Many-for-Many: Unify the Training of Multiple Video and Image Generation and Manipulation Tasks
Journal:
arXiv
Published Date:
Jun 2, 2025
Abstract
Diffusion models have shown impressive performance in many visual generation
and manipulation tasks. Many existing methods focus on training a model for a
specific task, especially, text-to-video (T2V) generation, while many other
works focus on finetuning the pretrained T2V model for image-to-video (I2V),
video-to-video (V2V), image and video manipulation tasks, etc. However,
training a strong T2V foundation model requires a large amount of high-quality
annotations, which is very costly. In addition, many existing models can
perform only one or several tasks. In this work, we introduce a unified
framework, namely many-for-many, which leverages the available training data
from many different visual generation and manipulation tasks to train a single
model for those different tasks. Specifically, we design a lightweight adapter
to unify the different conditions in different tasks, then employ a joint
image-video learning strategy to progressively train the model from scratch.
Our joint learning leads to a unified visual generation and manipulation model
with improved video generation performance. In addition, we introduce depth
maps as a condition to help our model better perceive the 3D space in visual
generation. Two versions of our model are trained with different model sizes
(8B and 2B), each of which can perform more than 10 different tasks. In
particular, our 8B model demonstrates highly competitive performance in video
generation tasks compared to open-source and even commercial engines. Our
models and source codes are available at https://github.com/leeruibin/MfM.git.
