Update pipelines/pipeline_seesr.py

pipelines/pipeline_seesr.py

@@ -1059,15 +1059,17 @@ class StableDiffusionControlNetPipeline(DiffusionPipeline, TextualInversionLoade
                     # Infer ControlNet only for the conditional batch.
                     controlnet_latent_model_input = latents
                     controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]
+                    print("well unexpected")
                     
                 else:
                     controlnet_latent_model_input = latent_model_input
                     controlnet_prompt_embeds = prompt_embeds
+                    print("a possiblity")
 
                 if h*w<=tile_size*tile_size: # tiled latent input
                     down_block_res_samples, mid_block_res_sample = [None]*10, None
 
-                    print(f"controlnet started with {controlnet_latent_model_input.shape}:{controlnet_prompt_embeds.shape}")
+                    print(f"controlnet 1 started with {controlnet_latent_model_input.shape}:{controlnet_prompt_embeds.shape}")
                     
                     down_block_res_samples, mid_block_res_sample = self.controlnet(
                         controlnet_latent_model_input,
@@ -1152,7 +1154,7 @@ class StableDiffusionControlNetPipeline(DiffusionPipeline, TextualInversionLoade
                                 cond_list_t = torch.cat(cond_list, dim=0)
                                 img_list_t = torch.cat(img_list, dim=0)
                                 #print(input_list_t.shape, cond_list_t.shape, img_list_t.shape, fg_mask_list_t.shape)
-                                print(f"controlnet started with {cond_list_t.shape}:{controlnet_prompt_embeds.shape}")
+                                print(f"controlnet 2 started with {cond_list_t.shape}:{controlnet_prompt_embeds.shape}")
 
                                 down_block_res_samples, mid_block_res_sample = self.controlnet(
                                     cond_list_t,