vllm-project--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
dg845/LTX-2.3-Diffusers	384x512	25	20	4.0	96GB-class GPU
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

LTX2 example:

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

LTX-2.3

python text_to_video.py \
  --model dg845/LTX-2.3-Diffusers \
  --model-class-name LTX23Pipeline \
  --prompt "Cherry blossoms swaying gently in the breeze with synchronized ambient sound" \
  --negative-prompt "worst quality, inconsistent motion, blurry, jittery, distorted" \
  --height 384 \
  --width 512 \
  --num-frames 25 \
  --num-inference-steps 20 \
  --guidance-scale 4.0 \
  --frame-rate 24 \
  --fps 24 \
  --audio-sample-rate 48000 \
  --output ltx23_t2v_output.mp4

Use a Diffusers-format checkpoint such as dg845/LTX-2.3-Diffusers; the upstream Lightricks/LTX-2.3 raw safetensors repo is not directly loadable by this pipeline. Pass --model-class-name LTX23Pipeline to select the LTX-2.3 text-to-video pipeline explicitly.

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 class. Use LTX23Pipeline for LTX-2.3 text-to-video.
• --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: audio sample rate for embedded audio (when the pipeline returns audio; LTX-2.3 outputs 48kHz 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"