标题： 多对多：统一多个视频和图像生成与操作任务的训练 显示英文标题

标题： Many-for-Many: Unify the Training of Multiple Video and Image Generation and Manipulation Tasks

摘要： 扩散模型在许多视觉生成和操作任务中表现出令人印象深刻的效果。 许多现有方法专注于为特定任务训练模型，特别是文本到视频（T2V）生成，而许多其他工作则专注于微调预训练的T2V模型以用于图像到视频（I2V）、视频到视频（V2V）、图像和视频操作等任务。 然而，训练一个强大的T2V基础模型需要大量的高质量标注，这非常昂贵。 此外，许多现有模型只能执行一个或几个任务。 在这项工作中，我们引入了一个统一的框架，即many-for-many，它利用来自多种不同视觉生成和操作任务的可用训练数据来训练一个适用于这些不同任务的单一模型。 具体来说，我们设计了一个轻量级适配器来统一不同任务中的不同条件，然后采用联合图像-视频学习策略从零开始逐步训练模型。 我们的联合学习导致了一个具有改进视频生成性能的统一视觉生成和操作模型。 此外，我们将深度图作为条件引入，以帮助我们的模型更好地感知视觉生成中的3D空间。 我们的两种模型版本使用了不同的模型大小（8B 和 2B），每种都可以执行超过10种不同的任务。 特别是，与开源甚至商业引擎相比，我们的8B模型在视频生成任务中表现出高度竞争力。 我们的模型和源代码可在https://github.com/leeruibin/MfM.git获取。 显示英文摘要

摘要： 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.