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Update app.py
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app.py
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@@ -1,47 +1,69 @@
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from fastapi import FastAPI, HTTPException
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from pydantic import BaseModel
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from
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from diffusers import FluxPipeline
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import torch
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import io
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from fastapi.responses import StreamingResponse
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import uvicorn
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pipe =
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pipe.enable_model_cpu_offload()
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prompt: str
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num_inference_steps=4,
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max_sequence_length=256,
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generator=torch.Generator("cpu").manual_seed(0)
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).images[0]
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img_io = io.BytesIO()
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image.save(img_io, 'PNG')
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img_io.seek(0)
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queue.put(img_io.getvalue())
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except Exception as e:
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queue.put(f"Error: {str(e)}")
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@app.post("/generate_image")
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async def generate_image(request: ImageRequest):
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queue = Queue()
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p = Process(target=generate_image_response, args=(request, queue))
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p.start()
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p.join()
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response = queue.get()
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if "Error" in response:
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raise HTTPException(status_code=500, detail=response)
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return StreamingResponse(io.BytesIO(response), media_type="image/png")
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if __name__ == "__main__":
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uvicorn.run(app, host="0.0.0.0", port=
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import numpy as np
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import random
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import torch
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from diffusers import DiffusionPipeline
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from fastapi import FastAPI, HTTPException
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from pydantic import BaseModel
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from fastapi.responses import JSONResponse
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import uvicorn
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dtype = torch.bfloat16
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device = "cuda" if torch.cuda.is_available() else "cpu"
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pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=dtype).to(device)
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MAX_SEED = np.iinfo(np.int32).max
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MAX_IMAGE_SIZE = 2048
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class InferenceRequest(BaseModel):
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prompt: str
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seed: int = 42
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randomize_seed: bool = False
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width: int = 1024
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height: int = 1024
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num_inference_steps: int = 4
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class InferenceResponse(BaseModel):
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image: str
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seed: int
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app = FastAPI()
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@app.post("/infer", response_model=InferenceResponse)
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async def infer(request: InferenceRequest):
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if request.randomize_seed:
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seed = random.randint(0, MAX_SEED)
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else:
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seed = request.seed
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if not (256 <= request.width <= MAX_IMAGE_SIZE) or not (256 <= request.height <= MAX_IMAGE_SIZE):
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raise HTTPException(status_code=400, detail="Width and height must be between 256 and 2048.")
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generator = torch.Generator().manual_seed(seed)
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image = pipe(
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prompt=request.prompt,
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width=request.width,
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height=request.height,
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num_inference_steps=request.num_inference_steps,
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generator=generator,
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guidance_scale=0.0
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).images[0]
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# Convert image to base64
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image_base64 = image_to_base64(image)
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return InferenceResponse(image=image_base64, seed=seed)
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def image_to_base64(image):
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import io
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import base64
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from PIL import Image
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buffered = io.BytesIO()
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image.save(buffered, format="PNG")
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img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
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return img_str
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if __name__ == "__main__":
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uvicorn.run(app, host="0.0.0.0", port=8000)
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