Stand-In - Tencent WeChat Visual Open Source Lightweight Video Generation Framework

What is Stand-In

Stand-In is a lightweight, plug-and-play identity-preserving video generation framework from Tencent's WeChat Vision team. Focusing on preserving specific identity features in video generation, Stand-In can achieve excellent results in face similarity and naturalness by training only the additional parameters of the base model 1%.Stand-In supports a variety of application scenarios, including identity-preserving text-to-video generation, video generation for non-human subjects, stylized video generation, face-switching video, and pose-guided video generation. The framework is efficiently trained, high-fidelity, plug-and-play, and highly scalable, compatible with community models such as LoRA, and supports a variety of downstream video tasks.

Features of Stand-In

• Efficient training: Only the additional parameters of the base model 1% need to be trained, which drastically reduces the training cost compared to other methods.
• high fidelity: Outperforms in face similarity and video naturalness, effectively preserving identity without sacrificing video quality.
• plug-and-play: Can be easily integrated into existing text-to-video (T2V) models without complex adjustments.
• Highly scalable: Compatible with community models such as LoRA, it supports a variety of downstream video tasks such as stylized video generation, video face-swapping, and so on.
• Diversified application scenarios: It supports various application scenarios such as text-to-video generation for identity preservation, video generation for non-human subjects, and pose-guided video generation.

Stand-In's Core Benefits

• efficiency: Only the additional parameters of the base model 1% need to be trained, dramatically reducing training cost and time.
• high fidelity: Excels in face similarity and video naturalness, accurately preserving identity features while ensuring video generation quality.
• Ease of integration: Plug-and-play, seamlessly integrating into existing text-to-video (T2V) models without complex adjustments.
• compatibility: Highly compatible with community models such as LoRA, supports multiple downstream video tasks, and is highly extensible.
• Rich application scenarios: Covering a wide range of scenarios such as text-to-video generation for identity preservation, video generation for non-human subjects, stylized video generation, video face-swapping, and pose-guided video generation.

What is the official Stand-In website?

• Project website:: https://www.stand-in.tech/
• GitHub repository:: https://github.com/WeChatCV/Stand-In
• HuggingFace Model Library:: https://huggingface.co/BowenXue/Stand-In
• arXiv Technical Paper:: https://arxiv.org/pdf/2508.07901

Who Stand-In is for

• Video content creators: Use Stand-In to quickly generate high-quality, personalized video content, saving time and money on filming and post-production.
• Special effects producer for film and televisionStand-In provides efficient and natural identity-preserving video generation when identity replacement or special effects compositing is required, increasing production efficiency.
• Advertising and marketing practitioners: Can be used to create more engaging and targeted advertising videos that are more relatable and persuasive by generating videos with characters that resemble the target audience.
• game developer: Use Stand-In to quickly generate character-appropriate video content for gameplay animations or video promotions, enhancing gameplay immersion.
• Researchers and educators: In research projects or educational video production, it can be used to generate identity-specific demo videos to assist teaching or to present research results.
• Social Media Operators: Quickly generate video content that matches a brand's image or a specific theme for promotion and user interaction on social media platforms.