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maxwelljones14/refVFX-lora
refVFX-lora is a image-to-video model from maxwelljones14. Use it for the image-to-video task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as cc-by-4.0.
This is a LoRA adapter for Wan-AI/Wan2.1-FLF2V-14B-720P, trained for the RefVFX project on tuning-free visual effect transfer across videos.
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From the Hugging Face model README
This is a LoRA adapter for Wan-AI/Wan2.1-FLF2V-14B-720P,
trained for the RefVFX project on tuning-free visual effect transfer across videos.
Note: This is an unofficial reimplementation produced at CMU. All code and training data were created from scratch using the publicly available arXiv paper and AI coding tools as the only resources.
maxwelljones14/refVFX_datasetWan-AI/Wan2.1-FLF2V-14B-720PWan-AI/Wan2.1-FLF2V-14B-720P (first-last-frame-to-video, 14B parameters, 720P)maxwelljones14/refVFX_datasetq, k, v, o),
cross-attention (q, k, v, o), and feed-forward (ffn.0, ffn.2)step-10000.safetensorsRefVFX performs tuning-free visual effect transfer across videos: given a reference effect, the model transfers that effect onto new input content while preserving the underlying motion and structure. The adapter was trained on the multi-part RefVFX dataset, which combines:
This is a LoRA adapter and must be applied on top of the base Wan2.1-FLF2V-14B-720P weights. See the GitHub repo for inference scripts and the full pipeline. At a high level:
from huggingface_hub import hf_hub_download
lora_path = hf_hub_download(
repo_id="maxwelljones14/refVFX-LoRA",
filename="step-10000.safetensors",
)
# Load Wan2.1-FLF2V-14B-720P, then apply the LoRA weights from `lora_path`.
# Refer to https://github.com/maxwelljones14/refVFX for the exact loading code.
The checkpoint stores LoRA A/B matrices keyed as
blocks.{i}.<module>.lora_A.default.weight / ...lora_B.default.weight.
@article{jones2026tuning,
title={Tuning-free Visual Effect Transfer across Videos},
author={Jones, Maxwell and Abdal, Rameen and Patashnik, Or and Salakhutdinov, Ruslan and Tulyakov, Sergey and Zhu, Jun-Yan and Wang, Kuan-Chieh Jackson},
journal={arXiv preprint arXiv:2601.07833},
year={2026}
}
Released under CC-BY-4.0. The adapter derives from
Wan-AI/Wan2.1-FLF2V-14B-720P; please also
review the base model's license terms before use.