Kling Omni Video O1: Multi-Modal Video Generation Model

Edited by Segmind Team on January 21, 2026.

---

What is Kling Omni Video O1?

Kling Omni Video O1, developed by WaveSpeedAI, stands out as a versatile multi-modal AI model that produces videos from reference objects such as characters, products, or even scenes. What sets it apart from conventional text-to-video models is its superior identity preservation, which ensures facial traits, attire, and object details remain consistent throughout every frame. Kling Omni Video O1 is ideal for character-focused stories, product showcases, and any creative video process needing seamless visual consistency. Furthermore, it processes input videos and images to craft novel narratives or visuals, all while preserving the originality of the subjects.

Key Features

• Multi-reference identity preservation works to maintain facial traits, clothing, and accessories across video frames.
• Multi-angle viewpoint support generates videos from varied camera angles and perspectives.
• Text-driven motion control directs scene progression through natural language prompts.
• Reference video continuation extends existing footage with new scenarios while preserving context.
• Flexible aspect ratios supports 16:9, 9:16, 1:1, and auto-detection for platform-specific outputs.
• Element-level control targets specific objects or subjects within complex scenes.
• Audio retention option preserves or removes original audio based on workflow needs.

Best Use Cases

• Content Creation: It generates character-consistent video series where the main characters seamlessly maintain appearance across episodes without reshoots.

• Product Marketing: It can be utilized to showcase products in multiple environments and angles while maintaining brand-accurate details and colors.

• E-commerce: It can create dynamic product videos showing items from different perspectives with consistent lighting and texture.

• Game Development: It can easily create prototype character animations and cinematics with consistent character models before full production.

• Social Media: It can be brought in to produce platform-optimized content (vertical shorts, square posts) featuring recognizable subjects or brand mascots.

Prompt Tips and Output Quality

Effective prompting strategies:

• Be action-specific: Use prompts that explain the action with clarity: instead of "person walks," write "person walks briskly through a sunlit forest, dodging low branches."
• Reference continuity: Mention context from the reference video: "Continuing from the rooftop scene, the character descends the fire escape."
• Control pacing: Use temporal language like "gradually," "suddenly," or "smoothly transitions" to guide motion flow.
• Describe camera movement: Include directives like "camera pans left revealing" or "zoom into character's expression."

Parameter impact:

• Duration: 5-second clips work for quick cuts and social media; 10 seconds allow complex actions to complete naturally.
• Image URLs: Multiple style references improve consistency: use 2-3 images that show the subject from different angles.
• Elements array: Specify target objects when there are complex scenes, to ensure the model prioritizes correct subjects.
• Aspect ratio: Match it with the platform: 16:9 for YouTube, 9:16 for TikTok/Reels, 1:1 for Instagram feed.

FAQs

What makes Kling Omni Video O1 different from other text-to-video models?
Kling Omni Video O1 uses reference videos and images to preserve subject identity across frames, while most models generate videos from text. This feature maintains a consistent appearance of characters and products, making Kling Omni Video O1 an ideal option to create assets for branded content and serialized storytelling.

Can I use this model for commercial projects?
Yes, Kling Omni Video O1 is API-ready for commercial integration; therefore, generated videos can be used in marketing, product demos, and content creation workflows.

How does identity preservation work technically?
The model extracts detailed feature embeddings from reference inputs, such as facial structure, clothing patterns, and object textures, and applies these constraints during video generation to maintain visual coherence across all frames.

What's the optimal number of reference images?
2-3 high-quality images showing the subject from different angles produce the best results. Using more images will further improve consistency but may increase processing complexity.

Does the model support custom camera movements?
Yes, if you describe camera motion in your prompt, such as "camera orbits around the subject" or "dolly zoom effect," this will guide the model's cinematography.

Can I extend existing video footage?
It is possible to extend an existing video's length by providing a reference video URL and describing what happens next in the prompt; the model continues the scene while maintaining visual consistency with the original footage.