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

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

Scans vibe-coded apps for launch-blocking issues like leaked keys and data exposure, and reports what to fix first, right from your coding agent.

Agent-Native Scanning: Tell Claude Code, Codex, Cursor or other agents to scan with CleanSlop and fix findings where you build.
System-Level Code Mapping: Maps routes, background jobs, data and external services so each finding follows a real call path.
Prioritized Reports: Findings grouped as needs attention, worth a look and minor, worst first.
Evidence for Every Finding: Shows where each issue lives in your code and why it was flagged.
Plain-English Fixes: Explains what is wrong, why it matters and what to do about it.
Copy for LLM: Hands any report to your AI tool so it can apply the fixes.
GitHub App Integration: Scan only the repositories you select, read-only access.
Expert Triage Calls: Book a 15-minute call with a CleanSlop engineer to walk through findings.

A solo founder checks a Next.js and Supabase app built with an AI coding assistant before launching.
A developer discovers an exposed AI API key bundled into browser code and moves it to a server route.
A side-project builder verifies that one user's data cannot be read by another through database policies.
A Claude Code user runs a scan from the terminal and has the agent fix the top findings.
A team lead gets a short prioritized list instead of hundreds of linter warnings.
An indie hacker books an expert triage call to confirm an ambiguous finding.