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Cleaned up code, moved main code contributions into soft_inpainting.py

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CodeHatchling
2023-12-04 16:06:58 -07:00
parent 259d33c3c8
commit 976c1053ef
4 changed files with 173 additions and 149 deletions
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56
modules/processing.py
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@@ -892,55 +892,13 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
# Generate the mask(s) based on similarity between the original and denoised latent vectors
if getattr(p, "image_mask", None) is not None and getattr(p, "soft_inpainting", None) is not None:
# latent_mask = p.nmask[0].float().cpu()
# convert the original mask into a form we use to scale distances for thresholding
# mask_scalar = 1-(torch.clamp(latent_mask, min=0, max=1) ** (p.mask_blend_scale / 2))
# mask_scalar = mask_scalar / (1.00001-mask_scalar)
# mask_scalar = mask_scalar.numpy()
latent_orig = p.init_latent
latent_proc = samples_ddim
latent_distance = torch.norm(latent_proc - latent_orig, p=2, dim=1)
kernel, kernel_center = images.get_gaussian_kernel(stddev_radius=1.5, max_radius=2)
for i, (distance_map, overlay_image) in enumerate(zip(latent_distance, p.overlay_images)):
converted_mask = distance_map.float().cpu().numpy()
converted_mask = images.weighted_histogram_filter(converted_mask, kernel, kernel_center,
percentile_min=0.9, percentile_max=1, min_width=1)
converted_mask = images.weighted_histogram_filter(converted_mask, kernel, kernel_center,
percentile_min=0.25, percentile_max=0.75, min_width=1)
# The distance at which opacity of original decreases to 50%
# half_weighted_distance = 1 # * mask_scalar
# converted_mask = converted_mask / half_weighted_distance
converted_mask = 1 / (1 + converted_mask ** 2)
converted_mask = images.smootherstep(converted_mask)
converted_mask = 1 - converted_mask
converted_mask = 255. * converted_mask
converted_mask = converted_mask.astype(np.uint8)
converted_mask = Image.fromarray(converted_mask)
converted_mask = images.resize_image(2, converted_mask, p.width, p.height)
converted_mask = create_binary_mask(converted_mask, round=False)
# Remove aliasing artifacts using a gaussian blur.
converted_mask = converted_mask.filter(ImageFilter.GaussianBlur(radius=4))
# Expand the mask to fit the whole image if needed.
if p.paste_to is not None:
converted_mask = uncrop(converted_mask,
(overlay_image.width, overlay_image.height),
p.paste_to)
p.masks_for_overlay[i] = converted_mask
image_masked = Image.new('RGBa', (overlay_image.width, overlay_image.height))
image_masked.paste(overlay_image.convert("RGBA").convert("RGBa"),
mask=ImageOps.invert(converted_mask.convert('L')))
p.overlay_images[i] = image_masked.convert('RGBA')
si.generate_adaptive_masks(latent_orig=p.init_latent,
latent_processed=samples_ddim,
overlay_images=p.overlay_images,
masks_for_overlay=p.masks_for_overlay,
width=p.width,
height=p.height,
paste_to=p.paste_to)
x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim,
target_device=devices.cpu,