Open-source SDXL-based AI model for native high-resolution anime art generation
Stage
MVP
Not given
Founded
Not given
Not given
Based in
NP
Not given
Pricing model
Freemium
Open source
Pricing
Free: unlimited builds and demos with watermarked exports up to 15 seconds. Indie: $29/month or $290/year (2 months free) for 1 app with up to 500 monthly editor users and no watermark. Team: $79/month or $790/year (2 months free) for 1 app with unlimited users, plus $29/month per additional app. Bu
Free for personal and research use; prohibits closed-source proprietary monetization
Key features
Effects on clips or specific moments
Karaoke captions with word-by-word timing
35 easing curves for text animation
Automatic audio mixing for music and voices
Voice changers including robot, chipmunk and telephone
Smooth transitions between clips
Ready-made text styles for titles and captions
Full customization of colors, fonts and video formats
MP4 export ready to post
On-device rendering with no servers
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
One view integration for iOS, Android, React Native and web with the same implementation
Full customization to match any app's brand and workflow
Complete feature set ready to use without building
On-device processing with no per-export cost or data leaving the device
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