Mirror/stable-diffusion-webui
1
0
Fork 0
mirror of https://github.com/AUTOMATIC1111/stable-diffusion-webui.git synced 2024-06-07 21:20:49 +00:00
Code Issues Packages Projects Releases Wiki Activity

Restored original formatting.

Browse Source
This commit is contained in:
CodeHatchling 2023-12-04 17:41:51 -07:00
parent 57f29bd61d
commit 60c602232f
1 changed files with 11 additions and 25 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

36
modules/processing.py
View File

@ -370,10 +370,7 @@ class StableDiffusionProcessing:
return self.edit_image_conditioning(source_image)
if self.sampler.conditioning_key in {'hybrid', 'concat'}:
return self.inpainting_image_conditioning(source_image,
latent_image,
image_mask=image_mask,
round_image_mask=round_image_mask)
return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask, round_image_mask=round_image_mask)
if self.sampler.conditioning_key == "crossattn-adm":
return self.unclip_image_conditioning(source_image)
@ -885,7 +882,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
if getattr(samples_ddim, 'already_decoded', False):
x_samples_ddim = samples_ddim
# todo: generate masks the old fashioned way
# todo: generate adaptive masks based on pixel differences.
# if p.masks_for_overlay is used, it will already be populated with masks
else:
if opts.sd_vae_decode_method != 'Full':
p.extra_generation_params['VAE Decoder'] = opts.sd_vae_decode_method
@ -900,9 +898,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
height=p.height,
paste_to=p.paste_to)
x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim,
target_device=devices.cpu,
check_for_nans=True)
x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True)
x_samples_ddim = torch.stack(x_samples_ddim).float()
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
@ -927,9 +923,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
x_samples_ddim = batch_params.images
def infotext(index=0, use_main_prompt=False):
return create_infotext(p, p.prompts, p.seeds, p.subseeds,
use_main_prompt=use_main_prompt, index=index,
all_negative_prompts=p.negative_prompts)
return create_infotext(p, p.prompts, p.seeds, p.subseeds, use_main_prompt=use_main_prompt, index=index, all_negative_prompts=p.negative_prompts)
save_samples = p.save_samples()
@ -972,8 +966,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
image = apply_overlay(image, p.paste_to, i, p.overlay_images)
if save_samples:
images.save_image(image, p.outpath_samples, "", p.seeds[i],
p.prompts[i], opts.samples_format, info=infotext(i), p=p)
images.save_image(image, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p)
text = infotext(i)
infotexts.append(text)
@ -983,14 +976,10 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
if save_samples and any([opts.save_mask, opts.save_mask_composite, opts.return_mask, opts.return_mask_composite]):
if hasattr(p, 'masks_for_overlay') and p.masks_for_overlay:
image_mask = p.masks_for_overlay[i].convert('RGB')
image_mask_composite = Image.composite(
original_denoised_image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size),
images.resize_image(2, p.masks_for_overlay[i], image.width, image.height).convert('L')).convert('RGBA')
image_mask_composite = Image.composite(original_denoised_image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, p.masks_for_overlay[i], image.width, image.height).convert('L')).convert('RGBA')
elif hasattr(p, 'mask_for_overlay') and p.mask_for_overlay:
image_mask = p.mask_for_overlay.convert('RGB')
image_mask_composite = Image.composite(
original_denoised_image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size),
images.resize_image(2, p.mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA')
image_mask_composite = Image.composite(original_denoised_image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, p.mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA')
else:
image_mask = None
image_mask_composite = None
@ -1515,8 +1504,8 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
self.masks_for_overlay.append(image_mask)
else:
image_masked = Image.new('RGBa', (image.width, image.height))
image_masked.paste(image.convert("RGBA").convert("RGBa"),
mask=ImageOps.invert(self.mask_for_overlay.convert('L')))
image_masked.paste(image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(self.mask_for_overlay.convert('L')))
self.overlay_images.append(image_masked.convert('RGBA'))
# crop_region is not None if we are doing inpaint full res
@ -1583,10 +1572,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
elif self.inpainting_fill == 3:
self.init_latent = self.init_latent * self.mask
self.image_conditioning = self.img2img_image_conditioning(image * 2 - 1,
self.init_latent,
image_mask,
self.soft_inpainting is None)
self.image_conditioning = self.img2img_image_conditioning(image * 2 - 1, self.init_latent, image_mask, self.soft_inpainting is None)
def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
x = self.rng.next()