EMA-VFI, released checkpoints as safetensors

The four checkpoints released with EMA-VFI, converted from the original pickles to safetensors and nothing else. The weights are unchanged, tensor for tensor.

Upstream: https://github.com/MCG-NJU/EMA-VFI. This is a mirror, not a new model. All credit for the network and the training belongs to its authors.

Why this exists

The originals are distributed as bare state_dict pickles inside a Google Drive folder. That is two problems for anyone building on them. A pickle executes arbitrary code when it is loaded, and a Drive folder gives out per-file links that are not stable enough to script against, so tools end up telling people to fetch a file by hand.

Safetensors cannot execute anything on load, and a Hub URL stays put.

The files

File	Size	Parameters	Quality	Multiplier above 2
ours_t.safetensors	250.5 MiB	65.66M	best	yes
ours.safetensors	250.5 MiB	65.66M	best	no
ours_small_t.safetensors	55.3 MiB	14.49M	lower	yes
ours_small.safetensors	55.3 MiB	14.49M	lower	no

Take ours_t unless you have a reason not to. The _t pair was trained to land anywhere between two frames, so it can do 3x, 4x and beyond. The other two only ever land halfway, which limits them to a multiplier of 2. The small pair is a narrower network: quicker, and visibly weaker on fast motion.

Provenance

Every file was downloaded from the authors' own Drive folder, 16jUa3HkQ85Z5lb5gce1yoaWkP-rdCd0o, linked from the upstream README. No third-party mirror was used.

Original	SHA-256 of the source pickle
ours_t.pkl	c09e82d193e303d7b91f1e36bf535886b820cb56105574355211d60a6b557a99
ours.pkl	f9085bb32d8b2eaa70eb1df08cc25bc7e973c50a74d74fb593ebe0d5e100a11f
ours_small_t.pkl	ad845671f7250d408937718aca19eaf87aa1ca79215070f7641eea332b703233
ours_small.pkl	ef0e455a7fe75c24d415bbcd83c29a9c0134ba853942bde0cd4dc4ed01e3f98f

Each hash is repeated in its safetensors file's own metadata, under source_sha256, alongside the architecture the weights belong to. conversion_report.json in this repo records the whole run.

What was checked before publishing

1. Loaded with torch.load(weights_only=True), which refuses arbitrary pickle opcodes, so a payload would have raised rather than run.
2. Built the network at the matching size and required load_state_dict(strict=True), which proves the tensors really are this architecture and that none is missing or spare.
3. Reloaded each converted file and required the same strict fit, then compared every tensor against the original with torch.equal. All exact.

What the conversion changed

Two things, both mechanical, and both what a loader had to do anyway:

• The module. prefix is stripped. The originals were saved from a DataParallel wrapper, so every key carried it.
• attn_mask and HW are dropped. These are shifted-window buffers the backbone registers during forward from whatever padded size it is given, so a saved pair belongs to the resolution it was trained at and is wrong at any other. Upstream's own loader drops them too. 8 such keys in the full models, 4 in the small ones.

Everything else is byte-identical, float32 throughout.

Use it in ComfyUI

With WAS Node Suite and its EMA-VFI Frame Interpolation node, either way round.

Let it fetch. Set features.network: true in the pack's config.yaml, pick a checkpoint in the node's menu and queue the graph. The file is downloaded once into ComfyUI/models/EMA-VFI and kept. Nothing is fetched at startup, only on the run that needs it.

Or place it yourself. Download the file and put it here:

ComfyUI/models/EMA-VFI/ours_t.safetensors

Create the EMA-VFI directory if it is not there, and restart ComfyUI. The checkpoint list is read when the node builds its schema, so a file added while ComfyUI is running does not appear until it restarts.

Keep the filename as it is. The node picks the architecture from the name, so a renamed file will not be recognised.

Use it in plain torch

The network itself is not in this repo. Take it from upstream, or from modules/vendor/ema_vfi in WAS Node Suite, then:

from safetensors.torch import load_file

state = load_file("ours_t.safetensors")
net.load_state_dict(state, strict=True)   # no prefix to strip, no keys to drop

Licence

Apache-2.0, as released by the authors: "This project is released under the Apache 2.0 license." The full text is in LICENSE beside these files.

Citation

@inproceedings{zhang2023extracting,
  title={Extracting motion and appearance via inter-frame attention for efficient video frame interpolation},
  author={Zhang, Guozhen and Zhu, Yuhan and Wang, Haonan and Chen, Youxin and Wu, Gangshan and Wang, Limin},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={5682--5692},
  year={2023}
}