Re-implemented soft inpainting via a script. Also fixed some mistakes with the previous hooks, removed unnecessary formatting changes, removed code that I had forgotten to.

1 files changed, 10 insertions, 13 deletions

diff --git a/modules/processing.py b/modules/processing.py
index 5a1a90af..f8d85bdf 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -879,14 +879,13 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if p.scripts is not None:

                 ps = scripts.PostSampleArgs(samples_ddim)

                 p.scripts.post_sample(p, ps)

-                samples_ddim = pp.samples

+                samples_ddim = ps.samples

 

             if getattr(samples_ddim, 'already_decoded', False):

                 x_samples_ddim = samples_ddim

             else:

                 if opts.sd_vae_decode_method != 'Full':

                     p.extra_generation_params['VAE Decoder'] = opts.sd_vae_decode_method

-

                 x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True)

 

             x_samples_ddim = torch.stack(x_samples_ddim).float()

@@ -944,7 +943,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                 if p.scripts is not None:

                     ppmo = scripts.PostProcessMaskOverlayArgs(i, mask_for_overlay, overlay_image)

                     p.scripts.postprocess_maskoverlay(p, ppmo)

-                    mask_for_overlay, overlay_image = pp.mask_for_overlay, pp.overlay_image

+                    mask_for_overlay, overlay_image = ppmo.mask_for_overlay, ppmo.overlay_image

 

                 if p.color_corrections is not None and i < len(p.color_corrections):

                     if save_samples and opts.save_images_before_color_correction:

@@ -959,7 +958,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                 original_denoised_image = image.copy()

 

                 if p.paste_to is not None:

-                    original_denoised_image = uncrop(original_denoised_image, (p.overlay_image.width, p.overlay_image.height), p.paste_to)

+                    original_denoised_image = uncrop(original_denoised_image, (overlay_image.width, overlay_image.height), p.paste_to)

 

                 image = apply_overlay(image, p.paste_to, overlay_image)

 

@@ -1512,9 +1511,6 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             if self.overlay_images is not None:

                 self.overlay_images = self.overlay_images * self.batch_size

 

-            if self.masks_for_overlay is not None:

-                self.masks_for_overlay = self.masks_for_overlay * self.batch_size

-

             if self.color_corrections is not None and len(self.color_corrections) == 1:

                 self.color_corrections = self.color_corrections * self.batch_size

 

@@ -1565,14 +1561,15 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
 

         samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning)

 

-        blended_samples = samples * self.nmask + self.init_latent * self.mask

+        if self.mask is not None:

+            blended_samples = samples * self.nmask + self.init_latent * self.mask

 

-        if self.scripts is not None:

-            mba = scripts.MaskBlendArgs(self, samples, self.nmask, self.init_latent, self.mask, blended_samples, sigma=None, is_final_blend=True)

-            self.scripts.on_mask_blend(self, mba)

-            blended_samples = mba.blended_latent

+            if self.scripts is not None:

+                mba = scripts.MaskBlendArgs(samples, self.nmask, self.init_latent, self.mask, blended_samples)

+                self.scripts.on_mask_blend(self, mba)

+                blended_samples = mba.blended_latent

 

-        samples = blended_samples

+            samples = blended_samples

 

         del x

         devices.torch_gc()