# Kling Omni Multimodal Video

> 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: `adu2021/kling-omni-multimodal-video` (Agent Skill)
- Install (CLI): `npx skillmds@latest add adu2021/kling-omni-multimodal-video`
- Raw SKILL.md: https://api.skillmd.com/api/skills/adu2021/kling-omni-multimodal-video/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- License: MIT
- Author: adu2021 (https://skillmd.com/u/adu2021)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/adu2021/kling-omni-multimodal-video

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## 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.

## Implementation Notes

Design prompt enhancer to interpret and unify diverse inputs. Build unified diffusion transformer with shared visual-textual embedding space. Implement multimodal super-resolution with adaptive conditioning. Follow progressive training strategy: pre-training, finetuning, alignment, distillation. Optimize inference through model distillation.

## References

- Original paper: Kling-Omni (Dec 2025)
- Multimodal generation frameworks
- Diffusion transformers for video