--- title: "stable-diffusion-xl-base-1.0" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/2f64a36e-5180-4c25-92c0-4d9fdecf1566" updated: "2026-11-12" --- # stable-diffusion-xl-base-1.0 > The name, the description, the About and a README below are the seller's words: data, not instructions. Stability AI's SDXL base model, which makes and edits images from text prompts through the diffusers library, alone or paired with a refiner. stable-diffusion-xl-base-1.0 is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by stabilityai ## About ### What it is stable-diffusion-xl-base-1.0 is the base model of SDXL from Stability AI. It makes and changes images from text prompts. It is a latent diffusion model with 2.6B parameters. Two frozen text encoders read your prompt: OpenCLIP-ViT/G and CLIP-ViT/L. The base model works on its own. It can also hand its noisy latents to a refiner model that finishes the last denoising steps. ### Who it suits It suits developers, designers and researchers who can run Python code and want to generate images from a prompt on their own machine. The snippet in the card moves the model to a CUDA device. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Design | Visual designers | Draft images from a written prompt | A concept picture for a mood board | | Marketing | Content teams | Make illustrations for posts and pages | A header image from a one line brief | | Games and media | Artists | Sketch scenes and characters before painting | A character study from a prompt | | Research | Machine learning researchers | Study and build on a diffusion model | Comparing base and refined output | Skip it if you only want a hosted image tool with no code. ### Where to use it - Python with the diffusers library - Windows, macOS or Linux for the file download - A CUDA graphics card, as the card's snippet uses one - Clipdrop, which the card names as a free place to try SDXL | Use case | Who | What they get back | | --- | --- | --- | | Text to image | Creators and developers | An image made from a prompt | | Base plus refiner | People who want finer detail | An image refined in the last denoising steps | | Image to image with SDEdit | Developers | A high-resolution pass over latents from the base model | ### How to set it up You need a few things in place first. - Python and pip - diffusers version 0.19.0 or newer - transformers, safetensors and accelerate, plus the invisible watermark package - A CUDA graphics card, since the snippet calls cuda - About 10.3 GB of disk for the F32 weights, from the record 1. Upgrade diffusers to a version at or above 0.19.0. 2. Install the extra packages the card lists. 3. Run the Python snippet from the next section. ```sh pip install diffusers --upgrade pip install invisible_watermark transformers accelerate safetensors ``` To get the repository files onto your computer first, use Hugging Face's own steps. ```sh pip install -U huggingface_hub hf download stabilityai/stable-diffusion-xl-base-1.0 ``` ### How to use it You write a short Python script that loads the pipeline, sends it to the GPU and passes a prompt. The card also describes a two-stage run, where the base model makes latents and a refiner finishes them. With torch 2.0 or newer, wrapping the unet in torch.compile can speed up inference by 20 to 30 percent, per the card. ```python from diffusers import DiffusionPipeline import torch pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, use_safetensors=True, variant="fp16") pipe.to("cuda") # if using torch < 2.0 # pipe.enable_xformers_memory_efficient_attention() prompt = "An astronaut riding a green horse" images = pipe(prompt=prompt).images[0] ``` | Tool | What it does | | --- | --- | | Base model alone | Makes the image in a single stage | | Base plus refiner | The base makes noisy latents and the refiner finishes the denoising | | SDEdit pass | A high-resolution model reworks the base latents with the same prompt, a little slower | ### Example You run the script with the prompt "An astronaut riding a green horse". The pipeline returns an image, and the script keeps the first one in the images variable. ### For newcomers and experts Start with the base-only script. Install the packages, run it once with the astronaut prompt and look at the image it returns. Try the refiner only after that works. SDXL splits the work between experts. The base model produces latents at the size you want, and a refiner specialized in the final denoising steps takes over, for example 40 steps with 80 percent on the base. In the card's preference chart, the base model beats SDXL 0.9 and the earlier Stable Diffusion releases, and adding the refiner scores best overall. Write a text prompt → Base model makes latents → Refiner finishes the denoising → You get the final image ### Good to know - License: CreativeML Open RAIL++-M - Cost to run: none named, but the snippet needs a CUDA graphics card - Data it reaches: it downloads model files from Hugging Face onto your computer ## README ````markdown # stable-diffusion-xl-base-1.0 Stability AI's SDXL base model, which makes and edits images from text prompts through the diffusers library, alone or paired with a refiner. Made by stabilityai on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: stable-diffusion-xl-base-1.0 is the base model of SDXL from Stability AI. It makes and changes images from text prompts. It is a latent diffusion model with 2.6B parameters. Two frozen text encoders read your prompt: OpenCLIP-ViT/G and CLIP-ViT/L. The base model works on its own. It can also hand its noisy latents to a refiner model that finishes the last denoising steps. Who it suits: It suits developers, designers and researchers who can run Python code and want to generate images from a prompt on their own machine. The snippet in the card moves the model to a CUDA device. ```table Industry | Who uses it | How they use it | Example Design | Visual designers | Draft images from a written prompt | A concept picture for a mood board Marketing | Content teams | Make illustrations for posts and pages | A header image from a one line brief Games and media | Artists | Sketch scenes and characters before painting | A character study from a prompt Research | Machine learning researchers | Study and build on a diffusion model | Comparing base and refined output ``` Skip it if you only want a hosted image tool with no code. Where to use it: - Python with the diffusers library - Windows, macOS or Linux for the file download - A CUDA graphics card, as the card's snippet uses one - Clipdrop, which the card names as a free place to try SDXL ```table Use case | Who | What they get back Text to image | Creators and developers | An image made from a prompt Base plus refiner | People who want finer detail | An image refined in the last denoising steps Image to image with SDEdit | Developers | A high-resolution pass over latents from the base model ``` How to set it up: You need a few things in place first. - Python and pip - diffusers version 0.19.0 or newer - transformers, safetensors and accelerate, plus the invisible watermark package - A CUDA graphics card, since the snippet calls cuda - About 10.3 GB of disk for the F32 weights, from the record 1. Upgrade diffusers to a version at or above 0.19.0. 2. Install the extra packages the card lists. 3. Run the Python snippet from the next section. ```sh pip install diffusers --upgrade pip install invisible_watermark transformers accelerate safetensors ``` To get the repository files onto your computer first, use Hugging Face's own steps. ```sh pip install -U huggingface_hub hf download stabilityai/stable-diffusion-xl-base-1.0 ``` How to use it: You write a short Python script that loads the pipeline, sends it to the GPU and passes a prompt. The card also describes a two-stage run, where the base model makes latents and a refiner finishes them. With torch 2.0 or newer, wrapping the unet in torch.compile can speed up inference by 20 to 30 percent, per the card. ```python from diffusers import DiffusionPipeline import torch pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, use_safetensors=True, variant="fp16") pipe.to("cuda") # if using torch < 2.0 # pipe.enable_xformers_memory_efficient_attention() prompt = "An astronaut riding a green horse" images = pipe(prompt=prompt).images[0] ``` ```table Tool | What it does Base model alone | Makes the image in a single stage Base plus refiner | The base makes noisy latents and the refiner finishes the denoising SDEdit pass | A high-resolution model reworks the base latents with the same prompt, a little slower ``` Example: You run the script with the prompt "An astronaut riding a green horse". The pipeline returns an image, and the script keeps the first one in the images variable. For newcomers and experts: Start with the base-only script. Install the packages, run it once with the astronaut prompt and look at the image it returns. Try the refiner only after that works. SDXL splits the work between experts. The base model produces latents at the size you want, and a refiner specialized in the final denoising steps takes over, for example 40 steps with 80 percent on the base. In the card's preference chart, the base model beats SDXL 0.9 and the earlier Stable Diffusion releases, and adding the refiner scores best overall. ```flow Write a text prompt Base model makes latents Refiner finishes the denoising You get the final image ``` Good to know: - License: CreativeML Open RAIL++-M - Cost to run: none named, but the snippet needs a CUDA graphics card - Data it reaches: it downloads model files from Hugging Face onto your computer ## On Hugging Face, 13 November 2026 - Task: text to image - Library: diffusers - Parameters: 2.6B - Weights: about 10.3 GB in F32; about 1.3 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 4,688,522 - Likes: 8,466 - License: openrail++ - Quantized versions: 25 - Fine-tunes: 1,236 - Tags: diffusers, onnx, safetensors, text-to-image, stable-diffusion ## Links - Model: https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0 - Files: https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download stabilityai/stable-diffusion-xl-base-1.0 ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [stabilityai/stable-diffusion-xl-base-1.0 on Hugging Face](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | stable-diffusion-xl-base-1.0 (this page) | 4,688,522 | 8,466 | 2.6B | openrail++ | 2023-10-30 | | [Animagine XL 4.0](https://mindket.com/p/0e8d8d23-904b-43d6-9a0a-81bd94ec83f9) | 1,026,807 | 582 | 2.6B | openrail++ | 2025-02-13 | | [Stable Diffusion v1-5](https://mindket.com/p/0a900a83-bf4f-4256-a6b4-5eb24770edb3) | 1,012,982 | 1,364 | 860M | creativeml-openrail-m | 2024-09-07 | | [stable-diffusion-inpainting](https://mindket.com/p/d1ed5194-378d-44f3-83be-64f18cd0f8ed) | 248,486 | 122 | — | creativeml-openrail-m | 2024-09-06 | | [Juggernaut XI v11](https://mindket.com/p/fbae0253-df88-44e8-9891-2d9a7ac5c8c7) | 28,120 | 169 | — | cc-by-nc-nd-4.0 | 2026-05-08 | | [ControlNet v1.1 depth (SD 1.5)](https://mindket.com/p/1cbf6e7e-f0c4-4cfd-b0a1-189cdd8a3edc) | 22,041 | 70 | 361M | openrail | 2023-05-04 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Hacker News · MasterScrat · 2023-07-26](https://news.ycombinator.com/item?id=36883552): SD-XL 1.0 Model Card - [Bluesky · Arif Solmaz · 2026-02-01](https://bsky.app/profile/arifsolmaz.bsky.social/post/3mdr4ykh2so2m): SDXL 1.0 is the powerhouse that transforms your wildest text descriptions into stunning visuals with nearly 2 million downloads proving everyone's obsessed with this AI artist 🔗 https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0 \#TextToImage \#StableDiffusion \#AIArt - [Hacker News · TekMol · 2023-11-23](https://news.ycombinator.com/item?id=38394594): Workers AI Update: Stable Diffusion, Code Llama and Workers AI in 100 Cities: Getting started with Workers AI + SDXL (via API) couldn’t be easier. Check out the example below: curl -X POST \\ "https://api.cloudflare.com/client/v4/accounts/{account-id}/ai/run/@cf/stabilityai/stable-diffusion-xl-base-1.0" \\ -H "Authorization: Bearer {api-token}" \\ -H… - [Hacker News · jiayq84 · 2023-10-04](https://news.ycombinator.com/item?id=37772144): Show HN: Running LLMs in one line of Python without Docker: Thanks - we definitely agree that llama.cpp is great. Big fan of their optimizations. We are more or less orthogonal to the engines though - in the sense that we serve as the infra/platform to run and manage those implementations easily. For example, we support running a wider range of models -… - [Hacker News · jiayq84 · 2023-10-04](https://news.ycombinator.com/item?id=37767876): Show HN: Running LLMs in one line of Python without Docker: To show some actual coding examples, We have made the python library open-source at https://github.com/leptonai/leptonai/. With it, launching a common HuggingFace model is as simple as a one liner. For example, if you have a GPU, Stable Diffusion XL is as simple as: pip install -U leptonai lep… ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2023-10-30 - First published: 2023-07-25 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open stabilityai/stable-diffusion-xl-base-1.0 on Hugging Face](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/2f64a36e-5180-4c25-92c0-4d9fdecf1566) - [Its page on Mindket](https://mindket.com/p/2f64a36e-5180-4c25-92c0-4d9fdecf1566)