Update model.py

model.py

@@ -124,13 +124,14 @@ class Model:
                 image=image,image_resolution=512, detect_resolution=512)
         elif preprocessor_name == 'No preprocess':
             condition_img = image
+        print('get edge')
         condition_img = condition_img.resize((512,512))
         W, H = condition_img.size
 
-        condition_img = torch.from_numpy(np.array(condition_img)).unsqueeze(0).permute(0,3,1,2).repeat(3,1,1,1)
+        condition_img = torch.from_numpy(np.array(condition_img)).unsqueeze(0).permute(0,3,1,2).repeat(1,1,1,1)
         condition_img = condition_img.to(self.device)
         condition_img = 2*(condition_img/255 - 0.5)
-        prompts = [prompt] * 3
+        prompts = [prompt] * 1
         caption_embs, emb_masks = self.t5_model.get_text_embeddings(prompts)
 
         print(f"processing left-padding...")
@@ -214,13 +215,14 @@ class Model:
                 )
         elif preprocessor_name == 'No preprocess':
             condition_img = image
+        print('get depth')
         condition_img = condition_img.resize((512,512))
         W, H = condition_img.size
 
-        condition_img = torch.from_numpy(np.array(condition_img)).unsqueeze(0).permute(0,3,1,2).repeat(3,1,1,1)
+        condition_img = torch.from_numpy(np.array(condition_img)).unsqueeze(0).permute(0,3,1,2).repeat(1,1,1,1)
         condition_img = condition_img.to(self.device)
         condition_img = 2*(condition_img/255 - 0.5)
-        prompts = [prompt] * 3
+        prompts = [prompt] * 1
         caption_embs, emb_masks = self.t5_model.get_text_embeddings(prompts)
 
         print(f"processing left-padding...")