stable-diffusion-v2-1-turbo-GGUF

Model creator: Stability AI
Original model: sd-turbo
GGUF quantization: based on stable-diffusion.cpp ac54e that patched by llama-box.

Quantization	OpenCLIP ViT-H/14 Quantization	VAE Quantization
FP16	FP16	FP16
Q8_0	FP16	FP16
Q4_1	FP16	FP16
Q4_0	FP16	FP16

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SD-Turbo Model Card

SD-Turbo is a fast generative text-to-image model that can synthesize photorealistic images from a text prompt in a single network evaluation. We release SD-Turbo as a research artifact, and to study small, distilled text-to-image models. For increased quality and prompt understanding, we recommend SDXL-Turbo.

Please note: For commercial use, please refer to https://stability.ai/license.

Model Details

Model Description

SD-Turbo is a distilled version of Stable Diffusion 2.1, trained for real-time synthesis. SD-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: Stable Diffusion 2.1

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 [for the bigger SDXL-Turbo]: http://clipdrop.co/stable-diffusion-turbo

Evaluation

The charts above evaluate user preference for SD-Turbo over other single- and multi-step models. SD-Turbo evaluated at a single step is preferred by human voters in terms of image quality and prompt following over LCM-Lora XL and LCM-Lora 1.5.

Note: For increased quality, we recommend the bigger version SDXL-Turbo. 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. 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:

SD-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/sd-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 SD-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/sd-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 quality and prompt alignment is lower than that of SDXL-Turbo.
• 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.

How to Get Started with the Model

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