test gradio

app.py

@@ -1,28 +1,28 @@
 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
 from diffusers.utils import load_image, make_image_grid
 import torch
-from diffusers import StableDiffusionXLAdapterPipeline,T2IAdapter
-from diffusers.schedulers import UniPCMultistepScheduler
 
 # Log in to Hugging Face with your token
 token = os.getenv("HF_TOKEN")
 login(token=token)
 
+model_id = 'stabilityai/sdxl-turbo'
 
-pipe = StableDiffusionXLAdapterPipeline.from_pretrained(
-    "stabilityai/sdxl-turbo",
-)
-pipe.load_ip_adapter("TencentARC/T2I-Adapter",
+adapter = StableDiffusionXLAdapterPipeline.load_ip_adapter("TencentARC/T2I-Adapter",
                      subfolder="models",
                      weight_name="t2iadapter_style_sd14v1.pth")
 
-# Set up the scheduler and device
-pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
+euler_a = EulerAncestralDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
+vae=AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix",)
+pipe = StableDiffusionXLAdapterPipeline.from_pretrained(
+    model_id, vae=vae, adapter=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)