README.md

metadata

title: ForgeWM
emoji: 🎮
colorFrom: yellow
colorTo: blue
sdk: gradio
sdk_version: 6.25.0
app_file: app.py
short_description: Few-step action-conditioned Minecraft world model
python_version: '3.12'
startup_duration_timeout: 1h
pinned: false
license: apache-2.0
models:
  - ForgeWM/ForgeWM
  - Skywork/Matrix-Game-2.0

ForgeWM — Progressive Causal Training for Few-Step Action-Conditioned Video World Models

Interactive demo for ForgeWM/ForgeWM (paper · code · project page).

Feed the model one Minecraft frame plus a short action script (forward, turn_right, look_up, …) and it rolls the world forward autoregressively: a block-causal diffusion transformer denoises 3 latent frames (≈1 second of 12 fps video) per step, conditioned on the keyboard/mouse actions for that window and a sliding KV cache of the past.

Three released students are selectable:

Student	Denoising steps	First-Frame Enhancement
ForgeWM-4	4	–
ForgeWM-2	2	4-step schedule on block 0
ForgeWM-1	1	4-step schedule on block 0

Fidelity notes

The app mirrors the repo's own inference.py and pipeline/causal_inference.py: 352×640, num_frame_per_block=3, local_attn_size=6, sink_size=0, warp_denoising_step=true, the published denoising_step_list / denoising_step_list_first_chunk schedules, MG2-style conditioning (CLIP ViT-H visual context + 4-channel mask concatenated with the first-frame latent), and the exact Minecraft action palette (CAM_VALUE=0.10).

Attention runs on PyTorch SDPA rather than FlashAttention-2. In the KV-cached inference path the repo calls attention(q, k, v) with no causal/window arguments, so the two are numerically equivalent — causality is enforced by the cache layout, not the kernel.

Credits

• Model and reference frames (examples/*.png): the ForgeWM repository, Apache-2.0.
• Base weights: Skywork/Matrix-Game-2.0, distributed under Skywork's own terms.
• Vendored wan/, pipeline/, utils/ are Apache-2.0; see NOTICE.