Kling
An AI video generation model developed by Kuaishou, supporting text-to-video generation. It is one of the mainstream AI video generation models in the Chinese market.
Core Facts
Cross-shot character consistency has long been the most central technical challenge in AI video generation.
Representative technologies for converting static images into dynamic video (Image-to-Video) include the Runway Gen series, Kling, and Sora.
Starting in 2023, diffusion models such as Stable Video Diffusion and Runway Gen-2 began to dominate the video generation market.
Timeline (last 90 days)
SmophyAI's Video Studio uses Seedance and Kling to create AI videos, supporting text-to-video and video ads.
Both Kling and Higgsfield have released important updates since that list was made.
A developer systematically tested mainstream video editing models including Kling, Higgsfield, MiniMax, and Seedance 2.5 while building the AI video generation tool Genjutsu AI.
fal.ai offers highly optimized inference acceleration for mainstream open-source models such as Stable Diffusion, FLUX, and Kling
Creators can use Midjourney to generate keyframes, then use video generation tools such as Runway or Kling to add motion to the visuals, and finally layer in a soundtrack, compressing the entire process to a few hours.
Static visual assets can be generated with Midjourney, Seedream, or Flux, while dynamic video clips can be handled by Runway, Kling, or Sora.
Higgs Field aggregates access to multiple top AI video generation models on the market through a single API, including Google's Veo, Nano Banana, Kling, and its own self-developed Soul model.
AI-generated lightweight Blender geometry scenes can serve as hard spatial constraint assets for video models like Kling, resolving issues with camera perspective and movement paths.
Pure image diffusion video models lack three-dimensional spatial awareness and are prone to perspective collapse and incoherent movement paths when the camera orbits an object or multiple characters move.
Mainstream T2V models include the Runway Gen series, Sora, Kling, and Wan.
12 more timeline events
All Facts (20)
Cross-shot character consistency has long been the most central technical challenge in AI video generation.
80%VerifiedRepresentative technologies for converting static images into dynamic video (Image-to-Video) include the Runway Gen series, Kling, and Sora.
75%VerifiedStarting in 2023, diffusion models such as Stable Video Diffusion and Runway Gen-2 began to dominate the video generation market.
70%VerifiedThe GPU computing resources consumed to generate a single 10-second high-quality AI video may be tens of times greater than those required to generate a single image.
70%VerifiedKling 3.0 is a video generation model developed by Kuaishou that specializes in converting static images into smooth dynamic videos.
65%UnverifiedGenerative AI currently covers nearly all media formats including text, images, audio, and video, encompassing tools such as GPT-4, Claude, Midjourney, DALL-E, Stable Diffusion, Sora, Runway, and Kling.
60%Unverified快手的可灵（Kling）在国内率先实现了高质量长视频生成
60%UnverifiedSmophyAI's Video Studio uses Seedance and Kling to create AI videos, supporting text-to-video and video ads.
50%UnverifiedBoth Kling and Higgsfield have released important updates since that list was made.
50%Unverifiedfal.ai offers highly optimized inference acceleration for mainstream open-source models such as Stable Diffusion, FLUX, and Kling
50%UnverifiedCreators can use Midjourney to generate keyframes, then use video generation tools such as Runway or Kling to add motion to the visuals, and finally layer in a soundtrack, compressing the entire process to a few hours.
50%UnverifiedStatic visual assets can be generated with Midjourney, Seedream, or Flux, while dynamic video clips can be handled by Runway, Kling, or Sora.
50%UnverifiedHiggs Field aggregates access to multiple top AI video generation models on the market through a single API, including Google's Veo, Nano Banana, Kling, and its own self-developed Soul model.
50%UnverifiedPure image diffusion video models lack three-dimensional spatial awareness and are prone to perspective collapse and incoherent movement paths when the camera orbits an object or multiple characters move.
50%UnverifiedAI-generated lightweight Blender geometry scenes can serve as hard spatial constraint assets for video models like Kling, resolving issues with camera perspective and movement paths.
50%UnverifiedMainstream T2V models include the Runway Gen series, Sora, Kling, and Wan.
50%UnverifiedMainstream text-to-video models (including Seedance, Kling, Sora, etc.) are all based on the diffusion Transformer architecture; each generation is an independent random sampling, and two generations with the same prompt can produce significantly different results.
50%UnverifiedKling and Pika support describing camera motion through text prompts
50%UnverifiedMiniMax H3 has a degree of competitiveness compared to Sora, Runway Gen series, Kling, and others in terms of motion smoothness and stylized rendering.
50%UnverifiedMainstream locally deployable video generation models include ByteDance's CogVideoX and Kuaishou's Wan, while on the commercial API side there are Runway, Pika, Kling, and others.
50%