test interpolate latent

pipeline.py

@@ -665,6 +665,7 @@ class StableDiffusionLongPromptWeightingPipeline(StableDiffusionPipeline):
         mask_image: Union[torch.FloatTensor, PIL.Image.Image] = None,
         height: int = 512,
         width: int = 512,
+        resize_scale: float = 1.2,
         num_inference_steps: int = 50,
         guidance_scale: float = 7.5,
         strength: float = 0.8,
@@ -841,10 +842,15 @@ class StableDiffusionLongPromptWeightingPipeline(StableDiffusionPipeline):
                     return None
         print(latents)
         print(latents.shape)
+        resized_image = torch.nn.functional.interpolate(
+            latents, size=(int(latents.shape[2]*resize_scale)//8, int(latents.shape[3]*resize_scale)//8))
+
+        print(resized_image.shape)
+        #do latent upscale  here
+
         # 9. Post-processing
         image = self.decode_latents(latents)
 
-        #do latent upscale  here
 
         # 10. Run safety checker
         image, has_nsfw_concept = self.run_safety_checker(image, device, text_embeddings.dtype)