AI photo enhancer to unblur images, restore clarity, and improve quality online with free preview and no sign-up required
Open-source SDXL-based AI model for native high-resolution anime art generation
Stage
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Founded
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Based in
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Pricing model
Freemium
Open source
Pricing
Free: Quick Enhance with local browser preview. Paid subscriptions: Pro $10/month (2,000 monthly credits or 24,000 yearly), Ultimate $20/month (5,000 monthly or 60,000 yearly), Max $40/month (10,000 monthly or 120,000 yearly). Credit packs: one-time purchase valid 180 days. Standard enhancement: 2 c
Free for personal and research use; prohibits closed-source proprietary monetization
Key features
Quick Enhance: free local browser processing with no upload or account needed
Portrait enhancement with facial feature and texture reconstruction
Text and screenshot sharpening for readability
Old photo restoration with grain and haze reduction
Support for JPG, PNG, and WebP files up to 10 MB
Mobile and desktop responsive web application
Image upscaling to 2K and 4K resolution
Native 1536x1536 high-resolution generation without upscaling
Hybrid Prompting System combining natural English and Danbooru tags
Stable text encoder fine-tuning for superior concept understanding
Trained on comprehensive Danbooru2023 dataset
Full compatibility with ComfyUI, Automatic1111, Forge, and Draw Things
Superior anatomy rendering with flawless hands and character details
V-Prediction architecture for enhanced dynamic range and contrast
Highly flexible base canvas for LoRA training and custom checkpoints
What makes it different
Free preview mode runs entirely in browser without uploading or account creation
Generative detail reconstruction instead of simple edge contrast enhancement
Commercial use rights retained by users
Watermark-free downloads with paid credits
Only open-source SDXL model natively trained at 1536x1536 resolution
Stylistically neutral untuned base canvas unlike Pony Diffusion
Continuous rapid iterations from v0.1 to v3.5 with aggressive optimization
Thousands of compatible community-trained LoRAs and derivatives