Image processing changes (51f81efb) · Commits · github_fork / Stable Diffusion Webui

modules/processing.py

+38 −38

Original line number	Diff line number	Diff line
		@@ -528,7 +528,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
		p.all_negative_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_negative_styles_to_prompt(p.negative_prompt, p.styles)]

		if type(p) == StableDiffusionProcessingTxt2Img:
		if p.enable_hr and p.hr_prompt != '':
		if p.enable_hr and p.hr_prompt == '':
		p.all_hr_prompts, p.all_hr_negative_prompts = p.all_prompts, p.all_negative_prompts
		elif p.enable_hr and p.hr_prompt != '':
		if type(p.prompt) == list:
		p.all_hr_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, p.styles) for x in p.hr_prompt]
		else:
		@@ -555,14 +557,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
		if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
		model_hijack.embedding_db.load_textual_inversion_embeddings()

		_, extra_network_data = extra_networks.parse_prompts(p.all_prompts[0:1])
		if type(p) == StableDiffusionProcessingTxt2Img:
		if p.enable_hr and p.hr_prompt != '':
		_, hr_extra_network_data = extra_networks.parse_prompts(p.all_hr_prompts[0:1])
		if p.all_hr_prompts != p.all_prompts:
		extra_network_data.update(hr_extra_network_data)


		if p.scripts is not None:
		p.scripts.process(p)

		@@ -600,13 +594,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
		if shared.opts.live_previews_enable and opts.show_progress_type == "Approx NN":
		sd_vae_approx.model()

		if not p.disable_extra_networks:
		extra_networks.activate(p, extra_network_data)

		with open(os.path.join(paths.data_path, "params.txt"), "w", encoding="utf8") as file:
		processed = Processed(p, [], p.seed, "")
		file.write(processed.infotext(p, 0))

		if state.job_count == -1:
		state.job_count = p.n_iter

		@@ -623,7 +610,10 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
		negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]

		if type(p) == StableDiffusionProcessingTxt2Img:
		if p.enable_hr and p.hr_prompt != '':
		if p.enable_hr:
		if p.hr_prompt == '':
		hr_prompts, hr_negative_prompts = prompts, negative_prompts
		else:
		hr_prompts = p.all_hr_prompts[n * p.batch_size:(n + 1) * p.batch_size]
		hr_negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]

		@@ -633,19 +623,40 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
		if len(prompts) == 0:
		break

		prompts, _ = extra_networks.parse_prompts(prompts)
		prompts, extra_network_data = extra_networks.parse_prompts(prompts)
		if type(p) == StableDiffusionProcessingTxt2Img:
		if p.enable_hr and hr_prompts != prompts:
		_, hr_extra_network_data = extra_networks.parse_prompts(hr_prompts)
		extra_network_data.update(hr_extra_network_data)



		if not p.disable_extra_networks:
		with devices.autocast():
		extra_networks.activate(p, extra_network_data)

		if p.scripts is not None:
		p.scripts.process_batch(p, batch_number=n, prompts=prompts, seeds=seeds, subseeds=subseeds)

		# params.txt should be saved after scripts.process_batch, since the
		# infotext could be modified by that callback
		# Example: a wildcard processed by process_batch sets an extra model
		# strength, which is saved as "Model Strength: 1.0" in the infotext
		if n == 0:
		with open(os.path.join(paths.data_path, "params.txt"), "w", encoding="utf8") as file:
		processed = Processed(p, [], p.seed, "")
		file.write(processed.infotext(p, 0))

		uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps, cached_uc)
		c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps, cached_c)

		if type(p) == StableDiffusionProcessingTxt2Img:
		if p.enable_hr and p.hr_prompt != '':
		hr_uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, hr_negative_prompts, p.steps,
		cached_uc)
		hr_c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, hr_prompts, p.steps,
		cached_c)
		if p.enable_hr:
		if prompts != hr_prompts:
		hr_uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, hr_negative_prompts, p.steps, cached_uc)
		hr_c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, hr_prompts, p.steps, cached_c)
		else:
		hr_uc, hr_c = uc, c

		if len(model_hijack.comments) > 0:
		for comment in model_hijack.comments:
		@@ -658,20 +669,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
		with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast():
		if type(p) == StableDiffusionProcessingTxt2Img:
		if p.enable_hr:
		if p.hr_prompt != '':
		samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, hr_conditioning=hr_c, hr_unconditional_conditioning=hr_uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)
		else:
		samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, hr_conditioning=c,
		hr_unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds,
		subseed_strength=p.subseed_strength, prompts=prompts)
		else:
		samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds,
		subseed_strength=p.subseed_strength, prompts=prompts)

		else:
		samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds,
		subseeds=subseeds,
		subseed_strength=p.subseed_strength, prompts=prompts)
		samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)

		x_samples_ddim = [decode_first_stage(p.sd_model, samples_ddim[i:i+1].to(dtype=devices.dtype_vae))[0].cpu() for i in range(samples_ddim.size(0))]
		for x in x_samples_ddim:

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