Update app.py

app.py

@@ -7,8 +7,10 @@ from diffusers import AutoPipelineForInpainting, UNet2DConditionModel
 import diffusers
 from share_btn import community_icon_html, loading_icon_html, share_js
 
+unet = UNet2DConditionModel.from_config("SG161222/Realistic_Vision_V5.0_noVAE", subfolder="unet").to("cuda", torch.float16)
+unet.load_state_dict(load_file(hf_hub_download("ByteDance/SDXL-Lightning", "sdxl_lightning_2step_unet.safetensors"), device="cuda"))
 vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse")
-pipe = AutoPipelineForInpainting.from_pretrained("SG161222/Realistic_Vision_V5.0_noVAE",vae=vae).to("cuda")
+pipe = AutoPipelineForInpainting.from_pretrained("SG161222/Realistic_Vision_V5.0_noVAE",unet=unet,vae=vae).to("cuda")
 
 def read_content(file_path: str) -> str:
     """read the content of target file
@@ -30,7 +32,9 @@ def predict(dict, prompt="", negative_prompt="", guidance_scale=7.5, steps=30, s
         add_kwargs["algorithm_type"] = "sde-dpmsolver++"
 
     scheduler = getattr(diffusers, scheduler_class_name)
-    pipe.scheduler = scheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler", **add_kwargs)
+    pipe.scheduler = scheduler.from_pretrained("SG161222/Realistic_Vision_V5.0_noVAE", subfolder="scheduler", **add_kwargs)
+
+    
     
     init_image = dict["image"]
     mask_image = dict["mask"]