FiDA-Gen-Img-Flux2-klein/litserve_serve.py

import uuid

import torch
from minio import Minio

import litserve as ls
from PIL import Image
import io
import base64
from diffusers import Flux2KleinPipeline

from utils.new_oss_client import oss_get_image, oss_upload_image, MINIO_URL, MINIO_ACCESS, MINIO_SECRET, MINIO_SECURE

minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE)


# 保持原有的辅助函数
def aspect_to_wh(aspect_ratio: str, base_long_edge: int) -> tuple[int, int]:
    w_str, h_str = aspect_ratio.split(":")
    w, h = float(w_str), float(h_str)
    if w >= h:
        width = base_long_edge
        height = int(round(base_long_edge * (h / w)))
    else:
        height = base_long_edge
        width = int(round(base_long_edge * (w / h)))
    width = max(64, (width // 8) * 8)
    height = max(64, (height // 8) * 8)
    return width, height


class FluxKleinAPI(ls.LitAPI):
    def setup(self, device):
        # 1. 模型初始化
        self.repo_id = "black-forest-labs/FLUX.2-klein-4B"
        self.device = device
        self.dtype = torch.bfloat16

        self.pipe = Flux2KleinPipeline.from_pretrained(
            self.repo_id,
            torch_dtype=self.dtype
        )
        self.pipe.to(device)

    def decode_request(self, request):
        """
        解析请求参数并加载OSS图片的接口函数

        接口入参说明（request字典结构）：
        ----------
        request : dict
            核心请求参数字典，各字段说明如下：
            - input_image_paths : list[str] | None (可选)
              OSS图片路径列表，格式为 "bucket/object_name"（如 "test/typical_b/uildi/ng_space_station.png"）
              若不传则为None，会导致后续图片加载失败，建议必传
            - width : int (可选，默认值512)
              图片宽度，默认512像素
            - height : int (可选，默认值512)
              图片高度，默认512像素
            - bucket_name : str | None (可选)
              OSS桶名，不传则为None
            - object_name : str | None (可选)
              OSS对象名（文件路径），不传则为None
            - prompt : str (可选，默认值空字符串)
              文本提示词，用于模型推理等场景
            - steps : int (可选，默认值28)
              推理步数，控制模型生成过程的迭代次数
            - guidance : float (可选，默认值4.0)
              引导系数，调节提示词对生成结果的影响程度
            - seed : int (可选，默认值42)
              随机种子，保证生成结果的可复现性

        返回值说明
        -------
        dict
            解析后的参数字典，包含：
            - bucket_name: 请求中的桶名（None/字符串）
            - object_name: 请求中的对象名（None/字符串）
            - images: 从OSS加载的图片列表（按input_image_paths顺序）
            - prompt: 文本提示词（默认空字符串）
            - steps: 推理步数（默认28）
            - guidance: 引导系数（默认4.0）
            - seed: 随机种子（默认42）
            - height: 图片高度（默认512）
            - width: 图片宽度（默认512）

        异常说明
        -------
        - 若input_image_paths非None但格式错误（无"/"分割且非空），可能导致rest[0]索引错误
        - 若OSS图片加载失败（如路径不存在），oss_get_image会抛出对应异常
        """
        input_image_paths = request.get("input_image_paths", None)
        W = request.get("width", 512)
        H = request.get("height", 512)
        images = []
        for path in input_image_paths:
            bucket, *rest = path.split("/", 1)  # 拆分为 ["test", "typical_b/uildi/ng_space_station.png"]
            object_name = rest[0] if rest else ""
            image = oss_get_image(oss_client=minio_client, bucket=bucket, object_name=object_name)
            images.append(image)
        return {
            "bucket_name": request.get("bucket_name", None),
            "object_name": request.get("object_name", None),
            "images": images,
            "prompt": request.get("prompt", ""),
            "steps": request.get("steps", 4),
            "guidance": request.get("guidance", 4.0),
            "seed": request.get("seed", 42),
            "height": H,
            "width": W
        }

    @torch.inference_mode()
    def predict(self, payload):
        # 3. 执行推理逻辑
        images = payload.get("images", [])
        prompt = payload.get("prompt", "")
        W, H = aspect_to_wh(payload["aspect_ratio"], payload["base_long_edge"])
        gen = torch.Generator(device=self.device)
        output = {}
        if images:
            output['im'] = self.pipe(
                image=images,
                prompt=prompt,
                height=H,
                width=W,
                guidance_scale=payload["guidance"],
                num_inference_steps=payload["steps"],
                generator=gen,
            ).images[0]
        else:
            output = self.pipe(
                prompt=prompt,
                height=H,
                width=W,
                guidance_scale=payload["guidance"],
                num_inference_steps=payload["steps"],
                generator=gen,
            ).images[0]
        image_data = io.BytesIO()
        output.save(image_data, format='PNG')
        image_data.seek(0)
        image_bytes = image_data.read()
        req = oss_upload_image(oss_client=minio_client, bucket=payload.get("bucket_name", "test"), object_name=payload.get("object_name", f"fida_generate_image/{uuid.uuid4().hex}.png"), image_bytes=image_bytes)
        output_path = req.bucket_name + "/" + req.object_name
        return output_path

    def encode_response(self, output_path):
        return {"output_path": output_path}


if __name__ == "__main__":
    # 启动服务器
    api = FluxKleinAPI()
    server = ls.LitServer(api, accelerator="cuda", devices=1)
    server.run(port=8451)