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Move controlnet code to comfy/controlnet.py

pull/1340/head
comfyanonymous 1 year ago
parent
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c77f02e1c6
4 changed files with 533 additions and 527 deletions
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  1. 483
      comfy/controlnet.py
  2. 558
      comfy/sd.py
  3. 14
      comfy/utils.py
  4. 5
      nodes.py

483
comfy/controlnet.py
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@ -0,0 +1,483 @@
import torch
import math
import comfy.utils
import comfy.sd
import comfy.model_management
import comfy.model_detection
import comfy.cldm.cldm
import comfy.t2i_adapter.adapter
def broadcast_image_to(tensor, target_batch_size, batched_number):
current_batch_size = tensor.shape[0]
#print(current_batch_size, target_batch_size)
if current_batch_size == 1:
return tensor
per_batch = target_batch_size // batched_number
tensor = tensor[:per_batch]
if per_batch > tensor.shape[0]:
tensor = torch.cat([tensor] * (per_batch // tensor.shape[0]) + [tensor[:(per_batch % tensor.shape[0])]], dim=0)
current_batch_size = tensor.shape[0]
if current_batch_size == target_batch_size:
return tensor
else:
return torch.cat([tensor] * batched_number, dim=0)
class ControlBase:
def __init__(self, device=None):
self.cond_hint_original = None
self.cond_hint = None
self.strength = 1.0
self.timestep_percent_range = (1.0, 0.0)
self.timestep_range = None
if device is None:
device = comfy.model_management.get_torch_device()
self.device = device
self.previous_controlnet = None
self.global_average_pooling = False
def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(1.0, 0.0)):
self.cond_hint_original = cond_hint
self.strength = strength
self.timestep_percent_range = timestep_percent_range
return self
def pre_run(self, model, percent_to_timestep_function):
self.timestep_range = (percent_to_timestep_function(self.timestep_percent_range[0]), percent_to_timestep_function(self.timestep_percent_range[1]))
if self.previous_controlnet is not None:
self.previous_controlnet.pre_run(model, percent_to_timestep_function)
def set_previous_controlnet(self, controlnet):
self.previous_controlnet = controlnet
return self
def cleanup(self):
if self.previous_controlnet is not None:
self.previous_controlnet.cleanup()
if self.cond_hint is not None:
del self.cond_hint
self.cond_hint = None
self.timestep_range = None
def get_models(self):
out = []
if self.previous_controlnet is not None:
out += self.previous_controlnet.get_models()
return out
def copy_to(self, c):
c.cond_hint_original = self.cond_hint_original
c.strength = self.strength
c.timestep_percent_range = self.timestep_percent_range
def inference_memory_requirements(self, dtype):
if self.previous_controlnet is not None:
return self.previous_controlnet.inference_memory_requirements(dtype)
return 0
def control_merge(self, control_input, control_output, control_prev, output_dtype):
out = {'input':[], 'middle':[], 'output': []}
if control_input is not None:
for i in range(len(control_input)):
key = 'input'
x = control_input[i]
if x is not None:
x *= self.strength
if x.dtype != output_dtype:
x = x.to(output_dtype)
out[key].insert(0, x)
if control_output is not None:
for i in range(len(control_output)):
if i == (len(control_output) - 1):
key = 'middle'
index = 0
else:
key = 'output'
index = i
x = control_output[i]
if x is not None:
if self.global_average_pooling:
x = torch.mean(x, dim=(2, 3), keepdim=True).repeat(1, 1, x.shape[2], x.shape[3])
x *= self.strength
if x.dtype != output_dtype:
x = x.to(output_dtype)
out[key].append(x)
if control_prev is not None:
for x in ['input', 'middle', 'output']:
o = out[x]
for i in range(len(control_prev[x])):
prev_val = control_prev[x][i]
if i >= len(o):
o.append(prev_val)
elif prev_val is not None:
if o[i] is None:
o[i] = prev_val
else:
o[i] += prev_val
return out
class ControlNet(ControlBase):
def __init__(self, control_model, global_average_pooling=False, device=None):
super().__init__(device)
self.control_model = control_model
self.control_model_wrapped = comfy.sd.ModelPatcher(self.control_model, load_device=comfy.model_management.get_torch_device(), offload_device=comfy.model_management.unet_offload_device())
self.global_average_pooling = global_average_pooling
def get_control(self, x_noisy, t, cond, batched_number):
control_prev = None
if self.previous_controlnet is not None:
control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number)
if self.timestep_range is not None:
if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]:
if control_prev is not None:
return control_prev
else:
return {}
output_dtype = x_noisy.dtype
if self.cond_hint is None or x_noisy.shape[2] * 8 != self.cond_hint.shape[2] or x_noisy.shape[3] * 8 != self.cond_hint.shape[3]:
if self.cond_hint is not None:
del self.cond_hint
self.cond_hint = None
self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * 8, x_noisy.shape[2] * 8, 'nearest-exact', "center").to(self.control_model.dtype).to(self.device)
if x_noisy.shape[0] != self.cond_hint.shape[0]:
self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number)
context = torch.cat(cond['c_crossattn'], 1)
y = cond.get('c_adm', None)
if y is not None:
y = y.to(self.control_model.dtype)
control = self.control_model(x=x_noisy.to(self.control_model.dtype), hint=self.cond_hint, timesteps=t, context=context.to(self.control_model.dtype), y=y)
return self.control_merge(None, control, control_prev, output_dtype)
def copy(self):
c = ControlNet(self.control_model, global_average_pooling=self.global_average_pooling)
self.copy_to(c)
return c
def get_models(self):
out = super().get_models()
out.append(self.control_model_wrapped)
return out
class ControlLoraOps:
class Linear(torch.nn.Module):
def __init__(self, in_features: int, out_features: int, bias: bool = True,
device=None, dtype=None) -> None:
factory_kwargs = {'device': device, 'dtype': dtype}
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.weight = None
self.up = None
self.down = None
self.bias = None
def forward(self, input):
if self.up is not None:
return torch.nn.functional.linear(input, self.weight.to(input.device) + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), self.bias)
else:
return torch.nn.functional.linear(input, self.weight.to(input.device), self.bias)
class Conv2d(torch.nn.Module):
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
bias=True,
padding_mode='zeros',
device=None,
dtype=None
):
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = kernel_size
self.stride = stride
self.padding = padding
self.dilation = dilation
self.transposed = False
self.output_padding = 0
self.groups = groups
self.padding_mode = padding_mode
self.weight = None
self.bias = None
self.up = None
self.down = None
def forward(self, input):
if self.up is not None:
return torch.nn.functional.conv2d(input, self.weight.to(input.device) + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), self.bias, self.stride, self.padding, self.dilation, self.groups)
else:
return torch.nn.functional.conv2d(input, self.weight.to(input.device), self.bias, self.stride, self.padding, self.dilation, self.groups)
def conv_nd(self, dims, *args, **kwargs):
if dims == 2:
return self.Conv2d(*args, **kwargs)
else:
raise ValueError(f"unsupported dimensions: {dims}")
class ControlLora(ControlNet):
def __init__(self, control_weights, global_average_pooling=False, device=None):
ControlBase.__init__(self, device)
self.control_weights = control_weights
self.global_average_pooling = global_average_pooling
def pre_run(self, model, percent_to_timestep_function):
super().pre_run(model, percent_to_timestep_function)
controlnet_config = model.model_config.unet_config.copy()
controlnet_config.pop("out_channels")
controlnet_config["hint_channels"] = self.control_weights["input_hint_block.0.weight"].shape[1]
controlnet_config["operations"] = ControlLoraOps()
self.control_model = comfy.cldm.cldm.ControlNet(**controlnet_config)
dtype = model.get_dtype()
self.control_model.to(dtype)
self.control_model.to(comfy.model_management.get_torch_device())
diffusion_model = model.diffusion_model
sd = diffusion_model.state_dict()
cm = self.control_model.state_dict()
for k in sd:
weight = sd[k]
if weight.device == torch.device("meta"): #lowvram NOTE: this depends on the inner working of the accelerate library so it might break.
key_split = k.split('.') # I have no idea why they don't just leave the weight there instead of using the meta device.
op = comfy.utils.get_attr(diffusion_model, '.'.join(key_split[:-1]))
weight = op._hf_hook.weights_map[key_split[-1]]
try:
comfy.utils.set_attr(self.control_model, k, weight)
except:
pass
for k in self.control_weights:
if k not in {"lora_controlnet"}:
comfy.utils.set_attr(self.control_model, k, self.control_weights[k].to(dtype).to(comfy.model_management.get_torch_device()))
def copy(self):
c = ControlLora(self.control_weights, global_average_pooling=self.global_average_pooling)
self.copy_to(c)
return c
def cleanup(self):
del self.control_model
self.control_model = None
super().cleanup()
def get_models(self):
out = ControlBase.get_models(self)
return out
def inference_memory_requirements(self, dtype):
return comfy.utils.calculate_parameters(self.control_weights) * comfy.model_management.dtype_size(dtype) + ControlBase.inference_memory_requirements(self, dtype)
def load_controlnet(ckpt_path, model=None):
controlnet_data = comfy.utils.load_torch_file(ckpt_path, safe_load=True)
if "lora_controlnet" in controlnet_data:
return ControlLora(controlnet_data)
controlnet_config = None
if "controlnet_cond_embedding.conv_in.weight" in controlnet_data: #diffusers format
use_fp16 = comfy.model_management.should_use_fp16()
controlnet_config = comfy.model_detection.unet_config_from_diffusers_unet(controlnet_data, use_fp16)
diffusers_keys = comfy.utils.unet_to_diffusers(controlnet_config)
diffusers_keys["controlnet_mid_block.weight"] = "middle_block_out.0.weight"
diffusers_keys["controlnet_mid_block.bias"] = "middle_block_out.0.bias"
count = 0
loop = True
while loop:
suffix = [".weight", ".bias"]
for s in suffix:
k_in = "controlnet_down_blocks.{}{}".format(count, s)
k_out = "zero_convs.{}.0{}".format(count, s)
if k_in not in controlnet_data:
loop = False
break
diffusers_keys[k_in] = k_out
count += 1
count = 0
loop = True
while loop:
suffix = [".weight", ".bias"]
for s in suffix:
if count == 0:
k_in = "controlnet_cond_embedding.conv_in{}".format(s)
else:
k_in = "controlnet_cond_embedding.blocks.{}{}".format(count - 1, s)
k_out = "input_hint_block.{}{}".format(count * 2, s)
if k_in not in controlnet_data:
k_in = "controlnet_cond_embedding.conv_out{}".format(s)
loop = False
diffusers_keys[k_in] = k_out
count += 1
new_sd = {}
for k in diffusers_keys:
if k in controlnet_data:
new_sd[diffusers_keys[k]] = controlnet_data.pop(k)
leftover_keys = controlnet_data.keys()
if len(leftover_keys) > 0:
print("leftover keys:", leftover_keys)
controlnet_data = new_sd
pth_key = 'control_model.zero_convs.0.0.weight'
pth = False
key = 'zero_convs.0.0.weight'
if pth_key in controlnet_data:
pth = True
key = pth_key
prefix = "control_model."
elif key in controlnet_data:
prefix = ""
else:
net = load_t2i_adapter(controlnet_data)
if net is None:
print("error checkpoint does not contain controlnet or t2i adapter data", ckpt_path)
return net
if controlnet_config is None:
use_fp16 = comfy.model_management.should_use_fp16()
controlnet_config = comfy.model_detection.model_config_from_unet(controlnet_data, prefix, use_fp16).unet_config
controlnet_config.pop("out_channels")
controlnet_config["hint_channels"] = controlnet_data["{}input_hint_block.0.weight".format(prefix)].shape[1]
control_model = comfy.cldm.cldm.ControlNet(**controlnet_config)
if pth:
if 'difference' in controlnet_data:
if model is not None:
comfy.model_management.load_models_gpu([model])
model_sd = model.model_state_dict()
for x in controlnet_data:
c_m = "control_model."
if x.startswith(c_m):
sd_key = "diffusion_model.{}".format(x[len(c_m):])
if sd_key in model_sd:
cd = controlnet_data[x]
cd += model_sd[sd_key].type(cd.dtype).to(cd.device)
else:
print("WARNING: Loaded a diff controlnet without a model. It will very likely not work.")
class WeightsLoader(torch.nn.Module):
pass
w = WeightsLoader()
w.control_model = control_model
missing, unexpected = w.load_state_dict(controlnet_data, strict=False)
else:
missing, unexpected = control_model.load_state_dict(controlnet_data, strict=False)
print(missing, unexpected)
if use_fp16:
control_model = control_model.half()
global_average_pooling = False
if ckpt_path.endswith("_shuffle.pth") or ckpt_path.endswith("_shuffle.safetensors") or ckpt_path.endswith("_shuffle_fp16.safetensors"): #TODO: smarter way of enabling global_average_pooling
global_average_pooling = True
control = ControlNet(control_model, global_average_pooling=global_average_pooling)
return control
class T2IAdapter(ControlBase):
def __init__(self, t2i_model, channels_in, device=None):
super().__init__(device)
self.t2i_model = t2i_model
self.channels_in = channels_in
self.control_input = None
def scale_image_to(self, width, height):
unshuffle_amount = self.t2i_model.unshuffle_amount
width = math.ceil(width / unshuffle_amount) * unshuffle_amount
height = math.ceil(height / unshuffle_amount) * unshuffle_amount
return width, height
def get_control(self, x_noisy, t, cond, batched_number):
control_prev = None
if self.previous_controlnet is not None:
control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number)
if self.timestep_range is not None:
if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]:
if control_prev is not None:
return control_prev
else:
return {}
if self.cond_hint is None or x_noisy.shape[2] * 8 != self.cond_hint.shape[2] or x_noisy.shape[3] * 8 != self.cond_hint.shape[3]:
if self.cond_hint is not None:
del self.cond_hint
self.control_input = None
self.cond_hint = None
width, height = self.scale_image_to(x_noisy.shape[3] * 8, x_noisy.shape[2] * 8)
self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, width, height, 'nearest-exact', "center").float().to(self.device)
if self.channels_in == 1 and self.cond_hint.shape[1] > 1:
self.cond_hint = torch.mean(self.cond_hint, 1, keepdim=True)
if x_noisy.shape[0] != self.cond_hint.shape[0]:
self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number)
if self.control_input is None:
self.t2i_model.to(x_noisy.dtype)
self.t2i_model.to(self.device)
self.control_input = self.t2i_model(self.cond_hint.to(x_noisy.dtype))
self.t2i_model.cpu()
control_input = list(map(lambda a: None if a is None else a.clone(), self.control_input))
mid = None
if self.t2i_model.xl == True:
mid = control_input[-1:]
control_input = control_input[:-1]
return self.control_merge(control_input, mid, control_prev, x_noisy.dtype)
def copy(self):
c = T2IAdapter(self.t2i_model, self.channels_in)
self.copy_to(c)
return c
def load_t2i_adapter(t2i_data):
keys = t2i_data.keys()
if 'adapter' in keys:
t2i_data = t2i_data['adapter']
keys = t2i_data.keys()
if "body.0.in_conv.weight" in keys:
cin = t2i_data['body.0.in_conv.weight'].shape[1]
model_ad = comfy.t2i_adapter.adapter.Adapter_light(cin=cin, channels=[320, 640, 1280, 1280], nums_rb=4)
elif 'conv_in.weight' in keys:
cin = t2i_data['conv_in.weight'].shape[1]
channel = t2i_data['conv_in.weight'].shape[0]
ksize = t2i_data['body.0.block2.weight'].shape[2]
use_conv = False
down_opts = list(filter(lambda a: a.endswith("down_opt.op.weight"), keys))
if len(down_opts) > 0:
use_conv = True
xl = False
if cin == 256:
xl = True
model_ad = comfy.t2i_adapter.adapter.Adapter(cin=cin, channels=[channel, channel*2, channel*4, channel*4][:4], nums_rb=2, ksize=ksize, sk=True, use_conv=use_conv, xl=xl)
else:
return None
missing, unexpected = model_ad.load_state_dict(t2i_data)
if len(missing) > 0:
print("t2i missing", missing)
if len(unexpected) > 0:
print("t2i unexpected", unexpected)
return T2IAdapter(model_ad, model_ad.input_channels)

558
comfy/sd.py
Unescape View File

@ -8,10 +8,9 @@ from comfy import model_management
from .ldm.util import instantiate_from_config from .ldm.util import instantiate_from_config
from .ldm.models.autoencoder import AutoencoderKL from .ldm.models.autoencoder import AutoencoderKL
import yaml import yaml
from .cldm import cldm
from .t2i_adapter import adapter
from . import utils import comfy.utils
from . import clip_vision from . import clip_vision
from . import gligen from . import gligen
from . import diffusers_convert from . import diffusers_convert
@ -23,6 +22,7 @@ from . import sd2_clip
from . import sdxl_clip from . import sdxl_clip
import comfy.lora import comfy.lora
import comfy.t2i_adapter.adapter
def load_model_weights(model, sd): def load_model_weights(model, sd):
m, u = model.load_state_dict(sd, strict=False) m, u = model.load_state_dict(sd, strict=False)
@ -50,26 +50,9 @@ def load_clip_weights(model, sd):
if ids.dtype == torch.float32: if ids.dtype == torch.float32:
sd['cond_stage_model.transformer.text_model.embeddings.position_ids'] = ids.round() sd['cond_stage_model.transformer.text_model.embeddings.position_ids'] = ids.round()
sd = utils.transformers_convert(sd, "cond_stage_model.model.", "cond_stage_model.transformer.text_model.", 24) sd = comfy.utils.transformers_convert(sd, "cond_stage_model.model.", "cond_stage_model.transformer.text_model.", 24)
return load_model_weights(model, sd) return load_model_weights(model, sd)
def set_attr(obj, attr, value):
attrs = attr.split(".")
for name in attrs[:-1]:
obj = getattr(obj, name)
prev = getattr(obj, attrs[-1])
setattr(obj, attrs[-1], torch.nn.Parameter(value))
del prev
def get_attr(obj, attr):
attrs = attr.split(".")
for name in attrs:
obj = getattr(obj, name)
return obj
class ModelPatcher: class ModelPatcher:
def __init__(self, model, load_device, offload_device, size=0, current_device=None): def __init__(self, model, load_device, offload_device, size=0, current_device=None):
self.size = size self.size = size
@ -224,7 +207,7 @@ class ModelPatcher:
else: else:
temp_weight = weight.to(torch.float32, copy=True) temp_weight = weight.to(torch.float32, copy=True)
out_weight = self.calculate_weight(self.patches[key], temp_weight, key).to(weight.dtype) out_weight = self.calculate_weight(self.patches[key], temp_weight, key).to(weight.dtype)
set_attr(self.model, key, out_weight) comfy.utils.set_attr(self.model, key, out_weight)
del temp_weight del temp_weight
if device_to is not None: if device_to is not None:
@ -327,7 +310,7 @@ class ModelPatcher:
keys = list(self.backup.keys()) keys = list(self.backup.keys())
for k in keys: for k in keys:
set_attr(self.model, k, self.backup[k]) comfy.utils.set_attr(self.model, k, self.backup[k])
self.backup = {} self.backup = {}
@ -431,7 +414,7 @@ class VAE:
self.first_stage_model = AutoencoderKL(**(config['params'])) self.first_stage_model = AutoencoderKL(**(config['params']))
self.first_stage_model = self.first_stage_model.eval() self.first_stage_model = self.first_stage_model.eval()
if ckpt_path is not None: if ckpt_path is not None:
sd = utils.load_torch_file(ckpt_path) sd = comfy.utils.load_torch_file(ckpt_path)
if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format
sd = diffusers_convert.convert_vae_state_dict(sd) sd = diffusers_convert.convert_vae_state_dict(sd)
self.first_stage_model.load_state_dict(sd, strict=False) self.first_stage_model.load_state_dict(sd, strict=False)
@ -444,29 +427,29 @@ class VAE:
self.first_stage_model.to(self.vae_dtype) self.first_stage_model.to(self.vae_dtype)
def decode_tiled_(self, samples, tile_x=64, tile_y=64, overlap = 16): def decode_tiled_(self, samples, tile_x=64, tile_y=64, overlap = 16):
steps = samples.shape[0] * utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x, tile_y, overlap) steps = samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x, tile_y, overlap)
steps += samples.shape[0] * utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x // 2, tile_y * 2, overlap) steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x // 2, tile_y * 2, overlap)
steps += samples.shape[0] * utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x * 2, tile_y // 2, overlap) steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x * 2, tile_y // 2, overlap)
pbar = utils.ProgressBar(steps) pbar = comfy.utils.ProgressBar(steps)
decode_fn = lambda a: (self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)) + 1.0).float() decode_fn = lambda a: (self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)) + 1.0).float()
output = torch.clamp(( output = torch.clamp((
(utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = 8, pbar = pbar) + (comfy.utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = 8, pbar = pbar) +
utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = 8, pbar = pbar) + comfy.utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = 8, pbar = pbar) +
utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = 8, pbar = pbar)) comfy.utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = 8, pbar = pbar))
/ 3.0) / 2.0, min=0.0, max=1.0) / 3.0) / 2.0, min=0.0, max=1.0)
return output return output
def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64): def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64):
steps = pixel_samples.shape[0] * utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap) steps = pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap)
steps += pixel_samples.shape[0] * utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x // 2, tile_y * 2, overlap) steps += pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x // 2, tile_y * 2, overlap)
steps += pixel_samples.shape[0] * utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap) steps += pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap)
pbar = utils.ProgressBar(steps) pbar = comfy.utils.ProgressBar(steps)
encode_fn = lambda a: self.first_stage_model.encode(2. * a.to(self.vae_dtype).to(self.device) - 1.).sample().float() encode_fn = lambda a: self.first_stage_model.encode(2. * a.to(self.vae_dtype).to(self.device) - 1.).sample().float()
samples = utils.tiled_scale(pixel_samples, encode_fn, tile_x, tile_y, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar) samples = comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x, tile_y, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)
samples += utils.tiled_scale(pixel_samples, encode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar) samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)
samples += utils.tiled_scale(pixel_samples, encode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar) samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)
samples /= 3.0 samples /= 3.0
return samples return samples
@ -528,481 +511,6 @@ class VAE:
def get_sd(self): def get_sd(self):
return self.first_stage_model.state_dict() return self.first_stage_model.state_dict()
def broadcast_image_to(tensor, target_batch_size, batched_number):
current_batch_size = tensor.shape[0]
#print(current_batch_size, target_batch_size)
if current_batch_size == 1:
return tensor
per_batch = target_batch_size // batched_number
tensor = tensor[:per_batch]
if per_batch > tensor.shape[0]:
tensor = torch.cat([tensor] * (per_batch // tensor.shape[0]) + [tensor[:(per_batch % tensor.shape[0])]], dim=0)
current_batch_size = tensor.shape[0]
if current_batch_size == target_batch_size:
return tensor
else:
return torch.cat([tensor] * batched_number, dim=0)
class ControlBase:
def __init__(self, device=None):
self.cond_hint_original = None
self.cond_hint = None
self.strength = 1.0
self.timestep_percent_range = (1.0, 0.0)
self.timestep_range = None
if device is None:
device = model_management.get_torch_device()
self.device = device
self.previous_controlnet = None
self.global_average_pooling = False
def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(1.0, 0.0)):
self.cond_hint_original = cond_hint
self.strength = strength
self.timestep_percent_range = timestep_percent_range
return self
def pre_run(self, model, percent_to_timestep_function):
self.timestep_range = (percent_to_timestep_function(self.timestep_percent_range[0]), percent_to_timestep_function(self.timestep_percent_range[1]))
if self.previous_controlnet is not None:
self.previous_controlnet.pre_run(model, percent_to_timestep_function)
def set_previous_controlnet(self, controlnet):
self.previous_controlnet = controlnet
return self
def cleanup(self):
if self.previous_controlnet is not None:
self.previous_controlnet.cleanup()
if self.cond_hint is not None:
del self.cond_hint
self.cond_hint = None
self.timestep_range = None
def get_models(self):
out = []
if self.previous_controlnet is not None:
out += self.previous_controlnet.get_models()
return out
def copy_to(self, c):
c.cond_hint_original = self.cond_hint_original
c.strength = self.strength
c.timestep_percent_range = self.timestep_percent_range
def inference_memory_requirements(self, dtype):
if self.previous_controlnet is not None:
return self.previous_controlnet.inference_memory_requirements(dtype)
return 0
def control_merge(self, control_input, control_output, control_prev, output_dtype):
out = {'input':[], 'middle':[], 'output': []}
if control_input is not None:
for i in range(len(control_input)):
key = 'input'
x = control_input[i]
if x is not None:
x *= self.strength
if x.dtype != output_dtype:
x = x.to(output_dtype)
out[key].insert(0, x)
if control_output is not None:
for i in range(len(control_output)):
if i == (len(control_output) - 1):
key = 'middle'
index = 0
else:
key = 'output'
index = i
x = control_output[i]
if x is not None:
if self.global_average_pooling:
x = torch.mean(x, dim=(2, 3), keepdim=True).repeat(1, 1, x.shape[2], x.shape[3])
x *= self.strength
if x.dtype != output_dtype:
x = x.to(output_dtype)
out[key].append(x)
if control_prev is not None:
for x in ['input', 'middle', 'output']:
o = out[x]
for i in range(len(control_prev[x])):
prev_val = control_prev[x][i]
if i >= len(o):
o.append(prev_val)
elif prev_val is not None:
if o[i] is None:
o[i] = prev_val
else:
o[i] += prev_val
return out
class ControlNet(ControlBase):
def __init__(self, control_model, global_average_pooling=False, device=None):
super().__init__(device)
self.control_model = control_model
self.control_model_wrapped = ModelPatcher(self.control_model, load_device=model_management.get_torch_device(), offload_device=model_management.unet_offload_device())
self.global_average_pooling = global_average_pooling
def get_control(self, x_noisy, t, cond, batched_number):
control_prev = None
if self.previous_controlnet is not None:
control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number)
if self.timestep_range is not None:
if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]:
if control_prev is not None:
return control_prev
else:
return {}
output_dtype = x_noisy.dtype
if self.cond_hint is None or x_noisy.shape[2] * 8 != self.cond_hint.shape[2] or x_noisy.shape[3] * 8 != self.cond_hint.shape[3]:
if self.cond_hint is not None:
del self.cond_hint
self.cond_hint = None
self.cond_hint = utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * 8, x_noisy.shape[2] * 8, 'nearest-exact', "center").to(self.control_model.dtype).to(self.device)
if x_noisy.shape[0] != self.cond_hint.shape[0]:
self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number)
context = torch.cat(cond['c_crossattn'], 1)
y = cond.get('c_adm', None)
if y is not None:
y = y.to(self.control_model.dtype)
control = self.control_model(x=x_noisy.to(self.control_model.dtype), hint=self.cond_hint, timesteps=t, context=context.to(self.control_model.dtype), y=y)
return self.control_merge(None, control, control_prev, output_dtype)
def copy(self):
c = ControlNet(self.control_model, global_average_pooling=self.global_average_pooling)
self.copy_to(c)
return c
def get_models(self):
out = super().get_models()
out.append(self.control_model_wrapped)
return out
class ControlLoraOps:
class Linear(torch.nn.Module):
def __init__(self, in_features: int, out_features: int, bias: bool = True,
device=None, dtype=None) -> None:
factory_kwargs = {'device': device, 'dtype': dtype}
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.weight = None
self.up = None
self.down = None
self.bias = None
def forward(self, input):
if self.up is not None:
return torch.nn.functional.linear(input, self.weight.to(input.device) + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), self.bias)
else:
return torch.nn.functional.linear(input, self.weight.to(input.device), self.bias)
class Conv2d(torch.nn.Module):
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
bias=True,
padding_mode='zeros',
device=None,
dtype=None
):
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = kernel_size
self.stride = stride
self.padding = padding
self.dilation = dilation
self.transposed = False
self.output_padding = 0
self.groups = groups
self.padding_mode = padding_mode
self.weight = None
self.bias = None
self.up = None
self.down = None
def forward(self, input):
if self.up is not None:
return torch.nn.functional.conv2d(input, self.weight.to(input.device) + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), self.bias, self.stride, self.padding, self.dilation, self.groups)
else:
return torch.nn.functional.conv2d(input, self.weight.to(input.device), self.bias, self.stride, self.padding, self.dilation, self.groups)
def conv_nd(self, dims, *args, **kwargs):
if dims == 2:
return self.Conv2d(*args, **kwargs)
else:
raise ValueError(f"unsupported dimensions: {dims}")
class ControlLora(ControlNet):
def __init__(self, control_weights, global_average_pooling=False, device=None):
ControlBase.__init__(self, device)
self.control_weights = control_weights
self.global_average_pooling = global_average_pooling
def pre_run(self, model, percent_to_timestep_function):
super().pre_run(model, percent_to_timestep_function)
controlnet_config = model.model_config.unet_config.copy()
controlnet_config.pop("out_channels")
controlnet_config["hint_channels"] = self.control_weights["input_hint_block.0.weight"].shape[1]
controlnet_config["operations"] = ControlLoraOps()
self.control_model = cldm.ControlNet(**controlnet_config)
dtype = model.get_dtype()
self.control_model.to(dtype)
self.control_model.to(model_management.get_torch_device())
diffusion_model = model.diffusion_model
sd = diffusion_model.state_dict()
cm = self.control_model.state_dict()
for k in sd:
weight = sd[k]
if weight.device == torch.device("meta"): #lowvram NOTE: this depends on the inner working of the accelerate library so it might break.
key_split = k.split('.') # I have no idea why they don't just leave the weight there instead of using the meta device.
op = get_attr(diffusion_model, '.'.join(key_split[:-1]))
weight = op._hf_hook.weights_map[key_split[-1]]
try:
set_attr(self.control_model, k, weight)
except:
pass
for k in self.control_weights:
if k not in {"lora_controlnet"}:
set_attr(self.control_model, k, self.control_weights[k].to(dtype).to(model_management.get_torch_device()))
def copy(self):
c = ControlLora(self.control_weights, global_average_pooling=self.global_average_pooling)
self.copy_to(c)
return c
def cleanup(self):
del self.control_model
self.control_model = None
super().cleanup()
def get_models(self):
out = ControlBase.get_models(self)
return out
def inference_memory_requirements(self, dtype):
return utils.calculate_parameters(self.control_weights) * model_management.dtype_size(dtype) + ControlBase.inference_memory_requirements(self, dtype)
def load_controlnet(ckpt_path, model=None):
controlnet_data = utils.load_torch_file(ckpt_path, safe_load=True)
if "lora_controlnet" in controlnet_data:
return ControlLora(controlnet_data)
controlnet_config = None
if "controlnet_cond_embedding.conv_in.weight" in controlnet_data: #diffusers format
use_fp16 = model_management.should_use_fp16()
controlnet_config = model_detection.unet_config_from_diffusers_unet(controlnet_data, use_fp16)
diffusers_keys = utils.unet_to_diffusers(controlnet_config)
diffusers_keys["controlnet_mid_block.weight"] = "middle_block_out.0.weight"
diffusers_keys["controlnet_mid_block.bias"] = "middle_block_out.0.bias"
count = 0
loop = True
while loop:
suffix = [".weight", ".bias"]
for s in suffix:
k_in = "controlnet_down_blocks.{}{}".format(count, s)
k_out = "zero_convs.{}.0{}".format(count, s)
if k_in not in controlnet_data:
loop = False
break
diffusers_keys[k_in] = k_out
count += 1
count = 0
loop = True
while loop:
suffix = [".weight", ".bias"]
for s in suffix:
if count == 0:
k_in = "controlnet_cond_embedding.conv_in{}".format(s)
else:
k_in = "controlnet_cond_embedding.blocks.{}{}".format(count - 1, s)
k_out = "input_hint_block.{}{}".format(count * 2, s)
if k_in not in controlnet_data:
k_in = "controlnet_cond_embedding.conv_out{}".format(s)
loop = False
diffusers_keys[k_in] = k_out
count += 1
new_sd = {}
for k in diffusers_keys:
if k in controlnet_data:
new_sd[diffusers_keys[k]] = controlnet_data.pop(k)
leftover_keys = controlnet_data.keys()
if len(leftover_keys) > 0:
print("leftover keys:", leftover_keys)
controlnet_data = new_sd
pth_key = 'control_model.zero_convs.0.0.weight'
pth = False
key = 'zero_convs.0.0.weight'
if pth_key in controlnet_data:
pth = True
key = pth_key
prefix = "control_model."
elif key in controlnet_data:
prefix = ""
else:
net = load_t2i_adapter(controlnet_data)
if net is None:
print("error checkpoint does not contain controlnet or t2i adapter data", ckpt_path)
return net
if controlnet_config is None:
use_fp16 = model_management.should_use_fp16()
controlnet_config = model_detection.model_config_from_unet(controlnet_data, prefix, use_fp16).unet_config
controlnet_config.pop("out_channels")
controlnet_config["hint_channels"] = controlnet_data["{}input_hint_block.0.weight".format(prefix)].shape[1]
control_model = cldm.ControlNet(**controlnet_config)
if pth:
if 'difference' in controlnet_data:
if model is not None:
model_management.load_models_gpu([model])
model_sd = model.model_state_dict()
for x in controlnet_data:
c_m = "control_model."
if x.startswith(c_m):
sd_key = "diffusion_model.{}".format(x[len(c_m):])
if sd_key in model_sd:
cd = controlnet_data[x]
cd += model_sd[sd_key].type(cd.dtype).to(cd.device)
else:
print("WARNING: Loaded a diff controlnet without a model. It will very likely not work.")
class WeightsLoader(torch.nn.Module):
pass
w = WeightsLoader()
w.control_model = control_model
missing, unexpected = w.load_state_dict(controlnet_data, strict=False)
else:
missing, unexpected = control_model.load_state_dict(controlnet_data, strict=False)
print(missing, unexpected)
if use_fp16:
control_model = control_model.half()
global_average_pooling = False
if ckpt_path.endswith("_shuffle.pth") or ckpt_path.endswith("_shuffle.safetensors") or ckpt_path.endswith("_shuffle_fp16.safetensors"): #TODO: smarter way of enabling global_average_pooling
global_average_pooling = True
control = ControlNet(control_model, global_average_pooling=global_average_pooling)
return control
class T2IAdapter(ControlBase):
def __init__(self, t2i_model, channels_in, device=None):
super().__init__(device)
self.t2i_model = t2i_model
self.channels_in = channels_in
self.control_input = None
def scale_image_to(self, width, height):
unshuffle_amount = self.t2i_model.unshuffle_amount
width = math.ceil(width / unshuffle_amount) * unshuffle_amount
height = math.ceil(height / unshuffle_amount) * unshuffle_amount
return width, height
def get_control(self, x_noisy, t, cond, batched_number):
control_prev = None
if self.previous_controlnet is not None:
control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number)
if self.timestep_range is not None:
if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]:
if control_prev is not None:
return control_prev
else:
return {}
if self.cond_hint is None or x_noisy.shape[2] * 8 != self.cond_hint.shape[2] or x_noisy.shape[3] * 8 != self.cond_hint.shape[3]:
if self.cond_hint is not None:
del self.cond_hint
self.control_input = None
self.cond_hint = None
width, height = self.scale_image_to(x_noisy.shape[3] * 8, x_noisy.shape[2] * 8)
self.cond_hint = utils.common_upscale(self.cond_hint_original, width, height, 'nearest-exact', "center").float().to(self.device)
if self.channels_in == 1 and self.cond_hint.shape[1] > 1:
self.cond_hint = torch.mean(self.cond_hint, 1, keepdim=True)
if x_noisy.shape[0] != self.cond_hint.shape[0]:
self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number)
if self.control_input is None:
self.t2i_model.to(x_noisy.dtype)
self.t2i_model.to(self.device)
self.control_input = self.t2i_model(self.cond_hint.to(x_noisy.dtype))
self.t2i_model.cpu()
control_input = list(map(lambda a: None if a is None else a.clone(), self.control_input))
mid = None
if self.t2i_model.xl == True:
mid = control_input[-1:]
control_input = control_input[:-1]
return self.control_merge(control_input, mid, control_prev, x_noisy.dtype)
def copy(self):
c = T2IAdapter(self.t2i_model, self.channels_in)
self.copy_to(c)
return c
def load_t2i_adapter(t2i_data):
keys = t2i_data.keys()
if 'adapter' in keys:
t2i_data = t2i_data['adapter']
keys = t2i_data.keys()
if "body.0.in_conv.weight" in keys:
cin = t2i_data['body.0.in_conv.weight'].shape[1]
model_ad = adapter.Adapter_light(cin=cin, channels=[320, 640, 1280, 1280], nums_rb=4)
elif 'conv_in.weight' in keys:
cin = t2i_data['conv_in.weight'].shape[1]
channel = t2i_data['conv_in.weight'].shape[0]
ksize = t2i_data['body.0.block2.weight'].shape[2]
use_conv = False
down_opts = list(filter(lambda a: a.endswith("down_opt.op.weight"), keys))
if len(down_opts) > 0:
use_conv = True
xl = False
if cin == 256:
xl = True
model_ad = adapter.Adapter(cin=cin, channels=[channel, channel*2, channel*4, channel*4][:4], nums_rb=2, ksize=ksize, sk=True, use_conv=use_conv, xl=xl)
else:
return None
missing, unexpected = model_ad.load_state_dict(t2i_data)
if len(missing) > 0:
print("t2i missing", missing)
if len(unexpected) > 0:
print("t2i unexpected", unexpected)
return T2IAdapter(model_ad, model_ad.input_channels)
class StyleModel: class StyleModel:
def __init__(self, model, device="cpu"): def __init__(self, model, device="cpu"):
self.model = model self.model = model
@ -1012,10 +520,10 @@ class StyleModel:
def load_style_model(ckpt_path): def load_style_model(ckpt_path):
model_data = utils.load_torch_file(ckpt_path, safe_load=True) model_data = comfy.utils.load_torch_file(ckpt_path, safe_load=True)
keys = model_data.keys() keys = model_data.keys()
if "style_embedding" in keys: if "style_embedding" in keys:
model = adapter.StyleAdapter(width=1024, context_dim=768, num_head=8, n_layes=3, num_token=8) model = comfy.t2i_adapter.adapter.StyleAdapter(width=1024, context_dim=768, num_head=8, n_layes=3, num_token=8)
else: else:
raise Exception("invalid style model {}".format(ckpt_path)) raise Exception("invalid style model {}".format(ckpt_path))
model.load_state_dict(model_data) model.load_state_dict(model_data)
@ -1025,14 +533,14 @@ def load_style_model(ckpt_path):
def load_clip(ckpt_paths, embedding_directory=None): def load_clip(ckpt_paths, embedding_directory=None):
clip_data = [] clip_data = []
for p in ckpt_paths: for p in ckpt_paths:
clip_data.append(utils.load_torch_file(p, safe_load=True)) clip_data.append(comfy.utils.load_torch_file(p, safe_load=True))
class EmptyClass: class EmptyClass:
pass pass
for i in range(len(clip_data)): for i in range(len(clip_data)):
if "transformer.resblocks.0.ln_1.weight" in clip_data[i]: if "transformer.resblocks.0.ln_1.weight" in clip_data[i]:
clip_data[i] = utils.transformers_convert(clip_data[i], "", "text_model.", 32) clip_data[i] = comfy.utils.transformers_convert(clip_data[i], "", "text_model.", 32)
clip_target = EmptyClass() clip_target = EmptyClass()
clip_target.params = {} clip_target.params = {}
@ -1061,7 +569,7 @@ def load_clip(ckpt_paths, embedding_directory=None):
return clip return clip
def load_gligen(ckpt_path): def load_gligen(ckpt_path):
data = utils.load_torch_file(ckpt_path, safe_load=True) data = comfy.utils.load_torch_file(ckpt_path, safe_load=True)
model = gligen.load_gligen(data) model = gligen.load_gligen(data)
if model_management.should_use_fp16(): if model_management.should_use_fp16():
model = model.half() model = model.half()
@ -1101,7 +609,7 @@ def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_cl
pass pass
if state_dict is None: if state_dict is None:
state_dict = utils.load_torch_file(ckpt_path) state_dict = comfy.utils.load_torch_file(ckpt_path)
class EmptyClass: class EmptyClass:
pass pass
@ -1148,7 +656,7 @@ def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_cl
return (ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=offload_device), clip, vae) return (ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=offload_device), clip, vae)
def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None): def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None):
sd = utils.load_torch_file(ckpt_path) sd = comfy.utils.load_torch_file(ckpt_path)
sd_keys = sd.keys() sd_keys = sd.keys()
clip = None clip = None
clipvision = None clipvision = None
@ -1156,7 +664,7 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
model = None model = None
clip_target = None clip_target = None
parameters = utils.calculate_parameters(sd, "model.diffusion_model.") parameters = comfy.utils.calculate_parameters(sd, "model.diffusion_model.")
fp16 = model_management.should_use_fp16(model_params=parameters) fp16 = model_management.should_use_fp16(model_params=parameters)
class WeightsLoader(torch.nn.Module): class WeightsLoader(torch.nn.Module):
@ -1206,8 +714,8 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
def load_unet(unet_path): #load unet in diffusers format def load_unet(unet_path): #load unet in diffusers format
sd = utils.load_torch_file(unet_path) sd = comfy.utils.load_torch_file(unet_path)
parameters = utils.calculate_parameters(sd) parameters = comfy.utils.calculate_parameters(sd)
fp16 = model_management.should_use_fp16(model_params=parameters) fp16 = model_management.should_use_fp16(model_params=parameters)
model_config = model_detection.model_config_from_diffusers_unet(sd, fp16) model_config = model_detection.model_config_from_diffusers_unet(sd, fp16)
@ -1215,7 +723,7 @@ def load_unet(unet_path): #load unet in diffusers format
print("ERROR UNSUPPORTED UNET", unet_path) print("ERROR UNSUPPORTED UNET", unet_path)
return None return None
diffusers_keys = utils.unet_to_diffusers(model_config.unet_config) diffusers_keys = comfy.utils.unet_to_diffusers(model_config.unet_config)
new_sd = {} new_sd = {}
for k in diffusers_keys: for k in diffusers_keys:
@ -1232,4 +740,4 @@ def load_unet(unet_path): #load unet in diffusers format
def save_checkpoint(output_path, model, clip, vae, metadata=None): def save_checkpoint(output_path, model, clip, vae, metadata=None):
model_management.load_models_gpu([model, clip.load_model()]) model_management.load_models_gpu([model, clip.load_model()])
sd = model.model.state_dict_for_saving(clip.get_sd(), vae.get_sd()) sd = model.model.state_dict_for_saving(clip.get_sd(), vae.get_sd())
utils.save_torch_file(sd, output_path, metadata=metadata) comfy.utils.save_torch_file(sd, output_path, metadata=metadata)

14
comfy/utils.py
Unescape View File

@ -237,6 +237,20 @@ def safetensors_header(safetensors_path, max_size=100*1024*1024):
return None return None
return f.read(length_of_header) return f.read(length_of_header)
def set_attr(obj, attr, value):
attrs = attr.split(".")
for name in attrs[:-1]:
obj = getattr(obj, name)
prev = getattr(obj, attrs[-1])
setattr(obj, attrs[-1], torch.nn.Parameter(value))
del prev
def get_attr(obj, attr):
attrs = attr.split(".")
for name in attrs:
obj = getattr(obj, name)
return obj
def bislerp(samples, width, height): def bislerp(samples, width, height):
def slerp(b1, b2, r): def slerp(b1, b2, r):
'''slerps batches b1, b2 according to ratio r, batches should be flat e.g. NxC''' '''slerps batches b1, b2 according to ratio r, batches should be flat e.g. NxC'''

5
nodes.py
Unescape View File

@ -22,6 +22,7 @@ import comfy.samplers
import comfy.sample import comfy.sample
import comfy.sd import comfy.sd
import comfy.utils import comfy.utils
import comfy.controlnet
import comfy.clip_vision import comfy.clip_vision
@ -569,7 +570,7 @@ class ControlNetLoader:
def load_controlnet(self, control_net_name): def load_controlnet(self, control_net_name):
controlnet_path = folder_paths.get_full_path("controlnet", control_net_name) controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
controlnet = comfy.sd.load_controlnet(controlnet_path) controlnet = comfy.controlnet.load_controlnet(controlnet_path)
return (controlnet,) return (controlnet,)
class DiffControlNetLoader: class DiffControlNetLoader:
@ -585,7 +586,7 @@ class DiffControlNetLoader:
def load_controlnet(self, model, control_net_name): def load_controlnet(self, model, control_net_name):
controlnet_path = folder_paths.get_full_path("controlnet", control_net_name) controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
controlnet = comfy.sd.load_controlnet(controlnet_path, model) controlnet = comfy.controlnet.load_controlnet(controlnet_path, model)
return (controlnet,) return (controlnet,)

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