wehub-resource-sync
122 lines
3.7 KiB
Markdown
122 lines
3.7 KiB
Markdown
# Quickstart
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This guide will help you quickly get started with vLLM-Omni to perform:
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- Offline batched inference
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- Online serving using OpenAI-compatible server
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## Prerequisites
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- OS: Linux
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- Python: 3.12
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## Installation
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For installation on GPU from source:
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```bash
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uv venv --python 3.12 --seed
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source .venv/bin/activate
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# On CUDA
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uv pip install vllm==0.25.0 --torch-backend=auto
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# On ROCm
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uv pip install vllm==0.25.0+rocm723 --extra-index-url https://wheels.vllm.ai/rocm/0.25.0/rocm723
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git clone https://github.com/vllm-project/vllm-omni.git
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cd vllm-omni
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uv pip install -e .
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```
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For additional installation methods — please see the [installation guide](installation/README.md).
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!!! note
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It is important to install the same major & minor version of vLLM and vLLM Omni, otherwise things may not work as expected. If the versions are misaligned, you will see a warning when you import vLLM Omni.
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If you are seeing strange behavior with the `vllm` command not handling the `--omni` flag correctly, you most likely have a version mismatch with vLLM < `0.25.0` and vLLM Omni `0.25.0`, as vLLM Omni no longer hijacks the vLLM entrypoint. Updating vLLM should resolve this issue.
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## Offline Inference
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Text-to-image generation quickstart with vLLM-Omni:
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```python
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from vllm_omni.entrypoints.omni import Omni
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if __name__ == "__main__":
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omni = Omni(model="Tongyi-MAI/Z-Image-Turbo")
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prompt = "a cup of coffee on the table"
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outputs = omni.generate(prompt)
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images = outputs[0].request_output.images
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images[0].save("coffee.png")
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```
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You can pass a list of prompts and wait for the independent requests to finish,
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as shown below.
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!!! info
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For diffusion pipelines, each prompt becomes a separate logical request.
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The runtime may automatically batch compatible in-flight requests through
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the scheduler and runner.
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```python
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from vllm_omni.entrypoints.omni import Omni
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if __name__ == "__main__":
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omni = Omni(
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model="Tongyi-MAI/Z-Image-Turbo",
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# stage_configs_path="./stage-config.yaml", # See below
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)
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prompts = [
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"a cup of coffee on a table",
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"a toy dinosaur on a sandy beach",
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"a fox waking up in bed and yawning",
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]
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omni_outputs = omni.generate(prompts)
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for i_prompt, prompt_output in enumerate(omni_outputs):
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this_request_output = prompt_output.request_output
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this_images = this_request_output.images
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for i_image, image in enumerate(this_images):
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image.save(f"p{i_prompt}-img{i_image}.jpg")
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print("saved to", f"p{i_prompt}-img{i_image}.jpg")
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# saved to p0-img0.jpg
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# saved to p1-img0.jpg
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# saved to p2-img0.jpg
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```
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!!! info
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For diffusion request-level batching controls such as `max_num_seqs` and
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`request_batch_max_wait_ms`, see
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[Request-Level Batching](../user_guide/diffusion/request_batching.md).
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For more usages, please refer to [offline inference](../user_guide/examples/offline_inference/qwen2_5_omni.md)
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## Online Serving with OpenAI-Completions API
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Text-to-image generation quickstart with vLLM-Omni:
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```bash
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vllm serve Tongyi-MAI/Z-Image-Turbo --omni --port 8091
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```
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```bash
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curl -s http://localhost:8091/v1/chat/completions \
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-H "Content-Type: application/json" \
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-d '{
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"messages": [
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{"role": "user", "content": "a cup of coffee on the table"}
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],
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"extra_body": {
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"height": 1024,
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"width": 1024,
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"num_inference_steps": 50,
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"guidance_scale": 4.0,
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"seed": 42
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}
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}' | jq -r '.choices[0].message.content[0].image_url.url' | cut -d',' -f2 | base64 -d > coffee.png
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```
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For more details, please refer to [online serving](../user_guide/examples/online_serving/text_to_image.md).
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