README.md

metadata

license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
  - en
tags:
  - flux
  - diffusers
  - lora
base_model: black-forest-labs/FLUX.1-dev
pipeline_tag: text-to-image
instance_prompt: DHANUSH

Tugce_Flux

Trained on Replicate using:

https://replicate.com/ostris/flux-dev-lora-trainer/train

Trigger words

You should use tugce to trigger the image generation.

Use it with the 🧨 diffusers library

from fastapi import FastAPI, HTTPException
from fastapi.responses import FileResponse
import torch
from diffusers import AutoPipelineForText2Image
import io

app = FastAPI()

# Model ve LoRA'yı yükle
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('codermert/tugce2-lora', weight_name='flux_train_replicate.safetensors')

@app.post("/generate_image")
async def generate_image(prompt: str, width: int, height: int):
    try:
        image = pipeline(
            prompt,
            width=width,
            height=height
        ).images[0]
        
        img_byte_arr = io.BytesIO()
        image.save(img_byte_arr, format='PNG')
        img_byte_arr.seek(0)
        
        return FileResponse(img_byte_arr, media_type="image/png")
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)

For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers