test latent fix

pipeline.py

@@ -641,8 +641,9 @@ class StableDiffusionLongPromptWeightingPipeline(StableDiffusionPipeline):
                 if upscale:
                         return latents, None, None
             # scale the initial noise by the standard deviation required by the scheduler
-                latents = latents * self.scheduler.init_noise_sigma
-                return latents, None, None
+                else:
+                    latents = latents * self.scheduler.init_noise_sigma
+                    return latents, None, None
         else:
             init_latent_dist = self.vae.encode(image).latent_dist
             init_latents = init_latent_dist.sample(generator=generator)
@@ -1039,7 +1040,7 @@ class StableDiffusionLongPromptWeightingPipeline(StableDiffusionPipeline):
         )
 
         latents = torch.nn.functional.interpolate(latents, size=(int(height*resize_scale)//8, int(width*resize_scale)//8))
-
+        co
         return self.__call__(
             prompt=prompt,
             negative_prompt=negative_prompt,