ABrush vs iisee.me: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of ABrush and iisee.me — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

ABrush

Freemium

AI image generation and editing studio that runs as a panel inside Adobe Photoshop, with 23+ models, ControlNet, LoRA styles and layer-native output.

Photoshop-native panel: Generation, editing and upscaling happen on the open document and land on real layers, with no export-import round trip
23+ models in one panel: Switch between Stable Diffusion, Flux, Qwen Image and others per stage of a piece rather than committing to one provider
Targeted editing: Inpaint or regenerate only the region that needs changing, keeping the rest of the composition untouched
Pro conditioning controls: ControlNet support plus IP-Adapter and reference images for pose, composition and style control
Custom LoRA styles: Load your own LoRA or style models to keep generations consistent with an established look
Generation history: Every generation is saved and recoverable, so artists can return to an earlier variation without regenerating
Shareable presets: Save prompts and settings as presets and share them across a team to reproduce a house style
Commercial-safe data policy: Generated images belong to the user and customer images are not used for model training

A concept artist generating multiple variations of a character directly in the working file and painting over the strongest one
A retoucher fixing a single element of a composite with inpainting rather than regenerating the whole image
A studio distributing a shared preset pack so several artists produce work in a consistent house style
A freelance illustrator using a custom LoRA to keep generated assets on-style with a client's brand
A designer upscaling and cleaning up a low-resolution asset without leaving Photoshop
An agency handling commercial client work that needs assurance the images aren't used for model training

Charlie Clark (GitHub: charlieclark)

Free

Web app to create customizable AI-generated expression grids for design, character exploration, and visualization.

Expression Grid Generation: Creates a multi-cell grid of AI-generated images showing variations in expression, pose, or style to support rapid visual ideation.
Interactive Web Editor: Web-based interface for previewing grids in real time and adjusting generation parameters without local tooling.
Customizable Parameters: Controls for seed, style, intensity, and layout to fine-tune how expressions and visual characteristics vary across the grid.
Export and Sharing: Options to export generated grids (image download or shareable link) for integration into design workflows or reviews.
Preset Templates: Ready-made templates for common grid layouts and expression sets to accelerate creation for character design and reference.
Open-Source Repository: Public GitHub repo enabling developers to self-host, inspect generation logic, or extend functionality for bespoke needs.
Generate AI-created expression grids (collections of expressions/images)
Web-based frontend built with Next.js
Database schema defined with Prisma
Repository includes npm scripts for setup (npm install) and database start (npm run db:start)
Deployment configuration present (vercel.json) for easy Vercel hosting
Scripts for database migration and startup (start-database.sh, db-deploy-migrations.sh)
Source code available on GitHub for self-hosting and extension

Character Design Ideation: Quickly generate a matrix of facial expressions to iterate on character looks for games, comics, or animation.
Animation Reference Sheets: Produce consistent expression grids to guide animators and ensure continuity across scenes.
Avatar and Emote Creation: Create multiple emotive avatar variants for chat apps, streaming, or social platforms.
UX and Concept Testing: Generate visual variations for user testing to evaluate emotional impact or recognizability of character designs.
Marketing Creative Exploration: Rapidly prototype visual concepts showing different emotional tones for ad or social creatives.
Educational and Reference Material: Produce expression collections for teaching acting, emotion recognition, or illustration techniques.
Create expression/pose sheets for character design and animation
Rapidly prototype facial expression variations for game or film art
Generate visual assets for avatars or social media content
Self-hosted research or experimentation with image-generation workflows
Integrate into artist pipelines by extending Next.js API routes or the source code