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mirror of https://github.com/vladmandic/automatic synced 2026-09-19 01:04:32 +02:00
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add callback

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This commit is contained in:
ljleb
2023-08-02 15:26:04 -04:00
parent 246989129f
commit 88cc6f63e1
2 changed files with 61 additions and 14 deletions
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modules/processing.py
+27 -14
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@@ -444,8 +444,13 @@ def fix_seed(p):
p.subseed = get_fixed_seed(p.subseed)
def create_infotext(p: StableDiffusionProcessing, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0): # pylint: disable=unused-argument
index = position_in_batch + iteration * p.batch_size
def create_infotext(p: StableDiffusionProcessing, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0, index=None, all_negative_prompts=None): # pylint: disable=unused-argument
if index is None:
index = position_in_batch + iteration * p.batch_size
if all_negative_prompts is None:
all_negative_prompts = p.all_negative_prompts
generation_params = {
"Steps": p.steps,
"Seed": all_seeds[index],
@@ -487,7 +492,7 @@ def create_infotext(p: StableDiffusionProcessing, all_prompts, all_seeds, all_su
generation_params['Token merging ratio hr'] = token_merging_ratio_hr if token_merging_ratio_hr != 0 else None
generation_params.update(p.extra_generation_params)
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])
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"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip()
@@ -599,9 +604,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
else:
p.all_subseeds = [int(subseed) + x for x in range(len(p.all_prompts))]
def infotext(iteration=0, position_in_batch=0):
return create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, comments, iteration, position_in_batch)
if os.path.exists(shared.opts.embeddings_dir) and not p.do_not_reload_embeddings:
model_hijack.embedding_db.load_textual_inversion_embeddings()
if p.scripts is not None:
@@ -709,6 +711,17 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
devices.torch_gc()
if p.scripts is not None:
p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
p.seeds = seeds
p.subseeds = subseeds
if p.scripts is not None:
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]
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):
return create_infotext(p, p.prompts, p.seeds, p.subseeds, index=index, all_negative_prompts=p.negative_prompts)
for i, x_sample in enumerate(x_samples_ddim):
p.batch_index = i
@@ -721,9 +734,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
if shared.opts.save and not p.do_not_save_samples and shared.opts.save_images_before_face_restoration:
orig = p.restore_faces
p.restore_faces = False
info=infotext(n, i)
info = infotext(i)
p.restore_faces = orig
images.save_image(Image.fromarray(x_sample), path=p.outpath_samples, basename="", seed=seeds[i], prompt=prompts[i], extension=shared.opts.samples_format, info=info, p=p, suffix="-before-face-restoration")
images.save_image(Image.fromarray(x_sample), path=p.outpath_samples, basename="", seed=p.seeds[i], prompt=p.prompts[i], extension=shared.opts.samples_format, info=info, p=p, suffix="-before-face-restoration")
p.ops.append('face')
x_sample = modules.face_restoration.restore_faces(x_sample)
image = Image.fromarray(x_sample)
@@ -735,16 +748,16 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
if shared.opts.save and not p.do_not_save_samples and shared.opts.save_images_before_color_correction:
orig = p.color_corrections
p.color_corrections = None
info=infotext(n, i)
info = infotext(i)
p.color_corrections = orig
image_without_cc = apply_overlay(image, p.paste_to, i, p.overlay_images)
images.save_image(image_without_cc, path=p.outpath_samples, basename="", seed=seeds[i], prompt=prompts[i], extension=shared.opts.samples_format, info=info, p=p, suffix="-before-color-correction")
images.save_image(image_without_cc, path=p.outpath_samples, basename="", seed=p.seeds[i], prompt=p.prompts[i], extension=shared.opts.samples_format, info=info, p=p, suffix="-before-color-correction")
p.ops.append('color')
image = apply_color_correction(p.color_corrections[i], image)
image = apply_overlay(image, p.paste_to, i, p.overlay_images)
if shared.opts.samples_save and not p.do_not_save_samples:
images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], shared.opts.samples_format, info=infotext(n, i), p=p)
text = infotext(n, i)
images.save_image(image, p.outpath_samples, "", p.seeds[i], p.prompts[i], shared.opts.samples_format, info=infotext(i), p=p)
text = infotext(i)
infotexts.append(text)
image.info["parameters"] = text
output_images.append(image)
@@ -752,9 +765,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
image_mask = p.mask_for_overlay.convert('RGB')
image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(3, p.mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA')
if shared.opts.save_mask:
images.save_image(image_mask, p.outpath_samples, "", seeds[i], prompts[i], shared.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], shared.opts.samples_format, info=infotext(i), p=p, suffix="-mask")
if shared.opts.save_mask_composite:
images.save_image(image_mask_composite, p.outpath_samples, "", seeds[i], prompts[i], shared.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], shared.opts.samples_format, info=infotext(i), p=p, suffix="-mask-composite")
if shared.opts.return_mask:
output_images.append(image_mask)
if shared.opts.return_mask_composite: