---
language:
- en
pipeline_tag: text-to-video
base_model: zhuhz22/Causal-Forcing
tags:
- causal-forcing
- video-compression
- memory-compression
- pytorch
- distributed-checkpoint
---

# Causal Forcing CR10 checkpoints: 2k, 3k, 4k, 5k

This public release contains four CR10 model checkpoints from the same Causal Forcing compressor training run: steps **2000, 3000, 4000, and 5000**. Tensor files are preserved byte-for-byte with SHA256 checksums.

## Available checkpoints

| Step | Checkpoint directory | Model size |
|---:|---|---:|
| 2000 | `checkpoints/cf-cr10-lr1e4-cos2e5-5k/iter_000002000` | 5.682 GB |
| 3000 | `checkpoints/cf-cr10-lr1e4-cos2e5-5k/iter_000003000` | 5.682 GB |
| 4000 | `checkpoints/cf-cr10-lr1e4-cos2e5-5k/iter_000004000` | 5.682 GB |
| 5000 | `checkpoints/cf-cr10-lr1e4-cos2e5-5k/iter_000005000` | 5.682 GB |

Total model size is approximately **22.73 GB**. The memory ratio is `0.1` (CR10). Original training provenance and exact file sizes are recorded in `inventory.json`.

Each `model/` directory is a PyTorch Distributed Checkpoint (DCP) containing both frozen `net.*` generator state and trained `net_compressor.*` state. Keep `.metadata` and all `.distcp` shards together, including empty rank shards. This release contains model weights and a training config; optimizer, scheduler, and trainer state are not included.

The saved config contains original cluster paths that must be adapted on another installation. Its credential fields are empty.

## Download

```python
from huggingface_hub import snapshot_download

step = 2000  # 2000, 3000, 4000, or 5000
root = snapshot_download(
    repo_id="weihang44/Causal-Forcing-Memory-Checkpoints",
    allow_patterns=[
        f"checkpoints/cf-cr10-lr1e4-cos2e5-5k/iter_{step:09d}/model/*",
        "checkpoints/cf-cr10-lr1e4-cos2e5-5k/config.yaml",
        "manifest.json",
    ],
    local_dir="cf-models",
)
```

To download all four checkpoints, omit `allow_patterns`.

## Load the compressor in the matching inference repository


Use the supported inference environment and the source pinned at [video-compress commit bdbf3f2e903392e855638f19b1d555c150904d96](https://github.com/a-little-hoof/video-compress/tree/bdbf3f2e903392e855638f19b1d555c150904d96). Its loader reads DCP checkpoints without requiring the original training world size, extracts `net_compressor.*`, and checks the target state dictionary strictly. It also handles the optional unused AE task embedding in older checkpoints.

From that repository root, after downloading the checkpoint:

```python
import sys
import torch

sys.path.insert(0, "scripts/context_compression")
from generate_compressed_context import build_compressor, load_compressor_weights

compressor = build_compressor("1.3B", 0.1, 1024)
load_compressor_weights(
    compressor,
    "cf-models/checkpoints/cf-cr10-lr1e4-cos2e5-5k/iter_000002000",
)
compressor = compressor.to(device="cuda", dtype=torch.bfloat16).eval()
```

This loads model weights; generation still requires the CF generator, text encoder, VAE, and the chosen cache policy. Selection-free fullraw21, sink count, and recent-frame count are inference policies, not different checkpoint identities. See the [published CF method and review index](https://github.com/a-little-hoof/video-compress/blob/bdbf3f2e903392e855638f19b1d555c150904d96/CF_MBENCH_REMOTE_REVIEW.md) for the exact evaluated setup and its limitations.

## Integrity and upstream dependencies

`manifest.json` and `SHA256SUMS` cover every model/config file in this release. After downloading all four checkpoints, run `sha256sum -c SHA256SUMS` from the download directory.

The original standalone generator can be obtained from [zhuhz22/Causal-Forcing](https://huggingface.co/zhuhz22/Causal-Forcing/blob/2f8eb8bb6eeb1238da9d13e5420d342a74d634a6/chunkwise/causal_forcing.pt). Its SHA256 is `cf75ee5cc6f4e2e336c59c973f5544655d8f0aa481761efe6de1b9cb2eb0cd9d`. The matching inference setup also requires its text encoder and VAE.

These are CF-trained CR10 checkpoints. The training run, iteration, and file hashes identify each checkpoint. See `NOTICE.md` for upstream attribution.