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, merged with the weights of sdxl_vae.safetensors 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.
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
issd_xl_base_1.0_inpainting_0.1.safetensors
B
is your fine-tuned checkpointC
issd_xl_base_1.0_fp16_vae.safetensors
Using ENFUGUE's Web UI:
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.
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