Model overview
Capybara is a unified visual creation framework designed to handle multiple content generation and editing tasks within a single architecture. Built by xgen-universe, it combines advanced diffusion models and transformer architectures to deliver high-quality visual synthesis capabilities. Unlike models focused on single tasks, this framework supports text-to-video, text-to-image, instruction-based video-to-video, and instruction-based image-to-image operations through one cohesive system. The distributed inference support enables efficient processing across multiple GPUs, making it practical for production environments.
Model inputs and outputs
Capybara accepts diverse input types depending on the task: text prompts for generation tasks, images or videos paired with text instructions for editing operations, and reference images for guided creation. The model produces high-fidelity visual outputs with temporal coherence for video tasks and precise control over specific visual elements like lighting, composition, and style for image operations.
Inputs
- Text prompts for describing desired visual content or editing instructions
- Images for instruction-based image editing and in-context generation
- Videos for instruction-based video editing and temporal manipulation
- Reference images for style transfer and conditional generation
- CSV files containing batch instructions and media paths for processing multiple samples
Outputs
- Generated images in 720p or custom resolutions with controlled aspect ratios
- Generated videos with up to 81 frames and natural motion coherence
- Edited images with local or global modifications including style changes and background replacement
- Edited videos with temporally consistent transformations that preserve identity and structure
Capabilities
The framework handles text-to-image synthesis with diverse artistic styles and photorealistic rendering. For video generation, it produces temporally coherent content with natural motion across both realistic and stylized aesthetics. Instruction-based editing enables local modifications like expression control or global changes such as time-of-day shifts and style transformations. Multi-turn sequential edits demonstrate the system's capacity for complex visual narratives. In-context operations include subject-conditioned generation, image-to-video conversion, and reference-driven editing that maintains visual consistency across transformations.
What can I use it for?
Content creators can use this framework for rapid prototyping of visual ideas across multiple formats without switching between specialized tools. Marketing teams could generate diverse product variations and lifestyle imagery at scale. Video production workflows benefit from instruction-based editing for quick iteration on motion, lighting, and style without manual compositing. Fashion and design industries can leverage style transfer and conditional generation for exploring variations within branded aesthetics. The batch processing capability through CSV files enables workflow automation for teams handling hundreds of visual assets.
Things to try
Experiment with multi-turn editing sequences to understand how the system maintains context across sequential modifications. Test the rewrite instruction feature with identical prompts to observe how instruction refinement improves output quality. Compare in-context generation with subject-conditioned video generation to explore consistency preservation when introducing new visual elements. Use reference-driven editing to establish visual style anchors, then apply the same style to completely different content to understand transfer capabilities. Explore the tension between detailed instruction precision and creative freedom by gradually simplifying or expanding your edit descriptions.
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