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Agnes-Video-2.0-MoE Inference Code

This folder contains the inference implementation for Agnes-Video-2.0-MoE. It does not contain model checkpoints or third-party model weights.

Contents

  • inference/test_infer_seedance.py: standard inference entry point.
  • crown/infer_entry.py: optional elastic-width inference entry point.
  • inference/: DiT, generation pipeline, checkpoint loading, and runtime utilities.
  • configs/config_template.json: configuration template. Update all local paths before running.
  • infer.bat: Windows CMD launcher for standard inference.
  • infer.sh: Linux launcher.

Required model files

Obtain the compatible Agnes-Video-2.0-MoE checkpoint and required auxiliary assets separately. Configure the paths in configs/config_template.json:

  • engine_config.load: DiT/base checkpoint directory. The inference entry also accepts --ckpt_dir for a .pt checkpoint.
  • evaluation_config.txt_model_path: T5Gemma text encoder.
  • evaluation_config.vae_model_path: Wan2.2-TI2V-5B video VAE/model files required by this pipeline.
  • evaluation_config.omnivae_audio_ckpt_dir: OmniVAE audio decoder checkpoint.
  • OmniVAE source code: set OMNIVAE_CODE_ROOT to the local OmniVAE vae package root.

External components and their weights are not included in this code package. Obtain them from their respective official sources and follow their license terms.

Environment

Use a Linux environment with a CUDA-capable NVIDIA GPU. This inference entry initializes CUDA/NCCL and is not supported on CPU or Windows native CMD. Windows users can invoke it from WSL/Linux; infer.bat is provided as a convenience wrapper for environments where Python and the CUDA runtime are available.

Install a PyTorch build compatible with your CUDA runtime first. Then install the Python dependencies listed in requirements-inference.txt. Dependency versions must match the release environment and compatible upstream components.

Inference

Edit configs/config_template.json and replace all C:/models/... paths with local paths. From this folder, run in CMD/PowerShell:

python inference\test_infer_seedance.py --config-load-path configs\config_template.json --generate --prompt "A cinematic scene of a quiet lake at sunrise." --seconds 5 --save_path_prefix outputs\sample

Outputs are written with the selected prefix. The entry point supports additional options such as --seed, --num_inference_steps, --br_width, and --br_height; run python inference\test_infer_seedance.py --help to see available arguments.

For elastic-width checkpoints, use a compatible checkpoint/config and run the elastic entry point in the supported Linux/CUDA environment:

ELASTIC=1 bash infer.sh --config-load-path configs/config_template.json --generate --prompt "A cinematic scene of a quiet lake at sunrise." --seconds 5 --save_path_prefix outputs/sample

The elastic entry requires checkpoint metadata supported by crown/infer_entry.py. The standard entry point is for regular inference.

License

The CROWN inference code is provided under Apache License 2.0. External models, libraries, and their weights are subject to their respective licenses. See LICENSE and the upstream license notices for details.