Re-implemented soft inpainting via a script. Also fixed some mistakes with the...

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.

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()

modules/scripts.py

@@ -12,12 +12,12 @@ from modules import shared, paths, script_callbacks, extensions, script_loading,
 AlwaysVisible = object()
 
 class MaskBlendArgs:
-    def __init__(self, current_latent, nmask, init_latent, mask, blended_samples, denoiser=None, sigma=None):
+    def __init__(self, current_latent, nmask, init_latent, mask, blended_latent, denoiser=None, sigma=None):
         self.current_latent = current_latent
         self.nmask = nmask
         self.init_latent = init_latent
         self.mask = mask
-        self.blended_samples = blended_samples
+        self.blended_latent = blended_latent
 
         self.denoiser = denoiser
         self.is_final_blend = denoiser is None