Update app.py

app.py

@@ -39,8 +39,8 @@ model_path = snapshot_download(
     token=huggingface_token, # type a new token-id.
 )
 
-tokenizer_2 = T5Tokenizer.from_pretrained("LPX55/FLUX.1-merged_uncensored", subfolder="tokenizer_2", token=huggingface_token)
-taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16).to(device)
+# tokenizer_2 = T5Tokenizer.from_pretrained("LPX55/FLUX.1-merged_uncensored", subfolder="tokenizer_2", token=huggingface_token)
+# taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16).to(device)
 good_vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae", torch_dtype=torch.bfloat16, token=huggingface_token).to(device)
 # Load pipeline
 controlnet = FluxControlNetModel.from_pretrained(
@@ -49,6 +49,7 @@ controlnet = FluxControlNetModel.from_pretrained(
 pipe = FluxControlNetPipeline.from_pretrained(
     "LPX55/FLUX.1-merged_uncensored", controlnet=controlnet, torch_dtype=torch.bfloat16, vae=good_vae, token=huggingface_token,
 )
+pipe.enable_vae_tiling()
 # pipe.load_lora_weights(
 #     hf_hub_download("ByteDance/Hyper-SD", "Hyper-FLUX.1-dev-8steps-lora.safetensors"), adapter_name="hyper-sd"
 # )