mirror of
https://github.com/vladmandic/automatic
synced 2026-09-18 16:54:33 +02:00
add reprocess plus major processing refactor
This commit is contained in:
Vladimir Mandic
+33
-39
@@ -272,9 +272,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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if p.scripts is not None and isinstance(p.scripts, scripts.ScriptRunner):
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p.scripts.process(p)
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def infotext(_inxex=0): # dummy function overriden if there are iterations
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return ''
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ema_scope_context = p.sd_model.ema_scope if not shared.native else nullcontext
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shared.state.job_count = p.n_iter
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with devices.inference_context(), ema_scope_context():
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@@ -312,55 +309,53 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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if p.scripts is not None and isinstance(p.scripts, scripts.ScriptRunner):
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p.scripts.process_batch(p, batch_number=n, prompts=p.prompts, seeds=p.seeds, subseeds=p.subseeds)
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x_samples_ddim = None
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samples = None
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timer.process.record('init')
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if p.scripts is not None and isinstance(p.scripts, scripts.ScriptRunner):
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x_samples_ddim = p.scripts.process_images(p)
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if x_samples_ddim is None:
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samples = p.scripts.process_images(p)
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if samples is None:
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if not shared.native:
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from modules.processing_original import process_original
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x_samples_ddim = process_original(p)
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samples = process_original(p)
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elif shared.native:
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from modules.processing_diffusers import process_diffusers
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x_samples_ddim = process_diffusers(p)
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samples = process_diffusers(p)
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else:
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raise ValueError(f"Unknown backend {shared.backend}")
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timer.process.record('process')
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if not shared.opts.keep_incomplete and shared.state.interrupted:
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x_samples_ddim = []
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samples = []
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if not shared.native and (shared.cmd_opts.lowvram or shared.cmd_opts.medvram):
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lowvram.send_everything_to_cpu()
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devices.torch_gc()
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if p.scripts is not None and isinstance(p.scripts, scripts.ScriptRunner):
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p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
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p.scripts.postprocess_batch(p, samples, batch_number=n)
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if p.scripts is not None and isinstance(p.scripts, scripts.ScriptRunner):
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p.prompts = p.all_prompts[n * p.batch_size:(n+1) * p.batch_size]
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p.negative_prompts = p.all_negative_prompts[n * p.batch_size:(n+1) * p.batch_size]
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batch_params = scripts.PostprocessBatchListArgs(list(x_samples_ddim))
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batch_params = scripts.PostprocessBatchListArgs(list(samples))
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p.scripts.postprocess_batch_list(p, batch_params, batch_number=n)
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x_samples_ddim = batch_params.images
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samples = batch_params.images
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def infotext(index): # pylint: disable=function-redefined
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return create_infotext(p, p.prompts, p.seeds, p.subseeds, index=index, all_negative_prompts=p.negative_prompts)
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for i, x_sample in enumerate(x_samples_ddim):
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debug(f'Processing result: index={i+1}/{len(x_samples_ddim)} iteration={n+1}/{p.n_iter}')
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for i, sample in enumerate(samples):
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debug(f'Processing result: index={i+1}/{len(samples)} iteration={n+1}/{p.n_iter}')
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p.batch_index = i
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if type(x_sample) == Image.Image:
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image = x_sample
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x_sample = np.array(x_sample)
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info = create_infotext(p, p.prompts, p.seeds, p.subseeds, index=i, all_negative_prompts=p.negative_prompts)
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if type(sample) == Image.Image:
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image = sample
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sample = np.array(sample)
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else:
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x_sample = validate_sample(x_sample)
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image = Image.fromarray(x_sample)
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sample = validate_sample(sample)
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image = Image.fromarray(sample)
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if p.restore_faces:
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if not p.do_not_save_samples and shared.opts.save_images_before_face_restoration:
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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=infotext(i), p=p, suffix="-before-face-restore")
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images.save_image(Image.fromarray(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-restore")
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p.ops.append('face')
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x_sample = face_restoration.restore_faces(x_sample, p)
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if x_sample is not None:
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image = Image.fromarray(x_sample)
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sample = face_restoration.restore_faces(sample, p)
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if sample is not None:
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image = Image.fromarray(sample)
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if p.scripts is not None and isinstance(p.scripts, scripts.ScriptRunner):
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pp = scripts.PostprocessImageArgs(image)
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p.scripts.postprocess_image(p, pp)
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@@ -370,7 +365,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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if not p.do_not_save_samples and shared.opts.save_images_before_color_correction:
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orig = p.color_corrections
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p.color_corrections = None
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info = infotext(i)
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p.color_corrections = orig
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image_without_cc = apply_overlay(image, p.paste_to, i, p.overlay_images)
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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-correct")
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@@ -378,12 +372,11 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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image = apply_color_correction(p.color_corrections[i], image)
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if shared.opts.mask_apply_overlay:
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image = apply_overlay(image, p.paste_to, i, p.overlay_images)
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text = infotext(i)
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infotexts.append(text)
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image.info["parameters"] = text
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infotexts.append(info)
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image.info["parameters"] = info
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output_images.append(image)
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if shared.opts.samples_save and not p.do_not_save_samples and p.outpath_samples is not None:
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images.save_image(image, p.outpath_samples, "", p.seeds[i], p.prompts[i], shared.opts.samples_format, info=text, p=p) # main save image
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images.save_image(image, p.outpath_samples, "", p.seeds[i], p.prompts[i], shared.opts.samples_format, info=info, p=p) # main save image
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if hasattr(p, 'mask_for_overlay') and p.mask_for_overlay and any([shared.opts.save_mask, shared.opts.save_mask_composite, shared.opts.return_mask, shared.opts.return_mask_composite]):
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image_mask = p.mask_for_overlay.convert('RGB')
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image1 = image.convert('RGBA').convert('RGBa')
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@@ -391,15 +384,15 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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mask = images.resize_image(3, p.mask_for_overlay, image.width, image.height).convert('L')
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image_mask_composite = Image.composite(image1, image2, mask).convert('RGBA')
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if shared.opts.save_mask:
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images.save_image(image_mask, p.outpath_samples, "", p.seeds[i], p.prompts[i], shared.opts.samples_format, info=text, p=p, suffix="-mask")
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images.save_image(image_mask, p.outpath_samples, "", p.seeds[i], p.prompts[i], shared.opts.samples_format, info=info, p=p, suffix="-mask")
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if shared.opts.save_mask_composite:
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images.save_image(image_mask_composite, p.outpath_samples, "", p.seeds[i], p.prompts[i], shared.opts.samples_format, info=text, p=p, suffix="-mask-composite")
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images.save_image(image_mask_composite, p.outpath_samples, "", p.seeds[i], p.prompts[i], shared.opts.samples_format, info=info, p=p, suffix="-mask-composite")
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if shared.opts.return_mask:
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output_images.append(image_mask)
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if shared.opts.return_mask_composite:
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output_images.append(image_mask_composite)
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timer.process.record('post')
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del x_samples_ddim
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del samples
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devices.torch_gc()
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if hasattr(shared.sd_model, 'restore_pipeline') and shared.sd_model.restore_pipeline is not None:
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@@ -413,15 +406,16 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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index_of_first_image = 0
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if (shared.opts.return_grid or shared.opts.grid_save) and not p.do_not_save_grid and len(output_images) > 1:
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if images.check_grid_size(output_images):
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r, c = images.get_grid_size(output_images, p.batch_size)
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grid = images.image_grid(output_images, p.batch_size)
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grid_text = f'{r}x{c}'
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grid_info = create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, index=0, grid=grid_text)
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if shared.opts.return_grid:
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text = infotext(-1)
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infotexts.insert(0, text)
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grid.info["parameters"] = text
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infotexts.insert(0, grid_info)
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output_images.insert(0, grid)
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index_of_first_image = 1
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if shared.opts.grid_save:
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images.save_image(grid, p.outpath_grids, "", p.all_seeds[0], p.all_prompts[0], shared.opts.grid_format, info=infotext(-1), p=p, grid=True, suffix="-grid") # main save grid
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images.save_image(grid, p.outpath_grids, "", p.all_seeds[0], p.all_prompts[0], shared.opts.grid_format, info=grid_info, p=p, grid=True, suffix="-grid") # main save grid
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if shared.native:
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from modules import ipadapter
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@@ -445,7 +439,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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p,
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images_list=output_images,
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seed=p.all_seeds[0],
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info=infotext(0),
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info=infotexts[0] if len(infotexts) > 0 else '',
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comments="\n".join(comments),
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subseed=p.all_subseeds[0],
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index_of_first_image=index_of_first_image,
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