test gradio

app.py

@@ -4,19 +4,34 @@ from diffusers import StableDiffusion3ControlNetPipeline, SD3ControlNetModel, Un
 from huggingface_hub import login
 import os
 import spaces
-from diffusers import StableDiffusion3ControlNetPipeline
-from diffusers.models import SD3ControlNetModel, SD3MultiControlNetModel
-from diffusers.utils import load_image
+from diffusers.utils import load_image, make_image_grid
+import torch
+from diffusers import StableDiffusionXLAdapterPipeline,T2IAdapter
+from diffusers.models import T2IAdapter
+from diffusers.schedulers import UniPCMultistepScheduler
 
 # Log in to Hugging Face with your token
 token = os.getenv("HF_TOKEN")
 login(token=token)
 
-controlnet = SD3ControlNetModel.from_pretrained("alimama-creative/SD3-Controlnet-Softedge", torch_dtype=torch.float16)
 
-pipe = StableDiffusion3ControlNetPipeline.from_pretrained("stabilityai/stable-diffusion-3-medium-diffusers", controlnet=controlnet)
+
+# Load the T2I-Style Adapter and the SDXL pipeline
+adapter = T2IAdapter.from_pretrained("TencentARC/t2i-adapter-style-sdxl")
+pipe = StableDiffusionXLAdapterPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0",
+    adapter=adapter,
+)
+
+# Set up the scheduler and device
+pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
 pipe.to("cuda", torch.float16)
 
+# controlnet = SD3ControlNetModel.from_pretrained("alimama-creative/SD3-Controlnet-Softedge", torch_dtype=torch.float16)
+#
+# pipe = StableDiffusion3ControlNetPipeline.from_pretrained("stabilityai/stable-diffusion-3-medium-diffusers", controlnet=controlnet)
+# pipe.to("cuda", torch.float16)
+
 
 @spaces.GPU
 def generate_image(prompt, reference_image, controlnet_conditioning_scale):