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remove mentions of specific samplers from CFG denoiser code

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AUTOMATIC1111 2024-07-06 10:31:08 +03:00
parent eb112c6f88
commit 0a6628bad0
2 changed files with 10 additions and 9 deletions
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16
modules/sd_samplers_cfg_denoiser.py
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@ -58,6 +58,9 @@ class CFGDenoiser(torch.nn.Module):
self.model_wrap = None self.model_wrap = None
self.p = None self.p = None
self.cond_scale_miltiplier = 1.0
self.need_last_noise_uncond = False
self.last_noise_uncond = None self.last_noise_uncond = None
# NOTE: masking before denoising can cause the original latents to be oversmoothed # NOTE: masking before denoising can cause the original latents to be oversmoothed
@ -162,8 +165,6 @@ class CFGDenoiser(torch.nn.Module):
# so is_edit_model is set to False to support AND composition. # 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 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
is_cfg_pp = 'CFG++' in self.sampler.config.name
conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step) conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step) uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
@ -277,18 +278,15 @@ class CFGDenoiser(torch.nn.Module):
denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps, self.inner_model) denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps, self.inner_model)
cfg_denoised_callback(denoised_params) cfg_denoised_callback(denoised_params)
if is_cfg_pp: if self.need_last_noise_uncond:
self.last_noise_uncond = x_out[-uncond.shape[0]:] self.last_noise_uncond = torch.clone(x_out[-uncond.shape[0]:])
self.last_noise_uncond = torch.clone(self.last_noise_uncond)
if is_edit_model: if is_edit_model:
denoised = self.combine_denoised_for_edit_model(x_out, cond_scale) denoised = self.combine_denoised_for_edit_model(x_out, cond_scale * self.cond_scale_miltiplier)
elif skip_uncond: elif skip_uncond:
denoised = self.combine_denoised(x_out, conds_list, uncond, 1.0) denoised = self.combine_denoised(x_out, conds_list, uncond, 1.0)
elif is_cfg_pp:
denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale/12.5) # CFG++ scale of (0, 1) maps to (1.0, 12.5)
else: else:
denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale) denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale * self.cond_scale_miltiplier)
# Blend in the original latents (after) # Blend in the original latents (after)
if not self.mask_before_denoising and self.mask is not None: if not self.mask_before_denoising and self.mask is not None:

3
modules/sd_samplers_timesteps_impl.py
View File

@ -52,6 +52,9 @@ def ddim_cfgpp(model, x, timesteps, extra_args=None, callback=None, disable=None
sqrt_one_minus_alphas = torch.sqrt(1 - alphas) sqrt_one_minus_alphas = torch.sqrt(1 - alphas)
sigmas = eta * np.sqrt((1 - alphas_prev.cpu().numpy()) / (1 - alphas.cpu()) * (1 - alphas.cpu() / alphas_prev.cpu().numpy())) sigmas = eta * np.sqrt((1 - alphas_prev.cpu().numpy()) / (1 - alphas.cpu()) * (1 - alphas.cpu() / alphas_prev.cpu().numpy()))
model.cond_scale_miltiplier = 1 / 12.5
model.need_last_noise_uncond = True
extra_args = {} if extra_args is None else extra_args extra_args = {} if extra_args is None else extra_args
s_in = x.new_ones((x.shape[0])) s_in = x.new_ones((x.shape[0]))
s_x = x.new_ones((x.shape[0], 1, 1, 1)) s_x = x.new_ones((x.shape[0], 1, 1, 1))