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KOALA-700M Model Card

KOALA Model Cards

Abstract

TL;DR

We propose a fast text-to-image model, called KOALA, by compressing SDXL's U-Net and distilling knowledge from SDXL into our model. KOALA-700M can generate a 1024x1024 image in less than 1.5 seconds on an NVIDIA 4090 GPU, which is more than 2x faster than SDXL. KOALA-700M can be used as a decent alternative between SDM and SDXL in limited resources.

FULL abstract Stable diffusion is the mainstay of the text-to-image (T2I) synthesis in the community due to its generation performance and open-source nature. Recently, Stable Diffusion XL (SDXL), the successor of stable diffusion, has received a lot of attention due to its significant performance improvements with a higher resolution of 1024x1024 and a larger model. However, its increased computation cost and model size require higher-end hardware (e.g., bigger VRAM GPU) for end-users, incurring higher costs of operation. To address this problem, in this work, we propose an efficient latent diffusion model for text-to-image synthesis obtained by distilling the knowledge of SDXL. To this end, we first perform an in-depth analysis of the denoising U-Net in SDXL, which is the main bottleneck of the model, and then design a more efficient U-Net based on the analysis. Secondly, we explore how to effectively distill the generation capability of SDXL into an efficient U-Net and eventually identify four essential factors, the core of which is that self-attention is the most important part. With our efficient U-Net and self-attention-based knowledge distillation strategy, we build our efficient T2I models, called KOALA-1B &-700M, while reducing the model size up to 54% and 69% of the original SDXL model. In particular, the KOALA-700M is more than twice as fast as SDXL while still retaining a decent generation quality. We hope that due to its balanced speed-performance tradeoff, our KOALA models can serve as a cost-effective alternative to SDXL in resource-constrained environments.

These 1024x1024 samples are generated by KOALA-700M with 25 denoising steps.

Architecture

There are two two types of compressed U-Net, KOALA-1B and KOALA-700M, which are realized by reducing residual blocks and transformer blocks.

U-Net comparison

U-Net SDM-v2.0 SDXL-Base-1.0 KOALA-1B KOALA-700M
Param. 865M 2,567M 1,161M 782M
CKPT size 3.46GB 10.3GB 4.4GB 3.0GB
Tx blocks [1, 1, 1, 1] [0, 2, 10] [0, 2, 6] [0, 2, 5]
Mid block
Latency 1.131s 3.133s 1.604s 1.257s
  • Tx menans transformer block and CKPT means the trained checkpoint file.
  • We measured latency with FP16-precision, and 25 denoising steps in NVIDIA 4090 GPU (24GB).
  • SDM-v2.0 uses 768x768 resolution, while SDXL and KOALA models uses 1024x1024 resolution.

Latency and memory usage comparison on different GPUs

We measure the inference time of SDM-v2.0 with 768x768 resolution and the other models with 1024x1024 using a variety of consumer-grade GPUs: NVIDIA 3060Ti (8GB), 2080Ti (11GB), and 4090 (24GB). We use 25 denoising steps and FP16/FP32 precisions. OOM means Out-of-Memory. Note that SDXL-Base cannot operate in the 8GB-GPU.

Key Features

  • Efficient U-Net Architecture: KOALA models use a simplified U-Net architecture that reduces the model size by up to 54% and 69% respectively compared to its predecessor, Stable Diffusion XL (SDXL).
  • Self-Attention-Based Knowledge Distillation: The core technique in KOALA focuses on the distillation of self-attention features, which proves crucial for maintaining image generation quality.

Model Description

Usage with 🤗Diffusers library

The inference code with denoising step 25

import torch
from diffusers import StableDiffusionXLPipeline

pipe = StableDiffusionXLPipeline.from_pretrained("etri-vilab/koala-700m", torch_dtype=torch.float16)
pipe = pipe.to("cuda")

prompt = "A portrait painting of a Golden Retriever like Leonard da Vinci"
negative = "worst quality, low quality, illustration, low resolution"
image = pipe(prompt=prompt, negative_prompt=negative).images[0]

Uses

Direct Use

The model is intended for research purposes only. Possible research areas and tasks include

  • Generation of artworks and use in design and other artistic processes.
  • Applications in educational or creative tools.
  • Research on generative models.
  • Safe deployment of models which have the potential to generate harmful content.
  • Probing and understanding the limitations and biases of generative models.
  • 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.

Limitations and Bias

  • Text Rendering: The models face challenges in rendering long, legible text within images.
  • Complex Prompts: KOALA sometimes struggles with complex prompts involving multiple attributes.
  • Dataset Dependencies: The current limitations are partially attributed to the characteristics of the training dataset (LAION-aesthetics-V2 6+).

Citation

@misc{Lee@koala,
    title={KOALA: Self-Attention Matters in Knowledge Distillation of Latent Diffusion Models for Memory-Efficient and Fast Image Synthesis}, 
    author={Youngwan Lee and Kwanyong Park and Yoorhim Cho and Yong-Ju Lee and Sung Ju Hwang},
    year={2023},
    eprint={2312.04005},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}
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