LongCat Video & LongCat Image: Free AI Video, Image, ComfyUI Guide

1. Introduction to LongCat Video

LongCat Video represents a groundbreaking development in AI video generation technology. This MIT-licensed video foundation model has been released as an open-source solution, making advanced video generation accessible to developers and creators worldwide. The model excels at creating high-quality, long-form videos that maintain consistency throughout their duration.

What sets LongCat Video apart is its ability to handle multiple generative tasks within a single unified system. Whether you need to create videos from text descriptions, animate static images, or extend existing video clips, LongCat Video provides a comprehensive solution that rivals commercial alternatives.

2. What is LongCat Video - A Unified Foundation Model

LongCat Video is a unified foundational video generation model that combines multiple capabilities into one cohesive system. Unlike traditional video generation models that focus on single tasks, LongCat Video supports three primary generation modes:

• Text-to-Video Generation: Create videos directly from text prompts
• Image-to-Video Generation: Animate static images into dynamic video content
• Video Continuation: Extend existing videos seamlessly with consistent style and motion

This unified approach means you only need one model to handle all your video generation needs, simplifying workflows and reducing the complexity of managing multiple specialized models.

3. Model Architecture and Parameters

LongCat Video is built with 13.6 billion parameters, making it a substantial yet efficient model. The architecture combines transformer-based diffusion modeling with reinforcement learning techniques to achieve high-quality output.

The parameter count is optimized to balance quality with accessibility. While 13.6 billion parameters is significant, the model is designed to run on hardware that many researchers and developers can access, particularly those with high-end GPUs like the H100 with 80GB of VRAM.

4. Three Generation Modes Explained

Text-to-Video

The text-to-video mode allows you to describe a scene in natural language, and LongCat Video will generate corresponding video content. The model understands complex descriptions including lighting, composition, movement, and style preferences.

Image-to-Video

With image-to-video generation, you can bring static images to life. Provide a starting image and optional prompts, and the model will animate the scene while maintaining visual consistency with the original image.

Video Continuation

Perhaps the most impressive feature is video continuation. You can provide an existing video clip and have LongCat Video seamlessly extend it, maintaining the same style, characters, and motion patterns throughout the extended duration.

5. Long Video Generation Capability

One of the most significant achievements of LongCat Video is its ability to generate minutes-long videos in a single generation. Traditional video generation models are typically limited to 5-10 seconds of output, often suffering from color drift and temporal inconsistency as the video progresses.

LongCat Video addresses these limitations through specialized training for video continuation. The model can generate coherent videos lasting 3-4 minutes or even longer while maintaining consistent colors, clear motion, and coherent storytelling throughout.

6. Open Source and MIT License

LongCat Video is released under the MIT license, making it freely available for both personal and commercial use. This open-source approach provides several benefits:

• Full access to model weights for local deployment
• Freedom to modify and fine-tune for specific use cases
• No licensing fees or usage restrictions
• Community-driven improvements and extensions

The open-source nature significantly lowers barriers for researchers and developers who want to experiment with or build upon this technology.

7. Diffusion Transformer Framework

Architecturally, LongCat Video is built on a diffusion transformer framework. This combines the generative power of diffusion models with the attention mechanisms of transformers, resulting in high-quality video generation with excellent temporal coherence.

The framework uses single-stream 3D transformer blocks that process both spatial (within-frame) and temporal (across-frame) information simultaneously, enabling the model to understand and generate coherent motion patterns.

8. 3D Transformer Blocks with Attention Layers

The core of LongCat Video's architecture consists of 3D transformer blocks equipped with both self-attention and cross-attention layers. These components serve distinct purposes:

• Self-Attention Layers: Allow the model to understand relationships between different parts of the video, both spatially within frames and temporally across frames
• Cross-Attention Layers: Enable the model to incorporate conditioning information from text prompts or reference images into the generation process

This dual-attention mechanism ensures that generated videos are both internally consistent and faithful to the provided prompts.

9. 3D Rotary Positional Embedding

For spatial and temporal understanding, LongCat Video employs 3D rotary positional embedding (RoPE). This technique helps the model understand:

• Where elements are positioned within each frame
• How those positions relate across different frames
• The temporal sequence of events in the video

The 3D nature of the positional embedding is crucial for video generation, as it allows the model to reason about time as a first-class dimension alongside the two spatial dimensions.

10. Video Compression with VAE

Video input and output in LongCat Video is compressed through a VAN 2.1 VAE (Variational Autoencoder). This compression serves multiple purposes:

• Reduces computational requirements by working in a lower-dimensional latent space
• Preserves important visual information while discarding redundant data
• Enables efficient processing of high-resolution video content

The VAE ensures that the model can handle video generation efficiently without sacrificing quality in the final output.

11. T5 Text Encoder for Multilingual Support

LongCat Video uses the T5 text encoder for processing text prompts, enabling multilingual prompt understanding. This means you can provide prompts in multiple languages, and the model will understand and respond appropriately.

The text encoder converts natural language descriptions into embeddings that the video generation model can use for conditioning, ensuring that the generated videos accurately reflect the described scenes.

12. KV Caching for Efficiency

Efficiency is enhanced through KV (key-value) caching, which allows the model to reuse computed condition features during generation. This optimization:

• Reduces redundant computations
• Speeds up the generation process
• Enables longer video generation without proportionally increasing computation time

KV caching is particularly important for video continuation, where previous frames' features can be efficiently reused when generating new frames.

13. Block Sparse Attention (BSA)

LongCat Video implements Block Sparse Attention (BSA) to maintain computational efficiency while generating high-quality videos. BSA works by:

• Retaining only the most relevant token interactions
• Reducing memory usage during attention computation
• Enabling processing of longer sequences without running out of memory

This technique achieves up to 10x computation reduction with minimal quality loss, making it possible to generate longer videos on available hardware.

14. Multi-Reward GRPO Training

The model is trained using multi-reward GRPO (Group Relative Policy Optimization) which balances three key aspects:

1. Visual Quality: Ensuring frames are clear, detailed, and visually appealing
2. Motion Realism: Making sure movements appear natural and physically plausible
3. Text-Video Alignment: Maintaining fidelity between the text prompt and generated content

This multi-reward approach prevents the model from over-optimizing for any single metric, resulting in well-balanced output that excels across all dimensions.

15. Coarse-to-Fine Generation Process

LongCat Video uses a coarse-to-fine generation process for efficient and high-quality output:

Coarse Stage:

• Generates video at lower resolution (480p)
• Lower frame rate (15 fps)
• Establishes core composition and motion
• Faster generation for initial preview

Fine Stage:

• Upscales to higher resolution (720p)
• Increases frame rate (30 fps)
• Adds texture, sharpness, and fine details
• Corrects distortions and improves color accuracy

This two-stage approach balances generation speed with final quality.

16. Tackling the Drift Problem

A major challenge in long video generation is drift - where visual elements gradually change over time due to accumulated errors. LongCat Video addresses this through:

• 3D Attention with RoPE: Better temporal understanding
• Video Continuation Pre-training: Specialized training on extending existing videos
• KV Caching: Maintaining consistent features across generation
• Multi-reward Training: Preventing gradual quality degradation

These techniques enable the generation of minutes-long videos without the characters, colors, or scenes drifting from their original appearance.

17. Benchmark Performance

LongCat Video achieves impressive results on industry benchmarks such as VBench. According to published benchmarks, the model ranks among the top open-source video generation models and competes favorably with commercial alternatives.

The model particularly excels at:

• Long-form coherence
• Motion quality
• Visual consistency
• Text-video alignment

These benchmark results validate that the open-source community now has access to professional-grade video generation capabilities.

18. VRAM Requirements

Running LongCat Video requires significant VRAM. Based on practical testing:

• Minimum: 40GB VRAM for basic operation
• Recommended: 80GB VRAM (H100) for optimal performance
• Generation: Approximately 42GB VRAM during active generation

For users with limited hardware, cloud computing platforms offer access to high-VRAM GPUs that can run the model effectively.

19. Generation Time and Quality

Generation times for LongCat Video vary based on video length and quality settings:

• 6-second video: Approximately 12-17 minutes total for all three quality versions
• Raw generation: Fastest, lower quality
• Distilled version: Medium speed, good quality
• Refined version: Slowest, production-grade quality

The refined version, while taking longer, produces 720p output at 30fps with excellent texture, sharpness, lighting, and color accuracy.

20. Three Output Versions

LongCat Video generates three versions of each video:

Raw Diffusion Output

• 480p resolution at 15fps
• Captures core composition and motion
• Softer textures, occasional noise
• Foundation clip before refinement

Distilled Version

• Uses classifier-free guidance distillation
• Only needs 16 inference steps
• Much faster generation
• Smoother frame quality

Refined High-Quality Version

• 720p resolution at 30fps
• Production-grade quality
• Improved texture, sharpness, lighting
• Uses BSA for efficient fine detail restoration

21. Video Continuation Feature

The video continuation feature allows you to create infinitely long videos by:

1. Generating an initial clip
2. Using that clip as input for the next generation
3. Providing new prompts to guide the continuation
4. Repeating the process as needed

This enables the creation of extended narratives and long-form content that would be impossible with single-shot generation.

22. Introduction to LongCat Image

Alongside LongCat Video, there is LongCat Image - a powerful image generation and editing model. This 6 billion parameter model punches well above its weight class, matching or exceeding much larger models on various benchmarks.

LongCat Image is designed to tackle the biggest challenges in AI image generation:

• Multilingual text rendering
• Photo realism
• Deployment efficiency
• Developer accessibility

23. LongCat Image Parameters and Architecture

LongCat Image is built with 6 billion parameters using a DIT (Diffusion Transformer) architecture. Despite being relatively small, it achieves performance comparable to 20+ billion parameter mixture-of-expert models.

The model features:

• Two types of transformer blocks (MM-DiT and Single-DiT)
• MROP for spatial information management
• Vision-language module for understanding inputs
• Efficient training with progressive pre-training and fine-tuning

24. Bilingual Support - Chinese and English

LongCat Image is a bilingual model supporting both Chinese and English:

• Understands prompts in either language
• Excels at rendering Chinese characters in images
• Industry-leading coverage of the Chinese dictionary
• Accurate English text rendering as well

This bilingual capability makes it particularly valuable for creating content that needs accurate text in either language.

25. Text Rendering Capabilities

One of LongCat Image's standout features is its text rendering capability:

Chinese Text:

• Superior accuracy in rendering Chinese characters
• Handles complex traditional characters
• Industry-leading dictionary coverage
• Stable text generation without gibberish

English Text:

• Accurate rendering of words and sentences
• Suitable for posters, advertisements, branded content
• Readable text without common AI text artifacts

26. Image Editing Capabilities

LongCat Image includes powerful editing capabilities through its Edit model:

• Object Addition/Removal: Add or remove elements from existing images
• Style Transfer: Convert images to different artistic styles
• Background Changes: Replace backgrounds while keeping subjects consistent
• Outfit Changes: Modify clothing while maintaining face consistency
• Multi-Step Editing: Perform multiple edits in a single prompt

The editing model maintains visual consistency and understands scene context for natural-looking modifications.

27. Photo Realism in Image Generation

LongCat Image achieves remarkable photo realism through:

• Innovative data strategy during training
• Reinforcement learning fine-tuning (GRPO and DPO)
• Progressive training stages
• Multi-stage quality optimization

Generated images feature accurate lighting, natural textures, and professional composition that can be difficult to distinguish from real photographs.

28. Apache 2.0 License

LongCat Image is released under the Apache 2.0 license, which provides:

• Free use for commercial and personal projects
• Access to intermediate checkpoints
• Full training code availability
• Complete tool chain for customization

This open-source ecosystem significantly lowers barriers for research and development.

29. Installation and Usage

LongCat Video Installation

# Clone the repository
git clone [repository-url]

# Create virtual environment
conda create -n longcat python=3.10
conda activate longcat

# Install PyTorch
pip install torch torchvision

# Install requirements
pip install -r requirements.txt

# Download model via Hugging Face CLI
huggingface-cli download [model-name]

LongCat Image Installation

# Clone the repository
git clone [repository-url]

# Install dependencies
pip install -r requirements.txt

# Launch Gradio demo
python app.py

Both models provide scripts for text-to-video/image generation and various generation modes.

30. Conclusion and Future Implications

LongCat Video and LongCat Image represent significant milestones in open-source AI generation:

LongCat Video:

• First open-source model for minutes-long coherent video generation
• Unified architecture handling multiple video tasks
• Production-grade quality rivaling commercial solutions

LongCat Image:

• Efficient 6B parameter model with 20B+ level performance
• Superior bilingual text rendering
• Comprehensive generation and editing capabilities

These models demonstrate that high-quality AI generation is becoming democratized through open-source development. The ability to generate long, coherent videos and edit images with precision opens new possibilities for content creators, researchers, and developers.

The significance extends beyond current capabilities:

• Long-term coherence proves memory persistence can be learned
• Clean architecture provides foundation for future improvements
• Time as a first-class dimension enables new AI applications

Whether you're creating cinematic content, animated art, marketing materials, or extending existing footage, LongCat Video and LongCat Image provide powerful, accessible tools for the future of AI-generated media.