KOALA-1B-LLaVA-Caption Model Card

KOALA-700M and -1B models originally were trained on LAION-aesthetics-V2 6+ which has some noisy or very short texts. So we construct synthesized captions of LAION-aesthetics-V2 6+ by using a large multimodal model, LLaVA. KOALA-700M-LLaVA-Caption and KOALA-1B-LLaVA-Caption is trained on the synthesized caption-image pairs of LAION-aesthetics-V2 6+.

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.

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

• 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

• Developed by ETRI Visual Intelligence Lab
• Developer: Youngwan Lee, Kwanyong Park, Yoorhim Cho, Young-Ju Lee, Sung Ju Hwang
• Model Description: Latent Diffusion based text-to-image generative model. KOALA models uses the same text encoders as SDXL-Base-1.0 and only replace the denoising U-Net with the compressed U-Nets.
• Training data: LAION-aesthetics-V2 6+
• Resources for more information: Check out KOALA report on arXiv and project page.

Usage with 🤗Diffusers library

The inference code with denoising step 25

import torch
from diffusers import StableDiffusionXLPipeline

pipe = StableDiffusionXLPipeline.from_pretrained("etri-vilab/koala-1b-llava-cap", 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}
}