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README.md

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 ---
+library_name: pytorch
+tags:
+  - world-model
+  - video-generation
+  - streaming-generation
+  - robotics
+  - camera-control
+  - diffusion
+  - rectified-flow
+  - video-dit
+  - droid
+  - realestate10k
 license: apache-2.0
 ---
+
+# MiniWorld
+
+**MiniWorld: Democratizing the Training of Video World Models from Scratch**
+
+<a href="https://zhao-yian.github.io/MiniWorld/"><img src="https://img.shields.io/badge/Project-Page-1f6feb?style=for-the-badge&logo=googlechrome&logoColor=white" alt="Project Page"></a>
+<img src="https://img.shields.io/badge/arXiv-Coming%20Soon-b31b1b?style=for-the-badge&logo=arxiv&logoColor=white" alt="arXiv">
+<a href="https://github.com/zhao-yian/MiniWorld"><img src="https://img.shields.io/badge/GitHub-Code-181717?style=for-the-badge&logo=github&logoColor=white" alt="GitHub"></a>
+
+MiniWorld is a minimal and reproducible framework for training streaming video
+world models from scratch. Instead of adapting a pretrained bidirectional video
+generator, it directly learns causal next-state prediction with a block-causal
+Video Diffusion Transformer and Rectified Flow.
+
+The same architecture supports two control modalities:
+
+- **DROID:** low-level robot actions for embodied world modeling.
+- **RealEstate10K:** camera poses for controllable scene prediction.
+
+This Hugging Face repository hosts the MiniWorld model checkpoints. Code,
+training scripts, and evaluation utilities live in the GitHub repository.
+
+## Model Summary
+
+MiniWorld uses a block-causal Video Diffusion Transformer trained with Rectified
+Flow in the latent space of the Wan2.2 VAE. During inference, MiniWorld performs
+streaming generation with a rolling KV cache and pipelined asynchronous
+denoising, enabling long-horizon generation under bounded online computation.
+
+Key components:
+
+- **Block-causal Video DiT** with bidirectional attention inside each chunk and
+  causal attention across chunks.
+- **Unified conditioning** for robot actions and camera poses through AdaLN-LoRA
+  modulation.
+- **Chunk-oriented Probability Propagation (CoPP)** for stable non-decreasing
+  diffusion schedules.
+- **Continued long-context training** from short clips to 253-frame sequences.
+- **Structured rolling KV cache** with a persistent sink and FIFO history.
+- **Pipelined asynchronous denoising** for a quality-throughput trade-off at
+  inference time.
+
+The complete model can be trained in several days on a single 8-GPU server.
+
+## Released Checkpoints
+
+Sampling requires matching the checkpoint with the corresponding dataset and
+model scale.
+
+| Dataset | Model | Status | Checkpoint |
+| --- | --- | --- | --- |
+| DROID | MiniWorld-0.5B | Available | [MiniWorld_0_5b_droid.pt](resolve/main/MiniWorld_0_5b_droid.pt) |
+| DROID | MiniWorld-1B | Available | [MiniWorld_1b_droid.pt](resolve/main/MiniWorld_1b_droid.pt) |
+| DROID | MiniWorld-3B | Coming soon | -- |
+| RealEstate10K | MiniWorld-0.5B | Available | [MiniWorld_0_5b_re10k.pt](resolve/main/MiniWorld_0_5b_re10k.pt) |
+| RealEstate10K | MiniWorld-1B | Available | [MiniWorld_1b_re10k.pt](resolve/main/MiniWorld_1b_re10k.pt) |
+| RealEstate10K | MiniWorld-3B | Coming soon | -- |
+
+Download a single checkpoint with:
+
+```bash
+hf download zhaoyian01/MiniWorld \
+  --include "MiniWorld_1b_droid.pt" \
+  --local-dir checkpoints/miniworld
+```
+
+## Model Configurations
+
+`MODEL` is the identifier expected by the training and sampling scripts in the
+GitHub repository.
+
+| Model | `MODEL` | Depth | Width | Heads | Parameters |
+| --- | --- | ---: | ---: | ---: | ---: |
+| MiniWorld-B | `B` | 12 | 768 | 12 | 0.12B |
+| MiniWorld-L | `L` | 24 | 1024 | 16 | 0.39B |
+| MiniWorld-0.5B | `0.5B` | 28 | 1152 | 16 | 0.55B |
+| MiniWorld-1B | `1B` | 28 | 1536 | 12 | 1B |
+| MiniWorld-3B | `3B` | 32 | 2560 | 20 | 3B |
+
+## Intended Use
+
+MiniWorld is intended for research on streaming video world models, including:
+
+- action-conditioned robot world modeling,
+- camera-pose-conditioned scene prediction,
+- long-horizon autoregressive video generation,
+- temporal memory and KV-cache mechanisms,
+- train-test alignment for streaming diffusion models.
+
+MiniWorld is a research baseline and is not intended as a general-purpose
+text-to-video model.
+
+## Requirements
+
+Inference requires the MiniWorld codebase and the pretrained Wan2.2 VAE:
+
+- Linux with an NVIDIA CUDA GPU
+- Python 3.11
+- CUDA-compatible PyTorch 2.x
+- FlashAttention
+- Wan2.2 VAE checkpoint from `Wan-AI/Wan2.2-TI2V-5B`
+
+Download the VAE:
+
+```bash
+hf download Wan-AI/Wan2.2-TI2V-5B \
+  --include "Wan2.2_VAE.pth" \
+  --local-dir checkpoints/wan2.2
+```
+
+## Usage
+
+Clone the [MiniWorld codebase](https://github.com/zhao-yian/MiniWorld), install
+its requirements, then download the desired checkpoint. All commands are run
+from the repository root.
+
+The default sampler uses one observed frame as initial context, eight in-flight
+chunks and a 24-chunk rolling KV cache (a 64-frame active attention window), one
+persistent sink frame, 100 denoising steps with classifier-free guidance at
+scale 2.0, and a 64-latent-frame rollout corresponding to 253 RGB frames.
+Generated videos are saved to `${SAMPLE_DIR}/pred/`.
+
+### DROID action-conditioned generation
+
+```bash
+DATA_ROOT=/path/to/droid_lerobot \
+CKPT=/path/to/MiniWorld_1b_droid.pt \
+VAE_CKPT=checkpoints/wan2.2/Wan2.2_VAE.pth \
+MODEL=1B \
+bash scripts/sample_droid.sh
+```
+
+### RealEstate10K camera-conditioned generation
+
+```bash
+DATA_ROOT=/path/to/re10k/videos \
+POSE_DIR=/path/to/re10k/poses \
+CKPT=/path/to/MiniWorld_1b_re10k.pt \
+VAE_CKPT=checkpoints/wan2.2/Wan2.2_VAE.pth \
+MODEL=1B \
+bash scripts/sample_re10k.sh
+```
+
+### Common inference controls
+
+```bash
+GPU=0 \
+TOTAL_LEN=96 \
+CFG_SCALE=2.0 \
+SAMPLE_NUM_VIDEOS=10 \
+STREAM_INFLIGHT_CHUNKS=8 \
+STREAM_MAX_CACHE_CHUNKS=24 \
+STREAM_SINK_SIZE=1 \
+bash scripts/sample_droid.sh
+```
+
+`TOTAL_LEN` sets the rollout length in latent frames and can exceed the trained
+window, since streaming keeps the attention span bounded; `TOTAL_LEN=96` yields
+381 RGB frames from a 64-frame checkpoint. MiniWorld is a streaming model and
+does not assume a fixed generation horizon.
+
+### Custom camera trajectories
+
+A RealEstate10K checkpoint can also animate a single image along a procedural
+camera trajectory, without any dataset on disk:
+
+```bash
+PYTHONPATH=. python -m miniworld.sample \
+  --dataset re10k \
+  --init_image /path/to/first_frame.png \
+  --custom_camera_trajectory orbit_right \
+  --checkpoint /path/to/MiniWorld_1b_re10k.pt \
+  --vae_checkpoint checkpoints/wan2.2/Wan2.2_VAE.pth \
+  --sample_dir samples/re10k_orbit_right \
+  --wm_model 1B \
+  --total_len 64 \
+  --sample_num_videos 1 \
+  --trajectory_magnitude 3.0
+```
+
+These checkpoints are trained on raw (unnormalized) translations, so
+`--trajectory_magnitude` is worth tuning: `1.0` is almost static, `3.0` is a
+good default at `--total_len 64`, and values above `5.0` degrade the second half
+of the rollout. Scale it with the rollout length to keep the same apparent
+speed. See the GitHub README for the full list of trajectories.
+
+
+
+## Limitations
+
+MiniWorld is a research model trained and evaluated at modest resolution and on
+limited domains. It may exhibit long-horizon drift, geometric errors, temporal
+inconsistencies, and failures under out-of-distribution actions, poses, scenes,
+or camera motions. It should not be used for safety-critical simulation or as a
+faithful physical simulator.
+
+## License
+
+These checkpoints are released under the Apache 2.0 license. Please also follow
+the licenses and usage terms of the underlying datasets (DROID, RealEstate10K)
+and of the Wan2.2 VAE.