pipeline_tag: text-to-image
inference: false
license: other
license_name: sai-nc-community
license_link: https://huggingface.co/stabilityai/sdxl-turbo/blob/main/LICENSE.md
stable-diffusion-xl-1.0-turbo-GGUF
!!! Experimental supported by gpustack/llama-box v0.0.75+ only !!!
Model creator: Stability AI
Original model: sdxl-turbo
GGUF quantization: based on stable-diffusion.cpp ac54e that patched by llama-box.
VAE From: madebyollin/sdxl-vae-fp16-fix.
| Quantization | OpenAI CLIP ViT-L/14 Quantization | OpenCLIP ViT-G/14 Quantization | VAE Quantization |
|---|---|---|---|
| FP16 | FP16 | FP16 | FP16 |
| Q8_0 | FP16 | FP16 | FP16 |
| Q4_1 | FP16 | FP16 | FP16 |
| Q4_0 | FP16 | FP16 | FP16 |
SDXL-Turbo 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.
Model Details
Model Description
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 diffusion models as a teacher signal and combines this with an adversarial loss to ensure high image fidelity even in the low-step regime of one or two sampling steps.
- Developed by: Stability AI
- Funded by: Stability AI
- Model type: Generative text-to-image model
- Finetuned from model: SDXL 1.0 Base
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).
- Repository: https://github.com/Stability-AI/generative-models
- Paper: https://stability.ai/research/adversarial-diffusion-distillation
- Demo: http://clipdrop.co/stable-diffusion-turbo
Evaluation
The charts above evaluate user preference for SDXL-Turbo over other single- and multi-step models.
SDXL-Turbo evaluated at a single step is preferred by human voters in terms of image quality and prompt following over LCM-XL evaluated at four (or fewer) steps.
In addition, we see that using four steps for SDXL-Turbo further improves performance.
For details on the user study, we refer to the research paper.
Uses
Direct Use
The model is intended for both non-commercial and commercial usage. You can use this model for non-commercial or research purposes under this license. Possible research areas and tasks include
- Research on generative models.
- Research on real-time applications of generative models.
- Research on the impact of real-time generative models.
- Safe deployment of models which have the potential to generate harmful content.
- Probing and understanding the limitations and biases of generative models.
- Generation of artworks and use in design and other artistic processes.
- Applications in educational or creative tools.
For commercial use, please refer to https://stability.ai/membership.
Excluded uses are described below.
Diffusers
pip install diffusers transformers accelerate --upgrade
- Text-to-image:
SDXL-Turbo does not make use of guidance_scale or negative_prompt, we disable it with guidance_scale=0.0.
Preferably, the model generates images of size 512x512 but higher image sizes work as well.
A single step is enough to generate high quality images.
from diffusers import AutoPipelineForText2Image
import torch
pipe = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16")
pipe.to("cuda")
prompt = "A cinematic shot of a baby racoon wearing an intricate italian priest robe."
image = pipe(prompt=prompt, num_inference_steps=1, guidance_scale=0.0).images[0]
- Image-to-image:
When using SDXL-Turbo for image-to-image generation, make sure that num_inference_steps * strength is larger or equal
to 1. The image-to-image pipeline will run for int(num_inference_steps * strength) steps, e.g. 0.5 * 2.0 = 1 step in our example
below.
from diffusers import AutoPipelineForImage2Image
from diffusers.utils import load_image
import torch
pipe = AutoPipelineForImage2Image.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16")
pipe.to("cuda")
init_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png").resize((512, 512))
prompt = "cat wizard, gandalf, lord of the rings, detailed, fantasy, cute, adorable, Pixar, Disney, 8k"
image = pipe(prompt, image=init_image, num_inference_steps=2, strength=0.5, guidance_scale=0.0).images[0]
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. The model should not be used in any way that violates Stability AI's Acceptable Use Policy.
Limitations and Bias
Limitations
- The generated images are of a fixed resolution (512x512 pix), and the model does not achieve perfect photorealism.
- The model cannot render legible text.
- Faces and people in general may not be generated properly.
- The autoencoding part of the model is lossy.
Recommendations
The model is intended for both non-commercial and commercial usage.