Clarity AI Upscaler is an open-source tool designed to enhance and upscale low-resolution images using Stable Diffusion-based models. Built as a free alternative to commercial tools like Magnific, it leverages advanced techniques such as LoRA adapters, Flux upscaling, and tiled diffusion to produce high-fidelity outputs up to 13k x 13k pixels. The project targets developers, digital artists, and AI enthusiasts who need precise control over image enhancement without relying on proprietary services. While the core functionality is open-sourced via Cog and ComfyUI integrations, the developer also offers paid web-based versions on ClarityAI.co for ease of use and additional features like Flux upscaling.
The tool supports multiple deployment options: a web app, API endpoints, ComfyUI nodes, and direct Cog execution. This flexibility allows users to integrate Clarity AI into workflows ranging from simple desktop use to automated pipelines in production environments. Its active development history, including support for custom safetensors checkpoints and anime-specific upscaling, makes it a robust choice for users seeking customizable, high-resolution image enhancement.
What You Get
- 13k x 13k resolution upscaling - Capable of generating ultra-high-resolution outputs, enabling detailed enlargements for print or high-DPI displays without artifacts.
- LoRA model support - Allows users to apply custom LoRA checkpoints for style-specific enhancements, including anime, photorealistic faces, or artistic styles.
- Flux upscaling (via paid API) - Advanced face and text enhancement using proprietary Flux models, accessible through ClarityAI.co/flux-upscaler with improved fidelity and error reduction.
- Multi-step upscaling - Enables progressive enhancement through multiple passes, improving detail retention and reducing noise in large-scale enlargements.
- ComfyUI integration - Official node available via ComfyUI Manager, with pre-built free workflows and API key configuration for seamless use in Stable Diffusion pipelines.
- Custom safetensors checkpoint support - Users can load and use their own trained models for personalized upscaling results.
- Multiple output formats - Supports PNG, JPG, and WebP for flexible integration into different workflows and platforms.
- Pre-downscaling and sharpening - Pre-processes images to reduce noise before upscaling, followed by optional sharpening for enhanced edge clarity.
- Tiled Diffusion and ControlNet support - Uses tiled inference with 4x-UltraSharp upscaler and tile-resample ControlNet for stable, high-quality results on large images.
Common Use Cases
- Building a digital art portfolio with high-res prints - Artists upscale low-resolution sketches or scans to 13k resolution for professional printing while preserving brushwork and detail using LoRA models.
- Creating product mockups for e-commerce with AI-enhanced textures - E-commerce teams enhance low-quality product images to 4K+ resolution for zoom functionality, using sharpening and pre-downscaling to reduce compression artifacts.
- Problem: Low-resolution AI-generated images → Solution: Clarity AI Upscaler - Users generate low-res images with Stable Diffusion and use Clarity to upscale them without losing style or introducing artifacts, using custom safetensors and LoRAs for consistency.
- DevOps teams deploying AI upscaling in production pipelines - Engineers use the Cog-based CLI or API to batch-process images via Docker, integrating Clarity into CI/CD workflows for automated image enhancement.
Under The Hood
This project is a web-based AI image generation platform built around Stable Diffusion and related machine learning models, offering a modular and extensible interface for image upscaling and creation. It provides a rich ecosystem of plugins and extensions while maintaining a clean separation of concerns in its architecture.
Architecture
This project adopts a monolithic yet modular architecture that supports extensibility through a plugin system. It emphasizes layered design and component-based initialization.
- The architecture follows a layered structure separating UI, core logic, and model handling for better maintainability.
- Extensions are implemented as dynamic plugins that enhance functionality without tight coupling to core modules.
- Strategy and factory design patterns are applied in model and upscaler handling for flexible runtime behavior.
- Centralized configuration and initialization systems manage component interactions and state.
Tech Stack
The project is built using Python as its primary language, integrating with deep learning and web technologies for a full-stack AI image generation experience.
- Python 3.9+ is the main runtime, with PyTorch as the core deep learning framework and libraries like PIL, NumPy, and Gradio for image and UI processing.
- It leverages stable-diffusion models, Lora networks, and a wide array of extensions for customization and enhanced capabilities.
- Modern frontend technologies such as JavaScript, HTML/CSS, and build tools like Vite are used for UI development.
- The system supports linting and formatting with ESLint and Ruff, ensuring code quality and consistency in both backend and frontend.
Code Quality
The project demonstrates a mixed level of code quality with strengths in test coverage and structural organization, though some inconsistencies remain.
- The codebase includes type annotations and comprehensive API documentation, enhancing clarity and maintainability.
- Functional tests are present in sufficient scope to validate key workflows and model interactions.
- Error handling practices vary across modules, with some areas showing more robust approaches than others.
- Code consistency is generally maintained but shows signs of technical debt in module organization and core implementation details.
What Makes It Unique
This project distinguishes itself through its plugin-driven architecture and extensibility model, offering a highly customizable AI image generation platform.
- The plugin system allows for dynamic extension and customization without modifying core logic, setting it apart from conventional tools.
- A modular API model generation system enables flexible integration of new models and upscalers without major architectural changes.
- It combines a rich set of built-in extensions with an extensible framework that supports community-driven enhancements.