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Sampling code changes.

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apply_model in model_base now returns the denoised output.

This means that sampling_function now computes things on the denoised
output instead of the model output. This should make things more consistent
across current and future models.
This commit is contained in:
comfyanonymous
2023-10-31 17:33:43 -04:00
parent c837a173fa
commit 1777b54d02
3 changed files with 136 additions and 65 deletions
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8
comfy/extra_samplers/uni_pc.py
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@@ -852,6 +852,12 @@ class SigmaConvert:
log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff))
return log_mean_coeff - log_std
def predict_eps_sigma(model, input, sigma_in, **kwargs):
sigma = sigma_in.view(sigma_in.shape[:1] + (1,) * (input.ndim - 1))
input = input * ((sigma ** 2 + 1.0) ** 0.5)
return (input - model(input, sigma_in, **kwargs)) / sigma
def sample_unipc(model, noise, image, sigmas, sampling_function, max_denoise, extra_args=None, callback=None, disable=False, noise_mask=None, variant='bh1'):
timesteps = sigmas.clone()
if sigmas[-1] == 0:
@@ -874,7 +880,7 @@ def sample_unipc(model, noise, image, sigmas, sampling_function, max_denoise, ex
model_type = "noise"
model_fn = model_wrapper(
model.predict_eps_sigma,
lambda input, sigma, **kwargs: predict_eps_sigma(model, input, sigma, **kwargs),
ns,
model_type=model_type,
guidance_type="uncond",