Update app.py

app.py

@@ -1,21 +1,43 @@
 import gradio as gr
 import numpy as np
 import random
+import os
 
 import spaces
 from diffusers import DiffusionPipeline
 import torch
+from huggingface_hub import HfFolder
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 model_repo_id = "stabilityai/stable-diffusion-3.5-large-turbo"
 
+def get_hf_token():
+    token = os.getenv("HF_TOKEN")  # First check environment variable
+    if not token:
+        token = HfFolder.get_token()  # Then check .huggingface/token
+    return token
+
 if torch.cuda.is_available():
     torch_dtype = torch.bfloat16
 else:
     torch_dtype = torch.float32
 
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
+# Initialize pipeline with token
+token = get_hf_token()
+if token:
+    pipe = DiffusionPipeline.from_pretrained(
+        model_repo_id,
+        torch_dtype=torch_dtype,
+        use_auth_token=token
+    )
+    pipe = pipe.to(device)
+else:
+    raise ValueError(
+        "HF token not found. Please set your Hugging Face token either:\n"
+        "1. As an environment variable: export HF_TOKEN='your_token'\n"
+        "2. Using the Hugging Face CLI: huggingface-cli login\n"
+        "You can get your token from: https://huggingface.co/settings/tokens"
+    )
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
@@ -82,8 +104,9 @@ with gr.Blocks(css=css) as demo:
             negative_prompt = gr.Text(
                 label="Negative prompt",
                 max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
+                placeholder="Enter things you don't want in the image (optional)",
+                value="",
+                visible=True,
             )
 
             seed = gr.Slider(
@@ -148,4 +171,4 @@ with gr.Blocks(css=css) as demo:
     )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()