| # Agnes-Video-2.0-MoE Inference Code |
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| This folder contains the inference implementation for Agnes-Video-2.0-MoE. It does not contain model checkpoints or third-party model weights. |
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| ## Contents |
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| - `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. |
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| ## Required model files |
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| Obtain the compatible Agnes-Video-2.0-MoE checkpoint and required auxiliary assets separately. Configure the paths in `configs/config_template.json`: |
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| - `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. |
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| External components and their weights are not included in this code package. Obtain them from their respective official sources and follow their license terms. |
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| ## Environment |
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| 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. |
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| 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. |
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| ## Inference |
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| Edit `configs/config_template.json` and replace all `C:/models/...` paths with local paths. From this folder, run in CMD/PowerShell: |
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| ```bat |
| 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 |
| ``` |
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| 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. |
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| For elastic-width checkpoints, use a compatible checkpoint/config and run the elastic entry point in the supported Linux/CUDA environment: |
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| ```bash |
| 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 |
| ``` |
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| The elastic entry requires checkpoint metadata supported by `crown/infer_entry.py`. The standard entry point is for regular inference. |
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| ## License |
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| 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.
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