| name | kling-omni-multimodal-video |
| title | Kling-Omni: Unified Multimodal Video Generation with Visual Language Paradigm |
| version | 0.0.2 |
| engine | skillxiv-v0.0.2-claude-opus-4.6 |
| license | MIT |
| url | https://arxiv.org/abs/2512.16776 |
| keywords | ["video-generation","multimodal","vision-language","diffusion-transformer","unified-model"] |
| description | Unify video generation, editing, and reasoning through Multimodal Visual Language (MVL) paradigm processing text instructions, reference images, and video contexts in shared embedding space. Employ prompt enhancer, omni-generator with diffusion transformer, and multimodal super-resolution. Support diverse user inputs with in-context generation and reasoning-based editing. |
Skill Summary
Kling-Omni introduces Multimodal Visual Language (MVL) as unified interaction paradigm for video generation, processing text instructions, reference images, and video contexts through shared embedding space. The system comprises three components: Prompt Enhancer (PE) interpreting diverse inputs to infer creative intent, Omni-Generator (diffusion transformer processing visual and textual tokens jointly), and Multimodal Super-Resolution (cascaded refinement). Progressive multi-stage training includes pre-training, supervised finetuning with complex MVL inputs, DPO for alignment, and distillation reducing inference cost from 150 to 10 function evaluations.
When To Use
- Building unified video generation systems supporting multiple input modalities
- Projects requiring both video generation and editing capabilities
- Scenarios where flexible user inputs (text, images, context) enhance creativity
- Research on multimodal generation beyond simple text-to-video
When NOT To Use
- Simple text-to-video-only applications where unified complexity adds overhead
- Real-time inference with strict latency requirements (multiple stages)
- Domains not benefiting from multimodal reasoning
- Scenarios with limited training data for complex MVL inputs
Core Technique
Three key components enable unified multimodal video generation:
1. Prompt Enhancer (PE)
MLLM module that interprets diverse user inputs (text, images, video context) and "infers the creator's specific creative intent and reformulates the prompt accordingly." This bridges diverse input modalities to coherent generation instructions.
2. Omni-Generator
Core diffusion transformer "processing visual and textual tokens within a shared embedding space, enabling deep cross-modal interaction." This unified architecture handles:
- Text-to-video generation
- Image-guided video generation
- Context-aware video editing and continuation
3. Multimodal Super-Resolution
Cascaded refinement module enhancing high-frequency details while conditioning on original MVL signals, preserving multimodal information through refinement.
4. Training Strategy
Progressive multi-stage approach:
- Pre-training: text-video pairs
- Supervised fine-tuning: complex MVL inputs
- DPO: human alignment
- Distillation: reduce inference cost (150 → 10 NFEs)
Key Distinction
Unlike fragmented "expert models" for specific tasks, Kling-Omni unifies video generation, editing, and reasoning into single system capable of handling diverse user inputs and supporting in-context generation, reasoning-based editing, and multimodal instruction following.
