Update app.py

app.py

@@ -32,7 +32,7 @@ enhancer_long = pipeline("summarization", model="prithivMLmods/t5-Flan-Prompt-En
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 2048
 
-# Qwen2VL caption function – updated with no_grad and autocast contexts, and explicit device moves
+# Qwen2VL caption function – updated to request plain text caption instead of JSON
 @spaces.GPU
 def qwen_caption(image):
     # Convert image to PIL if needed
@@ -48,7 +48,7 @@ def qwen_caption(image):
             ],
         }
     ]
-    
+
     text = qwen_processor.apply_chat_template(
         messages, tokenize=False, add_generation_prompt=True
     )
@@ -59,17 +59,11 @@ def qwen_caption(image):
         videos=video_inputs,
         padding=True,
         return_tensors="pt",
-    )
-    # Explicitly move each tensor to device
-    inputs = {k: v.to(device) for k, v in inputs.items()}
-    
-    # Wrap generation in no_grad and autocast contexts to prevent extra memory usage and potential caching issues
-    with torch.no_grad():
-        with torch.cuda.amp.autocast(device_type="cuda", dtype=torch.float16):
-            generated_ids = qwen_model.generate(**inputs, max_new_tokens=1024)
-    
+    ).to(device)
+
+    generated_ids = qwen_model.generate(**inputs, max_new_tokens=1024)
     generated_ids_trimmed = [
-        out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs["input_ids"], generated_ids)
+        out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
     ]
     output_text = qwen_processor.batch_decode(
         generated_ids_trimmed,
@@ -124,7 +118,7 @@ def process_workflow(image, text_prompt, use_enhancer, seed, randomize_seed, wid
 
 custom_css = """
 .input-group, .output-group {
-    /* You can add styling here if needed */
+
 }
 .submit-btn {
     background: linear-gradient(90deg, #4B79A1 0%, #283E51 100%) !important;