A custom blending function can be provided by p, replacing the use of soft_inpainting. (e90d4334) · Commits · github_fork / Stable Diffusion Webui

modules/sd_samplers_cfg_denoiser.py

+17 −17

Original line number	Diff line number	Diff line
		@@ -6,7 +6,6 @@ import modules.shared as shared
		from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
		from modules.script_callbacks import CFGDenoisedParams, cfg_denoised_callback
		from modules.script_callbacks import AfterCFGCallbackParams, cfg_after_cfg_callback
		import modules.soft_inpainting as si


		def catenate_conds(conds):
		@@ -44,7 +43,6 @@ class CFGDenoiser(torch.nn.Module):
		self.model_wrap = None
		self.mask = None
		self.nmask = None
		self.soft_inpainting: si.SoftInpaintingParameters = None
		self.init_latent = None
		self.steps = None
		"""number of steps as specified by user in UI"""
		@@ -94,7 +92,6 @@ class CFGDenoiser(torch.nn.Module):
		self.sampler.sampler_extra_args['uncond'] = uc

		def forward(self, x, sigma, uncond, cond, cond_scale, s_min_uncond, image_cond):

		if state.interrupted or state.skipped:
		raise sd_samplers_common.InterruptedException

		@@ -111,15 +108,24 @@ 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)"

		# If we use masks, blending between the denoised and original latent images occurs here.
		def apply_blend(latent):
		if hasattr(self.p, "denoiser_masked_blend_function") and callable(self.p.denoiser_masked_blend_function):
		return self.p.denoiser_masked_blend_function(
		self,
		# Using an argument dictionary so that arguments can be added without breaking extensions.
		args=
		{
		"denoiser": self,
		"current_latent": latent,
		"sigma": sigma
		})
		else:
		return self.init_latent * self.mask + self.nmask * latent

		# Blend in the original latents (before)
		if self.mask_before_denoising and self.mask is not None:
		if self.soft_inpainting is None:
		x = self.init_latent * self.mask + self.nmask * x
		else:
		x = si.latent_blend(self.soft_inpainting,
		self.init_latent,
		x,
		si.get_modified_nmask(self.soft_inpainting, self.nmask, sigma))
		x = apply_blend(x)

		batch_size = len(conds_list)
		repeats = [len(conds_list[i]) for i in range(batch_size)]
		@@ -222,13 +228,7 @@ class CFGDenoiser(torch.nn.Module):

		# Blend in the original latents (after)
		if not self.mask_before_denoising and self.mask is not None:
		if self.soft_inpainting is None:
		denoised = self.init_latent * self.mask + self.nmask * denoised
		else:
		denoised = si.latent_blend(self.soft_inpainting,
		self.init_latent,
		denoised,
		si.get_modified_nmask(self.soft_inpainting, self.nmask, sigma))
		denoised = apply_blend(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)

modules/sd_samplers_common.py

+0 −1

Original line number	Diff line number	Diff line
		@@ -277,7 +277,6 @@ class Sampler:
		self.model_wrap_cfg.p = p
		self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None
		self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None
		self.model_wrap_cfg.soft_inpainting = p.soft_inpainting if hasattr(p, 'soft_inpainting') else None
		self.model_wrap_cfg.step = 0
		self.model_wrap_cfg.image_cfg_scale = getattr(p, 'image_cfg_scale', None)
		self.eta = p.eta if p.eta is not None else getattr(opts, self.eta_option_field, 0.0)

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