---
license: openrail++
library_name: diffusers
pipeline_tag: text-to-image
tags:
- StableDiffusionXLPipeline
- StableDiffusionXLInpaintPipeline
- stable-diffusion-xl
- stable-diffusion-xl-inpainting
- stable-diffusion-xl-diffusers
- inpainting
---

This repository contains alternative or tuned versions of Stable Diffusion XL Base 1.0 in `.safetensors` format.

# Available Models
## sd_xl_base_1.0_fp16_vae.safetensors

This file contains the weights of [sd_xl_base_1.0.safetensors](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0), merged with the weights of [sdxl_vae.safetensors](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix) from MadeByOllin's SDXL FP16 VAE repository.

## sd_xl_base_1.0_inpainting_0.1.safetensors

This file contains the weights of `sd_xl_base_1.0_fp16_vae.safetensors` merged with the weights from [diffusers/stable-diffusion-xl-1.0-inpainting-0.1](https://huggingface.co/diffusers/stable-diffusion-xl-1.0-inpainting-0.1).

# How to Create an SDXL Inpainting Checkpoint from any SDXL Checkpoint

Using the `.safetensors` files here, you can calculate an inpainting model using the formula `A + (B - C)`, where:
- `A` is `sd_xl_base_1.0_inpainting_0.1.safetensors`
- `B` is your fine-tuned checkpoint
- `C` is `sd_xl_base_1.0_fp16_vae.safetensors`

Using [ENFUGUE](https://github.com/painebenjamin/app.enfugue.ai)'s Web UI:
![image/png](https://cdn-uploads.huggingface.co/production/uploads/64429aaf7feb866811b12f73/XLI5s3fubTup9qhThGs37.png)

You must specifically use the two files present in this repository for this to work. The Diffusers team trained XL Inpainting using FP16 XL VAE, so using a different XL base will result in an incorrect delta being applied to the inpainting checkpoint, and the resulting VAE will be nonsensical.

# Model Description
- Developed by: The Diffusers team
- Repackaged by: Benjamin Paine
- Model type: Diffusion-based text-to-image generative model
- License: CreativeML Open RAIL++-M License
- 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 two fixed, pretrained text encoders (OpenCLIP-ViT/G and CLIP-ViT/L).

# Uses
## Direct Use

The model is intended for research purposes only. Possible research areas and tasks include

- Generation of artworks and use in design and other artistic processes.
- Applications in educational or creative tools.
- Research on generative models.
- Safe deployment of models which have the potential to generate harmful content.
- Probing and understanding the limitations and biases of generative models.
- Excluded uses are described below.

## 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.

# Limitations and Bias
## Limitations
- The model does not achieve perfect photorealism
- The model cannot render legible text
- The model struggles with 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 autoencoding part of the model is lossy.
- When the strength parameter is set to 1 (i.e. starting in-painting from a fully masked image), the quality of the image is degraded. The model retains the non-masked contents of the image, but images look less sharp. We're investing this and working on the next version.
## Bias
- While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.