# Quickstart: Offline video generation with Wan2.2 on Neuron This quickstart shows you how to generate a video from a text prompt on AWS Trainium with the vLLM Omni Neuron plugin. When you finish, you have an MP4 clip produced offline by the [Wan2.2-T2V-A14B](../models/wan22-t2v-14b.md) text-to-video model. ## Prerequisites Before you start, make sure that you have the following: - One SSH-accessible `trn2.48xlarge` or `trn3` instance. - The environment prepared with either flow in the [setup guide](setup-guide.md). The DLC commands below use the `vllm-omni-neuron` container, with device access, persistent caches, and `--shm-size=2g`. - Network access to download the Wan2.2 weights (`Wan-AI/Wan2.2-T2V-A14B-Diffusers`) from Hugging Face. Run the following commands in an SSH session on the instance host. Each command uses `docker exec` to run the example script inside the local `vllm-omni-neuron` container. For manual installation, run the Python commands in the activated environment, omit `sudo docker exec vllm-omni-neuron`, and replace `/workspace` with `$VLLM_OMNI_HOME`. > **Note:** The script uses the vLLM Omni entrypoint (`Omni`), not the > `vllm.LLM` API. It reads its configuration from > `/workspace/plugin/examples/wan22/wan22_stage.yaml`. ## Step 1: Run a quick smoke test To verify the pipeline end to end, run the script in dev mode. Dev mode uses low resolution, few frames, and few denoising steps, so it finishes quickly. ```bash sudo docker exec vllm-omni-neuron python /workspace/plugin/examples/wan22/run.py --dev ``` The command writes an MP4 file to the container working directory (`/workspace`). When the smoke test passes, continue to the full run. ## Step 2: Run a full offline generation To generate the default clip, run the script with an output path. The default run produces 81 frames at 480x832 over 40 denoising steps. ```bash sudo docker exec vllm-omni-neuron python /workspace/plugin/examples/wan22/run.py \ --output /workspace/output/wan_output.mp4 ``` The command writes the clip to `/workspace/output/wan_output.mp4`, which is in the host-mounted output directory created by the setup guide. To set your own prompt and output file, add the `--prompt` and `--output` flags: ```bash sudo docker exec vllm-omni-neuron python /workspace/plugin/examples/wan22/run.py \ --prompt "A red hot air balloon rising over green hills at sunrise" \ --output /workspace/output/my_video.mp4 ``` ### Change the output shape and sampling controls `examples/wan22/run.py` sets the default height, width, frame count, and number of denoising steps. Override the output shape with `--height`, `--width`, and `--num-frames`. For example, to generate an 81-frame 720p clip: ```bash sudo docker exec vllm-omni-neuron python /workspace/plugin/examples/wan22/run.py \ --stage-config /workspace/plugin/examples/wan22/wan22_stage_tp4cp8cfg2_720p.yaml \ --height 720 \ --width 1280 \ --num-frames 81 \ --output /workspace/output/wan_720p.mp4 ``` Changing the output shape triggers compilation for that shape. Use frame counts of the form `4k + 1`, such as 5 or 81. The example runner fixes full-mode generation at 40 denoising steps; use the `OmniDiffusionSamplingParams` Python API or adapt the runner to change `num_inference_steps`. More steps increase latency approximately linearly and can improve detail or motion, but do not guarantee higher quality. See the [feature and configuration guide](../guides/features-guide.md#generation-controls) for the complete control and trade-off table. The run uses `/workspace/plugin/examples/wan22/wan22_stage.yaml` by default and the [720P stage config](https://github.com/aws-neuron/vllm-omni-neuron/blob/release-0.24.0.0.1.0/examples/wan22/wan22_stage_tp4cp8cfg2_720p.yaml) for the 720P command above. For parallelism, 720P, and quality tuning, see [Optimizing offline video generation](../model-dev/optimizing-offline-video-generation.md). To copy the clip to the host, use `docker cp`: ```bash sudo docker cp vllm-omni-neuron:/workspace/output/wan_output.mp4 . ``` > **Note:** The first run for a new output shape is slow because Neuron compiles > shape-specific NEFF graphs and loads large model weights (~116 GB). ## Common issues - **Silent crash or `SIGBUS` on output:** The container `/dev/shm` is too small for the returned video. Start the container with `--shm-size=2g`, as shown in the setup guide. - **`NRT_FAILURE` in `nrt_init()`, or Neuron cores not available:** The host driver or device access is incompatible with the runtime. Follow the host prerequisites and device checks in the setup guide. - **Very long first-run latency:** The Neuron compiler builds NEFF graphs on the first run. This is expected. - **Weights download fails:** The run cannot reach the Wan2.2 weights. Confirm network access and Hugging Face permission for `Wan-AI/Wan2.2-T2V-A14B-Diffusers`. ## Clean up To stop and remove the container, run the following commands: ```bash sudo docker stop vllm-omni-neuron sudo docker rm vllm-omni-neuron ``` ## Next steps - [Optimizing offline video generation](../model-dev/optimizing-offline-video-generation.md) — parallelism selection, 720p and VAE tiling, and quality tuning. - [Wan2.2-T2V-A14B model card](../models/wan22-t2v-14b.md) — architecture, feature support, and [VBench](https://arxiv.org/abs/2311.17982) accuracy. - [Entrypoint script (`run.py`)](https://github.com/aws-neuron/vllm-omni-neuron/blob/release-0.24.0.0.1.0/examples/wan22/run.py) — the offline entrypoint and its flags. - [Stage config (`wan22_stage.yaml`)](https://github.com/aws-neuron/vllm-omni-neuron/blob/release-0.24.0.0.1.0/examples/wan22/wan22_stage.yaml) — the configuration the run reads.