lllyasviel/stable-diffusion-webui-forge
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fix ddim inpaint

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lllyasviel
2024-02-04 03:34:20 -08:00
parent cbb9c0e8be
commit d32b5e5458
1 changed files with 8 additions and 15 deletions
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23
modules/sd_samplers_cfg_denoiser.py
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@@ -158,9 +158,7 @@ class CFGDenoiser(torch.nn.Module):
if classic_ddim_eps_estimation:
acd = self.inner_model.inner_model.alphas_cumprod
fake_sigmas = ((1 - acd) / acd) ** 0.5
tid = sigma.round().long().clip(0, int(fake_sigmas.shape[0]))
real_sigma = fake_sigmas[tid]
sqrt_alpha_cumprod = torch.sqrt(acd[tid])
real_sigma = fake_sigmas[sigma.round().long().clip(0, int(fake_sigmas.shape[0]))]
real_sigma_data = 1.0
x = x * (real_sigma ** 2.0 + real_sigma_data ** 2.0) ** 0.5
sigma = real_sigma
@@ -174,24 +172,19 @@ class CFGDenoiser(torch.nn.Module):
# If we use masks, blending between the denoised and original latent images occurs here.
def apply_blend(current_latent):
init_latent = self.init_latent
if classic_ddim_eps_estimation:
init_latent = init_latent / sqrt_alpha_cumprod
blended_latent = current_latent * self.nmask + init_latent * self.mask
blended_latent = current_latent * self.nmask + self.init_latent * self.mask
if self.p.scripts is not None:
from modules import scripts
mba = scripts.MaskBlendArgs(current_latent, self.nmask, init_latent, self.mask, blended_latent, denoiser=self, sigma=sigma)
mba = scripts.MaskBlendArgs(current_latent, self.nmask, self.init_latent, self.mask, blended_latent, denoiser=self, sigma=sigma)
self.p.scripts.on_mask_blend(self.p, mba)
blended_latent = mba.blended_latent
return blended_latent
# Blend in the original latents (before)
if self.mask_before_denoising and self.mask is not None:
x = apply_blend(x)
# # Blend in the original latents (before, wrong method)
# if self.mask is not None:
# x = apply_blend(x)
denoiser_params = CFGDenoiserParams(x, image_cond, sigma, state.sampling_step, state.sampling_steps, cond, uncond, self)
cfg_denoiser_callback(denoiser_params)
@@ -199,8 +192,8 @@ class CFGDenoiser(torch.nn.Module):
denoised = forge_sampler.forge_sample(self, denoiser_params=denoiser_params,
cond_scale=cond_scale, cond_composition=cond_composition)
# Blend in the original latents (after)
if not self.mask_before_denoising and self.mask is not None:
# Blend in the original latents (after, correct method)
if self.mask is not None:
denoised = apply_blend(denoised)
preview = self.sampler.last_latent = denoised