Model reference · open weights
stable-diffusion-2-inpainting is an open-weight image model from sd2-community, listed in the AxForge catalogue. AxForge can bring it up on EU-owned hardware for you on request — with the licence handled where one is required.
About
Stable Diffusion v2 Model Card ⚠️ This repository is a mirror of the now deprecated stabilityai/stable-diffusion-2-inpainting, this repository and organization are not affiliated in any way with Stability AI. This model card focuses on the model associated with the Stable Diffusion v2, available here. This stable-diffusion-2-inpainting model is resumed from stable-diffusion-2-base (512-base-ema.ckpt) and trained for another 200k steps. Follows the mask-generation strategy presented in LAMA which, in combination with the latent VAE representations of the masked image, are used as an additional conditioning. - Use it with the stablediffusion repository: download the 512-inpainting-ema.ckpt here. - Use it with 🧨 diffusers Model Details - Developed by: Robin Rombach, Patrick Esser - Model type: Diffusion-based text-to-image generation model - Language(s): English - License: CreativeML Open RAIL++-M License - Model Description: This is a model that can be used to generate and modify images based on text prompts. It is a Latent Diffusion Model that uses a fixed, pretrained text encoder (OpenCLIP-ViT/H). - Resources for more information: GitHub Repository. - Cite as: @InProceedings{Rombach2022CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } Examples Using the 🤗's Diffusers library to run Stable Diffusion 2 inpainting in a simple and efficient manner. Notes: - Despite not being a dependency, we highly recommend you to install xformers for memory efficient attention (better performance) - If you have low GPU RAM available, make sure to add a pipe.enableattentionslicing() after sending it to cuda for less VRAM usage (to the cost of speed) How it works: image | maskimage :-------------------------:|:-------------------------:| prompt | Output :-------------------------:|:-------------------------:| Uses Direct Use The model is intended for research purposes only. Possible research a
Summarised from the published model card. Read the full card on the HuggingFace links below.
Specifications
| Maker | sd2-community |
|---|---|
| Type | Image models |
| Parameters (lead) | 866M |
| Variants | 1 |
| Runs with | diffusers |
| Released | 2025-11-14 |
| Popularity | 30k downloads / month |
| Likes | 19 |
| Licence | Open weights |
How it works
Variants
Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.
| Variant | Params | Precision | VRAM | Fits 16 GB | Weights |
|---|---|---|---|---|---|
| stable-diffusion-2-inpainting | 866M | BF16 | ~2 GB | ✓ | Weights ↗ |
Using it via the API
Once AxForge deploys stable-diffusion-2-inpainting for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (stable-diffusion-2-inpainting below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/images/generations \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"stable-diffusion-2-inpainting","prompt":"a red bicycle","size":"1024x1024"}'
Details
Tags
Papers
Licence
Open weights under openrail++ — commercial use is permitted. Deploy it on AxForge EU hardware on request. Read the licence ↗