Update app.py

app.py

@@ -28,7 +28,7 @@ MAX_IMAGE_SIZE = 1344
 
 @spaces.GPU
 def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
-
+    print("start inference...")
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
         
@@ -41,10 +41,10 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
         {"role": "assistant", "content": "A gourmet scene in a high-end restaurant kitchen where a chef is presenting a plate of cooked beef testicles, garnished elegantly with herbs and spices. The chef, a middle-aged Caucasian man wearing a white chef's hat and coat, is inspecting the dish with a satisfied expression. The kitchen background is bustling with other chefs and kitchen staff, and the atmosphere is warm and inviting with hanging pots and pans, and a glowing, busy stove in the background. The focus is on the chef's proud presentation of this unusual but delicately prepared dish."},
         {"role": "user", "content": prompt},
     ]
-    tokenized_input = tokenizer.apply_chat_templete(messages, add_special_tokens=False, add_generate_prompt=True, return_tensors="pt").to(model.device)
+    tokenized_input = tokenizer.apply_chat_templete(messages, add_generation_prompt=True, return_tensors="pt")
     with torch.inference_mode():
         output = upsampler.generate(
-            tokenized_input,
+            tokenized_input.to(upsampler.device),
             max_new_tokens=512,
             do_sample=True,
             top_p=0.95,
@@ -52,7 +52,7 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
             repetition_penalty=1.05,
         )[0]
     print(tokenizer.decode(output))
-    upsampled_prompt=output['generated_text']
+    upsampled_prompt=tokenizer.decode(output)
 
     print(upsampled_prompt)