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EngEmmanuel/
EchoLVFM-Weights

Name: EchoLVFM-Weights
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EchoLVFM — Weights

One-step latent video flow matching for echocardiogram synthesis.

Paper

arXiv

Code

GitHub

This repo holds weights only. The training + inference code lives in the EchoLVFM code repository. You need both to run the model.

Three independent checkpoints, each in its own subfolder:

Subfolder	Flow	Inference	Notes
`echolvfm_h1/`	RMMFlow	one-step	Adaptive-weighting exponent `h=1` in the training loss
`echolvfm_h2/`	RMMFlow	one-step	Adaptive-weighting exponent `h=2` in the training loss
`linear/`	LinearFlow	multi-step ODE	Baseline for comparison

h is a loss hyperparameter (the exponent of the adaptive-weighting term), not a step count. Both RMMFlow variants are one-step generators — that's the defining property of RMMFlow.

Each subfolder contains:

model.safetensors — the flow-level state dict (~293 MB).
config.yaml — minimal config to rebuild the UNet3D + flow wrapper.

Subfolders load independently: a single call only downloads the requested variant's files (~293 MB), not the whole repo.

Loading

python

from utils.hub import load_model_from_hub

flow = load_model_from_hub(
    "EngEmmanuel/EchoLVFM-Weights",
    subfolder="echolvfm_h2",
    device="cuda",
)

You also need the paired VAE (HReynaud/EchoFlow, subfolder vae); see vae/util.py::load_vae_and_processor in the code repo.

Training data

The underlying models were trained on VAE-encoded latents of the public CAMUS dataset. Please respect the CAMUS dataset's license and citation requirements when using these weights.

Citation

If you use EchoLVFM, please cite the paper:

bibtex

@article{echolvfm2026,
  title   = {EchoLVFM: One-Step Video Generation via Latent Flow Matching for Echocardiogram Synthesis},
  author  = {Oladokun, Emmanuel and Thomas, Sarina and Šprem, Jurica and Grau, Vicente},
  journal = {arXiv preprint arXiv:2603.13967},
  year    = {2026}
}

EngEmmanuel/EchoLVFM-Weights