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Open-weight model · Text to image

sdxl-turbo

by Stability AI stabilityai/sdxl-turbo

SDXL-Turbo is a fast generative text-to-image model that can synthesize photorealistic images from a text prompt in a single network evaluation.

Parameters2.6B
Context
Weights42.5 GB
Licenseother
AccessOpen weights
Monthly Downloads790.4k

Runs On

What it takes to serve sdxl-turbo (2.6B parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

PrecisionWeightsMemory neededCheapest setupPer hourAlso fits
16-bit 5.1 GB 6.2 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
8-bit 2.6 GB 3.1 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
4-bit 1.3 GB 1.5 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00

Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Sep 18, 2026.

SAVRN's Notes on sdxl-turbo

Stability AI distilled this 2.6-billion-parameter text-to-image model from SDXL 1.0 so it samples in 1 to 4 steps, and with so few steps per image, throughput per card rather than memory sizes the deployment. At 16-bit it needs 6.2 GB, at 8-bit 3.1 GB, at 4-bit 1.5 GB, and the cheapest configuration we track, a single MI300X with 192 GB at $1.85 an hour, sits mostly idle unless you batch requests onto it. The repository itself is 39 files totaling 55.5 GB.

The license is listed as other, not a standard open-source grant, and the publisher points commercial users to stability.ai/license. Read that page before anything goes into production; the terms of a commercial deployment are set there, not in the model files. Released November 27, 2023 and last updated July 10, 2024, with no configuration record in our data, your own prompts on your own hardware are the check.

Model Card

SDXL-Turbo is a fast generative text-to-image model that can synthesize photorealistic images from a text prompt in a single network evaluation. A real-time demo is available here: http://clipdrop.co/stable-diffusion-turbo Please note: For commercial use, please refer to https://stability.ai/license. SDXL-Turbo is a distilled version of SDXL 1.0, trained for real-time synthesis. SDXL-Turbo is based on a novel training method called Adversarial Diffusion Distillation (ADD) (see the technical report), which allows sampling large-scale foundational image diffusion models in 1 to 4 steps at high image quality. This approach uses score distillation to leverage large-scale off-the-shelf image…

Excerpt from the card by Stability AI, licensed other.

Identity and Version

Repository
stabilityai/sdxl-turbo
Publisher
Stability AI
Task
Text to image
Modality
Image
Library
diffusers
Parameters
2.6B parameters
Languages
Not stated by the source
Revision
71153311d3dbb46851df1931d3ca6e939de83304
First published
2023-11-27
Last updated
2024-07-10

Files and Weights

39 files, 55.5 GB in total. The weights are 15 files totalling 42.5 GB in onnx, safetensors.

Weights15 files · 42.5 GB
Configuration10 files · 6.9 KB
Tokenizer6 files · 3.2 MB
Documentation2 files · 17.7 KB
Other5 files · 13.0 GB
Repository1 file · 1.5 KB
Every file
FileTypeSizeSHA-256
sd_xl_turbo_1.0.safetensorsWeights13.9 GB 2e58e3704b4c
sd_xl_turbo_1.0_fp16.safetensorsWeights6.9 GB e869ac7d6942
text_encoder/model.fp16.safetensorsWeights246.1 MB 660c6f5b1aba
text_encoder/model.onnxWeights492.6 MB 5522ae5f760e
text_encoder/model.safetensorsWeights492.3 MB 778d02eb9e70
text_encoder_2/model.fp16.safetensorsWeights1.4 GB ec310df2af79
text_encoder_2/model.onnxWeights1.0 MB 03856bf14b5d
text_encoder_2/model.safetensorsWeights2.8 GB fa5b2e6f4c2e
unet/diffusion_pytorch_model.fp16.safetensorsWeights5.1 GB 48fa46161a74
unet/diffusion_pytorch_model.safetensorsWeights10.3 GB 1968fc61aa84
unet/model.onnxWeights7.3 MB 3771e8ac1315
vae/diffusion_pytorch_model.fp16.safetensorsWeights167.3 MB 02ee4bd18e5d
vae/diffusion_pytorch_model.safetensorsWeights334.6 MB 716971093e34
vae_decoder/model.onnxWeights198.1 MB 558225daaa98
vae_encoder/model.onnxWeights136.8 MB 6be23fdf564e
model_index.jsonConfiguration685 B
scheduler/scheduler_config.jsonConfiguration459 B
text_encoder/config.jsonConfiguration565 B
text_encoder_2/config.jsonConfiguration575 B
tokenizer/special_tokens_map.jsonConfiguration586 B
tokenizer_2/special_tokens_map.jsonConfiguration460 B
unet/config.jsonConfiguration1.8 KB
vae/config.jsonConfiguration607 B
vae_decoder/config.jsonConfiguration607 B
vae_encoder/config.jsonConfiguration607 B
LICENSE.mdDocumentation11.9 KB
README.mdDocumentation5.9 KB
image_quality_one_step.pngOther203.7 KB
output_tile.jpgOther744.1 KB
prompt_alignment_one_step.pngOther204.3 KB
text_encoder_2/model.onnx_dataOther2.8 GB 0c29d6ace4f3
unet/model.onnx_dataOther10.3 GB 479e70b02ced
.gitattributesRepository1.5 KB
tokenizer/merges.txtTokenizer524.6 KB
tokenizer/tokenizer_config.jsonTokenizer704 B
tokenizer/vocab.jsonTokenizer1.1 MB
tokenizer_2/merges.txtTokenizer524.6 KB
tokenizer_2/tokenizer_config.jsonTokenizer855 B
tokenizer_2/vocab.jsonTokenizer1.1 MB

License and Download

License
other
Access
Open weights, no gate
Download size
42.5 GB
Download from Stability AI

Released by Stability AI through its official repository on Hugging Face. Read the license.

Memory Requirements

PrecisionWeights in memory
As published42.5 GB
16-bit5.1 GB
8-bit2.6 GB
4-bit1.3 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Compare sdxl-turbo

Questions About sdxl-turbo

How much GPU memory does sdxl-turbo need?

About 6.2 GB at 16-bit and 1.5 GB at 4-bit: the weights (2.6B parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run sdxl-turbo on?

At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

What license is sdxl-turbo released under?

other, as its publisher declares it. Read the license text before commercial use.

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