Update app.py

app.py

@@ -100,11 +100,11 @@ isomer_color_weights = torch.from_numpy(np.array([1, 0.5, 1, 0.5])).float().to(d
 taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16).to(device_0)
 good_vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae", torch_dtype=torch.bfloat16, token=access_token).to(device_0)
 # flux_pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16, token=access_token).to(device=device_0, dtype=torch.bfloat16)
-flux_pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16, vae=taef1).to(device)
+flux_pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16, vae=taef1, token=access_token).to(device)
 flux_lora_ckpt_path = hf_hub_download(repo_id="LTT/xxx-ckpt", filename="rgb_normal_large.safetensors", repo_type="model")
 flux_pipe.load_lora_weights(flux_lora_ckpt_path)
 flux_pipe.to(device=device_0, dtype=torch.bfloat16)
-flux_pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(flux_pipe)
+# flux_pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(flux_pipe)
 
 
 # lrm