- **Developed by:** More information needed - **Shared by [Optional]:** CompVis - **Model type:** Image Identification - **Language(s) (NLP):** More information needed - **License:** More information needed - **Parent Model:** CLIP - **Resources for more information:** - CLIP Paper - Stable Diffusion Model Card The CLIP model devlopers note in their model card : We primarily imagine the model will be used by researchers to better understand robustness, generalization, and other capabilities, biases, and constraints of computer vision models.

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stable-diffusion-v1-1

CompVis

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 D🧨iffusers blog. The **Stable-Diffusion-v1-1** was trained on 237,000 steps at resolution `256x256` on laion2B-en, followed by 194,000 steps at resolution `512x512` on laion-high-resolution (170M examples from LAION-5B with resolution `>= 1024x1024`). For more information, please refer to Training. This weights here are intended to be used with the D🧨iffusers library. If you are looking for the weights to be loaded into the CompVis Stable Diffusion codebase, come here

Image generation

Input Text→ Output Image

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stable-diffusion-v1-4

CompVis

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

Image generation

Input Text→ Output Image

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Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
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