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dcec1047e6
This doesn't affect how percentages behave in the frontend but breaks things if you relied on them in the backend. percent_to_sigma goes from 0 to 1.0 instead of 1.0 to 0 for less confusion. Make percent 0 return an extremely large sigma and percent 1.0 return a zero one to fix imprecision.
86 lines
3.4 KiB
Python
86 lines
3.4 KiB
Python
import torch
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import numpy as np
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from comfy.ldm.modules.diffusionmodules.util import make_beta_schedule
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class EPS:
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def calculate_input(self, sigma, noise):
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sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1))
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return noise / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
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def calculate_denoised(self, sigma, model_output, model_input):
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sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
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return model_input - model_output * sigma
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class V_PREDICTION(EPS):
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def calculate_denoised(self, sigma, model_output, model_input):
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sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
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return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) - model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
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class ModelSamplingDiscrete(torch.nn.Module):
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def __init__(self, model_config=None):
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super().__init__()
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beta_schedule = "linear"
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if model_config is not None:
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beta_schedule = model_config.beta_schedule
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self._register_schedule(given_betas=None, beta_schedule=beta_schedule, timesteps=1000, linear_start=0.00085, linear_end=0.012, cosine_s=8e-3)
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self.sigma_data = 1.0
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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 = torch.tensor(np.cumprod(alphas, axis=0), dtype=torch.float32)
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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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sigmas = ((1 - alphas_cumprod) / alphas_cumprod) ** 0.5
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self.set_sigmas(sigmas)
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def set_sigmas(self, sigmas):
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self.register_buffer('sigmas', sigmas)
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self.register_buffer('log_sigmas', sigmas.log())
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@property
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def sigma_min(self):
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return self.sigmas[0]
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@property
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def sigma_max(self):
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return self.sigmas[-1]
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def timestep(self, sigma):
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log_sigma = sigma.log()
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dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None]
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return dists.abs().argmin(dim=0).view(sigma.shape)
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def sigma(self, timestep):
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t = torch.clamp(timestep.float(), min=0, max=(len(self.sigmas) - 1))
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low_idx = t.floor().long()
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high_idx = t.ceil().long()
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w = t.frac()
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log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx]
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return log_sigma.exp()
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def percent_to_sigma(self, percent):
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if percent <= 0.0:
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return torch.tensor(999999999.9)
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if percent >= 1.0:
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return torch.tensor(0.0)
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percent = 1.0 - percent
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return self.sigma(torch.tensor(percent * 999.0))
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