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Removed code which forces the inpainting mask to be 0 or 1. Now fractional values (e.g. 0.5) are accepted.

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CodeHatchling 2023-11-28 15:05:01 -07:00
parent 4afaaf8a02
commit dec791d35d
1 changed files with 1 additions and 5 deletions
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6
modules/processing.py
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@ -83,7 +83,7 @@ def apply_overlay(image, paste_loc, index, overlays):
def create_binary_mask(image): def create_binary_mask(image):
if image.mode == 'RGBA' and image.getextrema()[-1] != (255, 255): if image.mode == 'RGBA' and image.getextrema()[-1] != (255, 255):
image = image.split()[-1].convert("L").point(lambda x: 255 if x > 128 else 0) image = image.split()[-1].convert("L")
else: else:
image = image.convert('L') image = image.convert('L')
return image return image
@ -319,9 +319,6 @@ class StableDiffusionProcessing:
conditioning_mask = np.array(image_mask.convert("L")) conditioning_mask = np.array(image_mask.convert("L"))
conditioning_mask = conditioning_mask.astype(np.float32) / 255.0 conditioning_mask = conditioning_mask.astype(np.float32) / 255.0
conditioning_mask = torch.from_numpy(conditioning_mask[None, None]) conditioning_mask = torch.from_numpy(conditioning_mask[None, None])
# Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0
conditioning_mask = torch.round(conditioning_mask)
else: else:
conditioning_mask = source_image.new_ones(1, 1, *source_image.shape[-2:]) conditioning_mask = source_image.new_ones(1, 1, *source_image.shape[-2:])
@ -1504,7 +1501,6 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
latmask = init_mask.convert('RGB').resize((self.init_latent.shape[3], self.init_latent.shape[2])) latmask = init_mask.convert('RGB').resize((self.init_latent.shape[3], self.init_latent.shape[2]))
latmask = np.moveaxis(np.array(latmask, dtype=np.float32), 2, 0) / 255 latmask = np.moveaxis(np.array(latmask, dtype=np.float32), 2, 0) / 255
latmask = latmask[0] latmask = latmask[0]
latmask = np.around(latmask)
latmask = np.tile(latmask[None], (4, 1, 1)) latmask = np.tile(latmask[None], (4, 1, 1))
self.mask = torch.asarray(1.0 - latmask).to(shared.device).type(self.sd_model.dtype) self.mask = torch.asarray(1.0 - latmask).to(shared.device).type(self.sd_model.dtype)