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import spaces
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@spaces.GPU
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def dummy_gpu():
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pass
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import gradio as gr
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import torch
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from pathlib import Path
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from diffusers import FluxPipeline, FluxTransformer2DModel
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from huggingface_hub import hf_hub_download, HfApi
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IS_TURBO = False
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TEMP_DIR = "./temp"
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repo_id = "camenduru/FLUX.1-dev-diffusers"
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dtype = torch.bfloat16
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cp = hf_hub_download("John6666/flux1-backup-202502", "jibMixFlux_v8AccentueightNSFW.safetensors", repo_type="dataset")
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transformer = FluxTransformer2DModel.from_single_file(cp, subfolder="transformer", torch_dtype=dtype, config=repo_id)
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pipe = FluxPipeline.from_pretrained(repo_id, transformer=transformer, torch_dtype=dtype)
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if IS_TURBO:
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pipe.to("cuda")
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pipe.load_lora_weights(hf_hub_download("ByteDance/Hyper-SD", "Hyper-FLUX.1-dev-8steps-lora.safetensors"), adapter_name="hyper-sd")
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pipe.set_adapters(["hyper-sd"], adapter_weights=[0.125])
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pipe.fuse_lora()
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pipe.unload_lora_weights()
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pipe.to("cpu")
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def upload_model(repo_id: str="", token: str="", progress=gr.Progress(track_tqdm=True)):
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if not token: return "Token not found."
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pipe.save_pretrained(TEMP_DIR)
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api = HfApi(token=token if token else False)
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api.create_repo(repo_id=repo_id, token=token, private=True, exist_ok=True)
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api.upload_folder(repo_id=repo_id, repo_type="model", folder_path=TEMP_DIR, path_in_repo=".")
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api.upload_file(repo_id=repo_id, repo_type="model", path_or_fileobj=cp, path_in_repo=Path(cp).name)
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return "Converted."
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with gr.Blocks() as demo:
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repo_id = gr.Textbox(label="Repo ID", value="")
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hf_token = gr.Textbox(label="Your HF write token", value="")
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run_button = gr.Button("Submit", variant="primary")
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info_md = gr.Markdown("<br><br><br>")
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run_button.click(upload_model, [repo_id, hf_token], [info_md])
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demo.launch()
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