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Private Try-On vs Linum

A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.

Private Try-On compared with Linum
DetailPrivate Try-On logo Private Try-OnLinum logo Linum
What it doesAI outfit preview from your photo and selected clothing Generative video models for animation
StageNot given Not given
FoundedNot given Not given
Based inCN US
Pricing modelFreemium Not given
PricingFree: 5 image credits (one-time for verified accounts). Starter: $2.99 USD once for 10 image credits. 50 Try-Ons: $9.99 USD once for 50 image credits. Pro: $24.99 USD/month for 500 image credits and 5 video credits. Pro Max: $45.99 USD/month for 1000 image credits and 15 video credits. Not given
Key features
  • Generate outfit previews combining a person photo with individual clothing images
  • Upload personal photos or use measurements mode to create a model
  • Use saved product photos without building a cloud wardrobe
  • Swap individual pieces to refine the complete look
  • Generate 10-second runway video clips (Pro plans)
  • Install as a progressive web app without app store
  • Download and save generated outfit previews
  • Edit background in generated previews
  • Pyramid-JiT (P-JiT) architecture with 32×32 token reduction
  • 4× more aggressive token reduction than Linum v2
  • Converges in 11× fewer samples than Linum v2
  • 2B-parameter model with frozen Qwen text encoder
  • Pixel-space diffusion with multi-resolution readouts
  • PixelREPA alignment with DINOv3-L
  • Curriculum learning over noise schedule
What makes it different
  • Private by design: uploads not published or shown in public galleries
  • One-time purchase options with no recurring subscription required
  • Style preview focused on styling decisions, not fit guarantee
  • Independent product with documented limits and test evidence
  • Uploads not used for AI model training (per Agnes confirmation)
  • Low-cost training: full 2B model trained for just $21K in compute
  • Controls baked into model training rather than post-hoc fine-tuning
  • Pixel-space compression without separate VAE bottleneck
  • Apache 2.0 open release with full model weights and code
Free planNot given Not given
Open sourceNot given Not given
PlatformsNot given Not given
Public APINot given Not given
Community votes00
DR (Domain Rating by Ahrefs)14 +2 (up) since last check44 +1 (up) since last check
Trust Flow (Majestic)25
Citation Flow (Majestic)2332
Referring domains (Majestic)31397

4 details filled in for both companies.

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