Kling 3.0 is a next-generation unified multimodal model designed to push the boundaries of AI-driven storytelling. It introduces a "native" framework that integrates text, image, and video references into a single generation process. The model focuses on long-form narrative continuity, featuring an "AI Director" for multi-shot cinematic sequences and a native cross-modal audio engine for deep audio-visual coherence.
Kling 3.0 Video
Key Features
- Multi-Shot Scenes: Unlike older models, Kling 3.0 can generate a full movie scene with multiple cuts and different camera angles in a single generation.
- Complex Motion: The model excels at high-difficulty physics and rapid movements, such as sports or fast-paced action, while maintaining natural-looking motion.
- Cinematic Effects: It supports sophisticated camera language like dolly zooms and lighting shifts (e.g., changing from natural light to a blue "horror" tint) triggered by prompt instructions.
- Subject Anchoring: Improved "spatial awareness" keeps subjects correctly positioned, such as keeping a rider physically attached to a moving animal (dragon).
Technical Capabilities
- Modalities: T2V, I2V
- Resolution: HD
- Ratios: 1:1, 16:9, 9:16
- Durations: Supports 3 to 15 seconds
- Language Support: Native support for English, Chinese, Japanese, Korean, and Spanish.
Kling 3.0 Image
Kling IMAGE 3.0 is a flagship static visual creation model designed to redefine cinematic storytelling through still frames. Moving beyond simple text-to-image, it focuses on high-fidelity narrative expression, utilizing advanced multimodal reasoning to ensure precise alignment with complex creative instructions. It is specifically engineered for professional workflows, including storyboard creation, concept art, and brand-consistent visual design.
Key Features
- Narrative Aesthetic Engine: A new data engine that deconstructs audiovisual elements (lighting, composition, emotion) to merge macro-narrative atmosphere with fine scene details.
- Batch Optimization: Enables unified style adjustments across multiple images, significantly improving efficiency for repetitive tasks and large-scale visual systems.
- Enhanced Detail Consistency: Improvements in the stability of textures and lighting reduce the "AI-generated feel" while maintaining key elements across an entire series.
Technical Capabilities
- Modalities: Text-to-Image (t2i), Image-to-Image (i2i)
- Resolution: Native 1K and 2K
- Variable Aspect Ratios: 16:9, 1:1, 4:3, 3:2, 2:3, 21:9, 9:16, 3:4, 2:3