will fix haha

app.py

@@ -40,13 +40,14 @@ unet.load_state_dict(load_file(hf_hub_download(sdxl_lightening, ckpt), device="c
 pipe = SDEmb.from_pretrained(model_id, unet=unet, torch_dtype=torch.float16, variant="fp16").to("cuda")
 pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taesdxl", torch_dtype=torch.float16)
 pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
-pipe.to(device='cuda')
 
-image_encoder = CLIPVisionModelWithProjection.from_pretrained('h94/IP-Adapter', subfolder='sdxl_models/image_encoder', torch_dtype=torch.float16).to("cuda")
+image_preproc = CLIPImageProcessor.from_pretrained('runwayml/stable-diffusion-v1-5', subfolder='feature_extractor')
+image_encoder = CLIPVisionModelWithProjection.from_pretrained('h94/IP-Adapter', subfolder='sdxl_models/image_encoder', torch_dtype=torch.float16)
 pipe.unet._load_ip_adapter_weights(torch.load(hf_hub_download('h94/IP-Adapter', 'sdxl_models/ip-adapter_sdxl.bin'), map_location="cpu"))
 pipe.register_modules(image_encoder = image_encoder)
+pipe.register_modules(feature_extractor = image_preproc)
 
-# pipe.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter_sdxl.bin")
+pipe.to(device='cuda')
 
 output_hidden_state = True
 #######################