test gradio

app.py

@@ -1,12 +1,12 @@
 import gradio as gr
 import torch
 # from diffusers import StableDiffusion3ControlNetPipeline, SD3ControlNetModel, UniPCMultistepScheduler
-from diffusers import StableDiffusionXLPipeline
+from diffusers import StableDiffusionXLPipeline,T2IAdapter
 from huggingface_hub import login
 import os
 import spaces
 from diffusers.utils import load_image, make_image_grid
-import torch
+from diffusers import StableDiffusionXLAdapterPipeline, T2IAdapter, EulerAncestralDiscreteScheduler, AutoencoderKL
 
 token = os.getenv("HF_TOKEN")
 login(token=token)
@@ -27,7 +27,18 @@ 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)
-pipe = StableDiffusionXLPipeline.from_pretrained("stabilityai/sdxl-turbo")
+adapter = T2IAdapter.from_pretrained(
+  "TencentARC/t2i-adapter-lineart-sdxl-1.0", torch_dtype=torch.float16, varient="fp16"
+)
+
+model_id = 'stabilityai/stable-diffusion-xl-base-1.0'
+euler_a = EulerAncestralDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
+vae=AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
+pipe = StableDiffusionXLAdapterPipeline.from_pretrained(
+    model_id, vae=vae, adapter=adapter, scheduler=euler_a, torch_dtype=torch.float16, variant="fp16",
+).to("cuda")
+pipe.enable_xformers_memory_efficient_attention()
+
 pipe.load_ip_adapter("TencentARC/T2I-Adapter", subfolder="models", weight_name="coadapter-style-sd15v1.pth")
 pipe.to("cuda", torch.float16)