revert to applying mask before denoising for k-diffusion, like it was before

modules/sd_samplers_cfg_denoiser.py

@@ -56,6 +56,7 @@ class CFGDenoiser(torch.nn.Module):
         self.sampler = sampler
         self.model_wrap = None
         self.p = None
+        self.mask_before_denoising = False
 
     @property
     def inner_model(self):
@@ -104,7 +105,7 @@ 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 self.mask is not None:
+        if self.mask_before_denoising and self.mask is not None:
             x = self.init_latent * self.mask + self.nmask * x
 
         batch_size = len(conds_list)
@@ -206,6 +207,9 @@ class CFGDenoiser(torch.nn.Module):
         else:
             denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
 
+        if not self.mask_before_denoising and self.mask is not None:
+            denoised = self.init_latent * self.mask + self.nmask * denoised
+
         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)
 
         if opts.live_preview_content == "Prompt":

modules/sd_samplers_timesteps.py

@@ -49,6 +49,7 @@ class CFGDenoiserTimesteps(CFGDenoiser):
         super().__init__(sampler)
 
         self.alphas = shared.sd_model.alphas_cumprod
+        self.mask_before_denoising = True
 
     def get_pred_x0(self, x_in, x_out, sigma):
         ts = sigma.to(dtype=int)