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import os
from typing import Any, Dict
from diffusers import FluxPipeline, FluxTransformer2DModel
from torchao.quantization import int8_weight_only, quantize_
from PIL.Image import Image
import torch
from huggingface_inference_toolkit.logging import logger
class EndpointHandler:
def __init__(self, **kwargs: Any) -> None: # type: ignore
repo_id = "camenduru/FLUX.1-dev-diffusers"
dtype = torch.bfloat16
transformer = FluxTransformer2DModel.from_pretrained(repo_id, subfolder="transformer", torch_dtype=dtype)
quantize_(transformer, int8_weight_only(), device="cuda")
transformer.to(memory_format=torch.channels_last)
transformer = torch.compile(transformer, mode="max-autotune", fullgraph=True)
self.pipeline = FluxPipeline.from_pretrained(repo_id, transformer=transformer, torch_dtype=torch.bfloat16).to("cuda")
self.pipeline.vae.to(memory_format=torch.channels_last)
self.pipeline.vae.decode = torch.compile(self.pipeline.vae.decode, mode="max-autotune", fullgraph=True)
def __call__(self, data: Dict[str, Any]) -> Image:
logger.info(f"Received incoming request with {data=}")
if "inputs" in data and isinstance(data["inputs"], str):
prompt = data.pop("inputs")
elif "prompt" in data and isinstance(data["prompt"], str):
prompt = data.pop("prompt")
else:
raise ValueError(
"Provided input body must contain either the key `inputs` or `prompt` with the"
" prompt to use for the image generation, and it needs to be a non-empty string."
)
parameters = data.pop("parameters", {})
num_inference_steps = parameters.get("num_inference_steps", 30)
width = parameters.get("width", 1024)
height = parameters.get("height", 768)
guidance_scale = parameters.get("guidance_scale", 3.5)
# seed generator (seed cannot be provided as is but via a generator)
seed = parameters.get("seed", 0)
generator = torch.manual_seed(seed)
return self.pipeline( # type: ignore
prompt,
height=height,
width=width,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
generator=generator,
).images[0] |