A custom blending function can be provided by p, replacing the use of soft_inpainting.

modules/sd_samplers_cfg_denoiser.py

@@ -6,7 +6,6 @@ import modules.shared as shared
 from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
 from modules.script_callbacks import CFGDenoisedParams, cfg_denoised_callback
 from modules.script_callbacks import AfterCFGCallbackParams, cfg_after_cfg_callback
-import modules.soft_inpainting as si
 
 
 def catenate_conds(conds):
@@ -44,7 +43,6 @@ class CFGDenoiser(torch.nn.Module):
         self.model_wrap = None
         self.mask = None
         self.nmask = None
-        self.soft_inpainting: si.SoftInpaintingParameters = None
         self.init_latent = None
         self.steps = None
         """number of steps as specified by user in UI"""
@@ -94,7 +92,6 @@ class CFGDenoiser(torch.nn.Module):
         self.sampler.sampler_extra_args['uncond'] = uc
 
     def forward(self, x, sigma, uncond, cond, cond_scale, s_min_uncond, image_cond):
-
         if state.interrupted or state.skipped:
             raise sd_samplers_common.InterruptedException
 
@@ -111,15 +108,24 @@ class CFGDenoiser(torch.nn.Module):
 
         assert not is_edit_model or all(len(conds) == 1 for conds in conds_list), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)"
 
+        # If we use masks, blending between the denoised and original latent images occurs here.
+        def apply_blend(latent):
+            if hasattr(self.p, "denoiser_masked_blend_function") and callable(self.p.denoiser_masked_blend_function):
+                return self.p.denoiser_masked_blend_function(
+                    self,
+                    # Using an argument dictionary so that arguments can be added without breaking extensions.
+                    args=
+                    {
+                        "denoiser": self,
+                        "current_latent": latent,
+                        "sigma": sigma
+                    })
+            else:
+                return self.init_latent * self.mask + self.nmask * latent
+
         # Blend in the original latents (before)
         if self.mask_before_denoising and self.mask is not None:
-            if self.soft_inpainting is None:
+            x = apply_blend(x)
-                x = self.init_latent * self.mask + self.nmask * x
-            else:
-                x = si.latent_blend(self.soft_inpainting,
-                                    self.init_latent,
-                                    x,
-                                    si.get_modified_nmask(self.soft_inpainting, self.nmask, sigma))
 
         batch_size = len(conds_list)
         repeats = [len(conds_list[i]) for i in range(batch_size)]
@@ -222,13 +228,7 @@ class CFGDenoiser(torch.nn.Module):
 
         # Blend in the original latents (after)
         if not self.mask_before_denoising and self.mask is not None:
-            if self.soft_inpainting is None:
+            denoised = apply_blend(denoised)
-                denoised = self.init_latent * self.mask + self.nmask * denoised
-            else:
-                denoised = si.latent_blend(self.soft_inpainting,
-                                           self.init_latent,
-                                           denoised,
-                                           si.get_modified_nmask(self.soft_inpainting, self.nmask, sigma))
 
         self.sampler.last_latent = self.get_pred_x0(torch.cat([x_in[i:i + 1] for i in denoised_image_indexes]), torch.cat([x_out[i:i + 1] for i in denoised_image_indexes]), sigma)
 

modules/sd_samplers_common.py

@@ -277,7 +277,6 @@ class Sampler:
         self.model_wrap_cfg.p = p
         self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None
         self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None
-        self.model_wrap_cfg.soft_inpainting = p.soft_inpainting if hasattr(p, 'soft_inpainting') else None
         self.model_wrap_cfg.step = 0
         self.model_wrap_cfg.image_cfg_scale = getattr(p, 'image_cfg_scale', None)
         self.eta = p.eta if p.eta is not None else getattr(opts, self.eta_option_field, 0.0)