diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py
index 7e5670d66f9f7ec0cb6962230f17541b3836ab46..d4d28d2e7dfe9940f0e121ad6530dcaabe2b446b 100644
--- a/modules/sd_hijack_inpainting.py
+++ b/modules/sd_hijack_inpainting.py
@@ -19,63 +19,35 @@ from ldm.models.diffusion.ddim import DDIMSampler, noise_like
 # https://github.com/runwayml/stable-diffusion/blob/main/ldm/models/diffusion/ddim.py
 # =================================================================================================
 @torch.no_grad()
-def sample(
-    self,
-    S,
-    batch_size,
-    shape,
-    conditioning=None,
-    callback=None,
-    normals_sequence=None,
-    img_callback=None,
-    quantize_x0=False,
-    eta=0.,
-    mask=None,
-    x0=None,
-    temperature=1.,
-    noise_dropout=0.,
-    score_corrector=None,
-    corrector_kwargs=None,
-    verbose=True,
-    x_T=None,
-    log_every_t=100,
-    unconditional_guidance_scale=1.,
-    unconditional_conditioning=None,
-    # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
-    **kwargs
-    ):
+def sample(self,
+            S,
+            batch_size,
+            shape,
+            conditioning=None,
+            callback=None,
+            normals_sequence=None,
+            img_callback=None,
+            quantize_x0=False,
+            eta=0.,
+            mask=None,
+            x0=None,
+            temperature=1.,
+            noise_dropout=0.,
+            score_corrector=None,
+            corrector_kwargs=None,
+            verbose=True,
+            x_T=None,
+            log_every_t=100,
+            unconditional_guidance_scale=1.,
+            unconditional_conditioning=None,
+            # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+            **kwargs
+            ):
     if conditioning is not None:
         if isinstance(conditioning, dict):
             ctmp = conditioning[list(conditioning.keys())[0]]
             while isinstance(ctmp, list):
-                ctmp = elf.inpainting_fill == 2:
-                self.init_latent = self.init_latent * self.mask + create_random_tensors(self.init_latent.shape[1:], all_seeds[0:self.init_latent.shape[0]]) * self.nmask
-            elif self.inpainting_fill == 3:
-                self.init_latent = self.init_latent * self.mask
-
-        if self.image_mask is not None:
-            conditioning_mask = np.array(self.image_mask.convert("L"))
-            conditioning_mask = conditioning_mask.astype(np.float32) / 255.0
-            conditioning_mask = torch.from_numpy(conditioning_mask[None, None])
-
-            # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0
-            conditioning_mask = torch.round(conditioning_mask)
-        else:
-            conditioning_mask = torch.ones(1, 1, *image.shape[-2:])
-
-        # Create another latent image, this time with a masked version of the original input.
-        conditioning_mask = conditioning_mask.to(image.device)
-        conditioning_image = image * (1.0 - conditioning_mask)
-        conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
-
-        # Create the concatenated conditioning tensor to be fed to `c_concat`
-        conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=self.init_latent.shape[-2:])
-        conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1)
-        self.image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1)
-        self.image_conditioning = self.image_conditioning.to(shared.device).type(self.sd_model.dtype)
-
-    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
-        x = create_random_tensors([opctmp[0]
+                ctmp = ctmp[0]
             cbs = ctmp.shape[0]
             if cbs != batch_size:
                 print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
@@ -106,7 +78,6 @@ def sample(
                                                 )
     return samples, intermediates
 
-
 @torch.no_grad()
 def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
                     temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,