test gradio

app.py

@@ -1,37 +1,33 @@
 import gradio as gr
+import torch
+from diffusers import StableDiffusion3ControlNetPipeline, SD3ControlNetModel, UniPCMultistepScheduler
 from huggingface_hub import login
 import os
 import spaces
-from diffusers.schedulers import UniPCMultistepScheduler
-from diffusers import StableDiffusionXLAdapterPipeline, T2IAdapter, EulerAncestralDiscreteScheduler, AutoencoderKL, \
-    StableDiffusionXLPipeline
 from diffusers.utils import load_image, make_image_grid
 import torch
 
-# Log in to Hugging Face with your token
 token = os.getenv("HF_TOKEN")
 login(token=token)
 
-model_id = 'stabilityai/sdxl-turbo'
-x = StableDiffusionXLPipeline.from_pretrained(model_id)
-
-euler_a = EulerAncestralDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
-vae=AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix",)
-x.load_ip_adapter(pretrained_model_name_or_path_or_dict="TencentARC/T2I-Adapter",
-                     subfolder="models",
-                     weight_name="t2iadapter_style_sd14v1.pth")
 
-pipe = StableDiffusionXLAdapterPipeline.from_pretrained(
-    model_id, vae=vae, adapter=x.adapter, scheduler=euler_a, variant="fp16",
-)
-pipe.enable_xformers_memory_efficient_attention()
-pipe.to("cuda", torch.float16)
 
-# controlnet = SD3ControlNetModel.from_pretrained("alimama-creative/SD3-Controlnet-Softedge", torch_dtype=torch.float16)
+# # 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,
+# )
 #
-# pipe = StableDiffusion3ControlNetPipeline.from_pretrained("stabilityai/stable-diffusion-3-medium-diffusers", controlnet=controlnet)
+# # 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):
@@ -39,7 +35,7 @@ def generate_image(prompt, reference_image, controlnet_conditioning_scale):
     # Generate the image with ControlNet conditioning
     generated_image = pipe(
         prompt=prompt,
-        ip_adapter_image=load_image(reference_image),
+        control_image=load_image(reference_image),
         controlnet_conditioning_scale=controlnet_conditioning_scale,
     ).images[0]
     return generated_image