sample gradients. |
||||
sq_norm_large_batch (float): The squared 2-norm of the mean of the microbatch or |
||||
per sample gradients. |
||||
n_small_batch (int): The batch size of the individual microbatch or per sample |
||||
gradients (1 if per sample). |
||||
n_large_batch (int): The total batch size of the mean of the microbatch or |
||||
per sample gradients. |
||||
""" |
||||
est_sq_norm = (n_large_batch * sq_norm_large_batch - n_small_batch * sq_norm_small_batch) / (n_large_batch - n_small_batch) |
||||
est_var = (sq_norm_small_batch - sq_norm_large_batch) / (1 / n_small_batch - 1 / n_large_batch) |
||||
self.ema_sq_norm = self.beta * self.ema_sq_norm + (1 - self.beta) * est_sq_norm |
||||
self.ema_var = self.beta * self.ema_var + (1 - self.beta) * est_var |
||||
self.beta_cumprod *= self.beta |
||||
self.gradient_noise_scale = max(self.ema_var, self.eps) / max(self.ema_sq_norm, self.eps) |
||||
return self.gradient_noise_scale |
||||
|
||||
def get_gns(self): |
||||
"""Returns the current gradient noise scale.""" |
||||
return self.gradient_noise_scale |
||||
|
||||
def get_stats(self): |
||||
"""Returns the current (debiased) estimates of the squared mean gradient |
||||
and gradient variance.""" |
||||
return self.ema_sq_norm / (1 - self.beta_cumprod), self.ema_var / (1 - self.beta_cumprod) |
||||
@ -0,0 +1,246 @@
|
||||
import math |
||||
|
||||
from einops import rearrange, repeat |
||||
import torch |
||||
from torch import nn |
||||
from torch.nn import functional as F |
||||
|
||||
from . import utils |
||||
|
||||
# Karras et al. preconditioned denoiser |
||||
|
||||
class Denoiser(nn.Module): |
||||
"""A Karras et al. preconditioner for denoising diffusion models.""" |
||||
|
||||
def __init__(self, inner_model, sigma_data=1.): |
||||
super().__init__() |
||||
self.inner_model = inner_model |
||||
self.sigma_data = sigma_data |
||||
|
||||
def get_scalings(self, sigma): |
||||
c_skip = self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) |
||||
c_out = sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 |
||||
c_in = 1 / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 |
||||
return c_skip, c_out, c_in |
||||
|
||||
def loss(self, input, noise, sigma, **kwargs): |
||||
c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)] |
||||
noised_input = input + noise * utils.append_dims(sigma, input.ndim) |
||||
model_output = self.inner_model(noised_input * c_in, sigma, **kwargs) |
||||
target = (input - c_skip * noised_input) / c_out |
||||
return (model_output - target).pow(2).flatten(1).mean(1) |
||||
|
||||
def forward(self, input, sigma, **kwargs): |
||||
c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)] |
||||
return self.inner_model(input * c_in, sigma, **kwargs) * c_out + input * c_skip |
||||
|
||||
|
||||
class DenoiserWithVariance(Denoiser): |
||||
def loss(self, input, noise, sigma, **kwargs): |
||||
c_skip, c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)] |
||||
noised_input = input + noise * utils.append_dims(sigma, input.ndim) |
||||
model_output, logvar = self.inner_model(noised_input * c_in, sigma, return_variance=True, **kwargs) |
||||
logvar = utils.append_dims(logvar, model_output.ndim) |
||||
target = (input - c_skip * noised_input) / c_out |
||||
losses = ((model_output - target) ** 2 / logvar.exp() + logvar) / 2 |
||||
return losses.flatten(1).mean(1) |
||||
|
||||
|
||||
# Residual blocks |
||||
|
||||
class ResidualBlock(nn.Module): |
||||
def __init__(self, *main, skip=None): |
||||
super().__init__() |
||||
self.main = nn.Sequential(*main) |
||||
self.skip = skip if skip else nn.Identity() |
||||
|
||||
def forward(self, input): |
||||
return self.main(input) + self.skip(input) |
||||
|
||||
|
||||
# Noise level (and other) conditioning |
||||
|
||||
class ConditionedModule(nn.Module): |
||||
pass |
||||
|
||||
|
||||
class UnconditionedModule(ConditionedModule): |
||||
def __init__(self, module): |
||||
super().__init__() |
||||
self.module = module |
||||
|
||||
def forward(self, input, cond=None): |
||||
return self.module(input) |
||||
|
||||
|
||||
class ConditionedSequential(nn.Sequential, ConditionedModule): |
||||
def forward(self, input, cond): |
||||
for module in self: |
||||
if isinstance(module, ConditionedModule): |
||||
input = module(input, cond) |
||||
else: |
||||
input = module(input) |
||||
return input |
||||
|
||||
|
||||
class ConditionedResidualBlock(ConditionedModule): |
||||
def __init__(self, *main, skip=None): |
||||
super().__init__() |
||||
self.main = ConditionedSequential(*main) |
||||
self.skip = skip if skip else nn.Identity() |
||||
|
||||
def forward(self, input, cond): |
||||
skip = self.skip(input, cond) if isinstance(self.skip, ConditionedModule) else self.skip(input) |
||||
return self.main(input, cond) + skip |
||||
|
||||
|
||||
class AdaGN(ConditionedModule): |
||||
def __init__(self, feats_in, c_out, num_groups, eps=1e-5, cond_key='cond'): |
||||
super().__init__() |
||||
self.num_groups = num_groups |
||||
self.eps = eps |
||||
self.cond_key = cond_key |
||||
self.mapper = nn.Linear(feats_in, c_out * 2) |
||||
|
||||
def forward(self, input, cond): |
||||
weight, bias = self.mapper(cond[self.cond_key]).chunk(2, dim=-1) |
||||
input = F.group_norm(input, self.num_groups, eps=self.eps) |
||||
return torch.addcmul(utils.append_dims(bias, input.ndim), input, utils.append_dims(weight, input.ndim) + 1) |
||||
|
||||
|
||||
# Attention |
||||
|
||||
class SelfAttention2d(ConditionedModule): |
||||
def __init__(self, c_in, n_head, norm, dropout_rate=0.): |
||||
super().__init__() |
||||
assert c_in % n_head == 0 |
||||
self.norm_in = norm(c_in) |
||||
self.n_head = n_head |
||||
self.qkv_proj = nn.Conv2d(c_in, c_in * 3, 1) |
||||
self.out_proj = nn.Conv2d(c_in, c_in, 1) |
||||
self.dropout = nn.Dropout(dropout_rate) |
||||
|
||||
def forward(self, input, cond): |
||||
n, c, h, w = input.shape |
||||
qkv = self.qkv_proj(self.norm_in(input, cond)) |
||||
qkv = qkv.view([n, self.n_head * 3, c // self.n_head, h * w]).transpose(2, 3) |
||||
q, k, v = qkv.chunk(3, dim=1) |
||||
scale = k.shape[3] ** -0.25 |
||||
att = ((q * scale) @ (k.transpose(2, 3) * scale)).softmax(3) |
||||
att = self.dropout(att) |
||||
y = (att @ v).transpose(2, 3).contiguous().view([n, c, h, w]) |
||||
return input + self.out_proj(y) |
||||
|
||||
|
||||
class CrossAttention2d(ConditionedModule): |
||||
def __init__(self, c_dec, c_enc, n_head, norm_dec, dropout_rate=0., |
||||
cond_key='cross', cond_key_padding='cross_padding'): |
||||
super().__init__() |
||||
assert c_dec % n_head == 0 |
||||
self.cond_key = cond_key |
||||
self.cond_key_padding = cond_key_padding |
||||
self.norm_enc = nn.LayerNorm(c_enc) |
||||
self.norm_dec = norm_dec(c_dec) |
||||
self.n_head = n_head |
||||
self.q_proj = nn.Conv2d(c_dec, c_dec, 1) |
||||
self.kv_proj = nn.Linear(c_enc, c_dec * 2) |
||||
self.out_proj = nn.Conv2d(c_dec, c_dec, 1) |
||||
self.dropout = nn.Dropout(dropout_rate) |
||||
|
||||
def forward(self, input, cond): |
||||
n, c, h, w = input.shape |
||||
q = self.q_proj(self.norm_dec(input, cond)) |
||||
q = q.view([n, self.n_head, c // self.n_head, h * w]).transpose(2, 3) |
||||
kv = self.kv_proj(self.norm_enc(cond[self.cond_key])) |
||||
kv = kv.view([n, -1, self.n_head * 2, c // self.n_head]).transpose(1, 2) |
||||
k, v = kv.chunk(2, dim=1) |
||||
scale = k.shape[3] ** -0.25 |
||||
att = ((q * scale) @ (k.transpose(2, 3) * scale)) |
||||
att = att - (cond[self.cond_key_padding][:, None, None, :]) * 10000 |
||||
att = att.softmax(3) |
||||
att = self.dropout(att) |
||||
y = (att @ v).transpose(2, 3) |
||||
y = y.contiguous().view([n, c, h, w]) |
||||
return input + self.out_proj(y) |
||||
|
||||
|
||||
# Downsampling/upsampling |
||||
|
||||
_kernels = { |
||||
'linear': |
||||
[1 / 8, 3 / 8, 3 / 8, 1 / 8], |
||||
'cubic': |
||||
[-0.01171875, -0.03515625, 0.11328125, 0.43359375, |
||||
0.43359375, 0.11328125, -0.03515625, -0.01171875], |
||||
'lanczos3': |
||||
[0.003689131001010537, 0.015056144446134567, -0.03399861603975296, |
||||
-0.066637322306633, 0.13550527393817902, 0.44638532400131226, |
||||
0.44638532400131226, 0.13550527393817902, -0.066637322306633, |
||||
-0.03399861603975296, 0.015056144446134567, 0.003689131001010537] |
||||
} |
||||
_kernels['bilinear'] = _kernels['linear'] |
||||
_kernels['bicubic'] = _kernels['cubic'] |
||||
|
||||
|
||||
class Downsample2d(nn.Module): |
||||
def __init__(self, kernel='linear', pad_mode='reflect'): |
||||
super().__init__() |
||||
self.pad_mode = pad_mode |
||||
kernel_1d = torch.tensor([_kernels[kernel]]) |
||||
self.pad = kernel_1d.shape[1] // 2 - 1 |
||||
self.register_buffer('kernel', kernel_1d.T @ kernel_1d) |
||||
|
||||
def forward(self, x): |
||||
x = F.pad(x, (self.pad,) * 4, self.pad_mode) |
||||
weight = x.new_zeros([x.shape[1], x.shape[1], self.kernel.shape[0], self.kernel.shape[1]]) |
||||
indices = torch.arange(x.shape[1], device=x.device) |
||||
weight[indices, indices] = self.kernel.to(weight) |
||||
return F.conv2d(x, weight, stride=2) |
||||
|
||||
|
||||
class Upsample2d(nn.Module): |
||||
def __init__(self, kernel='linear', pad_mode='reflect'): |
||||
super().__init__() |
||||
self.pad_mode = pad_mode |
||||
kernel_1d = torch.tensor([_kernels[kernel]]) * 2 |
||||
self.pad = kernel_1d.shape[1] // 2 - 1 |
||||
self.register_buffer('kernel', kernel_1d.T @ kernel_1d) |
||||
|
||||
def forward(self, x): |
||||
x = F.pad(x, ((self.pad + 1) // 2,) * 4, self.pad_mode) |
||||
weight = x.new_zeros([x.shape[1], x.shape[1], self.kernel.shape[0], self.kernel.shape[1]]) |
||||
indices = torch.arange(x.shape[1], device=x.device) |
||||
weight[indices, indices] = self.kernel.to(weight) |
||||
return F.conv_transpose2d(x, weight, stride=2, padding=self.pad * 2 + 1) |
||||
|
||||
|
||||
# Embeddings |
||||
|
||||
class FourierFeatures(nn.Module): |
||||
def __init__(self, in_features, out_features, std=1.): |
||||
super().__init__() |
||||
assert out_features % 2 == 0 |
||||
self.register_buffer('weight', torch.randn([out_features // 2, in_features]) * std) |
||||
|
||||
def forward(self, input): |
||||
f = 2 * math.pi * input @ self.weight.T |
||||
return torch.cat([f.cos(), f.sin()], dim=-1) |
||||
|
||||
|
||||
# U-Nets |
||||
|
||||
class UNet(ConditionedModule): |
||||
def __init__(self, d_blocks, u_blocks, skip_stages=0): |
||||
super().__init__() |
||||
self.d_blocks = nn.ModuleList(d_blocks) |
||||
self.u_blocks = nn.ModuleList(u_blocks) |
||||
self.skip_stages = skip_stages |
||||
|
||||
def forward(self, input, cond): |
||||
skips = [] |
||||
for block in self.d_blocks[self.skip_stages:]: |
||||
input = block(input, cond) |
||||
skips.append(input) |
||||
for i, (block, skip) in enumerate(zip(self.u_blocks, reversed(skips))): |
||||
input = block(input, cond, skip if i > 0 else None) |
||||
return input |
||||
@ -0,0 +1 @@
|
||||
from .image_v1 import ImageDenoiserModelV1 |
||||
@ -0,0 +1,156 @@
|
||||
import math |
||||
|
||||
import torch |
||||
from torch import nn |
||||
from torch.nn import functional as F |
||||
|
||||
from .. import layers, utils |
||||
|
||||
|
||||
def orthogonal_(module): |
||||
nn.init.orthogonal_(module.weight) |
||||
return module |
||||
|
||||
|
||||
class ResConvBlock(layers.ConditionedResidualBlock): |
||||
def __init__(self, feats_in, c_in, c_mid, c_out, group_size=32, dropout_rate=0.): |
||||
skip = None if c_in == c_out else orthogonal_(nn.Conv2d(c_in, c_out, 1, bias=False)) |
||||
super().__init__( |
||||
layers.AdaGN(feats_in, c_in, max(1, c_in // group_size)), |
||||
nn.GELU(), |
||||
nn.Conv2d(c_in, c_mid, 3, padding=1), |
||||
nn.Dropout2d(dropout_rate, inplace=True), |
||||
layers.AdaGN(feats_in, c_mid, max(1, c_mid // group_size)), |
||||
nn.GELU(), |
||||
nn.Conv2d(c_mid, c_out, 3, padding=1), |
||||
nn.Dropout2d(dropout_rate, inplace=True), |
||||
skip=skip) |
||||
|
||||
|
||||
class DBlock(layers.ConditionedSequential): |
||||
def __init__(self, n_layers, feats_in, c_in, c_mid, c_out, group_size=32, head_size=64, dropout_rate=0., downsample=False, self_attn=False, cross_attn=False, c_enc=0): |
||||
modules = [nn.Identity()] |
||||
for i in range(n_layers): |
||||
my_c_in = c_in if i == 0 else c_mid |
||||
my_c_out = c_mid if i < n_layers - 1 else c_out |
||||
modules.append(ResConvBlock(feats_in, my_c_in, c_mid, my_c_out, group_size, dropout_rate)) |
||||
if self_attn: |
||||
norm = lambda c_in: layers.AdaGN(feats_in, c_in, max(1, my_c_out // group_size)) |
||||
modules.append(layers.SelfAttention2d(my_c_out, max(1, my_c_out // head_size), norm, dropout_rate)) |
||||
if cross_attn: |
||||
norm = lambda c_in: layers.AdaGN(feats_in, c_in, max(1, my_c_out // group_size)) |
||||
modules.append(layers.CrossAttention2d(my_c_out, c_enc, max(1, my_c_out // head_size), norm, dropout_rate)) |
||||
super().__init__(*modules) |
||||
self.set_downsample(downsample) |
||||
|
||||
def set_downsample(self, downsample): |
||||
self[0] = layers.Downsample2d() if downsample else nn.Identity() |
||||
return self |
||||
|
||||
|
||||
class UBlock(layers.ConditionedSequential): |
||||
def __init__(self, n_layers, feats_in, c_in, c_mid, c_out, group_size=32, head_size=64, dropout_rate=0., upsample=False, self_attn=False, cross_attn=False, c_enc=0): |
||||
modules = [] |
||||
for i in range(n_layers): |
||||
my_c_in = c_in if i == 0 else c_mid |
||||
my_c_out = c_mid if i < n_layers - 1 else c_out |
||||
modules.append(ResConvBlock(feats_in, my_c_in, c_mid, my_c_out, group_size, dropout_rate)) |
||||
if self_attn: |
||||
norm = lambda c_in: layers.AdaGN(feats_in, c_in, max(1, my_c_out // group_size)) |
||||
modules.append(layers.SelfAttention2d(my_c_out, max(1, my_c_out // head_size), norm, dropout_rate)) |
||||
if cross_attn: |
||||
norm = lambda c_in: layers.AdaGN(feats_in, c_in, max(1, my_c_out // group_size)) |
||||
modules.append(layers.CrossAttention2d(my_c_out, c_enc, max(1, my_c_out // head_size), norm, dropout_rate)) |
||||
modules.append(nn.Identity()) |
||||
super().__init__(*modules) |
||||
self.set_upsample(upsample) |
||||
|
||||
def forward(self, input, cond, skip=None): |
||||
if skip is not None: |
||||
input = torch.cat([input, skip], dim=1) |
||||
return super().forward(input, cond) |
||||
|
||||
def set_upsample(self, upsample): |
||||
self[-1] = layers.Upsample2d() if upsample else nn.Identity() |
||||
return self |
||||
|
||||
|
||||
class MappingNet(nn.Sequential): |
||||
def __init__(self, feats_in, feats_out, n_layers=2): |
||||
layers = [] |
||||
for i in range(n_layers): |
||||
layers.append(orthogonal_(nn.Linear(feats_in if i == 0 else feats_out, feats_out))) |
||||
layers.append(nn.GELU()) |
||||
super().__init__(*layers) |
||||
|
||||
|
||||
class ImageDenoiserModelV1(nn.Module): |
||||
def __init__(self, c_in, feats_in, depths, channels, self_attn_depths, cross_attn_depths=None, mapping_cond_dim=0, unet_cond_dim=0, cross_cond_dim=0, dropout_rate=0., patch_size=1, skip_stages=0, has_variance=False): |
||||
super().__init__() |
||||
self.c_in = c_in |
||||
self.channels = channels |
||||
self.unet_cond_dim = unet_cond_dim |
||||
self.patch_size = patch_size |
||||
self.has_variance = has_variance |
||||
self.timestep_embed = layers.FourierFeatures(1, feats_in) |
||||
if mapping_cond_dim > 0: |
||||
self.mapping_cond = nn.Linear(mapping_cond_dim, feats_in, bias=False) |
||||
self.mapping = MappingNet(feats_in, feats_in) |
||||
self.proj_in = nn.Conv2d((c_in + unet_cond_dim) * self.patch_size ** 2, channels[max(0, skip_stages - 1)], 1) |
||||
self.proj_out = nn.Conv2d(channels[max(0, skip_stages - 1)], c_in * self.patch_size ** 2 + (1 if self.has_variance else 0), 1) |
||||
nn.init.zeros_(self.proj_out.weight) |
||||
nn.init.zeros_(self.proj_out.bias) |
||||
if cross_cond_dim == 0: |
||||
cross_attn_depths = [False] * len(self_attn_depths) |
||||
d_blocks, u_blocks = [], [] |
||||
for i in range(len(depths)): |
||||
my_c_in = channels[max(0, i - 1)] |
||||
d_blocks.append(DBlock(depths[i], feats_in, my_c_in, channels[i], channels[i], downsample=i > skip_stages, self_attn=self_attn_depths[i], cross_attn=cross_attn_depths[i], c_enc=cross_cond_dim, dropout_rate=dropout_rate)) |
||||
for i in range(len(depths)): |
||||
my_c_in = channels[i] * 2 if i < len(depths) - 1 else channels[i] |
||||
my_c_out = channels[max(0, i - 1)] |
||||
u_blocks.append(UBlock(depths[i], feats_in, my_c_in, channels[i], my_c_out, upsample=i > skip_stages, self_attn=self_attn_depths[i], cross_attn=cross_attn_depths[i], c_enc=cross_cond_dim, dropout_rate=dropout_rate)) |
||||
self.u_net = layers.UNet(d_blocks, reversed(u_blocks), skip_stages=skip_stages) |
||||
|
||||
def forward(self, input, sigma, mapping_cond=None, unet_cond=None, cross_cond=None, cross_cond_padding=None, return_variance=False): |
||||
c_noise = sigma.log() / 4 |
||||
timestep_embed = self.timestep_embed(utils.append_dims(c_noise, 2)) |
||||
mapping_cond_embed = torch.zeros_like(timestep_embed) if mapping_cond is None else self.mapping_cond(mapping_cond) |
||||
mapping_out = self.mapping(timestep_embed + mapping_cond_embed) |
||||
cond = {'cond': mapping_out} |
||||
if unet_cond is not None: |
||||
input = torch.cat([input, unet_cond], dim=1) |
||||
if cross_cond is not None: |
||||
cond['cross'] = cross_cond |
||||
cond['cross_padding'] = cross_cond_padding |
||||
if self.patch_size > 1: |
||||
input = F.pixel_unshuffle(input, self.patch_size) |
||||
input = self.proj_in(input) |
||||
input = self.u_net(input, cond) |
||||
input = self.proj_out(input) |
||||
if self.has_variance: |
||||
input, logvar = input[:, :-1], input[:, -1].flatten(1).mean(1) |
||||
if self.patch_size > 1: |
||||
input = F.pixel_shuffle(input, self.patch_size) |
||||
if self.has_variance and return_variance: |
||||
return input, logvar |
||||
return input |
||||
|
||||
def set_skip_stages(self, skip_stages): |
||||
self.proj_in = nn.Conv2d(self.proj_in.in_channels, self.channels[max(0, skip_stages - 1)], 1) |
||||
self.proj_out = nn.Conv2d(self.channels[max(0, skip_stages - 1)], self.proj_out.out_channels, 1) |
||||
nn.init.zeros_(self.proj_out.weight) |
||||
nn.init.zeros_(self.proj_out.bias) |
||||
self.u_net.skip_stages = skip_stages |
||||
for i, block in enumerate(self.u_net.d_blocks): |
||||
block.set_downsample(i > skip_stages) |
||||
for i, block in enumerate(reversed(self.u_net.u_blocks)): |
||||
block.set_upsample(i > skip_stages) |
||||
return self |
||||
|
||||
def set_patch_size(self, patch_size): |
||||
self.patch_size = patch_size |
||||
self.proj_in = nn.Conv2d((self.c_in + self.unet_cond_dim) * self.patch_size ** 2, self.channels[max(0, self.u_net.skip_stages - 1)], 1) |
||||
self.proj_out = nn.Conv2d(self.channels[max(0, self.u_net.skip_stages - 1)], self.c_in * self.patch_size ** 2 + (1 if self.has_variance else 0), 1) |
||||
nn.init.zeros_(self.proj_out.weight) |
||||
nn.init.zeros_(self.proj_out.bias) |
||||
@ -0,0 +1,607 @@
|
||||
import math |
||||
|
||||
from scipy import integrate |
||||
import torch |
||||
from torch import nn |
||||
from torchdiffeq import odeint |
||||
import torchsde |
||||
from tqdm.auto import trange, tqdm |
||||
|
||||
from . import utils |
||||
|
||||
|
||||
def append_zero(x): |
||||
return torch.cat([x, x.new_zeros([1])]) |
||||
|
||||
|
||||
def get_sigmas_karras(n, sigma_min, sigma_max, rho=7., device='cpu'): |
||||
"""Constructs the noise schedule of Karras et al. (2022).""" |
||||
ramp = torch.linspace(0, 1, n, device=device) |
||||
min_inv_rho = sigma_min ** (1 / rho) |
||||
max_inv_rho = sigma_max ** (1 / rho) |
||||
sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho |
||||
return append_zero(sigmas).to(device) |
||||
|
||||
|
||||
def get_sigmas_exponential(n, sigma_min, sigma_max, device='cpu'): |
||||
"""Constructs an exponential noise schedule.""" |
||||
sigmas = torch.linspace(math.log(sigma_max), math.log(sigma_min), n, device=device).exp() |
||||
return append_zero(sigmas) |
||||
|
||||
|
||||
def get_sigmas_polyexponential(n, sigma_min, sigma_max, rho=1., device='cpu'): |
||||
"""Constructs an polynomial in log sigma noise schedule.""" |
||||
ramp = torch.linspace(1, 0, n, device=device) ** rho |
||||
sigmas = torch.exp(ramp * (math.log(sigma_max) - math.log(sigma_min)) + math.log(sigma_min)) |
||||
return append_zero(sigmas) |
||||
|
||||
|
||||
def get_sigmas_vp(n, beta_d=19.9, beta_min=0.1, eps_s=1e-3, device='cpu'): |
||||
"""Constructs a continuous VP noise schedule.""" |
||||
t = torch.linspace(1, eps_s, n, device=device) |
||||
sigmas = torch.sqrt(torch.exp(beta_d * t ** 2 / 2 + beta_min * t) - 1) |
||||
return append_zero(sigmas) |
||||
|
||||
|
||||
def to_d(x, sigma, denoised): |
||||
"""Converts a denoiser output to a Karras ODE derivative.""" |
||||
return (x - denoised) / utils.append_dims(sigma, x.ndim) |
||||
|
||||
|
||||
def get_ancestral_step(sigma_from, sigma_to, eta=1.): |
||||
"""Calculates the noise level (sigma_down) to step down to and the amount |
||||
of noise to add (sigma_up) when doing an ancestral sampling step.""" |
||||
if not eta: |
||||
return sigma_to, 0. |
||||
sigma_up = min(sigma_to, eta * (sigma_to ** 2 * (sigma_from ** 2 - sigma_to ** 2) / sigma_from ** 2) ** 0.5) |
||||
sigma_down = (sigma_to ** 2 - sigma_up ** 2) ** 0.5 |
||||
return sigma_down, sigma_up |
||||
|
||||
|
||||
def default_noise_sampler(x): |
||||
return lambda sigma, sigma_next: torch.randn_like(x) |
||||
|
||||
|
||||
class BatchedBrownianTree: |
||||
"""A wrapper around torchsde.BrownianTree that enables batches of entropy.""" |
||||
|
||||
def __init__(self, x, t0, t1, seed=None, **kwargs): |
||||
t0, t1, self.sign = self.sort(t0, t1) |
||||
w0 = kwargs.get('w0', torch.zeros_like(x)) |
||||
if seed is None: |
||||
seed = torch.randint(0, 2 ** 63 - 1, []).item() |
||||
self.batched = True |
||||
try: |
||||
assert len(seed) == x.shape[0] |
||||
w0 = w0[0] |
||||
except TypeError: |
||||
seed = [seed] |
||||
self.batched = False |
||||
self.trees = [torchsde.BrownianTree(t0, w0, t1, entropy=s, **kwargs) for s in seed] |
||||
|
||||
@staticmethod |
||||
def sort(a, b): |
||||
return (a, b, 1) if a < b else (b, a, -1) |
||||
|
||||
def __call__(self, t0, t1): |
||||
t0, t1, sign = self.sort(t0, t1) |
||||
w = torch.stack([tree(t0, t1) for tree in self.trees]) * (self.sign * sign) |
||||
return w if self.batched else w[0] |
||||
|
||||
|
||||
class BrownianTreeNoiseSampler: |
||||
"""A noise sampler backed by a torchsde.BrownianTree. |
||||
|
||||
Args: |
||||
x (Tensor): The tensor whose shape, device and dtype to use to generate |
||||
random samples. |
||||
sigma_min (float): The low end of the valid interval. |
||||
sigma_max (float): The high end of the valid interval. |
||||
seed (int or List[int]): The random seed. If a list of seeds is |
||||
supplied instead of a single integer, then the noise sampler will |
||||
use one BrownianTree per batch item, each with its own seed. |
||||
transform (callable): A function that maps sigma to the sampler's |
||||
internal timestep. |
||||
""" |
||||
|
||||
def __init__(self, x, sigma_min, sigma_max, seed=None, transform=lambda x: x): |
||||
self.transform = transform |
||||
t0, t1 = self.transform(torch.as_tensor(sigma_min)), self.transform(torch.as_tensor(sigma_max)) |
||||
self.tree = BatchedBrownianTree(x, t0, t1, seed) |
||||
|
||||
def __call__(self, sigma, sigma_next): |
||||
t0, t1 = self.transform(torch.as_tensor(sigma)), self.transform(torch.as_tensor(sigma_next)) |
||||
return self.tree(t0, t1) / (t1 - t0).abs().sqrt() |
||||
|
||||
|
||||
@torch.no_grad() |
||||
def sample_euler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): |
||||
"""Implements Algorithm 2 (Euler steps) from Karras et al. (2022).""" |
||||
extra_args = {} if extra_args is None else extra_args |
||||
s_in = x.new_ones([x.shape[0]]) |
||||
for i in trange(len(sigmas) - 1, disable=disable): |
||||
gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. |
||||
eps = torch.randn_like(x) * s_noise |
||||
sigma_hat = sigmas[i] * (gamma + 1) |
||||
if gamma > 0: |
||||
x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 |
||||
denoised = model(x, sigma_hat * s_in, **extra_args) |
||||
d = to_d(x, sigma_hat, denoised) |
||||
if callback is not None: |
||||
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) |
||||
dt = sigmas[i + 1] - sigma_hat |
||||
# Euler method |
||||
x = x + d * dt |
||||
return x |
||||
|
||||
|
||||
@torch.no_grad() |
||||
def sample_euler_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): |
||||
"""Ancestral sampling with Euler method steps.""" |
||||
extra_args = {} if extra_args is None else extra_args |
||||
noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler |
||||
s_in = x.new_ones([x.shape[0]]) |
||||
for i in trange(len(sigmas) - 1, disable=disable): |
||||
denoised = model(x, sigmas[i] * s_in, **extra_args) |
||||
sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) |
||||
if callback is not None: |
||||
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) |
||||
d = to_d(x, sigmas[i], denoised) |
||||
# Euler method |
||||
dt = sigma_down - sigmas[i] |
||||
x = x + d * dt |
||||
if sigmas[i + 1] > 0: |
||||
x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up |
||||
return x |
||||
|
||||
|
||||
@torch.no_grad() |
||||
def sample_heun(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): |
||||
"""Implements Algorithm 2 (Heun steps) from Karras et al. (2022).""" |
||||
extra_args = {} if extra_args is None else extra_args |
||||
s_in = x.new_ones([x.shape[0]]) |
||||
for i in trange(len(sigmas) - 1, disable=disable): |
||||
gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. |
||||
eps = torch.randn_like(x) * s_noise |
||||
sigma_hat = sigmas[i] * (gamma + 1) |
||||
if gamma > 0: |
||||
x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 |
||||
denoised = model(x, sigma_hat * s_in, **extra_args) |
||||
d = to_d(x, sigma_hat, denoised) |
||||
if callback is not None: |
||||
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) |
||||
dt = sigmas[i + 1] - sigma_hat |
||||
if sigmas[i + 1] == 0: |
||||
# Euler method |
||||
x = x + d * dt |
||||
else: |
||||
# Heun's method |
||||
x_2 = x + d * dt |
||||
denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args) |
||||
d_2 = to_d(x_2, sigmas[i + 1], denoised_2) |
||||
d_prime = (d + d_2) / 2 |
||||
x = x + d_prime * dt |
||||
return x |
||||
|
||||
|
||||
@torch.no_grad() |
||||
def sample_dpm_2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): |
||||
"""A sampler inspired by DPM-Solver-2 and Algorithm 2 from Karras et al. (2022).""" |
||||
extra_args = {} if extra_args is None else extra_args |
||||
s_in = x.new_ones([x.shape[0]]) |
||||
for i in trange(len(sigmas) - 1, disable=disable): |
||||
gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. |
||||
eps = torch.randn_like(x) * s_noise |
||||
sigma_hat = sigmas[i] * (gamma + 1) |
||||
if gamma > 0: |
||||
x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 |
||||
denoised = model(x, sigma_hat * s_in, **extra_args) |
||||
d = to_d(x, sigma_hat, denoised) |
||||
if callback is not None: |
||||
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) |
||||
if sigmas[i + 1] == 0: |
||||
# Euler method |
||||
dt = sigmas[i + 1] - sigma_hat |
||||
x = x + d * dt |
||||
else: |
||||
# DPM-Solver-2 |
||||
sigma_mid = sigma_hat.log().lerp(sigmas[i + 1].log(), 0.5).exp() |
||||
dt_1 = sigma_mid - sigma_hat |
||||
dt_2 = sigmas[i + 1] - sigma_hat |
||||
x_2 = x + d * dt_1 |
||||
denoised_2 = model(x_2, sigma_mid * s_in, **extra_args) |
||||
d_2 = to_d(x_2, sigma_mid, denoised_2) |
||||
x = x + d_2 * dt_2 |
||||
return x |
||||
|
||||
|
||||
@torch.no_grad() |
||||
def sample_dpm_2_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): |
||||
"""Ancestral sampling with DPM-Solver second-order steps.""" |
||||
extra_args = {} if extra_args is None else extra_args |
||||
noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler |
||||
s_in = x.new_ones([x.shape[0]]) |
||||
for i in trange(len(sigmas) - 1, disable=disable): |
||||
denoised = model(x, sigmas[i] * s_in, **extra_args) |
||||
sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) |
||||
if callback is not None: |
||||
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) |
||||
d = to_d(x, sigmas[i], denoised) |
||||
if sigma_down == 0: |
||||
# Euler method |
||||
dt = sigma_down - sigmas[i] |
||||
x = x + d * dt |
||||
else: |
||||
# DPM-Solver-2 |
||||
sigma_mid = sigmas[i].log().lerp(sigma_down.log(), 0.5).exp() |
||||
dt_1 = sigma_mid - sigmas[i] |
||||
dt_2 = sigma_down - sigmas[i] |
||||
x_2 = x + d * dt_1 |
||||
denoised_2 = model(x_2, sigma_mid * s_in, **extra_args) |
||||
d_2 = to_d(x_2, sigma_mid, denoised_2) |
||||
x = x + d_2 * dt_2 |
||||
x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up |
||||
return x |
||||
|
||||
|
||||
def linear_multistep_coeff(order, t, i, j): |
||||
if order - 1 > i: |
||||
raise ValueError(f'Order {order} too high for step {i}') |
||||
def fn(tau): |
||||
prod = 1. |
||||
for k in range(order): |
||||
if j == k: |
||||
continue |
||||
prod *= (tau - t[i - k]) / (t[i - j] - t[i - k]) |
||||
return prod |
||||
return integrate.quad(fn, t[i], t[i + 1], epsrel=1e-4)[0] |
||||
|
||||
|
||||
@torch.no_grad() |
||||
def sample_lms(model, x, sigmas, extra_args=None, callback=None, disable=None, order=4): |
||||
extra_args = {} if extra_args is None else extra_args |
||||
s_in = x.new_ones([x.shape[0]]) |
||||
sigmas_cpu = sigmas.detach().cpu().numpy() |
||||
ds = [] |
||||
for i in trange(len(sigmas) - 1, disable=disable): |
||||
denoised = model(x, sigmas[i] * s_in, **extra_args) |
||||
d = to_d(x, sigmas[i], denoised) |
||||
ds.append(d) |
||||
if len(ds) > order: |
||||
ds.pop(0) |
||||
if callback is not None: |
||||
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) |
||||
cur_order = min(i + 1, order) |
||||
coeffs = [linear_multistep_coeff(cur_order, sigmas_cpu, i, j) for j in range(cur_order)] |
||||
x = x + sum(coeff * d for coeff, d in zip(coeffs, reversed(ds))) |
||||
return x |
||||
|
||||
|
||||
@torch.no_grad() |
||||
def log_likelihood(model, x, sigma_min, sigma_max, extra_args=None, atol=1e-4, rtol=1e-4): |
||||
extra_args = {} if extra_args is None else extra_args |
||||
s_in = x.new_ones([x.shape[0]]) |
||||
v = torch.randint_like(x, 2) * 2 - 1 |
||||
fevals = 0 |
||||
def ode_fn(sigma, x): |
||||
nonlocal fevals |
||||
with torch.enable_grad(): |
||||
x = x[0].detach().requires_grad_() |
||||
denoised = model(x, sigma * s_in, **extra_args) |
||||
d = to_d(x, sigma, denoised) |
||||
fevals += 1 |
||||
grad = torch.autograd.grad((d * v).sum(), x)[0] |
||||
d_ll = (v * grad).flatten(1).sum(1) |
||||
return d.detach(), d_ll |
||||
x_min = x, x.new_zeros([x.shape[0]]) |
||||
t = x.new_tensor([sigma_min, sigma_max]) |
||||
sol = odeint(ode_fn, x_min, t, atol=atol, rtol=rtol, method='dopri5') |
||||
latent, delta_ll = sol[0][-1], sol[1][-1] |
||||
ll_prior = torch.distributions.Normal(0, sigma_max).log_prob(latent).flatten(1).sum(1) |
||||
return ll_prior + delta_ll, {'fevals': fevals} |
||||
|
||||
|
||||
class PIDStepSizeController: |
||||
"""A PID controller for ODE adaptive step size control.""" |
||||
def __init__(self, h, pcoeff, icoeff, dcoeff, order=1, accept_safety=0.81, eps=1e-8): |
||||
self.h = h |
||||
self.b1 = (pcoeff + icoeff + dcoeff) / order |
||||
self.b2 = -(pcoeff + 2 * dcoeff) / order |
||||
self.b3 = dcoeff / order |
||||
self.accept_safety = accept_safety |
||||
self.eps = eps |
||||
self.errs = [] |
||||
|
||||
def limiter(self, x): |
||||
return 1 + math.atan(x - 1) |
||||
|
||||
def propose_step(self, error): |
||||
inv_error = 1 / (float(error) + self.eps) |
||||
if not self.errs: |
||||
self.errs = [inv_error, inv_error, inv_error] |
||||
self.errs[0] = inv_error |
||||
factor = self.errs[0] ** self.b1 * self.errs[1] ** self.b2 * self.errs[2] ** self.b3 |
||||
factor = self.limiter(factor) |
||||
accept = factor >= self.accept_safety |
||||
if accept: |
||||
self.errs[2] = self.errs[1] |
||||
self.errs[1] = self.errs[0] |
||||
self.h *= factor |
||||
return accept |
||||
|
||||
|
||||
class DPMSolver(nn.Module): |
||||
"""DPM-Solver. See https://arxiv.org/abs/2206.00927.""" |
||||
|
||||
def __init__(self, model, extra_args=None, eps_callback=None, info_callback=None): |
||||
super().__init__() |
||||
self.model = model |
||||
self.extra_args = {} if extra_args is None else extra_args |
||||
self.eps_callback = eps_callback |
||||
self.info_callback = info_callback |
||||
|
||||
def t(self, sigma): |
||||
return -sigma.log() |
||||
|
||||
def sigma(self, t): |
||||
return t.neg().exp() |
||||
|
||||
def eps(self, eps_cache, key, x, t, *args, **kwargs): |
||||
if key in eps_cache: |
||||
return eps_cache[key], eps_cache |
||||
sigma = self.sigma(t) * x.new_ones([x.shape[0]]) |
||||
eps = (x - self.model(x, sigma, *args, **self.extra_args, **kwargs)) / self.sigma(t) |
||||
if self.eps_callback is not None: |
||||
self.eps_callback() |
||||
return eps, {key: eps, **eps_cache} |
||||
|
||||
def dpm_solver_1_step(self, x, t, t_next, eps_cache=None): |
||||
eps_cache = {} if eps_cache is None else eps_cache |
||||
h = t_next - t |
||||
eps, eps_cache = self.eps(eps_cache, 'eps', x, t) |
||||
x_1 = x - self.sigma(t_next) * h.expm1() * eps |
||||
return x_1, eps_cache |
||||
|
||||
def dpm_solver_2_step(self, x, t, t_next, r1=1 / 2, eps_cache=None): |
||||
eps_cache = {} if eps_cache is None else eps_cache |
||||
h = t_next - t |
||||
eps, eps_cache = self.eps(eps_cache, 'eps', x, t) |
||||
s1 = t + r1 * h |
||||
u1 = x - self.sigma(s1) * (r1 * h).expm1() * eps |
||||
eps_r1, eps_cache = self.eps(eps_cache, 'eps_r1', u1, s1) |
||||
x_2 = x - self.sigma(t_next) * h.expm1() * eps - self.sigma(t_next) / (2 * r1) * h.expm1() * (eps_r1 - eps) |
||||
return x_2, eps_cache |
||||
|
||||
def dpm_solver_3_step(self, x, t, t_next, r1=1 / 3, r2=2 / 3, eps_cache=None): |
||||
eps_cache = {} if eps_cache is None else eps_cache |
||||
h = t_next - t |
||||
eps, eps_cache = self.eps(eps_cache, 'eps', x, t) |
||||
s1 = t + r1 * h |
||||
s2 = t + r2 * h |
||||
u1 = x - self.sigma(s1) * (r1 * h).expm1() * eps |
||||
eps_r1, eps_cache = self.eps(eps_cache, 'eps_r1', u1, s1) |
||||
u2 = x - self.sigma(s2) * (r2 * h).expm1() * eps - self.sigma(s2) * (r2 / r1) * ((r2 * h).expm1() / (r2 * h) - 1) * (eps_r1 - eps) |
||||
eps_r2, eps_cache = self.eps(eps_cache, 'eps_r2', u2, s2) |
||||
x_3 = x - self.sigma(t_next) * h.expm1() * eps - self.sigma(t_next) / r2 * (h.expm1() / h - 1) * (eps_r2 - eps) |
||||
return x_3, eps_cache |
||||
|
||||
def dpm_solver_fast(self, x, t_start, t_end, nfe, eta=0., s_noise=1., noise_sampler=None): |
||||
noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler |
||||
if not t_end > t_start and eta: |
||||
raise ValueError('eta must be 0 for reverse sampling') |
||||
|
||||
m = math.floor(nfe / 3) + 1 |
||||
ts = torch.linspace(t_start, t_end, m + 1, device=x.device) |
||||
|
||||
if nfe % 3 == 0: |
||||
orders = [3] * (m - 2) + [2, 1] |
||||
else: |
||||
orders = [3] * (m - 1) + [nfe % 3] |
||||
|
||||
for i in range(len(orders)): |
||||
eps_cache = {} |
||||
t, t_next = ts[i], ts[i + 1] |
||||
if eta: |
||||
sd, su = get_ancestral_step(self.sigma(t), self.sigma(t_next), eta) |
||||
t_next_ = torch.minimum(t_end, self.t(sd)) |
||||
su = (self.sigma(t_next) ** 2 - self.sigma(t_next_) ** 2) ** 0.5 |
||||
else: |
||||
t_next_, su = t_next, 0. |
||||
|
||||
eps, eps_cache = self.eps(eps_cache, 'eps', x, t) |
||||
denoised = x - self.sigma(t) * eps |
||||
if self.info_callback is not None: |
||||
self.info_callback({'x': x, 'i': i, 't': ts[i], 't_up': t, 'denoised': denoised}) |
||||
|
||||
if orders[i] == 1: |
||||
x, eps_cache = self.dpm_solver_1_step(x, t, t_next_, eps_cache=eps_cache) |
||||
elif orders[i] == 2: |
||||
x, eps_cache = self.dpm_solver_2_step(x, t, t_next_, eps_cache=eps_cache) |
||||
else: |
||||
x, eps_cache = self.dpm_solver_3_step(x, t, t_next_, eps_cache=eps_cache) |
||||
|
||||
x = x + su * s_noise * noise_sampler(self.sigma(t), self.sigma(t_next)) |
||||
|
||||
return x |
||||
|
||||
def dpm_solver_adaptive(self, x, t_start, t_end, order=3, rtol=0.05, atol=0.0078, h_init=0.05, pcoeff=0., icoeff=1., dcoeff=0., accept_safety=0.81, eta=0., s_noise=1., noise_sampler=None): |
||||
noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler |
||||
if order not in {2, 3}: |
||||
raise ValueError('order should be 2 or 3') |
||||
forward = t_end > t_start |
||||
if not forward and eta: |
||||
raise ValueError('eta must be 0 for reverse sampling') |
||||
h_init = abs(h_init) * (1 if forward else -1) |
||||
atol = torch.tensor(atol) |
||||
rtol = torch.tensor(rtol) |
||||
s = t_start |
||||
x_prev = x |
||||
accept = True |
||||
pid = PIDStepSizeController(h_init, pcoeff, icoeff, dcoeff, 1.5 if eta else order, accept_safety) |
||||
info = {'steps': 0, 'nfe': 0, 'n_accept': 0, 'n_reject': 0} |
||||
|
||||
while s < t_end - 1e-5 if forward else s > t_end + 1e-5: |
||||
eps_cache = {} |
||||
t = torch.minimum(t_end, s + pid.h) if forward else torch.maximum(t_end, s + pid.h) |
||||
if eta: |
||||
sd, su = get_ancestral_step(self.sigma(s), self.sigma(t), eta) |
||||
t_ = torch.minimum(t_end, self.t(sd)) |
||||
su = (self.sigma(t) ** 2 - self.sigma(t_) ** 2) ** 0.5 |
||||
else: |
||||
t_, su = t, 0. |
||||
|
||||
eps, eps_cache = self.eps(eps_cache, 'eps', x, s) |
||||
denoised = x - self.sigma(s) * eps |
||||
|
||||
if order == 2: |
||||
x_low, eps_cache = self.dpm_solver_1_step(x, s, t_, eps_cache=eps_cache) |
||||
x_high, eps_cache = self.dpm_solver_2_step(x, s, t_, eps_cache=eps_cache) |
||||
else: |
||||
x_low, eps_cache = self.dpm_solver_2_step(x, s, t_, r1=1 / 3, eps_cache=eps_cache) |
||||
x_high, eps_cache = self.dpm_solver_3_step(x, s, t_, eps_cache=eps_cache) |
||||
delta = torch.maximum(atol, rtol * torch.maximum(x_low.abs(), x_prev.abs())) |
||||
error = torch.linalg.norm((x_low - x_high) / delta) / x.numel() ** 0.5 |
||||
accept = pid.propose_step(error) |
||||
if accept: |
||||
x_prev = x_low |
||||
x = x_high + su * s_noise * noise_sampler(self.sigma(s), self.sigma(t)) |
||||
s = t |
||||
info['n_accept'] += 1 |
||||
else: |
||||
info['n_reject'] += 1 |
||||
info['nfe'] += order |
||||
info['steps'] += 1 |
||||
|
||||
if self.info_callback is not None: |
||||
self.info_callback({'x': x, 'i': info['steps'] - 1, 't': s, 't_up': s, 'denoised': denoised, 'error': error, 'h': pid.h, **info}) |
||||
|
||||
return x, info |
||||
|
||||
|
||||
@torch.no_grad() |
||||
def sample_dpm_fast(model, x, sigma_min, sigma_max, n, extra_args=None, callback=None, disable=None, eta=0., s_noise=1., noise_sampler=None): |
||||
"""DPM-Solver-Fast (fixed step size). See https://arxiv.org/abs/2206.00927.""" |
||||
if sigma_min <= 0 or sigma_max <= 0: |
||||
raise ValueError('sigma_min and sigma_max must not be 0') |
||||
with tqdm(total=n, disable=disable) as pbar: |
||||
dpm_solver = DPMSolver(model, extra_args, eps_callback=pbar.update) |
||||
if callback is not None: |
||||
dpm_solver.info_callback = lambda info: callback({'sigma': dpm_solver.sigma(info['t']), 'sigma_hat': dpm_solver.sigma(info['t_up']), **info}) |
||||
return dpm_solver.dpm_solver_fast(x, dpm_solver.t(torch.tensor(sigma_max)), dpm_solver.t(torch.tensor(sigma_min)), n, eta, s_noise, noise_sampler) |
||||
|
||||
|
||||
@torch.no_grad() |
||||
def sample_dpm_adaptive(model, x, sigma_min, sigma_max, extra_args=None, callback=None, disable=None, order=3, rtol=0.05, atol=0.0078, h_init=0.05, pcoeff=0., icoeff=1., dcoeff=0., accept_safety=0.81, eta=0., s_noise=1., noise_sampler=None, return_info=False): |
||||
"""DPM-Solver-12 and 23 (adaptive step size). See https://arxiv.org/abs/2206.00927.""" |
||||
if sigma_min <= 0 or sigma_max <= 0: |
||||
raise ValueError('sigma_min and sigma_max must not be 0') |
||||
with tqdm(disable=disable) as pbar: |
||||
dpm_solver = DPMSolver(model, extra_args, eps_callback=pbar.update) |
||||
if callback is not None: |
||||
dpm_solver.info_callback = lambda info: callback({'sigma': dpm_solver.sigma(info['t']), 'sigma_hat': dpm_solver.sigma(info['t_up']), **info}) |
||||
x, info = dpm_solver.dpm_solver_adaptive(x, dpm_solver.t(torch.tensor(sigma_max)), dpm_solver.t(torch.tensor(sigma_min)), order, rtol, atol, h_init, pcoeff, icoeff, dcoeff, accept_safety, eta, s_noise, noise_sampler) |
||||
if return_info: |
||||
return x, info |
||||
return x |
||||
|
||||
|
||||
@torch.no_grad() |
||||
def sample_dpmpp_2s_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): |
||||
"""Ancestral sampling with DPM-Solver++(2S) second-order steps.""" |
||||
extra_args = {} if extra_args is None else extra_args |
||||
noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler |
||||
s_in = x.new_ones([x.shape[0]]) |
||||
sigma_fn = lambda t: t.neg().exp() |
||||
t_fn = lambda sigma: sigma.log().neg() |
||||
|
||||
for i in trange(len(sigmas) - 1, disable=disable): |
||||
denoised = model(x, sigmas[i] * s_in, **extra_args) |
||||
sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) |
||||
if callback is not None: |
||||
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) |
||||
if sigma_down == 0: |
||||
# Euler method |
||||
d = to_d(x, sigmas[i], denoised) |
||||
dt = sigma_down - sigmas[i] |
||||
x = x + d * dt |
||||
else: |
||||
# DPM-Solver++(2S) |
||||
t, t_next = t_fn(sigmas[i]), t_fn(sigma_down) |
||||
r = 1 / 2 |
||||
h = t_next - t |
||||
s = t + r * h |
||||
x_2 = (sigma_fn(s) / sigma_fn(t)) * x - (-h * r).expm1() * denoised |
||||
denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args) |
||||
x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_2 |
||||
# Noise addition |
||||
if sigmas[i + 1] > 0: |
||||
x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up |
||||
return x |
||||
|
||||
|
||||
@torch.no_grad() |
||||
def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2): |
||||
"""DPM-Solver++ (stochastic).""" |
||||
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() |
||||
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max) if noise_sampler is None else noise_sampler |
||||
extra_args = {} if extra_args is None else extra_args |
||||
s_in = x.new_ones([x.shape[0]]) |
||||
sigma_fn = lambda t: t.neg().exp() |
||||
t_fn = lambda sigma: sigma.log().neg() |
||||
|
||||
for i in trange(len(sigmas) - 1, disable=disable): |
||||
denoised = model(x, sigmas[i] * s_in, **extra_args) |
||||
if callback is not None: |
||||
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) |
||||
if sigmas[i + 1] == 0: |
||||
# Euler method |
||||
d = to_d(x, sigmas[i], denoised) |
||||
dt = sigmas[i + 1] - sigmas[i] |
||||
x = x + d * dt |
||||
else: |
||||
# DPM-Solver++ |
||||
t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1]) |
||||
h = t_next - t |
||||
s = t + h * r |
||||
fac = 1 / (2 * r) |
||||
|
||||
# Step 1 |
||||
sd, su = get_ancestral_step(sigma_fn(t), sigma_fn(s), eta) |
||||
s_ = t_fn(sd) |
||||
x_2 = (sigma_fn(s_) / sigma_fn(t)) * x - (t - s_).expm1() * denoised |
||||
x_2 = x_2 + noise_sampler(sigma_fn(t), sigma_fn(s)) * s_noise * su |
||||
denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args) |
||||
|
||||
# Step 2 |
||||
sd, su = get_ancestral_step(sigma_fn(t), sigma_fn(t_next), eta) |
||||
t_next_ = t_fn(sd) |
||||
denoised_d = (1 - fac) * denoised + fac * denoised_2 |
||||
x = (sigma_fn(t_next_) / sigma_fn(t)) * x - (t - t_next_).expm1() * denoised_d |
||||
x = x + noise_sampler(sigma_fn(t), sigma_fn(t_next)) * s_noise * su |
||||
return x |
||||
|
||||
|
||||
@torch.no_grad() |
||||
def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, disable=None): |
||||
"""DPM-Solver++(2M).""" |
||||
extra_args = {} if extra_args is None else extra_args |
||||
s_in = x.new_ones([x.shape[0]]) |
||||
sigma_fn = lambda t: t.neg().exp() |
||||
t_fn = lambda sigma: sigma.log().neg() |
||||
old_denoised = None |
||||
|
||||
for i in trange(len(sigmas) - 1, disable=disable): |
||||
denoised = model(x, sigmas[i] * s_in, **extra_args) |
||||
if callback is not None: |
||||
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) |
||||
t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1]) |
||||
h = t_next - t |
||||
if old_denoised is None or sigmas[i + 1] == 0: |
||||
x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised |
||||
else: |
||||
h_last = t - t_fn(sigmas[i - 1]) |
||||
r = h_last / h |
||||
denoised_d = (1 + 1 / (2 * r)) * denoised - (1 / (2 * r)) * old_denoised |
||||
x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_d |
||||
old_denoised = denoised |
||||
return x |
||||
@ -0,0 +1,332 @@
|
||||
from contextlib import contextmanager |
||||
import hashlib |
||||
import math |
||||
from pathlib import Path |
||||
import shutil |
||||
import urllib |
||||
import warnings |
||||
|
||||
from PIL import Image |
||||
import torch |
||||
from torch import nn, optim |
||||
from torch.utils import data |
||||
from torchvision.transforms import functional as TF |
||||
|
||||
|
||||
def from_pil_image(x): |
||||
"""Converts from a PIL image to a tensor.""" |
||||
x = TF.to_tensor(x) |
||||
if x.ndim == 2: |
||||
x = x[..., None] |
||||
return x * 2 - 1 |
||||
|
||||
|
||||
def to_pil_image(x): |
||||
"""Converts from a tensor to a PIL image.""" |
||||
if x.ndim == 4: |
||||
assert x.shape[0] == 1 |
||||
x = x[0] |
||||
if x.shape[0] == 1: |
||||
x = x[0] |
||||
return TF.to_pil_image((x.clamp(-1, 1) + 1) / 2) |
||||
|
||||
|
||||
def hf_datasets_augs_helper(examples, transform, image_key, mode='RGB'): |
||||
"""Apply passed in transforms for HuggingFace Datasets.""" |
||||
images = [transform(image.convert(mode)) for image in examples[image_key]] |
||||
return {image_key: images} |
||||
|
||||
|
||||
def append_dims(x, target_dims): |
||||
"""Appends dimensions to the end of a tensor until it has target_dims dimensions.""" |
||||
dims_to_append = target_dims - x.ndim |
||||
if dims_to_append < 0: |
||||
raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less') |
||||
expanded = x[(...,) + (None,) * dims_to_append] |
||||
# MPS will get inf values if it tries to index into the new axes, but detaching fixes this. |
||||
# https://github.com/pytorch/pytorch/issues/84364 |
||||
return expanded.detach().clone() if expanded.device.type == 'mps' else expanded |
||||
|
||||
|
||||
def n_params(module): |
||||
"""Returns the number of trainable parameters in a module.""" |
||||
return sum(p.numel() for p in module.parameters()) |
||||
|
||||
|
||||
def download_file(path, url, digest=None): |
||||
"""Downloads a file if it does not exist, optionally checking its SHA-256 hash.""" |
||||
path = Path(path) |
||||
path.parent.mkdir(parents=True, exist_ok=True) |
||||
if not path.exists(): |
||||
with urllib.request.urlopen(url) as response, open(path, 'wb') as f: |
||||
shutil.copyfileobj(response, f) |
||||
if digest is not None: |
||||
file_digest = hashlib.sha256(open(path, 'rb').read()).hexdigest() |
||||
if digest != file_digest: |
||||
raise OSError(f'hash of {path} (url: {url}) failed to validate') |
||||
return path |
||||
|
||||
|
||||
@contextmanager |
||||
def train_mode(model, mode=True): |
||||
"""A context manager that places a model into training mode and restores |
||||
the previous mode on exit.""" |
||||
modes = [module.training for module in model.modules()] |
||||
try: |
||||
yield model.train(mode) |
||||
finally: |
||||
for i, module in enumerate(model.modules()): |
||||
module.training = modes[i] |
||||
|
||||
|
||||
def eval_mode(model): |
||||
"""A context manager that places a model into evaluation mode and restores |
||||
the previous mode on exit.""" |
||||
return train_mode(model, False) |
||||
|
||||
|
||||
@torch.no_grad() |
||||
def ema_update(model, averaged_model, decay): |
||||
"""Incorporates updated model parameters into an exponential moving averaged |
||||
version of a model. It should be called after each optimizer step.""" |
||||
model_params = dict(model.named_parameters()) |
||||
averaged_params = dict(averaged_model.named_parameters()) |
||||
assert model_params.keys() == averaged_params.keys() |
||||
|
||||
for name, param in model_params.items(): |
||||
averaged_params[name].mul_(decay).add_(param, alpha=1 - decay) |
||||
|
||||
model_buffers = dict(model.named_buffers()) |
||||
averaged_buffers = dict(averaged_model.named_buffers()) |
||||
assert model_buffers.keys() == averaged_buffers.keys() |
||||
|
||||
for name, buf in model_buffers.items(): |
||||
averaged_buffers[name].copy_(buf) |
||||
|
||||
|
||||
class EMAWarmup: |
||||
"""Implements an EMA warmup using an inverse decay schedule. |
||||
If inv_gamma=1 and power=1, implements a simple average. inv_gamma=1, power=2/3 are |
||||
good values for models you plan to train for a million or more steps (reaches decay |
||||
factor 0.999 at 31.6K steps, 0.9999 at 1M steps), inv_gamma=1, power=3/4 for models |
||||
you plan to train for less (reaches decay factor 0.999 at 10K steps, 0.9999 at |
||||
215.4k steps). |
||||
Args: |
||||
inv_gamma (float): Inverse multiplicative factor of EMA warmup. Default: 1. |
||||
power (float): Exponential factor of EMA warmup. Default: 1. |
||||
min_value (float): The minimum EMA decay rate. Default: 0. |
||||
max_value (float): The maximum EMA decay rate. Default: 1. |
||||
start_at (int): The epoch to start averaging at. Default: 0. |
||||
last_epoch (int): The index of last epoch. Default: 0. |
||||
""" |
||||
|
||||
def __init__(self, inv_gamma=1., power=1., min_value=0., max_value=1., start_at=0, |
||||
last_epoch=0): |
||||
self.inv_gamma = inv_gamma |
||||
self.power = power |
||||
self.min_value = min_value |
||||
self.max_value = max_value |
||||
self.start_at = start_at |
||||
self.last_epoch = last_epoch |
||||
|
||||
def state_dict(self): |
||||
"""Returns the state of the class as a :class:`dict`.""" |
||||
return dict(self.__dict__.items()) |
||||
|
||||
def load_state_dict(self, state_dict): |
||||
"""Loads the class's state. |
||||
Args: |
||||
state_dict (dict): scaler state. Should be an object returned |
||||
from a call to :meth:`state_dict`. |
||||
""" |
||||
self.__dict__.update(state_dict) |
||||
|
||||
def get_value(self): |
||||
"""Gets the current EMA decay rate.""" |
||||
epoch = max(0, self.last_epoch - self.start_at) |
||||
value = 1 - (1 + epoch / self.inv_gamma) ** -self.power |
||||
return 0. if epoch < 0 else min(self.max_value, max(self.min_value, value)) |
||||
|
||||
def step(self): |
||||
"""Updates the step count.""" |
||||
self.last_epoch += 1 |
||||
|
||||
|
||||
class InverseLR(optim.lr_scheduler._LRScheduler): |
||||
"""Implements an inverse decay learning rate schedule with an optional exponential |
||||
warmup. When last_epoch=-1, sets initial lr as lr. |
||||
inv_gamma is the number of steps/epochs required for the learning rate to decay to |
||||
(1 / 2)**power of its original value. |
||||
Args: |
||||
optimizer (Optimizer): Wrapped optimizer. |
||||
inv_gamma (float): Inverse multiplicative factor of learning rate decay. Default: 1. |
||||
power (float): Exponential factor of learning rate decay. Default: 1. |
||||
warmup (float): Exponential warmup factor (0 <= warmup < 1, 0 to disable) |
||||
Default: 0. |
||||
min_lr (float): The minimum learning rate. Default: 0. |
||||
last_epoch (int): The index of last epoch. Default: -1. |
||||
verbose (bool): If ``True``, prints a message to stdout for |
||||
each update. Default: ``False``. |
||||
""" |
||||
|
||||
def __init__(self, optimizer, inv_gamma=1., power=1., warmup=0., min_lr=0., |
||||
last_epoch=-1, verbose=False): |
||||
self.inv_gamma = inv_gamma |
||||
self.power = power |
||||
if not 0. <= warmup < 1: |
||||
raise ValueError('Invalid value for warmup') |
||||
self.warmup = warmup |
||||
self.min_lr = min_lr |
||||
super().__init__(optimizer, last_epoch, verbose) |
||||
|
||||
def get_lr(self): |
||||
if not self._get_lr_called_within_step: |
||||
warnings.warn("To get the last learning rate computed by the scheduler, " |
||||
"please use `get_last_lr()`.") |
||||
|
||||
return self._get_closed_form_lr() |
||||
|
||||
def _get_closed_form_lr(self): |
||||
warmup = 1 - self.warmup ** (self.last_epoch + 1) |
||||
lr_mult = (1 + self.last_epoch / self.inv_gamma) ** -self.power |
||||
return [warmup * max(self.min_lr, base_lr * lr_mult) |
||||
for base_lr in self.base_lrs] |
||||
|
||||
|
||||
class ExponentialLR(optim.lr_scheduler._LRScheduler): |
||||
"""Implements an exponential learning rate schedule with an optional exponential |
||||
warmup. When last_epoch=-1, sets initial lr as lr. Decays the learning rate |
||||
continuously by decay (default 0.5) every num_steps steps. |
||||
Args: |
||||
optimizer (Optimizer): Wrapped optimizer. |
||||
num_steps (float): The number of steps to decay the learning rate by decay in. |
||||
decay (float): The factor by which to decay the learning rate every num_steps |
||||
steps. Default: 0.5. |
||||
warmup (float): Exponential warmup factor (0 <= warmup < 1, 0 to disable) |
||||
Default: 0. |
||||
min_lr (float): The minimum learning rate. Default: 0. |
||||
last_epoch (int): The index of last epoch. Default: -1. |
||||
verbose (bool): If ``True``, prints a message to stdout for |
||||
each update. Default: ``False``. |
||||
""" |
||||
|
||||
def __init__(self, optimizer, num_steps, decay=0.5, warmup=0., min_lr=0., |
||||
last_epoch=-1, verbose=False): |
||||
self.num_steps = num_steps |
||||
self.decay = decay |
||||
if not 0. <= warmup < 1: |
||||
raise ValueError('Invalid value for warmup') |
||||
self.warmup = warmup |
||||
self.min_lr = min_lr |
||||
super().__init__(optimizer, last_epoch, verbose) |
||||
|
||||
def get_lr(self): |
||||
if not self._get_lr_called_within_step: |
||||
warnings.warn("To get the last learning rate computed by the scheduler, " |
||||
"please use `get_last_lr()`.") |
||||
|
||||
return self._get_closed_form_lr() |
||||
|
||||
def _get_closed_form_lr(self): |
||||
warmup = 1 - self.warmup ** (self.last_epoch + 1) |
||||
lr_mult = (self.decay ** (1 / self.num_steps)) ** self.last_epoch |
||||
return [warmup * max(self.min_lr, base_lr * lr_mult) |
||||
for base_lr in self.base_lrs] |
||||
|
||||
|
||||
def rand_log_normal(shape, loc=0., scale=1., device='cpu', dtype=torch.float32): |
||||
"""Draws samples from an lognormal distribution.""" |
||||
return (torch.randn(shape, device=device, dtype=dtype) * scale + loc).exp() |
||||
|
||||
|
||||
def rand_log_logistic(shape, loc=0., scale=1., min_value=0., max_value=float('inf'), device='cpu', dtype=torch.float32): |
||||
"""Draws samples from an optionally truncated log-logistic distribution.""" |
||||
min_value = torch.as_tensor(min_value, device=device, dtype=torch.float64) |
||||
max_value = torch.as_tensor(max_value, device=device, dtype=torch.float64) |
||||
min_cdf = min_value.log().sub(loc).div(scale).sigmoid() |
||||
max_cdf = max_value.log().sub(loc).div(scale).sigmoid() |
||||
u = torch.rand(shape, device=device, dtype=torch.float64) * (max_cdf - min_cdf) + min_cdf |
||||
return u.logit().mul(scale).add(loc).exp().to(dtype) |
||||
|
||||
|
||||
def rand_log_uniform(shape, min_value, max_value, device='cpu', dtype=torch.float32): |
||||
"""Draws samples from an log-uniform distribution.""" |
||||
min_value = math.log(min_value) |
||||
max_value = math.log(max_value) |
||||
return (torch.rand(shape, device=device, dtype=dtype) * (max_value - min_value) + min_value).exp() |
||||
|
||||
|
||||
def rand_v_diffusion(shape, sigma_data=1., min_value=0., max_value=float('inf'), device='cpu', dtype=torch.float32): |
||||
"""Draws samples from a truncated v-diffusion training timestep distribution.""" |
||||
min_cdf = math.atan(min_value / sigma_data) * 2 / math.pi |
||||
max_cdf = math.atan(max_value / sigma_data) * 2 / math.pi |
||||
u = torch.rand(shape, device=device, dtype=dtype) * (max_cdf - min_cdf) + min_cdf |
||||
return torch.tan(u * math.pi / 2) * sigma_data |
||||
|
||||
|
||||
def rand_split_log_normal(shape, loc, scale_1, scale_2, device='cpu', dtype=torch.float32): |
||||
"""Draws samples from a split lognormal distribution.""" |
||||
n = torch.randn(shape, device=device, dtype=dtype).abs() |
||||
u = torch.rand(shape, device=device, dtype=dtype) |
||||
n_left = n * -scale_1 + loc |
||||
n_right = n * scale_2 + loc |
||||
ratio = scale_1 / (scale_1 + scale_2) |
||||
return torch.where(u < ratio, n_left, n_right).exp() |
||||
|
||||
|
||||
class FolderOfImages(data.Dataset): |
||||
"""Recursively finds all images in a directory. It does not support |
||||
classes/targets.""" |
||||
|
||||
IMG_EXTENSIONS = {'.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif', '.tiff', '.webp'} |
||||
|
||||
def __init__(self, root, transform=None): |
||||
super().__init__() |
||||
self.root = Path(root) |
||||
self.transform = nn.Identity() if transform is None else transform |
||||
self.paths = sorted(path for path in self.root.rglob('*') if path.suffix.lower() in self.IMG_EXTENSIONS) |
||||
|
||||
def __repr__(self): |
||||
return f'FolderOfImages(root="{self.root}", len: {len(self)})' |
||||
|
||||
def __len__(self): |
||||
return len(self.paths) |
||||
|
||||
def __getitem__(self, key): |
||||
path = self.paths[key] |
||||
with open(path, 'rb') as f: |
||||
image = Image.open(f).convert('RGB') |
||||
image = self.transform(image) |
||||
return image, |
||||
|
||||
|
||||
class CSVLogger: |
||||
def __init__(self, filename, columns): |
||||
self.filename = Path(filename) |
||||
self.columns = columns |
||||
if self.filename.exists(): |
||||
self.file = open(self.filename, 'a') |
||||
else: |
||||
self.file = open(self.filename, 'w') |
||||
self.write(*self.columns) |
||||
|
||||
def write(self, *args): |
||||
print(*args, sep=',', file=self.file, flush=True) |
||||
|
||||
|
||||
@contextmanager |
||||
def tf32_mode(cudnn=None, matmul=None): |
||||
"""A context manager that sets whether TF32 is allowed on cuDNN or matmul.""" |
||||
cudnn_old = torch.backends.cudnn.allow_tf32 |
||||
matmul_old = torch.backends.cuda.matmul.allow_tf32 |
||||
try: |
||||
if cudnn is not None: |
||||
torch.backends.cudnn.allow_tf32 = cudnn |
||||
if matmul is not None: |
||||
torch.backends.cuda.matmul.allow_tf32 = matmul |
||||
yield |
||||
finally: |
||||
if cudnn is not None: |
||||
torch.backends.cudnn.allow_tf32 = cudnn_old |
||||
if matmul is not None: |
||||
torch.backends.cuda.matmul.allow_tf32 = matmul_old |
||||
@ -0,0 +1,24 @@
|
||||
import torch |
||||
|
||||
from ldm.modules.midas.api import load_midas_transform |
||||
|
||||
|
||||
class AddMiDaS(object): |
||||
def __init__(self, model_type): |
||||
super().__init__() |
||||
self.transform = load_midas_transform(model_type) |
||||
|
||||
def pt2np(self, x): |
||||
x = ((x + 1.0) * .5).detach().cpu().numpy() |
||||
return x |
||||
|
||||
def np2pt(self, x): |
||||
x = torch.from_numpy(x) * 2 - 1. |
||||
return x |
||||
|
||||
def __call__(self, sample): |
||||
# sample['jpg'] is tensor hwc in [-1, 1] at this point |
||||
x = self.pt2np(sample['jpg']) |
||||
x = self.transform({"image": x})["image"] |
||||
sample['midas_in'] = x |
||||
return sample |
||||
@ -0,0 +1,223 @@
|
||||
import torch |
||||
# import pytorch_lightning as pl |
||||
import torch.nn.functional as F |
||||
from contextlib import contextmanager |
||||
|
||||
from ldm.modules.diffusionmodules.model import Encoder, Decoder |
||||
from ldm.modules.distributions.distributions import DiagonalGaussianDistribution |
||||
|
||||
from ldm.util import instantiate_from_config |
||||
from ldm.modules.ema import LitEma |
||||
|
||||
# class AutoencoderKL(pl.LightningModule): |
||||
class AutoencoderKL(torch.nn.Module): |
||||
def __init__(self, |
||||
ddconfig, |
||||
lossconfig, |
||||
embed_dim, |
||||
ckpt_path=None, |
||||
ignore_keys=[], |
||||
image_key="image", |
||||
colorize_nlabels=None, |
||||
monitor=None, |
||||
ema_decay=None, |
||||
learn_logvar=False |
||||
): |
||||
super().__init__() |
||||
self.learn_logvar = learn_logvar |
||||
self.image_key = image_key |
||||
self.encoder = Encoder(**ddconfig) |
||||
self.decoder = Decoder(**ddconfig) |
||||
self.loss = instantiate_from_config(lossconfig) |
||||
assert ddconfig["double_z"] |
||||
self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1) |
||||
self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1) |
||||
self.embed_dim = embed_dim |
||||
if colorize_nlabels is not None: |
||||
assert type(colorize_nlabels)==int |
||||
self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1)) |
||||
if monitor is not None: |
||||
self.monitor = monitor |
||||
|
||||
self.use_ema = ema_decay is not None |
||||
if self.use_ema: |
||||
self.ema_decay = ema_decay |
||||
assert 0. < ema_decay < 1. |
||||
self.model_ema = LitEma(self, decay=ema_decay) |
||||
print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") |
||||
|
||||
if ckpt_path is not None: |
||||
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys) |
||||
|
||||
def init_from_ckpt(self, path, ignore_keys=list()): |
||||
if path.lower().endswith(".safetensors"): |
||||
import safetensors.torch |
||||
sd = safetensors.torch.load_file(path, device="cpu") |
||||
else: |
||||
sd = torch.load(path, map_location="cpu")["state_dict"] |
||||
keys = list(sd.keys()) |
||||
for k in keys: |
||||
for ik in ignore_keys: |
||||
if k.startswith(ik): |
||||
print("Deleting key {} from state_dict.".format(k)) |
||||
del sd[k] |
||||
self.load_state_dict(sd, strict=False) |
||||
print(f"Restored from {path}") |
||||
|
||||
@contextmanager |
||||
def ema_scope(self, context=None): |
||||
if self.use_ema: |
||||
self.model_ema.store(self.parameters()) |
||||
self.model_ema.copy_to(self) |
||||
if context is not None: |
||||
print(f"{context}: Switched to EMA weights") |
||||
try: |
||||
yield None |
||||
finally: |
||||
if self.use_ema: |
||||
self.model_ema.restore(self.parameters()) |
||||
if context is not None: |
||||
print(f"{context}: Restored training weights") |
||||
|
||||
def on_train_batch_end(self, *args, **kwargs): |
||||
if self.use_ema: |
||||
self.model_ema(self) |
||||
|
||||
def encode(self, x): |
||||
h = self.encoder(x) |
||||
moments = self.quant_conv(h) |
||||
posterior = DiagonalGaussianDistribution(moments) |
||||
return posterior |
||||
|
||||
def decode(self, z): |
||||
z = self.post_quant_conv(z) |
||||
dec = self.decoder(z) |
||||
return dec |
||||
|
||||
def forward(self, input, sample_posterior=True): |
||||
posterior = self.encode(input) |
||||
if sample_posterior: |
||||
z = posterior.sample() |
||||
else: |
||||
z = posterior.mode() |
||||
dec = self.decode(z) |
||||
return dec, posterior |
||||
|
||||
def get_input(self, batch, k): |
||||
x = batch[k] |
||||
if len(x.shape) == 3: |
||||
x = x[..., None] |
||||
x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float() |
||||
return x |
||||
|
||||
def training_step(self, batch, batch_idx, optimizer_idx): |
||||
inputs = self.get_input(batch, self.image_key) |
||||
reconstructions, posterior = self(inputs) |
||||
|
||||
if optimizer_idx == 0: |
||||
# train encoder+decoder+logvar |
||||
aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step, |
||||
last_layer=self.get_last_layer(), split="train") |
||||
self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True) |
||||
self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False) |
||||
return aeloss |
||||
|
||||
if optimizer_idx == 1: |
||||
# train the discriminator |
||||
discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step, |
||||
last_layer=self.get_last_layer(), split="train") |
||||
|
||||
self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True) |
||||
self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False) |
||||
return discloss |
||||
|
||||
def validation_step(self, batch, batch_idx): |
||||
log_dict = self._validation_step(batch, batch_idx) |
||||
with self.ema_scope(): |
||||
log_dict_ema = self._validation_step(batch, batch_idx, postfix="_ema") |
||||
return log_dict |
||||
|
||||
def _validation_step(self, batch, batch_idx, postfix=""): |
||||
inputs = self.get_input(batch, self.image_key) |
||||
reconstructions, posterior = self(inputs) |
||||
aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, self.global_step, |
||||
last_layer=self.get_last_layer(), split="val"+postfix) |
||||
|
||||
discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step, |
||||
last_layer=self.get_last_layer(), split="val"+postfix) |
||||
|
||||
self.log(f"val{postfix}/rec_loss", log_dict_ae[f"val{postfix}/rec_loss"]) |
||||
self.log_dict(log_dict_ae) |
||||
self.log_dict(log_dict_disc) |
||||
return self.log_dict |
||||
|
||||
def configure_optimizers(self): |
||||
lr = self.learning_rate |
||||
ae_params_list = list(self.encoder.parameters()) + list(self.decoder.parameters()) + list( |
||||
self.quant_conv.parameters()) + list(self.post_quant_conv.parameters()) |
||||
if self.learn_logvar: |
||||
print(f"{self.__class__.__name__}: Learning logvar") |
||||
ae_params_list.append(self.loss.logvar) |
||||
opt_ae = torch.optim.Adam(ae_params_list, |
||||
lr=lr, betas=(0.5, 0.9)) |
||||
opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(), |
||||
lr=lr, betas=(0.5, 0.9)) |
||||
return [opt_ae, opt_disc], [] |
||||
|
||||
def get_last_layer(self): |
||||
return self.decoder.conv_out.weight |
||||
|
||||
@torch.no_grad() |
||||
def log_images(self, batch, only_inputs=False, log_ema=False, **kwargs): |
||||
log = dict() |
||||
x = self.get_input(batch, self.image_key) |
||||
x = x.to(self.device) |
||||
if not only_inputs: |
||||
xrec, posterior = self(x) |
||||
if x.shape[1] > 3: |
||||
# colorize with random projection |
||||
assert xrec.shape[1] > 3 |
||||
x = self.to_rgb(x) |
||||
xrec = self.to_rgb(xrec) |
||||
log["samples"] = self.decode(torch.randn_like(posterior.sample())) |
||||
log["reconstructions"] = xrec |
||||
if log_ema or self.use_ema: |
||||
with self.ema_scope(): |
||||
xrec_ema, posterior_ema = self(x) |
||||
if x.shape[1] > 3: |
||||
# colorize with random projection |
||||
assert xrec_ema.shape[1] > 3 |
||||
xrec_ema = self.to_rgb(xrec_ema) |
||||
log["samples_ema"] = self.decode(torch.randn_like(posterior_ema.sample())) |
||||
log["reconstructions_ema"] = xrec_ema |
||||
log["inputs"] = x |
||||
return log |
||||
|
||||
def to_rgb(self, x): |
||||
assert self.image_key == "segmentation" |
||||
if not hasattr(self, "colorize"): |
||||
self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x)) |
||||
x = F.conv2d(x, weight=self.colorize) |
||||
x = 2.*(x-x.min())/(x.max()-x.min()) - 1. |
||||
return x |
||||
|
||||
|
||||
class IdentityFirstStage(torch.nn.Module): |
||||
def __init__(self, *args, vq_interface=False, **kwargs): |
||||
self.vq_interface = vq_interface |
||||
super().__init__() |
||||
|
||||
def encode(self, x, *args, **kwargs): |
||||
return x |
||||
|
||||
def decode(self, x, *args, **kwargs): |
||||
return x |
||||
|
||||
def quantize(self, x, *args, **kwargs): |
||||
if self.vq_interface: |
||||
return x, None, [None, None, None] |
||||
return x |
||||
|
||||
def forward(self, x, *args, **kwargs): |
||||
return x |
||||
|
||||
@ -0,0 +1,336 @@
|
||||
"""SAMPLING ONLY.""" |
||||
|
||||
import torch |
||||
import numpy as np |
||||
from tqdm import tqdm |
||||
|
||||
from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, extract_into_tensor |
||||
|
||||
|
||||
class DDIMSampler(object): |
||||
def __init__(self, model, schedule="linear", **kwargs): |
||||
super().__init__() |
||||
self.model = model |
||||
self.ddpm_num_timesteps = model.num_timesteps |
||||
self.schedule = schedule |
||||
|
||||
def register_buffer(self, name, attr): |
||||
if type(attr) == torch.Tensor: |
||||
if attr.device != torch.device("cuda"): |
||||
attr = attr.to(torch.device("cuda")) |
||||
setattr(self, name, attr) |
||||
|
||||
def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True): |
||||
self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps, |
||||
num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose) |
||||
alphas_cumprod = self.model.alphas_cumprod |
||||
assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep' |
||||
to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device) |
||||
|
||||
self.register_buffer('betas', to_torch(self.model.betas)) |
||||
self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) |
||||
self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev)) |
||||
|
||||
# calculations for diffusion q(x_t | x_{t-1}) and others |
||||
self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu()))) |
||||
self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu()))) |
||||
self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu()))) |
||||
self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu()))) |
||||
self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1))) |
||||
|
||||
# ddim sampling parameters |
||||
ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(), |
||||
ddim_timesteps=self.ddim_timesteps, |
||||
eta=ddim_eta,verbose=verbose) |
||||
self.register_buffer('ddim_sigmas', ddim_sigmas) |
||||
self.register_buffer('ddim_alphas', ddim_alphas) |
||||
self.register_buffer('ddim_alphas_prev', ddim_alphas_prev) |
||||
self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas)) |
||||
sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt( |
||||
(1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * ( |
||||
1 - self.alphas_cumprod / self.alphas_cumprod_prev)) |
||||
self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps) |
||||
|
||||
@torch.no_grad() |
||||
def sample(self, |
||||
S, |
||||
batch_size, |
||||
shape, |
||||
conditioning=None, |
||||
callback=None, |
||||
normals_sequence=None, |
||||
img_callback=None, |
||||
quantize_x0=False, |
||||
eta=0., |
||||
mask=None, |
||||
x0=None, |
||||
temperature=1., |
||||
noise_dropout=0., |
||||
score_corrector=None, |
||||
corrector_kwargs=None, |
||||
verbose=True, |
||||
x_T=None, |
||||
log_every_t=100, |
||||
unconditional_guidance_scale=1., |
||||
unconditional_conditioning=None, # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... |
||||
dynamic_threshold=None, |
||||
ucg_schedule=None, |
||||
**kwargs |
||||
): |
||||
if conditioning is not None: |
||||
if isinstance(conditioning, dict): |
||||
ctmp = conditioning[list(conditioning.keys())[0]] |
||||
while isinstance(ctmp, list): ctmp = ctmp[0] |
||||
cbs = ctmp.shape[0] |
||||
if cbs != batch_size: |
||||
print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") |
||||
|
||||
elif isinstance(conditioning, list): |
||||
for ctmp in conditioning: |
||||
if ctmp.shape[0] != batch_size: |
||||
print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") |
||||
|
||||
else: |
||||
if conditioning.shape[0] != batch_size: |
||||
print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}") |
||||
|
||||
self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose) |
||||
# sampling |
||||
C, H, W = shape |
||||
size = (batch_size, C, H, W) |
||||
print(f'Data shape for DDIM sampling is {size}, eta {eta}') |
||||
|
||||
samples, intermediates = self.ddim_sampling(conditioning, size, |
||||
callback=callback, |
||||
img_callback=img_callback, |
||||
quantize_denoised=quantize_x0, |
||||
mask=mask, x0=x0, |
||||
ddim_use_original_steps=False, |
||||
noise_dropout=noise_dropout, |
||||
temperature=temperature, |
||||
score_corrector=score_corrector, |
||||
corrector_kwargs=corrector_kwargs, |
||||
x_T=x_T, |
||||
log_every_t=log_every_t, |
||||
unconditional_guidance_scale=unconditional_guidance_scale, |
||||
unconditional_conditioning=unconditional_conditioning, |
||||
dynamic_threshold=dynamic_threshold, |
||||
ucg_schedule=ucg_schedule |
||||
) |
||||
return samples, intermediates |
||||
|
||||
@torch.no_grad() |
||||
def ddim_sampling(self, cond, shape, |
||||
x_T=None, ddim_use_original_steps=False, |
||||
callback=None, timesteps=None, quantize_denoised=False, |
||||
mask=None, x0=None, img_callback=None, log_every_t=100, |
||||
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, |
||||
unconditional_guidance_scale=1., unconditional_conditioning=None, dynamic_threshold=None, |
||||
ucg_schedule=None): |
||||
device = self.model.betas.device |
||||
b = shape[0] |
||||
if x_T is None: |
||||
img = torch.randn(shape, device=device) |
||||
else: |
||||
img = x_T |
||||
|
||||
if timesteps is None: |
||||
timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps |
||||
elif timesteps is not None and not ddim_use_original_steps: |
||||
subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1 |
||||
timesteps = self.ddim_timesteps[:subset_end] |
||||
|
||||
intermediates = {'x_inter': [img], 'pred_x0': [img]} |
||||
time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps) |
||||
total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0] |
||||
print(f"Running DDIM Sampling with {total_steps} timesteps") |
||||
|
||||
iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps) |
||||
|
||||
for i, step in enumerate(iterator): |
||||
index = total_steps - i - 1 |
||||
ts = torch.full((b,), step, device=device, dtype=torch.long) |
||||
|
||||
if mask is not None: |
||||
assert x0 is not None |
||||
img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass? |
||||
img = img_orig * mask + (1. - mask) * img |
||||
|
||||
if ucg_schedule is not None: |
||||
assert len(ucg_schedule) == len(time_range) |
||||
unconditional_guidance_scale = ucg_schedule[i] |
||||
|
||||
outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps, |
||||
quantize_denoised=quantize_denoised, temperature=temperature, |
||||
noise_dropout=noise_dropout, score_corrector=score_corrector, |
||||
corrector_kwargs=corrector_kwargs, |
||||
unconditional_guidance_scale=unconditional_guidance_scale, |
||||
unconditional_conditioning=unconditional_conditioning, |
||||
dynamic_threshold=dynamic_threshold) |
||||
img, pred_x0 = outs |
||||
if callback: callback(i) |
||||
if img_callback: img_callback(pred_x0, i) |
||||
|
||||
if index % log_every_t == 0 or index == total_steps - 1: |
||||
intermediates['x_inter'].append(img) |
||||
intermediates['pred_x0'].append(pred_x0) |
||||
|
||||
return img, intermediates |
||||
|
||||
@torch.no_grad() |
||||
def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, |
||||
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, |
||||
unconditional_guidance_scale=1., unconditional_conditioning=None, |
||||
dynamic_threshold=None): |
||||
b, *_, device = *x.shape, x.device |
||||
|
||||
if unconditional_conditioning is None or unconditional_guidance_scale == 1.: |
||||
model_output = self.model.apply_model(x, t, c) |
||||
else: |
||||
x_in = torch.cat([x] * 2) |
||||
t_in = torch.cat([t] * 2) |
||||
if isinstance(c, dict): |
||||
assert isinstance(unconditional_conditioning, dict) |
||||
c_in = dict() |
||||
for k in c: |
||||
if isinstance(c[k], list): |
||||
c_in[k] = [torch.cat([ |
||||
unconditional_conditioning[k][i], |
||||
c[k][i]]) for i in range(len(c[k]))] |
||||
else: |
||||
c_in[k] = torch.cat([ |
||||
unconditional_conditioning[k], |
||||
c[k]]) |
||||
elif isinstance(c, list): |
||||
c_in = list() |
||||
assert isinstance(unconditional_conditioning, list) |
||||
for i in range(len(c)): |
||||
c_in.append(torch.cat([unconditional_conditioning[i], c[i]])) |
||||
else: |
||||
c_in = torch.cat([unconditional_conditioning, c]) |
||||
model_uncond, model_t = self.model.apply_model(x_in, t_in, c_in).chunk(2) |
||||
model_output = model_uncond + unconditional_guidance_scale * (model_t - model_uncond) |
||||
|
||||
if self.model.parameterization == "v": |
||||
e_t = self.model.predict_eps_from_z_and_v(x, t, model_output) |
||||
else: |
||||
e_t = model_output |
||||
|
||||
if score_corrector is not None: |
||||
assert self.model.parameterization == "eps", 'not implemented' |
||||
e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs) |
||||
|
||||
alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas |
||||
alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev |
||||
sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas |
||||
sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas |
||||
# select parameters corresponding to the currently considered timestep |
||||
a_t = torch.full((b, 1, 1, 1), alphas[index], device=device) |
||||
a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device) |
||||
sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device) |
||||
sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device) |
||||
|
||||
# current prediction for x_0 |
||||
if self.model.parameterization != "v": |
||||
pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() |
||||
else: |
||||
pred_x0 = self.model.predict_start_from_z_and_v(x, t, model_output) |
||||
|
||||
if quantize_denoised: |
||||
pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) |
||||
|
||||
if dynamic_threshold is not None: |
||||
raise NotImplementedError() |
||||
|
||||
# direction pointing to x_t |
||||
dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t |
||||
noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature |
||||
if noise_dropout > 0.: |
||||
noise = torch.nn.functional.dropout(noise, p=noise_dropout) |
||||
x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise |
||||
return x_prev, pred_x0 |
||||
|
||||
@torch.no_grad() |
||||
def encode(self, x0, c, t_enc, use_original_steps=False, return_intermediates=None, |
||||
unconditional_guidance_scale=1.0, unconditional_conditioning=None, callback=None): |
||||
num_reference_steps = self.ddpm_num_timesteps if use_original_steps else self.ddim_timesteps.shape[0] |
||||
|
||||
assert t_enc <= num_reference_steps |
||||
num_steps = t_enc |
||||
|
||||
if use_original_steps: |
||||
alphas_next = self.alphas_cumprod[:num_steps] |
||||
alphas = self.alphas_cumprod_prev[:num_steps] |
||||
else: |
||||
alphas_next = self.ddim_alphas[:num_steps] |
||||
alphas = torch.tensor(self.ddim_alphas_prev[:num_steps]) |
||||
|
||||
x_next = x0 |
||||
intermediates = [] |
||||
inter_steps = [] |
||||
for i in tqdm(range(num_steps), desc='Encoding Image'): |
||||
t = torch.full((x0.shape[0],), i, device=self.model.device, dtype=torch.long) |
||||
if unconditional_guidance_scale == 1.: |
||||
noise_pred = self.model.apply_model(x_next, t, c) |
||||
else: |
||||
assert unconditional_conditioning is not None |
||||
e_t_uncond, noise_pred = torch.chunk( |
||||
self.model.apply_model(torch.cat((x_next, x_next)), torch.cat((t, t)), |
||||
torch.cat((unconditional_conditioning, c))), 2) |
||||
noise_pred = e_t_uncond + unconditional_guidance_scale * (noise_pred - e_t_uncond) |
||||
|
||||
xt_weighted = (alphas_next[i] / alphas[i]).sqrt() * x_next |
||||
weighted_noise_pred = alphas_next[i].sqrt() * ( |
||||
(1 / alphas_next[i] - 1).sqrt() - (1 / alphas[i] - 1).sqrt()) * noise_pred |
||||
x_next = xt_weighted + weighted_noise_pred |
||||
if return_intermediates and i % ( |
||||
num_steps // return_intermediates) == 0 and i < num_steps - 1: |
||||
intermediates.append(x_next) |
||||
inter_steps.append(i) |
||||
elif return_intermediates and i >= num_steps - 2: |
||||
intermediates.append(x_next) |
||||
inter_steps.append(i) |
||||
if callback: callback(i) |
||||
|
||||
out = {'x_encoded': x_next, 'intermediate_steps': inter_steps} |
||||
if return_intermediates: |
||||
out.update({'intermediates': intermediates}) |
||||
return x_next, out |
||||
|
||||
@torch.no_grad() |
||||
def stochastic_encode(self, x0, t, use_original_steps=False, noise=None): |
||||
# fast, but does not allow for exact reconstruction |
||||
# t serves as an index to gather the correct alphas |
||||
if use_original_steps: |
||||
sqrt_alphas_cumprod = self.sqrt_alphas_cumprod |
||||
sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod |
||||
else: |
||||
sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas) |
||||
sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas |
||||
|
||||
if noise is None: |
||||
noise = torch.randn_like(x0) |
||||
return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 + |
||||
extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise) |
||||
|
||||
@torch.no_grad() |
||||
def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None, |
||||
use_original_steps=False, callback=None): |
||||
|
||||
timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps |
||||
timesteps = timesteps[:t_start] |
||||
|
||||
time_range = np.flip(timesteps) |
||||
total_steps = timesteps.shape[0] |
||||
print(f"Running DDIM Sampling with {total_steps} timesteps") |
||||
|
||||
iterator = tqdm(time_range, desc='Decoding image', total=total_steps) |
||||
x_dec = x_latent |
||||
for i, step in enumerate(iterator): |
||||
index = total_steps - i - 1 |
||||
ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long) |
||||
x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps, |
||||
unconditional_guidance_scale=unconditional_guidance_scale, |
||||
unconditional_conditioning=unconditional_conditioning) |
||||
if callback: callback(i) |
||||
return x_dec |
||||
File diff suppressed because it is too large
Load Diff
@ -0,0 +1 @@
|
||||
from .sampler import DPMSolverSampler |
||||
File diff suppressed because it is too large
Load Diff
@ -0,0 +1,87 @@
|
||||
"""SAMPLING ONLY.""" |
||||
import torch |
||||
|
||||
from .dpm_solver import NoiseScheduleVP, model_wrapper, DPM_Solver |
||||
|
||||
|
||||
MODEL_TYPES = { |
||||
"eps": "noise", |
||||
"v": "v" |
||||
} |
||||
|
||||
|
||||
class DPMSolverSampler(object): |
||||
def __init__(self, model, **kwargs): |
||||
super().__init__() |
||||
self.model = model |
||||
to_torch = lambda x: x.clone().detach().to(torch.float32).to(model.device) |
||||
self.register_buffer('alphas_cumprod', to_torch(model.alphas_cumprod)) |
||||
|
||||
def register_buffer(self, name, attr): |
||||
if type(attr) == torch.Tensor: |
||||
if attr.device != torch.device("cuda"): |
||||
attr = attr.to(torch.device("cuda")) |
||||
setattr(self, name, attr) |
||||
|
||||
@torch.no_grad() |
||||
def sample(self, |
||||
S, |
||||
batch_size, |
||||
shape, |
||||
conditioning=None, |
||||
callback=None, |
||||
normals_sequence=None, |
||||
img_callback=None, |
||||
quantize_x0=False, |
||||
eta=0., |
||||
mask=None, |
||||
x0=None, |
||||
temperature=1., |
||||
noise_dropout=0., |
||||
score_corrector=None, |
||||
corrector_kwargs=None, |
||||
verbose=True, |
||||
x_T=None, |
||||
log_every_t=100, |
||||
unconditional_guidance_scale=1., |
||||
unconditional_conditioning=None, |
||||
# this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... |
||||
**kwargs |
||||
): |
||||
if conditioning is not None: |
||||
if isinstance(conditioning, dict): |
||||
cbs = conditioning[list(conditioning.keys())[0]].shape[0] |
||||
if cbs != batch_size: |
||||
print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") |
||||
else: |
||||
if conditioning.shape[0] != batch_size: |
||||
print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}") |
||||
|
||||
# sampling |
||||
C, H, W = shape |
||||
size = (batch_size, C, H, W) |
||||
|
||||
print(f'Data shape for DPM-Solver sampling is {size}, sampling steps {S}') |
||||
|
||||
device = self.model.betas.device |
||||
if x_T is None: |
||||
img = torch.randn(size, device=device) |
||||
else: |
||||
img = x_T |
||||
|
||||
ns = NoiseScheduleVP('discrete', alphas_cumprod=self.alphas_cumprod) |
||||
|
||||
model_fn = model_wrapper( |
||||
lambda x, t, c: self.model.apply_model(x, t, c), |
||||
ns, |
||||
model_type=MODEL_TYPES[self.model.parameterization], |
||||
guidance_type="classifier-free", |
||||
condition=conditioning, |
||||
unconditional_condition=unconditional_conditioning, |
||||
guidance_scale=unconditional_guidance_scale, |
||||
) |
||||
|
||||
dpm_solver = DPM_Solver(model_fn, ns, predict_x0=True, thresholding=False) |
||||
x = dpm_solver.sample(img, steps=S, skip_type="time_uniform", method="multistep", order=2, lower_order_final=True) |
||||
|
||||
return x.to(device), None |
||||
@ -0,0 +1,244 @@
|
||||
"""SAMPLING ONLY.""" |
||||
|
||||
import torch |
||||
import numpy as np |
||||
from tqdm import tqdm |
||||
from functools import partial |
||||
|
||||
from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like |
||||
from ldm.models.diffusion.sampling_util import norm_thresholding |
||||
|
||||
|
||||
class PLMSSampler(object): |
||||
def __init__(self, model, schedule="linear", **kwargs): |
||||
super().__init__() |
||||
self.model = model |
||||
self.ddpm_num_timesteps = model.num_timesteps |
||||
self.schedule = schedule |
||||
|
||||
def register_buffer(self, name, attr): |
||||
if type(attr) == torch.Tensor: |
||||
if attr.device != torch.device("cuda"): |
||||
attr = attr.to(torch.device("cuda")) |
||||
setattr(self, name, attr) |
||||
|
||||
def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True): |
||||
if ddim_eta != 0: |
||||
raise ValueError('ddim_eta must be 0 for PLMS') |
||||
self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps, |
||||
num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose) |
||||
alphas_cumprod = self.model.alphas_cumprod |
||||
assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep' |
||||
to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device) |
||||
|
||||
self.register_buffer('betas', to_torch(self.model.betas)) |
||||
self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) |
||||
self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev)) |
||||
|
||||
# calculations for diffusion q(x_t | x_{t-1}) and others |
||||
self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu()))) |
||||
self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu()))) |
||||
self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu()))) |
||||
self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu()))) |
||||
self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1))) |
||||
|
||||
# ddim sampling parameters |
||||
ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(), |
||||
ddim_timesteps=self.ddim_timesteps, |
||||
eta=ddim_eta,verbose=verbose) |
||||
self.register_buffer('ddim_sigmas', ddim_sigmas) |
||||
self.register_buffer('ddim_alphas', ddim_alphas) |
||||
self.register_buffer('ddim_alphas_prev', ddim_alphas_prev) |
||||
self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas)) |
||||
sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt( |
||||
(1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * ( |
||||
1 - self.alphas_cumprod / self.alphas_cumprod_prev)) |
||||
self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps) |
||||
|
||||
@torch.no_grad() |
||||
def sample(self, |
||||
S, |
||||
batch_size, |
||||
shape, |
||||
conditioning=None, |
||||
callback=None, |
||||
normals_sequence=None, |
||||
img_callback=None, |
||||
quantize_x0=False, |
||||
eta=0., |
||||
mask=None, |
||||
x0=None, |
||||
temperature=1., |
||||
noise_dropout=0., |
||||
score_corrector=None, |
||||
corrector_kwargs=None, |
||||
verbose=True, |
||||
x_T=None, |
||||
log_every_t=100, |
||||
unconditional_guidance_scale=1., |
||||
unconditional_conditioning=None, |
||||
# this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... |
||||
dynamic_threshold=None, |
||||
**kwargs |
||||
): |
||||
if conditioning is not None: |
||||
if isinstance(conditioning, dict): |
||||
cbs = conditioning[list(conditioning.keys())[0]].shape[0] |
||||
if cbs != batch_size: |
||||
print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") |
||||
else: |
||||
if conditioning.shape[0] != batch_size: |
||||
print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}") |
||||
|
||||
self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose) |
||||
# sampling |
||||
C, H, W = shape |
||||
size = (batch_size, C, H, W) |
||||
print(f'Data shape for PLMS sampling is {size}') |
||||
|
||||
samples, intermediates = self.plms_sampling(conditioning, size, |
||||
callback=callback, |
||||
img_callback=img_callback, |
||||
quantize_denoised=quantize_x0, |
||||
mask=mask, x0=x0, |
||||
ddim_use_original_steps=False, |
||||
noise_dropout=noise_dropout, |
||||
temperature=temperature, |
||||
score_corrector=score_corrector, |
||||
corrector_kwargs=corrector_kwargs, |
||||
x_T=x_T, |
||||
log_every_t=log_every_t, |
||||
unconditional_guidance_scale=unconditional_guidance_scale, |
||||
unconditional_conditioning=unconditional_conditioning, |
||||
dynamic_threshold=dynamic_threshold, |
||||
) |
||||
return samples, intermediates |
||||
|
||||
@torch.no_grad() |
||||
def plms_sampling(self, cond, shape, |
||||
x_T=None, ddim_use_original_steps=False, |
||||
callback=None, timesteps=None, quantize_denoised=False, |
||||
mask=None, x0=None, img_callback=None, log_every_t=100, |
||||
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, |
||||
unconditional_guidance_scale=1., unconditional_conditioning=None, |
||||
dynamic_threshold=None): |
||||
device = self.model.betas.device |
||||
b = shape[0] |
||||
if x_T is None: |
||||
img = torch.randn(shape, device=device) |
||||
else: |
||||
img = x_T |
||||
|
||||
if timesteps is None: |
||||
timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps |
||||
elif timesteps is not None and not ddim_use_original_steps: |
||||
subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1 |
||||
timesteps = self.ddim_timesteps[:subset_end] |
||||
|
||||
intermediates = {'x_inter': [img], 'pred_x0': [img]} |
||||
time_range = list(reversed(range(0,timesteps))) if ddim_use_original_steps else np.flip(timesteps) |
||||
total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0] |
||||
print(f"Running PLMS Sampling with {total_steps} timesteps") |
||||
|
||||
iterator = tqdm(time_range, desc='PLMS Sampler', total=total_steps) |
||||
old_eps = [] |
||||
|
||||
for i, step in enumerate(iterator): |
||||
index = total_steps - i - 1 |
||||
ts = torch.full((b,), step, device=device, dtype=torch.long) |
||||
ts_next = torch.full((b,), time_range[min(i + 1, len(time_range) - 1)], device=device, dtype=torch.long) |
||||
|
||||
if mask is not None: |
||||
assert x0 is not None |
||||
img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass? |
||||
img = img_orig * mask + (1. - mask) * img |
||||
|
||||
outs = self.p_sample_plms(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps, |
||||
quantize_denoised=quantize_denoised, temperature=temperature, |
||||
noise_dropout=noise_dropout, score_corrector=score_corrector, |
||||
corrector_kwargs=corrector_kwargs, |
||||
unconditional_guidance_scale=unconditional_guidance_scale, |
||||
unconditional_conditioning=unconditional_conditioning, |
||||
old_eps=old_eps, t_next=ts_next, |
||||
dynamic_threshold=dynamic_threshold) |
||||
img, pred_x0, e_t = outs |
||||
old_eps.append(e_t) |
||||
if len(old_eps) >= 4: |
||||
old_eps.pop(0) |
||||
if callback: callback(i) |
||||
if img_callback: img_callback(pred_x0, i) |
||||
|
||||
if index % log_every_t == 0 or index == total_steps - 1: |
||||
intermediates['x_inter'].append(img) |
||||
intermediates['pred_x0'].append(pred_x0) |
||||
|
||||
return img, intermediates |
||||
|
||||
@torch.no_grad() |
||||
def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, |
||||
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, |
||||
unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None, |
||||
dynamic_threshold=None): |
||||
b, *_, device = *x.shape, x.device |
||||
|
||||
def get_model_output(x, t): |
||||
if unconditional_conditioning is None or unconditional_guidance_scale == 1.: |
||||
e_t = self.model.apply_model(x, t, c) |
||||
else: |
||||
x_in = torch.cat([x] * 2) |
||||
t_in = torch.cat([t] * 2) |
||||
c_in = torch.cat([unconditional_conditioning, c]) |
||||
e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2) |
||||
e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond) |
||||
|
||||
if score_corrector is not None: |
||||
assert self.model.parameterization == "eps" |
||||
e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs) |
||||
|
||||
return e_t |
||||
|
||||
alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas |
||||
alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev |
||||
sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas |
||||
sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas |
||||
|
||||
def get_x_prev_and_pred_x0(e_t, index): |
||||
# select parameters corresponding to the currently considered timestep |
||||
a_t = torch.full((b, 1, 1, 1), alphas[index], device=device) |
||||
a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device) |
||||
sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device) |
||||
sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device) |
||||
|
||||
# current prediction for x_0 |
||||
pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() |
||||
if quantize_denoised: |
||||
pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) |
||||
if dynamic_threshold is not None: |
||||
pred_x0 = norm_thresholding(pred_x0, dynamic_threshold) |
||||
# direction pointing to x_t |
||||
dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t |
||||
noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature |
||||
if noise_dropout > 0.: |
||||
noise = torch.nn.functional.dropout(noise, p=noise_dropout) |
||||
x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise |
||||
return x_prev, pred_x0 |
||||
|
||||
e_t = get_model_output(x, t) |
||||
if len(old_eps) == 0: |
||||
# Pseudo Improved Euler (2nd order) |
||||
x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index) |
||||
e_t_next = get_model_output(x_prev, t_next) |
||||
e_t_prime = (e_t + e_t_next) / 2 |
||||
elif len(old_eps) == 1: |
||||
# 2nd order Pseudo Linear Multistep (Adams-Bashforth) |
||||
e_t_prime = (3 * e_t - old_eps[-1]) / 2 |
||||
elif len(old_eps) == 2: |
||||
# 3nd order Pseudo Linear Multistep (Adams-Bashforth) |
||||
e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12 |
||||
elif len(old_eps) >= 3: |
||||
# 4nd order Pseudo Linear Multistep (Adams-Bashforth) |
||||
e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24 |
||||
|
||||
x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index) |
||||
|
||||
return x_prev, pred_x0, e_t |
||||
@ -0,0 +1,22 @@
|
||||
import torch |
||||
import numpy as np |
||||
|
||||
|
||||
def append_dims(x, target_dims): |
||||
"""Appends dimensions to the end of a tensor until it has target_dims dimensions. |
||||
From https://github.com/crowsonkb/k-diffusion/blob/master/k_diffusion/utils.py""" |
||||
dims_to_append = target_dims - x.ndim |
||||
if dims_to_append < 0: |
||||
raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less') |
||||
return x[(...,) + (None,) * dims_to_append] |
||||
|
||||
|
||||
def norm_thresholding(x0, value): |
||||
s = append_dims(x0.pow(2).flatten(1).mean(1).sqrt().clamp(min=value), x0.ndim) |
||||
return x0 * (value / s) |
||||
|
||||
|
||||
def spatial_norm_thresholding(x0, value): |
||||
# b c h w |
||||
s = x0.pow(2).mean(1, keepdim=True).sqrt().clamp(min=value) |
||||
return x0 * (value / s) |
||||
@ -0,0 +1,533 @@
|
||||
from inspect import isfunction |
||||
import math |
||||
import torch |
||||
import torch.nn.functional as F |
||||
from torch import nn, einsum |
||||
from einops import rearrange, repeat |
||||
from typing import Optional, Any |
||||
|
||||
from ldm.modules.diffusionmodules.util import checkpoint |
||||
from .sub_quadratic_attention import efficient_dot_product_attention |
||||
|
||||
try: |
||||
import xformers |
||||
import xformers.ops |
||||
XFORMERS_IS_AVAILBLE = True |
||||
except: |
||||
XFORMERS_IS_AVAILBLE = False |
||||
|
||||
# CrossAttn precision handling |
||||
import os |
||||
_ATTN_PRECISION = os.environ.get("ATTN_PRECISION", "fp32") |
||||
|
||||
def exists(val): |
||||
return val is not None |
||||
|
||||
|
||||
def uniq(arr): |
||||
return{el: True for el in arr}.keys() |
||||
|
||||
|
||||
def default(val, d): |
||||
if exists(val): |
||||
return val |
||||
return d() if isfunction(d) else d |
||||
|
||||
|
||||
def max_neg_value(t): |
||||
return -torch.finfo(t.dtype).max |
||||
|
||||
|
||||
def init_(tensor): |
||||
dim = tensor.shape[-1] |
||||
std = 1 / math.sqrt(dim) |
||||
tensor.uniform_(-std, std) |
||||
return tensor |
||||
|
||||
|
||||
# feedforward |
||||
class GEGLU(nn.Module): |
||||
def __init__(self, dim_in, dim_out): |
||||
super().__init__() |
||||
self.proj = nn.Linear(dim_in, dim_out * 2) |
||||
|
||||
def forward(self, x): |
||||
x, gate = self.proj(x).chunk(2, dim=-1) |
||||
return x * F.gelu(gate) |
||||
|
||||
|
||||
class FeedForward(nn.Module): |
||||
def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.): |
||||
super().__init__() |
||||
inner_dim = int(dim * mult) |
||||
dim_out = default(dim_out, dim) |
||||
project_in = nn.Sequential( |
||||
nn.Linear(dim, inner_dim), |
||||
nn.GELU() |
||||
) if not glu else GEGLU(dim, inner_dim) |
||||
|
||||
self.net = nn.Sequential( |
||||
project_in, |
||||
nn.Dropout(dropout), |
||||
nn.Linear(inner_dim, dim_out) |
||||
) |
||||
|
||||
def forward(self, x): |
||||
return self.net(x) |
||||
|
||||
|
||||
def zero_module(module): |
||||
""" |
||||
Zero out the parameters of a module and return it. |
||||
""" |
||||
for p in module.parameters(): |
||||
p.detach().zero_() |
||||
return module |
||||
|
||||
|
||||
def Normalize(in_channels): |
||||
return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) |
||||
|
||||
|
||||
class SpatialSelfAttention(nn.Module): |
||||
def __init__(self, in_channels): |
||||
super().__init__() |
||||
self.in_channels = in_channels |
||||
|
||||
self.norm = Normalize(in_channels) |
||||
self.q = torch.nn.Conv2d(in_channels, |
||||
in_channels, |
||||
kernel_size=1, |
||||
stride=1, |
||||
padding=0) |
||||
self.k = torch.nn.Conv2d(in_channels, |
||||
in_channels, |
||||
kernel_size=1, |
||||
stride=1, |
||||
padding=0) |
||||
self.v = torch.nn.Conv2d(in_channels, |
||||
in_channels, |
||||
kernel_size=1, |
||||
stride=1, |
||||
padding=0) |
||||
self.proj_out = torch.nn.Conv2d(in_channels, |
||||
in_channels, |
||||
kernel_size=1, |
||||
stride=1, |
||||
padding=0) |
||||
|
||||
def forward(self, x): |
||||
h_ = x |
||||
h_ = self.norm(h_) |
||||
q = self.q(h_) |
||||
k = self.k(h_) |
||||
v = self.v(h_) |
||||
|
||||
# compute attention |
||||
b,c,h,w = q.shape |
||||
q = rearrange(q, 'b c h w -> b (h w) c') |
||||
k = rearrange(k, 'b c h w -> b c (h w)') |
||||
w_ = torch.einsum('bij,bjk->bik', q, k) |
||||
|
||||
w_ = w_ * (int(c)**(-0.5)) |
||||
w_ = torch.nn.functional.softmax(w_, dim=2) |
||||
|
||||
# attend to values |
||||
v = rearrange(v, 'b c h w -> b c (h w)') |
||||
w_ = rearrange(w_, 'b i j -> b j i') |
||||
h_ = torch.einsum('bij,bjk->bik', v, w_) |
||||
h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h) |
||||
h_ = self.proj_out(h_) |
||||
|
||||
return x+h_ |
||||
|
||||
|
||||
class CrossAttentionBirchSan(nn.Module): |
||||
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.): |
||||
super().__init__() |
||||
inner_dim = dim_head * heads |
||||
context_dim = default(context_dim, query_dim) |
||||
|
||||
self.scale = dim_head ** -0.5 |
||||
self.heads = heads |
||||
|
||||
self.to_q = nn.Linear(query_dim, inner_dim, bias=False) |
||||
self.to_k = nn.Linear(context_dim, inner_dim, bias=False) |
||||
self.to_v = nn.Linear(context_dim, inner_dim, bias=False) |
||||
|
||||
self.to_out = nn.Sequential( |
||||
nn.Linear(inner_dim, query_dim), |
||||
nn.Dropout(dropout) |
||||
) |
||||
|
||||
def forward(self, x, context=None, mask=None): |
||||
h = self.heads |
||||
|
||||
query = self.to_q(x) |
||||
context = default(context, x) |
||||
key = self.to_k(context) |
||||
value = self.to_v(context) |
||||
del context, x |
||||
|
||||
query = query.unflatten(-1, (self.heads, -1)).transpose(1,2).flatten(end_dim=1) |
||||
key_t = key.transpose(1,2).unflatten(1, (self.heads, -1)).flatten(end_dim=1) |
||||
del key |
||||
value = value.unflatten(-1, (self.heads, -1)).transpose(1,2).flatten(end_dim=1) |
||||
|
||||
dtype = query.dtype |
||||
# TODO: do we still need to do *everything* in float32, given how we delay the division? |
||||
# TODO: do we need to support upcast_softmax too? SD 2.1 seems to work without it |
||||
# if self.upcast_attention: |
||||
# query = query.float() |
||||
# key_t = key_t.float() |
||||
|
||||
bytes_per_token = torch.finfo(query.dtype).bits//8 |
||||
batch_x_heads, q_tokens, _ = query.shape |
||||
_, _, k_tokens = key_t.shape |
||||
qk_matmul_size_bytes = batch_x_heads * bytes_per_token * q_tokens * k_tokens |
||||
|
||||
stats = torch.cuda.memory_stats(query.device) |
||||
mem_active = stats['active_bytes.all.current'] |
||||
mem_reserved = stats['reserved_bytes.all.current'] |
||||
mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device()) |
||||
mem_free_torch = mem_reserved - mem_active |
||||
mem_free_total = mem_free_cuda + mem_free_torch |
||||
chunk_threshold_bytes = mem_free_torch * 0.5 #Using only this seems to work better on AMD |
||||
|
||||
kv_chunk_size_min = None |
||||
|
||||
query_chunk_size_x = 1024 * 4 |
||||
kv_chunk_size_min_x = None |
||||
kv_chunk_size_x = (int((chunk_threshold_bytes // (batch_x_heads * bytes_per_token * query_chunk_size_x)) * 1.2) // 1024) * 1024 |
||||
if kv_chunk_size_x < 1024: |
||||
kv_chunk_size_x = None |
||||
|
||||
if chunk_threshold_bytes is not None and qk_matmul_size_bytes <= chunk_threshold_bytes: |
||||
# the big matmul fits into our memory limit; do everything in 1 chunk, |
||||
# i.e. send it down the unchunked fast-path |
||||
query_chunk_size = q_tokens |
||||
kv_chunk_size = k_tokens |
||||
else: |
||||
query_chunk_size = query_chunk_size_x |
||||
kv_chunk_size = kv_chunk_size_x |
||||
kv_chunk_size_min = kv_chunk_size_min_x |
||||
|
||||
hidden_states = efficient_dot_product_attention( |
||||
query, |
||||
key_t, |
||||
value, |
||||
query_chunk_size=query_chunk_size, |
||||
kv_chunk_size=kv_chunk_size, |
||||
kv_chunk_size_min=kv_chunk_size_min, |
||||
use_checkpoint=self.training, |
||||
) |
||||
|
||||
hidden_states = hidden_states.to(dtype) |
||||
|
||||
hidden_states = hidden_states.unflatten(0, (-1, self.heads)).transpose(1,2).flatten(start_dim=2) |
||||
|
||||
out_proj, dropout = self.to_out |
||||
hidden_states = out_proj(hidden_states) |
||||
hidden_states = dropout(hidden_states) |
||||
|
||||
return hidden_states |
||||
|
||||
|
||||
class CrossAttentionDoggettx(nn.Module): |
||||
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.): |
||||
super().__init__() |
||||
inner_dim = dim_head * heads |
||||
context_dim = default(context_dim, query_dim) |
||||
|
||||
self.scale = dim_head ** -0.5 |
||||
self.heads = heads |
||||
|
||||
self.to_q = nn.Linear(query_dim, inner_dim, bias=False) |
||||
self.to_k = nn.Linear(context_dim, inner_dim, bias=False) |
||||
self.to_v = nn.Linear(context_dim, inner_dim, bias=False) |
||||
|
||||
self.to_out = nn.Sequential( |
||||
nn.Linear(inner_dim, query_dim), |
||||
nn.Dropout(dropout) |
||||
) |
||||
|
||||
def forward(self, x, context=None, mask=None): |
||||
h = self.heads |
||||
|
||||
q_in = self.to_q(x) |
||||
context = default(context, x) |
||||
k_in = self.to_k(context) |
||||
v_in = self.to_v(context) |
||||
del context, x |
||||
|
||||
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in)) |
||||
del q_in, k_in, v_in |
||||
|
||||
r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device) |
||||
|
||||
stats = torch.cuda.memory_stats(q.device) |
||||
mem_active = stats['active_bytes.all.current'] |
||||
mem_reserved = stats['reserved_bytes.all.current'] |
||||
mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device()) |
||||
mem_free_torch = mem_reserved - mem_active |
||||
mem_free_total = mem_free_cuda + mem_free_torch |
||||
|
||||
gb = 1024 ** 3 |
||||
tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size() |
||||
modifier = 3 if q.element_size() == 2 else 2.5 |
||||
mem_required = tensor_size * modifier |
||||
steps = 1 |
||||
|
||||
|
||||
if mem_required > mem_free_total: |
||||
steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2))) |
||||
# print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB " |
||||
# f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}") |
||||
|
||||
if steps > 64: |
||||
max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64 |
||||
raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). ' |
||||
f'Need: {mem_required/64/gb:0.1f}GB free, Have:{mem_free_total/gb:0.1f}GB free') |
||||
|
||||
# print("steps", steps, mem_required, mem_free_total, modifier, q.element_size(), tensor_size) |
||||
first_op_done = False |
||||
cleared_cache = False |
||||
while True: |
||||
try: |
||||
slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1] |
||||
for i in range(0, q.shape[1], slice_size): |
||||
end = i + slice_size |
||||
if _ATTN_PRECISION =="fp32": |
||||
with torch.autocast(enabled=False, device_type = 'cuda'): |
||||
s1 = einsum('b i d, b j d -> b i j', q[:, i:end].float(), k.float()) * self.scale |
||||
else: |
||||
s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k) * self.scale |
||||
first_op_done = True |
||||
|
||||
s2 = s1.softmax(dim=-1) |
||||
del s1 |
||||
|
||||
r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v) |
||||
del s2 |
||||
break |
||||
except torch.cuda.OutOfMemoryError as e: |
||||
if first_op_done == False: |
||||
torch.cuda.empty_cache() |
||||
torch.cuda.ipc_collect() |
||||
if cleared_cache == False: |
||||
cleared_cache = True |
||||
print("out of memory error, emptying cache and trying again") |
||||
continue |
||||
steps *= 2 |
||||
if steps > 64: |
||||
raise e |
||||
print("out of memory error, increasing steps and trying again", steps) |
||||
else: |
||||
raise e |
||||
|
||||
del q, k, v |
||||
|
||||
r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h) |
||||
del r1 |
||||
|
||||
return self.to_out(r2) |
||||
|
||||
class OriginalCrossAttention(nn.Module): |
||||
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.): |
||||
super().__init__() |
||||
inner_dim = dim_head * heads |
||||
context_dim = default(context_dim, query_dim) |
||||
|
||||
self.scale = dim_head ** -0.5 |
||||
self.heads = heads |
||||
|
||||
self.to_q = nn.Linear(query_dim, inner_dim, bias=False) |
||||
self.to_k = nn.Linear(context_dim, inner_dim, bias=False) |
||||
self.to_v = nn.Linear(context_dim, inner_dim, bias=False) |
||||
|
||||
self.to_out = nn.Sequential( |
||||
nn.Linear(inner_dim, query_dim), |
||||
nn.Dropout(dropout) |
||||
) |
||||
|
||||
def forward(self, x, context=None, mask=None): |
||||
h = self.heads |
||||
|
||||
q = self.to_q(x) |
||||
context = default(context, x) |
||||
k = self.to_k(context) |
||||
v = self.to_v(context) |
||||
|
||||
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v)) |
||||
|
||||
# force cast to fp32 to avoid overflowing |
||||
if _ATTN_PRECISION =="fp32": |
||||
with torch.autocast(enabled=False, device_type = 'cuda'): |
||||
q, k = q.float(), k.float() |
||||
sim = einsum('b i d, b j d -> b i j', q, k) * self.scale |
||||
else: |
||||
sim = einsum('b i d, b j d -> b i j', q, k) * self.scale |
||||
|
||||
del q, k |
||||
|
||||
if exists(mask): |
||||
mask = rearrange(mask, 'b ... -> b (...)') |
||||
max_neg_value = -torch.finfo(sim.dtype).max |
||||
mask = repeat(mask, 'b j -> (b h) () j', h=h) |
||||
sim.masked_fill_(~mask, max_neg_value) |
||||
|
||||
# attention, what we cannot get enough of |
||||
sim = sim.softmax(dim=-1) |
||||
|
||||
out = einsum('b i j, b j d -> b i d', sim, v) |
||||
out = rearrange(out, '(b h) n d -> b n (h d)', h=h) |
||||
return self.to_out(out) |
||||
|
||||
class CrossAttention(CrossAttentionDoggettx): |
||||
pass |
||||
|
||||
class MemoryEfficientCrossAttention(nn.Module): |
||||
# https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223 |
||||
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0): |
||||
super().__init__() |
||||
print(f"Setting up {self.__class__.__name__}. Query dim is {query_dim}, context_dim is {context_dim} and using " |
||||
f"{heads} heads.") |
||||
inner_dim = dim_head * heads |
||||
context_dim = default(context_dim, query_dim) |
||||
|
||||
self.heads = heads |
||||
self.dim_head = dim_head |
||||
|
||||
self.to_q = nn.Linear(query_dim, inner_dim, bias=False) |
||||
self.to_k = nn.Linear(context_dim, inner_dim, bias=False) |
||||
self.to_v = nn.Linear(context_dim, inner_dim, bias=False) |
||||
|
||||
self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout)) |
||||
self.attention_op: Optional[Any] = None |
||||
|
||||
def forward(self, x, context=None, mask=None): |
||||
q = self.to_q(x) |
||||
context = default(context, x) |
||||
k = self.to_k(context) |
||||
v = self.to_v(context) |
||||
|
||||
b, _, _ = q.shape |
||||
q, k, v = map( |
||||
lambda t: t.unsqueeze(3) |
||||
.reshape(b, t.shape[1], self.heads, self.dim_head) |
||||
.permute(0, 2, 1, 3) |
||||
.reshape(b * self.heads, t.shape[1], self.dim_head) |
||||
.contiguous(), |
||||
(q, k, v), |
||||
) |
||||
|
||||
# actually compute the attention, what we cannot get enough of |
||||
out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op) |
||||
|
||||
if exists(mask): |
||||
raise NotImplementedError |
||||
out = ( |
||||
out.unsqueeze(0) |
||||
.reshape(b, self.heads, out.shape[1], self.dim_head) |
||||
.permute(0, 2, 1, 3) |
||||
.reshape(b, out.shape[1], self.heads * self.dim_head) |
||||
) |
||||
return self.to_out(out) |
||||
|
||||
|
||||
class BasicTransformerBlock(nn.Module): |
||||
ATTENTION_MODES = { |
||||
"softmax": CrossAttention, # vanilla attention |
||||
"softmax-xformers": MemoryEfficientCrossAttention |
||||
} |
||||
def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True, |
||||
disable_self_attn=False): |
||||
super().__init__() |
||||
attn_mode = "softmax-xformers" if XFORMERS_IS_AVAILBLE else "softmax" |
||||
assert attn_mode in self.ATTENTION_MODES |
||||
attn_cls = self.ATTENTION_MODES[attn_mode] |
||||
self.disable_self_attn = disable_self_attn |
||||
self.attn1 = attn_cls(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout, |
||||
context_dim=context_dim if self.disable_self_attn else None) # is a self-attention if not self.disable_self_attn |
||||
self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff) |
||||
self.attn2 = attn_cls(query_dim=dim, context_dim=context_dim, |
||||
heads=n_heads, dim_head=d_head, dropout=dropout) # is self-attn if context is none |
||||
self.norm1 = nn.LayerNorm(dim) |
||||
self.norm2 = nn.LayerNorm(dim) |
||||
self.norm3 = nn.LayerNorm(dim) |
||||
self.checkpoint = checkpoint |
||||
|
||||
def forward(self, x, context=None): |
||||
return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint) |
||||
|
||||
def _forward(self, x, context=None): |
||||
x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None) + x |
||||
x = self.attn2(self.norm2(x), context=context) + x |
||||
x = self.ff(self.norm3(x)) + x |
||||
return x |
||||
|
||||
|
||||
class SpatialTransformer(nn.Module): |
||||
""" |
||||
Transformer block for image-like data. |
||||
First, project the input (aka embedding) |
||||
and reshape to b, t, d. |
||||
Then apply standard transformer action. |
||||
Finally, reshape to image |
||||
NEW: use_linear for more efficiency instead of the 1x1 convs |
||||
""" |
||||
def __init__(self, in_channels, n_heads, d_head, |
||||
depth=1, dropout=0., context_dim=None, |
||||
disable_self_attn=False, use_linear=False, |
||||
use_checkpoint=True): |
||||
super().__init__() |
||||
if exists(context_dim) and not isinstance(context_dim, list): |
||||
context_dim = [context_dim] |
||||
self.in_channels = in_channels |
||||
inner_dim = n_heads * d_head |
||||
self.norm = Normalize(in_channels) |
||||
if not use_linear: |
||||
self.proj_in = nn.Conv2d(in_channels, |
||||
inner_dim, |
||||
kernel_size=1, |
||||
stride=1, |
||||
padding=0) |
||||
else: |
||||
self.proj_in = nn.Linear(in_channels, inner_dim) |
||||
|
||||
self.transformer_blocks = nn.ModuleList( |
||||
[BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d], |
||||
disable_self_attn=disable_self_attn, checkpoint=use_checkpoint) |
||||
for d in range(depth)] |
||||
) |
||||
if not use_linear: |
||||
self.proj_out = zero_module(nn.Conv2d(inner_dim, |
||||
in_channels, |
||||
kernel_size=1, |
||||
stride=1, |
||||
padding=0)) |
||||
else: |
||||
self.proj_out = zero_module(nn.Linear(in_channels, inner_dim)) |
||||
self.use_linear = use_linear |
||||
|
||||
def forward(self, x, context=None): |
||||
# note: if no context is given, cross-attention defaults to self-attention |
||||
if not isinstance(context, list): |
||||
context = [context] |
||||
b, c, h, w = x.shape |
||||
x_in = x |
||||
x = self.norm(x) |
||||
if not self.use_linear: |
||||
x = self.proj_in(x) |
||||
x = rearrange(x, 'b c h w -> b (h w) c').contiguous() |
||||
if self.use_linear: |
||||
x = self.proj_in(x) |
||||
for i, block in enumerate(self.transformer_blocks): |
||||
x = block(x, context=context[i]) |
||||
if self.use_linear: |
||||
x = self.proj_out(x) |
||||
x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous() |
||||
if not self.use_linear: |
||||
x = self.proj_out(x) |
||||
return x + x_in |
||||
|
||||
@ -0,0 +1,852 @@
|
||||
# pytorch_diffusion + derived encoder decoder |
||||
import math |
||||
import torch |
||||
import torch.nn as nn |
||||
import numpy as np |
||||
from einops import rearrange |
||||
from typing import Optional, Any |
||||
|
||||
from ldm.modules.attention import MemoryEfficientCrossAttention |
||||
|
||||
try: |
||||
import xformers |
||||
import xformers.ops |
||||
XFORMERS_IS_AVAILBLE = True |
||||
except: |
||||
XFORMERS_IS_AVAILBLE = False |
||||
print("No module 'xformers'. Proceeding without it.") |
||||
|
||||
|
||||
def get_timestep_embedding(timesteps, embedding_dim): |
||||
""" |
||||
This matches the implementation in Denoising Diffusion Probabilistic Models: |
||||
From Fairseq. |
||||
Build sinusoidal embeddings. |
||||
This matches the implementation in tensor2tensor, but differs slightly |
||||
from the description in Section 3.5 of "Attention Is All You Need". |
||||
""" |
||||
assert len(timesteps.shape) == 1 |
||||
|
||||
half_dim = embedding_dim // 2 |
||||
emb = math.log(10000) / (half_dim - 1) |
||||
emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb) |
||||
emb = emb.to(device=timesteps.device) |
||||
emb = timesteps.float()[:, None] * emb[None, :] |
||||
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) |
||||
if embedding_dim % 2 == 1: # zero pad |
||||
emb = torch.nn.functional.pad(emb, (0,1,0,0)) |
||||
return emb |
||||
|
||||
|
||||
def nonlinearity(x): |
||||
# swish |
||||
return x*torch.sigmoid(x) |
||||
|
||||
|
||||
def Normalize(in_channels, num_groups=32): |
||||
return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True) |
||||
|
||||
|
||||
class Upsample(nn.Module): |
||||
def __init__(self, in_channels, with_conv): |
||||
super().__init__() |
||||
self.with_conv = with_conv |
||||
if self.with_conv: |
||||
self.conv = torch.nn.Conv2d(in_channels, |
||||
in_channels, |
||||
kernel_size=3, |
||||
stride=1, |
||||
padding=1) |
||||
|
||||
def forward(self, x): |
||||
x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") |
||||
if self.with_conv: |
||||
x = self.conv(x) |
||||
return x |
||||
|
||||
|
||||
class Downsample(nn.Module): |
||||
def __init__(self, in_channels, with_conv): |
||||
super().__init__() |
||||
self.with_conv = with_conv |
||||
if self.with_conv: |
||||
# no asymmetric padding in torch conv, must do it ourselves |
||||
self.conv = torch.nn.Conv2d(in_channels, |
||||
in_channels, |
||||
kernel_size=3, |
||||
stride=2, |
||||
padding=0) |
||||
|
||||
def forward(self, x): |
||||
if self.with_conv: |
||||
pad = (0,1,0,1) |
||||
x = torch.nn.functional.pad(x, pad, mode="constant", value=0) |
||||
x = self.conv(x) |
||||
else: |
||||
x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2) |
||||
return x |
||||
|
||||
|
||||
class ResnetBlock(nn.Module): |
||||
def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False, |
||||
dropout, temb_channels=512): |
||||
super().__init__() |
||||
self.in_channels = in_channels |
||||
out_channels = in_channels if out_channels is None else out_channels |
||||
self.out_channels = out_channels |
||||
self.use_conv_shortcut = conv_shortcut |
||||
|
||||
self.norm1 = Normalize(in_channels) |
||||
self.conv1 = torch.nn.Conv2d(in_channels, |
||||
out_channels, |
||||
kernel_size=3, |
||||
stride=1, |
||||
padding=1) |
||||
if temb_channels > 0: |
||||
self.temb_proj = torch.nn.Linear(temb_channels, |
||||
out_channels) |
||||
self.norm2 = Normalize(out_channels) |
||||
self.dropout = torch.nn.Dropout(dropout) |
||||
self.conv2 = torch.nn.Conv2d(out_channels, |
||||
out_channels, |
||||
kernel_size=3, |
||||
stride=1, |
||||
padding=1) |
||||
if self.in_channels != self.out_channels: |
||||
if self.use_conv_shortcut: |
||||
self.conv_shortcut = torch.nn.Conv2d(in_channels, |
||||
out_channels, |
||||
kernel_size=3, |
||||
stride=1, |
||||
padding=1) |
||||
else: |
||||
self.nin_shortcut = torch.nn.Conv2d(in_channels, |
||||
out_channels, |
||||
kernel_size=1, |
||||
stride=1, |
||||
padding=0) |
||||
|
||||
def forward(self, x, temb): |
||||
h = x |
||||
h = self.norm1(h) |
||||
h = nonlinearity(h) |
||||
h = self.conv1(h) |
||||
|
||||
if temb is not None: |
||||
h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None] |
||||
|
||||
h = self.norm2(h) |
||||
h = nonlinearity(h) |
||||
h = self.dropout(h) |
||||
h = self.conv2(h) |
||||
|
||||
if self.in_channels != self.out_channels: |
||||
if self.use_conv_shortcut: |
||||
x = self.conv_shortcut(x) |
||||
else: |
||||
x = self.nin_shortcut(x) |
||||
|
||||
return x+h |
||||
|
||||
|
||||
class AttnBlock(nn.Module): |
||||
def __init__(self, in_channels): |
||||
super().__init__() |
||||
self.in_channels = in_channels |
||||
|
||||
self.norm = Normalize(in_channels) |
||||
self.q = torch.nn.Conv2d(in_channels, |
||||
in_channels, |
||||
kernel_size=1, |
||||
stride=1, |
||||
padding=0) |
||||
self.k = torch.nn.Conv2d(in_channels, |
||||
in_channels, |
||||
kernel_size=1, |
||||
stride=1, |
||||
padding=0) |
||||
self.v = torch.nn.Conv2d(in_channels, |
||||
in_channels, |
||||
kernel_size=1, |
||||
stride=1, |
||||
padding=0) |
||||
self.proj_out = torch.nn.Conv2d(in_channels, |
||||
in_channels, |
||||
kernel_size=1, |
||||
stride=1, |
||||
padding=0) |
||||
|
||||
def forward(self, x): |
||||
h_ = x |
||||
h_ = self.norm(h_) |
||||
q = self.q(h_) |
||||
k = self.k(h_) |
||||
v = self.v(h_) |
||||
|
||||
# compute attention |
||||
b,c,h,w = q.shape |
||||
q = q.reshape(b,c,h*w) |
||||
q = q.permute(0,2,1) # b,hw,c |
||||
k = k.reshape(b,c,h*w) # b,c,hw |
||||
w_ = torch.bmm(q,k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j] |
||||
w_ = w_ * (int(c)**(-0.5)) |
||||
w_ = torch.nn.functional.softmax(w_, dim=2) |
||||
|
||||
# attend to values |
||||
v = v.reshape(b,c,h*w) |
||||
w_ = w_.permute(0,2,1) # b,hw,hw (first hw of k, second of q) |
||||
h_ = torch.bmm(v,w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j] |
||||
h_ = h_.reshape(b,c,h,w) |
||||
|
||||
h_ = self.proj_out(h_) |
||||
|
||||
return x+h_ |
||||
|
||||
class MemoryEfficientAttnBlock(nn.Module): |
||||
""" |
||||
Uses xformers efficient implementation, |
||||
see https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223 |
||||
Note: this is a single-head self-attention operation |
||||
""" |
||||
# |
||||
def __init__(self, in_channels): |
||||
super().__init__() |
||||
self.in_channels = in_channels |
||||
|
||||
self.norm = Normalize(in_channels) |
||||
self.q = torch.nn.Conv2d(in_channels, |
||||
in_channels, |
||||
kernel_size=1, |
||||
stride=1, |
||||
padding=0) |
||||
self.k = torch.nn.Conv2d(in_channels, |
||||
in_channels, |
||||
kernel_size=1, |
||||
stride=1, |
||||
padding=0) |
||||
self.v = torch.nn.Conv2d(in_channels, |
||||
in_channels, |
||||
kernel_size=1, |
||||
stride=1, |
||||
padding=0) |
||||
self.proj_out = torch.nn.Conv2d(in_channels, |
||||
in_channels, |
||||
kernel_size=1, |
||||
stride=1, |
||||
padding=0) |
||||
self.attention_op: Optional[Any] = None |
||||
|
||||
def forward(self, x): |
||||
h_ = x |
||||
h_ = self.norm(h_) |
||||
q = self.q(h_) |
||||
k = self.k(h_) |
||||
v = self.v(h_) |
||||
|
||||
# compute attention |
||||
B, C, H, W = q.shape |
||||
q, k, v = map(lambda x: rearrange(x, 'b c h w -> b (h w) c'), (q, k, v)) |
||||
|
||||
q, k, v = map( |
||||
lambda t: t.unsqueeze(3) |
||||
.reshape(B, t.shape[1], 1, C) |
||||
.permute(0, 2, 1, 3) |
||||
.reshape(B * 1, t.shape[1], C) |
||||
.contiguous(), |
||||
(q, k, v), |
||||
) |
||||
out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op) |
||||
|
||||
out = ( |
||||
out.unsqueeze(0) |
||||
.reshape(B, 1, out.shape[1], C) |
||||
.permute(0, 2, 1, 3) |
||||
.reshape(B, out.shape[1], C) |
||||
) |
||||
out = rearrange(out, 'b (h w) c -> b c h w', b=B, h=H, w=W, c=C) |
||||
out = self.proj_out(out) |
||||
return x+out |
||||
|
||||
|
||||
class MemoryEfficientCrossAttentionWrapper(MemoryEfficientCrossAttention): |
||||
def forward(self, x, context=None, mask=None): |
||||
b, c, h, w = x.shape |
||||
x = rearrange(x, 'b c h w -> b (h w) c') |
||||
out = super().forward(x, context=context, mask=mask) |
||||
out = rearrange(out, 'b (h w) c -> b c h w', h=h, w=w, c=c) |
||||
return x + out |
||||
|
||||
|
||||
def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None): |
||||
assert attn_type in ["vanilla", "vanilla-xformers", "memory-efficient-cross-attn", "linear", "none"], f'attn_type {attn_type} unknown' |
||||
if XFORMERS_IS_AVAILBLE and attn_type == "vanilla": |
||||
attn_type = "vanilla-xformers" |
||||
print(f"making attention of type '{attn_type}' with {in_channels} in_channels") |
||||
if attn_type == "vanilla": |
||||
assert attn_kwargs is None |
||||
return AttnBlock(in_channels) |
||||
elif attn_type == "vanilla-xformers": |
||||
print(f"building MemoryEfficientAttnBlock with {in_channels} in_channels...") |
||||
return MemoryEfficientAttnBlock(in_channels) |
||||
elif type == "memory-efficient-cross-attn": |
||||
attn_kwargs["query_dim"] = in_channels |
||||
return MemoryEfficientCrossAttentionWrapper(**attn_kwargs) |
||||
elif attn_type == "none": |
||||
return nn.Identity(in_channels) |
||||
else: |
||||
raise NotImplementedError() |
||||
|
||||
|
||||
class Model(nn.Module): |
||||
def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, |
||||
attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, |
||||
resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"): |
||||
super().__init__() |
||||
if use_linear_attn: attn_type = "linear" |
||||
self.ch = ch |
||||
self.temb_ch = self.ch*4 |
||||
self.num_resolutions = len(ch_mult) |
||||
self.num_res_blocks = num_res_blocks |
||||
self.resolution = resolution |
||||
self.in_channels = in_channels |
||||
|
||||
self.use_timestep = use_timestep |
||||
if self.use_timestep: |
||||
# timestep embedding |
||||
self.temb = nn.Module() |
||||
self.temb.dense = nn.ModuleList([ |
||||
torch.nn.Linear(self.ch, |
||||
self.temb_ch), |
||||
torch.nn.Linear(self.temb_ch, |
||||
self.temb_ch), |
||||
]) |
||||
|
||||
# downsampling |
||||
self.conv_in = torch.nn.Conv2d(in_channels, |
||||
self.ch, |
||||
kernel_size=3, |
||||
stride=1, |
||||
padding=1) |
||||
|
||||
curr_res = resolution |
||||
in_ch_mult = (1,)+tuple(ch_mult) |
||||
self.down = nn.ModuleList() |
||||
for i_level in range(self.num_resolutions): |
||||
block = nn.ModuleList() |
||||
attn = nn.ModuleList() |
||||
block_in = ch*in_ch_mult[i_level] |
||||
block_out = ch*ch_mult[i_level] |
||||
for i_block in range(self.num_res_blocks): |
||||
block.append(ResnetBlock(in_channels=block_in, |
||||
out_channels=block_out, |
||||
temb_channels=self.temb_ch, |
||||
dropout=dropout)) |
||||
block_in = block_out |
||||
if curr_res in attn_resolutions: |
||||
attn.append(make_attn(block_in, attn_type=attn_type)) |
||||
down = nn.Module() |
||||
down.block = block |
||||
down.attn = attn |
||||
if i_level != self.num_resolutions-1: |
||||
down.downsample = Downsample(block_in, resamp_with_conv) |
||||
curr_res = curr_res // 2 |
||||
self.down.append(down) |
||||
|
||||
# middle |
||||
self.mid = nn.Module() |
||||
self.mid.block_1 = ResnetBlock(in_channels=block_in, |
||||
out_channels=block_in, |
||||
temb_channels=self.temb_ch, |
||||
dropout=dropout) |
||||
self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) |
||||
self.mid.block_2 = ResnetBlock(in_channels=block_in, |
||||
out_channels=block_in, |
||||
temb_channels=self.temb_ch, |
||||
dropout=dropout) |
||||
|
||||
# upsampling |
||||
self.up = nn.ModuleList() |
||||
for i_level in reversed(range(self.num_resolutions)): |
||||
block = nn.ModuleList() |
||||
attn = nn.ModuleList() |
||||
block_out = ch*ch_mult[i_level] |
||||
skip_in = ch*ch_mult[i_level] |
||||
for i_block in range(self.num_res_blocks+1): |
||||
if i_block == self.num_res_blocks: |
||||
skip_in = ch*in_ch_mult[i_level] |
||||
block.append(ResnetBlock(in_channels=block_in+skip_in, |
||||
out_channels=block_out, |
||||
temb_channels=self.temb_ch, |
||||
dropout=dropout)) |
||||
block_in = block_out |
||||
if curr_res in attn_resolutions: |
||||
attn.append(make_attn(block_in, attn_type=attn_type)) |
||||
up = nn.Module() |
||||
up.block = block |
||||
up.attn = attn |
||||
if i_level != 0: |
||||
up.upsample = Upsample(block_in, resamp_with_conv) |
||||
curr_res = curr_res * 2 |
||||
self.up.insert(0, up) # prepend to get consistent order |
||||
|
||||
# end |
||||
self.norm_out = Normalize(block_in) |
||||
self.conv_out = torch.nn.Conv2d(block_in, |
||||
out_ch, |
||||
kernel_size=3, |
||||
stride=1, |
||||
padding=1) |
||||
|
||||
def forward(self, x, t=None, context=None): |
||||
#assert x.shape[2] == x.shape[3] == self.resolution |
||||
if context is not None: |
||||
# assume aligned context, cat along channel axis |
||||
x = torch.cat((x, context), dim=1) |
||||
if self.use_timestep: |
||||
# timestep embedding |
||||
assert t is not None |
||||
temb = get_timestep_embedding(t, self.ch) |
||||
temb = self.temb.dense[0](temb) |
||||
temb = nonlinearity(temb) |
||||
temb = self.temb.dense[1](temb) |
||||
else: |
||||
temb = None |
||||
|
||||
# downsampling |
||||
hs = [self.conv_in(x)] |
||||
for i_level in range(self.num_resolutions): |
||||
for i_block in range(self.num_res_blocks): |
||||
h = self.down[i_level].block[i_block](hs[-1], temb) |
||||
if len(self.down[i_level].attn) > 0: |
||||
h = self.down[i_level].attn[i_block](h) |
||||
hs.append(h) |
||||
if i_level != self.num_resolutions-1: |
||||
hs.append(self.down[i_level].downsample(hs[-1])) |
||||
|
||||
# middle |
||||
h = hs[-1] |
||||
h = self.mid.block_1(h, temb) |
||||
h = self.mid.attn_1(h) |
||||
h = self.mid.block_2(h, temb) |
||||
|
||||
# upsampling |
||||
for i_level in reversed(range(self.num_resolutions)): |
||||
for i_block in range(self.num_res_blocks+1): |
||||
h = self.up[i_level].block[i_block]( |
||||
torch.cat([h, hs.pop()], dim=1), temb) |
||||
if len(self.up[i_level].attn) > 0: |
||||
h = self.up[i_level].attn[i_block](h) |
||||
if i_level != 0: |
||||
h = self.up[i_level].upsample(h) |
||||
|
||||
# end |
||||
h = self.norm_out(h) |
||||
h = nonlinearity(h) |
||||
h = self.conv_out(h) |
||||
return h |
||||
|
||||
def get_last_layer(self): |
||||
return self.conv_out.weight |
||||
|
||||
|
||||
class Encoder(nn.Module): |
||||
def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, |
||||
attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, |
||||
resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla", |
||||
**ignore_kwargs): |
||||
super().__init__() |
||||
if use_linear_attn: attn_type = "linear" |
||||
self.ch = ch |
||||
self.temb_ch = 0 |
||||
self.num_resolutions = len(ch_mult) |
||||
self.num_res_blocks = num_res_blocks |
||||
self.resolution = resolution |
||||
self.in_channels = in_channels |
||||
|
||||
# downsampling |
||||
self.conv_in = torch.nn.Conv2d(in_channels, |
||||
self.ch, |
||||
kernel_size=3, |
||||
stride=1, |
||||
padding=1) |
||||
|
||||
curr_res = resolution |
||||
in_ch_mult = (1,)+tuple(ch_mult) |
||||
self.in_ch_mult = in_ch_mult |
||||
self.down = nn.ModuleList() |
||||
for i_level in range(self.num_resolutions): |
||||
block = nn.ModuleList() |
||||
attn = nn.ModuleList() |
||||
block_in = ch*in_ch_mult[i_level] |
||||
block_out = ch*ch_mult[i_level] |
||||
for i_block in range(self.num_res_blocks): |
||||
block.append(ResnetBlock(in_channels=block_in, |
||||
out_channels=block_out, |
||||
temb_channels=self.temb_ch, |
||||
dropout=dropout)) |
||||
block_in = block_out |
||||
if curr_res in attn_resolutions: |
||||
attn.append(make_attn(block_in, attn_type=attn_type)) |
||||
down = nn.Module() |
||||
down.block = block |
||||
down.attn = attn |
||||
if i_level != self.num_resolutions-1: |
||||
down.downsample = Downsample(block_in, resamp_with_conv) |
||||
curr_res = curr_res // 2 |
||||
self.down.append(down) |
||||
|
||||
# middle |
||||
self.mid = nn.Module() |
||||
self.mid.block_1 = ResnetBlock(in_channels=block_in, |
||||
out_channels=block_in, |
||||
temb_channels=self.temb_ch, |
||||
dropout=dropout) |
||||
self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) |
||||
self.mid.block_2 = ResnetBlock(in_channels=block_in, |
||||
out_channels=block_in, |
||||
temb_channels=self.temb_ch, |
||||
dropout=dropout) |
||||
|
||||
# end |
||||
self.norm_out = Normalize(block_in) |
||||
self.conv_out = torch.nn.Conv2d(block_in, |
||||
2*z_channels if double_z else z_channels, |
||||
kernel_size=3, |
||||
stride=1, |
||||
padding=1) |
||||
|
||||
def forward(self, x): |
||||
# timestep embedding |
||||
temb = None |
||||
|
||||
# downsampling |
||||
hs = [self.conv_in(x)] |
||||
for i_level in range(self.num_resolutions): |
||||
for i_block in range(self.num_res_blocks): |
||||
h = self.down[i_level].block[i_block](hs[-1], temb) |
||||
if len(self.down[i_level].attn) > 0: |
||||
h = self.down[i_level].attn[i_block](h) |
||||
hs.append(h) |
||||
if i_level != self.num_resolutions-1: |
||||
hs.append(self.down[i_level].downsample(hs[-1])) |
||||
|
||||
# middle |
||||
h = hs[-1] |
||||
h = self.mid.block_1(h, temb) |
||||
h = self.mid.attn_1(h) |
||||
h = self.mid.block_2(h, temb) |
||||
|
||||
# end |
||||
h = self.norm_out(h) |
||||
h = nonlinearity(h) |
||||
h = self.conv_out(h) |
||||
return h |
||||
|
||||
|
||||
class Decoder(nn.Module): |
||||
def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, |
||||
attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, |
||||
resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False, |
||||
attn_type="vanilla", **ignorekwargs): |
||||
super().__init__() |
||||
if use_linear_attn: attn_type = "linear" |
||||
self.ch = ch |
||||
self.temb_ch = 0 |
||||
self.num_resolutions = len(ch_mult) |
||||
self.num_res_blocks = num_res_blocks |
||||
self.resolution = resolution |
||||
self.in_channels = in_channels |
||||
self.give_pre_end = give_pre_end |
||||
self.tanh_out = tanh_out |
||||
|
||||
# compute in_ch_mult, block_in and curr_res at lowest res |
||||
in_ch_mult = (1,)+tuple(ch_mult) |
||||
block_in = ch*ch_mult[self.num_resolutions-1] |
||||
curr_res = resolution // 2**(self.num_resolutions-1) |
||||
self.z_shape = (1,z_channels,curr_res,curr_res) |
||||
print("Working with z of shape {} = {} dimensions.".format( |
||||
self.z_shape, np.prod(self.z_shape))) |
||||
|
||||
# z to block_in |
||||
self.conv_in = torch.nn.Conv2d(z_channels, |
||||
block_in, |
||||
kernel_size=3, |
||||
stride=1, |
||||
padding=1) |
||||
|
||||
# middle |
||||
self.mid = nn.Module() |
||||
self.mid.block_1 = ResnetBlock(in_channels=block_in, |
||||
out_channels=block_in, |
||||
temb_channels=self.temb_ch, |
||||
dropout=dropout) |
||||
self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) |
||||
self.mid.block_2 = ResnetBlock(in_channels=block_in, |
||||
out_channels=block_in, |
||||
temb_channels=self.temb_ch, |
||||
dropout=dropout) |
||||
|
||||
# upsampling |
||||
self.up = nn.ModuleList() |
||||
for i_level in reversed(range(self.num_resolutions)): |
||||
block = nn.ModuleList() |
||||
attn = nn.ModuleList() |
||||
block_out = ch*ch_mult[i_level] |
||||
for i_block in range(self.num_res_blocks+1): |
||||
block.append(ResnetBlock(in_channels=block_in, |
||||
out_channels=block_out, |
||||
temb_channels=self.temb_ch, |
||||
dropout=dropout)) |
||||
block_in = block_out |
||||
if curr_res in attn_resolutions: |
||||
attn.append(make_attn(block_in, attn_type=attn_type)) |
||||
up = nn.Module() |
||||
up.block = block |
||||
up.attn = attn |
||||
if i_level != 0: |
||||
up.upsample = Upsample(block_in, resamp_with_conv) |
||||
curr_res = curr_res * 2 |
||||
self.up.insert(0, up) # prepend to get consistent order |
||||
|
||||
# end |
||||
self.norm_out = Normalize(block_in) |
||||
self.conv_out = torch.nn.Conv2d(block_in, |
||||
out_ch, |
||||
kernel_size=3, |
||||
stride=1, |
||||
padding=1) |
||||
|
||||
def forward(self, z): |
||||
#assert z.shape[1:] == self.z_shape[1:] |
||||
self.last_z_shape = z.shape |
||||
|
||||
# timestep embedding |
||||
temb = None |
||||
|
||||
# z to block_in |
||||
h = self.conv_in(z) |
||||
|
||||
# middle |
||||
h = self.mid.block_1(h, temb) |
||||
h = self.mid.attn_1(h) |
||||
h = self.mid.block_2(h, temb) |
||||
|
||||
# upsampling |
||||
for i_level in reversed(range(self.num_resolutions)): |
||||
for i_block in range(self.num_res_blocks+1): |
||||
h = self.up[i_level].block[i_block](h, temb) |
||||
if len(self.up[i_level].attn) > 0: |
||||
h = self.up[i_level].attn[i_block](h) |
||||
if i_level != 0: |
||||
h = self.up[i_level].upsample(h) |
||||
|
||||
# end |
||||
if self.give_pre_end: |
||||
return h |
||||
|
||||
h = self.norm_out(h) |
||||
h = nonlinearity(h) |
||||
h = self.conv_out(h) |
||||
if self.tanh_out: |
||||
h = torch.tanh(h) |
||||
return h |
||||
|
||||
|
||||
class SimpleDecoder(nn.Module): |
||||
def __init__(self, in_channels, out_channels, *args, **kwargs): |
||||
super().__init__() |
||||
self.model = nn.ModuleList([nn.Conv2d(in_channels, in_channels, 1), |
||||
ResnetBlock(in_channels=in_channels, |
||||
out_channels=2 * in_channels, |
||||
temb_channels=0, dropout=0.0), |
||||
ResnetBlock(in_channels=2 * in_channels, |
||||
out_channels=4 * in_channels, |
||||
temb_channels=0, dropout=0.0), |
||||
ResnetBlock(in_channels=4 * in_channels, |
||||
out_channels=2 * in_channels, |
||||
temb_channels=0, dropout=0.0), |
||||
nn.Conv2d(2*in_channels, in_channels, 1), |
||||
Upsample(in_channels, with_conv=True)]) |
||||
# end |
||||
self.norm_out = Normalize(in_channels) |
||||
self.conv_out = torch.nn.Conv2d(in_channels, |
||||
out_channels, |
||||
kernel_size=3, |
||||
stride=1, |
||||
padding=1) |
||||
|
||||
def forward(self, x): |
||||
for i, layer in enumerate(self.model): |
||||
if i in [1,2,3]: |
||||
x = layer(x, None) |
||||
else: |
||||
x = layer(x) |
||||
|
||||
h = self.norm_out(x) |
||||
h = nonlinearity(h) |
||||
x = self.conv_out(h) |
||||
return x |
||||
|
||||
|
||||
class UpsampleDecoder(nn.Module): |
||||
def __init__(self, in_channels, out_channels, ch, num_res_blocks, resolution, |
||||
ch_mult=(2,2), dropout=0.0): |
||||
super().__init__() |
||||
# upsampling |
||||
self.temb_ch = 0 |
||||
self.num_resolutions = len(ch_mult) |
||||
self.num_res_blocks = num_res_blocks |
||||
block_in = in_channels |
||||
curr_res = resolution // 2 ** (self.num_resolutions - 1) |
||||
self.res_blocks = nn.ModuleList() |
||||
self.upsample_blocks = nn.ModuleList() |
||||
for i_level in range(self.num_resolutions): |
||||
res_block = [] |
||||
block_out = ch * ch_mult[i_level] |
||||
for i_block in range(self.num_res_blocks + 1): |
||||
res_block.append(ResnetBlock(in_channels=block_in, |
||||
out_channels=block_out, |
||||
temb_channels=self.temb_ch, |
||||
dropout=dropout)) |
||||
block_in = block_out |
||||
self.res_blocks.append(nn.ModuleList(res_block)) |
||||
if i_level != self.num_resolutions - 1: |
||||
self.upsample_blocks.append(Upsample(block_in, True)) |
||||
curr_res = curr_res * 2 |
||||
|
||||
# end |
||||
self.norm_out = Normalize(block_in) |
||||
self.conv_out = torch.nn.Conv2d(block_in, |
||||
out_channels, |
||||
kernel_size=3, |
||||
stride=1, |
||||
padding=1) |
||||
|
||||
def forward(self, x): |
||||
# upsampling |
||||
h = x |
||||
for k, i_level in enumerate(range(self.num_resolutions)): |
||||
for i_block in range(self.num_res_blocks + 1): |
||||
h = self.res_blocks[i_level][i_block](h, None) |
||||
if i_level != self.num_resolutions - 1: |
||||
h = self.upsample_blocks[k](h) |
||||
h = self.norm_out(h) |
||||
h = nonlinearity(h) |
||||
h = self.conv_out(h) |
||||
return h |
||||
|
||||
|
||||
class LatentRescaler(nn.Module): |
||||
def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2): |
||||
super().__init__() |
||||
# residual block, interpolate, residual block |
||||
self.factor = factor |
||||
self.conv_in = nn.Conv2d(in_channels, |
||||
mid_channels, |
||||
kernel_size=3, |
||||
stride=1, |
||||
padding=1) |
||||
self.res_block1 = nn.ModuleList([ResnetBlock(in_channels=mid_channels, |
||||
out_channels=mid_channels, |
||||
temb_channels=0, |
||||
dropout=0.0) for _ in range(depth)]) |
||||
self.attn = AttnBlock(mid_channels) |
||||
self.res_block2 = nn.ModuleList([ResnetBlock(in_channels=mid_channels, |
||||
out_channels=mid_channels, |
||||
temb_channels=0, |
||||
dropout=0.0) for _ in range(depth)]) |
||||
|
||||
self.conv_out = nn.Conv2d(mid_channels, |
||||
out_channels, |
||||
kernel_size=1, |
||||
) |
||||
|
||||
def forward(self, x): |
||||
x = self.conv_in(x) |
||||
for block in self.res_block1: |
||||
x = block(x, None) |
||||
x = torch.nn.functional.interpolate(x, size=(int(round(x.shape[2]*self.factor)), int(round(x.shape[3]*self.factor)))) |
||||
x = self.attn(x) |
||||
for block in self.res_block2: |
||||
x = block(x, None) |
||||
x = self.conv_out(x) |
||||
return x |
||||
|
||||
|
||||
class MergedRescaleEncoder(nn.Module): |
||||
def __init__(self, in_channels, ch, resolution, out_ch, num_res_blocks, |
||||
attn_resolutions, dropout=0.0, resamp_with_conv=True, |
||||
ch_mult=(1,2,4,8), rescale_factor=1.0, rescale_module_depth=1): |
||||
super().__init__() |
||||
intermediate_chn = ch * ch_mult[-1] |
||||
self.encoder = Encoder(in_channels=in_channels, num_res_blocks=num_res_blocks, ch=ch, ch_mult=ch_mult, |
||||
z_channels=intermediate_chn, double_z=False, resolution=resolution, |
||||
attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv, |
||||
out_ch=None) |
||||
self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=intermediate_chn, |
||||
mid_channels=intermediate_chn, out_channels=out_ch, depth=rescale_module_depth) |
||||
|
||||
def forward(self, x): |
||||
x = self.encoder(x) |
||||
x = self.rescaler(x) |
||||
return x |
||||
|
||||
|
||||
class MergedRescaleDecoder(nn.Module): |
||||
def __init__(self, z_channels, out_ch, resolution, num_res_blocks, attn_resolutions, ch, ch_mult=(1,2,4,8), |
||||
dropout=0.0, resamp_with_conv=True, rescale_factor=1.0, rescale_module_depth=1): |
||||
super().__init__() |
||||
tmp_chn = z_channels*ch_mult[-1] |
||||
self.decoder = Decoder(out_ch=out_ch, z_channels=tmp_chn, attn_resolutions=attn_resolutions, dropout=dropout, |
||||
resamp_with_conv=resamp_with_conv, in_channels=None, num_res_blocks=num_res_blocks, |
||||
ch_mult=ch_mult, resolution=resolution, ch=ch) |
||||
self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=z_channels, mid_channels=tmp_chn, |
||||
out_channels=tmp_chn, depth=rescale_module_depth) |
||||
|
||||
def forward(self, x): |
||||
x = self.rescaler(x) |
||||
x = self.decoder(x) |
||||
return x |
||||
|
||||
|
||||
class Upsampler(nn.Module): |
||||
def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2): |
||||
super().__init__() |
||||
assert out_size >= in_size |
||||
num_blocks = int(np.log2(out_size//in_size))+1 |
||||
factor_up = 1.+ (out_size % in_size) |
||||
print(f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}") |
||||
self.rescaler = LatentRescaler(factor=factor_up, in_channels=in_channels, mid_channels=2*in_channels, |
||||
out_channels=in_channels) |
||||
self.decoder = Decoder(out_ch=out_channels, resolution=out_size, z_channels=in_channels, num_res_blocks=2, |
||||
attn_resolutions=[], in_channels=None, ch=in_channels, |
||||
ch_mult=[ch_mult for _ in range(num_blocks)]) |
||||
|
||||
def forward(self, x): |
||||
x = self.rescaler(x) |
||||
x = self.decoder(x) |
||||
return x |
||||
|
||||
|
||||
class Resize(nn.Module): |
||||
def __init__(self, in_channels=None, learned=False, mode="bilinear"): |
||||
super().__init__() |
||||
self.with_conv = learned |
||||
self.mode = mode |
||||
if self.with_conv: |
||||
print(f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode") |
||||
raise NotImplementedError() |
||||
assert in_channels is not None |
||||
# no asymmetric padding in torch conv, must do it ourselves |
||||
self.conv = torch.nn.Conv2d(in_channels, |
||||
in_channels, |
||||
kernel_size=4, |
||||
stride=2, |
||||
padding=1) |
||||
|
||||
def forward(self, x, scale_factor=1.0): |
||||
if scale_factor==1.0: |
||||
return x |
||||
else: |
||||
x = torch.nn.functional.interpolate(x, mode=self.mode, align_corners=False, scale_factor=scale_factor) |
||||
return x |
||||
@ -0,0 +1,786 @@
|
||||
from abc import abstractmethod |
||||
import math |
||||
|
||||
import numpy as np |
||||
import torch as th |
||||
import torch.nn as nn |
||||
import torch.nn.functional as F |
||||
|
||||
from ldm.modules.diffusionmodules.util import ( |
||||
checkpoint, |
||||
conv_nd, |
||||
linear, |
||||
avg_pool_nd, |
||||
zero_module, |
||||
normalization, |
||||
timestep_embedding, |
||||
) |
||||
from ldm.modules.attention import SpatialTransformer |
||||
from ldm.util import exists |
||||
|
||||
|
||||
# dummy replace |
||||
def convert_module_to_f16(x): |
||||
pass |
||||
|
||||
def convert_module_to_f32(x): |
||||
pass |
||||
|
||||
|
||||
## go |
||||
class AttentionPool2d(nn.Module): |
||||
""" |
||||
Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py |
||||
""" |
||||
|
||||
def __init__( |
||||
self, |
||||
spacial_dim: int, |
||||
embed_dim: int, |
||||
num_heads_channels: int, |
||||
output_dim: int = None, |
||||
): |
||||
super().__init__() |
||||
self.positional_embedding = nn.Parameter(th.randn(embed_dim, spacial_dim ** 2 + 1) / embed_dim ** 0.5) |
||||
self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1) |
||||
self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1) |
||||
self.num_heads = embed_dim // num_heads_channels |
||||
self.attention = QKVAttention(self.num_heads) |
||||
|
||||
def forward(self, x): |
||||
b, c, *_spatial = x.shape |
||||
x = x.reshape(b, c, -1) # NC(HW) |
||||
x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1) # NC(HW+1) |
||||
x = x + self.positional_embedding[None, :, :].to(x.dtype) # NC(HW+1) |
||||
x = self.qkv_proj(x) |
||||
x = self.attention(x) |
||||
x = self.c_proj(x) |
||||
return x[:, :, 0] |
||||
|
||||
|
||||
class TimestepBlock(nn.Module): |
||||
""" |
||||
Any module where forward() takes timestep embeddings as a second argument. |
||||
""" |
||||
|
||||
@abstractmethod |
||||
def forward(self, x, emb): |
||||
""" |
||||
Apply the module to `x` given `emb` timestep embeddings. |
||||
""" |
||||
|
||||
|
||||
class TimestepEmbedSequential(nn.Sequential, TimestepBlock): |
||||
""" |
||||
A sequential module that passes timestep embeddings to the children that |
||||
support it as an extra input. |
||||
""" |
||||
|
||||
def forward(self, x, emb, context=None): |
||||
for layer in self: |
||||
if isinstance(layer, TimestepBlock): |
||||
x = layer(x, emb) |
||||
elif isinstance(layer, SpatialTransformer): |
||||
x = layer(x, context) |
||||
else: |
||||
x = layer(x) |
||||
return x |
||||
|
||||
|
||||
class Upsample(nn.Module): |
||||
""" |
||||
An upsampling layer with an optional convolution. |
||||
:param channels: channels in the inputs and outputs. |
||||
:param use_conv: a bool determining if a convolution is applied. |
||||
:param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then |
||||
upsampling occurs in the inner-two dimensions. |
||||
""" |
||||
|
||||
def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1): |
||||
super().__init__() |
||||
self.channels = channels |
||||
self.out_channels = out_channels or channels |
||||
self.use_conv = use_conv |
||||
self.dims = dims |
||||
if use_conv: |
||||
self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding) |
||||
|
||||
def forward(self, x): |
||||
assert x.shape[1] == self.channels |
||||
if self.dims == 3: |
||||
x = F.interpolate( |
||||
x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest" |
||||
) |
||||
else: |
||||
x = F.interpolate(x, scale_factor=2, mode="nearest") |
||||
if self.use_conv: |
||||
x = self.conv(x) |
||||
return x |
||||
|
||||
class TransposedUpsample(nn.Module): |
||||
'Learned 2x upsampling without padding' |
||||
def __init__(self, channels, out_channels=None, ks=5): |
||||
super().__init__() |
||||
self.channels = channels |
||||
self.out_channels = out_channels or channels |
||||
|
||||
self.up = nn.ConvTranspose2d(self.channels,self.out_channels,kernel_size=ks,stride=2) |
||||
|
||||
def forward(self,x): |
||||
return self.up(x) |
||||
|
||||
|
||||
class Downsample(nn.Module): |
||||
""" |
||||
A downsampling layer with an optional convolution. |
||||
:param channels: channels in the inputs and outputs. |
||||
:param use_conv: a bool determining if a convolution is applied. |
||||
:param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then |
||||
downsampling occurs in the inner-two dimensions. |
||||
""" |
||||
|
||||
def __init__(self, channels, use_conv, dims=2, out_channels=None,padding=1): |
||||
super().__init__() |
||||
self.channels = channels |
||||
self.out_channels = out_channels or channels |
||||
self.use_conv = use_conv |
||||
self.dims = dims |
||||
stride = 2 if dims != 3 else (1, 2, 2) |
||||
if use_conv: |
||||
self.op = conv_nd( |
||||
dims, self.channels, self.out_channels, 3, stride=stride, padding=padding |
||||
) |
||||
else: |
||||
assert self.channels == self.out_channels |
||||
self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride) |
||||
|
||||
def forward(self, x): |
||||
assert x.shape[1] == self.channels |
||||
return self.op(x) |
||||
|
||||
|
||||
class ResBlock(TimestepBlock): |
||||
""" |
||||
A residual block that can optionally change the number of channels. |
||||
:param channels: the number of input channels. |
||||
:param emb_channels: the number of timestep embedding channels. |
||||
:param dropout: the rate of dropout. |
||||
:param out_channels: if specified, the number of out channels. |
||||
:param use_conv: if True and out_channels is specified, use a spatial |
||||
convolution instead of a smaller 1x1 convolution to change the |
||||
channels in the skip connection. |
||||
:param dims: determines if the signal is 1D, 2D, or 3D. |
||||
:param use_checkpoint: if True, use gradient checkpointing on this module. |
||||
:param up: if True, use this block for upsampling. |
||||
:param down: if True, use this block for downsampling. |
||||
""" |
||||
|
||||
def __init__( |
||||
self, |
||||
channels, |
||||
emb_channels, |
||||
dropout, |
||||
out_channels=None, |
||||
use_conv=False, |
||||
use_scale_shift_norm=False, |
||||
dims=2, |
||||
use_checkpoint=False, |
||||
up=False, |
||||
down=False, |
||||
): |
||||
super().__init__() |
||||
self.channels = channels |
||||
self.emb_channels = emb_channels |
||||
self.dropout = dropout |
||||
self.out_channels = out_channels or channels |
||||
self.use_conv = use_conv |
||||
self.use_checkpoint = use_checkpoint |
||||
self.use_scale_shift_norm = use_scale_shift_norm |
||||
|
||||
self.in_layers = nn.Sequential( |
||||
normalization(channels), |
||||
nn.SiLU(), |
||||
conv_nd(dims, channels, self.out_channels, 3, padding=1), |
||||
) |
||||
|
||||
self.updown = up or down |
||||
|
||||
if up: |
||||
self.h_upd = Upsample(channels, False, dims) |
||||
self.x_upd = Upsample(channels, False, dims) |
||||
elif down: |
||||
self.h_upd = Downsample(channels, False, dims) |
||||
self.x_upd = Downsample(channels, False, dims) |
||||
else: |
||||
self.h_upd = self.x_upd = nn.Identity() |
||||
|
||||
self.emb_layers = nn.Sequential( |
||||
nn.SiLU(), |
||||
linear( |
||||
emb_channels, |
||||
2 * self.out_channels if use_scale_shift_norm else self.out_channels, |
||||
), |
||||
) |
||||
self.out_layers = nn.Sequential( |
||||
normalization(self.out_channels), |
||||
nn.SiLU(), |
||||
nn.Dropout(p=dropout), |
||||
zero_module( |
||||
conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1) |
||||
), |
||||
) |
||||
|
||||
if self.out_channels == channels: |
||||
self.skip_connection = nn.Identity() |
||||
elif use_conv: |
||||
self.skip_connection = conv_nd( |
||||
dims, channels, self.out_channels, 3, padding=1 |
||||
) |
||||
else: |
||||
self.skip_connection = conv_nd(dims, channels, self.out_channels, 1) |
||||
|
||||
def forward(self, x, emb): |
||||
""" |
||||
Apply the block to a Tensor, conditioned on a timestep embedding. |
||||
:param x: an [N x C x ...] Tensor of features. |
||||
:param emb: an [N x emb_channels] Tensor of timestep embeddings. |
||||
:return: an [N x C x ...] Tensor of outputs. |
||||
""" |
||||
return checkpoint( |
||||
self._forward, (x, emb), self.parameters(), self.use_checkpoint |
||||
) |
||||
|
||||
|
||||
def _forward(self, x, emb): |
||||
if self.updown: |
||||
in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1] |
||||
h = in_rest(x) |
||||
h = self.h_upd(h) |
||||
x = self.x_upd(x) |
||||
h = in_conv(h) |
||||
else: |
||||
h = self.in_layers(x) |
||||
emb_out = self.emb_layers(emb).type(h.dtype) |
||||
while len(emb_out.shape) < len(h.shape): |
||||
emb_out = emb_out[..., None] |
||||
if self.use_scale_shift_norm: |
||||
out_norm, out_rest = self.out_layers[0], self.out_layers[1:] |
||||
scale, shift = th.chunk(emb_out, 2, dim=1) |
||||
h = out_norm(h) * (1 + scale) + shift |
||||
h = out_rest(h) |
||||
else: |
||||
h = h + emb_out |
||||
h = self.out_layers(h) |
||||
return self.skip_connection(x) + h |
||||
|
||||
|
||||
class AttentionBlock(nn.Module): |
||||
""" |
||||
An attention block that allows spatial positions to attend to each other. |
||||
Originally ported from here, but adapted to the N-d case. |
||||
https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66. |
||||
""" |
||||
|
||||
def __init__( |
||||
self, |
||||
channels, |
||||
num_heads=1, |
||||
num_head_channels=-1, |
||||
use_checkpoint=False, |
||||
use_new_attention_order=False, |
||||
): |
||||
super().__init__() |
||||
self.channels = channels |
||||
if num_head_channels == -1: |
||||
self.num_heads = num_heads |
||||
else: |
||||
assert ( |
||||
channels % num_head_channels == 0 |
||||
), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}" |
||||
self.num_heads = channels // num_head_channels |
||||
self.use_checkpoint = use_checkpoint |
||||
self.norm = normalization(channels) |
||||
self.qkv = conv_nd(1, channels, channels * 3, 1) |
||||
if use_new_attention_order: |
||||
# split qkv before split heads |
||||
self.attention = QKVAttention(self.num_heads) |
||||
else: |
||||
# split heads before split qkv |
||||
self.attention = QKVAttentionLegacy(self.num_heads) |
||||
|
||||
self.proj_out = zero_module(conv_nd(1, channels, channels, 1)) |
||||
|
||||
def forward(self, x): |
||||
return checkpoint(self._forward, (x,), self.parameters(), True) # TODO: check checkpoint usage, is True # TODO: fix the .half call!!! |
||||
#return pt_checkpoint(self._forward, x) # pytorch |
||||
|
||||
def _forward(self, x): |
||||
b, c, *spatial = x.shape |
||||
x = x.reshape(b, c, -1) |
||||
qkv = self.qkv(self.norm(x)) |
||||
h = self.attention(qkv) |
||||
h = self.proj_out(h) |
||||
return (x + h).reshape(b, c, *spatial) |
||||
|
||||
|
||||
def count_flops_attn(model, _x, y): |
||||
""" |
||||
A counter for the `thop` package to count the operations in an |
||||
attention operation. |
||||
Meant to be used like: |
||||
macs, params = thop.profile( |
||||
model, |
||||
inputs=(inputs, timestamps), |
||||
custom_ops={QKVAttention: QKVAttention.count_flops}, |
||||
) |
||||
""" |
||||
b, c, *spatial = y[0].shape |
||||
num_spatial = int(np.prod(spatial)) |
||||
# We perform two matmuls with the same number of ops. |
||||
# The first computes the weight matrix, the second computes |
||||
# the combination of the value vectors. |
||||
matmul_ops = 2 * b * (num_spatial ** 2) * c |
||||
model.total_ops += th.DoubleTensor([matmul_ops]) |
||||
|
||||
|
||||
class QKVAttentionLegacy(nn.Module): |
||||
""" |
||||
A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping |
||||
""" |
||||
|
||||
def __init__(self, n_heads): |
||||
super().__init__() |
||||
self.n_heads = n_heads |
||||
|
||||
def forward(self, qkv): |
||||
""" |
||||
Apply QKV attention. |
||||
:param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs. |
||||
:return: an [N x (H * C) x T] tensor after attention. |
||||
""" |
||||
bs, width, length = qkv.shape |
||||
assert width % (3 * self.n_heads) == 0 |
||||
ch = width // (3 * self.n_heads) |
||||
q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1) |
||||
scale = 1 / math.sqrt(math.sqrt(ch)) |
||||
weight = th.einsum( |
||||
"bct,bcs->bts", q * scale, k * scale |
||||
) # More stable with f16 than dividing afterwards |
||||
weight = th.softmax(weight.float(), dim=-1).type(weight.dtype) |
||||
a = th.einsum("bts,bcs->bct", weight, v) |
||||
return a.reshape(bs, -1, length) |
||||
|
||||
@staticmethod |
||||
def count_flops(model, _x, y): |
||||
return count_flops_attn(model, _x, y) |
||||
|
||||
|
||||
class QKVAttention(nn.Module): |
||||
""" |
||||
A module which performs QKV attention and splits in a different order. |
||||
""" |
||||
|
||||
def __init__(self, n_heads): |
||||
super().__init__() |
||||
self.n_heads = n_heads |
||||
|
||||
def forward(self, qkv): |
||||
""" |
||||
Apply QKV attention. |
||||
:param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs. |
||||
:return: an [N x (H * C) x T] tensor after attention. |
||||
""" |
||||
bs, width, length = qkv.shape |
||||
assert width % (3 * self.n_heads) == 0 |
||||
ch = width // (3 * self.n_heads) |
||||
q, k, v = qkv.chunk(3, dim=1) |
||||
scale = 1 / math.sqrt(math.sqrt(ch)) |
||||
weight = th.einsum( |
||||
"bct,bcs->bts", |
||||
(q * scale).view(bs * self.n_heads, ch, length), |
||||
(k * scale).view(bs * self.n_heads, ch, length), |
||||
) # More stable with f16 than dividing afterwards |
||||
weight = th.softmax(weight.float(), dim=-1).type(weight.dtype) |
||||
a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length)) |
||||
return a.reshape(bs, -1, length) |
||||
|
||||
@staticmethod |
||||
def count_flops(model, _x, y): |
||||
return count_flops_attn(model, _x, y) |
||||
|
||||
|
||||
class UNetModel(nn.Module): |
||||
""" |
||||
The full UNet model with attention and timestep embedding. |
||||
:param in_channels: channels in the input Tensor. |
||||
:param model_channels: base channel count for the model. |
||||
:param out_channels: channels in the output Tensor. |
||||
:param num_res_blocks: number of residual blocks per downsample. |
||||
:param attention_resolutions: a collection of downsample rates at which |
||||
attention will take place. May be a set, list, or tuple. |
||||
For example, if this contains 4, then at 4x downsampling, attention |
||||
will be used. |
||||
:param dropout: the dropout probability. |
||||
:param channel_mult: channel multiplier for each level of the UNet. |
||||
:param conv_resample: if True, use learned convolutions for upsampling and |
||||
downsampling. |
||||
:param dims: determines if the signal is 1D, 2D, or 3D. |
||||
:param num_classes: if specified (as an int), then this model will be |
||||
class-conditional with `num_classes` classes. |
||||
:param use_checkpoint: use gradient checkpointing to reduce memory usage. |
||||
:param num_heads: the number of attention heads in each attention layer. |
||||
:param num_heads_channels: if specified, ignore num_heads and instead use |
||||
a fixed channel width per attention head. |
||||
:param num_heads_upsample: works with num_heads to set a different number |
||||
of heads for upsampling. Deprecated. |
||||
:param use_scale_shift_norm: use a FiLM-like conditioning mechanism. |
||||
:param resblock_updown: use residual blocks for up/downsampling. |
||||
:param use_new_attention_order: use a different attention pattern for potentially |
||||
increased efficiency. |
||||
""" |
||||
|
||||
def __init__( |
||||
self, |
||||
image_size, |
||||
in_channels, |
||||
model_channels, |
||||
out_channels, |
||||
num_res_blocks, |
||||
attention_resolutions, |
||||
dropout=0, |
||||
channel_mult=(1, 2, 4, 8), |
||||
conv_resample=True, |
||||
dims=2, |
||||
num_classes=None, |
||||
use_checkpoint=False, |
||||
use_fp16=False, |
||||
num_heads=-1, |
||||
num_head_channels=-1, |
||||
num_heads_upsample=-1, |
||||
use_scale_shift_norm=False, |
||||
resblock_updown=False, |
||||
use_new_attention_order=False, |
||||
use_spatial_transformer=False, # custom transformer support |
||||
transformer_depth=1, # custom transformer support |
||||
context_dim=None, # custom transformer support |
||||
n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model |
||||
legacy=True, |
||||
disable_self_attentions=None, |
||||
num_attention_blocks=None, |
||||
disable_middle_self_attn=False, |
||||
use_linear_in_transformer=False, |
||||
): |
||||
super().__init__() |
||||
if use_spatial_transformer: |
||||
assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...' |
||||
|
||||
if context_dim is not None: |
||||
assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...' |
||||
from omegaconf.listconfig import ListConfig |
||||
if type(context_dim) == ListConfig: |
||||
context_dim = list(context_dim) |
||||
|
||||
if num_heads_upsample == -1: |
||||
num_heads_upsample = num_heads |
||||
|
||||
if num_heads == -1: |
||||
assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set' |
||||
|
||||
if num_head_channels == -1: |
||||
assert num_heads != -1, 'Either num_heads or num_head_channels has to be set' |
||||
|
||||
self.image_size = image_size |
||||
self.in_channels = in_channels |
||||
self.model_channels = model_channels |
||||
self.out_channels = out_channels |
||||
if isinstance(num_res_blocks, int): |
||||
self.num_res_blocks = len(channel_mult) * [num_res_blocks] |
||||
else: |
||||
if len(num_res_blocks) != len(channel_mult): |
||||
raise ValueError("provide num_res_blocks either as an int (globally constant) or " |
||||
"as a list/tuple (per-level) with the same length as channel_mult") |
||||
self.num_res_blocks = num_res_blocks |
||||
if disable_self_attentions is not None: |
||||
# should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not |
||||
assert len(disable_self_attentions) == len(channel_mult) |
||||
if num_attention_blocks is not None: |
||||
assert len(num_attention_blocks) == len(self.num_res_blocks) |
||||
assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks)))) |
||||
print(f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. " |
||||
f"This option has LESS priority than attention_resolutions {attention_resolutions}, " |
||||
f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, " |
||||
f"attention will still not be set.") |
||||
|
||||
self.attention_resolutions = attention_resolutions |
||||
self.dropout = dropout |
||||
self.channel_mult = channel_mult |
||||
self.conv_resample = conv_resample |
||||
self.num_classes = num_classes |
||||
self.use_checkpoint = use_checkpoint |
||||
self.dtype = th.float16 if use_fp16 else th.float32 |
||||
self.num_heads = num_heads |
||||
self.num_head_channels = num_head_channels |
||||
self.num_heads_upsample = num_heads_upsample |
||||
self.predict_codebook_ids = n_embed is not None |
||||
|
||||
time_embed_dim = model_channels * 4 |
||||
self.time_embed = nn.Sequential( |
||||
linear(model_channels, time_embed_dim), |
||||
nn.SiLU(), |
||||
linear(time_embed_dim, time_embed_dim), |
||||
) |
||||
|
||||
if self.num_classes is not None: |
||||
if isinstance(self.num_classes, int): |
||||
self.label_emb = nn.Embedding(num_classes, time_embed_dim) |
||||
elif self.num_classes == "continuous": |
||||
print("setting up linear c_adm embedding layer") |
||||
self.label_emb = nn.Linear(1, time_embed_dim) |
||||
else: |
||||
raise ValueError() |
||||
|
||||
self.input_blocks = nn.ModuleList( |
||||
[ |
||||
TimestepEmbedSequential( |
||||
conv_nd(dims, in_channels, model_channels, 3, padding=1) |
||||
) |
||||
] |
||||
) |
||||
self._feature_size = model_channels |
||||
input_block_chans = [model_channels] |
||||
ch = model_channels |
||||
ds = 1 |
||||
for level, mult in enumerate(channel_mult): |
||||
for nr in range(self.num_res_blocks[level]): |
||||
layers = [ |
||||
ResBlock( |
||||
ch, |
||||
time_embed_dim, |
||||
dropout, |
||||
out_channels=mult * model_channels, |
||||
dims=dims, |
||||
use_checkpoint=use_checkpoint, |
||||
use_scale_shift_norm=use_scale_shift_norm, |
||||
) |
||||
] |
||||
ch = mult * model_channels |
||||
if ds in attention_resolutions: |
||||
if num_head_channels == -1: |
||||
dim_head = ch // num_heads |
||||
else: |
||||
num_heads = ch // num_head_channels |
||||
dim_head = num_head_channels |
||||
if legacy: |
||||
#num_heads = 1 |
||||
dim_head = ch // num_heads if use_spatial_transformer else num_head_channels |
||||
if exists(disable_self_attentions): |
||||
disabled_sa = disable_self_attentions[level] |
||||
else: |
||||
disabled_sa = False |
||||
|
||||
if not exists(num_attention_blocks) or nr < num_attention_blocks[level]: |
||||
layers.append( |
||||
AttentionBlock( |
||||
ch, |
||||
use_checkpoint=use_checkpoint, |
||||
num_heads=num_heads, |
||||
num_head_channels=dim_head, |
||||
use_new_attention_order=use_new_attention_order, |
||||
) if not use_spatial_transformer else SpatialTransformer( |
||||
ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim, |
||||
disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer, |
||||
use_checkpoint=use_checkpoint |
||||
) |
||||
) |
||||
self.input_blocks.append(TimestepEmbedSequential(*layers)) |
||||
self._feature_size += ch |
||||
input_block_chans.append(ch) |
||||
if level != len(channel_mult) - 1: |
||||
out_ch = ch |
||||
self.input_blocks.append( |
||||
TimestepEmbedSequential( |
||||
ResBlock( |
||||
ch, |
||||
time_embed_dim, |
||||
dropout, |
||||
out_channels=out_ch, |
||||
dims=dims, |
||||
use_checkpoint=use_checkpoint, |
||||
use_scale_shift_norm=use_scale_shift_norm, |
||||
down=True, |
||||
) |
||||
if resblock_updown |
||||
else Downsample( |
||||
ch, conv_resample, dims=dims, out_channels=out_ch |
||||
) |
||||
) |
||||
) |
||||
ch = out_ch |
||||
input_block_chans.append(ch) |
||||
ds *= 2 |
||||
self._feature_size += ch |
||||
|
||||
if num_head_channels == -1: |
||||
dim_head = ch // num_heads |
||||
else: |
||||
num_heads = ch // num_head_channels |
||||
dim_head = num_head_channels |
||||
if legacy: |
||||
#num_heads = 1 |
||||
dim_head = ch // num_heads if use_spatial_transformer else num_head_channels |
||||
self.middle_block = TimestepEmbedSequential( |
||||
ResBlock( |
||||
ch, |
||||
time_embed_dim, |
||||
dropout, |
||||
dims=dims, |
||||
use_checkpoint=use_checkpoint, |
||||
use_scale_shift_norm=use_scale_shift_norm, |
||||
), |
||||
AttentionBlock( |
||||
ch, |
||||
use_checkpoint=use_checkpoint, |
||||
num_heads=num_heads, |
||||
num_head_channels=dim_head, |
||||
use_new_attention_order=use_new_attention_order, |
||||
) if not use_spatial_transformer else SpatialTransformer( # always uses a self-attn |
||||
ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim, |
||||
disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer, |
||||
use_checkpoint=use_checkpoint |
||||
), |
||||
ResBlock( |
||||
ch, |
||||
time_embed_dim, |
||||
dropout, |
||||
dims=dims, |
||||
use_checkpoint=use_checkpoint, |
||||
use_scale_shift_norm=use_scale_shift_norm, |
||||
), |
||||
) |
||||
self._feature_size += ch |
||||
|
||||
self.output_blocks = nn.ModuleList([]) |
||||
for level, mult in list(enumerate(channel_mult))[::-1]: |
||||
for i in range(self.num_res_blocks[level] + 1): |
||||
ich = input_block_chans.pop() |
||||
layers = [ |
||||
ResBlock( |
||||
ch + ich, |
||||
time_embed_dim, |
||||
dropout, |
||||
out_channels=model_channels * mult, |
||||
dims=dims, |
||||
use_checkpoint=use_checkpoint, |
||||
use_scale_shift_norm=use_scale_shift_norm, |
||||
) |
||||
] |
||||
ch = model_channels * mult |
||||
if ds in attention_resolutions: |
||||
if num_head_channels == -1: |
||||
dim_head = ch // num_heads |
||||
else: |
||||
num_heads = ch // num_head_channels |
||||
dim_head = num_head_channels |
||||
if legacy: |
||||
#num_heads = 1 |
||||
dim_head = ch // num_heads if use_spatial_transformer else num_head_channels |
||||
if exists(disable_self_attentions): |
||||
disabled_sa = disable_self_attentions[level] |
||||
else: |
||||
disabled_sa = False |
||||
|
||||
if not exists(num_attention_blocks) or i < num_attention_blocks[level]: |
||||
layers.append( |
||||
AttentionBlock( |
||||
ch, |
||||
use_checkpoint=use_checkpoint, |
||||
num_heads=num_heads_upsample, |
||||
num_head_channels=dim_head, |
||||
use_new_attention_order=use_new_attention_order, |
||||
) if not use_spatial_transformer else SpatialTransformer( |
||||
ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim, |
||||
disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer, |
||||
use_checkpoint=use_checkpoint |
||||
) |
||||
) |
||||
if level and i == self.num_res_blocks[level]: |
||||
out_ch = ch |
||||
layers.append( |
||||
ResBlock( |
||||
ch, |
||||
time_embed_dim, |
||||
dropout, |
||||
out_channels=out_ch, |
||||
dims=dims, |
||||
use_checkpoint=use_checkpoint, |
||||
use_scale_shift_norm=use_scale_shift_norm, |
||||
up=True, |
||||
) |
||||
if resblock_updown |
||||
else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch) |
||||
) |
||||
ds //= 2 |
||||
self.output_blocks.append(TimestepEmbedSequential(*layers)) |
||||
self._feature_size += ch |
||||
|
||||
self.out = nn.Sequential( |
||||
normalization(ch), |
||||
nn.SiLU(), |
||||
zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)), |
||||
) |
||||
if self.predict_codebook_ids: |
||||
self.id_predictor = nn.Sequential( |
||||
normalization(ch), |
||||
conv_nd(dims, model_channels, n_embed, 1), |
||||
#nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits |
||||
) |
||||
|
||||
def convert_to_fp16(self): |
||||
""" |
||||
Convert the torso of the model to float16. |
||||
""" |
||||
self.input_blocks.apply(convert_module_to_f16) |
||||
self.middle_block.apply(convert_module_to_f16) |
||||
self.output_blocks.apply(convert_module_to_f16) |
||||
|
||||
def convert_to_fp32(self): |
||||
""" |
||||
Convert the torso of the model to float32. |
||||
""" |
||||
self.input_blocks.apply(convert_module_to_f32) |
||||
self.middle_block.apply(convert_module_to_f32) |
||||
self.output_blocks.apply(convert_module_to_f32) |
||||
|
||||
def forward(self, x, timesteps=None, context=None, y=None,**kwargs): |
||||
""" |
||||
Apply the model to an input batch. |
||||
:param x: an [N x C x ...] Tensor of inputs. |
||||
:param timesteps: a 1-D batch of timesteps. |
||||
:param context: conditioning plugged in via crossattn |
||||
:param y: an [N] Tensor of labels, if class-conditional. |
||||
:return: an [N x C x ...] Tensor of outputs. |
||||
""" |
||||
assert (y is not None) == ( |
||||
self.num_classes is not None |
||||
), "must specify y if and only if the model is class-conditional" |
||||
hs = [] |
||||
t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False) |
||||
emb = self.time_embed(t_emb) |
||||
|
||||
if self.num_classes is not None: |
||||
assert y.shape[0] == x.shape[0] |
||||
emb = emb + self.label_emb(y) |
||||
|
||||
h = x.type(self.dtype) |
||||
for module in self.input_blocks: |
||||
h = module(h, emb, context) |
||||
hs.append(h) |
||||
h = self.middle_block(h, emb, context) |
||||
for module in self.output_blocks: |
||||
h = th.cat([h, hs.pop()], dim=1) |
||||
h = module(h, emb, context) |
||||
h = h.type(x.dtype) |
||||
if self.predict_codebook_ids: |
||||
return self.id_predictor(h) |
||||
else: |
||||
return self.out(h) |
||||
@ -0,0 +1,81 @@
|
||||
import torch |
||||
import torch.nn as nn |
||||
import numpy as np |
||||
from functools import partial |
||||
|
||||
from ldm.modules.diffusionmodules.util import extract_into_tensor, make_beta_schedule |
||||
from ldm.util import default |
||||
|
||||
|
||||
class AbstractLowScaleModel(nn.Module): |
||||
# for concatenating a downsampled image to the latent representation |
||||
def __init__(self, noise_schedule_config=None): |
||||
super(AbstractLowScaleModel, self).__init__() |
||||
if noise_schedule_config is not None: |
||||
self.register_schedule(**noise_schedule_config) |
||||
|
||||
def register_schedule(self, beta_schedule="linear", timesteps=1000, |
||||
linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): |
||||
betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, |
||||
cosine_s=cosine_s) |
||||
alphas = 1. - betas |
||||
alphas_cumprod = np.cumprod(alphas, axis=0) |
||||
alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1]) |
||||
|
||||
timesteps, = betas.shape |
||||
self.num_timesteps = int(timesteps) |
||||
self.linear_start = linear_start |
||||
self.linear_end = linear_end |
||||
assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep' |
||||
|
||||
to_torch = partial(torch.tensor, dtype=torch.float32) |
||||
|
||||
self.register_buffer('betas', to_torch(betas)) |
||||
self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) |
||||
self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev)) |
||||
|
||||
# calculations for diffusion q(x_t | x_{t-1}) and others |
||||
self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))) |
||||
self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod))) |
||||
self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod))) |
||||
self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod))) |
||||
self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1))) |
||||
|
||||
def q_sample(self, x_start, t, noise=None): |
||||
noise = default(noise, lambda: torch.randn_like(x_start)) |
||||
return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + |
||||
extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise) |
||||
|
||||
def forward(self, x): |
||||
return x, None |
||||
|
||||
def decode(self, x): |
||||
return x |
||||
|
||||
|
||||
class SimpleImageConcat(AbstractLowScaleModel): |
||||
# no noise level conditioning |
||||
def __init__(self): |
||||
super(SimpleImageConcat, self).__init__(noise_schedule_config=None) |
||||
self.max_noise_level = 0 |
||||
|
||||
def forward(self, x): |
||||
# fix to constant noise level |
||||
return x, torch.zeros(x.shape[0], device=x.device).long() |
||||
|
||||
|
||||
class ImageConcatWithNoiseAugmentation(AbstractLowScaleModel): |
||||
def __init__(self, noise_schedule_config, max_noise_level=1000, to_cuda=False): |
||||
super().__init__(noise_schedule_config=noise_schedule_config) |
||||
self.max_noise_level = max_noise_level |
||||
|
||||
def forward(self, x, noise_level=None): |
||||
if noise_level is None: |
||||
noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long() |
||||
else: |
||||
assert isinstance(noise_level, torch.Tensor) |
||||
z = self.q_sample(x, noise_level) |
||||
return z, noise_level |
||||
|
||||
|
||||
|
||||
@ -0,0 +1,270 @@
|
||||
# adopted from |
||||
# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py |
||||
# and |
||||
# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py |
||||
# and |
||||
# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py |
||||
# |
||||
# thanks! |
||||
|
||||
|
||||
import os |
||||
import math |
||||
import torch |
||||
import torch.nn as nn |
||||
import numpy as np |
||||
from einops import repeat |
||||
|
||||
from ldm.util import instantiate_from_config |
||||
|
||||
|
||||
def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): |
||||
if schedule == "linear": |
||||
betas = ( |
||||
torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2 |
||||
) |
||||
|
||||
elif schedule == "cosine": |
||||
timesteps = ( |
||||
torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s |
||||
) |
||||
alphas = timesteps / (1 + cosine_s) * np.pi / 2 |
||||
alphas = torch.cos(alphas).pow(2) |
||||
alphas = alphas / alphas[0] |
||||
betas = 1 - alphas[1:] / alphas[:-1] |
||||
betas = np.clip(betas, a_min=0, a_max=0.999) |
||||
|
||||
elif schedule == "sqrt_linear": |
||||
betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) |
||||
elif schedule == "sqrt": |
||||
betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5 |
||||
else: |
||||
raise ValueError(f"schedule '{schedule}' unknown.") |
||||
return betas.numpy() |
||||
|
||||
|
||||
def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True): |
||||
if ddim_discr_method == 'uniform': |
||||
c = num_ddpm_timesteps // num_ddim_timesteps |
||||
ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c))) |
||||
elif ddim_discr_method == 'quad': |
||||
ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int) |
||||
else: |
||||
raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"') |
||||
|
||||
# assert ddim_timesteps.shape[0] == num_ddim_timesteps |
||||
# add one to get the final alpha values right (the ones from first scale to data during sampling) |
||||
steps_out = ddim_timesteps + 1 |
||||
if verbose: |
||||
print(f'Selected timesteps for ddim sampler: {steps_out}') |
||||
return steps_out |
||||
|
||||
|
||||
def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True): |
||||
# select alphas for computing the variance schedule |
||||
alphas = alphacums[ddim_timesteps] |
||||
alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist()) |
||||
|
||||
# according the the formula provided in https://arxiv.org/abs/2010.02502 |
||||
sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev)) |
||||
if verbose: |
||||
print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}') |
||||
print(f'For the chosen value of eta, which is {eta}, ' |
||||
f'this results in the following sigma_t schedule for ddim sampler {sigmas}') |
||||
return sigmas, alphas, alphas_prev |
||||
|
||||
|
||||
def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999): |
||||
""" |
||||
Create a beta schedule that discretizes the given alpha_t_bar function, |
||||
which defines the cumulative product of (1-beta) over time from t = [0,1]. |
||||
:param num_diffusion_timesteps: the number of betas to produce. |
||||
:param alpha_bar: a lambda that takes an argument t from 0 to 1 and |
||||
produces the cumulative product of (1-beta) up to that |
||||
part of the diffusion process. |
||||
:param max_beta: the maximum beta to use; use values lower than 1 to |
||||
prevent singularities. |
||||
""" |
||||
betas = [] |
||||
for i in range(num_diffusion_timesteps): |
||||
t1 = i / num_diffusion_timesteps |
||||
t2 = (i + 1) / num_diffusion_timesteps |
||||
betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta)) |
||||
return np.array(betas) |
||||
|
||||
|
||||
def extract_into_tensor(a, t, x_shape): |
||||
b, *_ = t.shape |
||||
out = a.gather(-1, t) |
||||
return out.reshape(b, *((1,) * (len(x_shape) - 1))) |
||||
|
||||
|
||||
def checkpoint(func, inputs, params, flag): |
||||
""" |
||||
Evaluate a function without caching intermediate activations, allowing for |
||||
reduced memory at the expense of extra compute in the backward pass. |
||||
:param func: the function to evaluate. |
||||
:param inputs: the argument sequence to pass to `func`. |
||||
:param params: a sequence of parameters `func` depends on but does not |
||||
explicitly take as arguments. |
||||
:param flag: if False, disable gradient checkpointing. |
||||
""" |
||||
if flag: |
||||
args = tuple(inputs) + tuple(params) |
||||
return CheckpointFunction.apply(func, len(inputs), *args) |
||||
else: |
||||
return func(*inputs) |
||||
|
||||
|
||||
class CheckpointFunction(torch.autograd.Function): |
||||
@staticmethod |
||||
def forward(ctx, run_function, length, *args): |
||||
ctx.run_function = run_function |
||||
ctx.input_tensors = list(args[:length]) |
||||
ctx.input_params = list(args[length:]) |
||||
ctx.gpu_autocast_kwargs = {"enabled": torch.is_autocast_enabled(), |
||||
"dtype": torch.get_autocast_gpu_dtype(), |
||||
"cache_enabled": torch.is_autocast_cache_enabled()} |
||||
with torch.no_grad(): |
||||
output_tensors = ctx.run_function(*ctx.input_tensors) |
||||
return output_tensors |
||||
|
||||
@staticmethod |
||||
def backward(ctx, *output_grads): |
||||
ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors] |
||||
with torch.enable_grad(), \ |
||||
torch.cuda.amp.autocast(**ctx.gpu_autocast_kwargs): |
||||
# Fixes a bug where the first op in run_function modifies the |
||||
# Tensor storage in place, which is not allowed for detach()'d |
||||
# Tensors. |
||||
shallow_copies = [x.view_as(x) for x in ctx.input_tensors] |
||||
output_tensors = ctx.run_function(*shallow_copies) |
||||
input_grads = torch.autograd.grad( |
||||
output_tensors, |
||||
ctx.input_tensors + ctx.input_params, |
||||
output_grads, |
||||
allow_unused=True, |
||||
) |
||||
del ctx.input_tensors |
||||
del ctx.input_params |
||||
del output_tensors |
||||
return (None, None) + input_grads |
||||
|
||||
|
||||
def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False): |
||||
""" |
||||
Create sinusoidal timestep embeddings. |
||||
:param timesteps: a 1-D Tensor of N indices, one per batch element. |
||||
These may be fractional. |
||||
:param dim: the dimension of the output. |
||||
:param max_period: controls the minimum frequency of the embeddings. |
||||
:return: an [N x dim] Tensor of positional embeddings. |
||||
""" |
||||
if not repeat_only: |
||||
half = dim // 2 |
||||
freqs = torch.exp( |
||||
-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half |
||||
).to(device=timesteps.device) |
||||
args = timesteps[:, None].float() * freqs[None] |
||||
embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) |
||||
if dim % 2: |
||||
embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) |
||||
else: |
||||
embedding = repeat(timesteps, 'b -> b d', d=dim) |
||||
return embedding |
||||
|
||||
|
||||
def zero_module(module): |
||||
""" |
||||
Zero out the parameters of a module and return it. |
||||
""" |
||||
for p in module.parameters(): |
||||
p.detach().zero_() |
||||
return module |
||||
|
||||
|
||||
def scale_module(module, scale): |
||||
""" |
||||
Scale the parameters of a module and return it. |
||||
""" |
||||
for p in module.parameters(): |
||||
p.detach().mul_(scale) |
||||
return module |
||||
|
||||
|
||||
def mean_flat(tensor): |
||||
""" |
||||
Take the mean over all non-batch dimensions. |
||||
""" |
||||
return tensor.mean(dim=list(range(1, len(tensor.shape)))) |
||||
|
||||
|
||||
def normalization(channels): |
||||
""" |
||||
Make a standard normalization layer. |
||||
:param channels: number of input channels. |
||||
:return: an nn.Module for normalization. |
||||
""" |
||||
return GroupNorm32(32, channels) |
||||
|
||||
|
||||
# PyTorch 1.7 has SiLU, but we support PyTorch 1.5. |
||||
class SiLU(nn.Module): |
||||
def forward(self, x): |
||||
return x * torch.sigmoid(x) |
||||
|
||||
|
||||
class GroupNorm32(nn.GroupNorm): |
||||
def forward(self, x): |
||||
return super().forward(x.float()).type(x.dtype) |
||||
|
||||
def conv_nd(dims, *args, **kwargs): |
||||
""" |
||||
Create a 1D, 2D, or 3D convolution module. |
||||
""" |
||||
if dims == 1: |
||||
return nn.Conv1d(*args, **kwargs) |
||||
elif dims == 2: |
||||
return nn.Conv2d(*args, **kwargs) |
||||
elif dims == 3: |
||||
return nn.Conv3d(*args, **kwargs) |
||||
raise ValueError(f"unsupported dimensions: {dims}") |
||||
|
||||
|
||||
def linear(*args, **kwargs): |
||||
""" |
||||
Create a linear module. |
||||
""" |
||||
return nn.Linear(*args, **kwargs) |
||||
|
||||
|
||||
def avg_pool_nd(dims, *args, **kwargs): |
||||
""" |
||||
Create a 1D, 2D, or 3D average pooling module. |
||||
""" |
||||
if dims == 1: |
||||
return nn.AvgPool1d(*args, **kwargs) |
||||
elif dims == 2: |
||||
return nn.AvgPool2d(*args, **kwargs) |
||||
elif dims == 3: |
||||
return nn.AvgPool3d(*args, **kwargs) |
||||
raise ValueError(f"unsupported dimensions: {dims}") |
||||
|
||||
|
||||
class HybridConditioner(nn.Module): |
||||
|
||||
def __init__(self, c_concat_config, c_crossattn_config): |
||||
super().__init__() |
||||
self.concat_conditioner = instantiate_from_config(c_concat_config) |
||||
self.crossattn_conditioner = instantiate_from_config(c_crossattn_config) |
||||
|
||||
def forward(self, c_concat, c_crossattn): |
||||
c_concat = self.concat_conditioner(c_concat) |
||||
c_crossattn = self.crossattn_conditioner(c_crossattn) |
||||
return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]} |
||||
|
||||
|
||||
def noise_like(shape, device, repeat=False): |
||||
repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1))) |
||||
noise = lambda: torch.randn(shape, device=device) |
||||
return repeat_noise() if repeat else noise() |
||||
@ -0,0 +1,92 @@
|
||||
import torch |
||||
import numpy as np |
||||
|
||||
|
||||
class AbstractDistribution: |
||||
def sample(self): |
||||
raise NotImplementedError() |
||||
|
||||
def mode(self): |
||||
raise NotImplementedError() |
||||
|
||||
|
||||
class DiracDistribution(AbstractDistribution): |
||||
def __init__(self, value): |
||||
self.value = value |
||||
|
||||
def sample(self): |
||||
return self.value |
||||
|
||||
def mode(self): |
||||
return self.value |
||||
|
||||
|
||||
class DiagonalGaussianDistribution(object): |
||||
def __init__(self, parameters, deterministic=False): |
||||
self.parameters = parameters |
||||
self.mean, self.logvar = torch.chunk(parameters, 2, dim=1) |
||||
self.logvar = torch.clamp(self.logvar, -30.0, 20.0) |
||||
self.deterministic = deterministic |
||||
self.std = torch.exp(0.5 * self.logvar) |
||||
self.var = torch.exp(self.logvar) |
||||
if self.deterministic: |
||||
self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device) |
||||
|
||||
def sample(self): |
||||
x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device) |
||||
return x |
||||
|
||||
def kl(self, other=None): |
||||
if self.deterministic: |
||||
return torch.Tensor([0.]) |
||||
else: |
||||
if other is None: |
||||
return 0.5 * torch.sum(torch.pow(self.mean, 2) |
||||
+ self.var - 1.0 - self.logvar, |
||||
dim=[1, 2, 3]) |
||||
else: |
||||
return 0.5 * torch.sum( |
||||
torch.pow(self.mean - other.mean, 2) / other.var |
||||
+ self.var / other.var - 1.0 - self.logvar + other.logvar, |
||||
dim=[1, 2, 3]) |
||||
|
||||
def nll(self, sample, dims=[1,2,3]): |
||||
if self.deterministic: |
||||
return torch.Tensor([0.]) |
||||
logtwopi = np.log(2.0 * np.pi) |
||||
return 0.5 * torch.sum( |
||||
logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, |
||||
dim=dims) |
||||
|
||||
def mode(self): |
||||
return self.mean |
||||
|
||||
|
||||
def normal_kl(mean1, logvar1, mean2, logvar2): |
||||
""" |
||||
source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12 |
||||
Compute the KL divergence between two gaussians. |
||||
Shapes are automatically broadcasted, so batches can be compared to |
||||
scalars, among other use cases. |
||||
""" |
||||
tensor = None |
||||
for obj in (mean1, logvar1, mean2, logvar2): |
||||
if isinstance(obj, torch.Tensor): |
||||
tensor = obj |
||||
break |
||||
assert tensor is not None, "at least one argument must be a Tensor" |
||||
|
||||
# Force variances to be Tensors. Broadcasting helps convert scalars to |
||||
# Tensors, but it does not work for torch.exp(). |
||||
logvar1, logvar2 = [ |
||||
x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor) |
||||
for x in (logvar1, logvar2) |
||||
] |
||||
|
||||
return 0.5 * ( |
||||
-1.0 |
||||
+ logvar2 |
||||
- logvar1 |
||||
+ torch.exp(logvar1 - logvar2) |
||||
+ ((mean1 - mean2) ** 2) * torch.exp(-logvar2) |
||||
) |
||||
@ -0,0 +1,80 @@
|
||||
import torch |
||||
from torch import nn |
||||
|
||||
|
||||
class LitEma(nn.Module): |
||||
def __init__(self, model, decay=0.9999, use_num_upates=True): |
||||
super().__init__() |
||||
if decay < 0.0 or decay > 1.0: |
||||
raise ValueError('Decay must be between 0 and 1') |
||||
|
||||
self.m_name2s_name = {} |
||||
self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32)) |
||||
self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int) if use_num_upates |
||||
else torch.tensor(-1, dtype=torch.int)) |
||||
|
||||
for name, p in model.named_parameters(): |
||||
if p.requires_grad: |
||||
# remove as '.'-character is not allowed in buffers |
||||
s_name = name.replace('.', '') |
||||
self.m_name2s_name.update({name: s_name}) |
||||
self.register_buffer(s_name, p.clone().detach().data) |
||||
|
||||
self.collected_params = [] |
||||
|
||||
def reset_num_updates(self): |
||||
del self.num_updates |
||||
self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int)) |
||||
|
||||
def forward(self, model): |
||||
decay = self.decay |
||||
|
||||
if self.num_updates >= 0: |
||||
self.num_updates += 1 |
||||
decay = min(self.decay, (1 + self.num_updates) / (10 + self.num_updates)) |
||||
|
||||
one_minus_decay = 1.0 - decay |
||||
|
||||
with torch.no_grad(): |
||||
m_param = dict(model.named_parameters()) |
||||
shadow_params = dict(self.named_buffers()) |
||||
|
||||
for key in m_param: |
||||
if m_param[key].requires_grad: |
||||
sname = self.m_name2s_name[key] |
||||
shadow_params[sname] = shadow_params[sname].type_as(m_param[key]) |
||||
shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key])) |
||||
else: |
||||
assert not key in self.m_name2s_name |
||||
|
||||
def copy_to(self, model): |
||||
m_param = dict(model.named_parameters()) |
||||
shadow_params = dict(self.named_buffers()) |
||||
for key in m_param: |
||||
if m_param[key].requires_grad: |
||||
m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data) |
||||
else: |
||||
assert not key in self.m_name2s_name |
||||
|
||||
def store(self, parameters): |
||||
""" |
||||
Save the current parameters for restoring later. |
||||
Args: |
||||
parameters: Iterable of `torch.nn.Parameter`; the parameters to be |
||||
temporarily stored. |
||||
""" |
||||
self.collected_params = [param.clone() for param in parameters] |
||||
|
||||
def restore(self, parameters): |
||||
""" |
||||
Restore the parameters stored with the `store` method. |
||||
Useful to validate the model with EMA parameters without affecting the |
||||
original optimization process. Store the parameters before the |
||||
`copy_to` method. After validation (or model saving), use this to |
||||
restore the former parameters. |
||||
Args: |
||||
parameters: Iterable of `torch.nn.Parameter`; the parameters to be |
||||
updated with the stored parameters. |
||||
""" |
||||
for c_param, param in zip(self.collected_params, parameters): |
||||
param.data.copy_(c_param.data) |
||||
@ -0,0 +1,213 @@
|
||||
import torch |
||||
import torch.nn as nn |
||||
from torch.utils.checkpoint import checkpoint |
||||
|
||||
from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel |
||||
|
||||
import open_clip |
||||
from ldm.util import default, count_params |
||||
|
||||
|
||||
class AbstractEncoder(nn.Module): |
||||
def __init__(self): |
||||
super().__init__() |
||||
|
||||
def encode(self, *args, **kwargs): |
||||
raise NotImplementedError |
||||
|
||||
|
||||
class IdentityEncoder(AbstractEncoder): |
||||
|
||||
def encode(self, x): |
||||
return x |
||||
|
||||
|
||||
class ClassEmbedder(nn.Module): |
||||
def __init__(self, embed_dim, n_classes=1000, key='class', ucg_rate=0.1): |
||||
super().__init__() |
||||
self.key = key |
||||
self.embedding = nn.Embedding(n_classes, embed_dim) |
||||
self.n_classes = n_classes |
||||
self.ucg_rate = ucg_rate |
||||
|
||||
def forward(self, batch, key=None, disable_dropout=False): |
||||
if key is None: |
||||
key = self.key |
||||
# this is for use in crossattn |
||||
c = batch[key][:, None] |
||||
if self.ucg_rate > 0. and not disable_dropout: |
||||
mask = 1. - torch.bernoulli(torch.ones_like(c) * self.ucg_rate) |
||||
c = mask * c + (1-mask) * torch.ones_like(c)*(self.n_classes-1) |
||||
c = c.long() |
||||
c = self.embedding(c) |
||||
return c |
||||
|
||||
def get_unconditional_conditioning(self, bs, device="cuda"): |
||||
uc_class = self.n_classes - 1 # 1000 classes --> 0 ... 999, one extra class for ucg (class 1000) |
||||
uc = torch.ones((bs,), device=device) * uc_class |
||||
uc = {self.key: uc} |
||||
return uc |
||||
|
||||
|
||||
def disabled_train(self, mode=True): |
||||
"""Overwrite model.train with this function to make sure train/eval mode |
||||
does not change anymore.""" |
||||
return self |
||||
|
||||
|
||||
class FrozenT5Embedder(AbstractEncoder): |
||||
"""Uses the T5 transformer encoder for text""" |
||||
def __init__(self, version="google/t5-v1_1-large", device="cuda", max_length=77, freeze=True): # others are google/t5-v1_1-xl and google/t5-v1_1-xxl |
||||
super().__init__() |
||||
self.tokenizer = T5Tokenizer.from_pretrained(version) |
||||
self.transformer = T5EncoderModel.from_pretrained(version) |
||||
self.device = device |
||||
self.max_length = max_length # TODO: typical value? |
||||
if freeze: |
||||
self.freeze() |
||||
|
||||
def freeze(self): |
||||
self.transformer = self.transformer.eval() |
||||
#self.train = disabled_train |
||||
for param in self.parameters(): |
||||
param.requires_grad = False |
||||
|
||||
def forward(self, text): |
||||
batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, |
||||
return_overflowing_tokens=False, padding="max_length", return_tensors="pt") |
||||
tokens = batch_encoding["input_ids"].to(self.device) |
||||
outputs = self.transformer(input_ids=tokens) |
||||
|
||||
z = outputs.last_hidden_state |
||||
return z |
||||
|
||||
def encode(self, text): |
||||
return self(text) |
||||
|
||||
|
||||
class FrozenCLIPEmbedder(AbstractEncoder): |
||||
"""Uses the CLIP transformer encoder for text (from huggingface)""" |
||||
LAYERS = [ |
||||
"last", |
||||
"pooled", |
||||
"hidden" |
||||
] |
||||
def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77, |
||||
freeze=True, layer="last", layer_idx=None): # clip-vit-base-patch32 |
||||
super().__init__() |
||||
assert layer in self.LAYERS |
||||
self.tokenizer = CLIPTokenizer.from_pretrained(version) |
||||
self.transformer = CLIPTextModel.from_pretrained(version) |
||||
self.device = device |
||||
self.max_length = max_length |
||||
if freeze: |
||||
self.freeze() |
||||
self.layer = layer |
||||
self.layer_idx = layer_idx |
||||
if layer == "hidden": |
||||
assert layer_idx is not None |
||||
assert 0 <= abs(layer_idx) <= 12 |
||||
|
||||
def freeze(self): |
||||
self.transformer = self.transformer.eval() |
||||
#self.train = disabled_train |
||||
for param in self.parameters(): |
||||
param.requires_grad = False |
||||
|
||||
def forward(self, text): |
||||
batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, |
||||
return_overflowing_tokens=False, padding="max_length", return_tensors="pt") |
||||
tokens = batch_encoding["input_ids"].to(self.device) |
||||
outputs = self.transformer(input_ids=tokens, output_hidden_states=self.layer=="hidden") |
||||
if self.layer == "last": |
||||
z = outputs.last_hidden_state |
||||
elif self.layer == "pooled": |
||||
z = outputs.pooler_output[:, None, :] |
||||
else: |
||||
z = outputs.hidden_states[self.layer_idx] |
||||
return z |
||||
|
||||
def encode(self, text): |
||||
return self(text) |
||||
|
||||
|
||||
class FrozenOpenCLIPEmbedder(AbstractEncoder): |
||||
""" |
||||
Uses the OpenCLIP transformer encoder for text |
||||
""" |
||||
LAYERS = [ |
||||
#"pooled", |
||||
"last", |
||||
"penultimate" |
||||
] |
||||
def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", device="cuda", max_length=77, |
||||
freeze=True, layer="last"): |
||||
super().__init__() |
||||
assert layer in self.LAYERS |
||||
model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'), pretrained=version) |
||||
del model.visual |
||||
self.model = model |
||||
|
||||
self.device = device |
||||
self.max_length = max_length |
||||
if freeze: |
||||
self.freeze() |
||||
self.layer = layer |
||||
if self.layer == "last": |
||||
self.layer_idx = 0 |
||||
elif self.layer == "penultimate": |
||||
self.layer_idx = 1 |
||||
else: |
||||
raise NotImplementedError() |
||||
|
||||
def freeze(self): |
||||
self.model = self.model.eval() |
||||
for param in self.parameters(): |
||||
param.requires_grad = False |
||||
|
||||
def forward(self, text): |
||||
tokens = open_clip.tokenize(text) |
||||
z = self.encode_with_transformer(tokens.to(self.device)) |
||||
return z |
||||
|
||||
def encode_with_transformer(self, text): |
||||
x = self.model.token_embedding(text) # [batch_size, n_ctx, d_model] |
||||
x = x + self.model.positional_embedding |
||||
x = x.permute(1, 0, 2) # NLD -> LND |
||||
x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask) |
||||
x = x.permute(1, 0, 2) # LND -> NLD |
||||
x = self.model.ln_final(x) |
||||
return x |
||||
|
||||
def text_transformer_forward(self, x: torch.Tensor, attn_mask = None): |
||||
for i, r in enumerate(self.model.transformer.resblocks): |
||||
if i == len(self.model.transformer.resblocks) - self.layer_idx: |
||||
break |
||||
if self.model.transformer.grad_checkpointing and not torch.jit.is_scripting(): |
||||
x = checkpoint(r, x, attn_mask) |
||||
else: |
||||
x = r(x, attn_mask=attn_mask) |
||||
return x |
||||
|
||||
def encode(self, text): |
||||
return self(text) |
||||
|
||||
|
||||
class FrozenCLIPT5Encoder(AbstractEncoder): |
||||
def __init__(self, clip_version="openai/clip-vit-large-patch14", t5_version="google/t5-v1_1-xl", device="cuda", |
||||
clip_max_length=77, t5_max_length=77): |
||||
super().__init__() |
||||
self.clip_encoder = FrozenCLIPEmbedder(clip_version, device, max_length=clip_max_length) |
||||
self.t5_encoder = FrozenT5Embedder(t5_version, device, max_length=t5_max_length) |
||||
print(f"{self.clip_encoder.__class__.__name__} has {count_params(self.clip_encoder)*1.e-6:.2f} M parameters, " |
||||
f"{self.t5_encoder.__class__.__name__} comes with {count_params(self.t5_encoder)*1.e-6:.2f} M params.") |
||||
|
||||
def encode(self, text): |
||||
return self(text) |
||||
|
||||
def forward(self, text): |
||||
clip_z = self.clip_encoder.encode(text) |
||||
t5_z = self.t5_encoder.encode(text) |
||||
return [clip_z, t5_z] |
||||
|
||||
|
||||
@ -0,0 +1,2 @@
|
||||
from ldm.modules.image_degradation.bsrgan import degradation_bsrgan_variant as degradation_fn_bsr |
||||
from ldm.modules.image_degradation.bsrgan_light import degradation_bsrgan_variant as degradation_fn_bsr_light |
||||
@ -0,0 +1,730 @@
|
||||
# -*- coding: utf-8 -*- |
||||
""" |
||||
# -------------------------------------------- |
||||
# Super-Resolution |
||||
# -------------------------------------------- |
||||
# |
||||
# Kai Zhang (cskaizhang@gmail.com) |
||||
# https://github.com/cszn |
||||
# From 2019/03--2021/08 |
||||
# -------------------------------------------- |
||||
""" |
||||
|
||||
import numpy as np |
||||
import cv2 |
||||
import torch |
||||
|
||||
from functools import partial |
||||
import random |
||||
from scipy import ndimage |
||||
import scipy |
||||
import scipy.stats as ss |
||||
from scipy.interpolate import interp2d |
||||
from scipy.linalg import orth |
||||
import albumentations |
||||
|
||||
import ldm.modules.image_degradation.utils_image as util |
||||
|
||||
|
||||
def modcrop_np(img, sf): |
||||
''' |
||||
Args: |
||||
img: numpy image, WxH or WxHxC |
||||
sf: scale factor |
||||
Return: |
||||
cropped image |
||||
''' |
||||
w, h = img.shape[:2] |
||||
im = np.copy(img) |
||||
return im[:w - w % sf, :h - h % sf, ...] |
||||
|
||||
|
||||
""" |
||||
# -------------------------------------------- |
||||
# anisotropic Gaussian kernels |
||||
# -------------------------------------------- |
||||
""" |
||||
|
||||
|
||||
def analytic_kernel(k): |
||||
"""Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)""" |
||||
k_size = k.shape[0] |
||||
# Calculate the big kernels size |
||||
big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2)) |
||||
# Loop over the small kernel to fill the big one |
||||
for r in range(k_size): |
||||
for c in range(k_size): |
||||
big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k |
||||
# Crop the edges of the big kernel to ignore very small values and increase run time of SR |
||||
crop = k_size // 2 |
||||
cropped_big_k = big_k[crop:-crop, crop:-crop] |
||||
# Normalize to 1 |
||||
return cropped_big_k / cropped_big_k.sum() |
||||
|
||||
|
||||
def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6): |
||||
""" generate an anisotropic Gaussian kernel |
||||
Args: |
||||
ksize : e.g., 15, kernel size |
||||
theta : [0, pi], rotation angle range |
||||
l1 : [0.1,50], scaling of eigenvalues |
||||
l2 : [0.1,l1], scaling of eigenvalues |
||||
If l1 = l2, will get an isotropic Gaussian kernel. |
||||
Returns: |
||||
k : kernel |
||||
""" |
||||
|
||||
v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.])) |
||||
V = np.array([[v[0], v[1]], [v[1], -v[0]]]) |
||||
D = np.array([[l1, 0], [0, l2]]) |
||||
Sigma = np.dot(np.dot(V, D), np.linalg.inv(V)) |
||||
k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize) |
||||
|
||||
return k |
||||
|
||||
|
||||
def gm_blur_kernel(mean, cov, size=15): |
||||
center = size / 2.0 + 0.5 |
||||
k = np.zeros([size, size]) |
||||
for y in range(size): |
||||
for x in range(size): |
||||
cy = y - center + 1 |
||||
cx = x - center + 1 |
||||
k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov) |
||||
|
||||
k = k / np.sum(k) |
||||
return k |
||||
|
||||
|
||||
def shift_pixel(x, sf, upper_left=True): |
||||
"""shift pixel for super-resolution with different scale factors |
||||
Args: |
||||
x: WxHxC or WxH |
||||
sf: scale factor |
||||
upper_left: shift direction |
||||
""" |
||||
h, w = x.shape[:2] |
||||
shift = (sf - 1) * 0.5 |
||||
xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0) |
||||
if upper_left: |
||||
x1 = xv + shift |
||||
y1 = yv + shift |
||||
else: |
||||
x1 = xv - shift |
||||
y1 = yv - shift |
||||
|
||||
x1 = np.clip(x1, 0, w - 1) |
||||
y1 = np.clip(y1, 0, h - 1) |
||||
|
||||
if x.ndim == 2: |
||||
x = interp2d(xv, yv, x)(x1, y1) |
||||
if x.ndim == 3: |
||||
for i in range(x.shape[-1]): |
||||
x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1) |
||||
|
||||
return x |
||||
|
||||
|
||||
def blur(x, k): |
||||
''' |
||||
x: image, NxcxHxW |
||||
k: kernel, Nx1xhxw |
||||
''' |
||||
n, c = x.shape[:2] |
||||
p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2 |
||||
x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate') |
||||
k = k.repeat(1, c, 1, 1) |
||||
k = k.view(-1, 1, k.shape[2], k.shape[3]) |
||||
x = x.view(1, -1, x.shape[2], x.shape[3]) |
||||
x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c) |
||||
x = x.view(n, c, x.shape[2], x.shape[3]) |
||||
|
||||
return x |
||||
|
||||
|
||||
def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0): |
||||
"""" |
||||
# modified version of https://github.com/assafshocher/BlindSR_dataset_generator |
||||
# Kai Zhang |
||||
# min_var = 0.175 * sf # variance of the gaussian kernel will be sampled between min_var and max_var |
||||
# max_var = 2.5 * sf |
||||
""" |
||||
# Set random eigen-vals (lambdas) and angle (theta) for COV matrix |
||||
lambda_1 = min_var + np.random.rand() * (max_var - min_var) |
||||
lambda_2 = min_var + np.random.rand() * (max_var - min_var) |
||||
theta = np.random.rand() * np.pi # random theta |
||||
noise = -noise_level + np.random.rand(*k_size) * noise_level * 2 |
||||
|
||||
# Set COV matrix using Lambdas and Theta |
||||
LAMBDA = np.diag([lambda_1, lambda_2]) |
||||
Q = np.array([[np.cos(theta), -np.sin(theta)], |
||||
[np.sin(theta), np.cos(theta)]]) |
||||
SIGMA = Q @ LAMBDA @ Q.T |
||||
INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :] |
||||
|
||||
# Set expectation position (shifting kernel for aligned image) |
||||
MU = k_size // 2 - 0.5 * (scale_factor - 1) # - 0.5 * (scale_factor - k_size % 2) |
||||
MU = MU[None, None, :, None] |
||||
|
||||
# Create meshgrid for Gaussian |
||||
[X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1])) |
||||
Z = np.stack([X, Y], 2)[:, :, :, None] |
||||
|
||||
# Calcualte Gaussian for every pixel of the kernel |
||||
ZZ = Z - MU |
||||
ZZ_t = ZZ.transpose(0, 1, 3, 2) |
||||
raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise) |
||||
|
||||
# shift the kernel so it will be centered |
||||
# raw_kernel_centered = kernel_shift(raw_kernel, scale_factor) |
||||
|
||||
# Normalize the kernel and return |
||||
# kernel = raw_kernel_centered / np.sum(raw_kernel_centered) |
||||
kernel = raw_kernel / np.sum(raw_kernel) |
||||
return kernel |
||||
|
||||
|
||||
def fspecial_gaussian(hsize, sigma): |
||||
hsize = [hsize, hsize] |
||||
siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0] |
||||
std = sigma |
||||
[x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1)) |
||||
arg = -(x * x + y * y) / (2 * std * std) |
||||
h = np.exp(arg) |
||||
h[h < scipy.finfo(float).eps * h.max()] = 0 |
||||
sumh = h.sum() |
||||
if sumh != 0: |
||||
h = h / sumh |
||||
return h |
||||
|
||||
|
||||
def fspecial_laplacian(alpha): |
||||
alpha = max([0, min([alpha, 1])]) |
||||
h1 = alpha / (alpha + 1) |
||||
h2 = (1 - alpha) / (alpha + 1) |
||||
h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]] |
||||
h = np.array(h) |
||||
return h |
||||
|
||||
|
||||
def fspecial(filter_type, *args, **kwargs): |
||||
''' |
||||
python code from: |
||||
https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py |
||||
''' |
||||
if filter_type == 'gaussian': |
||||
return fspecial_gaussian(*args, **kwargs) |
||||
if filter_type == 'laplacian': |
||||
return fspecial_laplacian(*args, **kwargs) |
||||
|
||||
|
||||
""" |
||||
# -------------------------------------------- |
||||
# degradation models |
||||
# -------------------------------------------- |
||||
""" |
||||
|
||||
|
||||
def bicubic_degradation(x, sf=3): |
||||
''' |
||||
Args: |
||||
x: HxWxC image, [0, 1] |
||||
sf: down-scale factor |
||||
Return: |
||||
bicubicly downsampled LR image |
||||
''' |
||||
x = util.imresize_np(x, scale=1 / sf) |
||||
return x |
||||
|
||||
|
||||
def srmd_degradation(x, k, sf=3): |
||||
''' blur + bicubic downsampling |
||||
Args: |
||||
x: HxWxC image, [0, 1] |
||||
k: hxw, double |
||||
sf: down-scale factor |
||||
Return: |
||||
downsampled LR image |
||||
Reference: |
||||
@inproceedings{zhang2018learning, |
||||
title={Learning a single convolutional super-resolution network for multiple degradations}, |
||||
author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, |
||||
booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, |
||||
pages={3262--3271}, |
||||
year={2018} |
||||
} |
||||
''' |
||||
x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') # 'nearest' | 'mirror' |
||||
x = bicubic_degradation(x, sf=sf) |
||||
return x |
||||
|
||||
|
||||
def dpsr_degradation(x, k, sf=3): |
||||
''' bicubic downsampling + blur |
||||
Args: |
||||
x: HxWxC image, [0, 1] |
||||
k: hxw, double |
||||
sf: down-scale factor |
||||
Return: |
||||
downsampled LR image |
||||
Reference: |
||||
@inproceedings{zhang2019deep, |
||||
title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels}, |
||||
author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, |
||||
booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, |
||||
pages={1671--1681}, |
||||
year={2019} |
||||
} |
||||
''' |
||||
x = bicubic_degradation(x, sf=sf) |
||||
x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') |
||||
return x |
||||
|
||||
|
||||
def classical_degradation(x, k, sf=3): |
||||
''' blur + downsampling |
||||
Args: |
||||
x: HxWxC image, [0, 1]/[0, 255] |
||||
k: hxw, double |
||||
sf: down-scale factor |
||||
Return: |
||||
downsampled LR image |
||||
''' |
||||
x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') |
||||
# x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2)) |
||||
st = 0 |
||||
return x[st::sf, st::sf, ...] |
||||
|
||||
|
||||
def add_sharpening(img, weight=0.5, radius=50, threshold=10): |
||||
"""USM sharpening. borrowed from real-ESRGAN |
||||
Input image: I; Blurry image: B. |
||||
1. K = I + weight * (I - B) |
||||
2. Mask = 1 if abs(I - B) > threshold, else: 0 |
||||
3. Blur mask: |
||||
4. Out = Mask * K + (1 - Mask) * I |
||||
Args: |
||||
img (Numpy array): Input image, HWC, BGR; float32, [0, 1]. |
||||
weight (float): Sharp weight. Default: 1. |
||||
radius (float): Kernel size of Gaussian blur. Default: 50. |
||||
threshold (int): |
||||
""" |
||||
if radius % 2 == 0: |
||||
radius += 1 |
||||
blur = cv2.GaussianBlur(img, (radius, radius), 0) |
||||
residual = img - blur |
||||
mask = np.abs(residual) * 255 > threshold |
||||
mask = mask.astype('float32') |
||||
soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0) |
||||
|
||||
K = img + weight * residual |
||||
K = np.clip(K, 0, 1) |
||||
return soft_mask * K + (1 - soft_mask) * img |
||||
|
||||
|
||||
def add_blur(img, sf=4): |
||||
wd2 = 4.0 + sf |
||||
wd = 2.0 + 0.2 * sf |
||||
if random.random() < 0.5: |
||||
l1 = wd2 * random.random() |
||||
l2 = wd2 * random.random() |
||||
k = anisotropic_Gaussian(ksize=2 * random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2) |
||||
else: |
||||
k = fspecial('gaussian', 2 * random.randint(2, 11) + 3, wd * random.random()) |
||||
img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror') |
||||
|
||||
return img |
||||
|
||||
|
||||
def add_resize(img, sf=4): |
||||
rnum = np.random.rand() |
||||
if rnum > 0.8: # up |
||||
sf1 = random.uniform(1, 2) |
||||
elif rnum < 0.7: # down |
||||
sf1 = random.uniform(0.5 / sf, 1) |
||||
else: |
||||
sf1 = 1.0 |
||||
img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3])) |
||||
img = np.clip(img, 0.0, 1.0) |
||||
|
||||
return img |
||||
|
||||
|
||||
# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25): |
||||
# noise_level = random.randint(noise_level1, noise_level2) |
||||
# rnum = np.random.rand() |
||||
# if rnum > 0.6: # add color Gaussian noise |
||||
# img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) |
||||
# elif rnum < 0.4: # add grayscale Gaussian noise |
||||
# img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) |
||||
# else: # add noise |
||||
# L = noise_level2 / 255. |
||||
# D = np.diag(np.random.rand(3)) |
||||
# U = orth(np.random.rand(3, 3)) |
||||
# conv = np.dot(np.dot(np.transpose(U), D), U) |
||||
# img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) |
||||
# img = np.clip(img, 0.0, 1.0) |
||||
# return img |
||||
|
||||
def add_Gaussian_noise(img, noise_level1=2, noise_level2=25): |
||||
noise_level = random.randint(noise_level1, noise_level2) |
||||
rnum = np.random.rand() |
||||
if rnum > 0.6: # add color Gaussian noise |
||||
img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) |
||||
elif rnum < 0.4: # add grayscale Gaussian noise |
||||
img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) |
||||
else: # add noise |
||||
L = noise_level2 / 255. |
||||
D = np.diag(np.random.rand(3)) |
||||
U = orth(np.random.rand(3, 3)) |
||||
conv = np.dot(np.dot(np.transpose(U), D), U) |
||||
img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) |
||||
img = np.clip(img, 0.0, 1.0) |
||||
return img |
||||
|
||||
|
||||
def add_speckle_noise(img, noise_level1=2, noise_level2=25): |
||||
noise_level = random.randint(noise_level1, noise_level2) |
||||
img = np.clip(img, 0.0, 1.0) |
||||
rnum = random.random() |
||||
if rnum > 0.6: |
||||
img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) |
||||
elif rnum < 0.4: |
||||
img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) |
||||
else: |
||||
L = noise_level2 / 255. |
||||
D = np.diag(np.random.rand(3)) |
||||
U = orth(np.random.rand(3, 3)) |
||||
conv = np.dot(np.dot(np.transpose(U), D), U) |
||||
img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) |
||||
img = np.clip(img, 0.0, 1.0) |
||||
return img |
||||
|
||||
|
||||
def add_Poisson_noise(img): |
||||
img = np.clip((img * 255.0).round(), 0, 255) / 255. |
||||
vals = 10 ** (2 * random.random() + 2.0) # [2, 4] |
||||
if random.random() < 0.5: |
||||
img = np.random.poisson(img * vals).astype(np.float32) / vals |
||||
else: |
||||
img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114]) |
||||
img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255. |
||||
noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray |
||||
img += noise_gray[:, :, np.newaxis] |
||||
img = np.clip(img, 0.0, 1.0) |
||||
return img |
||||
|
||||
|
||||
def add_JPEG_noise(img): |
||||
quality_factor = random.randint(30, 95) |
||||
img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR) |
||||
result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor]) |
||||
img = cv2.imdecode(encimg, 1) |
||||
img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB) |
||||
return img |
||||
|
||||
|
||||
def random_crop(lq, hq, sf=4, lq_patchsize=64): |
||||
h, w = lq.shape[:2] |
||||
rnd_h = random.randint(0, h - lq_patchsize) |
||||
rnd_w = random.randint(0, w - lq_patchsize) |
||||
lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :] |
||||
|
||||
rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf) |
||||
hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :] |
||||
return lq, hq |
||||
|
||||
|
||||
def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None): |
||||
""" |
||||
This is the degradation model of BSRGAN from the paper |
||||
"Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" |
||||
---------- |
||||
img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf) |
||||
sf: scale factor |
||||
isp_model: camera ISP model |
||||
Returns |
||||
------- |
||||
img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] |
||||
hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] |
||||
""" |
||||
isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25 |
||||
sf_ori = sf |
||||
|
||||
h1, w1 = img.shape[:2] |
||||
img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop |
||||
h, w = img.shape[:2] |
||||
|
||||
if h < lq_patchsize * sf or w < lq_patchsize * sf: |
||||
raise ValueError(f'img size ({h1}X{w1}) is too small!') |
||||
|
||||
hq = img.copy() |
||||
|
||||
if sf == 4 and random.random() < scale2_prob: # downsample1 |
||||
if np.random.rand() < 0.5: |
||||
img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])), |
||||
interpolation=random.choice([1, 2, 3])) |
||||
else: |
||||
img = util.imresize_np(img, 1 / 2, True) |
||||
img = np.clip(img, 0.0, 1.0) |
||||
sf = 2 |
||||
|
||||
shuffle_order = random.sample(range(7), 7) |
||||
idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3) |
||||
if idx1 > idx2: # keep downsample3 last |
||||
shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1] |
||||
|
||||
for i in shuffle_order: |
||||
|
||||
if i == 0: |
||||
img = add_blur(img, sf=sf) |
||||
|
||||
elif i == 1: |
||||
img = add_blur(img, sf=sf) |
||||
|
||||
elif i == 2: |
||||
a, b = img.shape[1], img.shape[0] |
||||
# downsample2 |
||||
if random.random() < 0.75: |
||||
sf1 = random.uniform(1, 2 * sf) |
||||
img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])), |
||||
interpolation=random.choice([1, 2, 3])) |
||||
else: |
||||
k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf)) |
||||
k_shifted = shift_pixel(k, sf) |
||||
k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel |
||||
img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror') |
||||
img = img[0::sf, 0::sf, ...] # nearest downsampling |
||||
img = np.clip(img, 0.0, 1.0) |
||||
|
||||
elif i == 3: |
||||
# downsample3 |
||||
img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3])) |
||||
img = np.clip(img, 0.0, 1.0) |
||||
|
||||
elif i == 4: |
||||
# add Gaussian noise |
||||
img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25) |
||||
|
||||
elif i == 5: |
||||
# add JPEG noise |
||||
if random.random() < jpeg_prob: |
||||
img = add_JPEG_noise(img) |
||||
|
||||
elif i == 6: |
||||
# add processed camera sensor noise |
||||
if random.random() < isp_prob and isp_model is not None: |
||||
with torch.no_grad(): |
||||
img, hq = isp_model.forward(img.copy(), hq) |
||||
|
||||
# add final JPEG compression noise |
||||
img = add_JPEG_noise(img) |
||||
|
||||
# random crop |
||||
img, hq = random_crop(img, hq, sf_ori, lq_patchsize) |
||||
|
||||
return img, hq |
||||
|
||||
|
||||
# todo no isp_model? |
||||
def degradation_bsrgan_variant(image, sf=4, isp_model=None): |
||||
""" |
||||
This is the degradation model of BSRGAN from the paper |
||||
"Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" |
||||
---------- |
||||
sf: scale factor |
||||
isp_model: camera ISP model |
||||
Returns |
||||
------- |
||||
img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] |
||||
hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] |
||||
""" |
||||
image = util.uint2single(image) |
||||
isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25 |
||||
sf_ori = sf |
||||
|
||||
h1, w1 = image.shape[:2] |
||||
image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop |
||||
h, w = image.shape[:2] |
||||
|
||||
hq = image.copy() |
||||
|
||||
if sf == 4 and random.random() < scale2_prob: # downsample1 |
||||
if np.random.rand() < 0.5: |
||||
image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])), |
||||
interpolation=random.choice([1, 2, 3])) |
||||
else: |
||||
image = util.imresize_np(image, 1 / 2, True) |
||||
image = np.clip(image, 0.0, 1.0) |
||||
sf = 2 |
||||
|
||||
shuffle_order = random.sample(range(7), 7) |
||||
idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3) |
||||
if idx1 > idx2: # keep downsample3 last |
||||
shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1] |
||||
|
||||
for i in shuffle_order: |
||||
|
||||
if i == 0: |
||||
image = add_blur(image, sf=sf) |
||||
|
||||
elif i == 1: |
||||
image = add_blur(image, sf=sf) |
||||
|
||||
elif i == 2: |
||||
a, b = image.shape[1], image.shape[0] |
||||
# downsample2 |
||||
if random.random() < 0.75: |
||||
sf1 = random.uniform(1, 2 * sf) |
||||
image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])), |
||||
interpolation=random.choice([1, 2, 3])) |
||||
else: |
||||
k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf)) |
||||
k_shifted = shift_pixel(k, sf) |
||||
k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel |
||||
image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror') |
||||
image = image[0::sf, 0::sf, ...] # nearest downsampling |
||||
image = np.clip(image, 0.0, 1.0) |
||||
|
||||
elif i == 3: |
||||
# downsample3 |
||||
image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3])) |
||||
image = np.clip(image, 0.0, 1.0) |
||||
|
||||
elif i == 4: |
||||
# add Gaussian noise |
||||
image = add_Gaussian_noise(image, noise_level1=2, noise_level2=25) |
||||
|
||||
elif i == 5: |
||||
# add JPEG noise |
||||
if random.random() < jpeg_prob: |
||||
image = add_JPEG_noise(image) |
||||
|
||||
# elif i == 6: |
||||
# # add processed camera sensor noise |
||||
# if random.random() < isp_prob and isp_model is not None: |
||||
# with torch.no_grad(): |
||||
# img, hq = isp_model.forward(img.copy(), hq) |
||||
|
||||
# add final JPEG compression noise |
||||
image = add_JPEG_noise(image) |
||||
image = util.single2uint(image) |
||||
example = {"image":image} |
||||
return example |
||||
|
||||
|
||||
# TODO incase there is a pickle error one needs to replace a += x with a = a + x in add_speckle_noise etc... |
||||
def degradation_bsrgan_plus(img, sf=4, shuffle_prob=0.5, use_sharp=True, lq_patchsize=64, isp_model=None): |
||||
""" |
||||
This is an extended degradation model by combining |
||||
the degradation models of BSRGAN and Real-ESRGAN |
||||
---------- |
||||
img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf) |
||||
sf: scale factor |
||||
use_shuffle: the degradation shuffle |
||||
use_sharp: sharpening the img |
||||
Returns |
||||
------- |
||||
img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] |
||||
hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] |
||||
""" |
||||
|
||||
h1, w1 = img.shape[:2] |
||||
img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop |
||||
h, w = img.shape[:2] |
||||
|
||||
if h < lq_patchsize * sf or w < lq_patchsize * sf: |
||||
raise ValueError(f'img size ({h1}X{w1}) is too small!') |
||||
|
||||
if use_sharp: |
||||
img = add_sharpening(img) |
||||
hq = img.copy() |
||||
|
||||
if random.random() < shuffle_prob: |
||||
shuffle_order = random.sample(range(13), 13) |
||||
else: |
||||
shuffle_order = list(range(13)) |
||||
# local shuffle for noise, JPEG is always the last one |
||||
shuffle_order[2:6] = random.sample(shuffle_order[2:6], len(range(2, 6))) |
||||
shuffle_order[9:13] = random.sample(shuffle_order[9:13], len(range(9, 13))) |
||||
|
||||
poisson_prob, speckle_prob, isp_prob = 0.1, 0.1, 0.1 |
||||
|
||||
for i in shuffle_order: |
||||
if i == 0: |
||||
img = add_blur(img, sf=sf) |
||||
elif i == 1: |
||||
img = add_resize(img, sf=sf) |
||||
elif i == 2: |
||||
img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25) |
||||
elif i == 3: |
||||
if random.random() < poisson_prob: |
||||
img = add_Poisson_noise(img) |
||||
elif i == 4: |
||||
if random.random() < speckle_prob: |
||||
img = add_speckle_noise(img) |
||||
elif i == 5: |
||||
if random.random() < isp_prob and isp_model is not None: |
||||
with torch.no_grad(): |
||||
img, hq = isp_model.forward(img.copy(), hq) |
||||
elif i == 6: |
||||
img = add_JPEG_noise(img) |
||||
elif i == 7: |
||||
img = add_blur(img, sf=sf) |
||||
elif i == 8: |
||||
img = add_resize(img, sf=sf) |
||||
elif i == 9: |
||||
img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25) |
||||
elif i == 10: |
||||
if random.random() < poisson_prob: |
||||
img = add_Poisson_noise(img) |
||||
elif i == 11: |
||||
if random.random() < speckle_prob: |
||||
img = add_speckle_noise(img) |
||||
elif i == 12: |
||||
if random.random() < isp_prob and isp_model is not None: |
||||
with torch.no_grad(): |
||||
img, hq = isp_model.forward(img.copy(), hq) |
||||
else: |
||||
print('check the shuffle!') |
||||
|
||||
# resize to desired size |
||||
img = cv2.resize(img, (int(1 / sf * hq.shape[1]), int(1 / sf * hq.shape[0])), |
||||
interpolation=random.choice([1, 2, 3])) |
||||
|
||||
# add final JPEG compression noise |
||||
img = add_JPEG_noise(img) |
||||
|
||||
# random crop |
||||
img, hq = random_crop(img, hq, sf, lq_patchsize) |
||||
|
||||
return img, hq |
||||
|
||||
|
||||
if __name__ == '__main__': |
||||
print("hey") |
||||
img = util.imread_uint('utils/test.png', 3) |
||||
print(img) |
||||
img = util.uint2single(img) |
||||
print(img) |
||||
img = img[:448, :448] |
||||
h = img.shape[0] // 4 |
||||
print("resizing to", h) |
||||
sf = 4 |
||||
deg_fn = partial(degradation_bsrgan_variant, sf=sf) |
||||
for i in range(20): |
||||
print(i) |
||||
img_lq = deg_fn(img) |
||||
print(img_lq) |
||||
img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img)["image"] |
||||
print(img_lq.shape) |
||||
print("bicubic", img_lq_bicubic.shape) |
||||
print(img_hq.shape) |
||||
lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])), |
||||
interpolation=0) |
||||
lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])), |
||||
interpolation=0) |
||||
img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1) |
||||
util.imsave(img_concat, str(i) + '.png') |
||||
|
||||
|
||||
@ -0,0 +1,651 @@
|
||||
# -*- coding: utf-8 -*- |
||||
import numpy as np |
||||
import cv2 |
||||
import torch |
||||
|
||||
from functools import partial |
||||
import random |
||||
from scipy import ndimage |
||||
import scipy |
||||
import scipy.stats as ss |
||||
from scipy.interpolate import interp2d |
||||
from scipy.linalg import orth |
||||
import albumentations |
||||
|
||||
import ldm.modules.image_degradation.utils_image as util |
||||
|
||||
""" |
||||
# -------------------------------------------- |
||||
# Super-Resolution |
||||
# -------------------------------------------- |
||||
# |
||||
# Kai Zhang (cskaizhang@gmail.com) |
||||
# https://github.com/cszn |
||||
# From 2019/03--2021/08 |
||||
# -------------------------------------------- |
||||
""" |
||||
|
||||
def modcrop_np(img, sf): |
||||
''' |
||||
Args: |
||||
img: numpy image, WxH or WxHxC |
||||
sf: scale factor |
||||
Return: |
||||
cropped image |
||||
''' |
||||
w, h = img.shape[:2] |
||||
im = np.copy(img) |
||||
return im[:w - w % sf, :h - h % sf, ...] |
||||
|
||||
|
||||
""" |
||||
# -------------------------------------------- |
||||
# anisotropic Gaussian kernels |
||||
# -------------------------------------------- |
||||
""" |
||||
|
||||
|
||||
def analytic_kernel(k): |
||||
"""Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)""" |
||||
k_size = k.shape[0] |
||||
# Calculate the big kernels size |
||||
big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2)) |
||||
# Loop over the small kernel to fill the big one |
||||
for r in range(k_size): |
||||
for c in range(k_size): |
||||
big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k |
||||
# Crop the edges of the big kernel to ignore very small values and increase run time of SR |
||||
crop = k_size // 2 |
||||
cropped_big_k = big_k[crop:-crop, crop:-crop] |
||||
# Normalize to 1 |
||||
return cropped_big_k / cropped_big_k.sum() |
||||
|
||||
|
||||
def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6): |
||||
""" generate an anisotropic Gaussian kernel |
||||
Args: |
||||
ksize : e.g., 15, kernel size |
||||
theta : [0, pi], rotation angle range |
||||
l1 : [0.1,50], scaling of eigenvalues |
||||
l2 : [0.1,l1], scaling of eigenvalues |
||||
If l1 = l2, will get an isotropic Gaussian kernel. |
||||
Returns: |
||||
k : kernel |
||||
""" |
||||
|
||||
v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.])) |
||||
V = np.array([[v[0], v[1]], [v[1], -v[0]]]) |
||||
D = np.array([[l1, 0], [0, l2]]) |
||||
Sigma = np.dot(np.dot(V, D), np.linalg.inv(V)) |
||||
k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize) |
||||
|
||||
return k |
||||
|
||||
|
||||
def gm_blur_kernel(mean, cov, size=15): |
||||
center = size / 2.0 + 0.5 |
||||
k = np.zeros([size, size]) |
||||
for y in range(size): |
||||
for x in range(size): |
||||
cy = y - center + 1 |
||||
cx = x - center + 1 |
||||
k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov) |
||||
|
||||
k = k / np.sum(k) |
||||
return k |
||||
|
||||
|
||||
def shift_pixel(x, sf, upper_left=True): |
||||
"""shift pixel for super-resolution with different scale factors |
||||
Args: |
||||
x: WxHxC or WxH |
||||
sf: scale factor |
||||
upper_left: shift direction |
||||
""" |
||||
h, w = x.shape[:2] |
||||
shift = (sf - 1) * 0.5 |
||||
xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0) |
||||
if upper_left: |
||||
x1 = xv + shift |
||||
y1 = yv + shift |
||||
else: |
||||
x1 = xv - shift |
||||
y1 = yv - shift |
||||
|
||||
x1 = np.clip(x1, 0, w - 1) |
||||
y1 = np.clip(y1, 0, h - 1) |
||||
|
||||
if x.ndim == 2: |
||||
x = interp2d(xv, yv, x)(x1, y1) |
||||
if x.ndim == 3: |
||||
for i in range(x.shape[-1]): |
||||
x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1) |
||||
|
||||
return x |
||||
|
||||
|
||||
def blur(x, k): |
||||
''' |
||||
x: image, NxcxHxW |
||||
k: kernel, Nx1xhxw |
||||
''' |
||||
n, c = x.shape[:2] |
||||
p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2 |
||||
x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate') |
||||
k = k.repeat(1, c, 1, 1) |
||||
k = k.view(-1, 1, k.shape[2], k.shape[3]) |
||||
x = x.view(1, -1, x.shape[2], x.shape[3]) |
||||
x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c) |
||||
x = x.view(n, c, x.shape[2], x.shape[3]) |
||||
|
||||
return x |
||||
|
||||
|
||||
def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0): |
||||
"""" |
||||
# modified version of https://github.com/assafshocher/BlindSR_dataset_generator |
||||
# Kai Zhang |
||||
# min_var = 0.175 * sf # variance of the gaussian kernel will be sampled between min_var and max_var |
||||
# max_var = 2.5 * sf |
||||
""" |
||||
# Set random eigen-vals (lambdas) and angle (theta) for COV matrix |
||||
lambda_1 = min_var + np.random.rand() * (max_var - min_var) |
||||
lambda_2 = min_var + np.random.rand() * (max_var - min_var) |
||||
theta = np.random.rand() * np.pi # random theta |
||||
noise = -noise_level + np.random.rand(*k_size) * noise_level * 2 |
||||
|
||||
# Set COV matrix using Lambdas and Theta |
||||
LAMBDA = np.diag([lambda_1, lambda_2]) |
||||
Q = np.array([[np.cos(theta), -np.sin(theta)], |
||||
[np.sin(theta), np.cos(theta)]]) |
||||
SIGMA = Q @ LAMBDA @ Q.T |
||||
INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :] |
||||
|
||||
# Set expectation position (shifting kernel for aligned image) |
||||
MU = k_size // 2 - 0.5 * (scale_factor - 1) # - 0.5 * (scale_factor - k_size % 2) |
||||
MU = MU[None, None, :, None] |
||||
|
||||
# Create meshgrid for Gaussian |
||||
[X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1])) |
||||
Z = np.stack([X, Y], 2)[:, :, :, None] |
||||
|
||||
# Calcualte Gaussian for every pixel of the kernel |
||||
ZZ = Z - MU |
||||
ZZ_t = ZZ.transpose(0, 1, 3, 2) |
||||
raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise) |
||||
|
||||
# shift the kernel so it will be centered |
||||
# raw_kernel_centered = kernel_shift(raw_kernel, scale_factor) |
||||
|
||||
# Normalize the kernel and return |
||||
# kernel = raw_kernel_centered / np.sum(raw_kernel_centered) |
||||
kernel = raw_kernel / np.sum(raw_kernel) |
||||
return kernel |
||||
|
||||
|
||||
def fspecial_gaussian(hsize, sigma): |
||||
hsize = [hsize, hsize] |
||||
siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0] |
||||
std = sigma |
||||
[x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1)) |
||||
arg = -(x * x + y * y) / (2 * std * std) |
||||
h = np.exp(arg) |
||||
h[h < scipy.finfo(float).eps * h.max()] = 0 |
||||
sumh = h.sum() |
||||
if sumh != 0: |
||||
h = h / sumh |
||||
return h |
||||
|
||||
|
||||
def fspecial_laplacian(alpha): |
||||
alpha = max([0, min([alpha, 1])]) |
||||
h1 = alpha / (alpha + 1) |
||||
h2 = (1 - alpha) / (alpha + 1) |
||||
h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]] |
||||
h = np.array(h) |
||||
return h |
||||
|
||||
|
||||
def fspecial(filter_type, *args, **kwargs): |
||||
''' |
||||
python code from: |
||||
https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py |
||||
''' |
||||
if filter_type == 'gaussian': |
||||
return fspecial_gaussian(*args, **kwargs) |
||||
if filter_type == 'laplacian': |
||||
return fspecial_laplacian(*args, **kwargs) |
||||
|
||||
|
||||
""" |
||||
# -------------------------------------------- |
||||
# degradation models |
||||
# -------------------------------------------- |
||||
""" |
||||
|
||||
|
||||
def bicubic_degradation(x, sf=3): |
||||
''' |
||||
Args: |
||||
x: HxWxC image, [0, 1] |
||||
sf: down-scale factor |
||||
Return: |
||||
bicubicly downsampled LR image |
||||
''' |
||||
x = util.imresize_np(x, scale=1 / sf) |
||||
return x |
||||
|
||||
|
||||
def srmd_degradation(x, k, sf=3): |
||||
''' blur + bicubic downsampling |
||||
Args: |
||||
x: HxWxC image, [0, 1] |
||||
k: hxw, double |
||||
sf: down-scale factor |
||||
Return: |
||||
downsampled LR image |
||||
Reference: |
||||
@inproceedings{zhang2018learning, |
||||
title={Learning a single convolutional super-resolution network for multiple degradations}, |
||||
author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, |
||||
booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, |
||||
pages={3262--3271}, |
||||
year={2018} |
||||
} |
||||
''' |
||||
x = ndimage.convolve(x, np.expand_dims(k, axis=2), mode='wrap') # 'nearest' | 'mirror' |
||||
x = bicubic_degradation(x, sf=sf) |
||||
return x |
||||
|
||||
|
||||
def dpsr_degradation(x, k, sf=3): |
||||
''' bicubic downsampling + blur |
||||
Args: |
||||
x: HxWxC image, [0, 1] |
||||
k: hxw, double |
||||
sf: down-scale factor |
||||
Return: |
||||
downsampled LR image |
||||
Reference: |
||||
@inproceedings{zhang2019deep, |
||||
title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels}, |
||||
author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, |
||||
booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, |
||||
pages={1671--1681}, |
||||
year={2019} |
||||
} |
||||
''' |
||||
x = bicubic_degradation(x, sf=sf) |
||||
x = ndimage.convolve(x, np.expand_dims(k, axis=2), mode='wrap') |
||||
return x |
||||
|
||||
|
||||
def classical_degradation(x, k, sf=3): |
||||
''' blur + downsampling |
||||
Args: |
||||
x: HxWxC image, [0, 1]/[0, 255] |
||||
k: hxw, double |
||||
sf: down-scale factor |
||||
Return: |
||||
downsampled LR image |
||||
''' |
||||
x = ndimage.convolve(x, np.expand_dims(k, axis=2), mode='wrap') |
||||
# x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2)) |
||||
st = 0 |
||||
return x[st::sf, st::sf, ...] |
||||
|
||||
|
||||
def add_sharpening(img, weight=0.5, radius=50, threshold=10): |
||||
"""USM sharpening. borrowed from real-ESRGAN |
||||
Input image: I; Blurry image: B. |
||||
1. K = I + weight * (I - B) |
||||
2. Mask = 1 if abs(I - B) > threshold, else: 0 |
||||
3. Blur mask: |
||||
4. Out = Mask * K + (1 - Mask) * I |
||||
Args: |
||||
img (Numpy array): Input image, HWC, BGR; float32, [0, 1]. |
||||
weight (float): Sharp weight. Default: 1. |
||||
radius (float): Kernel size of Gaussian blur. Default: 50. |
||||
threshold (int): |
||||
""" |
||||
if radius % 2 == 0: |
||||
radius += 1 |
||||
blur = cv2.GaussianBlur(img, (radius, radius), 0) |
||||
residual = img - blur |
||||
mask = np.abs(residual) * 255 > threshold |
||||
mask = mask.astype('float32') |
||||
soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0) |
||||
|
||||
K = img + weight * residual |
||||
K = np.clip(K, 0, 1) |
||||
return soft_mask * K + (1 - soft_mask) * img |
||||
|
||||
|
||||
def add_blur(img, sf=4): |
||||
wd2 = 4.0 + sf |
||||
wd = 2.0 + 0.2 * sf |
||||
|
||||
wd2 = wd2/4 |
||||
wd = wd/4 |
||||
|
||||
if random.random() < 0.5: |
||||
l1 = wd2 * random.random() |
||||
l2 = wd2 * random.random() |
||||
k = anisotropic_Gaussian(ksize=random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2) |
||||
else: |
||||
k = fspecial('gaussian', random.randint(2, 4) + 3, wd * random.random()) |
||||
img = ndimage.convolve(img, np.expand_dims(k, axis=2), mode='mirror') |
||||
|
||||
return img |
||||
|
||||
|
||||
def add_resize(img, sf=4): |
||||
rnum = np.random.rand() |
||||
if rnum > 0.8: # up |
||||
sf1 = random.uniform(1, 2) |
||||
elif rnum < 0.7: # down |
||||
sf1 = random.uniform(0.5 / sf, 1) |
||||
else: |
||||
sf1 = 1.0 |
||||
img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3])) |
||||
img = np.clip(img, 0.0, 1.0) |
||||
|
||||
return img |
||||
|
||||
|
||||
# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25): |
||||
# noise_level = random.randint(noise_level1, noise_level2) |
||||
# rnum = np.random.rand() |
||||
# if rnum > 0.6: # add color Gaussian noise |
||||
# img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) |
||||
# elif rnum < 0.4: # add grayscale Gaussian noise |
||||
# img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) |
||||
# else: # add noise |
||||
# L = noise_level2 / 255. |
||||
# D = np.diag(np.random.rand(3)) |
||||
# U = orth(np.random.rand(3, 3)) |
||||
# conv = np.dot(np.dot(np.transpose(U), D), U) |
||||
# img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) |
||||
# img = np.clip(img, 0.0, 1.0) |
||||
# return img |
||||
|
||||
def add_Gaussian_noise(img, noise_level1=2, noise_level2=25): |
||||
noise_level = random.randint(noise_level1, noise_level2) |
||||
rnum = np.random.rand() |
||||
if rnum > 0.6: # add color Gaussian noise |
||||
img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) |
||||
elif rnum < 0.4: # add grayscale Gaussian noise |
||||
img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) |
||||
else: # add noise |
||||
L = noise_level2 / 255. |
||||
D = np.diag(np.random.rand(3)) |
||||
U = orth(np.random.rand(3, 3)) |
||||
conv = np.dot(np.dot(np.transpose(U), D), U) |
||||
img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) |
||||
img = np.clip(img, 0.0, 1.0) |
||||
return img |
||||
|
||||
|
||||
def add_speckle_noise(img, noise_level1=2, noise_level2=25): |
||||
noise_level = random.randint(noise_level1, noise_level2) |
||||
img = np.clip(img, 0.0, 1.0) |
||||
rnum = random.random() |
||||
if rnum > 0.6: |
||||
img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) |
||||
elif rnum < 0.4: |
||||
img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) |
||||
else: |
||||
L = noise_level2 / 255. |
||||
D = np.diag(np.random.rand(3)) |
||||
U = orth(np.random.rand(3, 3)) |
||||
conv = np.dot(np.dot(np.transpose(U), D), U) |
||||
img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) |
||||
img = np.clip(img, 0.0, 1.0) |
||||
return img |
||||
|
||||
|
||||
def add_Poisson_noise(img): |
||||
img = np.clip((img * 255.0).round(), 0, 255) / 255. |
||||
vals = 10 ** (2 * random.random() + 2.0) # [2, 4] |
||||
if random.random() < 0.5: |
||||
img = np.random.poisson(img * vals).astype(np.float32) / vals |
||||
else: |
||||
img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114]) |
||||
img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255. |
||||
noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray |
||||
img += noise_gray[:, :, np.newaxis] |
||||
img = np.clip(img, 0.0, 1.0) |
||||
return img |
||||
|
||||
|
||||
def add_JPEG_noise(img): |
||||
quality_factor = random.randint(80, 95) |
||||
img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR) |
||||
result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor]) |
||||
img = cv2.imdecode(encimg, 1) |
||||
img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB) |
||||
return img |
||||
|
||||
|
||||
def random_crop(lq, hq, sf=4, lq_patchsize=64): |
||||
h, w = lq.shape[:2] |
||||
rnd_h = random.randint(0, h - lq_patchsize) |
||||
rnd_w = random.randint(0, w - lq_patchsize) |
||||
lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :] |
||||
|
||||
rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf) |
||||
hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :] |
||||
return lq, hq |
||||
|
||||
|
||||
def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None): |
||||
""" |
||||
This is the degradation model of BSRGAN from the paper |
||||
"Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" |
||||
---------- |
||||
img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf) |
||||
sf: scale factor |
||||
isp_model: camera ISP model |
||||
Returns |
||||
------- |
||||
img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] |
||||
hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] |
||||
""" |
||||
isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25 |
||||
sf_ori = sf |
||||
|
||||
h1, w1 = img.shape[:2] |
||||
img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop |
||||
h, w = img.shape[:2] |
||||
|
||||
if h < lq_patchsize * sf or w < lq_patchsize * sf: |
||||
raise ValueError(f'img size ({h1}X{w1}) is too small!') |
||||
|
||||
hq = img.copy() |
||||
|
||||
if sf == 4 and random.random() < scale2_prob: # downsample1 |
||||
if np.random.rand() < 0.5: |
||||
img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])), |
||||
interpolation=random.choice([1, 2, 3])) |
||||
else: |
||||
img = util.imresize_np(img, 1 / 2, True) |
||||
img = np.clip(img, 0.0, 1.0) |
||||
sf = 2 |
||||
|
||||
shuffle_order = random.sample(range(7), 7) |
||||
idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3) |
||||
if idx1 > idx2: # keep downsample3 last |
||||
shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1] |
||||
|
||||
for i in shuffle_order: |
||||
|
||||
if i == 0: |
||||
img = add_blur(img, sf=sf) |
||||
|
||||
elif i == 1: |
||||
img = add_blur(img, sf=sf) |
||||
|
||||
elif i == 2: |
||||
a, b = img.shape[1], img.shape[0] |
||||
# downsample2 |
||||
if random.random() < 0.75: |
||||
sf1 = random.uniform(1, 2 * sf) |
||||
img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])), |
||||
interpolation=random.choice([1, 2, 3])) |
||||
else: |
||||
k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf)) |
||||
k_shifted = shift_pixel(k, sf) |
||||
k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel |
||||
img = ndimage.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror') |
||||
img = img[0::sf, 0::sf, ...] # nearest downsampling |
||||
img = np.clip(img, 0.0, 1.0) |
||||
|
||||
elif i == 3: |
||||
# downsample3 |
||||
img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3])) |
||||
img = np.clip(img, 0.0, 1.0) |
||||
|
||||
elif i == 4: |
||||
# add Gaussian noise |
||||
img = add_Gaussian_noise(img, noise_level1=2, noise_level2=8) |
||||
|
||||
elif i == 5: |
||||
# add JPEG noise |
||||
if random.random() < jpeg_prob: |
||||
img = add_JPEG_noise(img) |
||||
|
||||
elif i == 6: |
||||
# add processed camera sensor noise |
||||
if random.random() < isp_prob and isp_model is not None: |
||||
with torch.no_grad(): |
||||
img, hq = isp_model.forward(img.copy(), hq) |
||||
|
||||
# add final JPEG compression noise |
||||
img = add_JPEG_noise(img) |
||||
|
||||
# random crop |
||||
img, hq = random_crop(img, hq, sf_ori, lq_patchsize) |
||||
|
||||
return img, hq |
||||
|
||||
|
||||
# todo no isp_model? |
||||
def degradation_bsrgan_variant(image, sf=4, isp_model=None, up=False): |
||||
""" |
||||
This is the degradation model of BSRGAN from the paper |
||||
"Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" |
||||
---------- |
||||
sf: scale factor |
||||
isp_model: camera ISP model |
||||
Returns |
||||
------- |
||||
img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] |
||||
hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] |
||||
""" |
||||
image = util.uint2single(image) |
||||
isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25 |
||||
sf_ori = sf |
||||
|
||||
h1, w1 = image.shape[:2] |
||||
image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop |
||||
h, w = image.shape[:2] |
||||
|
||||
hq = image.copy() |
||||
|
||||
if sf == 4 and random.random() < scale2_prob: # downsample1 |
||||
if np.random.rand() < 0.5: |
||||
image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])), |
||||
interpolation=random.choice([1, 2, 3])) |
||||
else: |
||||
image = util.imresize_np(image, 1 / 2, True) |
||||
image = np.clip(image, 0.0, 1.0) |
||||
sf = 2 |
||||
|
||||
shuffle_order = random.sample(range(7), 7) |
||||
idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3) |
||||
if idx1 > idx2: # keep downsample3 last |
||||
shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1] |
||||
|
||||
for i in shuffle_order: |
||||
|
||||
if i == 0: |
||||
image = add_blur(image, sf=sf) |
||||
|
||||
# elif i == 1: |
||||
# image = add_blur(image, sf=sf) |
||||
|
||||
if i == 0: |
||||
pass |
||||
|
||||
elif i == 2: |
||||
a, b = image.shape[1], image.shape[0] |
||||
# downsample2 |
||||
if random.random() < 0.8: |
||||
sf1 = random.uniform(1, 2 * sf) |
||||
image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])), |
||||
interpolation=random.choice([1, 2, 3])) |
||||
else: |
||||
k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf)) |
||||
k_shifted = shift_pixel(k, sf) |
||||
k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel |
||||
image = ndimage.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror') |
||||
image = image[0::sf, 0::sf, ...] # nearest downsampling |
||||
|
||||
image = np.clip(image, 0.0, 1.0) |
||||
|
||||
elif i == 3: |
||||
# downsample3 |
||||
image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3])) |
||||
image = np.clip(image, 0.0, 1.0) |
||||
|
||||
elif i == 4: |
||||
# add Gaussian noise |
||||
image = add_Gaussian_noise(image, noise_level1=1, noise_level2=2) |
||||
|
||||
elif i == 5: |
||||
# add JPEG noise |
||||
if random.random() < jpeg_prob: |
||||
image = add_JPEG_noise(image) |
||||
# |
||||
# elif i == 6: |
||||
# # add processed camera sensor noise |
||||
# if random.random() < isp_prob and isp_model is not None: |
||||
# with torch.no_grad(): |
||||
# img, hq = isp_model.forward(img.copy(), hq) |
||||
|
||||
# add final JPEG compression noise |
||||
image = add_JPEG_noise(image) |
||||
image = util.single2uint(image) |
||||
if up: |
||||
image = cv2.resize(image, (w1, h1), interpolation=cv2.INTER_CUBIC) # todo: random, as above? want to condition on it then |
||||
example = {"image": image} |
||||
return example |
||||
|
||||
|
||||
|
||||
|
||||
if __name__ == '__main__': |
||||
print("hey") |
||||
img = util.imread_uint('utils/test.png', 3) |
||||
img = img[:448, :448] |
||||
h = img.shape[0] // 4 |
||||
print("resizing to", h) |
||||
sf = 4 |
||||
deg_fn = partial(degradation_bsrgan_variant, sf=sf) |
||||
for i in range(20): |
||||
print(i) |
||||
img_hq = img |
||||
img_lq = deg_fn(img)["image"] |
||||
img_hq, img_lq = util.uint2single(img_hq), util.uint2single(img_lq) |
||||
print(img_lq) |
||||
img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img_hq)["image"] |
||||
print(img_lq.shape) |
||||
print("bicubic", img_lq_bicubic.shape) |
||||
print(img_hq.shape) |
||||
lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])), |
||||
interpolation=0) |
||||
lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic), |
||||
(int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])), |
||||
interpolation=0) |
||||
img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1) |
||||
util.imsave(img_concat, str(i) + '.png') |
||||
|
After Width: | Height: | Size: 431 KiB |
@ -0,0 +1,916 @@
|
||||
import os |
||||
import math |
||||
import random |
||||
import numpy as np |
||||
import torch |
||||
import cv2 |
||||
from torchvision.utils import make_grid |
||||
from datetime import datetime |
||||
#import matplotlib.pyplot as plt # TODO: check with Dominik, also bsrgan.py vs bsrgan_light.py |
||||
|
||||
|
||||
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE" |
||||
|
||||
|
||||
''' |
||||
# -------------------------------------------- |
||||
# Kai Zhang (github: https://github.com/cszn) |
||||
# 03/Mar/2019 |
||||
# -------------------------------------------- |
||||
# https://github.com/twhui/SRGAN-pyTorch |
||||
# https://github.com/xinntao/BasicSR |
||||
# -------------------------------------------- |
||||
''' |
||||
|
||||
|
||||
IMG_EXTENSIONS = ['.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', '.tif'] |
||||
|
||||
|
||||
def is_image_file(filename): |
||||
return any(filename.endswith(extension) for extension in IMG_EXTENSIONS) |
||||
|
||||
|
||||
def get_timestamp(): |
||||
return datetime.now().strftime('%y%m%d-%H%M%S') |
||||
|
||||
|
||||
def imshow(x, title=None, cbar=False, figsize=None): |
||||
plt.figure(figsize=figsize) |
||||
plt.imshow(np.squeeze(x), interpolation='nearest', cmap='gray') |
||||
if title: |
||||
plt.title(title) |
||||
if cbar: |
||||
plt.colorbar() |
||||
plt.show() |
||||
|
||||
|
||||
def surf(Z, cmap='rainbow', figsize=None): |
||||
plt.figure(figsize=figsize) |
||||
ax3 = plt.axes(projection='3d') |
||||
|
||||
w, h = Z.shape[:2] |
||||
xx = np.arange(0,w,1) |
||||
yy = np.arange(0,h,1) |
||||
X, Y = np.meshgrid(xx, yy) |
||||
ax3.plot_surface(X,Y,Z,cmap=cmap) |
||||
#ax3.contour(X,Y,Z, zdim='z',offset=-2，cmap=cmap) |
||||
plt.show() |
||||
|
||||
|
||||
''' |
||||
# -------------------------------------------- |
||||
# get image pathes |
||||
# -------------------------------------------- |
||||
''' |
||||
|
||||
|
||||
def get_image_paths(dataroot): |
||||
paths = None # return None if dataroot is None |
||||
if dataroot is not None: |
||||
paths = sorted(_get_paths_from_images(dataroot)) |
||||
return paths |
||||
|
||||
|
||||
def _get_paths_from_images(path): |
||||
assert os.path.isdir(path), '{:s} is not a valid directory'.format(path) |
||||
images = [] |
||||
for dirpath, _, fnames in sorted(os.walk(path)): |
||||
for fname in sorted(fnames): |
||||
if is_image_file(fname): |
||||
img_path = os.path.join(dirpath, fname) |
||||
images.append(img_path) |
||||
assert images, '{:s} has no valid image file'.format(path) |
||||
return images |
||||
|
||||
|
||||
''' |
||||
# -------------------------------------------- |
||||
# split large images into small images |
||||
# -------------------------------------------- |
||||
''' |
||||
|
||||
|
||||
def patches_from_image(img, p_size=512, p_overlap=64, p_max=800): |
||||
w, h = img.shape[:2] |
||||
patches = [] |
||||
if w > p_max and h > p_max: |
||||
w1 = list(np.arange(0, w-p_size, p_size-p_overlap, dtype=np.int)) |
||||
h1 = list(np.arange(0, h-p_size, p_size-p_overlap, dtype=np.int)) |
||||
w1.append(w-p_size) |
||||
h1.append(h-p_size) |
||||
# print(w1) |
||||
# print(h1) |
||||
for i in w1: |
||||
for j in h1: |
||||
patches.append(img[i:i+p_size, j:j+p_size,:]) |
||||
else: |
||||
patches.append(img) |
||||
|
||||
return patches |
||||
|
||||
|
||||
def imssave(imgs, img_path): |
||||
""" |
||||
imgs: list, N images of size WxHxC |
||||
""" |
||||
img_name, ext = os.path.splitext(os.path.basename(img_path)) |
||||
|
||||
for i, img in enumerate(imgs): |
||||
if img.ndim == 3: |
||||
img = img[:, :, [2, 1, 0]] |
||||
new_path = os.path.join(os.path.dirname(img_path), img_name+str('_s{:04d}'.format(i))+'.png') |
||||
cv2.imwrite(new_path, img) |
||||
|
||||
|
||||
def split_imageset(original_dataroot, taget_dataroot, n_channels=3, p_size=800, p_overlap=96, p_max=1000): |
||||
""" |
||||
split the large images from original_dataroot into small overlapped images with size (p_size)x(p_size), |
||||
and save them into taget_dataroot; only the images with larger size than (p_max)x(p_max) |
||||
will be splitted. |
||||
Args: |
||||
original_dataroot: |
||||
taget_dataroot: |
||||
p_size: size of small images |
||||
p_overlap: patch size in training is a good choice |
||||
p_max: images with smaller size than (p_max)x(p_max) keep unchanged. |
||||
""" |
||||
paths = get_image_paths(original_dataroot) |
||||
for img_path in paths: |
||||
# img_name, ext = os.path.splitext(os.path.basename(img_path)) |
||||
img = imread_uint(img_path, n_channels=n_channels) |
||||
patches = patches_from_image(img, p_size, p_overlap, p_max) |
||||
imssave(patches, os.path.join(taget_dataroot,os.path.basename(img_path))) |
||||
#if original_dataroot == taget_dataroot: |
||||
#del img_path |
||||
|
||||
''' |
||||
# -------------------------------------------- |
||||
# makedir |
||||
# -------------------------------------------- |
||||
''' |
||||
|
||||
|
||||
def mkdir(path): |
||||
if not os.path.exists(path): |
||||
os.makedirs(path) |
||||
|
||||
|
||||
def mkdirs(paths): |
||||
if isinstance(paths, str): |
||||
mkdir(paths) |
||||
else: |
||||
for path in paths: |
||||
mkdir(path) |
||||
|
||||
|
||||
def mkdir_and_rename(path): |
||||
if os.path.exists(path): |
||||
new_name = path + '_archived_' + get_timestamp() |
||||
print('Path already exists. Rename it to [{:s}]'.format(new_name)) |
||||
os.rename(path, new_name) |
||||
os.makedirs(path) |
||||
|
||||
|
||||
''' |
||||
# -------------------------------------------- |
||||
# read image from path |
||||
# opencv is fast, but read BGR numpy image |
||||
# -------------------------------------------- |
||||
''' |
||||
|
||||
|
||||
# -------------------------------------------- |
||||
# get uint8 image of size HxWxn_channles (RGB) |
||||
# -------------------------------------------- |
||||
def imread_uint(path, n_channels=3): |
||||
# input: path |
||||
# output: HxWx3(RGB or GGG), or HxWx1 (G) |
||||
if n_channels == 1: |
||||
img = cv2.imread(path, 0) # cv2.IMREAD_GRAYSCALE |
||||
img = np.expand_dims(img, axis=2) # HxWx1 |
||||
elif n_channels == 3: |
||||
img = cv2.imread(path, cv2.IMREAD_UNCHANGED) # BGR or G |
||||
if img.ndim == 2: |
||||
img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB) # GGG |
||||
else: |
||||
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # RGB |
||||
return img |
||||
|
||||
|
||||
# -------------------------------------------- |
||||
# matlab's imwrite |
||||
# -------------------------------------------- |
||||
def imsave(img, img_path): |
||||
img = np.squeeze(img) |
||||
if img.ndim == 3: |
||||
img = img[:, :, [2, 1, 0]] |
||||
cv2.imwrite(img_path, img) |
||||
|
||||
def imwrite(img, img_path): |
||||
img = np.squeeze(img) |
||||
if img.ndim == 3: |
||||
img = img[:, :, [2, 1, 0]] |
||||
cv2.imwrite(img_path, img) |
||||
|
||||
|
||||
|
||||
# -------------------------------------------- |
||||
# get single image of size HxWxn_channles (BGR) |
||||
# -------------------------------------------- |
||||
def read_img(path): |
||||
# read image by cv2 |
||||
# return: Numpy float32, HWC, BGR, [0,1] |
||||
img = cv2.imread(path, cv2.IMREAD_UNCHANGED) # cv2.IMREAD_GRAYSCALE |
||||
img = img.astype(np.float32) / 255. |
||||
if img.ndim == 2: |
||||
img = np.expand_dims(img, axis=2) |
||||
# some images have 4 channels |
||||
if img.shape[2] > 3: |
||||
img = img[:, :, :3] |
||||
return img |
||||
|
||||
|
||||
''' |
||||
# -------------------------------------------- |
||||
# image format conversion |
||||
# -------------------------------------------- |
||||
# numpy(single) <---> numpy(unit) |
||||
# numpy(single) <---> tensor |
||||
# numpy(unit) <---> tensor |
||||
# -------------------------------------------- |
||||
''' |
||||
|
||||
|
||||
# -------------------------------------------- |
||||
# numpy(single) [0, 1] <---> numpy(unit) |
||||
# -------------------------------------------- |
||||
|
||||
|
||||
def uint2single(img): |
||||
|
||||
return np.float32(img/255.) |
||||
|
||||
|
||||
def single2uint(img): |
||||
|
||||
return np.uint8((img.clip(0, 1)*255.).round()) |
||||
|
||||
|
||||
def uint162single(img): |
||||
|
||||
return np.float32(img/65535.) |
||||
|
||||
|
||||
def single2uint16(img): |
||||
|
||||
return np.uint16((img.clip(0, 1)*65535.).round()) |
||||
|
||||
|
||||
# -------------------------------------------- |
||||
# numpy(unit) (HxWxC or HxW) <---> tensor |
||||
# -------------------------------------------- |
||||
|
||||
|
||||
# convert uint to 4-dimensional torch tensor |
||||
def uint2tensor4(img): |
||||
if img.ndim == 2: |
||||
img = np.expand_dims(img, axis=2) |
||||
return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.).unsqueeze(0) |
||||
|
||||
|
||||
# convert uint to 3-dimensional torch tensor |
||||
def uint2tensor3(img): |
||||
if img.ndim == 2: |
||||
img = np.expand_dims(img, axis=2) |
||||
return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.) |
||||
|
||||
|
||||
# convert 2/3/4-dimensional torch tensor to uint |
||||
def tensor2uint(img): |
||||
img = img.data.squeeze().float().clamp_(0, 1).cpu().numpy() |
||||
if img.ndim == 3: |
||||
img = np.transpose(img, (1, 2, 0)) |
||||
return np.uint8((img*255.0).round()) |
||||
|
||||
|
||||
# -------------------------------------------- |
||||
# numpy(single) (HxWxC) <---> tensor |
||||
# -------------------------------------------- |
||||
|
||||
|
||||
# convert single (HxWxC) to 3-dimensional torch tensor |
||||
def single2tensor3(img): |
||||
return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float() |
||||
|
||||
|
||||
# convert single (HxWxC) to 4-dimensional torch tensor |
||||
def single2tensor4(img): |
||||
return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().unsqueeze(0) |
||||
|
||||
|
||||
# convert torch tensor to single |
||||
def tensor2single(img): |
||||
img = img.data.squeeze().float().cpu().numpy() |
||||
if img.ndim == 3: |
||||
img = np.transpose(img, (1, 2, 0)) |
||||
|
||||
return img |
||||
|
||||
# convert torch tensor to single |
||||
def tensor2single3(img): |
||||
img = img.data.squeeze().float().cpu().numpy() |
||||
if img.ndim == 3: |
||||
img = np.transpose(img, (1, 2, 0)) |
||||
elif img.ndim == 2: |
||||
img = np.expand_dims(img, axis=2) |
||||
return img |
||||
|
||||
|
||||
def single2tensor5(img): |
||||
return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float().unsqueeze(0) |
||||
|
||||
|
||||
def single32tensor5(img): |
||||
return torch.from_numpy(np.ascontiguousarray(img)).float().unsqueeze(0).unsqueeze(0) |
||||
|
||||
|
||||
def single42tensor4(img): |
||||
return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float() |
||||
|
||||
|
||||
# from skimage.io import imread, imsave |
||||
def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)): |
||||
''' |
||||
Converts a torch Tensor into an image Numpy array of BGR channel order |
||||
Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order |
||||
Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default) |
||||
''' |
||||
tensor = tensor.squeeze().float().cpu().clamp_(*min_max) # squeeze first, then clamp |
||||
tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0]) # to range [0,1] |
||||
n_dim = tensor.dim() |
||||
if n_dim == 4: |
||||
n_img = len(tensor) |
||||
img_np = make_grid(tensor, nrow=int(math.sqrt(n_img)), normalize=False).numpy() |
||||
img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR |
||||
elif n_dim == 3: |
||||
img_np = tensor.numpy() |
||||
img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR |
||||
elif n_dim == 2: |
||||
img_np = tensor.numpy() |
||||
else: |
||||
raise TypeError( |
||||
'Only support 4D, 3D and 2D tensor. But received with dimension: {:d}'.format(n_dim)) |
||||
if out_type == np.uint8: |
||||
img_np = (img_np * 255.0).round() |
||||
# Important. Unlike matlab, numpy.unit8() WILL NOT round by default. |
||||
return img_np.astype(out_type) |
||||
|
||||
|
||||
''' |
||||
# -------------------------------------------- |
||||
# Augmentation, flipe and/or rotate |
||||
# -------------------------------------------- |
||||
# The following two are enough. |
||||
# (1) augmet_img: numpy image of WxHxC or WxH |
||||
# (2) augment_img_tensor4: tensor image 1xCxWxH |
||||
# -------------------------------------------- |
||||
''' |
||||
|
||||
|
||||
def augment_img(img, mode=0): |
||||
'''Kai Zhang (github: https://github.com/cszn) |
||||
''' |
||||
if mode == 0: |
||||
return img |
||||
elif mode == 1: |
||||
return np.flipud(np.rot90(img)) |
||||
elif mode == 2: |
||||
return np.flipud(img) |
||||
elif mode == 3: |
||||
return np.rot90(img, k=3) |
||||
elif mode == 4: |
||||
return np.flipud(np.rot90(img, k=2)) |
||||
elif mode == 5: |
||||
return np.rot90(img) |
||||
elif mode == 6: |
||||
return np.rot90(img, k=2) |
||||
elif mode == 7: |
||||
return np.flipud(np.rot90(img, k=3)) |
||||
|
||||
|
||||
def augment_img_tensor4(img, mode=0): |
||||
'''Kai Zhang (github: https://github.com/cszn) |
||||
''' |
||||
if mode == 0: |
||||
return img |
||||
elif mode == 1: |
||||
return img.rot90(1, [2, 3]).flip([2]) |
||||
elif mode == 2: |
||||
return img.flip([2]) |
||||
elif mode == 3: |
||||
return img.rot90(3, [2, 3]) |
||||
elif mode == 4: |
||||
return img.rot90(2, [2, 3]).flip([2]) |
||||
elif mode == 5: |
||||
return img.rot90(1, [2, 3]) |
||||
elif mode == 6: |
||||
return img.rot90(2, [2, 3]) |
||||
elif mode == 7: |
||||
return img.rot90(3, [2, 3]).flip([2]) |
||||
|
||||
|
||||
def augment_img_tensor(img, mode=0): |
||||
'''Kai Zhang (github: https://github.com/cszn) |
||||
''' |
||||
img_size = img.size() |
||||
img_np = img.data.cpu().numpy() |
||||
if len(img_size) == 3: |
||||
img_np = np.transpose(img_np, (1, 2, 0)) |
||||
elif len(img_size) == 4: |
||||
img_np = np.transpose(img_np, (2, 3, 1, 0)) |
||||
img_np = augment_img(img_np, mode=mode) |
||||
img_tensor = torch.from_numpy(np.ascontiguousarray(img_np)) |
||||
if len(img_size) == 3: |
||||
img_tensor = img_tensor.permute(2, 0, 1) |
||||
elif len(img_size) == 4: |
||||
img_tensor = img_tensor.permute(3, 2, 0, 1) |
||||
|
||||
return img_tensor.type_as(img) |
||||
|
||||
|
||||
def augment_img_np3(img, mode=0): |
||||
if mode == 0: |
||||
return img |
||||
elif mode == 1: |
||||
return img.transpose(1, 0, 2) |
||||
elif mode == 2: |
||||
return img[::-1, :, :] |
||||
elif mode == 3: |
||||
img = img[::-1, :, :] |
||||
img = img.transpose(1, 0, 2) |
||||
return img |
||||
elif mode == 4: |
||||
return img[:, ::-1, :] |
||||
elif mode == 5: |
||||
img = img[:, ::-1, :] |
||||
img = img.transpose(1, 0, 2) |
||||
return img |
||||
elif mode == 6: |
||||
img = img[:, ::-1, :] |
||||
img = img[::-1, :, :] |
||||
return img |
||||
elif mode == 7: |
||||
img = img[:, ::-1, :] |
||||
img = img[::-1, :, :] |
||||
img = img.transpose(1, 0, 2) |
||||
return img |
||||
|
||||
|
||||
def augment_imgs(img_list, hflip=True, rot=True): |
||||
# horizontal flip OR rotate |
||||
hflip = hflip and random.random() < 0.5 |
||||
vflip = rot and random.random() < 0.5 |
||||
rot90 = rot and random.random() < 0.5 |
||||
|
||||
def _augment(img): |
||||
if hflip: |
||||
img = img[:, ::-1, :] |
||||
if vflip: |
||||
img = img[::-1, :, :] |
||||
if rot90: |
||||
img = img.transpose(1, 0, 2) |
||||
return img |
||||
|
||||
return [_augment(img) for img in img_list] |
||||
|
||||
|
||||
''' |
||||
# -------------------------------------------- |
||||
# modcrop and shave |
||||
# -------------------------------------------- |
||||
''' |
||||
|
||||
|
||||
def modcrop(img_in, scale): |
||||
# img_in: Numpy, HWC or HW |
||||
img = np.copy(img_in) |
||||
if img.ndim == 2: |
||||
H, W = img.shape |
||||
H_r, W_r = H % scale, W % scale |
||||
img = img[:H - H_r, :W - W_r] |
||||
elif img.ndim == 3: |
||||
H, W, C = img.shape |
||||
H_r, W_r = H % scale, W % scale |
||||
img = img[:H - H_r, :W - W_r, :] |
||||
else: |
||||
raise ValueError('Wrong img ndim: [{:d}].'.format(img.ndim)) |
||||
return img |
||||
|
||||
|
||||
def shave(img_in, border=0): |
||||
# img_in: Numpy, HWC or HW |
||||
img = np.copy(img_in) |
||||
h, w = img.shape[:2] |
||||
img = img[border:h-border, border:w-border] |
||||
return img |
||||
|
||||
|
||||
''' |
||||
# -------------------------------------------- |
||||
# image processing process on numpy image |
||||
# channel_convert(in_c, tar_type, img_list): |
||||
# rgb2ycbcr(img, only_y=True): |
||||
# bgr2ycbcr(img, only_y=True): |
||||
# ycbcr2rgb(img): |
||||
# -------------------------------------------- |
||||
''' |
||||
|
||||
|
||||
def rgb2ycbcr(img, only_y=True): |
||||
'''same as matlab rgb2ycbcr |
||||
only_y: only return Y channel |
||||
Input: |
||||
uint8, [0, 255] |
||||
float, [0, 1] |
||||
''' |
||||
in_img_type = img.dtype |
||||
img.astype(np.float32) |
||||
if in_img_type != np.uint8: |
||||
img *= 255. |
||||
# convert |
||||
if only_y: |
||||
rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0 |
||||
else: |
||||
rlt = np.matmul(img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786], |
||||
[24.966, 112.0, -18.214]]) / 255.0 + [16, 128, 128] |
||||
if in_img_type == np.uint8: |
||||
rlt = rlt.round() |
||||
else: |
||||
rlt /= 255. |
||||
return rlt.astype(in_img_type) |
||||
|
||||
|
||||
def ycbcr2rgb(img): |
||||
'''same as matlab ycbcr2rgb |
||||
Input: |
||||
uint8, [0, 255] |
||||
float, [0, 1] |
||||
''' |
||||
in_img_type = img.dtype |
||||
img.astype(np.float32) |
||||
if in_img_type != np.uint8: |
||||
img *= 255. |
||||
# convert |
||||
rlt = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0, -0.00153632, 0.00791071], |
||||
[0.00625893, -0.00318811, 0]]) * 255.0 + [-222.921, 135.576, -276.836] |
||||
if in_img_type == np.uint8: |
||||
rlt = rlt.round() |
||||
else: |
||||
rlt /= 255. |
||||
return rlt.astype(in_img_type) |
||||
|
||||
|
||||
def bgr2ycbcr(img, only_y=True): |
||||
'''bgr version of rgb2ycbcr |
||||
only_y: only return Y channel |
||||
Input: |
||||
uint8, [0, 255] |
||||
float, [0, 1] |
||||
''' |
||||
in_img_type = img.dtype |
||||
img.astype(np.float32) |
||||
if in_img_type != np.uint8: |
||||
img *= 255. |
||||
# convert |
||||
if only_y: |
||||
rlt = np.dot(img, [24.966, 128.553, 65.481]) / 255.0 + 16.0 |
||||
else: |
||||
rlt = np.matmul(img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786], |
||||
[65.481, -37.797, 112.0]]) / 255.0 + [16, 128, 128] |
||||
if in_img_type == np.uint8: |
||||
rlt = rlt.round() |
||||
else: |
||||
rlt /= 255. |
||||
return rlt.astype(in_img_type) |
||||
|
||||
|
||||
def channel_convert(in_c, tar_type, img_list): |
||||
# conversion among BGR, gray and y |
||||
if in_c == 3 and tar_type == 'gray': # BGR to gray |
||||
gray_list = [cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) for img in img_list] |
||||
return [np.expand_dims(img, axis=2) for img in gray_list] |
||||
elif in_c == 3 and tar_type == 'y': # BGR to y |
||||
y_list = [bgr2ycbcr(img, only_y=True) for img in img_list] |
||||
return [np.expand_dims(img, axis=2) for img in y_list] |
||||
elif in_c == 1 and tar_type == 'RGB': # gray/y to BGR |
||||
return [cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) for img in img_list] |
||||
else: |
||||
return img_list |
||||
|
||||
|
||||
''' |
||||
# -------------------------------------------- |
||||
# metric, PSNR and SSIM |
||||
# -------------------------------------------- |
||||
''' |
||||
|
||||
|
||||
# -------------------------------------------- |
||||
# PSNR |
||||
# -------------------------------------------- |
||||
def calculate_psnr(img1, img2, border=0): |
||||
# img1 and img2 have range [0, 255] |
||||
#img1 = img1.squeeze() |
||||
#img2 = img2.squeeze() |
||||
if not img1.shape == img2.shape: |
||||
raise ValueError('Input images must have the same dimensions.') |
||||
h, w = img1.shape[:2] |
||||
img1 = img1[border:h-border, border:w-border] |
||||
img2 = img2[border:h-border, border:w-border] |
||||
|
||||
img1 = img1.astype(np.float64) |
||||
img2 = img2.astype(np.float64) |
||||
mse = np.mean((img1 - img2)**2) |
||||
if mse == 0: |
||||
return float('inf') |
||||
return 20 * math.log10(255.0 / math.sqrt(mse)) |
||||
|
||||
|
||||
# -------------------------------------------- |
||||
# SSIM |
||||
# -------------------------------------------- |
||||
def calculate_ssim(img1, img2, border=0): |
||||
'''calculate SSIM |
||||
the same outputs as MATLAB's |
||||
img1, img2: [0, 255] |
||||
''' |
||||
#img1 = img1.squeeze() |
||||
#img2 = img2.squeeze() |
||||
if not img1.shape == img2.shape: |
||||
raise ValueError('Input images must have the same dimensions.') |
||||
h, w = img1.shape[:2] |
||||
img1 = img1[border:h-border, border:w-border] |
||||
img2 = img2[border:h-border, border:w-border] |
||||
|
||||
if img1.ndim == 2: |
||||
return ssim(img1, img2) |
||||
elif img1.ndim == 3: |
||||
if img1.shape[2] == 3: |
||||
ssims = [] |
||||
for i in range(3): |
||||
ssims.append(ssim(img1[:,:,i], img2[:,:,i])) |
||||
return np.array(ssims).mean() |
||||
elif img1.shape[2] == 1: |
||||
return ssim(np.squeeze(img1), np.squeeze(img2)) |
||||
else: |
||||
raise ValueError('Wrong input image dimensions.') |
||||
|
||||
|
||||
def ssim(img1, img2): |
||||
C1 = (0.01 * 255)**2 |
||||
C2 = (0.03 * 255)**2 |
||||
|
||||
img1 = img1.astype(np.float64) |
||||
img2 = img2.astype(np.float64) |
||||
kernel = cv2.getGaussianKernel(11, 1.5) |
||||
window = np.outer(kernel, kernel.transpose()) |
||||
|
||||
mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5] # valid |
||||
mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5] |
||||
mu1_sq = mu1**2 |
||||
mu2_sq = mu2**2 |
||||
mu1_mu2 = mu1 * mu2 |
||||
sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq |
||||
sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq |
||||
sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2 |
||||
|
||||
ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * |
||||
(sigma1_sq + sigma2_sq + C2)) |
||||
return ssim_map.mean() |
||||
|
||||
|
||||
''' |
||||
# -------------------------------------------- |
||||
# matlab's bicubic imresize (numpy and torch) [0, 1] |
||||
# -------------------------------------------- |
||||
''' |
||||
|
||||
|
||||
# matlab 'imresize' function, now only support 'bicubic' |
||||
def cubic(x): |
||||
absx = torch.abs(x) |
||||
absx2 = absx**2 |
||||
absx3 = absx**3 |
||||
return (1.5*absx3 - 2.5*absx2 + 1) * ((absx <= 1).type_as(absx)) + \ |
||||
(-0.5*absx3 + 2.5*absx2 - 4*absx + 2) * (((absx > 1)*(absx <= 2)).type_as(absx)) |
||||
|
||||
|
||||
def calculate_weights_indices(in_length, out_length, scale, kernel, kernel_width, antialiasing): |
||||
if (scale < 1) and (antialiasing): |
||||
# Use a modified kernel to simultaneously interpolate and antialias- larger kernel width |
||||
kernel_width = kernel_width / scale |
||||
|
||||
# Output-space coordinates |
||||
x = torch.linspace(1, out_length, out_length) |
||||
|
||||
# Input-space coordinates. Calculate the inverse mapping such that 0.5 |
||||
# in output space maps to 0.5 in input space, and 0.5+scale in output |
||||
# space maps to 1.5 in input space. |
||||
u = x / scale + 0.5 * (1 - 1 / scale) |
||||
|
||||
# What is the left-most pixel that can be involved in the computation? |
||||
left = torch.floor(u - kernel_width / 2) |
||||
|
||||
# What is the maximum number of pixels that can be involved in the |
||||
# computation? Note: it's OK to use an extra pixel here; if the |
||||
# corresponding weights are all zero, it will be eliminated at the end |
||||
# of this function. |
||||
P = math.ceil(kernel_width) + 2 |
||||
|
||||
# The indices of the input pixels involved in computing the k-th output |
||||
# pixel are in row k of the indices matrix. |
||||
indices = left.view(out_length, 1).expand(out_length, P) + torch.linspace(0, P - 1, P).view( |
||||
1, P).expand(out_length, P) |
||||
|
||||
# The weights used to compute the k-th output pixel are in row k of the |
||||
# weights matrix. |
||||
distance_to_center = u.view(out_length, 1).expand(out_length, P) - indices |
||||
# apply cubic kernel |
||||
if (scale < 1) and (antialiasing): |
||||
weights = scale * cubic(distance_to_center * scale) |
||||
else: |
||||
weights = cubic(distance_to_center) |
||||
# Normalize the weights matrix so that each row sums to 1. |
||||
weights_sum = torch.sum(weights, 1).view(out_length, 1) |
||||
weights = weights / weights_sum.expand(out_length, P) |
||||
|
||||
# If a column in weights is all zero, get rid of it. only consider the first and last column. |
||||
weights_zero_tmp = torch.sum((weights == 0), 0) |
||||
if not math.isclose(weights_zero_tmp[0], 0, rel_tol=1e-6): |
||||
indices = indices.narrow(1, 1, P - 2) |
||||
weights = weights.narrow(1, 1, P - 2) |
||||
if not math.isclose(weights_zero_tmp[-1], 0, rel_tol=1e-6): |
||||
indices = indices.narrow(1, 0, P - 2) |
||||
weights = weights.narrow(1, 0, P - 2) |
||||
weights = weights.contiguous() |
||||
indices = indices.contiguous() |
||||
sym_len_s = -indices.min() + 1 |
||||
sym_len_e = indices.max() - in_length |
||||
indices = indices + sym_len_s - 1 |
||||
return weights, indices, int(sym_len_s), int(sym_len_e) |
||||
|
||||
|
||||
# -------------------------------------------- |
||||
# imresize for tensor image [0, 1] |
||||
# -------------------------------------------- |
||||
def imresize(img, scale, antialiasing=True): |
||||
# Now the scale should be the same for H and W |
||||
# input: img: pytorch tensor, CHW or HW [0,1] |
||||
# output: CHW or HW [0,1] w/o round |
||||
need_squeeze = True if img.dim() == 2 else False |
||||
if need_squeeze: |
||||
img.unsqueeze_(0) |
||||
in_C, in_H, in_W = img.size() |
||||
out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale) |
||||
kernel_width = 4 |
||||
kernel = 'cubic' |
||||
|
||||
# Return the desired dimension order for performing the resize. The |
||||
# strategy is to perform the resize first along the dimension with the |
||||
# smallest scale factor. |
||||
# Now we do not support this. |
||||
|
||||
# get weights and indices |
||||
weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices( |
||||
in_H, out_H, scale, kernel, kernel_width, antialiasing) |
||||
weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices( |
||||
in_W, out_W, scale, kernel, kernel_width, antialiasing) |
||||
# process H dimension |
||||
# symmetric copying |
||||
img_aug = torch.FloatTensor(in_C, in_H + sym_len_Hs + sym_len_He, in_W) |
||||
img_aug.narrow(1, sym_len_Hs, in_H).copy_(img) |
||||
|
||||
sym_patch = img[:, :sym_len_Hs, :] |
||||
inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() |
||||
sym_patch_inv = sym_patch.index_select(1, inv_idx) |
||||
img_aug.narrow(1, 0, sym_len_Hs).copy_(sym_patch_inv) |
||||
|
||||
sym_patch = img[:, -sym_len_He:, :] |
||||
inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() |
||||
sym_patch_inv = sym_patch.index_select(1, inv_idx) |
||||
img_aug.narrow(1, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv) |
||||
|
||||
out_1 = torch.FloatTensor(in_C, out_H, in_W) |
||||
kernel_width = weights_H.size(1) |
||||
for i in range(out_H): |
||||
idx = int(indices_H[i][0]) |
||||
for j in range(out_C): |
||||
out_1[j, i, :] = img_aug[j, idx:idx + kernel_width, :].transpose(0, 1).mv(weights_H[i]) |
||||
|
||||
# process W dimension |
||||
# symmetric copying |
||||
out_1_aug = torch.FloatTensor(in_C, out_H, in_W + sym_len_Ws + sym_len_We) |
||||
out_1_aug.narrow(2, sym_len_Ws, in_W).copy_(out_1) |
||||
|
||||
sym_patch = out_1[:, :, :sym_len_Ws] |
||||
inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long() |
||||
sym_patch_inv = sym_patch.index_select(2, inv_idx) |
||||
out_1_aug.narrow(2, 0, sym_len_Ws).copy_(sym_patch_inv) |
||||
|
||||
sym_patch = out_1[:, :, -sym_len_We:] |
||||
inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long() |
||||
sym_patch_inv = sym_patch.index_select(2, inv_idx) |
||||
out_1_aug.narrow(2, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv) |
||||
|
||||
out_2 = torch.FloatTensor(in_C, out_H, out_W) |
||||
kernel_width = weights_W.size(1) |
||||
for i in range(out_W): |
||||
idx = int(indices_W[i][0]) |
||||
for j in range(out_C): |
||||
out_2[j, :, i] = out_1_aug[j, :, idx:idx + kernel_width].mv(weights_W[i]) |
||||
if need_squeeze: |
||||
out_2.squeeze_() |
||||
return out_2 |
||||
|
||||
|
||||
# -------------------------------------------- |
||||
# imresize for numpy image [0, 1] |
||||
# -------------------------------------------- |
||||
def imresize_np(img, scale, antialiasing=True): |
||||
# Now the scale should be the same for H and W |
||||
# input: img: Numpy, HWC or HW [0,1] |
||||
# output: HWC or HW [0,1] w/o round |
||||
img = torch.from_numpy(img) |
||||
need_squeeze = True if img.dim() == 2 else False |
||||
if need_squeeze: |
||||
img.unsqueeze_(2) |
||||
|
||||
in_H, in_W, in_C = img.size() |
||||
out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale) |
||||
kernel_width = 4 |
||||
kernel = 'cubic' |
||||
|
||||
# Return the desired dimension order for performing the resize. The |
||||
# strategy is to perform the resize first along the dimension with the |
||||
# smallest scale factor. |
||||
# Now we do not support this. |
||||
|
||||
# get weights and indices |
||||
weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices( |
||||
in_H, out_H, scale, kernel, kernel_width, antialiasing) |
||||
weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices( |
||||
in_W, out_W, scale, kernel, kernel_width, antialiasing) |
||||
# process H dimension |
||||
# symmetric copying |
||||
img_aug = torch.FloatTensor(in_H + sym_len_Hs + sym_len_He, in_W, in_C) |
||||
img_aug.narrow(0, sym_len_Hs, in_H).copy_(img) |
||||
|
||||
sym_patch = img[:sym_len_Hs, :, :] |
||||
inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long() |
||||
sym_patch_inv = sym_patch.index_select(0, inv_idx) |
||||
img_aug.narrow(0, 0, sym_len_Hs).copy_(sym_patch_inv) |
||||
|
||||
sym_patch = img[-sym_len_He:, :, :] |
||||
inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long() |
||||
sym_patch_inv = sym_patch.index_select(0, inv_idx) |
||||
img_aug.narrow(0, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv) |
||||
|
||||
out_1 = torch.FloatTensor(out_H, in_W, in_C) |
||||
kernel_width = weights_H.size(1) |
||||
for i in range(out_H): |
||||
idx = int(indices_H[i][0]) |
||||
for j in range(out_C): |
||||
out_1[i, :, j] = img_aug[idx:idx + kernel_width, :, j].transpose(0, 1).mv(weights_H[i]) |
||||
|
||||
# process W dimension |
||||
# symmetric copying |
||||
out_1_aug = torch.FloatTensor(out_H, in_W + sym_len_Ws + sym_len_We, in_C) |
||||
out_1_aug.narrow(1, sym_len_Ws, in_W).copy_(out_1) |
||||
|
||||
sym_patch = out_1[:, :sym_len_Ws, :] |
||||
inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() |
||||
sym_patch_inv = sym_patch.index_select(1, inv_idx) |
||||
out_1_aug.narrow(1, 0, sym_len_Ws).copy_(sym_patch_inv) |
||||
|
||||
sym_patch = out_1[:, -sym_len_We:, :] |
||||
inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() |
||||
sym_patch_inv = sym_patch.index_select(1, inv_idx) |
||||
out_1_aug.narrow(1, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv) |
||||
|
||||
out_2 = torch.FloatTensor(out_H, out_W, in_C) |
||||
kernel_width = weights_W.size(1) |
||||
for i in range(out_W): |
||||
idx = int(indices_W[i][0]) |
||||
for j in range(out_C): |
||||
out_2[:, i, j] = out_1_aug[:, idx:idx + kernel_width, j].mv(weights_W[i]) |
||||
if need_squeeze: |
||||
out_2.squeeze_() |
||||
|
||||
return out_2.numpy() |
||||
|
||||
|
||||
if __name__ == '__main__': |
||||
print('---') |
||||
# img = imread_uint('test.bmp', 3) |
||||
# img = uint2single(img) |
||||
# img_bicubic = imresize_np(img, 1/4) |
||||
@ -0,0 +1,170 @@
|
||||
# based on https://github.com/isl-org/MiDaS |
||||
|
||||
import cv2 |
||||
import torch |
||||
import torch.nn as nn |
||||
from torchvision.transforms import Compose |
||||
|
||||
from ldm.modules.midas.midas.dpt_depth import DPTDepthModel |
||||
from ldm.modules.midas.midas.midas_net import MidasNet |
||||
from ldm.modules.midas.midas.midas_net_custom import MidasNet_small |
||||
from ldm.modules.midas.midas.transforms import Resize, NormalizeImage, PrepareForNet |
||||
|
||||
|
||||
ISL_PATHS = { |
||||
"dpt_large": "midas_models/dpt_large-midas-2f21e586.pt", |
||||
"dpt_hybrid": "midas_models/dpt_hybrid-midas-501f0c75.pt", |
||||
"midas_v21": "", |
||||
"midas_v21_small": "", |
||||
} |
||||
|
||||
|
||||
def disabled_train(self, mode=True): |
||||
"""Overwrite model.train with this function to make sure train/eval mode |
||||
does not change anymore.""" |
||||
return self |
||||
|
||||
|
||||
def load_midas_transform(model_type): |
||||
# https://github.com/isl-org/MiDaS/blob/master/run.py |
||||
# load transform only |
||||
if model_type == "dpt_large": # DPT-Large |
||||
net_w, net_h = 384, 384 |
||||
resize_mode = "minimal" |
||||
normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) |
||||
|
||||
elif model_type == "dpt_hybrid": # DPT-Hybrid |
||||
net_w, net_h = 384, 384 |
||||
resize_mode = "minimal" |
||||
normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) |
||||
|
||||
elif model_type == "midas_v21": |
||||
net_w, net_h = 384, 384 |
||||
resize_mode = "upper_bound" |
||||
normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) |
||||
|
||||
elif model_type == "midas_v21_small": |
||||
net_w, net_h = 256, 256 |
||||
resize_mode = "upper_bound" |
||||
normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) |
||||
|
||||
else: |
||||
assert False, f"model_type '{model_type}' not implemented, use: --model_type large" |
||||
|
||||
transform = Compose( |
||||
[ |
||||
Resize( |
||||
net_w, |
||||
net_h, |
||||
resize_target=None, |
||||
keep_aspect_ratio=True, |
||||
ensure_multiple_of=32, |
||||
resize_method=resize_mode, |
||||
image_interpolation_method=cv2.INTER_CUBIC, |
||||
), |
||||
normalization, |
||||
PrepareForNet(), |
||||
] |
||||
) |
||||
|
||||
return transform |
||||
|
||||
|
||||
def load_model(model_type): |
||||
# https://github.com/isl-org/MiDaS/blob/master/run.py |
||||
# load network |
||||
model_path = ISL_PATHS[model_type] |
||||
if model_type == "dpt_large": # DPT-Large |
||||
model = DPTDepthModel( |
||||
path=model_path, |
||||
backbone="vitl16_384", |
||||
non_negative=True, |
||||
) |
||||
net_w, net_h = 384, 384 |
||||
resize_mode = "minimal" |
||||
normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) |
||||
|
||||
elif model_type == "dpt_hybrid": # DPT-Hybrid |
||||
model = DPTDepthModel( |
||||
path=model_path, |
||||
backbone="vitb_rn50_384", |
||||
non_negative=True, |
||||
) |
||||
net_w, net_h = 384, 384 |
||||
resize_mode = "minimal" |
||||
normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) |
||||
|
||||
elif model_type == "midas_v21": |
||||
model = MidasNet(model_path, non_negative=True) |
||||
net_w, net_h = 384, 384 |
||||
resize_mode = "upper_bound" |
||||
normalization = NormalizeImage( |
||||
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] |
||||
) |
||||
|
||||
elif model_type == "midas_v21_small": |
||||
model = MidasNet_small(model_path, features=64, backbone="efficientnet_lite3", exportable=True, |
||||
non_negative=True, blocks={'expand': True}) |
||||
net_w, net_h = 256, 256 |
||||
resize_mode = "upper_bound" |
||||
normalization = NormalizeImage( |
||||
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] |
||||
) |
||||
|
||||
else: |
||||
print(f"model_type '{model_type}' not implemented, use: --model_type large") |
||||
assert False |
||||
|
||||
transform = Compose( |
||||
[ |
||||
Resize( |
||||
net_w, |
||||
net_h, |
||||
resize_target=None, |
||||
keep_aspect_ratio=True, |
||||
ensure_multiple_of=32, |
||||
resize_method=resize_mode, |
||||
image_interpolation_method=cv2.INTER_CUBIC, |
||||
), |
||||
normalization, |
||||
PrepareForNet(), |
||||
] |
||||
) |
||||
|
||||
return model.eval(), transform |
||||
|
||||
|
||||
class MiDaSInference(nn.Module): |
||||
MODEL_TYPES_TORCH_HUB = [ |
||||
"DPT_Large", |
||||
"DPT_Hybrid", |
||||
"MiDaS_small" |
||||
] |
||||
MODEL_TYPES_ISL = [ |
||||
"dpt_large", |
||||
"dpt_hybrid", |
||||
"midas_v21", |
||||
"midas_v21_small", |
||||
] |
||||
|
||||
def __init__(self, model_type): |
||||
super().__init__() |
||||
assert (model_type in self.MODEL_TYPES_ISL) |
||||
model, _ = load_model(model_type) |
||||
self.model = model |
||||
self.model.train = disabled_train |
||||
|
||||
def forward(self, x): |
||||
# x in 0..1 as produced by calling self.transform on a 0..1 float64 numpy array |
||||
# NOTE: we expect that the correct transform has been called during dataloading. |
||||
with torch.no_grad(): |
||||
prediction = self.model(x) |
||||
prediction = torch.nn.functional.interpolate( |
||||
prediction.unsqueeze(1), |
||||
size=x.shape[2:], |
||||
mode="bicubic", |
||||
align_corners=False, |
||||
) |
||||
assert prediction.shape == (x.shape[0], 1, x.shape[2], x.shape[3]) |
||||
return prediction |
||||
|
||||
@ -0,0 +1,16 @@
|
||||
import torch |
||||
|
||||
|
||||
class BaseModel(torch.nn.Module): |
||||
def load(self, path): |
||||
"""Load model from file. |
||||
|
||||
Args: |
||||
path (str): file path |
||||
""" |
||||
parameters = torch.load(path, map_location=torch.device('cpu')) |
||||
|
||||
if "optimizer" in parameters: |
||||
parameters = parameters["model"] |
||||
|
||||
self.load_state_dict(parameters) |
||||
@ -0,0 +1,342 @@
|
||||
import torch |
||||
import torch.nn as nn |
||||
|
||||
from .vit import ( |
||||
_make_pretrained_vitb_rn50_384, |
||||
_make_pretrained_vitl16_384, |
||||
_make_pretrained_vitb16_384, |
||||
forward_vit, |
||||
) |
||||
|
||||
def _make_encoder(backbone, features, use_pretrained, groups=1, expand=False, exportable=True, hooks=None, use_vit_only=False, use_readout="ignore",): |
||||
if backbone == "vitl16_384": |
||||
pretrained = _make_pretrained_vitl16_384( |
||||
use_pretrained, hooks=hooks, use_readout=use_readout |
||||
) |
||||
scratch = _make_scratch( |
||||
[256, 512, 1024, 1024], features, groups=groups, expand=expand |
||||
) # ViT-L/16 - 85.0% Top1 (backbone) |
||||
elif backbone == "vitb_rn50_384": |
||||
pretrained = _make_pretrained_vitb_rn50_384( |
||||
use_pretrained, |
||||
hooks=hooks, |
||||
use_vit_only=use_vit_only, |
||||
use_readout=use_readout, |
||||
) |
||||
scratch = _make_scratch( |
||||
[256, 512, 768, 768], features, groups=groups, expand=expand |
||||
) # ViT-H/16 - 85.0% Top1 (backbone) |
||||
elif backbone == "vitb16_384": |
||||
pretrained = _make_pretrained_vitb16_384( |
||||
use_pretrained, hooks=hooks, use_readout=use_readout |
||||
) |
||||
scratch = _make_scratch( |
||||
[96, 192, 384, 768], features, groups=groups, expand=expand |
||||
) # ViT-B/16 - 84.6% Top1 (backbone) |
||||
elif backbone == "resnext101_wsl": |
||||
pretrained = _make_pretrained_resnext101_wsl(use_pretrained) |
||||
scratch = _make_scratch([256, 512, 1024, 2048], features, groups=groups, expand=expand) # efficientnet_lite3 |
||||
elif backbone == "efficientnet_lite3": |
||||
pretrained = _make_pretrained_efficientnet_lite3(use_pretrained, exportable=exportable) |
||||
scratch = _make_scratch([32, 48, 136, 384], features, groups=groups, expand=expand) # efficientnet_lite3 |
||||
else: |
||||
print(f"Backbone '{backbone}' not implemented") |
||||
assert False |
||||
|
||||
return pretrained, scratch |
||||
|
||||
|
||||
def _make_scratch(in_shape, out_shape, groups=1, expand=False): |
||||
scratch = nn.Module() |
||||
|
||||
out_shape1 = out_shape |
||||
out_shape2 = out_shape |
||||
out_shape3 = out_shape |
||||
out_shape4 = out_shape |
||||
if expand==True: |
||||
out_shape1 = out_shape |
||||
out_shape2 = out_shape*2 |
||||
out_shape3 = out_shape*4 |
||||
out_shape4 = out_shape*8 |
||||
|
||||
scratch.layer1_rn = nn.Conv2d( |
||||
in_shape[0], out_shape1, kernel_size=3, stride=1, padding=1, bias=False, groups=groups |
||||
) |
||||
scratch.layer2_rn = nn.Conv2d( |
||||
in_shape[1], out_shape2, kernel_size=3, stride=1, padding=1, bias=False, groups=groups |
||||
) |
||||
scratch.layer3_rn = nn.Conv2d( |
||||
in_shape[2], out_shape3, kernel_size=3, stride=1, padding=1, bias=False, groups=groups |
||||
) |
||||
scratch.layer4_rn = nn.Conv2d( |
||||
in_shape[3], out_shape4, kernel_size=3, stride=1, padding=1, bias=False, groups=groups |
||||
) |
||||
|
||||
return scratch |
||||
|
||||
|
||||
def _make_pretrained_efficientnet_lite3(use_pretrained, exportable=False): |
||||
efficientnet = torch.hub.load( |
||||
"rwightman/gen-efficientnet-pytorch", |
||||
"tf_efficientnet_lite3", |
||||
pretrained=use_pretrained, |
||||
exportable=exportable |
||||
) |
||||
return _make_efficientnet_backbone(efficientnet) |
||||
|
||||
|
||||
def _make_efficientnet_backbone(effnet): |
||||
pretrained = nn.Module() |
||||
|
||||
pretrained.layer1 = nn.Sequential( |
||||
effnet.conv_stem, effnet.bn1, effnet.act1, *effnet.blocks[0:2] |
||||
) |
||||
pretrained.layer2 = nn.Sequential(*effnet.blocks[2:3]) |
||||
pretrained.layer3 = nn.Sequential(*effnet.blocks[3:5]) |
||||
pretrained.layer4 = nn.Sequential(*effnet.blocks[5:9]) |
||||
|
||||
return pretrained |
||||
|
||||
|
||||
def _make_resnet_backbone(resnet): |
||||
pretrained = nn.Module() |
||||
pretrained.layer1 = nn.Sequential( |
||||
resnet.conv1, resnet.bn1, resnet.relu, resnet.maxpool, resnet.layer1 |
||||
) |
||||
|
||||
pretrained.layer2 = resnet.layer2 |
||||
pretrained.layer3 = resnet.layer3 |
||||
pretrained.layer4 = resnet.layer4 |
||||
|
||||
return pretrained |
||||
|
||||
|
||||
def _make_pretrained_resnext101_wsl(use_pretrained): |
||||
resnet = torch.hub.load("facebookresearch/WSL-Images", "resnext101_32x8d_wsl") |
||||
return _make_resnet_backbone(resnet) |
||||
|
||||
|
||||
|
||||
class Interpolate(nn.Module): |
||||
"""Interpolation module. |
||||
""" |
||||
|
||||
def __init__(self, scale_factor, mode, align_corners=False): |
||||
"""Init. |
||||
|
||||
Args: |
||||
scale_factor (float): scaling |
||||
mode (str): interpolation mode |
||||
""" |
||||
super(Interpolate, self).__init__() |
||||
|
||||
self.interp = nn.functional.interpolate |
||||
self.scale_factor = scale_factor |
||||
self.mode = mode |
||||
self.align_corners = align_corners |
||||
|
||||
def forward(self, x): |
||||
"""Forward pass. |
||||
|
||||
Args: |
||||
x (tensor): input |
||||
|
||||
Returns: |
||||
tensor: interpolated data |
||||
""" |
||||
|
||||
x = self.interp( |
||||
x, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners |
||||
) |
||||
|
||||
return x |
||||
|
||||
|
||||
class ResidualConvUnit(nn.Module): |
||||
"""Residual convolution module. |
||||
""" |
||||
|
||||
def __init__(self, features): |
||||
"""Init. |
||||
|
||||
Args: |
||||
features (int): number of features |
||||
""" |
||||
super().__init__() |
||||
|
||||
self.conv1 = nn.Conv2d( |
||||
features, features, kernel_size=3, stride=1, padding=1, bias=True |
||||
) |
||||
|
||||
self.conv2 = nn.Conv2d( |
||||
features, features, kernel_size=3, stride=1, padding=1, bias=True |
||||
) |
||||
|
||||
self.relu = nn.ReLU(inplace=True) |
||||
|
||||
def forward(self, x): |
||||
"""Forward pass. |
||||
|
||||
Args: |
||||
x (tensor): input |
||||
|
||||
Returns: |
||||
tensor: output |
||||
""" |
||||
out = self.relu(x) |
||||
out = self.conv1(out) |
||||
out = self.relu(out) |
||||
out = self.conv2(out) |
||||
|
||||
return out + x |
||||
|
||||
|
||||
class FeatureFusionBlock(nn.Module): |
||||
"""Feature fusion block. |
||||
""" |
||||
|
||||
def __init__(self, features): |
||||
"""Init. |
||||
|
||||
Args: |
||||
features (int): number of features |
||||
""" |
||||
super(FeatureFusionBlock, self).__init__() |
||||
|
||||
self.resConfUnit1 = ResidualConvUnit(features) |
||||
self.resConfUnit2 = ResidualConvUnit(features) |
||||
|
||||
def forward(self, *xs): |
||||
"""Forward pass. |
||||
|
||||
Returns: |
||||
tensor: output |
||||
""" |
||||
output = xs[0] |
||||
|
||||
if len(xs) == 2: |
||||
output += self.resConfUnit1(xs[1]) |
||||
|
||||
output = self.resConfUnit2(output) |
||||
|
||||
output = nn.functional.interpolate( |
||||
output, scale_factor=2, mode="bilinear", align_corners=True |
||||
) |
||||
|
||||
return output |
||||
|
||||
|
||||
|
||||
|
||||
class ResidualConvUnit_custom(nn.Module): |
||||
"""Residual convolution module. |
||||
""" |
||||
|
||||
def __init__(self, features, activation, bn): |
||||
"""Init. |
||||
|
||||
Args: |
||||
features (int): number of features |
||||
""" |
||||
super().__init__() |
||||
|
||||
self.bn = bn |
||||
|
||||
self.groups=1 |
||||
|
||||
self.conv1 = nn.Conv2d( |
||||
features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups |
||||
) |
||||
|
||||
self.conv2 = nn.Conv2d( |
||||
features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups |
||||
) |
||||
|
||||
if self.bn==True: |
||||
self.bn1 = nn.BatchNorm2d(features) |
||||
self.bn2 = nn.BatchNorm2d(features) |
||||
|
||||
self.activation = activation |
||||
|
||||
self.skip_add = nn.quantized.FloatFunctional() |
||||
|
||||
def forward(self, x): |
||||
"""Forward pass. |
||||
|
||||
Args: |
||||
x (tensor): input |
||||
|
||||
Returns: |
||||
tensor: output |
||||
""" |
||||
|
||||
out = self.activation(x) |
||||
out = self.conv1(out) |
||||
if self.bn==True: |
||||
out = self.bn1(out) |
||||
|
||||
out = self.activation(out) |
||||
out = self.conv2(out) |
||||
if self.bn==True: |
||||
out = self.bn2(out) |
||||
|
||||
if self.groups > 1: |
||||
out = self.conv_merge(out) |
||||
|
||||
return self.skip_add.add(out, x) |
||||
|
||||
# return out + x |
||||
|
||||
|
||||
class FeatureFusionBlock_custom(nn.Module): |
||||
"""Feature fusion block. |
||||
""" |
||||
|
||||
def __init__(self, features, activation, deconv=False, bn=False, expand=False, align_corners=True): |
||||
"""Init. |
||||
|
||||
Args: |
||||
features (int): number of features |
||||
""" |
||||
super(FeatureFusionBlock_custom, self).__init__() |
||||
|
||||
self.deconv = deconv |
||||
self.align_corners = align_corners |
||||
|
||||
self.groups=1 |
||||
|
||||
self.expand = expand |
||||
out_features = features |
||||
if self.expand==True: |
||||
out_features = features//2 |
||||
|
||||
self.out_conv = nn.Conv2d(features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1) |
||||
|
||||
self.resConfUnit1 = ResidualConvUnit_custom(features, activation, bn) |
||||
self.resConfUnit2 = ResidualConvUnit_custom(features, activation, bn) |
||||
|
||||
self.skip_add = nn.quantized.FloatFunctional() |
||||
|
||||
def forward(self, *xs): |
||||
"""Forward pass. |
||||
|
||||
Returns: |
||||
tensor: output |
||||
""" |
||||
output = xs[0] |
||||
|
||||
if len(xs) == 2: |
||||
res = self.resConfUnit1(xs[1]) |
||||
output = self.skip_add.add(output, res) |
||||
# output += res |
||||
|
||||
output = self.resConfUnit2(output) |
||||
|
||||
output = nn.functional.interpolate( |
||||
output, scale_factor=2, mode="bilinear", align_corners=self.align_corners |
||||
) |
||||
|
||||
output = self.out_conv(output) |
||||
|
||||
return output |
||||
|
||||
@ -0,0 +1,109 @@
|
||||
import torch |
||||
import torch.nn as nn |
||||
import torch.nn.functional as F |
||||
|
||||
from .base_model import BaseModel |
||||
from .blocks import ( |
||||
FeatureFusionBlock, |
||||
FeatureFusionBlock_custom, |
||||
Interpolate, |
||||
_make_encoder, |
||||
forward_vit, |
||||
) |
||||
|
||||
|
||||
def _make_fusion_block(features, use_bn): |
||||
return FeatureFusionBlock_custom( |
||||
features, |
||||
nn.ReLU(False), |
||||
deconv=False, |
||||
bn=use_bn, |
||||
expand=False, |
||||
align_corners=True, |
||||
) |
||||
|
||||
|
||||
class DPT(BaseModel): |
||||
def __init__( |
||||
self, |
||||
head, |
||||
features=256, |
||||
backbone="vitb_rn50_384", |
||||
readout="project", |
||||
channels_last=False, |
||||
use_bn=False, |
||||
): |
||||
|
||||
super(DPT, self).__init__() |
||||
|
||||
self.channels_last = channels_last |
||||
|
||||
hooks = { |
||||
"vitb_rn50_384": [0, 1, 8, 11], |
||||
"vitb16_384": [2, 5, 8, 11], |
||||
"vitl16_384": [5, 11, 17, 23], |
||||
} |
||||
|
||||
# Instantiate backbone and reassemble blocks |
||||
self.pretrained, self.scratch = _make_encoder( |
||||
backbone, |
||||
features, |
||||
False, # Set to true of you want to train from scratch, uses ImageNet weights |
||||
groups=1, |
||||
expand=False, |
||||
exportable=False, |
||||
hooks=hooks[backbone], |
||||
use_readout=readout, |
||||
) |
||||
|
||||
self.scratch.refinenet1 = _make_fusion_block(features, use_bn) |
||||
self.scratch.refinenet2 = _make_fusion_block(features, use_bn) |
||||
self.scratch.refinenet3 = _make_fusion_block(features, use_bn) |
||||
self.scratch.refinenet4 = _make_fusion_block(features, use_bn) |
||||
|
||||
self.scratch.output_conv = head |
||||
|
||||
|
||||
def forward(self, x): |
||||
if self.channels_last == True: |
||||
x.contiguous(memory_format=torch.channels_last) |
||||
|
||||
layer_1, layer_2, layer_3, layer_4 = forward_vit(self.pretrained, x) |
||||
|
||||
layer_1_rn = self.scratch.layer1_rn(layer_1) |
||||
layer_2_rn = self.scratch.layer2_rn(layer_2) |
||||
layer_3_rn = self.scratch.layer3_rn(layer_3) |
||||
layer_4_rn = self.scratch.layer4_rn(layer_4) |
||||
|
||||
path_4 = self.scratch.refinenet4(layer_4_rn) |
||||
path_3 = self.scratch.refinenet3(path_4, layer_3_rn) |
||||
path_2 = self.scratch.refinenet2(path_3, layer_2_rn) |
||||
path_1 = self.scratch.refinenet1(path_2, layer_1_rn) |
||||
|
||||
out = self.scratch.output_conv(path_1) |
||||
|
||||
return out |
||||
|
||||
|
||||
class DPTDepthModel(DPT): |
||||
def __init__(self, path=None, non_negative=True, **kwargs): |
||||
features = kwargs["features"] if "features" in kwargs else 256 |
||||
|
||||
head = nn.Sequential( |
||||
nn.Conv2d(features, features // 2, kernel_size=3, stride=1, padding=1), |
||||
Interpolate(scale_factor=2, mode="bilinear", align_corners=True), |
||||
nn.Conv2d(features // 2, 32, kernel_size=3, stride=1, padding=1), |
||||
nn.ReLU(True), |
||||
nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0), |
||||
nn.ReLU(True) if non_negative else nn.Identity(), |
||||
nn.Identity(), |
||||
) |
||||
|
||||
super().__init__(head, **kwargs) |
||||
|
||||
if path is not None: |
||||
self.load(path) |
||||
|
||||
def forward(self, x): |
||||
return super().forward(x).squeeze(dim=1) |
||||
|
||||
@ -0,0 +1,76 @@
|
||||
"""MidashNet: Network for monocular depth estimation trained by mixing several datasets. |
||||
This file contains code that is adapted from |
||||
https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py |
||||
""" |
||||
import torch |
||||
import torch.nn as nn |
||||
|
||||
from .base_model import BaseModel |
||||
from .blocks import FeatureFusionBlock, Interpolate, _make_encoder |
||||
|
||||
|
||||
class MidasNet(BaseModel): |
||||
"""Network for monocular depth estimation. |
||||
""" |
||||
|
||||
def __init__(self, path=None, features=256, non_negative=True): |
||||
"""Init. |
||||
|
||||
Args: |
||||
path (str, optional): Path to saved model. Defaults to None. |
||||
features (int, optional): Number of features. Defaults to 256. |
||||
backbone (str, optional): Backbone network for encoder. Defaults to resnet50 |
||||
""" |
||||
print("Loading weights: ", path) |
||||
|
||||
super(MidasNet, self).__init__() |
||||
|
||||
use_pretrained = False if path is None else True |
||||
|
||||
self.pretrained, self.scratch = _make_encoder(backbone="resnext101_wsl", features=features, use_pretrained=use_pretrained) |
||||
|
||||
self.scratch.refinenet4 = FeatureFusionBlock(features) |
||||
self.scratch.refinenet3 = FeatureFusionBlock(features) |
||||
self.scratch.refinenet2 = FeatureFusionBlock(features) |
||||
self.scratch.refinenet1 = FeatureFusionBlock(features) |
||||
|
||||
self.scratch.output_conv = nn.Sequential( |
||||
nn.Conv2d(features, 128, kernel_size=3, stride=1, padding=1), |
||||
Interpolate(scale_factor=2, mode="bilinear"), |
||||
nn.Conv2d(128, 32, kernel_size=3, stride=1, padding=1), |
||||
nn.ReLU(True), |
||||
nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0), |
||||
nn.ReLU(True) if non_negative else nn.Identity(), |
||||
) |
||||
|
||||
if path: |
||||
self.load(path) |
||||
|
||||
def forward(self, x): |
||||
"""Forward pass. |
||||
|
||||
Args: |
||||
x (tensor): input data (image) |
||||
|
||||
Returns: |
||||
tensor: depth |
||||
""" |
||||
|
||||
layer_1 = self.pretrained.layer1(x) |
||||
layer_2 = self.pretrained.layer2(layer_1) |
||||
layer_3 = self.pretrained.layer3(layer_2) |
||||
layer_4 = self.pretrained.layer4(layer_3) |
||||
|
||||
layer_1_rn = self.scratch.layer1_rn(layer_1) |
||||
layer_2_rn = self.scratch.layer2_rn(layer_2) |
||||
layer_3_rn = self.scratch.layer3_rn(layer_3) |
||||
layer_4_rn = self.scratch.layer4_rn(layer_4) |
||||
|
||||
path_4 = self.scratch.refinenet4(layer_4_rn) |
||||
path_3 = self.scratch.refinenet3(path_4, layer_3_rn) |
||||
path_2 = self.scratch.refinenet2(path_3, layer_2_rn) |
||||
path_1 = self.scratch.refinenet1(path_2, layer_1_rn) |
||||
|
||||
out = self.scratch.output_conv(path_1) |
||||
|
||||
return torch.squeeze(out, dim=1) |
||||
@ -0,0 +1,128 @@
|
||||
"""MidashNet: Network for monocular depth estimation trained by mixing several datasets. |
||||
This file contains code that is adapted from |
||||
https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py |
||||
""" |
||||
import torch |
||||
import torch.nn as nn |
||||
|
||||
from .base_model import BaseModel |
||||
from .blocks import FeatureFusionBlock, FeatureFusionBlock_custom, Interpolate, _make_encoder |
||||
|
||||
|
||||
class MidasNet_small(BaseModel): |
||||
"""Network for monocular depth estimation. |
||||
""" |
||||
|
||||
def __init__(self, path=None, features=64, backbone="efficientnet_lite3", non_negative=True, exportable=True, channels_last=False, align_corners=True, |
||||
blocks={'expand': True}): |
||||
"""Init. |
||||
|
||||
Args: |
||||
path (str, optional): Path to saved model. Defaults to None. |
||||
features (int, optional): Number of features. Defaults to 256. |
||||
backbone (str, optional): Backbone network for encoder. Defaults to resnet50 |
||||
""" |
||||
print("Loading weights: ", path) |
||||
|
||||
super(MidasNet_small, self).__init__() |
||||
|
||||
use_pretrained = False if path else True |
||||
|
||||
self.channels_last = channels_last |
||||
self.blocks = blocks |
||||
self.backbone = backbone |
||||
|
||||
self.groups = 1 |
||||
|
||||
features1=features |
||||
features2=features |
||||
features3=features |
||||
features4=features |
||||
self.expand = False |
||||
if "expand" in self.blocks and self.blocks['expand'] == True: |
||||
self.expand = True |
||||
features1=features |
||||
features2=features*2 |
||||
features3=features*4 |
||||
features4=features*8 |
||||
|
||||
self.pretrained, self.scratch = _make_encoder(self.backbone, features, use_pretrained, groups=self.groups, expand=self.expand, exportable=exportable) |
||||
|
||||
self.scratch.activation = nn.ReLU(False) |
||||
|
||||
self.scratch.refinenet4 = FeatureFusionBlock_custom(features4, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners) |
||||
self.scratch.refinenet3 = FeatureFusionBlock_custom(features3, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners) |
||||
self.scratch.refinenet2 = FeatureFusionBlock_custom(features2, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners) |
||||
self.scratch.refinenet1 = FeatureFusionBlock_custom(features1, self.scratch.activation, deconv=False, bn=False, align_corners=align_corners) |
||||
|
||||
|
||||
self.scratch.output_conv = nn.Sequential( |
||||
nn.Conv2d(features, features//2, kernel_size=3, stride=1, padding=1, groups=self.groups), |
||||
Interpolate(scale_factor=2, mode="bilinear"), |
||||
nn.Conv2d(features//2, 32, kernel_size=3, stride=1, padding=1), |
||||
self.scratch.activation, |
||||
nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0), |
||||
nn.ReLU(True) if non_negative else nn.Identity(), |
||||
nn.Identity(), |
||||
) |
||||
|
||||
if path: |
||||
self.load(path) |
||||
|
||||
|
||||
def forward(self, x): |
||||
"""Forward pass. |
||||
|
||||
Args: |
||||
x (tensor): input data (image) |
||||
|
||||
Returns: |
||||
tensor: depth |
||||
""" |
||||
if self.channels_last==True: |
||||
print("self.channels_last = ", self.channels_last) |
||||
x.contiguous(memory_format=torch.channels_last) |
||||
|
||||
|
||||
layer_1 = self.pretrained.layer1(x) |
||||
layer_2 = self.pretrained.layer2(layer_1) |
||||
layer_3 = self.pretrained.layer3(layer_2) |
||||
layer_4 = self.pretrained.layer4(layer_3) |
||||
|
||||
layer_1_rn = self.scratch.layer1_rn(layer_1) |
||||
layer_2_rn = self.scratch.layer2_rn(layer_2) |
||||
layer_3_rn = self.scratch.layer3_rn(layer_3) |
||||
layer_4_rn = self.scratch.layer4_rn(layer_4) |
||||
|
||||
|
||||
path_4 = self.scratch.refinenet4(layer_4_rn) |
||||
path_3 = self.scratch.refinenet3(path_4, layer_3_rn) |
||||
path_2 = self.scratch.refinenet2(path_3, layer_2_rn) |
||||
path_1 = self.scratch.refinenet1(path_2, layer_1_rn) |
||||
|
||||
out = self.scratch.output_conv(path_1) |
||||
|
||||
return torch.squeeze(out, dim=1) |
||||
|
||||
|
||||
|
||||
def fuse_model(m): |
||||
prev_previous_type = nn.Identity() |
||||
prev_previous_name = '' |
||||
previous_type = nn.Identity() |
||||
previous_name = '' |
||||
for name, module in m.named_modules(): |
||||
if prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d and type(module) == nn.ReLU: |
||||
# print("FUSED ", prev_previous_name, previous_name, name) |
||||
torch.quantization.fuse_modules(m, [prev_previous_name, previous_name, name], inplace=True) |
||||
elif prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d: |
||||
# print("FUSED ", prev_previous_name, previous_name) |
||||
torch.quantization.fuse_modules(m, [prev_previous_name, previous_name], inplace=True) |
||||
# elif previous_type == nn.Conv2d and type(module) == nn.ReLU: |
||||
# print("FUSED ", previous_name, name) |
||||
# torch.quantization.fuse_modules(m, [previous_name, name], inplace=True) |
||||
|
||||
prev_previous_type = previous_type |
||||
prev_previous_name = previous_name |
||||
previous_type = type(module) |
||||
previous_name = name |
||||
@ -0,0 +1,234 @@
|
||||
import numpy as np |
||||
import cv2 |
||||
import math |
||||
|
||||
|
||||
def apply_min_size(sample, size, image_interpolation_method=cv2.INTER_AREA): |
||||
"""Rezise the sample to ensure the given size. Keeps aspect ratio. |
||||
|
||||
Args: |
||||
sample (dict): sample |
||||
size (tuple): image size |
||||
|
||||
Returns: |
||||
tuple: new size |
||||
""" |
||||
shape = list(sample["disparity"].shape) |
||||
|
||||
if shape[0] >= size[0] and shape[1] >= size[1]: |
||||
return sample |
||||
|
||||
scale = [0, 0] |
||||
scale[0] = size[0] / shape[0] |
||||
scale[1] = size[1] / shape[1] |
||||
|
||||
scale = max(scale) |
||||
|
||||
shape[0] = math.ceil(scale * shape[0]) |
||||
shape[1] = math.ceil(scale * shape[1]) |
||||
|
||||
# resize |
||||
sample["image"] = cv2.resize( |
||||
sample["image"], tuple(shape[::-1]), interpolation=image_interpolation_method |
||||
) |
||||
|
||||
sample["disparity"] = cv2.resize( |
||||
sample["disparity"], tuple(shape[::-1]), interpolation=cv2.INTER_NEAREST |
||||
) |
||||
sample["mask"] = cv2.resize( |
||||
sample["mask"].astype(np.float32), |
||||
tuple(shape[::-1]), |
||||
interpolation=cv2.INTER_NEAREST, |
||||
) |
||||
sample["mask"] = sample["mask"].astype(bool) |
||||
|
||||
return tuple(shape) |
||||
|
||||
|
||||
class Resize(object): |
||||
"""Resize sample to given size (width, height). |
||||
""" |
||||
|
||||
def __init__( |
||||
self, |
||||
width, |
||||
height, |
||||
resize_target=True, |
||||
keep_aspect_ratio=False, |
||||
ensure_multiple_of=1, |
||||
resize_method="lower_bound", |
||||
image_interpolation_method=cv2.INTER_AREA, |
||||
): |
||||
"""Init. |
||||
|
||||
Args: |
||||
width (int): desired output width |
||||
height (int): desired output height |
||||
resize_target (bool, optional): |
||||
True: Resize the full sample (image, mask, target). |
||||
False: Resize image only. |
||||
Defaults to True. |
||||
keep_aspect_ratio (bool, optional): |
||||
True: Keep the aspect ratio of the input sample. |
||||
Output sample might not have the given width and height, and |
||||
resize behaviour depends on the parameter 'resize_method'. |
||||
Defaults to False. |
||||
ensure_multiple_of (int, optional): |
||||
Output width and height is constrained to be multiple of this parameter. |
||||
Defaults to 1. |
||||
resize_method (str, optional): |
||||
"lower_bound": Output will be at least as large as the given size. |
||||
"upper_bound": Output will be at max as large as the given size. (Output size might be smaller than given size.) |
||||
"minimal": Scale as least as possible. (Output size might be smaller than given size.) |
||||
Defaults to "lower_bound". |
||||
""" |
||||
self.__width = width |
||||
self.__height = height |
||||
|
||||
self.__resize_target = resize_target |
||||
self.__keep_aspect_ratio = keep_aspect_ratio |
||||
self.__multiple_of = ensure_multiple_of |
||||
self.__resize_method = resize_method |
||||
self.__image_interpolation_method = image_interpolation_method |
||||
|
||||
def constrain_to_multiple_of(self, x, min_val=0, max_val=None): |
||||
y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int) |
||||
|
||||
if max_val is not None and y > max_val: |
||||
y = (np.floor(x / self.__multiple_of) * self.__multiple_of).astype(int) |
||||
|
||||
if y < min_val: |
||||
y = (np.ceil(x / self.__multiple_of) * self.__multiple_of).astype(int) |
||||
|
||||
return y |
||||
|
||||
def get_size(self, width, height): |
||||
# determine new height and width |
||||
scale_height = self.__height / height |
||||
scale_width = self.__width / width |
||||
|
||||
if self.__keep_aspect_ratio: |
||||
if self.__resize_method == "lower_bound": |
||||
# scale such that output size is lower bound |
||||
if scale_width > scale_height: |
||||
# fit width |
||||
scale_height = scale_width |
||||
else: |
||||
# fit height |
||||
scale_width = scale_height |
||||
elif self.__resize_method == "upper_bound": |
||||
# scale such that output size is upper bound |
||||
if scale_width < scale_height: |
||||
# fit width |
||||
scale_height = scale_width |
||||
else: |
||||
# fit height |
||||
scale_width = scale_height |
||||
elif self.__resize_method == "minimal": |
||||
# scale as least as possbile |
||||
if abs(1 - scale_width) < abs(1 - scale_height): |
||||
# fit width |
||||
scale_height = scale_width |
||||
else: |
||||
# fit height |
||||
scale_width = scale_height |
||||
else: |
||||
raise ValueError( |
||||
f"resize_method {self.__resize_method} not implemented" |
||||
) |
||||
|
||||
if self.__resize_method == "lower_bound": |
||||
new_height = self.constrain_to_multiple_of( |
||||
scale_height * height, min_val=self.__height |
||||
) |
||||
new_width = self.constrain_to_multiple_of( |
||||
scale_width * width, min_val=self.__width |
||||
) |
||||
elif self.__resize_method == "upper_bound": |
||||
new_height = self.constrain_to_multiple_of( |
||||
scale_height * height, max_val=self.__height |
||||
) |
||||
new_width = self.constrain_to_multiple_of( |
||||
scale_width * width, max_val=self.__width |
||||
) |
||||
elif self.__resize_method == "minimal": |
||||
new_height = self.constrain_to_multiple_of(scale_height * height) |
||||
new_width = self.constrain_to_multiple_of(scale_width * width) |
||||
else: |
||||
raise ValueError(f"resize_method {self.__resize_method} not implemented") |
||||
|
||||
return (new_width, new_height) |
||||
|
||||
def __call__(self, sample): |
||||
width, height = self.get_size( |
||||
sample["image"].shape[1], sample["image"].shape[0] |
||||
) |
||||
|
||||
# resize sample |
||||
sample["image"] = cv2.resize( |
||||
sample["image"], |
||||
(width, height), |
||||
interpolation=self.__image_interpolation_method, |
||||
) |
||||
|
||||
if self.__resize_target: |
||||
if "disparity" in sample: |
||||
sample["disparity"] = cv2.resize( |
||||
sample["disparity"], |
||||
(width, height), |
||||
interpolation=cv2.INTER_NEAREST, |
||||
) |
||||
|
||||
if "depth" in sample: |
||||
sample["depth"] = cv2.resize( |
||||
sample["depth"], (width, height), interpolation=cv2.INTER_NEAREST |
||||
) |
||||
|
||||
sample["mask"] = cv2.resize( |
||||
sample["mask"].astype(np.float32), |
||||
(width, height), |
||||
interpolation=cv2.INTER_NEAREST, |
||||
) |
||||
sample["mask"] = sample["mask"].astype(bool) |
||||
|
||||
return sample |
||||
|
||||
|
||||
class NormalizeImage(object): |
||||
"""Normlize image by given mean and std. |
||||
""" |
||||
|
||||
def __init__(self, mean, std): |
||||
self.__mean = mean |
||||
self.__std = std |
||||
|
||||
def __call__(self, sample): |
||||
sample["image"] = (sample["image"] - self.__mean) / self.__std |
||||
|
||||
return sample |
||||
|
||||
|
||||
class PrepareForNet(object): |
||||
"""Prepare sample for usage as network input. |
||||
""" |
||||
|
||||
def __init__(self): |
||||
pass |
||||
|
||||
def __call__(self, sample): |
||||
image = np.transpose(sample["image"], (2, 0, 1)) |
||||
sample["image"] = np.ascontiguousarray(image).astype(np.float32) |
||||
|
||||
if "mask" in sample: |
||||
sample["mask"] = sample["mask"].astype(np.float32) |
||||
sample["mask"] = np.ascontiguousarray(sample["mask"]) |
||||
|
||||
if "disparity" in sample: |
||||
disparity = sample["disparity"].astype(np.float32) |
||||
sample["disparity"] = np.ascontiguousarray(disparity) |
||||
|
||||
if "depth" in sample: |
||||
depth = sample["depth"].astype(np.float32) |
||||
sample["depth"] = np.ascontiguousarray(depth) |
||||
|
||||
return sample |
||||
@ -0,0 +1,491 @@
|
||||
import torch |
||||
import torch.nn as nn |
||||
import timm |
||||
import types |
||||
import math |
||||
import torch.nn.functional as F |
||||
|
||||
|
||||
class Slice(nn.Module): |
||||
def __init__(self, start_index=1): |
||||
super(Slice, self).__init__() |
||||
self.start_index = start_index |
||||
|
||||
def forward(self, x): |
||||
return x[:, self.start_index :] |
||||
|
||||
|
||||
class AddReadout(nn.Module): |
||||
def __init__(self, start_index=1): |
||||
super(AddReadout, self).__init__() |
||||
self.start_index = start_index |
||||
|
||||
def forward(self, x): |
||||
if self.start_index == 2: |
||||
readout = (x[:, 0] + x[:, 1]) / 2 |
||||
else: |
||||
readout = x[:, 0] |
||||
return x[:, self.start_index :] + readout.unsqueeze(1) |
||||
|
||||
|
||||
class ProjectReadout(nn.Module): |
||||
def __init__(self, in_features, start_index=1): |
||||
super(ProjectReadout, self).__init__() |
||||
self.start_index = start_index |
||||
|
||||
self.project = nn.Sequential(nn.Linear(2 * in_features, in_features), nn.GELU()) |
||||
|
||||
def forward(self, x): |
||||
readout = x[:, 0].unsqueeze(1).expand_as(x[:, self.start_index :]) |
||||
features = torch.cat((x[:, self.start_index :], readout), -1) |
||||
|
||||
return self.project(features) |
||||
|
||||
|
||||
class Transpose(nn.Module): |
||||
def __init__(self, dim0, dim1): |
||||
super(Transpose, self).__init__() |
||||
self.dim0 = dim0 |
||||
self.dim1 = dim1 |
||||
|
||||
def forward(self, x): |
||||
x = x.transpose(self.dim0, self.dim1) |
||||
return x |
||||
|
||||
|
||||
def forward_vit(pretrained, x): |
||||
b, c, h, w = x.shape |
||||
|
||||
glob = pretrained.model.forward_flex(x) |
||||
|
||||
layer_1 = pretrained.activations["1"] |
||||
layer_2 = pretrained.activations["2"] |
||||
layer_3 = pretrained.activations["3"] |
||||
layer_4 = pretrained.activations["4"] |
||||
|
||||
layer_1 = pretrained.act_postprocess1[0:2](layer_1) |
||||
layer_2 = pretrained.act_postprocess2[0:2](layer_2) |
||||
layer_3 = pretrained.act_postprocess3[0:2](layer_3) |
||||
layer_4 = pretrained.act_postprocess4[0:2](layer_4) |
||||
|
||||
unflatten = nn.Sequential( |
||||
nn.Unflatten( |
||||
2, |
||||
torch.Size( |
||||
[ |
||||
h // pretrained.model.patch_size[1], |
||||
w // pretrained.model.patch_size[0], |
||||
] |
||||
), |
||||
) |
||||
) |
||||
|
||||
if layer_1.ndim == 3: |
||||
layer_1 = unflatten(layer_1) |
||||
if layer_2.ndim == 3: |
||||
layer_2 = unflatten(layer_2) |
||||
if layer_3.ndim == 3: |
||||
layer_3 = unflatten(layer_3) |
||||
if layer_4.ndim == 3: |
||||
layer_4 = unflatten(layer_4) |
||||
|
||||
layer_1 = pretrained.act_postprocess1[3 : len(pretrained.act_postprocess1)](layer_1) |
||||
layer_2 = pretrained.act_postprocess2[3 : len(pretrained.act_postprocess2)](layer_2) |
||||
layer_3 = pretrained.act_postprocess3[3 : len(pretrained.act_postprocess3)](layer_3) |
||||
layer_4 = pretrained.act_postprocess4[3 : len(pretrained.act_postprocess4)](layer_4) |
||||
|
||||
return layer_1, layer_2, layer_3, layer_4 |
||||
|
||||
|
||||
def _resize_pos_embed(self, posemb, gs_h, gs_w): |
||||
posemb_tok, posemb_grid = ( |
||||
posemb[:, : self.start_index], |
||||
posemb[0, self.start_index :], |
||||
) |
||||
|
||||
gs_old = int(math.sqrt(len(posemb_grid))) |
||||
|
||||
posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2) |
||||
posemb_grid = F.interpolate(posemb_grid, size=(gs_h, gs_w), mode="bilinear") |
||||
posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_h * gs_w, -1) |
||||
|
||||
posemb = torch.cat([posemb_tok, posemb_grid], dim=1) |
||||
|
||||
return posemb |
||||
|
||||
|
||||
def forward_flex(self, x): |
||||
b, c, h, w = x.shape |
||||
|
||||
pos_embed = self._resize_pos_embed( |
||||
self.pos_embed, h // self.patch_size[1], w // self.patch_size[0] |
||||
) |
||||
|
||||
B = x.shape[0] |
||||
|
||||
if hasattr(self.patch_embed, "backbone"): |
||||
x = self.patch_embed.backbone(x) |
||||
if isinstance(x, (list, tuple)): |
||||
x = x[-1] # last feature if backbone outputs list/tuple of features |
||||
|
||||
x = self.patch_embed.proj(x).flatten(2).transpose(1, 2) |
||||
|
||||
if getattr(self, "dist_token", None) is not None: |
||||
cls_tokens = self.cls_token.expand( |
||||
B, -1, -1 |
||||
) # stole cls_tokens impl from Phil Wang, thanks |
||||
dist_token = self.dist_token.expand(B, -1, -1) |
||||
x = torch.cat((cls_tokens, dist_token, x), dim=1) |
||||
else: |
||||
cls_tokens = self.cls_token.expand( |
||||
B, -1, -1 |
||||
) # stole cls_tokens impl from Phil Wang, thanks |
||||
x = torch.cat((cls_tokens, x), dim=1) |
||||
|
||||
x = x + pos_embed |
||||
x = self.pos_drop(x) |
||||
|
||||
for blk in self.blocks: |
||||
x = blk(x) |
||||
|
||||
x = self.norm(x) |
||||
|
||||
return x |
||||
|
||||
|
||||
activations = {} |
||||
|
||||
|
||||
def get_activation(name): |
||||
def hook(model, input, output): |
||||
activations[name] = output |
||||
|
||||
return hook |
||||
|
||||
|
||||
def get_readout_oper(vit_features, features, use_readout, start_index=1): |
||||
if use_readout == "ignore": |
||||
readout_oper = [Slice(start_index)] * len(features) |
||||
elif use_readout == "add": |
||||
readout_oper = [AddReadout(start_index)] * len(features) |
||||
elif use_readout == "project": |
||||
readout_oper = [ |
||||
ProjectReadout(vit_features, start_index) for out_feat in features |
||||
] |
||||
else: |
||||
assert ( |
||||
False |
||||
), "wrong operation for readout token, use_readout can be 'ignore', 'add', or 'project'" |
||||
|
||||
return readout_oper |
||||
|
||||
|
||||
def _make_vit_b16_backbone( |
||||
model, |
||||
features=[96, 192, 384, 768], |
||||
size=[384, 384], |
||||
hooks=[2, 5, 8, 11], |
||||
vit_features=768, |
||||
use_readout="ignore", |
||||
start_index=1, |
||||
): |
||||
pretrained = nn.Module() |
||||
|
||||
pretrained.model = model |
||||
pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1")) |
||||
pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2")) |
||||
pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3")) |
||||
pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4")) |
||||
|
||||
pretrained.activations = activations |
||||
|
||||
readout_oper = get_readout_oper(vit_features, features, use_readout, start_index) |
||||
|
||||
# 32, 48, 136, 384 |
||||
pretrained.act_postprocess1 = nn.Sequential( |
||||
readout_oper[0], |
||||
Transpose(1, 2), |
||||
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), |
||||
nn.Conv2d( |
||||
in_channels=vit_features, |
||||
out_channels=features[0], |
||||
kernel_size=1, |
||||
stride=1, |
||||
padding=0, |
||||
), |
||||
nn.ConvTranspose2d( |
||||
in_channels=features[0], |
||||
out_channels=features[0], |
||||
kernel_size=4, |
||||
stride=4, |
||||
padding=0, |
||||
bias=True, |
||||
dilation=1, |
||||
groups=1, |
||||
), |
||||
) |
||||
|
||||
pretrained.act_postprocess2 = nn.Sequential( |
||||
readout_oper[1], |
||||
Transpose(1, 2), |
||||
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), |
||||
nn.Conv2d( |
||||
in_channels=vit_features, |
||||
out_channels=features[1], |
||||
kernel_size=1, |
||||
stride=1, |
||||
padding=0, |
||||
), |
||||
nn.ConvTranspose2d( |
||||
in_channels=features[1], |
||||
out_channels=features[1], |
||||
kernel_size=2, |
||||
stride=2, |
||||
padding=0, |
||||
bias=True, |
||||
dilation=1, |
||||
groups=1, |
||||
), |
||||
) |
||||
|
||||
pretrained.act_postprocess3 = nn.Sequential( |
||||
readout_oper[2], |
||||
Transpose(1, 2), |
||||
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), |
||||
nn.Conv2d( |
||||
in_channels=vit_features, |
||||
out_channels=features[2], |
||||
kernel_size=1, |
||||
stride=1, |
||||
padding=0, |
||||
), |
||||
) |
||||
|
||||
pretrained.act_postprocess4 = nn.Sequential( |
||||
readout_oper[3], |
||||
Transpose(1, 2), |
||||
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), |
||||
nn.Conv2d( |
||||
in_channels=vit_features, |
||||
out_channels=features[3], |
||||
kernel_size=1, |
||||
stride=1, |
||||
padding=0, |
||||
), |
||||
nn.Conv2d( |
||||
in_channels=features[3], |
||||
out_channels=features[3], |
||||
kernel_size=3, |
||||
stride=2, |
||||
padding=1, |
||||
), |
||||
) |
||||
|
||||
pretrained.model.start_index = start_index |
||||
pretrained.model.patch_size = [16, 16] |
||||
|
||||
# We inject this function into the VisionTransformer instances so that |
||||
# we can use it with interpolated position embeddings without modifying the library source. |
||||
pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model) |
||||
pretrained.model._resize_pos_embed = types.MethodType( |
||||
_resize_pos_embed, pretrained.model |
||||
) |
||||
|
||||
return pretrained |
||||
|
||||
|
||||
def _make_pretrained_vitl16_384(pretrained, use_readout="ignore", hooks=None): |
||||
model = timm.create_model("vit_large_patch16_384", pretrained=pretrained) |
||||
|
||||
hooks = [5, 11, 17, 23] if hooks == None else hooks |
||||
return _make_vit_b16_backbone( |
||||
model, |
||||
features=[256, 512, 1024, 1024], |
||||
hooks=hooks, |
||||
vit_features=1024, |
||||
use_readout=use_readout, |
||||
) |
||||
|
||||
|
||||
def _make_pretrained_vitb16_384(pretrained, use_readout="ignore", hooks=None): |
||||
model = timm.create_model("vit_base_patch16_384", pretrained=pretrained) |
||||
|
||||
hooks = [2, 5, 8, 11] if hooks == None else hooks |
||||
return _make_vit_b16_backbone( |
||||
model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout |
||||
) |
||||
|
||||
|
||||
def _make_pretrained_deitb16_384(pretrained, use_readout="ignore", hooks=None): |
||||
model = timm.create_model("vit_deit_base_patch16_384", pretrained=pretrained) |
||||
|
||||
hooks = [2, 5, 8, 11] if hooks == None else hooks |
||||
return _make_vit_b16_backbone( |
||||
model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout |
||||
) |
||||
|
||||
|
||||
def _make_pretrained_deitb16_distil_384(pretrained, use_readout="ignore", hooks=None): |
||||
model = timm.create_model( |
||||
"vit_deit_base_distilled_patch16_384", pretrained=pretrained |
||||
) |
||||
|
||||
hooks = [2, 5, 8, 11] if hooks == None else hooks |
||||
return _make_vit_b16_backbone( |
||||
model, |
||||
features=[96, 192, 384, 768], |
||||
hooks=hooks, |
||||
use_readout=use_readout, |
||||
start_index=2, |
||||
) |
||||
|
||||
|
||||
def _make_vit_b_rn50_backbone( |
||||
model, |
||||
features=[256, 512, 768, 768], |
||||
size=[384, 384], |
||||
hooks=[0, 1, 8, 11], |
||||
vit_features=768, |
||||
use_vit_only=False, |
||||
use_readout="ignore", |
||||
start_index=1, |
||||
): |
||||
pretrained = nn.Module() |
||||
|
||||
pretrained.model = model |
||||
|
||||
if use_vit_only == True: |
||||
pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1")) |
||||
pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2")) |
||||
else: |
||||
pretrained.model.patch_embed.backbone.stages[0].register_forward_hook( |
||||
get_activation("1") |
||||
) |
||||
pretrained.model.patch_embed.backbone.stages[1].register_forward_hook( |
||||
get_activation("2") |
||||
) |
||||
|
||||
pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3")) |
||||
pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4")) |
||||
|
||||
pretrained.activations = activations |
||||
|
||||
readout_oper = get_readout_oper(vit_features, features, use_readout, start_index) |
||||
|
||||
if use_vit_only == True: |
||||
pretrained.act_postprocess1 = nn.Sequential( |
||||
readout_oper[0], |
||||
Transpose(1, 2), |
||||
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), |
||||
nn.Conv2d( |
||||
in_channels=vit_features, |
||||
out_channels=features[0], |
||||
kernel_size=1, |
||||
stride=1, |
||||
padding=0, |
||||
), |
||||
nn.ConvTranspose2d( |
||||
in_channels=features[0], |
||||
out_channels=features[0], |
||||
kernel_size=4, |
||||
stride=4, |
||||
padding=0, |
||||
bias=True, |
||||
dilation=1, |
||||
groups=1, |
||||
), |
||||
) |
||||
|
||||
pretrained.act_postprocess2 = nn.Sequential( |
||||
readout_oper[1], |
||||
Transpose(1, 2), |
||||
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), |
||||
nn.Conv2d( |
||||
in_channels=vit_features, |
||||
out_channels=features[1], |
||||
kernel_size=1, |
||||
stride=1, |
||||
padding=0, |
||||
), |
||||
nn.ConvTranspose2d( |
||||
in_channels=features[1], |
||||
out_channels=features[1], |
||||
kernel_size=2, |
||||
stride=2, |
||||
padding=0, |
||||
bias=True, |
||||
dilation=1, |
||||
groups=1, |
||||
), |
||||
) |
||||
else: |
||||
pretrained.act_postprocess1 = nn.Sequential( |
||||
nn.Identity(), nn.Identity(), nn.Identity() |
||||
) |
||||
pretrained.act_postprocess2 = nn.Sequential( |
||||
nn.Identity(), nn.Identity(), nn.Identity() |
||||
) |
||||
|
||||
pretrained.act_postprocess3 = nn.Sequential( |
||||
readout_oper[2], |
||||
Transpose(1, 2), |
||||
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), |
||||
nn.Conv2d( |
||||
in_channels=vit_features, |
||||
out_channels=features[2], |
||||
kernel_size=1, |
||||
stride=1, |
||||
padding=0, |
||||
), |
||||
) |
||||
|
||||
pretrained.act_postprocess4 = nn.Sequential( |
||||
readout_oper[3], |
||||
Transpose(1, 2), |
||||
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), |
||||
nn.Conv2d( |
||||
in_channels=vit_features, |
||||
out_channels=features[3], |
||||
kernel_size=1, |
||||
stride=1, |
||||
padding=0, |
||||
), |
||||
nn.Conv2d( |
||||
in_channels=features[3], |
||||
out_channels=features[3], |
||||
kernel_size=3, |
||||
stride=2, |
||||
padding=1, |
||||
), |
||||
) |
||||
|
||||
pretrained.model.start_index = start_index |
||||
pretrained.model.patch_size = [16, 16] |
||||
|
||||
# We inject this function into the VisionTransformer instances so that |
||||
# we can use it with interpolated position embeddings without modifying the library source. |
||||
pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model) |
||||
|
||||
# We inject this function into the VisionTransformer instances so that |
||||
# we can use it with interpolated position embeddings without modifying the library source. |
||||
pretrained.model._resize_pos_embed = types.MethodType( |
||||
_resize_pos_embed, pretrained.model |
||||
) |
||||
|
||||
return pretrained |
||||
|
||||
|
||||
def _make_pretrained_vitb_rn50_384( |
||||
pretrained, use_readout="ignore", hooks=None, use_vit_only=False |
||||
): |
||||
model = timm.create_model("vit_base_resnet50_384", pretrained=pretrained) |
||||
|
||||
hooks = [0, 1, 8, 11] if hooks == None else hooks |
||||
return _make_vit_b_rn50_backbone( |
||||
model, |
||||
features=[256, 512, 768, 768], |
||||
size=[384, 384], |
||||
hooks=hooks, |
||||
use_vit_only=use_vit_only, |
||||
use_readout=use_readout, |
||||
) |
||||
@ -0,0 +1,189 @@
|
||||
"""Utils for monoDepth.""" |
||||
import sys |
||||
import re |
||||
import numpy as np |
||||
import cv2 |
||||
import torch |
||||
|
||||
|
||||
def read_pfm(path): |
||||
"""Read pfm file. |
||||
|
||||
Args: |
||||
path (str): path to file |
||||
|
||||
Returns: |
||||
tuple: (data, scale) |
||||
""" |
||||
with open(path, "rb") as file: |
||||
|
||||
color = None |
||||
width = None |
||||
height = None |
||||
scale = None |
||||
endian = None |
||||
|
||||
header = file.readline().rstrip() |
||||
if header.decode("ascii") == "PF": |
||||
color = True |
||||
elif header.decode("ascii") == "Pf": |
||||
color = False |
||||
else: |
||||
raise Exception("Not a PFM file: " + path) |
||||
|
||||
dim_match = re.match(r"^(\d+)\s(\d+)\s$", file.readline().decode("ascii")) |
||||
if dim_match: |
||||
width, height = list(map(int, dim_match.groups())) |
||||
else: |
||||
raise Exception("Malformed PFM header.") |
||||
|
||||
scale = float(file.readline().decode("ascii").rstrip()) |
||||
if scale < 0: |
||||
# little-endian |
||||
endian = "<" |
||||
scale = -scale |
||||
else: |
||||
# big-endian |
||||
endian = ">" |
||||
|
||||
data = np.fromfile(file, endian + "f") |
||||
shape = (height, width, 3) if color else (height, width) |
||||
|
||||
data = np.reshape(data, shape) |
||||
data = np.flipud(data) |
||||
|
||||
return data, scale |
||||
|
||||
|
||||
def write_pfm(path, image, scale=1): |
||||
"""Write pfm file. |
||||
|
||||
Args: |
||||
path (str): pathto file |
||||
image (array): data |
||||
scale (int, optional): Scale. Defaults to 1. |
||||
""" |
||||
|
||||
with open(path, "wb") as file: |
||||
color = None |
||||
|
||||
if image.dtype.name != "float32": |
||||
raise Exception("Image dtype must be float32.") |
||||
|
||||
image = np.flipud(image) |
||||
|
||||
if len(image.shape) == 3 and image.shape[2] == 3: # color image |
||||
color = True |
||||
elif ( |
||||
len(image.shape) == 2 or len(image.shape) == 3 and image.shape[2] == 1 |
||||
): # greyscale |
||||
color = False |
||||
else: |
||||
raise Exception("Image must have H x W x 3, H x W x 1 or H x W dimensions.") |
||||
|
||||
file.write("PF\n" if color else "Pf\n".encode()) |
||||
file.write("%d %d\n".encode() % (image.shape[1], image.shape[0])) |
||||
|
||||
endian = image.dtype.byteorder |
||||
|
||||
if endian == "<" or endian == "=" and sys.byteorder == "little": |
||||
scale = -scale |
||||
|
||||
file.write("%f\n".encode() % scale) |
||||
|
||||
image.tofile(file) |
||||
|
||||
|
||||
def read_image(path): |
||||
"""Read image and output RGB image (0-1). |
||||
|
||||
Args: |
||||
path (str): path to file |
||||
|
||||
Returns: |
||||
array: RGB image (0-1) |
||||
""" |
||||
img = cv2.imread(path) |
||||
|
||||
if img.ndim == 2: |
||||
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) |
||||
|
||||
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) / 255.0 |
||||
|
||||
return img |
||||
|
||||
|
||||
def resize_image(img): |
||||
"""Resize image and make it fit for network. |
||||
|
||||
Args: |
||||
img (array): image |
||||
|
||||
Returns: |
||||
tensor: data ready for network |
||||
""" |
||||
height_orig = img.shape[0] |
||||
width_orig = img.shape[1] |
||||
|
||||
if width_orig > height_orig: |
||||
scale = width_orig / 384 |
||||
else: |
||||
scale = height_orig / 384 |
||||
|
||||
height = (np.ceil(height_orig / scale / 32) * 32).astype(int) |
||||
width = (np.ceil(width_orig / scale / 32) * 32).astype(int) |
||||
|
||||
img_resized = cv2.resize(img, (width, height), interpolation=cv2.INTER_AREA) |
||||
|
||||
img_resized = ( |
||||
torch.from_numpy(np.transpose(img_resized, (2, 0, 1))).contiguous().float() |
||||
) |
||||
img_resized = img_resized.unsqueeze(0) |
||||
|
||||
return img_resized |
||||
|
||||
|
||||
def resize_depth(depth, width, height): |
||||
"""Resize depth map and bring to CPU (numpy). |
||||
|
||||
Args: |
||||
depth (tensor): depth |
||||
width (int): image width |
||||
height (int): image height |
||||
|
||||
Returns: |
||||
array: processed depth |
||||
""" |
||||
depth = torch.squeeze(depth[0, :, :, :]).to("cpu") |
||||
|
||||
depth_resized = cv2.resize( |
||||
depth.numpy(), (width, height), interpolation=cv2.INTER_CUBIC |
||||
) |
||||
|
||||
return depth_resized |
||||
|
||||
def write_depth(path, depth, bits=1): |
||||
"""Write depth map to pfm and png file. |
||||
|
||||
Args: |
||||
path (str): filepath without extension |
||||
depth (array): depth |
||||
""" |
||||
write_pfm(path + ".pfm", depth.astype(np.float32)) |
||||
|
||||
depth_min = depth.min() |
||||
depth_max = depth.max() |
||||
|
||||
max_val = (2**(8*bits))-1 |
||||
|
||||
if depth_max - depth_min > np.finfo("float").eps: |
||||
out = max_val * (depth - depth_min) / (depth_max - depth_min) |
||||
else: |
||||
out = np.zeros(depth.shape, dtype=depth.type) |
||||
|
||||
if bits == 1: |
||||
cv2.imwrite(path + ".png", out.astype("uint8")) |
||||
elif bits == 2: |
||||
cv2.imwrite(path + ".png", out.astype("uint16")) |
||||
|
||||
return |
||||
@ -0,0 +1,204 @@
|
||||
# original source: |
||||
# https://github.com/AminRezaei0x443/memory-efficient-attention/blob/1bc0d9e6ac5f82ea43a375135c4e1d3896ee1694/memory_efficient_attention/attention_torch.py |
||||
# license: |
||||
# MIT |
||||
# credit: |
||||
# Amin Rezaei (original author) |
||||
# Alex Birch (optimized algorithm for 3D tensors, at the expense of removing bias, masking and callbacks) |
||||
# implementation of: |
||||
# Self-attention Does Not Need O(n2) Memory": |
||||
# https://arxiv.org/abs/2112.05682v2 |
||||
|
||||
from functools import partial |
||||
import torch |
||||
from torch import Tensor |
||||
from torch.utils.checkpoint import checkpoint |
||||
import math |
||||
from typing import Optional, NamedTuple, Protocol, List |
||||
|
||||
from torch import Tensor |
||||
from typing import List |
||||
|
||||
def dynamic_slice( |
||||
x: Tensor, |
||||
starts: List[int], |
||||
sizes: List[int], |
||||
) -> Tensor: |
||||
slicing = [slice(start, start + size) for start, size in zip(starts, sizes)] |
||||
return x[slicing] |
||||
|
||||
class AttnChunk(NamedTuple): |
||||
exp_values: Tensor |
||||
exp_weights_sum: Tensor |
||||
max_score: Tensor |
||||
|
||||
class SummarizeChunk(Protocol): |
||||
@staticmethod |
||||
def __call__( |
||||
query: Tensor, |
||||
key_t: Tensor, |
||||
value: Tensor, |
||||
) -> AttnChunk: ... |
||||
|
||||
class ComputeQueryChunkAttn(Protocol): |
||||
@staticmethod |
||||
def __call__( |
||||
query: Tensor, |
||||
key_t: Tensor, |
||||
value: Tensor, |
||||
) -> Tensor: ... |
||||
|
||||
def _summarize_chunk( |
||||
query: Tensor, |
||||
key_t: Tensor, |
||||
value: Tensor, |
||||
scale: float, |
||||
) -> AttnChunk: |
||||
attn_weights = torch.baddbmm( |
||||
torch.empty(1, 1, 1, device=query.device, dtype=query.dtype), |
||||
query, |
||||
key_t, |
||||
alpha=scale, |
||||
beta=0, |
||||
) |
||||
max_score, _ = torch.max(attn_weights, -1, keepdim=True) |
||||
max_score = max_score.detach() |
||||
exp_weights = torch.exp(attn_weights - max_score) |
||||
exp_values = torch.bmm(exp_weights, value) |
||||
max_score = max_score.squeeze(-1) |
||||
return AttnChunk(exp_values, exp_weights.sum(dim=-1), max_score) |
||||
|
||||
def _query_chunk_attention( |
||||
query: Tensor, |
||||
key_t: Tensor, |
||||
value: Tensor, |
||||
summarize_chunk: SummarizeChunk, |
||||
kv_chunk_size: int, |
||||
) -> Tensor: |
||||
batch_x_heads, k_channels_per_head, k_tokens = key_t.shape |
||||
_, _, v_channels_per_head = value.shape |
||||
|
||||
def chunk_scanner(chunk_idx: int) -> AttnChunk: |
||||
key_chunk = dynamic_slice( |
||||
key_t, |
||||
(0, 0, chunk_idx), |
||||
(batch_x_heads, k_channels_per_head, kv_chunk_size) |
||||
) |
||||
value_chunk = dynamic_slice( |
||||
value, |
||||
(0, chunk_idx, 0), |
||||
(batch_x_heads, kv_chunk_size, v_channels_per_head) |
||||
) |
||||
return summarize_chunk(query, key_chunk, value_chunk) |
||||
|
||||
chunks: List[AttnChunk] = [ |
||||
chunk_scanner(chunk) for chunk in torch.arange(0, k_tokens, kv_chunk_size) |
||||
] |
||||
acc_chunk = AttnChunk(*map(torch.stack, zip(*chunks))) |
||||
chunk_values, chunk_weights, chunk_max = acc_chunk |
||||
|
||||
global_max, _ = torch.max(chunk_max, 0, keepdim=True) |
||||
max_diffs = torch.exp(chunk_max - global_max) |
||||
chunk_values *= torch.unsqueeze(max_diffs, -1) |
||||
chunk_weights *= max_diffs |
||||
|
||||
all_values = chunk_values.sum(dim=0) |
||||
all_weights = torch.unsqueeze(chunk_weights, -1).sum(dim=0) |
||||
return all_values / all_weights |
||||
|
||||
# TODO: refactor CrossAttention#get_attention_scores to share code with this |
||||
def _get_attention_scores_no_kv_chunking( |
||||
query: Tensor, |
||||
key_t: Tensor, |
||||
value: Tensor, |
||||
scale: float, |
||||
) -> Tensor: |
||||
attn_scores = torch.baddbmm( |
||||
torch.empty(1, 1, 1, device=query.device, dtype=query.dtype), |
||||
query, |
||||
key_t, |
||||
alpha=scale, |
||||
beta=0, |
||||
) |
||||
attn_probs = attn_scores.softmax(dim=-1) |
||||
del attn_scores |
||||
hidden_states_slice = torch.bmm(attn_probs, value) |
||||
return hidden_states_slice |
||||
|
||||
class ScannedChunk(NamedTuple): |
||||
chunk_idx: int |
||||
attn_chunk: AttnChunk |
||||
|
||||
def efficient_dot_product_attention( |
||||
query: Tensor, |
||||
key_t: Tensor, |
||||
value: Tensor, |
||||
query_chunk_size=1024, |
||||
kv_chunk_size: Optional[int] = None, |
||||
kv_chunk_size_min: Optional[int] = None, |
||||
use_checkpoint=True, |
||||
): |
||||
"""Computes efficient dot-product attention given query, transposed key, and value. |
||||
This is efficient version of attention presented in |
||||
https://arxiv.org/abs/2112.05682v2 which comes with O(sqrt(n)) memory requirements. |
||||
Args: |
||||
query: queries for calculating attention with shape of |
||||
`[batch * num_heads, tokens, channels_per_head]`. |
||||
key_t: keys for calculating attention with shape of |
||||
`[batch * num_heads, channels_per_head, tokens]`. |
||||
value: values to be used in attention with shape of |
||||
`[batch * num_heads, tokens, channels_per_head]`. |
||||
query_chunk_size: int: query chunks size |
||||
kv_chunk_size: Optional[int]: key/value chunks size. if None: defaults to sqrt(key_tokens) |
||||
kv_chunk_size_min: Optional[int]: key/value minimum chunk size. only considered when kv_chunk_size is None. changes `sqrt(key_tokens)` into `max(sqrt(key_tokens), kv_chunk_size_min)`, to ensure our chunk sizes don't get too small (smaller chunks = more chunks = less concurrent work done). |
||||
use_checkpoint: bool: whether to use checkpointing (recommended True for training, False for inference) |
||||
Returns: |
||||
Output of shape `[batch * num_heads, query_tokens, channels_per_head]`. |
||||
""" |
||||
batch_x_heads, q_tokens, q_channels_per_head = query.shape |
||||
_, _, k_tokens = key_t.shape |
||||
scale = q_channels_per_head ** -0.5 |
||||
|
||||
kv_chunk_size = min(kv_chunk_size or int(math.sqrt(k_tokens)), k_tokens) |
||||
if kv_chunk_size_min is not None: |
||||
kv_chunk_size = max(kv_chunk_size, kv_chunk_size_min) |
||||
|
||||
def get_query_chunk(chunk_idx: int) -> Tensor: |
||||
return dynamic_slice( |
||||
query, |
||||
(0, chunk_idx, 0), |
||||
(batch_x_heads, min(query_chunk_size, q_tokens), q_channels_per_head) |
||||
) |
||||
|
||||
summarize_chunk: SummarizeChunk = partial(_summarize_chunk, scale=scale) |
||||
summarize_chunk: SummarizeChunk = partial(checkpoint, summarize_chunk) if use_checkpoint else summarize_chunk |
||||
compute_query_chunk_attn: ComputeQueryChunkAttn = partial( |
||||
_get_attention_scores_no_kv_chunking, |
||||
scale=scale |
||||
) if k_tokens <= kv_chunk_size else ( |
||||
# fast-path for when there's just 1 key-value chunk per query chunk (this is just sliced attention btw) |
||||
partial( |
||||
_query_chunk_attention, |
||||
kv_chunk_size=kv_chunk_size, |
||||
summarize_chunk=summarize_chunk, |
||||
) |
||||
) |
||||
|
||||
if q_tokens <= query_chunk_size: |
||||
# fast-path for when there's just 1 query chunk |
||||
return compute_query_chunk_attn( |
||||
query=query, |
||||
key_t=key_t, |
||||
value=value, |
||||
) |
||||
|
||||
# TODO: maybe we should use torch.empty_like(query) to allocate storage in-advance, |
||||
# and pass slices to be mutated, instead of torch.cat()ing the returned slices |
||||
res = torch.cat([ |
||||
compute_query_chunk_attn( |
||||
query=get_query_chunk(i * query_chunk_size), |
||||
key_t=key_t, |
||||
value=value, |
||||
) for i in range(math.ceil(q_tokens / query_chunk_size)) |
||||
], dim=1) |
||||
return res |
||||
@ -0,0 +1,197 @@
|
||||
import importlib |
||||
|
||||
import torch |
||||
from torch import optim |
||||
import numpy as np |
||||
|
||||
from inspect import isfunction |
||||
from PIL import Image, ImageDraw, ImageFont |
||||
|
||||
|
||||
def log_txt_as_img(wh, xc, size=10): |
||||
# wh a tuple of (width, height) |
||||
# xc a list of captions to plot |
||||
b = len(xc) |
||||
txts = list() |
||||
for bi in range(b): |
||||
txt = Image.new("RGB", wh, color="white") |
||||
draw = ImageDraw.Draw(txt) |
||||
font = ImageFont.truetype('data/DejaVuSans.ttf', size=size) |
||||
nc = int(40 * (wh[0] / 256)) |
||||
lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc)) |
||||
|
||||
try: |
||||
draw.text((0, 0), lines, fill="black", font=font) |
||||
except UnicodeEncodeError: |
||||
print("Cant encode string for logging. Skipping.") |
||||
|
||||
txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0 |
||||
txts.append(txt) |
||||
txts = np.stack(txts) |
||||
txts = torch.tensor(txts) |
||||
return txts |
||||
|
||||
|
||||
def ismap(x): |
||||
if not isinstance(x, torch.Tensor): |
||||
return False |
||||
return (len(x.shape) == 4) and (x.shape[1] > 3) |
||||
|
||||
|
||||
def isimage(x): |
||||
if not isinstance(x,torch.Tensor): |
||||
return False |
||||
return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1) |
||||
|
||||
|
||||
def exists(x): |
||||
return x is not None |
||||
|
||||
|
||||
def default(val, d): |
||||
if exists(val): |
||||
return val |
||||
return d() if isfunction(d) else d |
||||
|
||||
|
||||
def mean_flat(tensor): |
||||
""" |
||||
https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86 |
||||
Take the mean over all non-batch dimensions. |
||||
""" |
||||
return tensor.mean(dim=list(range(1, len(tensor.shape)))) |
||||
|
||||
|
||||
def count_params(model, verbose=False): |
||||
total_params = sum(p.numel() for p in model.parameters()) |
||||
if verbose: |
||||
print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.") |
||||
return total_params |
||||
|
||||
|
||||
def instantiate_from_config(config): |
||||
if not "target" in config: |
||||
if config == '__is_first_stage__': |
||||
return None |
||||
elif config == "__is_unconditional__": |
||||
return None |
||||
raise KeyError("Expected key `target` to instantiate.") |
||||
return get_obj_from_str(config["target"])(**config.get("params", dict())) |
||||
|
||||
|
||||
def get_obj_from_str(string, reload=False): |
||||
module, cls = string.rsplit(".", 1) |
||||
if reload: |
||||
module_imp = importlib.import_module(module) |
||||
importlib.reload(module_imp) |
||||
return getattr(importlib.import_module(module, package=None), cls) |
||||
|
||||
|
||||
class AdamWwithEMAandWings(optim.Optimizer): |
||||
# credit to https://gist.github.com/crowsonkb/65f7265353f403714fce3b2595e0b298 |
||||
def __init__(self, params, lr=1.e-3, betas=(0.9, 0.999), eps=1.e-8, # TODO: check hyperparameters before using |
||||
weight_decay=1.e-2, amsgrad=False, ema_decay=0.9999, # ema decay to match previous code |
||||
ema_power=1., param_names=()): |
||||
"""AdamW that saves EMA versions of the parameters.""" |
||||
if not 0.0 <= lr: |
||||
raise ValueError("Invalid learning rate: {}".format(lr)) |
||||
if not 0.0 <= eps: |
||||
raise ValueError("Invalid epsilon value: {}".format(eps)) |
||||
if not 0.0 <= betas[0] < 1.0: |
||||
raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0])) |
||||
if not 0.0 <= betas[1] < 1.0: |
||||
raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1])) |
||||
if not 0.0 <= weight_decay: |
||||
raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) |
||||
if not 0.0 <= ema_decay <= 1.0: |
||||
raise ValueError("Invalid ema_decay value: {}".format(ema_decay)) |
||||
defaults = dict(lr=lr, betas=betas, eps=eps, |
||||
weight_decay=weight_decay, amsgrad=amsgrad, ema_decay=ema_decay, |
||||
ema_power=ema_power, param_names=param_names) |
||||
super().__init__(params, defaults) |
||||
|
||||
def __setstate__(self, state): |
||||
super().__setstate__(state) |
||||
for group in self.param_groups: |
||||
group.setdefault('amsgrad', False) |
||||
|
||||
@torch.no_grad() |
||||
def step(self, closure=None): |
||||
"""Performs a single optimization step. |
||||
Args: |
||||
closure (callable, optional): A closure that reevaluates the model |
||||
and returns the loss. |
||||
""" |
||||
loss = None |
||||
if closure is not None: |
||||
with torch.enable_grad(): |
||||
loss = closure() |
||||
|
||||
for group in self.param_groups: |
||||
params_with_grad = [] |
||||
grads = [] |
||||
exp_avgs = [] |
||||
exp_avg_sqs = [] |
||||
ema_params_with_grad = [] |
||||
state_sums = [] |
||||
max_exp_avg_sqs = [] |
||||
state_steps = [] |
||||
amsgrad = group['amsgrad'] |
||||
beta1, beta2 = group['betas'] |
||||
ema_decay = group['ema_decay'] |
||||
ema_power = group['ema_power'] |
||||
|
||||
for p in group['params']: |
||||
if p.grad is None: |
||||
continue |
||||
params_with_grad.append(p) |
||||
if p.grad.is_sparse: |
||||
raise RuntimeError('AdamW does not support sparse gradients') |
||||
grads.append(p.grad) |
||||
|
||||
state = self.state[p] |
||||
|
||||
# State initialization |
||||
if len(state) == 0: |
||||
state['step'] = 0 |
||||
# Exponential moving average of gradient values |
||||
state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format) |
||||
# Exponential moving average of squared gradient values |
||||
state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format) |
||||
if amsgrad: |
||||
# Maintains max of all exp. moving avg. of sq. grad. values |
||||
state['max_exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format) |
||||
# Exponential moving average of parameter values |
||||
state['param_exp_avg'] = p.detach().float().clone() |
||||
|
||||
exp_avgs.append(state['exp_avg']) |
||||
exp_avg_sqs.append(state['exp_avg_sq']) |
||||
ema_params_with_grad.append(state['param_exp_avg']) |
||||
|
||||
if amsgrad: |
||||
max_exp_avg_sqs.append(state['max_exp_avg_sq']) |
||||
|
||||
# update the steps for each param group update |
||||
state['step'] += 1 |
||||
# record the step after step update |
||||
state_steps.append(state['step']) |
||||
|
||||
optim._functional.adamw(params_with_grad, |
||||
grads, |
||||
exp_avgs, |
||||
exp_avg_sqs, |
||||
max_exp_avg_sqs, |
||||
state_steps, |
||||
amsgrad=amsgrad, |
||||
beta1=beta1, |
||||
beta2=beta2, |
||||
lr=group['lr'], |
||||
weight_decay=group['weight_decay'], |
||||
eps=group['eps'], |
||||
maximize=False) |
||||
|
||||
cur_ema_decay = min(ema_decay, 1 - state['step'] ** -ema_power) |
||||
for param, ema_param in zip(params_with_grad, ema_params_with_grad): |
||||
ema_param.mul_(cur_ema_decay).add_(param.float(), alpha=1 - cur_ema_decay) |
||||
|
||||
return loss |
||||
@ -0,0 +1,114 @@
|
||||
import k_diffusion.sampling |
||||
import k_diffusion.external |
||||
import torch |
||||
import contextlib |
||||
|
||||
class CFGDenoiser(torch.nn.Module): |
||||
def __init__(self, model): |
||||
super().__init__() |
||||
self.inner_model = model |
||||
|
||||
def forward(self, x, sigma, uncond, cond, cond_scale): |
||||
if len(uncond[0]) == len(cond[0]) and x.shape[0] * x.shape[2] * x.shape[3] <= (96 * 96): #TODO check memory instead |
||||
x_in = torch.cat([x] * 2) |
||||
sigma_in = torch.cat([sigma] * 2) |
||||
cond_in = torch.cat([uncond, cond]) |
||||
uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2) |
||||
else: |
||||
cond = self.inner_model(x, sigma, cond=cond) |
||||
uncond = self.inner_model(x, sigma, cond=uncond) |
||||
return uncond + (cond - uncond) * cond_scale |
||||
|
||||
|
||||
def simple_scheduler(model, steps): |
||||
sigs = [] |
||||
ss = len(model.sigmas) / steps |
||||
for x in range(steps): |
||||
sigs += [float(model.sigmas[-(1 + int(x * ss))])] |
||||
sigs += [0.0] |
||||
return torch.FloatTensor(sigs) |
||||
|
||||
|
||||
class KSampler: |
||||
SCHEDULERS = ["karras", "normal", "simple"] |
||||
SAMPLERS = ["sample_euler", "sample_euler_ancestral", "sample_heun", "sample_dpm_2", "sample_dpm_2_ancestral", |
||||
"sample_lms", "sample_dpm_fast", "sample_dpm_adaptive", "sample_dpmpp_2s_ancestral", "sample_dpmpp_sde", |
||||
"sample_dpmpp_2m"] |
||||
|
||||
def __init__(self, model, steps, device, sampler=None, scheduler=None, denoise=None): |
||||
self.model = model |
||||
if self.model.parameterization == "v": |
||||
self.model_wrap = k_diffusion.external.CompVisVDenoiser(self.model, quantize=True) |
||||
else: |
||||
self.model_wrap = k_diffusion.external.CompVisDenoiser(self.model, quantize=True) |
||||
self.model_k = CFGDenoiser(self.model_wrap) |
||||
self.device = device |
||||
if scheduler not in self.SCHEDULERS: |
||||
scheduler = self.SCHEDULERS[0] |
||||
if sampler not in self.SAMPLERS: |
||||
sampler = self.SAMPLERS[0] |
||||
self.scheduler = scheduler |
||||
self.sampler = sampler |
||||
self.sigma_min=float(self.model_wrap.sigmas[0]) |
||||
self.sigma_max=float(self.model_wrap.sigmas[-1]) |
||||
self.set_steps(steps, denoise) |
||||
|
||||
def _calculate_sigmas(self, steps): |
||||
sigmas = None |
||||
|
||||
discard_penultimate_sigma = False |
||||
if self.sampler in ['sample_dpm_2', 'sample_dpm_2_ancestral']: |
||||
steps += 1 |
||||
discard_penultimate_sigma = True |
||||
|
||||
if self.scheduler == "karras": |
||||
sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=self.sigma_min, sigma_max=self.sigma_max, device=self.device) |
||||
elif self.scheduler == "normal": |
||||
sigmas = self.model_wrap.get_sigmas(steps).to(self.device) |
||||
elif self.scheduler == "simple": |
||||
sigmas = simple_scheduler(self.model_wrap, steps).to(self.device) |
||||
else: |
||||
print("error invalid scheduler", self.scheduler) |
||||
|
||||
if discard_penultimate_sigma: |
||||
sigmas = torch.cat([sigmas[:-2], sigmas[-1:]]) |
||||
return sigmas |
||||
|
||||
def set_steps(self, steps, denoise=None): |
||||
self.steps = steps |
||||
if denoise is None: |
||||
self.sigmas = self._calculate_sigmas(steps) |
||||
else: |
||||
new_steps = int(steps/denoise) |
||||
sigmas = self._calculate_sigmas(new_steps) |
||||
self.sigmas = sigmas[-(steps + 1):] |
||||
|
||||
|
||||
def sample(self, noise, positive, negative, cfg, latent_image=None, start_step=None, last_step=None): |
||||
sigmas = self.sigmas |
||||
sigma_min = self.sigma_min |
||||
|
||||
if last_step is not None: |
||||
sigma_min = sigmas[last_step] |
||||
sigmas = sigmas[:last_step + 1] |
||||
if start_step is not None: |
||||
sigmas = sigmas[start_step:] |
||||
|
||||
|
||||
noise *= sigmas[0] |
||||
if latent_image is not None: |
||||
noise += latent_image |
||||
|
||||
if self.model.model.diffusion_model.dtype == torch.float16: |
||||
precision_scope = torch.autocast |
||||
else: |
||||
precision_scope = contextlib.nullcontext |
||||
|
||||
with precision_scope(self.device): |
||||
if self.sampler == "sample_dpm_fast": |
||||
samples = k_diffusion.sampling.sample_dpm_fast(self.model_k, noise, sigma_min, sigmas[0], self.steps, extra_args={"cond":positive, "uncond":negative, "cond_scale": cfg}) |
||||
elif self.sampler == "sample_dpm_adaptive": |
||||
samples = k_diffusion.sampling.sample_dpm_adaptive(self.model_k, noise, sigma_min, sigmas[0], extra_args={"cond":positive, "uncond":negative, "cond_scale": cfg}) |
||||
else: |
||||
samples = getattr(k_diffusion.sampling, self.sampler)(self.model_k, noise, sigmas, extra_args={"cond":positive, "uncond":negative, "cond_scale": cfg}) |
||||
return samples.to(torch.float32) |
||||
@ -0,0 +1,124 @@
|
||||
import torch |
||||
|
||||
import sd1_clip |
||||
import sd2_clip |
||||
from ldm.util import instantiate_from_config |
||||
from ldm.models.autoencoder import AutoencoderKL |
||||
from omegaconf import OmegaConf |
||||
|
||||
|
||||
def load_model_from_config(config, ckpt, verbose=False, load_state_dict_to=[]): |
||||
print(f"Loading model from {ckpt}") |
||||
|
||||
if ckpt.lower().endswith(".safetensors"): |
||||
import safetensors.torch |
||||
sd = safetensors.torch.load_file(ckpt, device="cpu") |
||||
else: |
||||
pl_sd = torch.load(ckpt, map_location="cpu") |
||||
if "global_step" in pl_sd: |
||||
print(f"Global Step: {pl_sd['global_step']}") |
||||
sd = pl_sd["state_dict"] |
||||
model = instantiate_from_config(config.model) |
||||
|
||||
m, u = model.load_state_dict(sd, strict=False) |
||||
|
||||
k = list(sd.keys()) |
||||
for x in k: |
||||
# print(x) |
||||
if x.startswith("cond_stage_model.transformer.") and not x.startswith("cond_stage_model.transformer.text_model."): |
||||
y = x.replace("cond_stage_model.transformer.", "cond_stage_model.transformer.text_model.") |
||||
sd[y] = sd.pop(x) |
||||
|
||||
for x in load_state_dict_to: |
||||
x.load_state_dict(sd, strict=False) |
||||
|
||||
if len(m) > 0 and verbose: |
||||
print("missing keys:") |
||||
print(m) |
||||
if len(u) > 0 and verbose: |
||||
print("unexpected keys:") |
||||
print(u) |
||||
|
||||
model.eval() |
||||
return model |
||||
|
||||
|
||||
|
||||
class CLIP: |
||||
def __init__(self, config): |
||||
self.target_clip = config["target"] |
||||
if self.target_clip == "ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder": |
||||
clip = sd2_clip.SD2ClipModel |
||||
tokenizer = sd2_clip.SD2Tokenizer |
||||
elif self.target_clip == "ldm.modules.encoders.modules.FrozenCLIPEmbedder": |
||||
clip = sd1_clip.SD1ClipModel |
||||
tokenizer = sd1_clip.SD1Tokenizer |
||||
if "params" in config: |
||||
self.cond_stage_model = clip(**(config["params"])) |
||||
else: |
||||
self.cond_stage_model = clip() |
||||
self.tokenizer = tokenizer() |
||||
|
||||
def encode(self, text): |
||||
tokens = self.tokenizer.tokenize_with_weights(text) |
||||
cond = self.cond_stage_model.encode_token_weights(tokens) |
||||
return cond |
||||
|
||||
|
||||
class VAE: |
||||
def __init__(self, ckpt_path=None, scale_factor=0.18215, device="cuda", config=None): |
||||
if config is None: |
||||
#default SD1.x/SD2.x VAE parameters |
||||
ddconfig = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0} |
||||
self.first_stage_model = AutoencoderKL(ddconfig, {'target': 'torch.nn.Identity'}, 4, monitor="val/rec_loss", ckpt_path=ckpt_path) |
||||
else: |
||||
self.first_stage_model = AutoencoderKL(**(config['params']), ckpt_path=ckpt_path) |
||||
self.first_stage_model = self.first_stage_model.eval() |
||||
self.scale_factor = scale_factor |
||||
self.device = device |
||||
|
||||
def decode(self, samples): |
||||
self.first_stage_model = self.first_stage_model.to(self.device) |
||||
samples = samples.to(self.device) |
||||
pixel_samples = self.first_stage_model.decode(1. / self.scale_factor * samples) |
||||
pixel_samples = torch.clamp((pixel_samples + 1.0) / 2.0, min=0.0, max=1.0) |
||||
self.first_stage_model = self.first_stage_model.cpu() |
||||
pixel_samples = pixel_samples.cpu().movedim(1,-1) |
||||
return pixel_samples |
||||
|
||||
def encode(self, pixel_samples): |
||||
self.first_stage_model = self.first_stage_model.to(self.device) |
||||
pixel_samples = pixel_samples.movedim(-1,1).to(self.device) |
||||
samples = self.first_stage_model.encode(2. * pixel_samples - 1.).sample() * self.scale_factor |
||||
self.first_stage_model = self.first_stage_model.cpu() |
||||
samples = samples.cpu() |
||||
return samples |
||||
|
||||
|
||||
def load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True): |
||||
config = OmegaConf.load(config_path) |
||||
model_config_params = config['model']['params'] |
||||
clip_config = model_config_params['cond_stage_config'] |
||||
scale_factor = model_config_params['scale_factor'] |
||||
vae_config = model_config_params['first_stage_config'] |
||||
|
||||
clip = None |
||||
vae = None |
||||
|
||||
class WeightsLoader(torch.nn.Module): |
||||
pass |
||||
|
||||
w = WeightsLoader() |
||||
load_state_dict_to = [] |
||||
if output_vae: |
||||
vae = VAE(scale_factor=scale_factor, config=vae_config) |
||||
w.first_stage_model = vae.first_stage_model |
||||
load_state_dict_to = [w] |
||||
|
||||
if output_clip: |
||||
clip = CLIP(config=clip_config) |
||||
w.cond_stage_model = clip.cond_stage_model |
||||
load_state_dict_to = [w] |
||||
|
||||
model = load_model_from_config(config, ckpt_path, verbose=False, load_state_dict_to=load_state_dict_to) |
||||
return (model, clip, vae) |
||||
@ -0,0 +1,178 @@
|
||||
import os |
||||
|
||||
from transformers import CLIPTokenizer, CLIPTextModel, CLIPTextConfig |
||||
import torch |
||||
|
||||
class ClipTokenWeightEncoder: |
||||
def encode_token_weights(self, token_weight_pairs): |
||||
z_empty = self.encode(self.empty_tokens) |
||||
output = [] |
||||
for x in token_weight_pairs: |
||||
tokens = [list(map(lambda a: a[0], x))] |
||||
z = self.encode(tokens) |
||||
for i in range(len(z)): |
||||
for j in range(len(z[i])): |
||||
weight = x[j][1] |
||||
z[i][j] = (z[i][j] - z_empty[0][j]) * weight + z_empty[0][j] |
||||
output += [z] |
||||
if (len(output) == 0): |
||||
return self.encode(self.empty_tokens) |
||||
return torch.cat(output, dim=-2) |
||||
|
||||
class SD1ClipModel(torch.nn.Module, ClipTokenWeightEncoder): |
||||
"""Uses the CLIP transformer encoder for text (from huggingface)""" |
||||
LAYERS = [ |
||||
"last", |
||||
"pooled", |
||||
"hidden" |
||||
] |
||||
def __init__(self, version="openai/clip-vit-large-patch14", device="cpu", max_length=77, |
||||
freeze=True, layer="last", layer_idx=None, textmodel_json_config=None, textmodel_path=None): # clip-vit-base-patch32 |
||||
super().__init__() |
||||
assert layer in self.LAYERS |
||||
if textmodel_path is not None: |
||||
self.transformer = CLIPTextModel.from_pretrained(textmodel_path) |
||||
else: |
||||
if textmodel_json_config is None: |
||||
textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_clip_config.json") |
||||
config = CLIPTextConfig.from_json_file(textmodel_json_config) |
||||
self.transformer = CLIPTextModel(config) |
||||
|
||||
self.device = device |
||||
self.max_length = max_length |
||||
if freeze: |
||||
self.freeze() |
||||
self.layer = layer |
||||
self.layer_idx = None |
||||
self.empty_tokens = [[49406] + [49407] * 76] |
||||
if layer == "hidden": |
||||
assert layer_idx is not None |
||||
assert abs(layer_idx) <= 12 |
||||
self.clip_layer(layer_idx) |
||||
|
||||
def freeze(self): |
||||
self.transformer = self.transformer.eval() |
||||
#self.train = disabled_train |
||||
for param in self.parameters(): |
||||
param.requires_grad = False |
||||
|
||||
def clip_layer(self, layer_idx): |
||||
if abs(layer_idx) >= 12: |
||||
self.layer = "last" |
||||
else: |
||||
self.layer = "hidden" |
||||
self.layer_idx = layer_idx |
||||
|
||||
def forward(self, tokens): |
||||
tokens = torch.LongTensor(tokens).to(self.device) |
||||
outputs = self.transformer(input_ids=tokens, output_hidden_states=self.layer=="hidden") |
||||
|
||||
if self.layer == "last": |
||||
z = outputs.last_hidden_state |
||||
elif self.layer == "pooled": |
||||
z = outputs.pooler_output[:, None, :] |
||||
else: |
||||
z = outputs.hidden_states[self.layer_idx] |
||||
z = self.transformer.text_model.final_layer_norm(z) |
||||
|
||||
return z |
||||
|
||||
def encode(self, tokens): |
||||
return self(tokens) |
||||
|
||||
def parse_parentheses(string): |
||||
result = [] |
||||
current_item = "" |
||||
nesting_level = 0 |
||||
for char in string: |
||||
if char == "(": |
||||
if nesting_level == 0: |
||||
if current_item: |
||||
result.append(current_item) |
||||
current_item = "(" |
||||
else: |
||||
current_item = "(" |
||||
else: |
||||
current_item += char |
||||
nesting_level += 1 |
||||
elif char == ")": |
||||
nesting_level -= 1 |
||||
if nesting_level == 0: |
||||
result.append(current_item + ")") |
||||
current_item = "" |
||||
else: |
||||
current_item += char |
||||
else: |
||||
current_item += char |
||||
if current_item: |
||||
result.append(current_item) |
||||
return result |
||||
|
||||
def token_weights(string, current_weight): |
||||
a = parse_parentheses(string) |
||||
out = [] |
||||
for x in a: |
||||
weight = current_weight |
||||
if len(x) >= 2 and x[-1] == ')' and x[0] == '(': |
||||
x = x[1:-1] |
||||
xx = x.rfind(":") |
||||
weight *= 1.1 |
||||
if xx > 0: |
||||
try: |
||||
weight = float(x[xx+1:]) |
||||
x = x[:xx] |
||||
except: |
||||
pass |
||||
out += token_weights(x, weight) |
||||
else: |
||||
out += [(x, current_weight)] |
||||
return out |
||||
|
||||
def escape_important(text): |
||||
text = text.replace("\\)", "\0\1") |
||||
text = text.replace("\\(", "\0\2") |
||||
return text |
||||
|
||||
def unescape_important(text): |
||||
text = text.replace("\0\1", ")") |
||||
text = text.replace("\0\2", "(") |
||||
return text |
||||
|
||||
class SD1Tokenizer: |
||||
def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True): |
||||
if tokenizer_path is None: |
||||
tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_tokenizer") |
||||
self.tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path) |
||||
self.max_length = max_length |
||||
empty = self.tokenizer('')["input_ids"] |
||||
self.start_token = empty[0] |
||||
self.end_token = empty[1] |
||||
self.pad_with_end = pad_with_end |
||||
vocab = self.tokenizer.get_vocab() |
||||
self.inv_vocab = {v: k for k, v in vocab.items()} |
||||
|
||||
def tokenize_with_weights(self, text): |
||||
text = escape_important(text) |
||||
parsed_weights = token_weights(text, 1.0) |
||||
|
||||
tokens = [] |
||||
for t in parsed_weights: |
||||
tt = self.tokenizer(unescape_important(t[0]))["input_ids"][1:-1] |
||||
for x in tt: |
||||
tokens += [(x, t[1])] |
||||
|
||||
out_tokens = [] |
||||
for x in range(0, len(tokens), self.max_length - 2): |
||||
o_token = [(self.start_token, 1.0)] + tokens[x:min(self.max_length - 2 + x, len(tokens))] |
||||
o_token += [(self.end_token, 1.0)] |
||||
if self.pad_with_end: |
||||
o_token +=[(self.end_token, 1.0)] * (self.max_length - len(o_token)) |
||||
else: |
||||
o_token +=[(0, 1.0)] * (self.max_length - len(o_token)) |
||||
|
||||
out_tokens += [o_token] |
||||
|
||||
return out_tokens |
||||
|
||||
def untokenize(self, token_weight_pair): |
||||
return list(map(lambda a: (a, self.inv_vocab[a[0]]), token_weight_pair)) |
||||
@ -0,0 +1,25 @@
|
||||
{ |
||||
"_name_or_path": "openai/clip-vit-large-patch14", |
||||
"architectures": [ |
||||
"CLIPTextModel" |
||||
], |
||||
"attention_dropout": 0.0, |
||||
"bos_token_id": 0, |
||||
"dropout": 0.0, |
||||
"eos_token_id": 2, |
||||
"hidden_act": "quick_gelu", |
||||
"hidden_size": 768, |
||||
"initializer_factor": 1.0, |
||||
"initializer_range": 0.02, |
||||
"intermediate_size": 3072, |
||||
"layer_norm_eps": 1e-05, |
||||
"max_position_embeddings": 77, |
||||
"model_type": "clip_text_model", |
||||
"num_attention_heads": 12, |
||||
"num_hidden_layers": 12, |
||||
"pad_token_id": 1, |
||||
"projection_dim": 768, |
||||
"torch_dtype": "float32", |
||||
"transformers_version": "4.24.0", |
||||
"vocab_size": 49408 |
||||
} |
||||
File diff suppressed because it is too large
Load Diff
@ -0,0 +1,24 @@
|
||||
{ |
||||
"bos_token": { |
||||
"content": "<|startoftext|>", |
||||
"lstrip": false, |
||||
"normalized": true, |
||||
"rstrip": false, |
||||
"single_word": false |
||||
}, |
||||
"eos_token": { |
||||
"content": "<|endoftext|>", |
||||
"lstrip": false, |
||||
"normalized": true, |
||||
"rstrip": false, |
||||
"single_word": false |
||||
}, |
||||
"pad_token": "<|endoftext|>", |
||||
"unk_token": { |
||||
"content": "<|endoftext|>", |
||||
"lstrip": false, |
||||
"normalized": true, |
||||
"rstrip": false, |
||||
"single_word": false |
||||
} |
||||
} |
||||
@ -0,0 +1,34 @@
|
||||
{ |
||||
"add_prefix_space": false, |
||||
"bos_token": { |
||||
"__type": "AddedToken", |
||||
"content": "<|startoftext|>", |
||||
"lstrip": false, |
||||
"normalized": true, |
||||
"rstrip": false, |
||||
"single_word": false |
||||
}, |
||||
"do_lower_case": true, |
||||
"eos_token": { |
||||
"__type": "AddedToken", |
||||
"content": "<|endoftext|>", |
||||
"lstrip": false, |
||||
"normalized": true, |
||||
"rstrip": false, |
||||
"single_word": false |
||||
}, |
||||
"errors": "replace", |
||||
"model_max_length": 77, |
||||
"name_or_path": "openai/clip-vit-large-patch14", |
||||
"pad_token": "<|endoftext|>", |
||||
"special_tokens_map_file": "./special_tokens_map.json", |
||||
"tokenizer_class": "CLIPTokenizer", |
||||
"unk_token": { |
||||
"__type": "AddedToken", |
||||
"content": "<|endoftext|>", |
||||
"lstrip": false, |
||||
"normalized": true, |
||||
"rstrip": false, |
||||
"single_word": false |
||||
} |
||||
} |
||||
File diff suppressed because it is too large
Load Diff
@ -0,0 +1,89 @@
|
||||
import sd1_clip |
||||
import open_clip |
||||
import torch |
||||
|
||||
class SD2ClipModel(torch.nn.Module, sd1_clip.ClipTokenWeightEncoder): |
||||
""" |
||||
Uses the OpenCLIP transformer encoder for text |
||||
""" |
||||
LAYERS = [ |
||||
#"pooled", |
||||
"last", |
||||
"penultimate", |
||||
"hidden" |
||||
] |
||||
#version="laion2b_s32b_b79k" |
||||
def __init__(self, arch="ViT-H-14", device="cpu", max_length=77, |
||||
freeze=True, layer="penultimate", layer_idx=None): |
||||
super().__init__() |
||||
assert layer in self.LAYERS |
||||
model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu')) |
||||
del model.visual |
||||
self.model = model |
||||
|
||||
self.device = device |
||||
self.max_length = max_length |
||||
self.empty_tokens = [[49406] + [49407] + [0] * 75] |
||||
if freeze: |
||||
self.freeze() |
||||
self.layer = layer |
||||
if self.layer == "last": |
||||
self.layer_idx = 0 |
||||
elif self.layer == "penultimate": |
||||
self.layer_idx = 1 |
||||
elif self.layer == "hidden": |
||||
assert layer_idx is not None |
||||
assert abs(layer_idx) < 24 |
||||
self.clip_layer(layer_idx) |
||||
else: |
||||
raise NotImplementedError() |
||||
|
||||
def freeze(self): |
||||
self.model = self.model.eval() |
||||
for param in self.parameters(): |
||||
param.requires_grad = False |
||||
|
||||
def clip_layer(self, layer_idx): |
||||
#layer_idx should have the same logic as the one for SD1 |
||||
if abs(layer_idx) >= 24: |
||||
self.layer_idx = 0 |
||||
else: |
||||
if layer_idx < 0: |
||||
self.layer_idx = -(layer_idx + 1) |
||||
else: |
||||
self.layer_idx = 24 - (layer_idx + 1) |
||||
|
||||
def forward(self, tokens): |
||||
tokens = torch.LongTensor(tokens).to(self.device) |
||||
z = self.encode_with_transformer(tokens) |
||||
return z |
||||
|
||||
def encode_with_transformer(self, tokens): |
||||
x = self.model.token_embedding(tokens) # [batch_size, n_ctx, d_model] |
||||
x = x + self.model.positional_embedding |
||||
x = x.permute(1, 0, 2) # NLD -> LND |
||||
x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask) |
||||
x = x.permute(1, 0, 2) # LND -> NLD |
||||
x = self.model.ln_final(x) |
||||
return x |
||||
|
||||
def text_transformer_forward(self, x: torch.Tensor, attn_mask = None): |
||||
for i, r in enumerate(self.model.transformer.resblocks): |
||||
if i == len(self.model.transformer.resblocks) - self.layer_idx: |
||||
break |
||||
if self.model.transformer.grad_checkpointing and not torch.jit.is_scripting(): |
||||
x = checkpoint(r, x, attn_mask) |
||||
else: |
||||
x = r(x, attn_mask=attn_mask) |
||||
return x |
||||
|
||||
def encode(self, tokens): |
||||
return self(tokens) |
||||
|
||||
|
||||
|
||||
class SD2Tokenizer(sd1_clip.SD1Tokenizer): |
||||
def __init__(self, tokenizer_path=None): |
||||
super().__init__(tokenizer_path, pad_with_end=False) |
||||
|
||||
|
||||
|
After Width: | Height: | Size: 115 KiB |
@ -0,0 +1,314 @@
|
||||
import os |
||||
import sys |
||||
import copy |
||||
import json |
||||
import threading |
||||
import queue |
||||
import traceback |
||||
|
||||
import torch |
||||
|
||||
import nodes |
||||
|
||||
|
||||
def recursive_execute(prompt, outputs, current_item, extra_data={}): |
||||
unique_id = current_item |
||||
inputs = prompt[unique_id]['inputs'] |
||||
class_type = prompt[unique_id]['class_type'] |
||||
c_obj = nodes.NODE_CLASS_MAPPINGS[class_type] |
||||
valid_inputs = c_obj.INPUT_TYPES() |
||||
if unique_id in outputs: |
||||
return [] |
||||
|
||||
executed = [] |
||||
|
||||
for x in inputs: |
||||
input_data = inputs[x] |
||||
|
||||
if isinstance(input_data, list): |
||||
input_unique_id = input_data[0] |
||||
output_index = input_data[1] |
||||
if input_unique_id not in outputs: |
||||
executed += recursive_execute(prompt, outputs, input_unique_id, extra_data) |
||||
|
||||
input_data_all = {} |
||||
for x in inputs: |
||||
input_data = inputs[x] |
||||
if isinstance(input_data, list): |
||||
input_unique_id = input_data[0] |
||||
output_index = input_data[1] |
||||
obj = outputs[input_unique_id][output_index] |
||||
input_data_all[x] = obj |
||||
else: |
||||
if ("required" in valid_inputs and x in valid_inputs["required"]) or ("optional" in valid_inputs and x in valid_inputs["optional"]): |
||||
input_data_all[x] = input_data |
||||
|
||||
|
||||
obj = c_obj() |
||||
if "hidden" in valid_inputs: |
||||
h = valid_inputs["hidden"] |
||||
for x in h: |
||||
if h[x] == "PROMPT": |
||||
input_data_all[x] = prompt |
||||
if h[x] == "EXTRA_PNGINFO": |
||||
if "extra_pnginfo" in extra_data: |
||||
input_data_all[x] = extra_data['extra_pnginfo'] |
||||
|
||||
outputs[unique_id] = getattr(obj, obj.FUNCTION)(**input_data_all) |
||||
return executed + [unique_id] |
||||
|
||||
def recursive_output_delete_if_changed(prompt, old_prompt, outputs, current_item): |
||||
unique_id = current_item |
||||
inputs = prompt[unique_id]['inputs'] |
||||
class_type = prompt[unique_id]['class_type'] |
||||
|
||||
if unique_id not in outputs: |
||||
return True |
||||
|
||||
to_delete = False |
||||
if unique_id not in old_prompt: |
||||
to_delete = True |
||||
elif inputs == old_prompt[unique_id]['inputs']: |
||||
for x in inputs: |
||||
input_data = inputs[x] |
||||
|
||||
if isinstance(input_data, list): |
||||
input_unique_id = input_data[0] |
||||
output_index = input_data[1] |
||||
if input_unique_id in outputs: |
||||
to_delete = recursive_output_delete_if_changed(prompt, old_prompt, outputs, input_unique_id) |
||||
else: |
||||
to_delete = True |
||||
if to_delete: |
||||
break |
||||
else: |
||||
to_delete = True |
||||
|
||||
if to_delete: |
||||
print("deleted", unique_id) |
||||
d = outputs.pop(unique_id) |
||||
del d |
||||
return to_delete |
||||
|
||||
class PromptExecutor: |
||||
def __init__(self): |
||||
self.outputs = {} |
||||
self.old_prompt = {} |
||||
|
||||
def execute(self, prompt, extra_data={}): |
||||
with torch.no_grad(): |
||||
for x in prompt: |
||||
recursive_output_delete_if_changed(prompt, self.old_prompt, self.outputs, x) |
||||
|
||||
current_outputs = set(self.outputs.keys()) |
||||
executed = [] |
||||
try: |
||||
for x in prompt: |
||||
class_ = nodes.NODE_CLASS_MAPPINGS[prompt[x]['class_type']] |
||||
if hasattr(class_, 'OUTPUT_NODE'): |
||||
if class_.OUTPUT_NODE == True: |
||||
valid = False |
||||
try: |
||||
m = validate_inputs(prompt, x) |
||||
valid = m[0] |
||||
except: |
||||
valid = False |
||||
if valid: |
||||
executed += recursive_execute(prompt, self.outputs, x, extra_data) |
||||
except Exception as e: |
||||
print(traceback.format_exc()) |
||||
to_delete = [] |
||||
for o in self.outputs: |
||||
if o not in current_outputs: |
||||
to_delete += [o] |
||||
if o in self.old_prompt: |
||||
d = self.old_prompt.pop(o) |
||||
del d |
||||
for o in to_delete: |
||||
d = self.outputs.pop(o) |
||||
del d |
||||
else: |
||||
executed = set(executed) |
||||
for x in executed: |
||||
self.old_prompt[x] = copy.deepcopy(prompt[x]) |
||||
|
||||
def validate_inputs(prompt, item): |
||||
unique_id = item |
||||
inputs = prompt[unique_id]['inputs'] |
||||
class_type = prompt[unique_id]['class_type'] |
||||
obj_class = nodes.NODE_CLASS_MAPPINGS[class_type] |
||||
|
||||
class_inputs = obj_class.INPUT_TYPES() |
||||
required_inputs = class_inputs['required'] |
||||
for x in required_inputs: |
||||
if x not in inputs: |
||||
return (False, "Required input is missing. {}, {}".format(class_type, x)) |
||||
val = inputs[x] |
||||
info = required_inputs[x] |
||||
type_input = info[0] |
||||
if isinstance(val, list): |
||||
if len(val) != 2: |
||||
return (False, "Bad Input. {}, {}".format(class_type, x)) |
||||
o_id = val[0] |
||||
o_class_type = prompt[o_id]['class_type'] |
||||
r = nodes.NODE_CLASS_MAPPINGS[o_class_type].RETURN_TYPES |
||||
if r[val[1]] != type_input: |
||||
return (False, "Return type mismatch. {}, {}".format(class_type, x)) |
||||
r = validate_inputs(prompt, o_id) |
||||
if r[0] == False: |
||||
return r |
||||
else: |
||||
if type_input == "INT": |
||||
val = int(val) |
||||
inputs[x] = val |
||||
if type_input == "FLOAT": |
||||
val = float(val) |
||||
inputs[x] = val |
||||
if type_input == "STRING": |
||||
val = str(val) |
||||
inputs[x] = val |
||||
|
||||
if len(info) > 1: |
||||
if "min" in info[1] and val < info[1]["min"]: |
||||
return (False, "Value smaller than min. {}, {}".format(class_type, x)) |
||||
if "max" in info[1] and val > info[1]["max"]: |
||||
return (False, "Value bigger than max. {}, {}".format(class_type, x)) |
||||
|
||||
if isinstance(type_input, list): |
||||
if val not in type_input: |
||||
return (False, "Value not in list. {}, {}".format(class_type, x)) |
||||
return (True, "") |
||||
|
||||
def validate_prompt(prompt): |
||||
outputs = set() |
||||
for x in prompt: |
||||
class_ = nodes.NODE_CLASS_MAPPINGS[prompt[x]['class_type']] |
||||
if hasattr(class_, 'OUTPUT_NODE') and class_.OUTPUT_NODE == True: |
||||
outputs.add(x) |
||||
|
||||
if len(outputs) == 0: |
||||
return (False, "Prompt has no outputs") |
||||
|
||||
good_outputs = set() |
||||
for o in outputs: |
||||
valid = False |
||||
reason = "" |
||||
try: |
||||
m = validate_inputs(prompt, o) |
||||
valid = m[0] |
||||
reason = m[1] |
||||
except: |
||||
valid = False |
||||
reason = "Parsing error" |
||||
|
||||
if valid == True: |
||||
good_outputs.add(x) |
||||
else: |
||||
print("Failed to validate prompt for output {} {}".format(o, reason)) |
||||
print("output will be ignored") |
||||
|
||||
if len(good_outputs) == 0: |
||||
return (False, "Prompt has no properly connected outputs") |
||||
|
||||
return (True, "") |
||||
|
||||
def prompt_worker(q): |
||||
e = PromptExecutor() |
||||
while True: |
||||
item = q.get() |
||||
e.execute(item[-2], item[-1]) |
||||
q.task_done() |
||||
|
||||
|
||||
from http.server import BaseHTTPRequestHandler, HTTPServer |
||||
|
||||
class PromptServer(BaseHTTPRequestHandler): |
||||
def _set_headers(self, code=200, ct='text/html'): |
||||
self.send_response(code) |
||||
self.send_header('Content-type', ct) |
||||
self.end_headers() |
||||
def log_message(self, format, *args): |
||||
pass |
||||
def do_GET(self): |
||||
if self.path == "/prompt": |
||||
self._set_headers(ct='application/json') |
||||
prompt_info = {} |
||||
exec_info = {} |
||||
exec_info['queue_remaining'] = self.server.prompt_queue.unfinished_tasks |
||||
prompt_info['exec_info'] = exec_info |
||||
self.wfile.write(json.dumps(prompt_info).encode('utf-8')) |
||||
elif self.path == "/object_info": |
||||
self._set_headers(ct='application/json') |
||||
out = {} |
||||
for x in nodes.NODE_CLASS_MAPPINGS: |
||||
obj_class = nodes.NODE_CLASS_MAPPINGS[x] |
||||
info = {} |
||||
info['input'] = obj_class.INPUT_TYPES() |
||||
info['output'] = obj_class.RETURN_TYPES |
||||
info['name'] = x #TODO |
||||
info['description'] = '' |
||||
out[x] = info |
||||
self.wfile.write(json.dumps(out).encode('utf-8')) |
||||
elif self.path[1:] in os.listdir(self.server.server_dir): |
||||
self._set_headers() |
||||
with open(os.path.join(self.server.server_dir, self.path[1:]), "rb") as f: |
||||
self.wfile.write(f.read()) |
||||
else: |
||||
self._set_headers() |
||||
with open(os.path.join(self.server.server_dir, "index.html"), "rb") as f: |
||||
self.wfile.write(f.read()) |
||||
|
||||
def do_HEAD(self): |
||||
self._set_headers() |
||||
|
||||
def do_POST(self): |
||||
resp_code = 200 |
||||
out_string = "" |
||||
if self.path == "/prompt": |
||||
print("got prompt") |
||||
self.data_string = self.rfile.read(int(self.headers['Content-Length'])) |
||||
json_data = json.loads(self.data_string) |
||||
if "number" in json_data: |
||||
number = float(json_data['number']) |
||||
else: |
||||
number = self.server.number |
||||
self.server.number += 1 |
||||
if "prompt" in json_data: |
||||
prompt = json_data["prompt"] |
||||
valid = validate_prompt(prompt) |
||||
extra_data = {} |
||||
if "extra_data" in json_data: |
||||
extra_data = json_data["extra_data"] |
||||
if valid[0]: |
||||
self.server.prompt_queue.put((number, id(prompt), prompt, extra_data)) |
||||
else: |
||||
resp_code = 400 |
||||
out_string = valid[1] |
||||
print("invalid prompt:", valid[1]) |
||||
self._set_headers(code=resp_code) |
||||
self.end_headers() |
||||
self.wfile.write(out_string.encode('utf8')) |
||||
return |
||||
|
||||
|
||||
def run(prompt_queue, address='', port=8188): |
||||
server_address = (address, port) |
||||
httpd = HTTPServer(server_address, PromptServer) |
||||
httpd.server_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "webshit") |
||||
httpd.prompt_queue = prompt_queue |
||||
httpd.number = 0 |
||||
if server_address[0] == '': |
||||
addr = '0.0.0.0' |
||||
else: |
||||
addr = server_address[0] |
||||
print("Starting server\n") |
||||
print("To see the GUI go to: http://{}:{}".format(addr, server_address[1])) |
||||
httpd.serve_forever() |
||||
|
||||
|
||||
if __name__ == "__main__": |
||||
q = queue.PriorityQueue() |
||||
threading.Thread(target=prompt_worker, daemon=True, args=(q,)).start() |
||||
run(q, address='127.0.0.1', port=8188) |
||||
|
||||
|
||||
@ -0,0 +1,73 @@
|
||||
model: |
||||
base_learning_rate: 1.0e-04 |
||||
target: ldm.models.diffusion.ddpm.LatentDiffusion |
||||
params: |
||||
linear_start: 0.00085 |
||||
linear_end: 0.0120 |
||||
num_timesteps_cond: 1 |
||||
log_every_t: 200 |
||||
timesteps: 1000 |
||||
first_stage_key: "jpg" |
||||
cond_stage_key: "txt" |
||||
image_size: 64 |
||||
channels: 4 |
||||
cond_stage_trainable: false # Note: different from the one we trained before |
||||
conditioning_key: crossattn |
||||
monitor: val/loss_simple_ema |
||||
scale_factor: 0.18215 |
||||
use_ema: False |
||||
|
||||
scheduler_config: # 10000 warmup steps |
||||
target: ldm.lr_scheduler.LambdaLinearScheduler |
||||
params: |
||||
warm_up_steps: [ 10000 ] |
||||
cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases |
||||
f_start: [ 1.e-6 ] |
||||
f_max: [ 1. ] |
||||
f_min: [ 1. ] |
||||
|
||||
unet_config: |
||||
target: ldm.modules.diffusionmodules.openaimodel.UNetModel |
||||
params: |
||||
image_size: 32 # unused |
||||
in_channels: 4 |
||||
out_channels: 4 |
||||
model_channels: 320 |
||||
attention_resolutions: [ 4, 2, 1 ] |
||||
num_res_blocks: 2 |
||||
channel_mult: [ 1, 2, 4, 4 ] |
||||
num_heads: 8 |
||||
use_spatial_transformer: True |
||||
transformer_depth: 1 |
||||
context_dim: 768 |
||||
use_checkpoint: True |
||||
legacy: False |
||||
|
||||
first_stage_config: |
||||
target: ldm.models.autoencoder.AutoencoderKL |
||||
params: |
||||
embed_dim: 4 |
||||
monitor: val/rec_loss |
||||
ddconfig: |
||||
double_z: true |
||||
z_channels: 4 |
||||
resolution: 256 |
||||
in_channels: 3 |
||||
out_ch: 3 |
||||
ch: 128 |
||||
ch_mult: |
||||
- 1 |
||||
- 2 |
||||
- 4 |
||||
- 4 |
||||
num_res_blocks: 2 |
||||
attn_resolutions: [] |
||||
dropout: 0.0 |
||||
lossconfig: |
||||
target: torch.nn.Identity |
||||
|
||||
cond_stage_config: |
||||
target: ldm.modules.encoders.modules.FrozenCLIPEmbedder |
||||
params: |
||||
layer: "hidden" |
||||
layer_idx: -2 |
||||
@ -0,0 +1,70 @@
|
||||
model: |
||||
base_learning_rate: 1.0e-04 |
||||
target: ldm.models.diffusion.ddpm.LatentDiffusion |
||||
params: |
||||
linear_start: 0.00085 |
||||
linear_end: 0.0120 |
||||
num_timesteps_cond: 1 |
||||
log_every_t: 200 |
||||
timesteps: 1000 |
||||
first_stage_key: "jpg" |
||||
cond_stage_key: "txt" |
||||
image_size: 64 |
||||
channels: 4 |
||||
cond_stage_trainable: false # Note: different from the one we trained before |
||||
conditioning_key: crossattn |
||||
monitor: val/loss_simple_ema |
||||
scale_factor: 0.18215 |
||||
use_ema: False |
||||
|
||||
scheduler_config: # 10000 warmup steps |
||||
target: ldm.lr_scheduler.LambdaLinearScheduler |
||||
params: |
||||
warm_up_steps: [ 10000 ] |
||||
cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases |
||||
f_start: [ 1.e-6 ] |
||||
f_max: [ 1. ] |
||||
f_min: [ 1. ] |
||||
|
||||
unet_config: |
||||
target: ldm.modules.diffusionmodules.openaimodel.UNetModel |
||||
params: |
||||
image_size: 32 # unused |
||||
in_channels: 4 |
||||
out_channels: 4 |
||||
model_channels: 320 |
||||
attention_resolutions: [ 4, 2, 1 ] |
||||
num_res_blocks: 2 |
||||
channel_mult: [ 1, 2, 4, 4 ] |
||||
num_heads: 8 |
||||
use_spatial_transformer: True |
||||
transformer_depth: 1 |
||||
context_dim: 768 |
||||
use_checkpoint: True |
||||
legacy: False |
||||
|
||||
first_stage_config: |
||||
target: ldm.models.autoencoder.AutoencoderKL |
||||
params: |
||||
embed_dim: 4 |
||||
monitor: val/rec_loss |
||||
ddconfig: |
||||
double_z: true |
||||
z_channels: 4 |
||||
resolution: 256 |
||||
in_channels: 3 |
||||
out_ch: 3 |
||||
ch: 128 |
||||
ch_mult: |
||||
- 1 |
||||
- 2 |
||||
- 4 |
||||
- 4 |
||||
num_res_blocks: 2 |
||||
attn_resolutions: [] |
||||
dropout: 0.0 |
||||
lossconfig: |
||||
target: torch.nn.Identity |
||||
|
||||
cond_stage_config: |
||||
target: ldm.modules.encoders.modules.FrozenCLIPEmbedder |
||||
@ -0,0 +1,73 @@
|
||||
model: |
||||
base_learning_rate: 1.0e-04 |
||||
target: ldm.models.diffusion.ddpm.LatentDiffusion |
||||
params: |
||||
linear_start: 0.00085 |
||||
linear_end: 0.0120 |
||||
num_timesteps_cond: 1 |
||||
log_every_t: 200 |
||||
timesteps: 1000 |
||||
first_stage_key: "jpg" |
||||
cond_stage_key: "txt" |
||||
image_size: 64 |
||||
channels: 4 |
||||
cond_stage_trainable: false # Note: different from the one we trained before |
||||
conditioning_key: crossattn |
||||
monitor: val/loss_simple_ema |
||||
scale_factor: 0.18215 |
||||
use_ema: False |
||||
|
||||
scheduler_config: # 10000 warmup steps |
||||
target: ldm.lr_scheduler.LambdaLinearScheduler |
||||
params: |
||||
warm_up_steps: [ 10000 ] |
||||
cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases |
||||
f_start: [ 1.e-6 ] |
||||
f_max: [ 1. ] |
||||
f_min: [ 1. ] |
||||
|
||||
unet_config: |
||||
target: ldm.modules.diffusionmodules.openaimodel.UNetModel |
||||
params: |
||||
image_size: 32 # unused |
||||
in_channels: 4 |
||||
out_channels: 4 |
||||
model_channels: 320 |
||||
attention_resolutions: [ 4, 2, 1 ] |
||||
num_res_blocks: 2 |
||||
channel_mult: [ 1, 2, 4, 4 ] |
||||
num_heads: 8 |
||||
use_spatial_transformer: True |
||||
transformer_depth: 1 |
||||
context_dim: 768 |
||||
use_checkpoint: True |
||||
legacy: False |
||||
|
||||
first_stage_config: |
||||
target: ldm.models.autoencoder.AutoencoderKL |
||||
params: |
||||
embed_dim: 4 |
||||
monitor: val/rec_loss |
||||
ddconfig: |
||||
double_z: true |
||||
z_channels: 4 |
||||
resolution: 256 |
||||
in_channels: 3 |
||||
out_ch: 3 |
||||
ch: 128 |
||||
ch_mult: |
||||
- 1 |
||||
- 2 |
||||
- 4 |
||||
- 4 |
||||
num_res_blocks: 2 |
||||
attn_resolutions: [] |
||||
dropout: 0.0 |
||||
lossconfig: |
||||
target: torch.nn.Identity |
||||
|
||||
cond_stage_config: |
||||
target: ldm.modules.encoders.modules.FrozenCLIPEmbedder |
||||
params: |
||||
layer: "hidden" |
||||
layer_idx: -2 |
||||
@ -0,0 +1,68 @@
|
||||
model: |
||||
base_learning_rate: 1.0e-4 |
||||
target: ldm.models.diffusion.ddpm.LatentDiffusion |
||||
params: |
||||
parameterization: "v" |
||||
linear_start: 0.00085 |
||||
linear_end: 0.0120 |
||||
num_timesteps_cond: 1 |
||||
log_every_t: 200 |
||||
timesteps: 1000 |
||||
first_stage_key: "jpg" |
||||
cond_stage_key: "txt" |
||||
image_size: 64 |
||||
channels: 4 |
||||
cond_stage_trainable: false |
||||
conditioning_key: crossattn |
||||
monitor: val/loss_simple_ema |
||||
scale_factor: 0.18215 |
||||
use_ema: False # we set this to false because this is an inference only config |
||||
|
||||
unet_config: |
||||
target: ldm.modules.diffusionmodules.openaimodel.UNetModel |
||||
params: |
||||
use_checkpoint: True |
||||
use_fp16: True |
||||
image_size: 32 # unused |
||||
in_channels: 4 |
||||
out_channels: 4 |
||||
model_channels: 320 |
||||
attention_resolutions: [ 4, 2, 1 ] |
||||
num_res_blocks: 2 |
||||
channel_mult: [ 1, 2, 4, 4 ] |
||||
num_head_channels: 64 # need to fix for flash-attn |
||||
use_spatial_transformer: True |
||||
use_linear_in_transformer: True |
||||
transformer_depth: 1 |
||||
context_dim: 1024 |
||||
legacy: False |
||||
|
||||
first_stage_config: |
||||
target: ldm.models.autoencoder.AutoencoderKL |
||||
params: |
||||
embed_dim: 4 |
||||
monitor: val/rec_loss |
||||
ddconfig: |
||||
#attn_type: "vanilla-xformers" |
||||
double_z: true |
||||
z_channels: 4 |
||||
resolution: 256 |
||||
in_channels: 3 |
||||
out_ch: 3 |
||||
ch: 128 |
||||
ch_mult: |
||||
- 1 |
||||
- 2 |
||||
- 4 |
||||
- 4 |
||||
num_res_blocks: 2 |
||||
attn_resolutions: [] |
||||
dropout: 0.0 |
||||
lossconfig: |
||||
target: torch.nn.Identity |
||||
|
||||
cond_stage_config: |
||||
target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder |
||||
params: |
||||
freeze: True |
||||
layer: "penultimate" |
||||
@ -0,0 +1,68 @@
|
||||
model: |
||||
base_learning_rate: 1.0e-4 |
||||
target: ldm.models.diffusion.ddpm.LatentDiffusion |
||||
params: |
||||
parameterization: "v" |
||||
linear_start: 0.00085 |
||||
linear_end: 0.0120 |
||||
num_timesteps_cond: 1 |
||||
log_every_t: 200 |
||||
timesteps: 1000 |
||||
first_stage_key: "jpg" |
||||
cond_stage_key: "txt" |
||||
image_size: 64 |
||||
channels: 4 |
||||
cond_stage_trainable: false |
||||
conditioning_key: crossattn |
||||
monitor: val/loss_simple_ema |
||||
scale_factor: 0.18215 |
||||
use_ema: False # we set this to false because this is an inference only config |
||||
|
||||
unet_config: |
||||
target: ldm.modules.diffusionmodules.openaimodel.UNetModel |
||||
params: |
||||
use_checkpoint: True |
||||
use_fp16: False |
||||
image_size: 32 # unused |
||||
in_channels: 4 |
||||
out_channels: 4 |
||||
model_channels: 320 |
||||
attention_resolutions: [ 4, 2, 1 ] |
||||
num_res_blocks: 2 |
||||
channel_mult: [ 1, 2, 4, 4 ] |
||||
num_head_channels: 64 # need to fix for flash-attn |
||||
use_spatial_transformer: True |
||||
use_linear_in_transformer: True |
||||
transformer_depth: 1 |
||||
context_dim: 1024 |
||||
legacy: False |
||||
|
||||
first_stage_config: |
||||
target: ldm.models.autoencoder.AutoencoderKL |
||||
params: |
||||
embed_dim: 4 |
||||
monitor: val/rec_loss |
||||
ddconfig: |
||||
#attn_type: "vanilla-xformers" |
||||
double_z: true |
||||
z_channels: 4 |
||||
resolution: 256 |
||||
in_channels: 3 |
||||
out_ch: 3 |
||||
ch: 128 |
||||
ch_mult: |
||||
- 1 |
||||
- 2 |
||||
- 4 |
||||
- 4 |
||||
num_res_blocks: 2 |
||||
attn_resolutions: [] |
||||
dropout: 0.0 |
||||
lossconfig: |
||||
target: torch.nn.Identity |
||||
|
||||
cond_stage_config: |
||||
target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder |
||||
params: |
||||
freeze: True |
||||
layer: "penultimate" |
||||
@ -0,0 +1,221 @@
|
||||
import torch |
||||
|
||||
import os |
||||
import sys |
||||
import json |
||||
|
||||
from PIL import Image |
||||
from PIL.PngImagePlugin import PngInfo |
||||
import numpy as np |
||||
|
||||
sys.path.append(os.path.join(sys.path[0], "comfy")) |
||||
|
||||
|
||||
import comfy.samplers |
||||
import comfy.sd |
||||
|
||||
supported_ckpt_extensions = ['.ckpt'] |
||||
try: |
||||
import safetensors.torch |
||||
supported_ckpt_extensions += ['.safetensors'] |
||||
except: |
||||
print("Could not import safetensors, safetensors support disabled.") |
||||
|
||||
def filter_files_extensions(files, extensions): |
||||
return sorted(list(filter(lambda a: os.path.splitext(a)[-1].lower() in extensions, files))) |
||||
|
||||
class CLIPTextEncode: |
||||
@classmethod |
||||
def INPUT_TYPES(s): |
||||
return {"required": {"text": ("STRING", ), "clip": ("CLIP", )}} |
||||
RETURN_TYPES = ("CONDITIONING",) |
||||
FUNCTION = "encode" |
||||
|
||||
def encode(self, clip, text): |
||||
return (clip.encode(text), ) |
||||
|
||||
class VAEDecode: |
||||
def __init__(self, device="cpu"): |
||||
self.device = device |
||||
|
||||
@classmethod |
||||
def INPUT_TYPES(s): |
||||
return {"required": { "samples": ("LATENT", ), "vae": ("VAE", )}} |
||||
RETURN_TYPES = ("IMAGE",) |
||||
FUNCTION = "decode" |
||||
|
||||
def decode(self, vae, samples): |
||||
return (vae.decode(samples), ) |
||||
|
||||
class VAEEncode: |
||||
def __init__(self, device="cpu"): |
||||
self.device = device |
||||
|
||||
@classmethod |
||||
def INPUT_TYPES(s): |
||||
return {"required": { "pixels": ("IMAGE", ), "vae": ("VAE", )}} |
||||
RETURN_TYPES = ("LATENT",) |
||||
FUNCTION = "encode" |
||||
|
||||
def encode(self, vae, pixels): |
||||
return (vae.encode(pixels), ) |
||||
|
||||
class CheckpointLoader: |
||||
models_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "models") |
||||
config_dir = os.path.join(models_dir, "configs") |
||||
ckpt_dir = os.path.join(models_dir, "checkpoints") |
||||
|
||||
@classmethod |
||||
def INPUT_TYPES(s): |
||||
return {"required": { "config_name": (filter_files_extensions(os.listdir(s.config_dir), '.yaml'), ), |
||||
"ckpt_name": (filter_files_extensions(os.listdir(s.ckpt_dir), supported_ckpt_extensions), )}} |
||||
RETURN_TYPES = ("MODEL", "CLIP", "VAE") |
||||
FUNCTION = "load_checkpoint" |
||||
|
||||
def load_checkpoint(self, config_name, ckpt_name, output_vae=True, output_clip=True): |
||||
config_path = os.path.join(self.config_dir, config_name) |
||||
ckpt_path = os.path.join(self.ckpt_dir, ckpt_name) |
||||
return comfy.sd.load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True) |
||||
|
||||
class VAELoader: |
||||
models_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "models") |
||||
vae_dir = os.path.join(models_dir, "vae") |
||||
@classmethod |
||||
def INPUT_TYPES(s): |
||||
return {"required": { "vae_name": (filter_files_extensions(os.listdir(s.vae_dir), supported_ckpt_extensions), )}} |
||||
RETURN_TYPES = ("VAE",) |
||||
FUNCTION = "load_vae" |
||||
|
||||
#TODO: scale factor? |
||||
def load_vae(self, vae_name): |
||||
vae_path = os.path.join(self.vae_dir, vae_name) |
||||
vae = comfy.sd.VAE(ckpt_path=vae_path) |
||||
return (vae,) |
||||
|
||||
class EmptyLatentImage: |
||||
def __init__(self, device="cpu"): |
||||
self.device = device |
||||
|
||||
@classmethod |
||||
def INPUT_TYPES(s): |
||||
return {"required": { "width": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}), |
||||
"height": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}), |
||||
"batch_size": ("INT", {"default": 1, "min": 1, "max": 64})}} |
||||
RETURN_TYPES = ("LATENT",) |
||||
FUNCTION = "generate" |
||||
|
||||
def generate(self, width, height, batch_size=1): |
||||
latent = torch.zeros([batch_size, 4, height // 8, width // 8]) |
||||
return (latent, ) |
||||
|
||||
class LatentUpscale: |
||||
upscale_methods = ["nearest-exact", "bilinear", "area"] |
||||
|
||||
@classmethod |
||||
def INPUT_TYPES(s): |
||||
return {"required": { "samples": ("LATENT",), "upscale_method": (s.upscale_methods,), |
||||
"width": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}), |
||||
"height": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}),}} |
||||
RETURN_TYPES = ("LATENT",) |
||||
FUNCTION = "upscale" |
||||
|
||||
def upscale(self, samples, upscale_method, width, height): |
||||
s = torch.nn.functional.interpolate(samples, size=(height // 8, width // 8), mode=upscale_method) |
||||
return (s,) |
||||
|
||||
class KSampler: |
||||
def __init__(self, device="cuda"): |
||||
self.device = device |
||||
|
||||
@classmethod |
||||
def INPUT_TYPES(s): |
||||
return {"required": |
||||
{"model": ("MODEL",), |
||||
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), |
||||
"steps": ("INT", {"default": 20, "min": 1, "max": 10000}), |
||||
"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}), |
||||
"sampler_name": (comfy.samplers.KSampler.SAMPLERS, ), |
||||
"scheduler": (comfy.samplers.KSampler.SCHEDULERS, ), |
||||
"positive": ("CONDITIONING", ), |
||||
"negative": ("CONDITIONING", ), |
||||
"latent_image": ("LATENT", ), |
||||
"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), |
||||
}} |
||||
|
||||
RETURN_TYPES = ("LATENT",) |
||||
FUNCTION = "sample" |
||||
|
||||
def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0): |
||||
noise = torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=torch.manual_seed(seed), device="cpu") |
||||
model = model.to(self.device) |
||||
noise = noise.to(self.device) |
||||
latent_image = latent_image.to(self.device) |
||||
|
||||
if positive.shape[0] < noise.shape[0]: |
||||
positive = torch.cat([positive] * noise.shape[0]) |
||||
|
||||
if negative.shape[0] < noise.shape[0]: |
||||
negative = torch.cat([negative] * noise.shape[0]) |
||||
|
||||
positive = positive.to(self.device) |
||||
negative = negative.to(self.device) |
||||
|
||||
if sampler_name in comfy.samplers.KSampler.SAMPLERS: |
||||
sampler = comfy.samplers.KSampler(model, steps=steps, device=self.device, sampler=sampler_name, scheduler=scheduler, denoise=denoise) |
||||
else: |
||||
#other samplers |
||||
pass |
||||
|
||||
samples = sampler.sample(noise, positive, negative, cfg=cfg, latent_image=latent_image) |
||||
samples = samples.cpu() |
||||
model = model.cpu() |
||||
return (samples, ) |
||||
|
||||
|
||||
class SaveImage: |
||||
def __init__(self): |
||||
self.output_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "output") |
||||
try: |
||||
self.counter = int(max(filter(lambda a: 'ComfyUI_' in a, os.listdir(self.output_dir))).split('_')[1]) + 1 |
||||
except: |
||||
self.counter = 1 |
||||
|
||||
@classmethod |
||||
def INPUT_TYPES(s): |
||||
return {"required": |
||||
{"images": ("IMAGE", )}, |
||||
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, |
||||
} |
||||
|
||||
RETURN_TYPES = () |
||||
FUNCTION = "save_images" |
||||
|
||||
OUTPUT_NODE = True |
||||
|
||||
def save_images(self, images, prompt=None, extra_pnginfo=None): |
||||
for image in images: |
||||
i = 255. * image.cpu().numpy() |
||||
img = Image.fromarray(i.astype(np.uint8)) |
||||
metadata = PngInfo() |
||||
if prompt is not None: |
||||
metadata.add_text("prompt", json.dumps(prompt)) |
||||
if extra_pnginfo is not None: |
||||
for x in extra_pnginfo: |
||||
metadata.add_text(x, json.dumps(extra_pnginfo[x])) |
||||
img.save(f"output/ComfyUI_{self.counter:05}_.png", pnginfo=metadata, optimize=True) |
||||
self.counter += 1 |
||||
|
||||
|
||||
NODE_CLASS_MAPPINGS = { |
||||
"KSampler": KSampler, |
||||
"CheckpointLoader": CheckpointLoader, |
||||
"CLIPTextEncode": CLIPTextEncode, |
||||
"VAEDecode": VAEDecode, |
||||
"VAEEncode": VAEEncode, |
||||
"VAELoader": VAELoader, |
||||
"EmptyLatentImage": EmptyLatentImage, |
||||
"LatentUpscale": LatentUpscale, |
||||
"SaveImage": SaveImage, |
||||
} |
||||
|
||||
|
||||
@ -0,0 +1,10 @@
|
||||
torch |
||||
torchdiffeq |
||||
torchsde |
||||
omegaconf |
||||
einops |
||||
open-clip-torch |
||||
transformers |
||||
safetensors |
||||
pytorch_lightning |
||||
|
||||
@ -0,0 +1,465 @@
|
||||
<html> |
||||
<head> |
||||
<link rel="stylesheet" type="text/css" href="litegraph.css"> |
||||
<script type="text/javascript" src="litegraph.core.js"></script> |
||||
</head> |
||||
<body style='width:100%; height:100%'> |
||||
<canvas id='mycanvas' width='1000' height='1000' style='width: 100%; height: 100%;'></canvas> |
||||
<script> |
||||
var graph = new LGraph(); |
||||
|
||||
var canvas = new LGraphCanvas("#mycanvas", graph); |
||||
|
||||
const ccc = document.getElementById("mycanvas"); |
||||
const ctx = ccc.getContext("2d"); |
||||
|
||||
// Resize the canvas to match the size of the canvas element |
||||
function resizeCanvas() { |
||||
ccc.width = ccc.offsetWidth; |
||||
ccc.height = ccc.offsetHeight; |
||||
canvas.draw(true, true); |
||||
} |
||||
// call the function when the page loads |
||||
resizeCanvas(); |
||||
// call the function when the window is resized |
||||
window.addEventListener("resize", resizeCanvas); |
||||
|
||||
|
||||
// var node_const = LiteGraph.createNode("basic/const"); |
||||
// node_const.pos = [200,200]; |
||||
// graph.add(node_const); |
||||
// node_const.setValue(4.5); |
||||
// |
||||
// var node_watch = LiteGraph.createNode("basic/watch"); |
||||
// node_watch.pos = [700,200]; |
||||
// graph.add(node_watch); |
||||
// |
||||
// node_const.connect(0, node_watch, 0 ); |
||||
|
||||
//node constructor class |
||||
// function MyAddNode() |
||||
// { |
||||
// this.addInput("A","number"); |
||||
// this.addInput("B","number"); |
||||
// this.addInput("XXX","TESTESTE"); |
||||
// this.addInput("NNN","TESTES"); |
||||
// this.addOutput("A+B","number"); |
||||
// this.addOutput("TTT","TESTESTE"); |
||||
// this.properties = { precision: 1 }; |
||||
// } |
||||
|
||||
//name to show |
||||
// MyAddNode.title = "Sum"; |
||||
|
||||
//function to call when the node is executed |
||||
// MyAddNode.prototype.onExecute = function() |
||||
// { |
||||
// var A = this.getInputData(0); |
||||
// if( A === undefined ) |
||||
// A = 0; |
||||
// var B = this.getInputData(1); |
||||
// if( B === undefined ) |
||||
// B = 0; |
||||
// this.setOutputData( 0, A + B ); |
||||
// } |
||||
|
||||
var default_graph = {"last_node_id":9,"last_link_id":9,"nodes":[{"id":7,"type":"CLIPTextEncode","pos":[413,389],"size":{"0":425.27801513671875,"1":180.6060791015625},"flags":{},"order":3,"mode":0,"inputs":[{"name":"clip","type":"CLIP","link":5}],"outputs":[{"name":"CONDITIONING","type":"CONDITIONING","links":[6],"slot_index":0}],"properties":{},"widgets_values":["bad hands"]},{"id":6,"type":"CLIPTextEncode","pos":[415,186],"size":{"0":422.84503173828125,"1":164.31304931640625},"flags":{},"order":2,"mode":0,"inputs":[{"name":"clip","type":"CLIP","link":3}],"outputs":[{"name":"CONDITIONING","type":"CONDITIONING","links":[4],"slot_index":0}],"properties":{},"widgets_values":["masterpiece best quality girl"]},{"id":5,"type":"EmptyLatentImage","pos":[473,609],"size":{"0":315,"1":106},"flags":{},"order":1,"mode":0,"outputs":[{"name":"LATENT","type":"LATENT","links":[2],"slot_index":0}],"properties":{},"widgets_values":[512,512,1]},{"id":3,"type":"KSampler","pos":[863,186],"size":{"0":315,"1":262},"flags":{},"order":4,"mode":0,"inputs":[{"name":"model","type":"MODEL","link":1},{"name":"positive","type":"CONDITIONING","link":4},{"name":"negative","type":"CONDITIONING","link":6},{"name":"latent_image","type":"LATENT","link":2}],"outputs":[{"name":"LATENT","type":"LATENT","links":[7],"slot_index":0}],"properties":{},"widgets_values":[8566257,true,20,8,"sample_euler","normal",1]},{"id":8,"type":"VAEDecode","pos":[1209,188],"size":{"0":210,"1":46},"flags":{},"order":5,"mode":0,"inputs":[{"name":"samples","type":"LATENT","link":7},{"name":"vae","type":"VAE","link":8}],"outputs":[{"name":"IMAGE","type":"IMAGE","links":[9],"slot_index":0}],"properties":{}},{"id":9,"type":"SaveImage","pos":[1451,189],"size":{"0":210,"1":26},"flags":{},"order":6,"mode":0,"inputs":[{"name":"images","type":"IMAGE","link":9}],"properties":{}},{"id":4,"type":"CheckpointLoader","pos":[26,474],"size":{"0":315,"1":122},"flags":{},"order":0,"mode":0,"outputs":[{"name":"MODEL","type":"MODEL","links":[1],"slot_index":0},{"name":"CLIP","type":"CLIP","links":[3,5],"slot_index":1},{"name":"VAE","type":"VAE","links":[8],"slot_index":2}],"properties":{},"widgets_values":["v1-inference.yaml","v1-5-pruned-emaonly.ckpt"]}],"links":[[1,4,0,3,0,"MODEL"],[2,5,0,3,3,"LATENT"],[3,4,1,6,0,"CLIP"],[4,6,0,3,1,"CONDITIONING"],[5,4,1,7,0,"CLIP"],[6,7,0,3,2,"CONDITIONING"],[7,3,0,8,0,"LATENT"],[8,4,2,8,1,"VAE"],[9,8,0,9,0,"IMAGE"]],"groups":[],"config":{},"extra":{},"version":0.4} |
||||
|
||||
function afterLoadGraph() |
||||
{ |
||||
let workflow = null; |
||||
try { |
||||
workflow = JSON.parse(localStorage.getItem("workflow")); |
||||
graph.configure(workflow); |
||||
} catch(err) { |
||||
} |
||||
|
||||
if (!workflow) { |
||||
graph.configure(default_graph); |
||||
} |
||||
|
||||
function saveGraph() { |
||||
localStorage.setItem("workflow", JSON.stringify(graph.serialize())); |
||||
} |
||||
|
||||
setInterval(saveGraph, 1000); |
||||
|
||||
} |
||||
|
||||
function onObjectInfo(json) { |
||||
for (let key in json) { |
||||
function MyNode() |
||||
{ |
||||
j = MyNode.__json_data; |
||||
inp = j['input']['required']; |
||||
this.class_comfy = MyNode.class_type_comfy; |
||||
this._widgets = [] |
||||
min_height = 1; |
||||
min_width = 1; |
||||
for (let x in inp) { |
||||
let default_val = min_val = max_val = step_val = undefined; |
||||
if (inp[x].length > 1) { |
||||
default_val = inp[x][1]['default']; |
||||
min_val = inp[x][1]['min']; |
||||
max_val = inp[x][1]['max']; |
||||
step_val = inp[x][1]['step']; |
||||
} |
||||
|
||||
let type = inp[x][0]; |
||||
if (Array.isArray(type)) { |
||||
w = this.addWidget("combo", x, type[0], function(v){}, { values: type } ); |
||||
this._widgets += [w] |
||||
} else if (type == "INT") { |
||||
if (default_val == undefined) default_val = 0; |
||||
if (min_val == undefined) min_val = 0; |
||||
if (max_val == undefined) max_val = 2048; |
||||
if (step_val == undefined) step_val = 1; |
||||
w = this.addWidget("number", x, default_val, function(v){let s = this.options.step / 10;this.value = Math.round( v / s ) * s;}, { min: min_val, max: max_val, step: 10.0 * step_val} ); |
||||
this._widgets += [w] |
||||
if (x == "seed") { |
||||
w1 = this.addWidget("toggle", "Random seed after every gen", true, function(v){}, { on: "enabled", off:"disabled"} ); |
||||
w1.to_randomize = w; |
||||
this._widgets += [w1] |
||||
} |
||||
} else if (type == "FLOAT") { |
||||
if (default_val == undefined) default_val = 0; |
||||
if (min_val == undefined) min_val = 0; |
||||
if (max_val == undefined) max_val = 2048; |
||||
if (step_val == undefined) step_val = 0.5; |
||||
|
||||
// if (min_val == 0.0 && max_val == 1.0) { |
||||
// w = this.slider = this.addWidget("slider", x, default_val, function(v){}, { min: min_val, max: max_val} ); |
||||
// } else { |
||||
w = this.addWidget("number", x, default_val, function(v){}, { min: min_val, max: max_val, step: 10.0 * step_val} ); |
||||
// } |
||||
this._widgets += [w] |
||||
} else if (type == "STRING") { |
||||
var w = { |
||||
type: "customtext", |
||||
name: x, |
||||
get value() { return this.input_div.innerText;}, |
||||
set value(x) { this.input_div.innerText = x;}, |
||||
callback: function(v){console.log(v);}, |
||||
options: {}, |
||||
draw: function(ctx, node, widget_width, y, H){ |
||||
var show_text = canvas.ds.scale > 0.5; |
||||
// this.input_div.style.top = `${y}px`; |
||||
let t = ctx.getTransform(); |
||||
let margin = 15; |
||||
let x_div = t.a * margin * 2 + t.e; |
||||
let y_div = t.d * (y + H) + t.f; |
||||
let width_div = (widget_width - margin * 2) * t.a; |
||||
let height_div = (this.parent.size[1] - (y + H))* t.d; |
||||
this.input_div.style.left = `${x_div}px`; |
||||
this.input_div.style.top = `${y_div}px`; |
||||
this.input_div.style.width = width_div; |
||||
this.input_div.style.height = height_div; |
||||
this.input_div.style.position = 'absolute'; |
||||
this.input_div.style.zIndex = 1; |
||||
this.input_div.style.fontSize = t.d * 10.0; |
||||
|
||||
if (show_text) { |
||||
this.input_div.hidden = false; |
||||
} else { |
||||
this.input_div.hidden = true; |
||||
} |
||||
|
||||
ctx.save(); |
||||
// ctx.fillText(String(this.value).substr(0,30), 0, y + H * 0.7); |
||||
ctx.restore(); |
||||
}, |
||||
}; |
||||
w.input_div = document.createElement('div'); |
||||
w.input_div.contentEditable = true; |
||||
w.input_div.style.backgroundColor = "#FFFFFF"; |
||||
w.input_div.style.overflow = 'hidden'; |
||||
document.addEventListener('click', function(event) { |
||||
if (!w.input_div.contains(event.target)) { |
||||
w.input_div.blur(); |
||||
} |
||||
}); |
||||
w.parent = this; |
||||
min_height = Math.max(min_height, 200); |
||||
min_width = Math.max(min_width, 400); |
||||
ccc.parentNode.appendChild(w.input_div); |
||||
|
||||
w = this.addCustomWidget(w); |
||||
// w = this.addWidget("text", x, "", function(v){}, { multiline:true } ); |
||||
console.log(w, this); |
||||
this._widgets += [w] |
||||
this.onRemoved = function() { |
||||
for (let y in this.widgets) { |
||||
if (this.widgets[y].input_div) { |
||||
this.widgets[y].input_div.remove(); |
||||
} |
||||
} |
||||
} |
||||
} else { |
||||
this.addInput(x, type); |
||||
} |
||||
} |
||||
|
||||
out = j['output']; |
||||
for (let x in out) { |
||||
this.addOutput(out[x], out[x]); |
||||
} |
||||
s = this.computeSize(); |
||||
s[0] *= 1.5; |
||||
s[0] = Math.max(min_width, s[0]); |
||||
s[1] = Math.max(min_height, s[1]); |
||||
this.size = s; |
||||
this.serialize_widgets = true; |
||||
} |
||||
MyNode.title = json[key]['name']; |
||||
MyNode.class_type_comfy = json[key]['name']; |
||||
MyNode.__json_data = json[key] |
||||
|
||||
LiteGraph.registerNodeType(key, MyNode); |
||||
MyNode.category = "sd"; //TODO: proper categories |
||||
}; |
||||
|
||||
afterLoadGraph(); |
||||
// graph.configure(JSON.parse(base_txt2img_graph)); |
||||
} |
||||
|
||||
fetch("object_info", {cache: "no-store"}) |
||||
.then(response => response.json()) |
||||
.then(json => onObjectInfo(json)); |
||||
|
||||
|
||||
//register in the system |
||||
graph.start(); |
||||
// LiteGraph.registerNodeType("testing", MyAddNode); |
||||
|
||||
|
||||
graph.onNodeRemoved = function(n) { |
||||
for (let y in n.widgets) { |
||||
if (n.widgets[y].input_div) { |
||||
n.widgets[y].input_div.remove(); |
||||
} |
||||
} |
||||
} |
||||
|
||||
function graphToPrompt() { |
||||
let s = graph.serialize(); |
||||
let output = {}; |
||||
// console.log(s['nodes']); |
||||
nodes = s['nodes'] |
||||
|
||||
for (let x in nodes) { |
||||
let n = graph.getNodeById(nodes[x].id); |
||||
let input_ = {}; |
||||
for (let y in n.widgets) { |
||||
input_[n.widgets[y].name] = n.widgets[y].value; |
||||
} |
||||
for (let y in n.inputs) { |
||||
let parent_node = n.getInputNode(y); |
||||
if (parent_node) { |
||||
for (let z in parent_node.outputs) { |
||||
let c_nodes = parent_node.getOutputNodes(z); |
||||
// console.log(c_nodes, z); |
||||
if (c_nodes) { |
||||
for (let zz in c_nodes) { |
||||
if (c_nodes[zz].id == n.id) { |
||||
input_[n.inputs[y].name] = [String(parent_node.id), parseInt(z)]; |
||||
break; |
||||
} |
||||
} |
||||
} |
||||
} |
||||
} |
||||
} |
||||
let node = {} |
||||
node['inputs'] = input_; |
||||
node['class_type'] = n.class_comfy; |
||||
// inputs = x['inputs'] |
||||
// inputs['name'], inputs['id'] |
||||
// console.log(x, n); |
||||
// console.log(node); |
||||
output[String(n.id)] = node; |
||||
} |
||||
|
||||
return output; |
||||
} |
||||
|
||||
|
||||
function promptPosted(data) |
||||
{ |
||||
if (data.status == 400) { |
||||
data.text().then(dt => alert(dt)); |
||||
return; |
||||
} |
||||
|
||||
let s = graph.serialize(); |
||||
let output = {}; |
||||
// console.log(s['nodes']); |
||||
nodes = s['nodes'] |
||||
|
||||
for (let x in nodes) { |
||||
let n = graph.getNodeById(nodes[x].id); |
||||
for (let w in n.widgets) { |
||||
let wid = n.widgets[w]; |
||||
if (Object.hasOwn(wid, 'to_randomize')) { |
||||
if (wid.value) { |
||||
wid.to_randomize.value = Math.floor(Math.random() * 1125899906842624); |
||||
} |
||||
} |
||||
} |
||||
} |
||||
|
||||
canvas.draw(true, true); |
||||
} |
||||
|
||||
function postPrompt() { |
||||
let prompt = graphToPrompt(); |
||||
let full_data = {prompt: prompt, extra_data: {extra_pnginfo: {workflow: graph.serialize()}}}; |
||||
|
||||
fetch('/prompt', { |
||||
method: 'POST', |
||||
headers: { |
||||
'Content-Type': 'application/json' |
||||
}, |
||||
body: JSON.stringify(full_data) |
||||
}) |
||||
.then(data => promptPosted(data)) |
||||
.catch(error => console.error(error)) |
||||
|
||||
// console.log(JSON.stringify(prompt)); |
||||
// console.log(JSON.stringify(graph.serialize())); |
||||
} |
||||
|
||||
|
||||
function promptToGraph(prompt) { |
||||
for (let x in prompt) { |
||||
|
||||
} |
||||
} |
||||
|
||||
function prompt_file_load(file) |
||||
{ |
||||
if (file.type === 'image/png') { |
||||
const reader = new FileReader(); |
||||
reader.onload = (event) => { |
||||
// Get the PNG data as a Uint8Array |
||||
const pngData = new Uint8Array(event.target.result); |
||||
const dataView = new DataView(pngData.buffer); |
||||
|
||||
// Check that the PNG signature is present |
||||
if (dataView.getUint32(0) !== 0x89504e47) { |
||||
console.error('Not a valid PNG file'); |
||||
return; |
||||
} |
||||
|
||||
// Start searching for chunks after the PNG signature |
||||
let offset = 8; |
||||
let txt_chunks = {} |
||||
// Loop through the chunks in the PNG file |
||||
while (offset < pngData.length) { |
||||
// Get the length of the chunk |
||||
const length = dataView.getUint32(offset); |
||||
// Get the chunk type |
||||
const type = String.fromCharCode(...pngData.slice(offset + 4, offset + 8)); |
||||
if (type === 'tEXt') { |
||||
// Get the keyword |
||||
let keyword_end = offset + 8; |
||||
while (pngData[keyword_end] !== 0) { |
||||
keyword_end++; |
||||
} |
||||
const keyword = String.fromCharCode(...pngData.slice(offset + 8, keyword_end)); |
||||
// Get the text |
||||
const text = String.fromCharCode(...pngData.slice(keyword_end + 1, offset + 8 + length)); |
||||
txt_chunks[keyword] = text; |
||||
} |
||||
|
||||
// Get the next chunk |
||||
offset += 12 + length; |
||||
} |
||||
console.log(txt_chunks); |
||||
console.log(JSON.parse(txt_chunks["prompt"])); |
||||
graph.configure(JSON.parse(txt_chunks["workflow"])); |
||||
}; |
||||
reader.readAsArrayBuffer(file); |
||||
} else if (file.type === "application/json" || file.name.endsWith(".json")) { |
||||
var reader = new FileReader(); |
||||
reader.onload = function() { |
||||
console.log(reader.result); |
||||
var jsonData = JSON.parse(reader.result); |
||||
graph.configure(jsonData); |
||||
}; |
||||
reader.readAsText(file); |
||||
} |
||||
} |
||||
|
||||
// Get prompt from dropped PNG or json |
||||
document.addEventListener('drop', (event) => { |
||||
event.preventDefault(); |
||||
event.stopPropagation(); |
||||
const file = event.dataTransfer.files[0]; |
||||
console.log(file.type); |
||||
prompt_file_load(file); |
||||
}); |
||||
|
||||
|
||||
setInterval(function(){ |
||||
fetch('/prompt') |
||||
.then(response => response.json()) |
||||
.then(data => { |
||||
document.getElementById("queuesize").innerHTML = "Queue size: " + data.exec_info.queue_remaining + ""; |
||||
}).catch((response) => {document.getElementById("queuesize").innerHTML = "Queue size: ERR"}); |
||||
}, 500); |
||||
|
||||
function clearGraph() { |
||||
graph.clear(); |
||||
} |
||||
|
||||
function loadTxt2Img() { |
||||
graph.configure(default_graph); |
||||
} |
||||
|
||||
function saveGraph() { |
||||
var json = JSON.stringify(graph.serialize()); // convert the data to a JSON string |
||||
var blob = new Blob([json], {type: "application/json"}); |
||||
var url = URL.createObjectURL(blob); |
||||
var a = document.createElement("a"); |
||||
a.style = "display: none"; |
||||
a.href = url; |
||||
a.download = "workflow.json"; |
||||
document.body.appendChild(a); |
||||
a.click(); |
||||
setTimeout(function() { |
||||
document.body.removeChild(a); |
||||
window.URL.revokeObjectURL(url); |
||||
}, 0); |
||||
} |
||||
|
||||
var input = document.createElement("input"); |
||||
input.setAttribute("type", "file"); |
||||
input.setAttribute("accept", ".json,image/png"); |
||||
input.style.display = "none"; |
||||
document.body.appendChild(input); |
||||
|
||||
input.addEventListener('change', function() { |
||||
var file = input.files[0]; |
||||
prompt_file_load(file); |
||||
|
||||
}); |
||||
|
||||
function loadGraph() { |
||||
input.click(); |
||||
} |
||||
|
||||
|
||||
</script> |
||||
|
||||
<span style="font-size: 15px;position: absolute; top: 50%; right: 0%; background-color: white; text-align: center;"> |
||||
<span id="queuesize">Queue size: X</span><br> |
||||
<button style="font-size: 20px;" id="queuebutton" onclick="postPrompt()">Queue Prompt</button><br> |
||||
<br> |
||||
<br> |
||||
<button style="font-size: 20px;" onclick="saveGraph()">Save</button><br> |
||||
<button style="font-size: 20px;" onclick="loadGraph()">Load</button><br> |
||||
<button style="font-size: 20px;" onclick="clearGraph()">Clear</button><br> |
||||
<button style="font-size: 20px;" onclick="loadTxt2Img()">Load Default</button><br> |
||||
</span> |
||||
</body> |
||||
</html> |
||||
@ -0,0 +1,680 @@
|
||||
/* this CSS contains only the basic CSS needed to run the app and use it */ |
||||
|
||||
.lgraphcanvas { |
||||
/*cursor: crosshair;*/ |
||||
user-select: none; |
||||
-moz-user-select: none; |
||||
-webkit-user-select: none; |
||||
outline: none; |
||||
font-family: Tahoma, sans-serif; |
||||
} |
||||
|
||||
.lgraphcanvas * { |
||||
box-sizing: border-box; |
||||
} |
||||
|
||||
.litegraph.litecontextmenu { |
||||
font-family: Tahoma, sans-serif; |
||||
position: fixed; |
||||
top: 100px; |
||||
left: 100px; |
||||
min-width: 100px; |
||||
color: #aaf; |
||||
padding: 0; |
||||
box-shadow: 0 0 10px black !important; |
||||
background-color: #2e2e2e !important; |
||||
z-index: 10; |
||||
} |
||||
|
||||
.litegraph.litecontextmenu.dark { |
||||
background-color: #000 !important; |
||||
} |
||||
|
||||
.litegraph.litecontextmenu .litemenu-title img { |
||||
margin-top: 2px; |
||||
margin-left: 2px; |
||||
margin-right: 4px; |
||||
} |
||||
|
||||
.litegraph.litecontextmenu .litemenu-entry { |
||||
margin: 2px; |
||||
padding: 2px; |
||||
} |
||||
|
||||
.litegraph.litecontextmenu .litemenu-entry.submenu { |
||||
background-color: #2e2e2e !important; |
||||
} |
||||
|
||||
.litegraph.litecontextmenu.dark .litemenu-entry.submenu { |
||||
background-color: #000 !important; |
||||
} |
||||
|
||||
.litegraph .litemenubar ul { |
||||
font-family: Tahoma, sans-serif; |
||||
margin: 0; |
||||
padding: 0; |
||||
} |
||||
|
||||
.litegraph .litemenubar li { |
||||
font-size: 14px; |
||||
color: #999; |
||||
display: inline-block; |
||||
min-width: 50px; |
||||
padding-left: 10px; |
||||
padding-right: 10px; |
||||
user-select: none; |
||||
-moz-user-select: none; |
||||
-webkit-user-select: none; |
||||
cursor: pointer; |
||||
} |
||||
|
||||
.litegraph .litemenubar li:hover { |
||||
background-color: #777; |
||||
color: #eee; |
||||
} |
||||
|
||||
.litegraph .litegraph .litemenubar-panel { |
||||
position: absolute; |
||||
top: 5px; |
||||
left: 5px; |
||||
min-width: 100px; |
||||
background-color: #444; |
||||
box-shadow: 0 0 3px black; |
||||
padding: 4px; |
||||
border-bottom: 2px solid #aaf; |
||||
z-index: 10; |
||||
} |
||||
|
||||
.litegraph .litemenu-entry, |
||||
.litemenu-title { |
||||
font-size: 12px; |
||||
color: #aaa; |
||||
padding: 0 0 0 4px; |
||||
margin: 2px; |
||||
padding-left: 2px; |
||||
-moz-user-select: none; |
||||
-webkit-user-select: none; |
||||
user-select: none; |
||||
cursor: pointer; |
||||
} |
||||
|
||||
.litegraph .litemenu-entry .icon { |
||||
display: inline-block; |
||||
width: 12px; |
||||
height: 12px; |
||||
margin: 2px; |
||||
vertical-align: top; |
||||
} |
||||
|
||||
.litegraph .litemenu-entry.checked .icon { |
||||
background-color: #aaf; |
||||
} |
||||
|
||||
.litegraph .litemenu-entry .more { |
||||
float: right; |
||||
padding-right: 5px; |
||||
} |
||||
|
||||
.litegraph .litemenu-entry.disabled { |
||||
opacity: 0.5; |
||||
cursor: default; |
||||
} |
||||
|
||||
.litegraph .litemenu-entry.separator { |
||||
display: block; |
||||
border-top: 1px solid #333; |
||||
border-bottom: 1px solid #666; |
||||
width: 100%; |
||||
height: 0px; |
||||
margin: 3px 0 2px 0; |
||||
background-color: transparent; |
||||
padding: 0 !important; |
||||
cursor: default !important; |
||||
} |
||||
|
||||
.litegraph .litemenu-entry.has_submenu { |
||||
border-right: 2px solid cyan; |
||||
} |
||||
|
||||
.litegraph .litemenu-title { |
||||
color: #dde; |
||||
background-color: #111; |
||||
margin: 0; |
||||
padding: 2px; |
||||
cursor: default; |
||||
} |
||||
|
||||
.litegraph .litemenu-entry:hover:not(.disabled):not(.separator) { |
||||
background-color: #444 !important; |
||||
color: #eee; |
||||
transition: all 0.2s; |
||||
} |
||||
|
||||
.litegraph .litemenu-entry .property_name { |
||||
display: inline-block; |
||||
text-align: left; |
||||
min-width: 80px; |
||||
min-height: 1.2em; |
||||
} |
||||
|
||||
.litegraph .litemenu-entry .property_value { |
||||
display: inline-block; |
||||
background-color: rgba(0, 0, 0, 0.5); |
||||
text-align: right; |
||||
min-width: 80px; |
||||
min-height: 1.2em; |
||||
vertical-align: middle; |
||||
padding-right: 10px; |
||||
} |
||||
|
||||
.litegraph.litesearchbox { |
||||
font-family: Tahoma, sans-serif; |
||||
position: absolute; |
||||
background-color: rgba(0, 0, 0, 0.5); |
||||
padding-top: 4px; |
||||
} |
||||
|
||||
.litegraph.litesearchbox input, |
||||
.litegraph.litesearchbox select { |
||||
margin-top: 3px; |
||||
min-width: 60px; |
||||
min-height: 1.5em; |
||||
background-color: black; |
||||
border: 0; |
||||
color: white; |
||||
padding-left: 10px; |
||||
margin-right: 5px; |
||||
} |
||||
|
||||
.litegraph.litesearchbox .name { |
||||
display: inline-block; |
||||
min-width: 60px; |
||||
min-height: 1.5em; |
||||
padding-left: 10px; |
||||
} |
||||
|
||||
.litegraph.litesearchbox .helper { |
||||
overflow: auto; |
||||
max-height: 200px; |
||||
margin-top: 2px; |
||||
} |
||||
|
||||
.litegraph.lite-search-item { |
||||
font-family: Tahoma, sans-serif; |
||||
background-color: rgba(0, 0, 0, 0.5); |
||||
color: white; |
||||
padding-top: 2px; |
||||
} |
||||
|
||||
.litegraph.lite-search-item.not_in_filter{ |
||||
/*background-color: rgba(50, 50, 50, 0.5);*/ |
||||
/*color: #999;*/ |
||||
color: #B99; |
||||
font-style: italic; |
||||
} |
||||
|
||||
.litegraph.lite-search-item.generic_type{ |
||||
/*background-color: rgba(50, 50, 50, 0.5);*/ |
||||
/*color: #DD9;*/ |
||||
color: #999; |
||||
font-style: italic; |
||||
} |
||||
|
||||
.litegraph.lite-search-item:hover, |
||||
.litegraph.lite-search-item.selected { |
||||
cursor: pointer; |
||||
background-color: white; |
||||
color: black; |
||||
} |
||||
|
||||
/* DIALOGs ******/ |
||||
|
||||
.litegraph .dialog { |
||||
position: absolute; |
||||
top: 50%; |
||||
left: 50%; |
||||
margin-top: -150px; |
||||
margin-left: -200px; |
||||
|
||||
background-color: #2A2A2A; |
||||
|
||||
min-width: 400px; |
||||
min-height: 200px; |
||||
box-shadow: 0 0 4px #111; |
||||
border-radius: 6px; |
||||
} |
||||
|
||||
.litegraph .dialog.settings { |
||||
left: 10px; |
||||
top: 10px; |
||||
height: calc( 100% - 20px ); |
||||
margin: auto; |
||||
max-width: 50%; |
||||
} |
||||
|
||||
.litegraph .dialog.centered { |
||||
top: 50px; |
||||
left: 50%; |
||||
position: absolute; |
||||
transform: translateX(-50%); |
||||
min-width: 600px; |
||||
min-height: 300px; |
||||
height: calc( 100% - 100px ); |
||||
margin: auto; |
||||
} |
||||
|
||||
.litegraph .dialog .close { |
||||
float: right; |
||||
margin: 4px; |
||||
margin-right: 10px; |
||||
cursor: pointer; |
||||
font-size: 1.4em; |
||||
} |
||||
|
||||
.litegraph .dialog .close:hover { |
||||
color: white; |
||||
} |
||||
|
||||
.litegraph .dialog .dialog-header { |
||||
color: #AAA; |
||||
border-bottom: 1px solid #161616; |
||||
} |
||||
|
||||
.litegraph .dialog .dialog-header { height: 40px; } |
||||
.litegraph .dialog .dialog-footer { height: 50px; padding: 10px; border-top: 1px solid #1a1a1a;} |
||||
|
||||
.litegraph .dialog .dialog-header .dialog-title { |
||||
font: 20px "Arial"; |
||||
margin: 4px; |
||||
padding: 4px 10px; |
||||
display: inline-block; |
||||
} |
||||
|
||||
.litegraph .dialog .dialog-content, .litegraph .dialog .dialog-alt-content { |
||||
height: calc(100% - 90px); |
||||
width: 100%; |
||||
min-height: 100px; |
||||
display: inline-block; |
||||
color: #AAA; |
||||
/*background-color: black;*/ |
||||
overflow: auto; |
||||
} |
||||
|
||||
.litegraph .dialog .dialog-content h3 { |
||||
margin: 10px; |
||||
} |
||||
|
||||
.litegraph .dialog .dialog-content .connections { |
||||
flex-direction: row; |
||||
} |
||||
|
||||
.litegraph .dialog .dialog-content .connections .connections_side { |
||||
width: calc(50% - 5px); |
||||
min-height: 100px; |
||||
background-color: black; |
||||
display: flex; |
||||
} |
||||
|
||||
.litegraph .dialog .node_type { |
||||
font-size: 1.2em; |
||||
display: block; |
||||
margin: 10px; |
||||
} |
||||
|
||||
.litegraph .dialog .node_desc { |
||||
opacity: 0.5; |
||||
display: block; |
||||
margin: 10px; |
||||
} |
||||
|
||||
.litegraph .dialog .separator { |
||||
display: block; |
||||
width: calc( 100% - 4px ); |
||||
height: 1px; |
||||
border-top: 1px solid #000; |
||||
border-bottom: 1px solid #333; |
||||
margin: 10px 2px; |
||||
padding: 0; |
||||
} |
||||
|
||||
.litegraph .dialog .property { |
||||
margin-bottom: 2px; |
||||
padding: 4px; |
||||
} |
||||
|
||||
.litegraph .dialog .property:hover { |
||||
background: #545454; |
||||
} |
||||
|
||||
.litegraph .dialog .property_name { |
||||
color: #737373; |
||||
display: inline-block; |
||||
text-align: left; |
||||
vertical-align: top; |
||||
width: 160px; |
||||
padding-left: 4px; |
||||
overflow: hidden; |
||||
margin-right: 6px; |
||||
} |
||||
|
||||
.litegraph .dialog .property:hover .property_name { |
||||
color: white; |
||||
} |
||||
|
||||
.litegraph .dialog .property_value { |
||||
display: inline-block; |
||||
text-align: right; |
||||
color: #AAA; |
||||
background-color: #1A1A1A; |
||||
/*width: calc( 100% - 122px );*/ |
||||
max-width: calc( 100% - 162px ); |
||||
min-width: 200px; |
||||
max-height: 300px; |
||||
min-height: 20px; |
||||
padding: 4px; |
||||
padding-right: 12px; |
||||
overflow: hidden; |
||||
cursor: pointer; |
||||
border-radius: 3px; |
||||
} |
||||
|
||||
.litegraph .dialog .property_value:hover { |
||||
color: white; |
||||
} |
||||
|
||||
.litegraph .dialog .property.boolean .property_value { |
||||
padding-right: 30px; |
||||
color: #A88; |
||||
/*width: auto; |
||||
float: right;*/ |
||||
} |
||||
|
||||
.litegraph .dialog .property.boolean.bool-on .property_name{ |
||||
color: #8A8; |
||||
} |
||||
.litegraph .dialog .property.boolean.bool-on .property_value{ |
||||
color: #8A8; |
||||
} |
||||
|
||||
.litegraph .dialog .btn { |
||||
border: 0; |
||||
border-radius: 4px; |
||||
padding: 4px 20px; |
||||
margin-left: 0px; |
||||
background-color: #060606; |
||||
color: #8e8e8e; |
||||
} |
||||
|
||||
.litegraph .dialog .btn:hover { |
||||
background-color: #111; |
||||
color: #FFF; |
||||
} |
||||
|
||||
.litegraph .dialog .btn.delete:hover { |
||||
background-color: #F33; |
||||
color: black; |
||||
} |
||||
|
||||
.litegraph .subgraph_property { |
||||
padding: 4px; |
||||
} |
||||
|
||||
.litegraph .subgraph_property:hover { |
||||
background-color: #333; |
||||
} |
||||
|
||||
.litegraph .subgraph_property.extra { |
||||
margin-top: 8px; |
||||
} |
||||
|
||||
.litegraph .subgraph_property span.name { |
||||
font-size: 1.3em; |
||||
padding-left: 4px; |
||||
} |
||||
|
||||
.litegraph .subgraph_property span.type { |
||||
opacity: 0.5; |
||||
margin-right: 20px; |
||||
padding-left: 4px; |
||||
} |
||||
|
||||
.litegraph .subgraph_property span.label { |
||||
display: inline-block; |
||||
width: 60px; |
||||
padding: 0px 10px; |
||||
} |
||||
|
||||
.litegraph .subgraph_property input { |
||||
width: 140px; |
||||
color: #999; |
||||
background-color: #1A1A1A; |
||||
border-radius: 4px; |
||||
border: 0; |
||||
margin-right: 10px; |
||||
padding: 4px; |
||||
padding-left: 10px; |
||||
} |
||||
|
||||
.litegraph .subgraph_property button { |
||||
background-color: #1c1c1c; |
||||
color: #aaa; |
||||
border: 0; |
||||
border-radius: 2px; |
||||
padding: 4px 10px; |
||||
cursor: pointer; |
||||
} |
||||
|
||||
.litegraph .subgraph_property.extra { |
||||
color: #ccc; |
||||
} |
||||
|
||||
.litegraph .subgraph_property.extra input { |
||||
background-color: #111; |
||||
} |
||||
|
||||
.litegraph .bullet_icon { |
||||
margin-left: 10px; |
||||
border-radius: 10px; |
||||
width: 12px; |
||||
height: 12px; |
||||
background-color: #666; |
||||
display: inline-block; |
||||
margin-top: 2px; |
||||
margin-right: 4px; |
||||
transition: background-color 0.1s ease 0s; |
||||
-moz-transition: background-color 0.1s ease 0s; |
||||
} |
||||
|
||||
.litegraph .bullet_icon:hover { |
||||
background-color: #698; |
||||
cursor: pointer; |
||||
} |
||||
|
||||
/* OLD */ |
||||
|
||||
.graphcontextmenu { |
||||
padding: 4px; |
||||
min-width: 100px; |
||||
} |
||||
|
||||
.graphcontextmenu-title { |
||||
color: #dde; |
||||
background-color: #222; |
||||
margin: 0; |
||||
padding: 2px; |
||||
cursor: default; |
||||
} |
||||
|
||||
.graphmenu-entry { |
||||
box-sizing: border-box; |
||||
margin: 2px; |
||||
padding-left: 20px; |
||||
user-select: none; |
||||
-moz-user-select: none; |
||||
-webkit-user-select: none; |
||||
transition: all linear 0.3s; |
||||
} |
||||
|
||||
.graphmenu-entry.event, |
||||
.litemenu-entry.event { |
||||
border-left: 8px solid orange; |
||||
padding-left: 12px; |
||||
} |
||||
|
||||
.graphmenu-entry.disabled { |
||||
opacity: 0.3; |
||||
} |
||||
|
||||
.graphmenu-entry.submenu { |
||||
border-right: 2px solid #eee; |
||||
} |
||||
|
||||
.graphmenu-entry:hover { |
||||
background-color: #555; |
||||
} |
||||
|
||||
.graphmenu-entry.separator { |
||||
background-color: #111; |
||||
border-bottom: 1px solid #666; |
||||
height: 1px; |
||||
width: calc(100% - 20px); |
||||
-moz-width: calc(100% - 20px); |
||||
-webkit-width: calc(100% - 20px); |
||||
} |
||||
|
||||
.graphmenu-entry .property_name { |
||||
display: inline-block; |
||||
text-align: left; |
||||
min-width: 80px; |
||||
min-height: 1.2em; |
||||
} |
||||
|
||||
.graphmenu-entry .property_value, |
||||
.litemenu-entry .property_value { |
||||
display: inline-block; |
||||
background-color: rgba(0, 0, 0, 0.5); |
||||
text-align: right; |
||||
min-width: 80px; |
||||
min-height: 1.2em; |
||||
vertical-align: middle; |
||||
padding-right: 10px; |
||||
} |
||||
|
||||
.graphdialog { |
||||
position: absolute; |
||||
top: 10px; |
||||
left: 10px; |
||||
min-height: 2em; |
||||
background-color: #333; |
||||
font-size: 1.2em; |
||||
box-shadow: 0 0 10px black !important; |
||||
z-index: 10; |
||||
} |
||||
|
||||
.graphdialog.rounded { |
||||
border-radius: 12px; |
||||
padding-right: 2px; |
||||
} |
||||
|
||||
.graphdialog .name { |
||||
display: inline-block; |
||||
min-width: 60px; |
||||
min-height: 1.5em; |
||||
padding-left: 10px; |
||||
} |
||||
|
||||
.graphdialog input, |
||||
.graphdialog textarea, |
||||
.graphdialog select { |
||||
margin: 3px; |
||||
min-width: 60px; |
||||
min-height: 1.5em; |
||||
background-color: black; |
||||
border: 0; |
||||
color: white; |
||||
padding-left: 10px; |
||||
outline: none; |
||||
} |
||||
|
||||
.graphdialog textarea { |
||||
min-height: 150px; |
||||
} |
||||
|
||||
.graphdialog button { |
||||
margin-top: 3px; |
||||
vertical-align: top; |
||||
background-color: #999; |
||||
border: 0; |
||||
} |
||||
|
||||
.graphdialog button.rounded, |
||||
.graphdialog input.rounded { |
||||
border-radius: 0 12px 12px 0; |
||||
} |
||||
|
||||
.graphdialog .helper { |
||||
overflow: auto; |
||||
max-height: 200px; |
||||
} |
||||
|
||||
.graphdialog .help-item { |
||||
padding-left: 10px; |
||||
} |
||||
|
||||
.graphdialog .help-item:hover, |
||||
.graphdialog .help-item.selected { |
||||
cursor: pointer; |
||||
background-color: white; |
||||
color: black; |
||||
} |
||||
|
||||
.litegraph .dialog { |
||||
min-height: 0; |
||||
} |
||||
.litegraph .dialog .dialog-content { |
||||
display: block; |
||||
} |
||||
.litegraph .dialog .dialog-content .subgraph_property { |
||||
padding: 5px; |
||||
} |
||||
.litegraph .dialog .dialog-footer { |
||||
margin: 0; |
||||
} |
||||
.litegraph .dialog .dialog-footer .subgraph_property { |
||||
margin-top: 0; |
||||
display: flex; |
||||
align-items: center; |
||||
padding: 5px; |
||||
} |
||||
.litegraph .dialog .dialog-footer .subgraph_property .name { |
||||
flex: 1; |
||||
} |
||||
.litegraph .graphdialog { |
||||
display: flex; |
||||
align-items: center; |
||||
border-radius: 20px; |
||||
padding: 4px 10px; |
||||
position: fixed; |
||||
} |
||||
.litegraph .graphdialog .name { |
||||
padding: 0; |
||||
min-height: 0; |
||||
font-size: 16px; |
||||
vertical-align: middle; |
||||
} |
||||
.litegraph .graphdialog .value { |
||||
font-size: 16px; |
||||
min-height: 0; |
||||
margin: 0 10px; |
||||
padding: 2px 5px; |
||||
} |
||||
.litegraph .graphdialog input[type="checkbox"] { |
||||
width: 16px; |
||||
height: 16px; |
||||
} |
||||
.litegraph .graphdialog button { |
||||
padding: 4px 18px; |
||||
border-radius: 20px; |
||||
cursor: pointer; |
||||
} |
||||
|
||||
Loading…
Reference in new issue