Model reference · open weights
stable-diffusion-4 is an open-weight image model from CompVis, 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 v1-4 Model Card Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. For more information about how Stable Diffusion functions, please have a look at 🤗's Stable Diffusion with 🧨Diffusers blog. The Stable-Diffusion-v1-4 checkpoint was initialized with the weights of the Stable-Diffusion-v1-2 checkpoint and subsequently fine-tuned on 225k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve classifier-free guidance sampling. This weights here are intended to be used with the 🧨 Diffusers library. If you are looking for the weights to be loaded into the CompVis Stable Diffusion codebase, come here Model Details - Developed by: Robin Rombach, Patrick Esser - Model type: Diffusion-based text-to-image generation model - Language(s): English - License: The CreativeML OpenRAIL M license is an Open RAIL M license, adapted from the work that BigScience and the RAIL Initiative are jointly carrying in the area of responsible AI licensing. See also the article about the BLOOM Open RAIL license on which our license is based. - 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 (CLIP ViT-L/14) as suggested in the Imagen paper. - Resources for more information: GitHub Repository, Paper. - 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 We recommend using 🤗's Diffusers library to run Stable Diffusion. PyTorch Running the pipeline with the default PNDM scheduler: Note: If you are limited by GPU memory and have less than 4GB of GPU RAM available, please make sure to load the StableDiffusionPipeline in float16 precision instead of the default float32 precision as done above. You
Summarised from the published model card. Read the full card on the HuggingFace links below.
Specifications
| Maker | CompVis |
|---|---|
| Type | Image models |
| Parameters (lead) | 860M |
| Variants | 1 |
| Runs with | diffusers |
| Released | 2022-08-20 |
| Popularity | 482k downloads / month |
| Likes | 7,056 |
| 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-v1-4 | 860M | BF16 | ~2 GB | ✓ | Weights ↗ |
Using it via the API
Once AxForge deploys stable-diffusion-4 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (stable-diffusion-4 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-4","prompt":"a red bicycle","size":"1024x1024"}'
Details
Tags
Papers
Licence
Open weights under creativeml-openrail-m — commercial use is permitted. Deploy it on AxForge EU hardware on request. Read the licence ↗