stable diffusion v1 5

---
license: creativeml-openrail-m
tags:

• stable-diffusion

• stable-diffusion-diffusers

• text-to-image

Stable Diffusion v1-5 Model Card

⚠️ This repository is a mirror of the now deprecated ruwnayml/stable-diffusion-v1-5, this repository or organization are not affiliated in any way with RunwayML.

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 blog.

The Stable-Diffusion-v1-5 checkpoint was initialized with the weights of the Stable-Diffusion-v1-2
checkpoint and subsequently fine-tuned on 595k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve classifier-free guidance sampling.

You can use this both with the 🧨Diffusers library and RunwayML GitHub repository (<span style="color:crimson">now deprecated</span>), <span style="color:darkgreen">ComfyUI, Automatic1111, SD.Next, InvokeAI</span>.

Use with Diffusers

from diffusers import StableDiffusionPipeline
import torch
model_id = "sd-legacy/stable-diffusion-v1-5"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "a photo of an astronaut riding a horse on mars"
image = pipe(prompt).images[0]  
    
image.save("astronaut_rides_horse.png")

Use with GitHub Repository <span style="color:crimson">(now deprecated)</span>, <span style="color:darkgreen">ComfyUI or Automatic1111</span>

1. Download the weights
- v1-5-pruned-emaonly.safetensors - ema-only weight. uses less VRAM - suitable for inference
- v1-5-pruned.safetensors - ema+non-ema weights. uses more VRAM - suitable for fine-tuning

2. Follow instructions here. <span style="color:crimson">(now deprecated)</span>

3. <span style="color:darkgreen">Use locally with <a href="https://github.com/comfyanonymous/ComfyUI">ComfyUI</a>, <a href="https://github.com/AUTOMATIC1111/stable-diffusion-webui">AUTOMATIC1111</a>, <a href="https://github.com/vladmandic/automatic">SD.Next</a>, <a href="https://github.com/invoke-ai/InvokeAI">InvokeAI</a></span>

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{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}
}

Uses

Direct Use

• Safe deployment of models which have the potential to generate harmful content.

• Probing and understanding the limitations and biases of generative models.

• Generation of artworks and use in design and other artistic processes.

• Applications in educational or creative tools.

• Research on generative models.

Excluded uses are described below.

### Misuse, Malicious Use, and Out-of-Scope Use
_Note: This section is taken from the DALLE-MINI model card, but applies in the same way to Stable Diffusion v1_.

The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.

#### Out-of-Scope Use
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.

#### Misuse and Malicious Use
Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:

• Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.

• Intentionally promoting or propagating discriminatory content or harmful stereotypes.

• Impersonating individuals without their consent.

• Sexual content without consent of the people who might see it.

• Mis- and disinformation

• Representations of egregious violence and gore

• Sharing of copyrighted or licensed material in violation of its terms of use.

• Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.

Limitations and Bias

Limitations

• The model does not achieve perfect photorealism

• The model cannot render legible text

• The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”

• Faces and people in general may not be generated properly.

• The model was trained mainly with English captions and will not work as well in other languages.

• The autoencoding part of the model is lossy

• The model was trained on a large-scale dataset

• No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the training data.

Bias

While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b