Multimodal Visual Language (MVL)
Advanced MVL system integrates multimodal inputs including image references and video clips, enabling sophisticated editing and creative control through natural language.
將 創意化為現實
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surreal scene of a giant Fanta can pouring orange liquid like a waterfall through a miniature mountain
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A beautiful woman smiles while looking forward. She gently touches her lips with her fingers and gracefully blows a kiss forward. Her gesture is full of affection and charm — her lips softly puckered, and her hand flicks outward as pink heart-shaped bubbles float from her fingertips and drift through the air. Her hair sways slightly with the motion. The background is soft and romantic, such as a starry sky, a sunset, or blurred lights.
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Pour the syrup, letting it find its own path across the cake.
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Advanced MVL system integrates multimodal inputs including image references and video clips, enabling sophisticated editing and creative control through natural language.
Kling 2.1 achieves 182% win-loss ratio against Google Veo2 and 178% against Runway Gen-4 in image-to-video generation benchmarks.
Generate 4 different audio tracks and dialogues that perfectly match video scenes, adding immersive audio experiences to visual content.
Built on enhanced DiT with Kuaishou's advanced latent space encoding and optimized temporal modeling for superior motion understanding.
Trusted by over 22 million users worldwide with 65+ million videos and 175+ million images generated, proving real-world reliability.
AI-powered prompting assistant helps generate optimized descriptions for better results, accessible to users of all skill levels.
Multi-image reference technology analyzes and integrates diverse subjects from multiple uploaded images, enabling dynamic interactions between different characters and addressing visual consistency challenges.
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