mirror of
https://git.datalinker.icu/comfyanonymous/ComfyUI
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50 lines
1.4 KiB
Python
50 lines
1.4 KiB
Python
from . import gaussian_diffusion as gd
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from .respace import SpacedDiffusion, space_timesteps
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def create_diffusion(
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*,
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steps=1000,
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learn_sigma=True,
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sigma_small=False,
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noise_schedule="linear",
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use_kl=False,
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predict_type='epsilon',
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rescale_timesteps=False,
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rescale_learned_sigmas=False,
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timestep_respacing="",
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mse_loss_weight_type='constant',
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beta_start=0.0001,
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beta_end=0.02,
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noise_offset=0.0,
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):
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betas = gd.get_named_beta_schedule(noise_schedule, steps, beta_start, beta_end)
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if use_kl:
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loss_type = gd.LossType.RESCALED_KL
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elif rescale_learned_sigmas:
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loss_type = gd.LossType.RESCALED_MSE
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else:
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loss_type = gd.LossType.MSE
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if timestep_respacing is None or timestep_respacing == "":
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timestep_respacing = [steps]
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mean_type = gd.predict_type_dict[predict_type]
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return SpacedDiffusion(
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use_timesteps=space_timesteps(steps, timestep_respacing),
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betas=betas,
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model_mean_type=mean_type,
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model_var_type=(
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(
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gd.ModelVarType.FIXED_LARGE
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if not sigma_small
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else gd.ModelVarType.FIXED_SMALL
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)
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if not learn_sigma
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else gd.ModelVarType.LEARNED_RANGE
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),
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loss_type=loss_type,
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rescale_timesteps=rescale_timesteps,
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mse_loss_weight_type=mse_loss_weight_type,
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noise_offset=noise_offset,
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)
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