Update README.md

README.md

@@ -1,49 +1,22 @@
 ---
 license: mit
 pipeline_tag: image-to-video
+datasets:
+- MIN-Lab/minWM-data
+tags:
+- Video
+- WorldModels
+- Stream
+- Diffusion
 ---
 
-# Causal Forcing
+# 🌍 minWM: The First Full-Stack Open-Source World Model Framework
 
-[**Causal Forcing**](https://huggingface.co/papers/2602.02214) is a framework for high-quality real-time interactive video generation. It distills pretrained bidirectional video diffusion models into few-step autoregressive (AR) models by bridging the architectural gap between bidirectional and causal attention.
+>  ***A full-stack framework and tutorial for newcomers, rather than a specific model.***
 
-- **Project Page:** [https://thu-ml.github.io/CausalForcing.github.io/](https://thu-ml.github.io/CausalForcing.github.io/)
-- **Code:** [https://github.com/thu-ml/Causal-Forcing](https://github.com/thu-ml/Causal-Forcing)
-- **Paper:** [Causal Forcing: Autoregressive Diffusion Distillation Done Right for High-Quality Real-Time Interactive Video Generation](https://huggingface.co/papers/2602.02214)
+**minWM** is our contribution to the world-model community: a **full-stack open-source framework** that walks you end-to-end through turning a bidirectional T2V foundation model into an action-conditioned video world model — with example data, runnable scripts, **Claude skills** capturing our hands-on experience, and **onboarding knowledge** for newcomers. We hope more researchers and developers join us in growing the community together.
 
-## Overview
-
-Causal Forcing uses an autoregressive teacher for ODE initialization to bridge the architectural gap, then applies an asymmetric DMD procedure. It significantly outperforms existing baselines in visual quality and motion dynamics while maintaining inference efficiency. The frame-wise models natively support both Text-to-Video (T2V) and Image-to-Video (I2V) generation.
-
-## Inference
-
-Please refer to the [official GitHub repository](https://github.com/thu-ml/Causal-Forcing) for installation instructions.
-
-### Text-to-Video (T2V)
-
-To generate video using the chunk-wise model:
-
-```bash
-python inference.py \
-  --config_path configs/causal_forcing_dmd_chunkwise.yaml \
-  --output_folder output/chunkwise \
-  --checkpoint_path checkpoints/chunkwise/causal_forcing.pt \
-  --data_path prompts/demos.txt
-```
-
-### Image-to-Video (I2V)
-
-The frame-wise setting natively supports I2V. Set the first latent initial frame as your conditional image:
-
-```bash
-python inference.py \
-  --config_path configs/causal_forcing_dmd_framewise.yaml \
-  --output_folder output/framewise \
-  --checkpoint_path  checkpoints/framewise/causal_forcing.pt \
-  --data_path prompts/i2v \
-  --i2v \
-  --use_ema
-```
+## Code: https://github.com/shengshu-ai/minWM
 
 ## Citation