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fix batch processing

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Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
2025-07-31 09:56:36 -04:00
parent a1fad27b0e
commit 1b3f5405a3
4 changed files with 39 additions and 40 deletions
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modules/processing.py
+35 -36
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@@ -372,43 +372,42 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
else:
image.info["parameters"] = info
output_images.append(image)
for i, image in enumerate(output_images):
is_grid = len(output_images) == p.batch_size * p.n_iter + 1 and i == 0
# 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
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')
devices.torch_gc()
del samples
devices.torch_gc()
for i, image in enumerate(output_images):
is_grid = len(output_images) == p.batch_size * p.n_iter + 1 and i == 0
# 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
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')
if not p.xyz:
if hasattr(shared.sd_model, 'restore_pipeline') and (shared.sd_model.restore_pipeline is not None):
@@ -462,5 +461,5 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
shared.log.debug(f'Processed: timers={timer.process.dct()}')
shared.log.debug(f'Processed: memory={memstats.memory_stats()}')
devices.torch_gc(force=True, reason='final')
# devices.torch_gc(force=True, reason='final')
return processed