FastVideo CLI Inference

FastVideo CLI Inference#

The FastVideo CLI provides a quick way to access the FastVideo inference pipeline for video generation. For more advanced usage, see the Python interface here.

Basic Usage#

The basic command to generate a video is:

fastvideo generate --model-path {MODEL_PATH} --prompt {PROMPT}

Required Parameters#

  • --model-path {MODEL_PATH}: Path to the model or model ID

  • --prompt {PROMPT}: Text description for the video you want to generate

Common Arguments#

To see all the options, you can use the --help flag:

fastvideo generate --help

Hardware Configuration#

  • --num-gpus {NUM_GPUS}: Number of GPUs to use

  • --tp-size {TP_SIZE}: Tensor parallelism size (Typically should match the number of GPUs)

  • --sp-size {SP_SIZE}: Sequence parallelism size (Typically should match the number of GPUs)

Video Configuration#

  • --height {HEIGHT}: Height of the generated video

  • --width {WIDTH}: Width of the generated video

  • --num-frames {NUM_FRAMES}: Number of frames to generate

  • --fps {FPS}: Frames per second for the saved video

Generation Parameters#

  • --num-inference-steps {STEPS}: Number of denoising steps

  • --negative-prompt {PROMPT}: Negative prompt to guide generation away from certain concepts

  • --seed {SEED}: Random seed for reproducible generation

Output Options#

  • --output-path {PATH}: Directory to save the generated video

  • --save-video: Whether to save the video to disk

  • --return-frames: Whether to return the raw frames

Using Configuration Files#

Instead of specifying all parameters on the command line, you can use a configuration file:

fastvideo generate --config {CONFIG_FILE_PATH}

The config file should be in JSON or YAML format with the same parameter names as the CLI options. Command-line arguments will take precedence over settings in the configuration file, allowing you to override specific values while keeping the rest from the config file.

Example configuration file (config.json):

{
    "model_path": "FastVideo/FastHunyuan-diffusers",
    "prompt": "A beautiful woman in a red dress walking down a street",
    "output_path": "outputs/",
    "num_gpus": 2,
    "sp_size": 2,
    "tp_size": 2,
    "num_frames": 45,
    "height": 720,
    "width": 1280,
    "num_inference_steps": 6,
    "seed": 1024,
    "fps": 24,
    "precision": "bf16",
    "vae_precision": "fp16",
    "vae_tiling": true,
    "vae_sp": true,
    "vae_config": {
        "load_encoder": false,
        "load_decoder": true,
        "tile_sample_min_height": 256,
        "tile_sample_min_width": 256
    },
    "text_encoder_precisions": [
        "fp16",
        "fp16"
    ],
    "mask_strategy_file_path": null,
    "enable_torch_compile": false
}

Or using YAML format (config.yaml):

model_path: "FastVideo/FastHunyuan-diffusers"
prompt: "A beautiful woman in a red dress walking down a street"
output_path: "outputs/"
num_gpus: 2
sp_size: 2
tp_size: 2
num_frames: 45
height: 720
width: 1280
num_inference_steps: 6
seed: 1024
fps: 24
precision: "bf16"
vae_precision: "fp16"
vae_tiling: true
vae_sp: true
vae_config:
  load_encoder: false
  load_decoder: true
  tile_sample_min_height: 256
  tile_sample_min_width: 256
text_encoder_precisions:
  - "fp16"
  - "fp16"
mask_strategy_file_path: null
enable_torch_compile: false

Examples#

Generating a simple video:

fastvideo generate --model-path FastVideo/FastHunyuan-diffusers --prompt "A cat playing with a ball of yarn" --num-frames 45 --height 720 --width 1280 --num-inference-steps 6 --seed 1024 --output-path outputs/

Using a negative prompt to avoid certain elements:

fastvideo generate --model-path FastVideo/FastHunyuan-diffusers --prompt "A beautiful forest landscape" --negative-prompt "people, buildings, roads"

Combining command line arguments and a configuration file:

fastvideo generate --config config.json --prompt "A capybara lounging in a hammock"

Troubleshooting#

  • If you encounter CUDA out-of-memory errors, try reducing the video dimensions or number of frames, or the number of inference steps.

  • For reproducible results, set the same seed value between runs.

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