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import os
from typing import Any, Dict
from PIL import Image
import torch
from diffusers import FluxPipeline
from huggingface_inference_toolkit.logging import logger
from para_attn.first_block_cache.diffusers_adapters import apply_cache_on_pipe
import time
class EndpointHandler:
def __init__(self, path=""):
self.pipe = FluxPipeline.from_pretrained(
"NoMoreCopyrightOrg/flux-dev",
torch_dtype=torch.bfloat16,
).to("cuda")
apply_cache_on_pipe(self.pipe, residual_diff_threshold=0.12)
self.pipe.transformer = torch.compile(
self.pipe.transformer, mode="max-autotune-no-cudagraphs",
)
self.pipe.vae = torch.compile(
self.pipe.vae, mode="max-autotune-no-cudagraphs",
)
def __call__(self, data: Dict[str, Any]) -> str:
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", 28)
width = parameters.get("width", 1024)
height = parameters.get("height", 1024)
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)
start_time = time.time()
result = self.pipe( # type: ignore
prompt,
height=height,
width=width,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
generator=generator,
).images[0]
end_time = time.time()
time_taken = end_time - start_time
print(f"Time taken: {time_taken:.2f} seconds")
return result
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