---
license: creativeml-openrail-m
tags:
- stable-diffusion
- text-to-image
inference: false
---

# Rodent Diffusion 1.5 Model Card

Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.
The **Rodent-Diffusion-1-5** checkpoint was created from a custom Stable Diffusion v1.4 model
From the base model, small merges (0.1-0.3) were included from the following models:
- analogDiffusion
- Knolling Case
- RPGDiffusion
- classicnegative
- cuteRich
- inkpunk
- evoartMj4
- dreamshaper
- deliberate



## Original Stable Diffusion 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](https://huggingface.co/spaces/CompVis/stable-diffusion-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.huggingface.co/blog/the-bigscience-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](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487).
- **Resources for more information:** [GitHub Repository](https://github.com/CompVis/stable-diffusion), [Paper](https://arxiv.org/abs/2112.10752).
- **Cite as:**

      @InProceedings{Rombach_2022_CVPR,
          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}
      }