Lynx AI by ByteDance: High-Fidelity Personalized Video Generation
Lynx AI by ByteDance creates short videos from a single input image and a prompt while keeping the person’s identity intact. It builds on a Diffusion Transformer (DiT) foundation with two lightweight adapters: an ID-adapter that turns ArcFace-based features into compact identity tokens, and a Ref-adapter that injects dense VAE features through cross‑attention to keep fine details consistent across frames. The result is strong identity preservation, steady motion, and prompt-following that feels natural and coherent across time.
What is Lynx AI by ByteDance?
Lynx AI by ByteDance focuses on identity‑faithful video generation from a single reference image. The system is oriented around two ideas: keep identity features clear and stable, and carry over local details that make the subject recognizable in every frame. To meet these goals, Lynx uses a DiT backbone and two adapters trained to condition the generator on identity and reference signals without heavy cost.
The ID‑adapter uses a Perceiver Resampler to convert features derived from ArcFace into a small set of tokens. These identity tokens are then used to condition the transformer at generation time. The Ref‑adapter integrates dense VAE features from a frozen reference pathway, using cross‑attention to inject texture and structure into all layers of the transformer. Combined, these two inputs stabilize identity and details while the DiT backbone handles temporal consistency.
Lynx was evaluated on a simple but balanced benchmark: 40 subjects and 20 neutral prompts, for a total of 800 cases. The results show strong identity resemblance and competitive prompt adherence, with high overall perceptual quality. The approach aims for consistent faces, stable lighting, and natural motion rather than flashiness. It is meant to be predictable and usable by a wide audience.
Key Capabilities
Identity Preservation
ID‑adapter conditioning keeps facial structure and traits stable across frames so the subject remains recognizable.
Fine Detail Retention
Ref‑adapter cross‑attention injects local features like texture, edges, and hair details to reduce drift.
Temporal Coherence
The DiT backbone supports consistent motion, lighting continuity, and stable content over time.
How Lynx AI Works
1) ID‑adapter
A Perceiver Resampler compresses ArcFace‑derived features into a small set of identity tokens. These tokens condition the transformer during generation, guiding the model to maintain the subject’s face and key traits across frames.
2) Ref‑adapter
Dense VAE features are fed through a frozen reference pathway. Cross‑attention layers inject these features at all transformer depths, helping the model keep hair strands, skin texture, and other small elements consistent.
3) DiT Backbone
The diffusion transformer structure governs the temporal process, keeping scene layout, lighting, and motion stable while following the prompt. The adapters condition each step with identity and reference cues.
4) Single‑Image to Video
Lynx starts from one image of the person and a short prompt. It produces a brief clip where the person moves naturally, with the same face and overall appearance, guided by the prompt content.
Quantitative Evaluation
Measurements on identity resemblance and perceptual quality indicate strong performance. The table below summarizes results across common metrics. Scores closer to 1.0 are better.
| Model | facexlib | Insightface | In-house | Prompt Follow | Aesthetic | Motion | Quality |
|---|---|---|---|---|---|---|---|
| SkyReels-A2 | 0.715 | 0.678 | 0.725 | 0.471 | 0.704 | 0.824 | 0.870 |
| VACE | 0.594 | 0.548 | 0.615 | 0.691 | 0.846 | 0.851 | 0.935 |
| Phantom | 0.664 | 0.659 | 0.689 | 0.690 | 0.825 | 0.828 | 0.888 |
| MAGREF | 0.575 | 0.510 | 0.591 | 0.612 | 0.787 | 0.812 | 0.886 |
| Stand-In | 0.611 | 0.576 | 0.634 | 0.582 | 0.807 | 0.823 | 0.926 |
| Lynx (ours) | 0.779 | 0.699 | 0.781 | 0.722 | 0.871 | 0.837 | 0.956 |
Prompts and Usage Tips
Guidelines
- • Use 20–30 words with concrete descriptors.
- • Describe clothing, textures, lighting, and shot type.
- • Avoid vague phrasing; be specific about the subject.
- • Keep a single primary goal per prompt for best consistency.
Example Prompts
Simple movement:
“A short walking clip, soft afternoon light, natural head turn, calm expression, mid‑shot.”
Outfit emphasis:
“Blue jacket with matte texture, neat collar, gentle sway, indoor lighting, shoulder‑level framing.”
Environment cue:
“Light breeze, golden hour, subtle background blur, steady pace, natural hand motion.”
Where Lynx AI Fits
Media and Marketing
Create short identity‑faithful clips for campaigns and product pages with consistent looks.
Education
Produce tutorial intros or presenter segments from a single portrait and prompt.
Pre‑visualization
Explore ideas quickly with reference‑driven motion tests while keeping identity stable.
Research and Tuning
Study adapter‑based conditioning and temporal behavior with a clear, simple baseline.
Demo Videos
Frequently Asked Questions
What input does Lynx AI require?
One clear image of the subject and a short prompt. Better lighting and a sharp face improve identity stability.
How is identity preserved?
Identity tokens from the ID‑adapter condition the transformer across steps, while the Ref‑adapter adds dense features at each layer.
How long are the outputs?
Short clips are typical. The approach can be extended, but the recommended range is brief segments for stable results.
Does Lynx AI edit existing videos?
The focus is single‑image to video. For full video editing, use dedicated editing pipelines designed for that task.
Can I specify clothing or background?
Yes. Add concrete descriptors to the prompt. Lynx follows clear cues about apparel, lighting, and framing.
What about bias and fairness?
Like all models, Lynx may reflect training data patterns. Use neutral prompts and review outputs carefully for your use case.
Start with Lynx AI by ByteDance
Visit Getting Started to set up inputs and prompts for identity‑faithful short clips.