PowerPaint
One open-source inpainting model that handles text-guided insertion, object removal, outpainting, and shape-guided generation via learnable task prompts.
About
PowerPaint (ECCV 2024, arXiv 2312.03594) is a versatile image inpainting model from the OpenMMLab community that packs four tasks into a single checkpoint: text-guided object insertion, text-free object removal, image outpainting, and shape-guided object generation. The trick is a set of learnable task prompts, special tokens prepended to the text conditioning that switch the model's behavior, so one model serves where you would otherwise need several specialized ones. A v2 release rebuilds the approach on top of BrushNet with a higher-quality base model.
The code is MIT-licensed on GitHub with a Gradio interface and an online demo, and v1/v2 checkpoints are distributed via Hugging Face. The project is pitched as an open alternative to Adobe Firefly's generative fill. It is a research artifact rather than a hosted product: there is no official API, and quality beyond the paper's own reported results has no widely cited independent benchmark.
Specs
Context window
Pricing
Free quota
API access
Weights
Released
Benchmarks
Author-reported results in the ECCV 2024 paper (arXiv 2312.03594); no widely-cited independent benchmark scores found
Provider-reported unless a source says otherwise. Benchmarks are marketing-adjacent by nature — treat them as directional.
Use cases
Trust score — 7/10
Scored 7/10 (verified tier, metrics-v1). Evidence: official repo github.com/open-mmlab/PowerPaint (MIT license confirmed via org listing), paper arXiv 2312.03594, checkpoints on Hugging Face, ECCV 2024 venue. Strengths: genuinely versatile single-model design, open code and weights, active repo through 2025. Docked for: (1) model-weight license terms not explicitly stated on the checkpoint pages; (2) no official API or hosted product, so it is DIY only; (3) no independent benchmark validation beyond the paper. License: MIT for code; checkpoint license terms unverified.
How the trust score is built
Full methodology →- Adoption · stars/downloads, log-scaled vs peers
- 7.0/10
- Maintenance · recency, releases, issue load
- 6.0/10
- Openness · license + docs
- 8.0/10
- Manual review · sanity read + red-flag check
- 7.0/10
- · ECCV 2024 paper arXiv 2312.03594; official repo github.com/open-mmlab/PowerPaint
- · MIT license confirmed via OpenMMLab org repository listing
- · v1 (SD1.5) and v2 (BrushNet-based) checkpoints on Hugging Face under JunhaoZhuang
- · No official API; self-hosted Gradio demo only