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66 lines
2.9 KiB
66 lines
2.9 KiB
import torch |
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from comfy.ldm.modules.diffusionmodules.openaimodel import UNetModel |
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from comfy.ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugmentation |
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from comfy.ldm.modules.diffusionmodules.util import make_beta_schedule |
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import numpy as np |
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class BaseModel(torch.nn.Module): |
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def __init__(self, unet_config, v_prediction=False): |
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super().__init__() |
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self.register_schedule(given_betas=None, beta_schedule="linear", timesteps=1000, linear_start=0.00085, linear_end=0.012, cosine_s=8e-3) |
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self.diffusion_model = UNetModel(**unet_config) |
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self.v_prediction = v_prediction |
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if self.v_prediction: |
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self.parameterization = "v" |
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else: |
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self.parameterization = "eps" |
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if "adm_in_channels" in unet_config: |
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self.adm_channels = unet_config["adm_in_channels"] |
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else: |
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self.adm_channels = 0 |
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print("v_prediction", v_prediction) |
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print("adm", self.adm_channels) |
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def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000, |
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linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): |
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if given_betas is not None: |
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betas = given_betas |
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else: |
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betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s) |
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alphas = 1. - betas |
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alphas_cumprod = np.cumprod(alphas, axis=0) |
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alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1]) |
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timesteps, = betas.shape |
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self.num_timesteps = int(timesteps) |
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self.linear_start = linear_start |
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self.linear_end = linear_end |
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self.register_buffer('betas', torch.tensor(betas, dtype=torch.float32)) |
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self.register_buffer('alphas_cumprod', torch.tensor(alphas_cumprod, dtype=torch.float32)) |
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self.register_buffer('alphas_cumprod_prev', torch.tensor(alphas_cumprod_prev, dtype=torch.float32)) |
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def apply_model(self, x, t, c_concat=None, c_crossattn=None, c_adm=None, control=None, transformer_options={}): |
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if c_concat is not None: |
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xc = torch.cat([x] + c_concat, dim=1) |
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else: |
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xc = x |
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context = torch.cat(c_crossattn, 1) |
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return self.diffusion_model(xc, t, context=context, y=c_adm, control=control, transformer_options=transformer_options) |
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def get_dtype(self): |
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return self.diffusion_model.dtype |
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def is_adm(self): |
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return self.adm_channels > 0 |
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class SD21UNCLIP(BaseModel): |
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def __init__(self, unet_config, noise_aug_config, v_prediction=True): |
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super().__init__(unet_config, v_prediction) |
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self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**noise_aug_config) |
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class SDInpaint(BaseModel): |
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def __init__(self, unet_config, v_prediction=False): |
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super().__init__(unet_config, v_prediction) |
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self.concat_keys = ("mask", "masked_image")
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