vllm-omni/docs/user_guide/examples/offline_inference/text_to_video.md

# Text-To-Video

Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/text_to_video>.


A unified script for text-to-video generation. Supports multiple models with model-aware defaults.

For backend selection and SageAttention usage, see the [Diffusion Attention Backends](../../diffusion/attention_backends.md) guide.

## Supported Models

| Model | Default Resolution | Default Frames | Default Steps | Guidance | VRAM (BF16) |
|---|---|---|---|---|---|
| `Wan-AI/Wan2.2-T2V-A14B-Diffusers` | 720x1280 | 81 | 40 | 4.0 | ~60 GiB |
| `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v` | 480x832 | 121 | 50 | 6.0 | 1×A100 80GB |
| `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v` | 720x1280 | 121 | 50 | 6.0 | FP8 + VAE tiling required |

## Local CLI Usage

### Wan2.2 (default)

```bash
python text_to_video.py \
  --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage." \
  --negative-prompt "<optional quality filter>" \
  --height 480 \
  --width 832 \
  --num-frames 33 \
  --guidance-scale 4.0 \
  --guidance-scale-high 3.0 \
  --flow-shift 12.0 \
  --num-inference-steps 40 \
  --fps 16 \
  --output t2v_out.mp4
```

LTX2 example:

```bash
python text_to_video.py \
  --model "Lightricks/LTX-2" \
  --prompt "A cinematic close-up of ocean waves at golden hour." \
  --negative-prompt "worst quality, inconsistent motion, blurry, jittery, distorted" \
  --height 512 \
  --width 768 \
  --num-frames 121 \
  --num-inference-steps 40 \
  --guidance-scale 4.0 \
  --frame-rate 24 \
  --output ltx2_out.mp4
```

### HunyuanVideo-1.5 (480p)

```bash
python text_to_video.py \
  --model hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v \
  --prompt "A cat walks through a sunlit garden, flowers swaying gently in the breeze." \
  --height 480 \
  --width 832 \
  --num-frames 121 \
  --guidance-scale 6.0 \
  --flow-shift 5.0 \
  --num-inference-steps 50 \
  --fps 24 \
  --output hunyuan_video_15_output.mp4
```

### HunyuanVideo-1.5 (720p)

```bash
python text_to_video.py \
  --model hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v \
  --prompt "A serene lakeside sunrise with mist over the water." \
  --height 720 \
  --width 1280 \
  --num-frames 121 \
  --guidance-scale 6.0 \
  --flow-shift 9.0 \
  --num-inference-steps 50 \
  --fps 24 \
  --output hunyuan_720p.mp4
```

### HunyuanVideo-1.5 with FP8 Quantization

```bash
python text_to_video.py \
  --model hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v \
  --prompt "A dog running across a field of golden wheat." \
  --quantization fp8 \
  --height 480 --width 832 --num-frames 121 \
  --guidance-scale 6.0 --flow-shift 5.0 \
  --output hunyuan_fp8.mp4
```

Quick test (smaller resolution, fewer frames):

```bash
python text_to_video.py \
  --model hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v \
  --prompt "A serene lakeside sunrise with mist over the water." \
  --height 320 --width 576 --num-frames 17 --num-inference-steps 30 \
  --flow-shift 5.0 \
  --output quick_test.mp4
```

## Key Arguments

### Common

- `--model`: Diffusers model ID or local path.
- `--prompt`: text description (string).
- `--height/--width`: output resolution. Default depends on model.
- `--num-frames`: number of frames. Default depends on model.
- `--guidance-scale`: CFG scale. Default depends on model.
- `--num-inference-steps`: sampling steps. Default depends on model.
- `--fps`: frames per second for the saved MP4.
- `--output`: path to save the generated video.
- `--vae-use-slicing`: enable VAE slicing for memory optimization.
- `--vae-use-tiling`: enable VAE tiling for memory optimization.
- `--cfg-parallel-size`: set it to 2 to enable CFG Parallel. See more examples in [`user_guide`](https://github.com/vllm-project/vllm-omni/tree/main/docs/user_guide/diffusion/parallelism_acceleration.md#cfg-parallel).
- `--tensor-parallel-size`: tensor parallel size (effective for models that support TP, e.g. LTX2).
- `--enable-cpu-offload`: enable CPU offloading for diffusion models.
- `--enable-layerwise-offload`: enable layerwise offloading on DiT modules.
- `--frame-rate`: generation FPS for pipelines that require it (e.g., LTX2).
- `--audio-sample-rate`: audio sample rate for embedded audio (when the pipeline returns audio).
- `--quantization`: quantization method (`fp8` for FP8, `gguf` for GGUF).
- `--flow-shift`: scheduler flow_shift parameter.

### Wan2.2-specific

- `--negative-prompt`: artifacts to suppress.
- `--guidance-scale-high`: separate CFG scale for high-noise stage.
- `--boundary-ratio`: boundary split for low/high DiT (default 0.875).
- `--flow-shift`: scheduler flow_shift (5.0 for 720p, 12.0 for 480p).
- `--cache-backend`: `cache_dit` for acceleration.

### HunyuanVideo-1.5 Optimal Configs

| Variant | flow_shift | guidance_scale | steps |
|---------|-----------|----------------|-------|
| 480p T2V | 5.0 | 6.0 | 50 |
| 720p T2V | 9.0 | 6.0 | 50 |
| 480p I2V | 5.0 | 6.0 | 50 |
| 720p I2V | 7.0 | 6.0 | 50 |
| CFG-distilled | (same) | 1.0 | 50 |

> If you encounter OOM errors, try `--vae-use-slicing`, `--vae-use-tiling`, `--enable-cpu-offload`, or `--quantization fp8`.

## Example materials

??? abstract "text_to_video.py"
    ``````py
    --8<-- "examples/offline_inference/text_to_video/text_to_video.py"
    ``````