Update app.py

app.py

@@ -45,12 +45,10 @@ from torchvision.transforms import InterpolationMode
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")  
 
-stable_diffusion_repo_path = "stabilityai/stable-diffusion-2-1-unclip"
-vae = AutoencoderKL.from_pretrained(stable_diffusion_repo_path, subfolder='vae')
-scheduler = DDIMScheduler.from_pretrained(stable_diffusion_repo_path, subfolder='scheduler')
-sd_image_variations_diffusers_path = 'lambdalabs/sd-image-variations-diffusers'
-image_encoder = CLIPVisionModelWithProjection.from_pretrained(sd_image_variations_diffusers_path, subfolder="image_encoder")
-feature_extractor = CLIPImageProcessor.from_pretrained(sd_image_variations_diffusers_path, subfolder="feature_extractor")
+vae = AutoencoderKL.from_pretrained("./", subfolder='vae')
+scheduler = DDIMScheduler.from_pretrained("./", subfolder='scheduler')
+image_encoder = CLIPVisionModelWithProjection.from_pretrained("./", subfolder="image_encoder")
+feature_extractor = CLIPImageProcessor.from_pretrained("./", subfolder="feature_extractor")
 unet = UNet2DConditionModel.from_pretrained('.', subfolder="unet")
 
 pipe = DepthNormalEstimationPipeline(vae=vae,