💻 computer science

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

This paper introduces "Many-for-Many," a unified framework that employs lightweight adapters and a joint image-video learning strategy to train a single foundation model capable of performing over ten diverse visual generation and manipulation tasks, achieving competitive performance while leveraging data from multiple sources to overcome the high cost of task-specific training.

Original authors: Ruibin Li, Tao Yang, Yangming Shi, Weiguo Feng, Shilei Wen, Bingyue Peng, Lei Zhang

Published 2026-02-06

📖 4 min read☕ Coffee break read

CC BY 4.0

Original authors: Ruibin Li, Tao Yang, Yangming Shi, Weiguo Feng, Shilei Wen, Bingyue Peng, Lei Zhang

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine you have a massive library of instructions for creating art. Some instructions say, "Draw a cat," others say, "Turn this photo of a cat into a video," and some say, "Fix the blurry parts of this video" or "Colorize this black-and-white film."

Usually, to do all these things, you need a different specialist for every single job. You need a "Text-to-Video" chef, an "Image-to-Video" chef, and a "Video-Editing" chef. Training each of these chefs from scratch is incredibly expensive and requires huge amounts of high-quality ingredients (data).

Enter "Many-for-Many" (MfM).

The authors of this paper built a "Super-Chef" that can do all these jobs at once. Instead of hiring ten different specialists, they trained one single model that can handle over 10 different types of visual tasks, from creating videos from scratch to editing existing ones.

Here is how they did it, using simple analogies:

1. The Universal Adapter (The "Swiss Army Knife" Handle)

Different tasks require different "inputs."

Text-to-Video: You give it a sentence.
Image-to-Video: You give it a picture.
Video Editing: You give it a video with a "mask" (a stencil showing which parts to change).

Usually, these inputs are like different shapes of puzzle pieces that don't fit the same hole. The MfM team designed a lightweight adapter. Think of this as a universal handle or a Swiss Army knife attachment. It takes the text, the image, the video, the mask, and even a depth map (a blueprint of the 3D space, like a topographical map of the scene) and reshapes them all into the same format. This allows the model to understand any instruction, no matter what form it comes in.

2. The "Joint Learning" Strategy (The "Apprentice" Method)

Training a video AI from scratch is usually like trying to teach someone to run a marathon before they can walk. It requires millions of expensive video examples.

MfM uses a clever training trick called Joint Image-Video Learning.

Phase 1: They start by teaching the model with simple "Image" tasks (like drawing a picture from text). This is cheap and easy.
Phase 2: They slowly introduce "Video" tasks.
The Magic: Because the model learned the basics of "visuals" from the images, it doesn't need as many expensive video examples to learn how to make things move. It's like an apprentice who learns to chop vegetables (images) first; when they move on to cooking a stew (video), they already know how to handle the ingredients.

This means they could train a powerful 8-billion-parameter model using only 10% of the video data that other top models use.

3. The "3D RoPE" (The GPS for Time and Space)

To make videos look natural, the model needs to understand not just where things are (left, right, up, down) but also when they happen (first frame, last frame).
The team upgraded the model's internal "GPS" (called 3D RoPE). Instead of just knowing the position of a pixel on a flat screen, the model now understands the position in 3D space and through time. This helps the model create smooth, realistic movements rather than jerky, unnatural ones.

4. The Results: One Model, Many Superpowers

The paper tested this "Super-Chef" on two sizes: a smaller 2-billion version and a larger 8-billion version.

Versatility: The model can perform over 10 different tasks, including:
- Creation: Turning text into video, or images into video.
- Extension: Taking a short clip and making it longer.
- Editing: Removing objects (inpainting), adding new scenery (outpainting), or changing colors.
- Enhancement: Making blurry videos sharp (super-resolution).
Performance: In head-to-head tests against famous models (like Wan2.1, Hunyuan, and even commercial giants like Sora), the MfM model was highly competitive. It often ranked #1 or #2 in overall consistency and motion quality, despite using far less data.

The Bottom Line

The paper claims that by unifying the training process and using a smart "adapter" to mix different types of data, they created a single, efficient model that is just as good (and sometimes better) than specialized models that were trained for just one specific job. They proved you don't need a separate tool for every job if you build one versatile tool that learns from a wide variety of experiences.

What the paper does not claim:
The paper focuses strictly on the technical training and performance of the model on standard benchmarks. It does not claim to have solved specific clinical issues, nor does it detail specific future commercial products beyond the open-source release of the code and model weights. It is a foundational research step, not a finished consumer app.

Technical Summary: Many-for-Many (MfM)

Problem Statement

Current visual generation and manipulation research is fragmented. While diffusion models have achieved impressive results, most existing approaches focus on training separate models for specific tasks (e.g., Text-to-Video or Image-to-Video) or fine-tuning a pre-trained Text-to-Video (T2V) foundation model for downstream tasks like video manipulation. This paradigm presents two primary challenges:

High Cost of Foundation Training: Training a robust T2V foundation model from scratch requires massive amounts of high-quality, annotated data, which is prohibitively expensive.
Task Fragmentation: Existing models are often limited to one or a few tasks. To perform multiple generation or manipulation tasks (e.g., inpainting, super-resolution, colorization), distinct models or complex fine-tuning pipelines are typically required, leading to inefficiency and inconsistent performance.

Methodology

The authors propose Many-for-Many (MfM), a unified framework designed to train a single model from scratch capable of performing over 10 different visual generation and manipulation tasks. The core methodology involves three key components:

1. Unified Architecture and Adapter

The MfM model is built upon a Diffusion Transformer (DiT) with 3D full attention, trained using Flow Matching. To handle the diverse input conditions across different tasks (ranging from 0D text prompts to 3D video masks and depth maps), the authors introduce a lightweight adapter:

Condition Unification: Instead of processing pixel data, masks, and depth maps separately, the adapter concatenates these 3D inputs into a unified feature map.
Resolution Alignment: The adapter uses convolutional layers and downsampling blocks to adjust the spatial and temporal resolution of the input conditions to match the latent space of the video VAE (e.g., converting $T \times H \times W \times C$ to $t \times h \times w \times c$ ).
Task Identification: Task names (e.g., "text-to-video," "image-to-video") are appended to text prompts to clarify the specific operation, especially when tasks share common inputs like masks.
Depth Integration: Depth maps are introduced as a conditional input to enhance the model's perception of 3D space.

2. Model Variants

Two versions of the model are trained to accommodate different computational needs:

MfM-2B: 2 billion parameters, 28 layers, 28 attention heads.
MfM-8B: 8 billion parameters, 40 layers, 48 attention heads.
Both utilize 3D Rotary Position Embedding (RoPE) to handle varying resolutions and lengths, and Query-Key Normalization (QK-Norm) to ensure training stability.

3. Training Strategy

The training process employs a progressive joint learning strategy:

Multi-Task Joint Learning: The model is trained on a mix of image and video data. The ratio of image-to-video data is progressively adjusted, starting with text-image pairs to establish semantic connections and gradually introducing video data.
Data Augmentation: To maximize the utility of available data, the framework generates diverse training samples from existing videos. For example, video inpainting tasks are created by applying random masks, and video colorization tasks are generated by converting videos to grayscale. This allows the model to learn manipulation tasks without requiring separate, dedicated datasets for each.
Resolution Progression: Training begins at low resolutions (e.g., 49×128×224) and progresses to higher resolutions (up to 97×720×1280), concluding with a multi-resolution stage using NaViT to support arbitrary aspect ratios.

Key Contributions

Unified Framework: MfM is the first framework to train a single model from scratch that effectively handles a wide spectrum of tasks, including T2V, I2V, video extension, inpainting, outpainting, colorization, super-resolution, and style transfer.
Efficient Data Utilization: By leveraging a multi-task learning strategy and a unified adapter, MfM utilizes data that was previously unusable for standard T2V training (e.g., low-resolution or watermarked data), significantly reducing the reliance on costly high-quality T2V annotations.
Performance with Fewer Resources: The 8B model achieves competitive performance using only 10% of the training data typically required by state-of-the-art open-source T2V foundation models.

Experimental Results

The authors evaluated MfM on the VBench benchmarks for T2V and I2V, as well as a custom MfM-benchmark covering 16 distinct tasks.

Text-to-Video (T2V): On the VBench-T2V benchmark, MfM-8B achieved the best average rank (3.04) among evaluated models, outperforming larger models like Wan2.1 (14B), Hunyuan (13B), and commercial systems like Sora and Gen-3. It demonstrated superior performance in appearance, overall consistency, and dynamic degree.
Image-to-Video (I2V): MfM achieved the best average rank (3.33) on VBench-I2V, showing balanced performance in static consistency and dynamic generation, outperforming specialized I2V models.
Video Manipulation: In tasks like video inpainting, outpainting, and colorization, MfM demonstrated significant improvements in reference-based metrics (e.g., FID improvements of 16.4% to 72.9% over specialized baselines) and superior temporal coherence.
Ablation Study: Comparing a T2V model trained with MfM's multi-task strategy versus one trained without it revealed that the multi-task approach significantly improved motion dynamic scores (0.819 vs. 0.402), suggesting that exposure to diverse manipulation tasks enhances the model's understanding of motion complexity.

Significance

The paper claims that MfM represents a shift towards economical and unified visual foundation models. By unifying the training of generation and manipulation tasks, MfM demonstrates that a single model can achieve highly competitive performance across diverse visual tasks while requiring substantially less data and computational resources than current specialized or large-scale foundation models. The work suggests that knowledge transfer between image and video tasks, and between generation and manipulation tasks, is a viable path to improving video generation quality without the prohibitive costs associated with training massive, task-specific foundation models from scratch.

The authors note limitations, specifically that the current architecture processes 1D (text) and 2D/3D (visual) conditions separately before fusing them via self-attention. They suggest future work could explore vision-language models for earlier, explicit multimodal fusion to better handle complex input conditions.