ABrush vs qarunbook: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of ABrush and qarunbook — 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

Freemium

qarunbook is an app testing runbook where AI drafts a test plan from your code and your team or AI works through it, tracking issues and retests.

AI-Drafted Test Plans: The /test-checklist skill looks through your app and drafts checks with steps and expected results already filled in.
MCP Connection: Connect Claude Code, Claude Desktop, Codex, Cursor or any MCP client so the assistant can read the plan and record results.
Issue-to-Check Linking: Every issue sits on the check that found it with the affected platforms, and raising one removes the pass.
Retest Flow: When an issue is fixed the check waits for someone to look again before being marked passed.
Roles and Join Links: Give people access to only the projects they need and invite whole groups with a join link.
Multi-App Workspaces: Keep each app's sections and platforms separate so web, driver and marketing sites are tested side by side.
GitHub and Linear Integration: Connect issue trackers so problems found in testing flow into existing engineering workflows.
QA Runbook Skill: The /qa-runbook skill works through the plan on web and mobile, recording each result and re-checking fixes.

Release Testing: Run a structured test plan before each release across web, Android and iOS.
AI-Assisted QA: Let a coding assistant execute checks and record pass/fail results through MCP.
Team Test Coordination: Split test checklists among teammates and see progress and results in one workspace.
Regression Follow-up: Track bugs against specific checks and retest them after fixes ship.
Solo Developer Testing: Generate a test plan from the codebase so a one-person team knows what to check.
Agency Multi-Client QA: Manage separate apps and clients with scoped access and a flat workspace price.