WorldCrafter: Consistent Video World Model with Implicit 3D-aware Memory
Paper • 2609.24984 • Published • 143
WorldCrafter-Context
An autoregressive video baseline with standard historical-frame context memory and UCPE camera control, without a memory encoder.
WorldCrafter-Context is an intermediate training checkpoint released to help researchers fine-tune the model and develop their own memory mechanisms, such as point-cloud conditioning. It precedes the learned memory encoder used by WorldCrafter-Base and WorldCrafter-Fast.
Components
| Component | Source |
|---|---|
| Transformer | TencentARC/WorldCrafter-Base, transformer/ |
| UCPE camera adapter | This repository, camera_adapter.bin (FP32, approximately 2.10 GB) |
| Text encoder, tokenizer, VAE and scheduler | TencentARC/WorldCrafter-Fast |
| Memory encoder | None; history is provided as video latents |
Shared components are referenced at fixed revisions in inference_config.json
and model_index.json; their weights are not duplicated here. manifest.json
records file hashes and sizes.
Download
Install the Hugging Face CLI with pip install huggingface_hub, then run:
hf download Drexubery/WorldCrafter-Context --local-dir weights/WorldCrafter-Context
hf download TencentARC/WorldCrafter-Base \
--revision d6b4731d4cec53f90b6afaee5a0f1b77d29979aa \
--include "transformer/*" --local-dir weights/WorldCrafter-Base
hf download TencentARC/WorldCrafter-Fast \
--revision d2ac12a654f8cafde4cb2e0ae7d5127ce2624939 \
--include "text_encoder/*" "tokenizer/*" "vae/*" "scheduler/*" \
--local-dir weights/WorldCrafter-Fast
These commands download only the shared components needed by Context. Existing matching files can be reused. Keep the downloaded configuration and weight files together.
Construct the baseline
Combine Base's shared transformer, this repository's UCPE adapter and Fast's text encoder, tokenizer, VAE and scheduler. Use historical video latents as context without a memory encoder; do not load Base/Fast's additional LoRA, camera adapters or RepEncoder. See the training documentation.
camera_adapter.bin is the original FP32 PyTorch state dictionary, with 400
blocks.<index>.cam_self_attn.* tensors and no outer checkpoint wrapper.
This release is for custom integration; the current Base/Fast entrypoints and
standalone DiffusionPipeline.from_pretrained do not load Context automatically.
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