test in latnent upcale

pipeline.py

@@ -841,6 +841,7 @@ class StableDiffusionLongPromptWeightingPipeline(StableDiffusionPipeline):
                     callback(i, t, latents)
                 if is_cancelled_callback is not None and is_cancelled_callback():
                     return None
+        print("after first step denoise latents")
         print(latents)
         print(latents.shape)
         latents = torch.nn.functional.interpolate(
@@ -850,6 +851,13 @@ class StableDiffusionLongPromptWeightingPipeline(StableDiffusionPipeline):
             # expand the latents if we are doing classifier free guidance
             latent_model_input = torch.cat(
                 [latents] * 2) if do_classifier_free_guidance else latents
+            print("latent_model_input")
+            print(latent_model_input)
+            print(latent_model_input.shape)
+
+            print("2nd step timestep")
+            print(t)
+
             latent_model_input = self.scheduler.scale_model_input(
                 latent_model_input, t)