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Visuali vs Linum

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

Visuali compared with Linum
Detail VisualiLinum logo Linum
What it doesAI image generator and editor on an infinite canvas for creating, editing, and expanding images Generative video models for animation
StageNot given Not given
FoundedNot given Not given
Based inNO US
Pricing modelFree trial Not given
PricingFree trial for 7 days. Starter plan $20/mo with 1,000 tokens monthly. Pro plan $60/mo with 3,000 tokens monthly. Studio plan $200/mo with 10,000 tokens monthly. Unlimited plan available at custom pricing. 50% discount when paying annually. Not given
Key features
  • Inpainting to mask and regenerate image regions
  • Outpainting to extend images in any direction without boundaries
  • Instruction edits to describe changes in plain words
  • Multi-image input and compositing from reference images
  • AI agent for bulk creation and autonomous asset generation
  • Non-destructive editing with layers and reversible changes
  • Drawing, text, and shape tools on canvas
  • Runs in browser and installs as app on mobile and desktop
  • Remove background in a single step
  • 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
  • Non-destructive infinite canvas with full professional editing capabilities
  • Integrated AI agent that works autonomously on the canvas to generate and compose full layouts
  • Works across all devices without installation directly in browser
  • Multiple reference images as context for precise AI-guided edits
  • 10+ integrated high-performance AI models in one workspace
  • 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)51 no change since last check44 +1 (up) since last check
Trust Flow (Majestic)55
Citation Flow (Majestic)3332
Referring domains (Majestic)194397

4 details filled in for both companies.

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