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Now fully launched and available for all public community usersMarch 2026

Wan 2.7 AI Video Generator

Wan 2.7 is Alibaba's latest video generation model with first/last frame control, multi-reference input, and instruction-based editing. Generate 5–15 second videos in 720P or 1080P.

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준수하지 않은 생성은 실패합니다. 이는 사기나 사이트 오류가 아닙니다. Seedance 2.0 모델은 최근 콘텐츠 제한을 강화했으며, 실제 사람 얼굴 입력(셀카, 초상화, 유명인)은 이제 모델 수준에서 차단됩니다. 폭력적 및 NSFW 콘텐츠도 거부됩니다. 그림, 애니메이션 캐릭터, AI 생성 얼굴 또는 스타일화된 아트워크로 멋진 동영상을 계속 만들 수 있습니다.
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Make songs from an idea, not a DAW.

Prompt, generate, extend, and export in one workflow built for creators who need music fast.

Output
2 tracks
Use case
Ads, video, social
AI video workflow

Wan 2.7 AI Video Generator -built for focused video creation

Wan 2.7 is Alibaba's latest video generation model with first/last frame control, multi-reference input, and instruction-based editing. Generate 5–15 second videos in 720P or 1080P.

Start creating
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Prompt
Live
Results
2 variations
Generating...
Scene setup

Video controls -match the model to the shot

Choose the right video mode, prompt, references, and model before generating so the first clip matches the intended scene.

Adjust settings
Original: 2:30
Original Track
Extended Track
Extended: 4:15
Clip workflow

Creator output -review and continue

Use the generated clip as a production draft, social video, storyboard beat, or starting point for another video iteration.

Generate video
Core capabilities of Wan 2.7

What makes Wan 2.7 stand out

Wan 2.7 brings first/last frame control, multi-reference input, instruction-based editing, and extended 15-second output to Alibaba's video generation line.

First / Last Frame Control

Set the opening and closing frame. Wan 2.7 generates the motion between them — precise cinematic control without complex prompting.

Lock in both the starting and ending composition before generation begins.
Ideal for product reveals, character transitions, and scene cuts.
Removes the guesswork of achieving a specific end state.

Multi-Reference Input

Feed up to 5 reference videos simultaneously. The model uses them to guide character, environment, and motion style.

Upload up to 5 reference videos to shape the output.
Maintains visual consistency across character and scene.
Useful for brand content, fashion, and product continuity.

Instruction-Based Editing

Edit existing videos with natural language. Change backgrounds, lighting, clothing, or style without re-generating from scratch.

Describe changes in plain text — no timeline editing required.
Swap backgrounds, update clothing, or adjust lighting with a prompt.
Iterate fast without losing the core motion of the original clip.

Up to 15 Seconds

Generate clips up to 15 seconds — 3× longer than earlier Wan models. Enough for a complete product shot or scene.

Output at 5, 10, or 15 seconds depending on the scene.
Supports 480P, 720P, and 1080P resolution.
Works in 16:9 landscape and 9:16 portrait orientations.

Made with Wan 2.7

Creator-shared community content posts from the X Social Platform

Vibrant Reddit Community Conversation Threads

Explore more models

Other Models to Try

Compare Wan 2.7 with other top video generation models available on this platform.

Kling v3.0

Native audio-enabled video generation with Kling's 3.x motion language.

Explore Our Curated Set of Associated AI Models

Kling v3.0 Pro

Professional-grade Kling 3.x output with higher fidelity and detail.

Explore Our Curated Set of Associated AI Models

Doubao Seedance 1.5 Pro AI Video Generator

Built to integrate with text-to-video and image-to-video creative workflows, Doubao Seedance 1.5 Pro is ByteDance’s purpose-built audio-native video generator. Use it to make audio-centric clips, locked first-frame compositions, smooth cross-frame transition sequences, and short videos up to 12 seconds long.

Explore Our Curated Set of Associated AI Models

Seedance 2.0 AI Video Generator

Launched in February 2026 by ByteDance, Seedance 2.0 is a purpose-built AI video generator engineered exclusively for extended clip lengths, tight motion control, and perfectly synchronized audio output. Here, you can explore text-to-video, first-frame image-to-video, and cross-frame start/end transition workflows, with support for clips up to 15 seconds in total runtime.

Explore Our Curated Set of Associated AI Models

Common.faqTitle

결정을 돕는 빠른 답변

1

What is Wan 2.7?

Wan 2.7 is the latest video generation model from Alibaba Tongyi Lab, released in March 2026. It upgrades Wan 2.6 with first/last frame control, up to 5 simultaneous reference video inputs, 9-grid image input, instruction-based video editing, and improved motion physics.
2

What is first/last frame control in Wan 2.7?

First/last frame control (FLF2V) lets you set both the opening and closing frames of a video. Wan 2.7 generates the motion between them. This gives you precise cinematic control — set a start position and an end position, and the model interpolates the action.
3

How long can videos be with Wan 2.7?

Wan 2.7 supports 2 to 15 seconds per clip. This is a significant improvement over earlier Wan models, which capped at around 5 seconds. On this platform, you can generate 5, 10, or 15-second clips.
4

What modes does Wan 2.7 support?

Wan 2.7 supports text-to-video, image-to-video, first/last frame video (FLF2V), and instruction-based video editing. On this page, text-to-video and image-to-video are available.
5

What resolutions does Wan 2.7 support?

Wan 2.7 supports 480P, 720P, and 1080P output. Both 16:9 landscape and 9:16 portrait aspect ratios are supported.
6

Is Wan 2.7 open source?

Wan 2.1 was fully open source under Apache 2.0. Wan 2.7's open-source release status had not been officially confirmed at launch — check the Alibaba Wan GitHub (github.com/Wan-Video) for the latest.
7

How does Wan 2.7 compare to Wan 2.6?

Wan 2.7 adds first/last frame control, 9-grid multi-image input, up to 5 reference video inputs, and instruction-based editing — none of which were in Wan 2.6. Maximum duration increased to 15 seconds. Motion physics accuracy and character consistency were also improved.

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