基于昇腾910B的文生图模型推理服务化部署

本次部署模型Qwen-Image。

1. vllm-Omni

vLLM 最初是为了支持文本自动回归生成任务的大型语言模型而设计的。vLLM-Omni 是一个框架，它扩展了对全模态模型推理和服务的支持

• 全模态：文本、图像、视频和音频数据处理
• 非自回归架构：将 vLLM 的 AR 支持扩展到 Diffusion Transformers (DiT) 和其他并行生成模型
• 异构输出：从传统的文本生成到多模态输出

2.环境准备

vllm环境准备

使用vllm-ascend镜像，本例使用

quay.io/ascend/vllm-ascend:v0.12.0rc1-openeuler

镜像下载地址：Quay

vllm-omni环境准备

github上下载vllm-omni代码仓库

git clone https://github.com/vllm-project/vllm-omni.git cd vllm-omni pip install -e -v . 。。。 Successfully installed accelerate-1.12.0 aiofiles-24.1.0 antlr4-python3-runtime-4.9.3 brotli-1.2.0 cache-dit-1.1.8 diffusers-0.36.0 ffmpy-1.0.0 gradio-5.50.0 gradio-client-1.14.0 groovy-0.1.2 importlib_metadata-8.7.1 omegaconf-2.3.0 orjson-3.11.5 pillow-11.3.0 pydantic-2.12.3 pydantic-core-2.41.4 pydub-0.25.1 resampy-0.4.3 ruff-0.14.10 safehttpx-0.1.7 semantic-version-2.10.0 tomlkit-0.13.3 vllm-omni-0.12.0rc1 zipp-3.23.0

安装成功版本查看

vllm 0.12.0+empty /vllm-workspace/vllm vllm_ascend 0.12.0rc1 /vllm-workspace/vllm-ascend vllm-omni 0.12.0rc1

模型下载

Qwen-Image · 模型库

3.服务启动

2.1 启动vllm 模型推理服务

export ASCEND_RT_VISIBLE_DEVICES=0,1 export VLLM_WORKER_MULTIPROC_METHOD=spawn vllm serve /opt/models/Qwen-Image/ --omni --port 10027 ... Loading safetensors checkpoint shards: 100% Completed | 9/9 [01:01<00:00, 7.35s/it] Loading safetensors checkpoint shards: 100% Completed | 9/9 [01:01<00:00, 6.85s/it] [Stage-0] INFO 12-29 15:15:08 [diffusers_loader.py:214] Loading weights took 61.97 seconds [Stage-0] INFO 12-29 15:15:09 [npu_worker.py:79] Model loading took 53.7445 GiB and 91.143213 seconds [Stage-0] INFO 12-29 15:15:09 [npu_worker.py:84] Worker 0: Model loaded successfully. [Stage-0] INFO 12-29 15:15:09 [npu_worker.py:118] Worker 0: Scheduler loop started. [Stage-0] INFO 12-29 15:15:09 [gpu_worker.py:229] Worker 0 ready to receive requests via shared memory [Stage-0] INFO 12-29 15:15:09 [scheduler.py:46] SyncScheduler initialized result MessageQueue [Stage-0] INFO 12-29 15:15:09 [async_omni_diffusion.py:83] 。。。 INFO: Started server process [1118] (APIServer pid=1118) INFO: Waiting for application startup. (APIServer pid=1118) INFO: Application startup complete.

服务测试：

测试脚本 openai_chat_t2i.py

​ python openai_chat_t2i.py \ --prompt "A beautiful landscape painting" \ --output output.png \ --server http://127.0.0.1:10027 \ --seed 21 \ --height 224--width 224 ​

2.2 启动图形界面服务

​ python gradio_demo.py --server http://127.0.0.1:10027 --port 10029 ​

使用浏览器打开界面：

http://127.0.0.1:10029

4.加速方法

3.1 TeaCache

vllm serve /opt/models/Qwen-Image/ --omni --port 10027 \ --cache-backend tea_cache \ --cache-config '{"rel_l1_thresh": 0.2}'

3.2 Cache-DiT

vllm serve /opt/models/Qwen-Image/ --omni --port 10027 \ --cache-backend cache_dit \ --cache-config '{"Fn_compute_blocks": 1, "Bn_compute_blocks": 0, "max_warmup_steps": 4, "residual_diff_threshold": 0.12}'

3.3 效果对比

prompt='生成一个ultraman', ref_images=0, params={'height': 384, 'width': 512, 'num_inference_steps': 10, 'true_cfg_scale': 4, 'num_outputs_per_prompt': 1