Wan 2.7 R2V model features
Core generation
- Reference-to-Video (R2V) — Core capability: upload a reference image and Wan 2.7 R2V generates a cinematic video that faithfully preserves the original composition, style, and subject details.
Editing and references
- Wan 2.7 Architecture — Alibaba's latest-generation video model, delivering significant improvements in motion quality, temporal consistency, and visual fidelity over Wan 2.1 and Wan 2.6.
Creative workflow
- Text-to-Video Support — Generate videos from detailed text prompts with advanced language understanding and faithful visual translation.
More capabilities
- Cinematic Motion Realism — Wan 2.7 R2V excels at natural character movement, realistic physics, and smooth camera transitions for professional-looking output. - 1080p High-Resolution Output — Download your videos in crisp Full HD quality, suitable for professional content creation, YouTube, and marketing campaigns. - Superior Style Preservation — When generating from reference images, Wan 2.7 R2V maintains the original visual style, color palette, and subject identity with industry-leading accuracy. - Multi-Aspect Ratio Support — Create videos in landscape (16:9), portrait (9:16), and square (1:1) formats optimized for every platform. - Open-Source Heritage — Built on Alibaba's commitment to open-source AI, with the Wan model family being one of the most widely adopted open-source video generation frameworks. - No Watermark, Free Access — Generate and download Wan 2.7 R2V videos instantly with no watermarks, no subscriptions, and no signup walls. - Fast Generation Speed — Optimized inference pipeline delivers quick video generation without compromising output quality.
最新のAIクリエイティブツール
AI画像ツールとワークフローを探索
モデル生成と最新の画像編集・拡張・背景生成ツールを組み合わせて、制作ワークフローを完成させましょう。
AI写真編集
スマートなレタッチ、スタイル保持、生成修復を備えたフル機能のAI画像編集スタジオ。
画像から画像 (Image to Image)
プロンプト指示により参考画像をスタジオクオリティで自在に変形・スタイル変換。
AI画像拡張 (Image Extender)
枠外のシーンを自然にAIで拡張・補完し、あらゆるアスペクト比にリサイズ。
AI背景削除
被写体を高精度に切り抜き、透過PNG画像を瞬時に作成・エクスポート。
AI消しゴムマジック
不要な人物やオブジェクトをなぞるだけで、背景を自然に補完・削除。
AI背景ジェネレーター
商品やポートレート写真のためにリアルなスタジオシーンや背景を自動生成。
AI年齢変化ツール
幼少期から高齢期までのリアルな顔立ちの変化をシミュレート。
AI結婚式カードジェネレーター
結婚式の招待状やカップルポートレートをオーダーメイドで生成。
AIインフルエンサージェネレーター
高品質なバーチャルモデルやSNS向けライフスタイルポートレートを生成。
リファレンス動画生成
静止画やポーズリファレンスからダイナミックなAI動画を生成。
Frequently asked questions about Wan 2.7 R2V
What is Wan 2.7 R2V?
Wan 2.7 R2V is Alibaba's latest AI video generation model, part of the Tongyi Wanxiang (通义万相) family. The "R2V" stands for Reference-to-Video — it specializes in transforming reference images into high-quality, cinematic videos while preserving the original image's composition, style, and subject details. It is the successor to Wan 2.1 and Wan 2.6, with significant improvements in motion quality and generation speed.
Is Wan 2.7 R2V free to use?
Yes, our Wan 2.7 R2V video generator is completely free. You can generate and download videos without watermarks, subscriptions, or mandatory signups.
What does R2V mean?
R2V stands for Reference-to-Video. It describes Wan 2.7 R2V's core capability — taking a reference image as input and generating a video that faithfully follows the reference's visual style, composition, and subject. This makes it ideal for product visualizations, character animations, and any scenario where you need precise visual control over the output.
How does Wan 2.7 R2V compare to Wan 2.1 and earlier versions?
Wan 2.7 R2V represents a major leap over Wan 2.1. Key improvements include significantly better motion realism with fewer artifacts, stronger temporal consistency (characters and objects remain coherent throughout the video), faster generation speed, and the specialized R2V capability for reference-guided video generation. Wan 2.1 was already a strong open-source model; Wan 2.7 R2V pushes the quality bar significantly higher.
How does Wan 2.7 R2V compare to Kling, Sora, and other AI video generators?
Wan 2.7 R2V competes in the top tier of AI video generators. Its unique strength is the R2V (Reference-to-Video) capability, which gives it a distinct advantage for use cases requiring precise visual control. In terms of overall video quality, motion realism, and prompt adherence, it stands alongside Kling 3.0 and other leading models. As an open-source framework, the Wan family also benefits from a large community of developers and continuous improvements.
What video resolution does Wan 2.7 R2V output?
Wan 2.7 R2V generates videos in 1080p (Full HD) resolution, suitable for professional use across YouTube, TikTok, Instagram, marketing campaigns, and creative projects.
Can I use Wan 2.7 R2V videos for commercial projects?
Yes, Wan 2.7 R2V-generated videos can be used for commercial purposes. Please review Alibaba's terms of service for the latest commercial usage rights and attribution requirements.
Does Wan 2.7 R2V support text-to-video generation?
Yes, in addition to its core R2V (Reference-to-Video) capability, Wan 2.7 R2V also supports standard text-to-video generation. You can generate videos from text prompts alone, or combine a text prompt with a reference image for the best of both worlds.
How long can Wan 2.7 R2V videos be?
Wan 2.7 R2V supports video generation up to 10 seconds per clip. For longer content, you can generate multiple clips and combine them using video editing software.
How do I write good prompts for Wan 2.7 R2V?
For best results, describe your scene in detail: subject, action, environment, camera angle, lighting, and style. When using a reference image, describe how you want the reference to be animated. Example: "The product in the reference image slowly rotating on a minimalist pedestal, soft studio lighting, cinematic close-up, smooth motion, photorealistic." Specific, vivid prompts consistently outperform vague ones.
What types of reference images work best with Wan 2.7 R2V?
For best results, use clear, well-lit images with distinct subjects and simple backgrounds. High-resolution images with good contrast produce better video output. Avoid images with heavy compression artifacts, extreme shadows, or overly complex backgrounds, as these can reduce the quality of the generated video.
Is Wan 2.7 R2V open source?
Wan 2.7 R2V is part of Alibaba's Wan model family, which has a strong open-source heritage. Previous Wan models (including Wan 2.1) were released as open-source, and the Wan series is one of the most widely adopted open-source video generation frameworks. Check Alibaba's official channels for the latest open-source release status of Wan 2.7 R2V.
What are the system requirements to use Wan 2.7 R2V?
There are no special system requirements. Wan 2.7 R2V runs entirely in the cloud — all you need is a web browser and an internet connection. No GPU, software installation, or powerful hardware is needed.
Who developed Wan 2.7 R2V?
Wan 2.7 R2V is developed by Alibaba's Tongyi Lab (通义实验室), the AI research division behind the Tongyi Wanxiang (通义万相) model family. The Wan series has been a leading force in open-source video generation since Wan 2.1 was first released in early 2025.
Ready to work with Wan 2.7 R2V?
Keep the selected model, original brief, settings, and review notes together before sharing a result.
Open generation workspaceWhat Wan 2.7 R2V is designed to handle
- reference-to-video work
- identity-conscious scene studies
- style continuity
Useful workflows for Wan 2.7 R2V
- reference-to-video work
- identity-conscious scene studies
- style continuity
How to use Wan 2.7 R2V
- Choose the Wan 2.7 R2V taskDecide whether the brief is video creation and planning and identify the single visual or planning outcome that matters most.
- Prepare inputs and constraintsUse the current workspace controls, keep references authorized, and record the input role described in the Wan 2.7 R2V case.
- Run a small comparisonKeep the prompt and settings together, then change one decision at a time so the comparison remains explainable.
- Review before reuseInspect the full result or structured text, proofread details, and complete rights, disclosure, and factual review before sharing.
Capabilities and target notes
- Modality
- Video
- Best for
- reference-to-video work, identity-conscious scene studies
- Creative task
- video creation and planning
- Available workflow
- reference-to-video with image and video inputs for subject and style continuityUse the workspace above to confirm the controls currently available for this model.
Limits and review checks for Wan 2.7 R2V
- Reference consistency is not identity verification.
- The exact live form determines which media combinations can be submitted.
What Wan 2.7 R2V is designed to handle
Wan 2.7 R2V is a video model available in PerchanceAI. a continuity brief that separates identity references from the scene and camera action. Use the model picker in the workspace to confirm the currently available option and controls.
- • Best suited to: video creation and planning.
- • Supported workflow: reference-to-video with image and video inputs for subject and style continuity.
Inputs and settings for Wan 2.7 R2V
Build a Wan 2.7 R2V request from the input notes rather than from a neighboring model's schema. The shared workspace exposes the current controls; preserve the source role and record the setting used for each comparison.
- • Assign every reference image or video a purpose before describing the action.
- • Keep voice, likeness, and source-media permissions documented.
Where Wan 2.7 R2V fits a creative workflow
The five cases below use distinct briefs for Wan 2.7 R2V. Media, when present, states its provenance and is not presented as a PerchanceAI-generated benchmark.
- • reference-to-video work
- • identity-conscious scene studies
- • style continuity
Review limits for Wan 2.7 R2V
Review a Wan 2.7 R2V result at its intended size, compare it with the original brief, and check identity, text, audio, factual detail, and source rights before sharing. Reference consistency is not identity verification.
- • Reference consistency is not identity verification.
- • The exact live form determines which media combinations can be submitted.
A repeatable Wan 2.7 R2V evaluation
A useful comparison keeps the prompt, input role, selected settings, and output review together. Change one variable at a time so a later decision can be traced back to the Wan 2.7 R2V brief.
- • Keep the original prompt beside the revised prompt.
- • Save the selected settings with the output review.
- • Do not infer an unsupported capability from a visual result.
Prompt examples for Wan 2.7 R2V
1. referenced person in a street
Wan 2.7 R2V: referenced person in a street; one focal subject, deliberate framing, coherent lighting or camera movement, restrained palette, and a clear review condition for video creation and planning.
2. style-preserving product move
Wan 2.7 R2V: style-preserving product move; one focal subject, deliberate framing, coherent lighting or camera movement, restrained palette, and a clear review condition for video creation and planning.
3. character entrance from a source clip
Wan 2.7 R2V: character entrance from a source clip; one focal subject, deliberate framing, coherent lighting or camera movement, restrained palette, and a clear review condition for video creation and planning.
4. voice-aware reference scene
Wan 2.7 R2V: voice-aware reference scene; one focal subject, deliberate framing, coherent lighting or camera movement, restrained palette, and a clear review condition for video creation and planning.
5. multi-asset campaign shot
Wan 2.7 R2V: multi-asset campaign shot; one focal subject, deliberate framing, coherent lighting or camera movement, restrained palette, and a clear review condition for video creation and planning.