diff options
Diffstat (limited to 'modules/processing.py')
| -rw-r--r-- | modules/processing.py | 14 |
1 files changed, 7 insertions, 7 deletions
diff --git a/modules/processing.py b/modules/processing.py index 7c4bcd74..df4c2bfc 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -150,11 +150,11 @@ 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
@@ -202,7 +202,7 @@ 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))
@@ -540,7 +540,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: for n in range(p.n_iter):
if state.skipped:
state.skipped = False
-
+
if state.interrupted:
break
@@ -615,7 +615,7 @@ 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()
@@ -707,7 +707,7 @@ 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
@@ -723,7 +723,7 @@ 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)
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