---
license: apache-2.0
pretty_name: DecoupAttn Plans
tags:
- video-diffusion
- sparse-attention
- training-free
- acceleration
size_categories:
- n<1K
---

# DecoupAttn Plans

The offline plans and calibration tables of **DecoupAttn: Decoupled Sparse Attention for Training-Free Acceleration of
Video Diffusion**. Code: [github.com/YJLi-new/DecoupAttn](https://github.com/YJLi-new/DecoupAttn).

DecoupAttn compiles one plan per model offline. Distortion Budget gives every unit u = (t, l, h) one option
(Dense, Static_θ with θ ∈ {0.05, 0.10, 0.15, 0.20, 0.30, 0.45}, or CompOnly) and every call one estimator (Full or
Centroid) at density 0.20, from the block ranking D_u, the distortion ε_u and the sensitivity J_u. This dataset holds the
plans of the paper and the ε_u and J_u files they were solved from.

## Contents

| Key | Model | Block shape | Reassign | ε_u clips | J_u | PSNR of this plan | PSNR in Table 1 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| `cp1` | Cosmos-Predict1 | 2×4×16 | yes | 4 | here | – (plan lost) | 27.77 |
| `cp25` | Cosmos-Predict2.5 | 4×4×8 | yes | 4 | GitHub `data/sensitivity/` | 24.70 | 24.70 |
| `wan21` | Wan2.1-14B (480p) | 4×4×8 | yes | 4 | GitHub `data/sensitivity/` | 24.70 | 24.70 |
| `wan22` | Wan2.2-A14B | 1×16×8 | yes | 4 | here | 18.43 | 18.43 |
| `hy` | HunyuanVideo | 1×8×16 | yes | 1 | here | 27.25 | 27.47 |
| `nano` | Cosmos3-Nano (I2W) | 4×4×8 | yes | 4 | here | 21.95 | 21.56 |
| `t25` | Cosmos-Transfer2.5 (edge) | 2×4×16 | yes | 4 | GitHub `data/sensitivity/` | 33.81 | 33.81 |
| `dr` | DiffusionRenderer | 2×4×16 | once | 4 | GitHub `data/sensitivity/` | 50.87 | 50.87 |
| `alaya` | AlayaRenderer | 4×4×8 | yes | 4 | GitHub `data/sensitivity/` | 28.15 | 28.15 |
| `unirelight` | UniRelight | 1×16×8 | yes | 4 | here | 42.75 | 42.75 |

PSNR (dB) is measured against the same-seed dense output on the test prompts or clips of each model, as in Table 1.
Block shapes are frames × rows × cols of one key block. "Reassign" is the Reassign Cache rule (δ_stale > 1.0 dB); for
DiffusionRenderer the clusters are built once.

**HunyuanVideo and Cosmos3-Nano.** The plan files behind Table 1 were lost. The plans here are re-runs of the same
calibration with the same constants; their PSNR differs from Table 1 by −0.22 dB (HunyuanVideo, 8 test prompts) and
+0.39 dB (Cosmos3-Nano, 32 test prompts).

**Cosmos-Predict1.** The plan file was lost. Its ε_u and J_u files are included; the block ranking D_u has to be measured
again before the plan can be solved (see below).

## Layout

```text
<key>/
├── plan.npz                        the plan, read by decoupattn.plan.load_plan
├── plan.json                       summary of the run that compiled it: block shape, reassignment, option histogram,
│                                   executed density, sensitivity source, solver
├── distortion/<clip>_s1000.npz     distortion ε_u on the calibration clips (seed 1000)
└── sensitivity/J_s1000.jsonl       group-injection runs behind J_u (models whose J_u is not in the GitHub repository)
```

Each folder has the layout of a DecoupAttn run directory, so it can be passed as `--run-dir`. `plan.json` keeps the
keys of the runs behind the paper, which differ from the summary that `decoupattn calibrate` writes. The block ranking
tables D_u are not included (up to 53 GB per model).

## Usage

```bash
pip install -U huggingface_hub
hf download LLKLL/DecoupAttn-plans --repo-type dataset --local-dir plans
```

Generate the test videos of a model with its plan (installation and checkpoints: see the GitHub README):

```bash
python -m decoupattn generate --model cp25 --run-dir runs/cp25 --plan plans/cp25/plan.npz --split test
python -m decoupattn generate --model cp25 --run-dir runs/cp25 --dense --split test
python -m decoupattn evaluate --model cp25 --run-dir runs/cp25 --split test
```

To solve a plan again, copy the model folder into a new run directory and run `decoupattn calibrate` there. The GPU
stages skip clips whose outputs exist, so the distortion and sensitivity files here are reused and only the block shape,
the reassignment probe and D_u are measured. The Cosmos-Predict1 sensitivity file names its groups `r0_t0`, … and its
floor `dense2`; set `prefix = "r"` and `floor = "dense2"` in `[planner.sensitivity]` of `configs/models/cp1.toml` to
read it.

## Citation

```bibtex
@misc{li2026decoupattn,
  title  = {{DecoupAttn}: Decoupled Sparse Attention for Training-Free Acceleration of Video Diffusion},
  author = {Li, Yanjin and Qiu, Bingbing and Huang, Qiang and Qian, Rui and Wang, Jian and Zhang, Mingda},
  year   = {2026}
}
```

## License

Apache License 2.0. The plans were compiled from the outputs of the models listed above, which keep their own licenses
and terms of use; the calibration prompts of the video models are from MovieGen Video Bench (CC BY-NC 4.0), and only
their line numbers are stored in the GitHub repository.