LongCat-Video

Product Introduction

1. Definition: LongCat-Video is an open-source, foundational video generation and understanding framework built on a 13.6 billion parameter Diffusion Transformer (DiT) architecture. It is a technical framework designed for advanced AI video tasks.
2. Core Value Proposition: It exists to provide a unified, scalable, and efficient solution for generating high-quality, long-duration videos from text, images, or existing video clips, addressing the computational and quality challenges in AI video synthesis.

Main Features

1. Unified Multi-Task Architecture: LongCat-Video integrates Text-to-Video (T2V), Image-to-Video (I2V), and Video-Continuation (V2V) generation into a single model. This eliminates the need for separate, task-specific models, streamlining the workflow for complex video production pipelines. It uses a single DiT backbone conditioned on different input modalities (text, image, video latent codes) to natively support all tasks.
2. Native Long Video Generation: The model is pre-trained explicitly on Video-Continuation tasks, enabling it to generate coherent, multi-minute videos without suffering from common issues like color drift, object distortion, or quality degradation over time. This is achieved through a temporal autoregressive generation strategy that conditions new segments on previously generated content.
3. Coarse-to-Fine Efficient Inference: To generate high-resolution (720p, 30fps) videos efficiently, LongCat-Video employs a multi-stage generation process. It first generates a low-resolution, low-frame-rate video and then progressively refines it along both spatial (resolution) and temporal (frame rate) axes. This is combined with Block Sparse Attention mechanisms to reduce computational overhead, especially at higher resolutions.
4. Audio-Driven Avatar Generation (LongCat-Video-Avatar): This specialized model variant adds precise lip-sync and expressive character animation driven by audio input. Key technical components include an audio encoder (Wav2Vec2 in v1.0, Whisper-Large-v3 in v1.5 for improved accuracy), and support for multi-stream audio inputs for multi-character dialogues. Version 1.5 introduces step distillation for 8-step inference and INT8 quantization for reduced VRAM usage.

Problems Solved

1. Pain Point: The high computational cost and technical complexity of generating long, high-quality, and coherent videos using AI. Traditional methods often produce short clips or suffer from inconsistency.
2. Target Audience: AI researchers in computer vision and generative AI, machine learning engineers building video generation applications, developers at multimedia and content creation startups, and technical teams in enterprises needing scalable video synthesis tools.
3. Use Cases: Generating long-form narrative content (short films, explainer videos), creating dynamic social media content from a single image, extending or editing existing video footage, producing audio-synced talking head videos for education or digital avatars, and prototyping visual concepts for film and game development.

Unique Advantages

1. Differentiation: Unlike many open-source models that specialize in one task (e.g., only T2V), LongCat-Video offers a unified framework. Compared to proprietary services (e.g., Veo, Sora), it provides full transparency, customization, and offline deployment. Its 13.6B dense architecture often matches or exceeds the performance of larger Mixture-of-Expert (MoE) models like Wan 2.2 in human evaluations.
2. Key Innovation: The integration of Multi-reward Group Relative Policy Optimization (GRPO) for Reinforcement Learning from Human Feedback (RLHF). This advanced training technique aligns the model with multiple human preference dimensions (text alignment, visual quality, motion quality) simultaneously, leading to superior overall output quality compared to standard supervised fine-tuning or single-reward RLHF approaches.

Frequently Asked Questions (FAQ)

1. What are the hardware requirements to run LongCat-Video? Running the full 13.6B parameter model efficiently requires significant GPU memory. Multi-GPU inference (e.g., 2+ high-end GPUs like NVIDIA A100 or H100) is recommended for standard operation, though the INT8 quantized Avatar-1.5 model reduces VRAM requirements for avatar-specific tasks.
2. How does LongCat-Video compare to Stable Video Diffusion or other open-source models? LongCat-Video is a larger, more capable foundational model (13.6B vs. ~1B params) designed natively for long video generation and multiple tasks. It demonstrates superior performance in length, coherence, and resolution (720p) and uses a modern DiT architecture versus the U-Net backbone of Stable Video Diffusion.
3. Can LongCat-Video be used for commercial applications? Yes, the model weights are released under the permissive MIT License, allowing for commercial use, modification, and distribution. However, users must ensure compliance with all applicable laws and are responsible for the content generated.
4. What is the difference between LongCat-Video and LongCat-Video-Avatar? LongCat-Video is the base model for general video generation from text, image, or video. LongCat-Video-Avatar is a specialized variant fine-tuned and equipped with an audio encoder to generate videos of characters (human or stylized) with accurate lip-synchronization to input audio, supporting single and multi-speaker scenarios.
5. How do I improve lip-sync accuracy in LongCat-Video-Avatar? For v1.5, use the Whisper-based model (--model_type avatar-v1.5) and adjust the audio classifier-free guidance (CFG) scale between 3 and 5. For all versions, include explicit verbal-action cues (e.g., "a woman speaking") in the text prompt and use longer, descriptive prompts for better context.