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Add stage3_new entry to README

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  1. README.md +11 -3
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@@ -19,8 +19,15 @@ Training checkpoints for [ForgeWM](https://github.com/asdfo123/ForgeWM).
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  | `stage1/model.pt` | Stage 1 | Teacher-Forcing Causal AR (10000 steps) |
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  | `stage2/model.pt` | Stage 2 | Consistency Distillation (6000 steps) |
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  | `stage3/model.pt` | Stage 3 | DMD final model (4-step real-time inference) |
 
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  ## Usage
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@@ -28,12 +35,13 @@ Training checkpoints for [ForgeWM](https://github.com/asdfo123/ForgeWM).
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  # Download
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  huggingface-cli download asdfo123/ForgeWM --local-dir ./ckpts --repo-type model
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- # Inference
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- python inference.py --checkpoint_path ckpts/stage3/model.pt --image_path demo_images/forest.png --action_type forward
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  ```
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  ## 🚧 Coming Soon
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  - More intermediate checkpoints
 
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  ## Citation
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@@ -47,4 +55,4 @@ python inference.py --checkpoint_path ckpts/stage3/model.pt --image_path demo_im
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  ```
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  ## Contact
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- [Xinye Li](https://asdfo123.github.io) leeasdfo123@gmail.com
 
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  | `stage1/model.pt` | Stage 1 | Teacher-Forcing Causal AR (10000 steps) |
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  | `stage2/model.pt` | Stage 2 | Consistency Distillation (6000 steps) |
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  | `stage3/model.pt` | Stage 3 | DMD final model (4-step real-time inference) |
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+ | `stage3_new/model.pt` | Stage 3 (improved) | DMD with cache-refresh fix in training pipeline + sliding-window (las=6, sink=0) inference alignment (4000 steps). Better long-video stability and reduced HUD shrinkage / OOD drift on 22s rollouts. |
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+ ### Notes on `stage3_new`
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+ `stage3_new` was retrained from the same Stage 2 starting point as the original `stage3`, but with two pipeline-level fixes:
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+ 1. **Train-time KV cache refresh** — the per-chunk t=0 cache rerun is now applied unconditionally. Previously it was skipped for grad-enabled chunks, creating a train/inference distribution shift on the KV cache (training cache was noisy at the exit_flags step, while inference cache reaches near-clean t≈0). See https://github.com/asdfo123/ForgeWM/blob/main/pipeline/self_forcing_training.py for the fix.
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+ 2. **Inference-time sliding-window alignment** — the inference pipeline now correctly forwards the top-level `local_attn_size` / `sink_size` from the training config into `WanDiffusionWrapper`. Previously these defaulted to `-1` / `0` and inference silently ran with full causal attention while the model was trained with a 6-frame window.
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+
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+ These changes are most visible on long-horizon (22s+) rollouts.
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  ## Usage
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  # Download
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  huggingface-cli download asdfo123/ForgeWM --local-dir ./ckpts --repo-type model
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+ # Inference (default 4-step recipe)
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+ python inference.py --checkpoint_path ckpts/stage3_new/model.pt --image_path demo_images/forest.png --action_type forward
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  ```
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  ## 🚧 Coming Soon
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  - More intermediate checkpoints
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+ - Few-step variants (2-step / 1-step with first-chunk FFE schedule)
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  ## Citation
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  ```
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  ## Contact
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+ [Xinye Li](https://asdfo123.github.io) leeasdfo123@gmail.com