| --- |
| license: apache-2.0 |
| base_model: robbyant/lingbot-world-fast |
| library_name: peft |
| pipeline_tag: image-to-video |
| inference: false |
| tags: |
| - worl |
| - lingbot |
| - world-model |
| - video-generation |
| - camera-control |
| - reinforcement-learning |
| - lora |
| - safetensors |
| --- |
| |
| # WORL-Lingbot |
|
|
| **WORL-Lingbot** is a camera-conditioned video world-model LoRA adapter trained with |
| WORL on top of [LingBot-World-Fast](https://huggingface.co/robbyant/lingbot-world-fast). |
| It generates video continuations conditioned on an initial image, text and |
| camera trajectories. |
|
|
| <img src="assets/worl-teaser.webp" alt="WORL long-horizon video generation examples" width="100%"> |
|
|
| Related releases: [WORL-NVRM](https://huggingface.co/Efficient-Large-Model/WORL-NVRM) |
| and [WORL-Helios](https://huggingface.co/Efficient-Large-Model/WORL-Helios). |
|
|
| ## Model details |
|
|
| | Field | Description | |
| | --- | --- | |
| | Base model | `robbyant/lingbot-world-fast` | |
| | Base revision | `b7a1db2da9bd3ba675ee01c228f41db0309b981e` | |
| | Base subfolder | `lingbot_world_fast` | |
| | Architecture | Camera-conditioned `WanModelFast` transformer | |
| | Release format | BF16 PEFT LoRA adapter | |
| | LoRA rank / alpha | 128 / 128 | |
| | Output geometry documented by the source release | 81 frames, 832 × 464 pixels, 16 FPS | |
|
|
| Load this adapter on top of the pinned LingBot-World-Fast base transformer. |
| The compatible base transformer, text encoder, tokenizer, VAE, scheduler and |
| LingBot runtime are required separately. |
|
|
| ## Inputs and outputs |
|
|
| | Input | Format | |
| | --- | --- | |
| | First frame | RGB image | |
| | Prompt | Text describing the scene and intended motion | |
| | Camera trajectory | Per-frame camera-to-world matrices, `(F, 4, 4)` | |
| | Camera intrinsics | Per-frame intrinsic matrices, `(F, 3, 3)` | |
|
|
| The output is a camera-conditioned video continuation. The source evaluation |
| profile uses an 80-frame stride between 81-frame clips, sharing one boundary |
| frame between adjacent clips. |
|
|
| ## Training data and method |
|
|
| WORL trains policies using autoregressive rollouts, reward evaluation, |
| candidate selection and policy updates. The project's LingBot reference |
| configuration combines HPSv3, NVRM and VGGT-Omega PI Camera rewards. |
|
|
| The team supplied [WORL-Lingbot-Training-Data](https://huggingface.co/datasets/Efficient-Large-Model/WORL-Lingbot-Training-Data), an OpenScene/revisit training bundle |
| containing 2,218 records with prompts, initial frames and camera-conditioning |
| arrays. Initial scene imagery was generated using gpt-image. This describes |
| the supplied project dataset and reference configuration; the exported model |
| does not record an exact per-run dataset/configuration binding. The training |
| dataset is distributed separately under CC BY 4.0. |
|
|
| ## Download and load |
|
|
| ```bash |
| hf download Efficient-Large-Model/WORL-Lingbot --local-dir artifacts/WORL-Lingbot |
| ``` |
|
|
| With a compatible LingBot runtime installed, attach the adapter to the pinned |
| base transformer: |
|
|
| ```python |
| import torch |
| from peft import PeftModel |
| from wan.modules.model_fast import WanModelFast |
| |
| base = WanModelFast.from_pretrained( |
| "robbyant/lingbot-world-fast", |
| revision="b7a1db2da9bd3ba675ee01c228f41db0309b981e", |
| subfolder="lingbot_world_fast", |
| torch_dtype=torch.bfloat16, |
| control_type="cam", |
| ) |
| model = PeftModel.from_pretrained( |
| base, |
| "Efficient-Large-Model/WORL-Lingbot", |
| is_trainable=False, |
| ).eval() |
| ``` |
|
|
| This example loads the base transformer and adapter. Integrate them with the remaining compatible |
| pipeline components for complete video generation. |
|
|
| ## Files and provenance |
|
|
| | Path | Contents | |
| | --- | --- | |
| | `adapter_model.safetensors` | Approximately 1.23 GB BF16 adapter | |
| | `adapter_config.json` | PEFT configuration | |
| | `RELEASE_MANIFEST.json` | Source revision and SHA-256 inventory | |
|
|
| Adapter weights and configuration are unchanged from |
| [qyoo/lingbot-worl at d3e449c675f5](https://huggingface.co/qyoo/lingbot-worl/tree/d3e449c675f5a8bfb3afd29e30f1675e4445b9c3). |
| Documentation and release manifests were updated for this repository's |
| adapter-only release. The original export record remains available at the |
| pinned source revision. File integrity is checked against that source snapshot; |
| no new GPU benchmark was performed for this publication. |
|
|
| ## Intended use and limitations |
|
|
| This model supports research on camera-conditioned video generation and video |
| world-model reinforcement learning. Long rollouts can drift in scene identity, |
| geometry, camera motion and physical consistency. Synthetic outputs are not |
| validated physical simulations or autonomous-control signals. Base-model and |
| training-data biases can affect generations; review outputs for the intended |
| application and respect rights in input media. |
|
|
| ## License |
|
|
| This release retains the source model card's |
| [Apache License 2.0](LICENSE.txt). The base model and separately obtained runtime |
| components retain their upstream terms and notices. See [NOTICE.txt](NOTICE.txt) |
| for source attribution. |
|
|
