diff --git a/modules/processing.py b/modules/processing.py
index 50343846..64edfa04 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -643,6 +643,27 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
             self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f
             self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f
+    
+    def create_dummy_mask(self, x, width=None, height=None):
+        if self.sampler.conditioning_key in {'hybrid', 'concat'}:
+            height = height or self.height
+            width = width or self.width
+
+            # The "masked-image" in this case will just be all zeros since the entire image is masked.
+            image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
+            image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning)) 
+
+            # Add the fake full 1s mask to the first dimension.
+            image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
+            image_conditioning = image_conditioning.to(x.dtype)
+
+        else:
+            # Dummy zero conditioning if we're not using inpainting model.
+            # Still takes up a bit of memory, but no encoder call.
+            # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
+            image_conditioning = torch.zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)
+
+        return image_conditioning
 
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
         self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
@@ -690,11 +711,14 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         x = None
         devices.torch_gc()
 
-        image_conditioning = self.img2img_image_conditioning(
-            decoded_samples, 
-            samples, 
-            decoded_samples.new_ones(decoded_samples.shape[0], 1, decoded_samples.shape[2], decoded_samples.shape[3])
-        )
+        if opts.use_scale_latent_for_hires_fix:
+            image_conditioning = self.create_dummy_mask(samples)
+        else:
+            image_conditioning = self.img2img_image_conditioning(
+                decoded_samples, 
+                samples, 
+                decoded_samples.new_ones(decoded_samples.shape[0], 1, decoded_samples.shape[2], decoded_samples.shape[3])
+            )
         samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps, image_conditioning=image_conditioning)
 
         return samples