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

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

In-browser, on-device piano transcription: drag & drop audio to get a piano-roll view and slowed playback with no uploads.

On-Device Transcription: Runs the transcription model fully in the browser so audio is processed locally, minimizing latency and ensuring recordings are not uploaded to remote servers.
Drag-and-Drop Audio Input: Accepts common file formats (MP3, WAV, FLAC, M4A) via simple drag-and-drop to quickly start analysis without complex imports.
Piano-Roll Visualization: Generates an interactive piano-roll view of detected notes, showing pitch and timing to help users inspect performance details visually.
Variable Playback Speed: Allows slowing down audio playback to inspect fast passages or subtle timing details while keeping the visual transcription synchronized.
Local Privacy by Design: Architecture avoids cloud uploads, making the tool suitable for users concerned about confidentiality of recordings and intellectual property.
Immediate, Low-Latency Analysis: Browser execution provides near-instant transcription and visualization for rapid iteration during practice or review.
On-device piano transcription performed in the browser (no uploads to a server)
Drag-and-drop support for MP3, WAV, FLAC, M4A audio files
Piano-roll visual representation of detected notes
Playback controls including slow-down for practice and analysis
Local processing to preserve user privacy and eliminate file transfers

Practice Analysis: Students and teachers can upload practice recordings to inspect missed notes, timing, and articulation using the piano-roll and slowed playback.
Performance Review: Pianists can quickly analyze live or recorded performances for phrasing and timing issues without sending files to third-party servers.
Music Transcription Prep: Musicians can extract note-level information from recordings as a starting point for creating scores or MIDI arrangements.
Rehearsal Feedback: Teachers can generate visual feedback for pupils showing where notes deviate from intended pitches or rhythms during lessons.
Research & Analysis: Music researchers can use on-device transcriptions to study performance characteristics across takes while preserving data privacy.
Musicians transcribing piano recordings into a piano-roll for practice and analysis
Students slowing down passages to learn difficult sections
Composers extracting note information from recorded takes
Music teachers demonstrating performance details visually without uploading student recordings
Quick on-device analysis of piano audio where privacy is required