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Fix various typos

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Jim Hays
2022-12-14 21:01:32 -05:00
parent 685f9631b5
commit c0355caefe
16 changed files with 68 additions and 68 deletions
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14
modules/processing.py
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@@ -147,11 +147,11 @@ class StableDiffusionProcessing():
# The "masked-image" in this case will just be all zeros since the entire image is masked.
image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning))
image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning))
# Add the fake full 1s mask to the first dimension.
image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
image_conditioning = image_conditioning.to(x.dtype)
image_conditioning = image_conditioning.to(x.dtype)
return image_conditioning
@@ -199,7 +199,7 @@ class StableDiffusionProcessing():
source_image * (1.0 - conditioning_mask),
getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight)
)
# Encode the new masked image using first stage of network.
conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
@@ -537,7 +537,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
for n in range(p.n_iter):
if state.skipped:
state.skipped = False
if state.interrupted:
break
@@ -612,7 +612,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
image.info["parameters"] = text
output_images.append(image)
del x_samples_ddim
del x_samples_ddim
devices.torch_gc()
@@ -704,7 +704,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2]
"""saves image before applying hires fix, if enabled in options; takes as an arguyment either an image or batch with latent space images"""
"""saves image before applying hires fix, if enabled in options; takes as an argument either an image or batch with latent space images"""
def save_intermediate(image, index):
if not opts.save or self.do_not_save_samples or not opts.save_images_before_highres_fix:
return
@@ -720,7 +720,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
# Avoid making the inpainting conditioning unless necessary as
# Avoid making the inpainting conditioning unless necessary as
# this does need some extra compute to decode / encode the image again.
if getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) < 1.0:
image_conditioning = self.img2img_image_conditioning(decode_first_stage(self.sd_model, samples), samples)