About Lynx AI by ByteDance

Lynx AI by ByteDance focuses on personalized video generation from a single image. It uses a Diffusion Transformer (DiT) with two light adapters—an ID-adapter and a Ref-adapter—to keep identity and fine details consistent across frames while following the prompt. The approach aims for clear faces, steady motion, and stable lighting.

The ID-adapter converts ArcFace-based facial features into compact identity tokens that condition the transformer. The Ref-adapter supplies dense VAE features through cross-attention at every transformer layer. Together, they reinforce identity fidelity and keep subtle traits intact while the DiT backbone maintains temporal coherence.

Core Technology

Adapters with DiT Backbone

Lynx AI by ByteDance couples a DiT backbone with two adapters optimized for conditioning signals:

  • • ID-adapter: Perceiver Resampler turns ArcFace features into identity tokens
  • • Ref-adapter: Dense VAE features injected via cross-attention
  • • DiT backbone: Temporal stability with prompt following
  • • Efficiency: Lightweight conditioning without heavy fine-tuning

Identity Conditioning

Identity tokens guide the generator to keep facial structure and key traits intact.

  • • ArcFace-derived embeddings
  • • Perceiver Resampler
  • • Compact token set
  • • Conditioning at generation time

Reference Pathway

A frozen pathway provides dense VAE features as reference for fine details.

  • • Dense features
  • • Cross-attention across layers
  • • Texture and edge cues
  • • Detail retention

Generation Capabilities

Prompt-Guided Appearance

Lynx responds to prompts about clothing, lighting, motion style, and framing while keeping identity stable:

  • • Clothing descriptors and colors
  • • Lighting tone and background hints
  • • Movement cues and pace
  • • Shot type and framing

Identity Stability

The adapters minimize drift so facial traits, hair patterns, and contours stay consistent from frame to frame.

  • • Consistent geometry
  • • Stable textures
  • • Natural lighting continuity
  • • Prompt adherence

Object Manipulation

Precise object editing with structural preservation:

  • • Object replacement and substitution
  • • Material and texture changes
  • • Size and scale modifications
  • • Environmental object additions

Scene Transformation

Global scene modifications with atmospheric control:

  • • Background and environment changes
  • • Lighting and weather modifications
  • • Artistic style transformations
  • • Seasonal and time-of-day alterations

Performance Characteristics

Consistency

Lynx aims for predictable outputs with reduced drift:

  • • Identity tokens at each step
  • • Cross-attention detail injection
  • • Stable motion cues
  • • Balanced prompt following

Motion Preservation

Temporal coherence is supported by the DiT backbone and adapter conditioning:

  • • Natural pacing
  • • Lighting stability
  • • Composition maintenance
  • • Frame-to-frame consistency

Inputs and Recommendations

Input

Single reference image
Short text prompt
Optional background hints

Recommended Specifications

Reference Quality

  • • Sharp, well-lit face
  • • Neutral expression works well
  • • Minimal occlusions
  • • Centered subject

Prompt Suggestions

  • • Use concrete descriptors
  • • Include lighting and framing
  • • One main intent per prompt
  • • Keep length concise

Workflow at a Glance

Single Image → Tokens

Extract identity features and build identity tokens with the ID-adapter.

  • • ArcFace features
  • • Perceiver Resampler
  • • Compact token set
  • • Conditioning hook

Reference Pathway → Features

Provide dense VAE features through a frozen path and cross-attend in all transformer layers.

  • • Dense feature maps
  • • Cross-attention injection
  • • Detail retention
  • • Frame consistency cues

Explore Lynx AI by ByteDance

Read the overview and then visit Getting Started to try prompts and inputs.

Getting StartedHome