test gradio

app.py

@@ -1,6 +1,7 @@
 import gradio as gr
 import torch
-from diffusers import StableDiffusion3ControlNetPipeline, SD3ControlNetModel, UniPCMultistepScheduler
+# from diffusers import StableDiffusion3ControlNetPipeline, SD3ControlNetModel, UniPCMultistepScheduler
+from diffusers import AutoPipelineForText2Image
 from huggingface_hub import login
 import os
 import spaces
@@ -23,9 +24,11 @@ login(token=token)
 # 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)
+# 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 = StableDiffusion3ControlNetPipeline.from_pretrained("stabilityai/stable-diffusion-3-medium-diffusers", controlnet=controlnet)
+pipe = AutoPipelineForText2Image.from_pretrained("stabilityai/stable-diffusion-3-medium-diffusers")
+pipe.load_ip_adapter("TencentARC/T2I-Adapter", subfolder="models", weight_name="coadapter-style-sd15v1.pth")
 pipe.to("cuda", torch.float16)
 
 
@@ -35,7 +38,7 @@ def generate_image(prompt, reference_image, controlnet_conditioning_scale):
     # Generate the image with ControlNet conditioning
     generated_image = pipe(
         prompt=prompt,
-        control_image=load_image(reference_image),
+        ip_adapter_image=load_image(reference_image),
         controlnet_conditioning_scale=controlnet_conditioning_scale,
     ).images[0]
     return generated_image