lllyasviel/stable-diffusion-webui-forge
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lllyasviel
2024-01-27 13:53:18 -08:00
parent 2591f16eb3
commit 7b1a1e510f
2 changed files with 25 additions and 18 deletions
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10
modules/sd_samplers_cfg_denoiser.py
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@@ -100,15 +100,9 @@ class CFGDenoiser(torch.nn.Module):
cond = self.sampler.sampler_extra_args['cond']
uncond = self.sampler.sampler_extra_args['uncond']
# at self.image_cfg_scale == 1.0 produced results for edit model are the same as with normal sampling,
# so is_edit_model is set to False to support AND composition.
is_edit_model = shared.sd_model.cond_stage_key == "edit" and self.image_cfg_scale is not None and self.image_cfg_scale != 1.0
conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
cond = prompt_parser.reconstruct_multicond_batch(cond, self.step)
uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
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 we use masks, blending between the denoised and original latent images occurs here.
def apply_blend(current_latent):
blended_latent = current_latent * self.nmask + self.init_latent * self.mask
@@ -125,7 +119,7 @@ class CFGDenoiser(torch.nn.Module):
if self.mask_before_denoising and self.mask is not None:
x = apply_blend(x)
denoiser_params = CFGDenoiserParams(x, image_cond, sigma, state.sampling_step, state.sampling_steps, tensor, uncond, self)
denoiser_params = CFGDenoiserParams(x, image_cond, sigma, state.sampling_step, state.sampling_steps, cond, uncond, self)
cfg_denoiser_callback(denoiser_params)
denoised = forge_sampler.forge_sample(self, denoiser_params=denoiser_params, cond_scale=cond_scale)