Implement distributed inference API server with FastAPI (#60)

* Implement distributed inference API server with FastAPI

- Added a new file `api_server_dist.py` to handle video generation tasks using distributed inference.
- Introduced endpoints for task submission, status checking, and result retrieval.
- Implemented image downloading and task management with error handling.
- Enhanced `infer.py` to ensure proper initialization of distributed processes.
- Created a shell script `start_api_with_dist_inference.sh` for easy server startup with environment setup.

This commit establishes a robust framework for managing video generation tasks in a distributed manner.

* Enhance file download endpoint with path traversal protection and error handling

* style: format

* refactor: Enhance video generation functionality with task interruption support

* feat: Add image upload and video generation endpoint with unique task handling

- Introduced a new endpoint `/v1/local/video/generate_form` for video generation that accepts image uploads.
- Implemented unique filename generation for uploaded images to prevent conflicts.
- Enhanced directory management for input and output paths.
- Improved file download response with detailed status and size information.
- Added error handling for distributed inference processes and graceful shutdown procedures.

lightx2v/infer.py

@@ -25,7 +25,8 @@ def init_runner(config):
     seed_all(config.seed)
 
     if config.parallel_attn_type:
-        dist.init_process_group(backend="nccl")
+        if not dist.is_initialized():
+            dist.init_process_group(backend="nccl")
 
     if CHECK_ENABLE_GRAPH_MODE():
         default_runner = RUNNER_REGISTER[config.model_cls](config)

scripts/start_api_with_dist_inference.sh

+#!/bin/bash
+
+# 设置路径
+lightx2v_path=/mnt/aigc/users/lijiaqi2/ComfyUI/custom_nodes/ComfyUI-Lightx2vWrapper/lightx2v
+model_path=/mnt/aigc/users/lijiaqi2/wan_model/Wan2.1-I2V-14B-720P-cfg
+
+# 检查参数
+if [ -z "${CUDA_VISIBLE_DEVICES}" ]; then
+    cuda_devices=2,3
+    echo "Warn: CUDA_VISIBLE_DEVICES is not set, using default value: ${cuda_devices}"
+    export CUDA_VISIBLE_DEVICES=${cuda_devices}
+fi
+
+if [ -z "${lightx2v_path}" ]; then
+    echo "Error: lightx2v_path is not set. Please set this variable first."
+    exit 1
+fi
+
+if [ -z "${model_path}" ]; then
+    echo "Error: model_path is not set. Please set this variable first."
+    exit 1
+fi
+
+# 设置环境变量
+export TOKENIZERS_PARALLELISM=false
+export PYTHONPATH=${lightx2v_path}:$PYTHONPATH
+export ENABLE_PROFILING_DEBUG=true
+export ENABLE_GRAPH_MODE=false
+
+echo "=========================================="
+echo "启动分布式推理API服务器"
+echo "模型路径: $model_path"
+echo "CUDA设备: $CUDA_VISIBLE_DEVICES"
+echo "API端口: 8000"
+echo "=========================================="
+
+# 启动API服务器，同时启动分布式推理服务
+python -m lightx2v.api_server_dist \
+--model_cls wan2.1 \
+--task i2v \
+--model_path $model_path \
+--config_json ${lightx2v_path}/configs/wan_i2v_dist.json \
+--port 8000 \
+--start_inference \
+--nproc_per_node 2
+
+echo "服务已停止"