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 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
robbyant/lingbot-video-dense-1.3b / robbyant/lingbot-video-moe-30b-a3b	192x320	9	2	3.0	~68 GiB (MoE smoke)
Lightricks/LTX-2	512x768	121	40	video 3.0 / audio 7.0	Model-dependent
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)

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

LingBot-Video

The shared runner recognizes both official LingBot checkpoint IDs and selects the native LingBotVideoPipeline automatically:

python text_to_video.py \
  --model robbyant/lingbot-video-dense-1.3b \
  --prompt "a robotic arm picks up a red block" \
  --height 192 --width 320 --num-frames 9 --num-inference-steps 2 \
  --guidance-scale 3.0 --flow-shift 3.0 --fps 24 \
  --output lingbot_t2v.mp4

Use robbyant/lingbot-video-moe-30b-a3b for the MoE checkpoint. For local paths without lingbot in the directory name, add --model-class-name LingBotVideoPipeline. Model-only options use --extra-body, for example '{"batch_cfg": true, "output_type": "np"}'.

LTX-2

python text_to_video.py \
  --model Lightricks/LTX-2 \
  --prompt "Cherry blossoms swaying gently in the breeze with synchronized ambient sound" \
  --output ltx2_output.mp4

See the LTX-2 recipe for all checkpoints, pipeline selection, I2V, defaults, and advanced options.

HunyuanVideo-1.5 (480p)

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)

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

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):

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.
• --model-class-name: Optional explicit pipeline override.
• --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.
• --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: fallback audio sample rate when the pipeline returns audio.
• --quantization: quantization method (fp8 for FP8, gguf for GGUF).
• --flow-shift: scheduler flow_shift parameter.
• --lora-path: path to PEFT LoRA adapter folder or checkpoint file.
• --lora-scale: scale factor for LoRA weights.
• --lora-backend: backend for loading LoRA adapters. Default: peft. Available options: peft, distill.

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"