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--- |
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license: other |
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license_name: flux-1-dev-non-commercial-license |
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license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md |
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language: |
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- en |
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tags: |
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- flux |
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- diffusers |
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- lora |
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base_model: "black-forest-labs/FLUX.1-dev" |
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pipeline_tag: text-to-image |
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instance_prompt: DHANUSH |
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--- |
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# Tugce_Flux |
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Trained on Replicate using: |
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https://replicate.com/ostris/flux-dev-lora-trainer/train |
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## Trigger words |
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You should use `tugce` to trigger the image generation. |
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## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) |
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```py |
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from fastapi import FastAPI, HTTPException |
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from fastapi.responses import FileResponse |
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import torch |
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from diffusers import AutoPipelineForText2Image |
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import io |
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app = FastAPI() |
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# Model ve LoRA'yı yükle |
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pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') |
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pipeline.load_lora_weights('codermert/tugce2-lora', weight_name='flux_train_replicate.safetensors') |
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@app.post("/generate_image") |
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async def generate_image(prompt: str, width: int, height: int): |
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try: |
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image = pipeline( |
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prompt, |
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width=width, |
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height=height |
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).images[0] |
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img_byte_arr = io.BytesIO() |
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image.save(img_byte_arr, format='PNG') |
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img_byte_arr.seek(0) |
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return FileResponse(img_byte_arr, media_type="image/png") |
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except Exception as e: |
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raise HTTPException(status_code=500, detail=str(e)) |
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if __name__ == "__main__": |
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import uvicorn |
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uvicorn.run(app, host="0.0.0.0", port=8000) |
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``` |
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For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) |
