Update app.py

app.py

@@ -28,21 +28,22 @@ pipe = ChatsSDXLPipeline.from_pretrained(
 pipe.to(DEVICE)
 
 @spaces.GPU(duration=75)
-def generate(prompt, seed, randomize_seed=False, steps=50, guidance_scale=7.5):
+def generate(prompt, seed=0, randomize_seed=False, steps=50, guidance_scale=5.0):
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
-
+    
+    print('inference with prompt : {}, seed : {}, step : {}, cfg : {}'.format(prompt, seed, steps, guidance_scale))
     output = pipe(
         prompt=prompt,
         num_inference_steps=steps,
         guidance_scale=guidance_scale,
         seed=seed
     )
-    return output['images']
+    return output['images'][0]
 
 examples = [
-    "The image is a digital art headshot of an owlfolk character with high detail and dramatic lighting",
     "Solar punk vehicle in a bustling city",
+    "An anthropomorphic cat riding a Harley Davidson in Arizona with sunglasses and a leather jacket",
     "An elderly woman poses for a high fashion photoshoot in colorful, patterned clothes with a cyberpunk 2077 vibe",
 ]
 
@@ -57,7 +58,7 @@ with gr.Blocks(css=css) as demo:
     
     with gr.Column(elem_id="col-container"):
         gr.Markdown(f"""# CHATS-SDXL
-SDXL diffusion models finetuned using preference optimization framework CHATS. [[paper] (https://arxiv.org/pdf/2502.12579)] [[code](https://github.com/AIDC-AI/CHATS)] [[model](https://huggingface.co/AIDC-AI/CHATS)]
+SDXL diffusion models finetuned using preference optimization framework CHATS. [[paper](https://arxiv.org/pdf/2502.12579)] [[code](https://github.com/AIDC-AI/CHATS)] [[model](https://huggingface.co/AIDC-AI/CHATS)]
         """)
         
         with gr.Row():
@@ -84,7 +85,7 @@ SDXL diffusion models finetuned using preference optimization framework CHATS. [
                 value=0,
             )
             
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+            randomize_seed = gr.Checkbox(label="Randomize seed", value=False)
                         
             with gr.Row():