Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A latent text-to-image diffusion model
| Date | Stars |
|---|---|
| 2026-07-31 | 73239 |
| 2026-08-01 | 73239 |
| 2026-08-02 | 73259 |
| 2026-08-12 | 73305 |
| 2026-08-18 | 73301 |
| 2026-08-19 | 73306 |
| 2026-08-20 | 73322 |
| 2026-08-21 | 73332 |
| 2026-08-22 | 73337 |
| 2026-08-23 | 73338 |
| 2026-08-24 | 73343 |
| 2026-08-25 | 73347 |
| 2026-08-26 | 73344 |
| 2026-08-27 | 73348 |
| 2026-08-28 | 73354 |
| 2026-08-29 | 73354 |
| 2026-08-30 | 73360 |
| 2026-08-31 | 73361 |
| 2026-09-01 | 73366 |
| 2026-09-02 | 73365 |
| 2026-09-03 | 73366 |
| 2026-09-04 | 73369 |
| 2026-09-05 | 73371 |
| 2026-09-06 | 73381 |
| 2026-09-07 | 73393 |
| 2026-09-08 | 73399 |
| 2026-09-09 | 73403 |
| 2026-09-10 | 73404 |
| 2026-09-11 | 73410 |
| 2026-09-12 | 73418 |
| 2026-09-13 | 73421 |
| 2026-09-14 | 73423 |
| 2026-09-15 | 73431 |
| 2026-09-16 | 73435 |
| 2026-09-17 | 73442 |
| 2026-09-18 | 73443 |
| 2026-09-19 | 73448 |
| 2026-09-20 | 73449 |
Today
+1 stars today
This week
+28 stars this week
This month
+117 stars this month
Momentum
0.0
growth rate 0.04%/day
# Stable Diffusion *Stable Diffusion was made possible thanks to a collaboration with [Stability AI](https://stability.ai/) and [Runway](https://runwayml.com/) and builds upon our previous work:* [**High-Resolution Image Synthesis with Latent Diffusion Models**](https://ommer-lab.com/research/latent-diffusion-models/)<br/> [Robin Rombach](https://github.com/rromb)\*, [Andreas Blattmann](https://github.com/ablattmann)\*, [Dominik Lorenz](https://github.com/qp-qp)\, [Patrick Esser](https://github.com/pesser), [Björn Ommer](https://hci.iwr.uni-heidelberg.de/Staff/bommer)<br/> _[CVPR '22 Oral](https://openaccess.thecvf.com/content/CVPR2022/html/Rombach_High-Resolution_Image_Synthesis_With_Latent_Diffusion_Models_CVPR_2022_paper.html) | [GitHub](https://github.com/CompVis/latent-diffusion) | [arXiv](https://arxiv.org/abs/2112.10752) | [Project page](https://ommer-lab.com/research/latent-diffusion-models/)_  [Stable Diffusion](#stable-diffusion-v1) is a latent text-to-image diffusion model. Thanks to a generous compute donation from [Stability AI](https://stability.ai/) and support from [LAION](https://laion.ai/), we were able to train a Latent Diffusion Model on 512x512 images from a subset of the [LAION-5B](https://laion.ai/blog/laion-5b/) database. Similar to Google's [Imagen](https://arxiv.org/abs/2205.11487), this model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts. With its 860M UNet and 123M text encoder, the model is relatively lightweight and runs on a GPU with at least 10GB VRAM. See [this section](#stable-diffusion-v1) below and the [model card](https://huggingface.co/CompVis/stable-diffusion). ## Requirements A suitable [conda](https://conda.io/) environment named `ldm` can be created and activated with: ``` conda env create -f environment.yaml conda activate ldm ``` You can also update an existing [latent diffusion](https://github.com/CompVis/latent-diffusion) environment by running ``` conda install pytorch torchvision -c pytorch pip install transformers==4.19.2 diffusers invisible-watermark pip install -e . ``` ## Stable Diffusion v1 Stable Diffusion v1 refers to a specific configuration of the model architecture that uses a downsampling-factor 8 autoencoder with an 860M UNet and CLIP ViT-L/14 text encoder for the diffusion model. The model was pretrained on 256x256 images and then finetuned on 512x512 images. *Note: Stable Diffusion v1 is a general text-to-image diffusion model and therefore mirrors biases and (mis-)conceptions that are present in its training data. Details on the training procedure and data, as well as the intended use of the model can be found in the corresponding [model card](Stable_Diffusion_v1_Model_Card.md).* The weights are available via [the CompVis organization at Hugging Face](https://huggingface.co/CompVis) under [a license which contains specific use-based restrictions to prevent misuse and harm as informed by the model card, but otherwise remains permissive](LICENSE). While commercial use is permitted under the terms of the license, **we do not recommend using the provided weights for services or products without additional safety mechanisms and considerations**, since there are [known limitations and biases](Stable_Diffusion_v1_Model_Card.md#limitations-and-bias) of the weights, and research on safe and ethical deployment of general text-to-image models is an ongoing effort. **The weights are research artifacts and should be treated as such.** [The CreativeML OpenRAIL M license](LICENSE) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingfa
Excerpt of 12,439 characters
Read on GitHub
Robin Rombach
9
Patrick Esser
7
Patrick von Platen
6
2
Cheng Lu · OpenAI · United States
2
apolinário
1
Charles Packer · @letta-ai · United States
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:94fe07c6450d5781, desc:text-to-image, desc:diffusion model, name:stable diffusion