Update

app.py

@@ -14,6 +14,9 @@ import gc
 from gradio import processing_utils
 from typing import Optional
 
+import warnings
+warnings.filterwarnings("ignore")
+
 from huggingface_hub import hf_hub_download
 hf_hub_download = partial(hf_hub_download, library_name="gligen_demo")
 
@@ -56,10 +59,13 @@ class Instance:
         )
         self.capacity = capacity
 
-    def get_model(self, model_type):
+    def _log(self, batch_size, instruction, phrase_list):
+        print(dict(self.counter), f'samples: {batch_size}', f'prompt: {instruction}', f'phrases: {phrase_list}', sep=', ')
+
+    def get_model(self, model_type, batch_size, instruction, phrase_list):
         if model_type in self.loaded_model_list:
             self.counter[model_type] += 1
-            print(self.counter)
+            self._log(batch_size, instruction, phrase_list)
             return self.loaded_model_list[model_type]
 
         if self.capacity == len(self.loaded_model_list):
@@ -71,7 +77,7 @@ class Instance:
 
         self.counter[model_type] = 1
         self.loaded_model_list[model_type] = self._get_model(model_type)
-        print(self.counter)
+        self._log(batch_size, instruction, phrase_list)
         return self.loaded_model_list[model_type]
 
     def _get_model(self, model_type):
@@ -218,16 +224,21 @@ def inference(task, language_instruction, grounding_instruction, inpainting_boxe
         inpainting_boxes_nodrop = inpainting_boxes_nodrop,
     )
 
+    get_model = partial(instance.get_model,
+                        batch_size=batch_size,
+                        instruction=language_instruction,
+                        phrase_list=phrase_list)
+
     with torch.autocast(device_type='cuda', dtype=torch.float16):
         if task == 'Grounded Generation':
             if style_image == None:
-                return grounded_generation_box(instance.get_model('base'), instruction, *args, **kwargs)
+                return grounded_generation_box(get_model('base'), instruction, *args, **kwargs)
             else:
-                return grounded_generation_box(instance.get_model('style'), instruction, *args, **kwargs)
+                return grounded_generation_box(get_model('style'), instruction, *args, **kwargs)
         elif task == 'Grounded Inpainting':
             assert image is not None
             instruction['input_image'] = image.convert("RGB")
-            return grounded_generation_box(instance.get_model('inpaint'), instruction, *args, **kwargs)
+            return grounded_generation_box(get_model('inpaint'), instruction, *args, **kwargs)
 
 
 def draw_box(boxes=[], texts=[], img=None):
@@ -264,7 +275,6 @@ def auto_append_grounding(language_instruction, grounding_texts):
     for grounding_text in grounding_texts:
         if grounding_text not in language_instruction and grounding_text != 'auto':
             language_instruction += "; " + grounding_text
-    print(language_instruction)
     return language_instruction