mirror of
https://github.com/vladmandic/automatic
synced 2026-09-20 01:31:13 +02:00
fix state interrupted checks
Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
@@ -217,6 +217,8 @@ class YoloRestorer(Detailer):
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return [merged]
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def restore(self, np_image, p: processing.StableDiffusionProcessing = None):
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if shared.state.interrupted or shared.state.skipped:
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return np_image
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if hasattr(p, 'recursion'):
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return np_image
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if not hasattr(p, 'detailer_active'):
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+72
-70
@@ -282,81 +282,83 @@ def process_samples(p: StableDiffusionProcessing, samples):
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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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p.ops.append('restore')
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if not p.do_not_save_samples and shared.opts.save_images_before_detailer:
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if not shared.state.interrupted and not shared.state.skipped:
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if p.restore_faces:
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p.ops.append('restore')
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if not p.do_not_save_samples and shared.opts.save_images_before_detailer:
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info = create_infotext(p, p.prompts, p.seeds, p.subseeds, index=i)
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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-restore")
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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.detailer_enabled:
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p.ops.append('detailer')
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if not p.do_not_save_samples and shared.opts.save_images_before_detailer:
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info = create_infotext(p, p.prompts, p.seeds, p.subseeds, index=i)
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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-detailer")
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sample = detailer.detail(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.color_corrections is not None and i < len(p.color_corrections):
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p.ops.append('color')
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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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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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info = create_infotext(p, p.prompts, p.seeds, p.subseeds, index=i)
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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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image = apply_color_correction(p.color_corrections[i], image)
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if p.scripts is not None and isinstance(p.scripts, scripts_manager.ScriptRunner):
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pp = scripts_manager.PostprocessImageArgs(image)
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p.scripts.postprocess_image(p, pp)
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if pp.image is not None:
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image = pp.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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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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image2 = Image.new('RGBa', image.size)
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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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info = create_infotext(p, p.prompts, p.seeds, p.subseeds, index=i)
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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-restore")
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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.detailer_enabled:
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p.ops.append('detailer')
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if not p.do_not_save_samples and shared.opts.save_images_before_detailer:
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info = create_infotext(p, p.prompts, p.seeds, p.subseeds, index=i)
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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-detailer")
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sample = detailer.detail(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.color_corrections is not None and i < len(p.color_corrections):
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p.ops.append('color')
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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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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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info = create_infotext(p, p.prompts, p.seeds, p.subseeds, index=i)
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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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image = apply_color_correction(p.color_corrections[i], image)
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if p.scripts is not None and isinstance(p.scripts, scripts_manager.ScriptRunner):
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pp = scripts_manager.PostprocessImageArgs(image)
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p.scripts.postprocess_image(p, pp)
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if pp.image is not None:
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image = pp.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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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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image2 = Image.new('RGBa', image.size)
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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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info = create_infotext(p, p.prompts, p.seeds, p.subseeds, index=i)
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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=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=info, p=p, suffix="-mask-composite")
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if shared.opts.return_mask:
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out_infotexts.append(info)
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out_images.append(image_mask)
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if shared.opts.return_mask_composite:
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out_infotexts.append(info)
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out_images.append(image_mask_composite)
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if shared.opts.include_mask:
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info = create_infotext(p, p.prompts, p.seeds, p.subseeds, index=i)
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if shared.opts.mask_apply_overlay and p.overlay_images is not None and len(p.overlay_images) > 0:
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p.image_mask = create_binary_mask(p.overlay_images[0])
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p.image_mask = ImageOps.invert(p.image_mask)
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out_infotexts.append(info)
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out_images.append(p.image_mask)
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elif getattr(p, 'image_mask', None) is not None and isinstance(p.image_mask, Image.Image):
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if getattr(p, 'mask_for_detailer', None) is not None:
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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=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=info, p=p, suffix="-mask-composite")
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if shared.opts.return_mask:
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out_infotexts.append(info)
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out_images.append(p.mask_for_detailer)
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else:
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out_images.append(image_mask)
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if shared.opts.return_mask_composite:
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out_infotexts.append(info)
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out_images.append(image_mask_composite)
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if shared.opts.include_mask:
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info = create_infotext(p, p.prompts, p.seeds, p.subseeds, index=i)
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if shared.opts.mask_apply_overlay and p.overlay_images is not None and len(p.overlay_images) > 0:
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p.image_mask = create_binary_mask(p.overlay_images[0])
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p.image_mask = ImageOps.invert(p.image_mask)
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out_infotexts.append(info)
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out_images.append(p.image_mask)
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elif getattr(p, 'image_mask', None) is not None and isinstance(p.image_mask, Image.Image):
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if getattr(p, 'mask_for_detailer', None) is not None:
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out_infotexts.append(info)
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out_images.append(p.mask_for_detailer)
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else:
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out_infotexts.append(info)
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out_images.append(p.image_mask)
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if p.selected_scale_tab_after == 1:
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p.width_after, p.height_after = int(image.width * p.scale_by_after), int(image.height * p.scale_by_after)
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if p.resize_mode_after != 0 and p.resize_name_after != 'None':
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image = images.resize_image(p.resize_mode_after, image, p.width_after, p.height_after, p.resize_name_after, context=p.resize_context_after)
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if p.selected_scale_tab_after == 1:
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p.width_after, p.height_after = int(image.width * p.scale_by_after), int(image.height * p.scale_by_after)
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if p.resize_mode_after != 0 and p.resize_name_after != 'None':
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image = images.resize_image(p.resize_mode_after, image, p.width_after, p.height_after, p.resize_name_after, context=p.resize_context_after)
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info = create_infotext(p, p.prompts, p.seeds, p.subseeds, index=i)
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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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@@ -547,8 +547,9 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
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output = SimpleNamespace(images=images)
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if (output is None or len(output.images) == 0) and has_images:
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shared.log.debug('Processing: using input as base output')
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output.images = p.init_images
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if output is not None:
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shared.log.debug('Processing: using input as base output')
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output.images = p.init_images
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if shared.state.interrupted or shared.state.skipped:
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shared.sd_model = orig_pipeline
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@@ -298,28 +298,33 @@ def resize_init_images(p):
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def resize_hires(p, latents): # input=latents output=pil if not latent_upscaler else latent
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jobid = shared.state.begin('Resize')
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if not torch.is_tensor(latents):
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shared.log.warning('Hires: input is not tensor')
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decoded = processing_vae.vae_decode(latents=latents, model=shared.sd_model, vae_type=p.vae_type, output_type='pil', width=p.width, height=p.height)
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shared.state.end(jobid)
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return decoded
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if (p.hr_upscale_to_x == 0 or p.hr_upscale_to_y == 0) and hasattr(p, 'init_hr'):
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shared.log.error('Hires: missing upscaling dimensions')
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shared.state.end(jobid)
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return decoded
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return latents
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jobid = shared.state.begin('Resize')
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if p.hr_upscaler.lower().startswith('latent'):
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if isinstance(latents, list):
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try:
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for i in range(len(latents)):
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if not torch.is_tensor(latents[i]):
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shared.log.warning(f'Hires: input[{i}]={type(latents[i])} not tensor')
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latents[i] = processing_vae.vae_encode(image=latents[i], model=shared.sd_model, vae_type=p.vae_type)
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latents = torch.cat(latents, dim=0)
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except Exception as e:
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shared.log.error(f'Hires: prepare latents: {e}')
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resized = latents
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elif not torch.is_tensor(latents):
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shared.log.warning(f'Hires: input={type(latents)} not tensor')
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resized = images.resize_image(p.hr_resize_mode, latents, p.hr_upscale_to_x, p.hr_upscale_to_y, upscaler_name=p.hr_upscaler, context=p.hr_resize_context)
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shared.state.end(jobid)
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return resized
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else:
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decoded = processing_vae.vae_decode(latents=latents, model=shared.sd_model, vae_type=p.vae_type, output_type='pil', width=p.width, height=p.height)
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resized = []
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for image in decoded:
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resize = images.resize_image(p.hr_resize_mode, image, p.hr_upscale_to_x, p.hr_upscale_to_y, upscaler_name=p.hr_upscaler, context=p.hr_resize_context)
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resized.append(resize)
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decoded = processing_vae.vae_decode(latents=latents, model=shared.sd_model, vae_type=p.vae_type, output_type='pil', width=p.width, height=p.height)
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resized = []
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for image in decoded:
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resize = images.resize_image(p.hr_resize_mode, image, p.hr_upscale_to_x, p.hr_upscale_to_y, upscaler_name=p.hr_upscaler, context=p.hr_resize_context)
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resized.append(resize)
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devices.torch_gc()
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shared.state.end(jobid)
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return resized
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@@ -189,13 +189,13 @@ class State:
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self.preview_job = -1
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self.duration = None
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self.paused = False
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self.interrupted = False
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self.skipped = False
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self.results = []
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def begin(self, title="", task_id=0, api=None):
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import modules.devices
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self.clear()
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self.interrupted = False
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self.skipped = False
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self.job_history += 1
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self.total_jobs += 1
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self.current_image = None
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