WORL-Lingbot

WORL-Lingbot is a camera-conditioned video world-model LoRA adapter trained with WORL on top of LingBot-World-Fast. It generates video continuations conditioned on an initial image, text and camera trajectories.

Related releases: WORL-NVRM and 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, 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

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:

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. 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. The base model and separately obtained runtime components retain their upstream terms and notices. See NOTICE.txt for source attribution.