diff --git a/modules/processing.py b/modules/processing.py
index 09cd8d9a..8742ba3a 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -343,24 +343,24 @@ class StableDiffusionProcessing:
         conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=latent_image.shape[-2:])
         conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1)
         image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1)
-        image_conditioning = image_conditioning.to(shared.device).type(self.sd_model.dtype)
+        # image_conditioning = image_conditioning.to(shared.device).type(self.sd_model.dtype)
 
         return image_conditioning
 
     def img2img_image_conditioning(self, source_image, latent_image, image_mask=None, round_image_mask=True):
         source_image = devices.cond_cast_float(source_image)
 
-        if self.sd_model.cond_stage_key == "edit":
-            return self.edit_image_conditioning(source_image)
+        # if self.sd_model.cond_stage_key == "edit":
+        #     return self.edit_image_conditioning(source_image)
 
-        if self.sampler.conditioning_key in {'hybrid', 'concat'}:
+        if self.sd_model.is_inpaint:
             return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask, round_image_mask=round_image_mask)
 
-        if self.sampler.conditioning_key == "crossattn-adm":
-            return self.unclip_image_conditioning(source_image)
-
-        if self.sampler.model_wrap.inner_model.is_sdxl_inpaint:
-            return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask)
+        # if self.sampler.conditioning_key == "crossattn-adm":
+        #     return self.unclip_image_conditioning(source_image)
+        #
+        # if self.sampler.model_wrap.inner_model.is_sdxl_inpaint:
+        #     return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask)
 
         # Dummy zero conditioning if we're not using inpainting or depth model.
         return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)