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revert to applying mask before denoising for k-diffusion, like it was before

This commit is contained in:
AUTOMATIC1111 2023-08-14 08:59:15 +03:00
parent cda2f0a162
commit c1a31ec9f7
2 changed files with 6 additions and 1 deletions
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6
modules/sd_samplers_cfg_denoiser.py
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@ -56,6 +56,7 @@ class CFGDenoiser(torch.nn.Module):
self.sampler = sampler self.sampler = sampler
self.model_wrap = None self.model_wrap = None
self.p = None self.p = None
self.mask_before_denoising = False
@property @property
def inner_model(self): def inner_model(self):
@ -104,7 +105,7 @@ class CFGDenoiser(torch.nn.Module):
assert not is_edit_model or all(len(conds) == 1 for conds in conds_list), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)" assert not is_edit_model or all(len(conds) == 1 for conds in conds_list), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)"
if self.mask is not None: if self.mask_before_denoising and self.mask is not None:
x = self.init_latent * self.mask + self.nmask * x x = self.init_latent * self.mask + self.nmask * x
batch_size = len(conds_list) batch_size = len(conds_list)
@ -206,6 +207,9 @@ class CFGDenoiser(torch.nn.Module):
else: else:
denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale) denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
if not self.mask_before_denoising and self.mask is not None:
denoised = self.init_latent * self.mask + self.nmask * denoised
self.sampler.last_latent = self.get_pred_x0(torch.cat([x_in[i:i + 1] for i in denoised_image_indexes]), torch.cat([x_out[i:i + 1] for i in denoised_image_indexes]), sigma) self.sampler.last_latent = self.get_pred_x0(torch.cat([x_in[i:i + 1] for i in denoised_image_indexes]), torch.cat([x_out[i:i + 1] for i in denoised_image_indexes]), sigma)
if opts.live_preview_content == "Prompt": if opts.live_preview_content == "Prompt":

1
modules/sd_samplers_timesteps.py
View File

@ -49,6 +49,7 @@ class CFGDenoiserTimesteps(CFGDenoiser):
super().__init__(sampler) super().__init__(sampler)
self.alphas = shared.sd_model.alphas_cumprod self.alphas = shared.sd_model.alphas_cumprod
self.mask_before_denoising = True
def get_pred_x0(self, x_in, x_out, sigma): def get_pred_x0(self, x_in, x_out, sigma):
ts = sigma.to(dtype=int) ts = sigma.to(dtype=int)