Update app.py

app.py

@@ -10,13 +10,13 @@ dtype = torch.bfloat16
 device = "cuda"
 
 bfl_repo = "black-forest-labs/FLUX.1-dev"
-scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(bfl_repo, subfolder="scheduler", revision="refs/pr/1")
+scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(bfl_repo, subfolder="scheduler", revision="refs/pr/3")
 text_encoder = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14", torch_dtype=dtype)
 tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14", torch_dtype=dtype)
-text_encoder_2 = T5EncoderModel.from_pretrained(bfl_repo, subfolder="text_encoder_2", torch_dtype=dtype, revision="refs/pr/1")
-tokenizer_2 = T5TokenizerFast.from_pretrained(bfl_repo, subfolder="tokenizer_2", torch_dtype=dtype, revision="refs/pr/1")
-vae = AutoencoderKL.from_pretrained(bfl_repo, subfolder="vae", torch_dtype=dtype, revision="refs/pr/1")
-transformer = FluxTransformer2DModel.from_pretrained(bfl_repo, torch_dtype=dtype, revision="refs/pr/1")
+text_encoder_2 = T5EncoderModel.from_pretrained(bfl_repo, subfolder="text_encoder_2", torch_dtype=dtype, revision="refs/pr/3")
+tokenizer_2 = T5TokenizerFast.from_pretrained(bfl_repo, subfolder="tokenizer_2", torch_dtype=dtype, revision="refs/pr/3")
+vae = AutoencoderKL.from_pretrained(bfl_repo, subfolder="vae", torch_dtype=dtype, revision="refs/pr/3")
+transformer = FluxTransformer2DModel.from_pretrained(bfl_repo, torch_dtype=dtype, revision="refs/pr/3")
 
 device = "cuda" if torch.cuda.is_available() else "cpu"