Model reference · open weights
optical-flow-MEMFOF-Tartan-T-TSKH is an open-weight image model from egorchistov. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Released by | egorchistov |
|---|---|
| Type | Image models |
| Task | Image edit |
| Parameters (lead) | 76M |
| Runs with | pytorch |
| Based on | egorchistov/optical-flow-MEMFOF-Tartan-T |
| Released | 2025-06-02 |
| Popularity | 5k downloads / month |
| Licence | Open weights |
About
🔍 MEMFOF is a memory-efficient optical flow method for Full HD video that combines high accuracy with low VRAM usage.
✅ Note: This particular checkpoint is intended for real-world videos — it is trained with higher diversity and robustness in mind.
Install MEMFOF via the package manager:
pip3 install git+https://github.com/msu-video-group/memfof
Then use the following snippet to compute backward and forward optical flow for three consecutive frames:
import torch
from memfof import MEMFOF
device = "cuda" if torch.cuda.is_available() else "cpu"
model = MEMFOF.from_pretrained("egorchistov/optical-flow-MEMFOF-Tartan-T-TSKH").eval().to(device)
with torch.inference_mode():
# [B=1, T=3, C=3, H=1080, W=1920]
example_input = torch.randint(0, 256, [1, 3, 3, 1080, 1920], device=device)
# [B=1, C=2, H=1080, W=1920]
backward_flow, forward_flow = model(example_input)["flow"][-1].unbind(dim=1)
@article{bargatin2025memfof,
title={MEMFOF: High-Resolution Training for Memory-Efficient Multi-Frame Optical Flow Estimation},
author={Bargatin, Vladislav and Chistov, Egor and Yakovenko, Alexander and Vatolin, Dmitriy},
journal={arXiv preprint arXiv:2506.23151},
year={2025}
}
From the published model card. Full card on the HuggingFace links in the sidebar.
How it works
Using it via the API
Once AxForge deploys optical-flow-memfof-tartan-t-tskh for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (optical-flow-memfof-tartan-t-tskh below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/images/generations \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"optical-flow-memfof-tartan-t-tskh","prompt":"a red bicycle","size":"1024x1024"}'
Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.