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Merge pull request #12023 from AUTOMATIC1111/create_infotext_fix

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Create infotext fix
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AUTOMATIC1111 2023-07-26 08:07:07 +03:00
parent e16eb3d0cb
commit 5360ae2cc5
1 changed files with 25 additions and 38 deletions
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61
modules/processing.py
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@ -600,9 +600,13 @@ def program_version():
return res return res
def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0, use_main_prompt=False): def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0, use_main_prompt=False, index=None, all_negative_prompts=None):
if index is None:
index = position_in_batch + iteration * p.batch_size index = position_in_batch + iteration * p.batch_size
if all_negative_prompts is None:
all_negative_prompts = p.all_negative_prompts
clip_skip = getattr(p, 'clip_skip', opts.CLIP_stop_at_last_layers) clip_skip = getattr(p, 'clip_skip', opts.CLIP_stop_at_last_layers)
enable_hr = getattr(p, 'enable_hr', False) enable_hr = getattr(p, 'enable_hr', False)
token_merging_ratio = p.get_token_merging_ratio() token_merging_ratio = p.get_token_merging_ratio()
@ -617,12 +621,12 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
"Sampler": p.sampler_name, "Sampler": p.sampler_name,
"CFG scale": p.cfg_scale, "CFG scale": p.cfg_scale,
"Image CFG scale": getattr(p, 'image_cfg_scale', None), "Image CFG scale": getattr(p, 'image_cfg_scale', None),
"Seed": all_seeds[index], "Seed": p.all_seeds[0] if use_main_prompt else all_seeds[index],
"Face restoration": (opts.face_restoration_model if p.restore_faces else None), "Face restoration": (opts.face_restoration_model if p.restore_faces else None),
"Size": f"{p.width}x{p.height}", "Size": f"{p.width}x{p.height}",
"Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash), "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
"Model": (None if not opts.add_model_name_to_info else shared.sd_model.sd_checkpoint_info.name_for_extra), "Model": (None if not opts.add_model_name_to_info else shared.sd_model.sd_checkpoint_info.name_for_extra),
"Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]), "Variation seed": (None if p.subseed_strength == 0 else (p.all_subseeds[0] if use_main_prompt else all_subseeds[index])),
"Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength), "Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
"Seed resize from": (None if p.seed_resize_from_w <= 0 or p.seed_resize_from_h <= 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"), "Seed resize from": (None if p.seed_resize_from_w <= 0 or p.seed_resize_from_h <= 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
"Denoising strength": getattr(p, 'denoising_strength', None), "Denoising strength": getattr(p, 'denoising_strength', None),
@ -642,7 +646,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None]) generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None])
prompt_text = p.prompt if use_main_prompt else all_prompts[index] prompt_text = p.prompt if use_main_prompt else all_prompts[index]
negative_prompt_text = f"\nNegative prompt: {p.all_negative_prompts[index]}" if p.all_negative_prompts[index] else "" negative_prompt_text = f"\nNegative prompt: {all_negative_prompts[index]}" if all_negative_prompts[index] else ""
return f"{prompt_text}{negative_prompt_text}\n{generation_params_text}".strip() return f"{prompt_text}{negative_prompt_text}\n{generation_params_text}".strip()
@ -716,29 +720,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
else: else:
p.all_subseeds = [int(subseed) + x for x in range(len(p.all_prompts))] p.all_subseeds = [int(subseed) + x for x in range(len(p.all_prompts))]
def infotext(iteration=0, position_in_batch=0, use_main_prompt=False):
all_prompts = p.all_prompts[:]
all_negative_prompts = p.all_negative_prompts[:]
all_seeds = p.all_seeds[:]
all_subseeds = p.all_subseeds[:]
# apply changes to generation data
all_prompts[iteration * p.batch_size:(iteration + 1) * p.batch_size] = p.prompts
all_negative_prompts[iteration * p.batch_size:(iteration + 1) * p.batch_size] = p.negative_prompts
all_seeds[iteration * p.batch_size:(iteration + 1) * p.batch_size] = p.seeds
all_subseeds[iteration * p.batch_size:(iteration + 1) * p.batch_size] = p.subseeds
# update p.all_negative_prompts in case extensions changed the size of the batch
# create_infotext below uses it
old_negative_prompts = p.all_negative_prompts
p.all_negative_prompts = all_negative_prompts
try:
return create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration, position_in_batch, use_main_prompt)
finally:
# restore p.all_negative_prompts in case extensions changed the size of the batch
p.all_negative_prompts = old_negative_prompts
if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings: if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
model_hijack.embedding_db.load_textual_inversion_embeddings() model_hijack.embedding_db.load_textual_inversion_embeddings()
@ -826,9 +807,15 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
if p.scripts is not None: if p.scripts is not None:
p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n) p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
postprocess_batch_list_args = scripts.PostprocessBatchListArgs(list(x_samples_ddim)) p.prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
p.scripts.postprocess_batch_list(p, postprocess_batch_list_args, batch_number=n) p.negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
x_samples_ddim = postprocess_batch_list_args.images
batch_params = scripts.PostprocessBatchListArgs(list(x_samples_ddim))
p.scripts.postprocess_batch_list(p, batch_params, batch_number=n)
x_samples_ddim = batch_params.images
def infotext(index=0, use_main_prompt=False):
return create_infotext(p, p.prompts, p.seeds, p.subseeds, use_main_prompt=use_main_prompt, index=index, all_negative_prompts=p.negative_prompts)
for i, x_sample in enumerate(x_samples_ddim): for i, x_sample in enumerate(x_samples_ddim):
p.batch_index = i p.batch_index = i
@ -838,7 +825,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
if p.restore_faces: if p.restore_faces:
if opts.save and not p.do_not_save_samples and opts.save_images_before_face_restoration: if opts.save and not p.do_not_save_samples and opts.save_images_before_face_restoration:
images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-face-restoration") images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-before-face-restoration")
devices.torch_gc() devices.torch_gc()
@ -855,15 +842,15 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
if p.color_corrections is not None and i < len(p.color_corrections): if p.color_corrections is not None and i < len(p.color_corrections):
if opts.save and not p.do_not_save_samples and opts.save_images_before_color_correction: if opts.save and not p.do_not_save_samples and opts.save_images_before_color_correction:
image_without_cc = apply_overlay(image, p.paste_to, i, p.overlay_images) image_without_cc = apply_overlay(image, p.paste_to, i, p.overlay_images)
images.save_image(image_without_cc, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-color-correction") images.save_image(image_without_cc, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-before-color-correction")
image = apply_color_correction(p.color_corrections[i], image) image = apply_color_correction(p.color_corrections[i], image)
image = apply_overlay(image, p.paste_to, i, p.overlay_images) image = apply_overlay(image, p.paste_to, i, p.overlay_images)
if opts.samples_save and not p.do_not_save_samples: if opts.samples_save and not p.do_not_save_samples:
images.save_image(image, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p) images.save_image(image, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p)
text = infotext(n, i) text = infotext(i)
infotexts.append(text) infotexts.append(text)
if opts.enable_pnginfo: if opts.enable_pnginfo:
image.info["parameters"] = text image.info["parameters"] = text
@ -874,10 +861,10 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, p.mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA') image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, p.mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA')
if opts.save_mask: if opts.save_mask:
images.save_image(image_mask, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask") images.save_image(image_mask, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask")
if opts.save_mask_composite: if opts.save_mask_composite:
images.save_image(image_mask_composite, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask-composite") images.save_image(image_mask_composite, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask-composite")
if opts.return_mask: if opts.return_mask:
output_images.append(image_mask) output_images.append(image_mask)
@ -918,7 +905,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
p, p,
images_list=output_images, images_list=output_images,
seed=p.all_seeds[0], seed=p.all_seeds[0],
info=infotext(), info=infotexts[0],
comments="".join(f"{comment}\n" for comment in comments), comments="".join(f"{comment}\n" for comment in comments),
subseed=p.all_subseeds[0], subseed=p.all_subseeds[0],
index_of_first_image=index_of_first_image, index_of_first_image=index_of_first_image,