send noisy latent into refiner without adding noise (68659838) · Commits · github_fork / Stable Diffusion Webui

modules/processing.py

+17 −16

Original line number	Diff line number	Diff line
		@@ -384,11 +384,11 @@ class StableDiffusionProcessing:
		shared.state.nextjob()

		stopped_at = self.sampler.stop_at
		noisy_output = self.sampler.noisy_output
		self.sampler = None

		a_is_sdxl = shared.sd_model.is_sdxl

		decoded_samples = decode_latent_batch(shared.sd_model, samples, target_device=devices.cpu, check_for_nans=True)
		decoded_noisy = decode_latent_batch(shared.sd_model, noisy_output, target_device=devices.cpu, check_for_nans=True)

		refiner_checkpoint_info = sd_models.get_closet_checkpoint_match(shared.opts.sd_refiner_checkpoint)
		if refiner_checkpoint_info is None:
		@@ -408,21 +408,21 @@ class StableDiffusionProcessing:
		b_is_sdxl = shared.sd_model.is_sdxl

		if a_is_sdxl != b_is_sdxl:
		decoded_samples = torch.stack(decoded_samples).float()
		decoded_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
		latent = images_tensor_to_samples(decoded_samples, approximation_indexes.get(opts.sd_vae_encode_method), shared.sd_model)
		decoded_noisy = torch.stack(decoded_noisy).float()
		decoded_noisy = torch.clamp((decoded_noisy + 1.0) / 2.0, min=0.0, max=1.0)
		noisy_latent = images_tensor_to_samples(decoded_noisy, approximation_indexes.get(opts.sd_vae_encode_method), shared.sd_model)
		else:
		latent = samples
		noisy_latent = noisy_output

		x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=self.seeds, subseeds=self.subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
		x = torch.zeros_like(noisy_latent)

		with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast():
		denoising_strength = self.denoising_strength

		self.denoising_strength = 1.0 - stopped_at / self.steps
		self.image_conditioning = txt2img_image_conditioning(shared.sd_model, latent, self.width, self.height)
		self.denoising_strength = 1.0 - (stopped_at + 1) / self.steps
		self.image_conditioning = txt2img_image_conditioning(shared.sd_model, noisy_latent, self.width, self.height)
		self.sampler = sd_samplers.create_sampler(self.sampler_name, shared.sd_model)
		samples = self.sampler.sample_img2img(self, latent, x, self.c, self.uc, image_conditioning=self.image_conditioning, steps=max(1, self.steps - stopped_at - 1))
		samples = self.sampler.sample_img2img(self, noisy_latent, x, self.c, self.uc, image_conditioning=self.image_conditioning, steps=max(1, self.steps - stopped_at - 1))

		self.denoising_strength = denoising_strength

		@@ -823,6 +823,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
		if state.interrupted:
		break

		sd_models.reload_model_weights() # model can be changed for example by refiner

		p.prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
		p.negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
		p.seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
		@@ -862,10 +864,12 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
		if p.n_iter > 1:
		shared.state.job = f"Batch {n+1} out of {p.n_iter}"

		with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast():
		p.sampler = sd_samplers.create_sampler(p.sampler_name, p.sd_model)

		if have_refiner:
		p.sampler.stop_at = max(1, int(shared.opts.sd_refiner_switch_at * p.steps - 1))

		with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast():
		samples_ddim = p.sample(conditioning=p.c, unconditional_conditioning=p.uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.prompts)

		if opts.sd_vae_decode_method != 'Full':
		@@ -1056,8 +1060,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
		self.hr_uc = None

		def init(self, all_prompts, all_seeds, all_subseeds):
		self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)

		if self.enable_hr:
		if self.hr_checkpoint_name:
		self.hr_checkpoint_info = sd_models.get_closet_checkpoint_match(self.hr_checkpoint_name)
		@@ -1355,7 +1357,6 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
		self.image_conditioning = None

		def init(self, all_prompts, all_seeds, all_subseeds):
		self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
		crop_region = None

		image_mask = self.image_mask

modules/sd_samplers_kdiffusion.py

+2 −0

Original line number	Diff line number	Diff line
		@@ -276,6 +276,7 @@ class KDiffusionSampler:
		self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
		self.sampler_noises = None
		self.stop_at = None
		self.noisy_output = None
		self.eta = None
		self.config = None # set by the function calling the constructor
		self.last_latent = None
		@@ -297,6 +298,7 @@ class KDiffusionSampler:
		if opts.live_preview_content == "Combined":
		sd_samplers_common.store_latent(latent)
		self.last_latent = latent
		self.noisy_output = d['x']

		if self.stop_at is not None and step > self.stop_at:
		raise sd_samplers_common.InterruptedException

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