Stable Diffusion Xl 1.0 Turbo GGUF

🚀 stable-diffusion-xl-1.0-turbo-GGUF

A text-to-image model based on SDXL-Turbo with GGUF quantization

🚀 Quick Start

Check out https://github.com/Stability-AI/generative-models

⚠️ Important Note

This is an experimental model supported by gpustack/llama-box v0.0.75+ only.

✨ Features

• Fast Generation: SDXL-Turbo is a fast generative text-to-image model that can synthesize photorealistic images from a text prompt in a single network evaluation.
• Multiple Quantization Options: Supports different quantization methods including FP16, Q8_0, Q4_1, and Q4_0.

📦 Installation

Diffusers

pip install diffusers transformers accelerate --upgrade

💻 Usage Examples

Basic Usage

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]

📚 Documentation

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.

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.

🔧 Technical Details

📄 License

This model is licensed under sai-nc-community.