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applied fill to all but one inpainting modes, fixed issue with mask resizing

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AUTOMATIC 2022-08-30 14:39:26 +03:00
parent ff98e09d72
commit bb2faa5fb8
1 changed files with 16 additions and 13 deletions
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29
webui.py
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@ -1145,27 +1145,16 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
self.sampler = samplers_for_img2img[self.sampler_index].constructor()
if self.original_mask is not None:
if self.mask_blur > 0:
self.original_mask = self.original_mask.filter(ImageFilter.GaussianBlur(self.mask_blur)).convert('L')
latmask = self.original_mask.convert('RGB').resize((64, 64))
latmask = np.moveaxis(np.array(latmask, dtype=np.float), 2, 0) / 255
latmask = latmask[0]
latmask = np.tile(latmask[None], (4, 1, 1))
self.mask = torch.asarray(1.0 - latmask).to(device).type(sd_model.dtype)
self.nmask = torch.asarray(latmask).to(device).type(sd_model.dtype)
self.original_mask = resize_image(self.resize_mode, self.original_mask, self.width, self.height)
self.overlay_images = []
imgs = []
for img in self.init_images:
image = img.convert("RGB")
image = resize_image(self.resize_mode, image, self.width, self.height)
if self.original_mask is not None:
if self.inpainting_fill == 0:
if self.inpainting_fill != 1:
image = fill(image, self.original_mask)
image_masked = Image.new('RGBa', (image.width, image.height))
@ -1194,6 +1183,20 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
self.init_latent = sd_model.get_first_stage_encoding(sd_model.encode_first_stage(image))
if self.original_mask is not None:
if self.mask_blur > 0:
self.original_mask = self.original_mask.filter(ImageFilter.GaussianBlur(self.mask_blur)).convert('L')
latmask = self.original_mask.convert('RGB').resize((self.init_latent.shape[3], self.init_latent.shape[2]))
latmask = np.moveaxis(np.array(latmask, dtype=np.float), 2, 0) / 255
latmask = latmask[0]
latmask = np.tile(latmask[None], (4, 1, 1))
self.mask = torch.asarray(1.0 - latmask).to(device).type(sd_model.dtype)
self.nmask = torch.asarray(latmask).to(device).type(sd_model.dtype)
def sample(self, x, conditioning, unconditional_conditioning):
t_enc = int(min(self.denoising_strength, 0.999) * self.steps)