Public/automatic
1
0
Fork 0
mirror of https://github.com/vladmandic/automatic synced 2026-09-18 16:54:33 +02:00
Code Issues Packages Projects Releases Wiki Activity

refactor control processing

Browse Source 
Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
2025-07-16 11:04:48 -04:00
parent 3c66c353da
commit 878fda65ab
13 changed files with 475 additions and 436 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/processing.py
+40 -25
View File
@@ -387,37 +387,52 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
else:
image.info["parameters"] = info
output_images.append(image)
if shared.opts.samples_save and not p.do_not_save_samples and p.outpath_samples is not None:
info = create_infotext(p, p.prompts, p.seeds, p.subseeds, index=i)
if isinstance(image, list):
for img in image:
images.save_image(img, p.outpath_samples, "", p.seeds[i], p.prompts[i], shared.opts.samples_format, info=info, p=p) # main save image
else:
images.save_image(image, p.outpath_samples, "", p.seeds[i], p.prompts[i], shared.opts.samples_format, info=info, p=p) # main save image
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]):
image_mask = p.mask_for_overlay.convert('RGB')
image1 = image.convert('RGBA').convert('RGBa')
image2 = Image.new('RGBa', image.size)
mask = images.resize_image(3, p.mask_for_overlay, image.width, image.height).convert('L')
image_mask_composite = Image.composite(image1, image2, mask).convert('RGBA')
if shared.opts.save_mask:
images.save_image(image_mask, p.outpath_samples, "", p.seeds[i], p.prompts[i], shared.opts.samples_format, info=info, p=p, suffix="-mask")
if shared.opts.save_mask_composite:
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")
if shared.opts.return_mask:
output_images.append(image_mask)
if shared.opts.return_mask_composite:
output_images.append(image_mask_composite)
is_grid = len(output_images) == p.batch_size * p.n_iter + 1 and i == 0
for image in output_images:
# resize after
if p.selected_scale_tab_after == 1:
p.width_after, p.height_after = int(image.width * p.scale_by_after), int(image.height * p.scale_by_after)
if p.resize_mode_after != 0 and p.resize_name_after != 'None' and not is_grid:
image = images.resize_image(p.resize_mode_after, image, p.width_after, p.height_after, p.resize_name_after, context=p.resize_context_after)
# save images
if shared.opts.samples_save and not p.do_not_save_samples and p.outpath_samples is not None:
info = create_infotext(p, p.prompts, p.seeds, p.subseeds, index=i)
if isinstance(image, list):
for img in image:
images.save_image(img, p.outpath_samples, "", p.seeds[i], p.prompts[i], shared.opts.samples_format, info=info, p=p) # main save image
else:
images.save_image(image, p.outpath_samples, "", p.seeds[i], p.prompts[i], shared.opts.samples_format, info=info, p=p) # main save image
# add masks
if shared.opts.include_mask and not script_run:
if processed_image is not None and isinstance(processed_image, Image.Image):
output_images.append(processed_image)
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]):
image_mask = p.mask_for_overlay.convert('RGB')
image1 = image.convert('RGBA').convert('RGBa')
image2 = Image.new('RGBa', image.size)
mask = images.resize_image(3, p.mask_for_overlay, image.width, image.height).convert('L')
image_mask_composite = Image.composite(image1, image2, mask).convert('RGBA')
if shared.opts.save_mask:
images.save_image(image_mask, p.outpath_samples, "", p.seeds[i], p.prompts[i], shared.opts.samples_format, info=info, p=p, suffix="-mask")
if shared.opts.save_mask_composite:
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")
if shared.opts.return_mask:
output_images.append(image_mask)
if shared.opts.return_mask_composite:
output_images.append(image_mask_composite)
timer.process.record('post')
del samples
devices.torch_gc()
# if not p.xyz:
if hasattr(shared.sd_model, 'restore_pipeline') and (shared.sd_model.restore_pipeline is not None):
shared.sd_model.restore_pipeline()
shared.sd_model = sd_models.set_diffuser_pipe(shared.sd_model, sd_models.DiffusersTaskType.TEXT_2_IMAGE)
if not p.xyz:
if hasattr(shared.sd_model, 'restore_pipeline') and (shared.sd_model.restore_pipeline is not None):
shared.sd_model.restore_pipeline()
shared.sd_model = sd_models.set_diffuser_pipe(shared.sd_model, sd_models.DiffusersTaskType.TEXT_2_IMAGE)
t1 = time.time()