stable-diffusion-xl-1.0

About

What stable-diffusion-xl-1.0 is

SD-XL 1.0-base Model Card Model SDXL consists of an ensemble of experts pipeline for latent diffusion: In a first step, the base model is used to generate (noisy) latents, which are then further processed with a refinement model (available here: https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/) specialized for the final denoising steps. Note that the base model can be used as a standalone module. Alternatively, we can use a two-stage pipeline as follows: First, the base model is used to generate latents of the desired output size. In the second step, we use a specialized high-resolution model and apply a technique called SDEdit (https://arxiv.org/abs/2108.01073, also known as "img2img") to the latents generated in the first step, using the same prompt. This technique is slightly slower than the first one, as it requires more function evaluations. Source code is available at https://github.com/Stability-AI/generative-models . Model Description - Developed by: Stability AI - 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). - Resources for more information: Check out our GitHub Repository and the SDXL report on arXiv. Model Sources For research purposes, we recommend our generative-models Github repository (https://github.com/Stability-AI/generative-models), which implements the most popular diffusion frameworks (both training and inference) and for which new functionalities like distillation will be added over time. Clipdrop provides free SDXL inference. - Repository: https://github.com/Stability-AI/generative-models - Demo: https://clipdrop.co/stable-diffusion Evaluation The chart above evaluates user preference for SDXL (with and without refinement) over SDXL 0.9 and Stable Diffusion 1.5 and 2.1. The SDXL base model performs significantly better than the previous variants, and the model combined with the refinement module achieves the best overall performance. 🧨 Diffusers Make

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How image models work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys stable-diffusion-xl-1-0 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (stable-diffusion-xl-1-0 below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/images/generations \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"stable-diffusion-xl-1-0","prompt":"a red bicycle","size":"1024x1024"}'

Details

Languages, data & research

Papers

Licence

Open weights

Sources

Weights & code

Explore

More image models