可灵 AI

1. Text-to-video: Generates videos from natural-language descriptions, with control over characters, environments, actions, camera movement, and visual style.
2. Image-to-video: Animates static images containing people, products, landscapes, and other subjects.
3. Motion Brush: Specifies which areas of the frame should move and their approximate direction, reducing the amount of movement left entirely to the model.
4. Camera controls: Controls camera movements such as push-ins, pull-backs, and pans, helping the result match the intended cinematic language.
5. Video extension: Continues an existing generation to create longer, more continuous shots.
6. Subject library: Saves people, characters, or objects as references for future generations, helping maintain subject consistency across different shots.
7. Video enhancement: Provides quality enhancement and basic post-production features such as subtitles and music, reducing the need to switch tools after generation.
8. International Kling AI: Offers a version for overseas users that supports image and video creation with prompts in multiple languages.

Kling’s web interface is less complicated than it may appear.

The main workflow is simply to choose a model, enter a prompt, and adjust the duration, aspect ratio, and generation mode.

I started with:

“A traditional Chinese sailing ship travels across the sea at sunset, with light and shadow shimmering across the waves.”

I then selected the high-quality mode.

After a few minutes, it produced a video lasting around five seconds.

At first glance, the texture of the water and sunset looked genuinely good.

The ship’s movement also did not show any obvious violations of basic physical logic.

These kinds of large-scale scenes, natural environments, and slow movements have consistently been areas where Kling performs well.

The problems become more noticeable with people.

I asked a character to pick up a cup from a table, take a sip, and put it back down.

The model understood the general action, but the fingers, the cup’s position, and its contact with the mouth occasionally looked unnatural.

Kling is currently better at understanding that “this person is drinking water” than reliably executing a precise sequence such as:

“Hold the cup with the left hand, raise it to the mouth, pause for two seconds, and return it to its original position.”

That difference may still be acceptable for ordinary short-form videos.

For advertising demonstrations or continuous cinematic action, however, it significantly affects the percentage of usable outputs.

Image-to-video is generally easier to control than pure text-to-video.

If you already have a character or product image, uploading it first and then animating it makes the composition and subject more likely to match your expectations.

Even then, the result is not completely reliable.

When a person turns their head, becomes obstructed, or makes a large movement, their face and clothing may still change.

The subject library can improve consistency across shots, but it does not guarantee that a saved character will have exactly the same face in every future generation.

Queueing also affects the experience.

Free users may face longer waits during peak periods.

Sometimes progress moves quickly, while at other times even a short video takes noticeably longer.

Tasks may occasionally fail or remain stuck without visible progress.

Paid plans receive better priority, but AI video generation still takes considerably longer than image generation.

If you have a firm delivery deadline, you cannot assume that “three minutes per video means ten videos will take half an hour.”

You need to allow extra time for failures, regenerations, and queueing.

Pros

• Works well with Chinese prompts: There is no need to translate every description into English before generating.
• Strong performance with natural scenes: Water, skies, animals, and large-scale motion are more likely to produce good results.
• Practical image-to-video: Existing character and product images provide more control than generating from scratch.
• A solid set of motion controls: Includes motion brushes, camera controls, extensions, and related features.
• Accessible within China: Users do not need to navigate overseas services to try mainstream AI video generation.
• Frequent updates: New models and control features continue to be added.

Cons

• Fine movements remain unreliable: Fingers, object interactions, and complex character actions can produce errors.
• Video consumes Inspiration Credits quickly: Repeated generation is often the real source of high usage.
• Character consistency is not guaranteed: Consecutive shots still require individual review.
• Queues occur during peak periods: This is especially noticeable for free users.
• Outputs are unpredictable: Repeating the same prompt can produce substantially different results.
• It cannot replace complete film production: Editing, sound, performance, and precise shot control still require other tools or human work.

Best for / Not ideal for

Best for

• Short-form video creators: Useful for establishing shots, visual sequences, and footage that would be difficult to film in reality.
• AI video enthusiasts: Offers an accessible way to try text-to-video and image-to-video generation.
• Advertising and marketing teams: Useful for rapidly testing creative directions, product moods, and visual concepts.
• Film pre-production teams: Suitable for concept videos, animated storyboards, and shot previews.
• People with existing image assets: Product and character images can be extended into dynamic content.

Not ideal for

• People who require every movement to be exact: Hands and complex object interactions are still not consistently reliable.
• People creating long, continuous stories: Character and scene consistency across multiple shots requires substantial manual maintenance.
• People who cannot accept unusable generations: The same request often needs several attempts before producing a usable result.
• High-volume users with very limited budgets: Inspiration Credits can disappear quickly during batch production.
• Teams with strict deadlines: Queues, failures, and regenerations make production time difficult to predict precisely.

Kling AI is no longer merely a Chinese model used for comparisons with Sora.

For ordinary creators, its more practical significance is that AI video generation has finally become a tool they can open and use whenever they need it.

It can generate videos from Chinese prompts.

If you already have an image, you can animate it directly.

If the result is unsatisfactory, you can adjust the motion, switch models, or generate it again.

This workflow is already practical enough for creating short-form video assets, advertising concepts, and animated storyboards.

However, there is still a clear gap between this and “an ordinary person typing one sentence and producing a movie.”

Character movements can fail, subjects may drift in appearance, and consecutive shots need to be checked repeatedly.

Once you begin using it seriously, the main cost is not the membership fee. It is the Inspiration Credits consumed by failed attempts and repeated generations.

There is no need to purchase a high-tier membership when using it for the first time.

Start with the free allowance and run three tests: one natural scene, one clip involving human movement, and one image-to-video generation using your own picture.

Then see which type most closely matches your actual needs.

If producing one usable shot takes only one or two attempts on average, the Gold membership can already support a substantial amount of work.

If a five-second shot regularly requires five or six attempts, calculate your true monthly Inspiration Credit consumption before moving to a higher tier.

Whether Kling genuinely belongs in your workflow should not be judged by the platform’s most polished official demos.

What matters is this: Out of ten generations using your own prompts, how many results are actually usable?