test gradio

app.py

@@ -3,7 +3,7 @@ import torch
 from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
 from huggingface_hub import login
 import os
-
+import spaces
 # Log in to Hugging Face with your token
 token = os.getenv("HF_TOKEN")
 login(token=token)
@@ -18,9 +18,9 @@ pipe = StableDiffusionControlNetPipeline.from_pretrained(
     model_id, controlnet=controlnet, torch_dtype=torch.float16
 )
 pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
-pipe = pipe.to("cuda") if torch.cuda.is_available() else pipe
+pipe = pipe.to("cuda")
 
-# Define the Gradio interface function
+@spaces.GPU
 def generate_image(prompt, reference_image):
     # Prepare the reference image for ControlNet
     reference_image = reference_image.convert("RGB").resize((512, 512))

requirements.txt

@@ -3,4 +3,5 @@ diffusers
 transformers
 accelerate
 gradio
-sentencepiece
+sentencepiece
+spaces