test latent fix

pipeline.py

@@ -625,7 +625,8 @@ class StableDiffusionLongPromptWeightingPipeline(StableDiffusionPipeline):
                 height // self.vae_scale_factor,
                 width // self.vae_scale_factor,
             )
-
+            print("shape in prepare_latents")
+            print(shape)
             if latents is None:
                 if device.type == "mps":
                     # randn does not work reproducibly on mps
@@ -654,6 +655,9 @@ class StableDiffusionLongPromptWeightingPipeline(StableDiffusionPipeline):
             else:
                 noise = torch.randn(shape, generator=generator, device=device, dtype=dtype)
             latents = self.scheduler.add_noise(init_latents, noise, timestep)
+            print("noise in prepare_latents")
+            print(noise)
+        
             return latents, init_latents_orig, noise
 
     @torch.no_grad()
@@ -809,9 +813,8 @@ class StableDiffusionLongPromptWeightingPipeline(StableDiffusionPipeline):
             generator,
             latents,
         )
-        print("noise")
-        print(noise.shape)
-        print(noise)
+        # print("noise")
+        # print(noise)
 
         # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
         extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)