krita-ai-diffusion
Krita AI Diffusion is a Krita plugin enabling AI-powered inpainting, outpainting, and image generation directly within the editor. Users can refine existing artwork with text prompts and ControlNet guidance while maintaining precise control over generation regions and strength.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | Acly/krita-ai-diffusion |
| Owner | Acly |
| Primary language | Python |
| License | GPL-3.0 — OSI-approved |
| Stars | 10.3k |
| Forks | 600 |
| Open issues | 110 |
| Latest release | v1.52.1 (2026-06-30) |
| Last updated | 2026-06-30 |
| Source | https://github.com/Acly/krita-ai-diffusion |
What krita-ai-diffusion is
Python-based Krita plugin leveraging ComfyUI as a diffusion backend, supporting Stable Diffusion, Flux, and ControlNet models with local GPU acceleration (NVIDIA/CUDA, AMD/ROCm, Intel/XPU, Apple/MPS) or cloud fallback. Integrates IP-Adapter for style transfer and region-based multi-prompt generation.
Get the krita-ai-diffusion source
Clone the repository and explore it locally.
git clone https://github.com/Acly/krita-ai-diffusion.gitcd krita-ai-diffusion# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- ComfyUI backend must be installed locally or a remote server configured; automatic setup available but may fail on atypical system configurations—FAQ and Discord support required for troubleshooting.
- GPU drivers and libraries (CUDA/ROCm/XPU/MPS) must be pre-installed and compatible; plugin does not bundle or validate driver state, only detects supported hardware at runtime.
- Model downloading is on-demand (Flux 2, Illustrious, Stable Diffusion variants); first-run can be slow and requires significant disk/network bandwidth; no built-in quota or caching strategy documented.
- Krita version pinned to 5.2.0+; verify compatibility matrix as plugin evolves; official Krita support channels explicitly exclude this plugin (direct community support via Discord/GitHub).
- Optional segmentation features require a separate companion plugin (krita-ai-tools) for object selection; LoRA and custom checkpoint support documented but requires manual model management.
When to avoid it — and what to weigh
- No GPU / CPU-only deployment — Local CPU-based generation is explicitly noted as 'very slow.' Requires at least 6GB VRAM (GPU) for practical performance; cloud fallback available but adds latency and potential cost.
- Closed-source / Proprietary AI pipelines required — Plugin is built on open-source models (Stable Diffusion, Flux, ComfyUI). If vendor-locked or proprietary diffusion models are mandated, this will not satisfy requirements.
- Standalone headless/API-only use case — Tightly coupled to Krita UI and workflow; not designed as an independent inference service or REST API. Requires Krita 5.2.0+ as a hard dependency.
- Zero tolerance for GPL licensing — GPL-3.0 requires derivative works and linked codebases to also adopt GPL-3.0. Commercial products embedding this plugin must meet GPL-3.0 obligations; internal/non-redistributed use is permissible.
License & commercial use
GPL-3.0 (GNU General Public License v3.0). This is a copyleft license requiring all derivative works, modifications, and linked code to be distributed under GPL-3.0 and made publicly available with source code. Internal use without redistribution is permitted; commercial use is permissible but only if source and license obligations are met.
Commercial use is legally permissible under GPL-3.0 if (1) any modifications or derivative works are published under GPL-3.0 with source code, and (2) end-user rights to access/modify source are preserved. Embedding this plugin in a closed-source commercial product without GPL-3.0 compliance is non-compliant. Consult legal counsel if commercial distribution is intended. Internal use within a company (non-distributed) is allowed.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Strong |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Good |
| Assessment confidence | High |
Local GPU execution avoids cloud data transmission, reducing exposure of user images if deployed on-premises. ComfyUI backend runs locally by default but network-exposed if configured for remote access (e.g., across a company LAN); no built-in authentication or encryption for remote servers—network security is user's responsibility. Cloud generation option (provider unknown) introduces third-party data handling risk; terms and retention policies are not disclosed in publicly available sources. Models (Stable Diffusion, Flux) are open-source; no attestation of model integrity or provenance beyond upstream repos. No explicit supply-chain security (e.g., signed releases, SBOMs) mentioned.
Alternatives to consider
Automatic1111 WebUI (standalone Stable Diffusion UI)
Web-based, browser-agnostic alternative to Krita plugin; broader model ecosystem; no Krita dependency. Trade-off: requires manual asset pipeline integration and lacks native painting workflow.
Clipdrop / Adobe Firefly (cloud-based inpainting)
Commercial SaaS options with proprietary models and no local setup complexity. Trade-off: vendor lock-in, per-use costs, and data privacy concerns; no open-source models.
ComfyUI standalone with custom frontends (e.g., Invoke, OpenArt)
Direct ComfyUI usage with alternative UIs; maximum flexibility and no Krita coupling. Trade-off: steeper learning curve, no native Krita workflow integration, separate tool ecosystem management.
Build on krita-ai-diffusion with DEV.co software developers
Evaluate krita-ai-diffusion for concept art, asset refinement, and design iteration. Assess GPU requirements, ComfyUI deployment complexity, and GPL-3.0 licensing implications. Contact our team to review integration, security, and commercial use considerations.
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krita-ai-diffusion FAQ
Can I run this entirely offline without cloud services?
What are the minimum GPU requirements?
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Integrate AI-Powered Image Generation into Your Krita Workflow
Evaluate krita-ai-diffusion for concept art, asset refinement, and design iteration. Assess GPU requirements, ComfyUI deployment complexity, and GPL-3.0 licensing implications. Contact our team to review integration, security, and commercial use considerations.