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import os
from typing import Any, Dict
from diffusers import FluxPipeline, FluxTransformer2DModel, AutoencoderKL, TorchAoConfig
from PIL.Image import Image
import torch
import torch._dynamo
torch._dynamo.config.suppress_errors = True
#from huggingface_inference_toolkit.logging import logger
def compile_pipeline(pipe):
pipe.transformer.to(memory_format=torch.channels_last)
pipe.transformer = torch.compile(pipe.transformer, mode="reduce-overhead", fullgraph=False, dynamic=False, backend="inductor")
return pipe
class EndpointHandler:
def __init__(self, **kwargs: Any) -> None: # type: ignore
is_compile = False
#repo_id = "camenduru/FLUX.1-dev-diffusers"
repo_id = "NoMoreCopyright/FLUX.1-dev-test"
dtype = torch.bfloat16
quantization_config = TorchAoConfig("int4dq")
vae = AutoencoderKL.from_pretrained(repo_id, subfolder="vae", torch_dtype=dtype)
#transformer = FluxTransformer2DModel.from_pretrained(repo_id, subfolder="transformer", torch_dtype=dtype).to("cuda")
self.pipeline = FluxPipeline.from_pretrained(repo_id, vae=vae, torch_dtype=dtype, quantization_config=quantization_config)
if is_compile: self.pipeline = compile_pipeline(self.pipeline)
self.pipeline.to("cuda")
@torch.inference_mode()
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] |