README.md

---
tags:
- text-to-image
- KOALA
---

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  <img src="https://dl.dropboxusercontent.com/scl/fi/yosvi68jvyarbvymxc4hm/github_logo.png?rlkey=r9ouwcd7cqxjbvio43q9b3djd&dl=1" width="1024px" />
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<div style="display:flex;justify-content: center">
    <a href="https://youngwanlee.github.io/KOALA/"><img src="https://img.shields.io/static/v1?label=Project%20Page&message=Github&color=blue&logo=github-pages"></a> &ensp;
    <a href="https://github.com/youngwanLEE/sdxl-koala"><img src="https://img.shields.io/static/v1?label=Code&message=Github&color=blue&logo=github"></a> &ensp;
    <a href="https://arxiv.org/abs/2312.04005"><img src="https://img.shields.io/static/v1?label=Paper&message=Arxiv:KOALA&color=red&logo=arxiv"></a> &ensp;
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# KOALA-700M-LLaVA-Caption Model Card

KOALA-700M and -1B models originally were trained on [LAION-aesthetics-V2 6+](https://laion.ai/blog/laion-aesthetics/) 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](https://llava-vl.github.io/). 
KOALA-700M-LLaVA-Caption and KOALA-1B-LLaVA-Caption is trained on the synthesized caption-image pairs of LAION-aesthetics-V2 6+. 


## 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.

<details><summary>FULL abstract</summary>
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.
</details>

<br>


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

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  <img src="https://dl.dropboxusercontent.com/scl/fi/rjsqqgfney7be069y2yr7/teaser.png?rlkey=7lq0m90xpjcoqclzl4tieajpo&dl=1" width="1024px" />
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## 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.

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  <img src="https://dl.dropboxusercontent.com/scl/fi/5ydeywgiyt1d3njw63dpk/arch.png?rlkey=1p6imbjs4lkmfpcxy153i1a2t&dl=1" width="1024px" />
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### 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.


<div align="center">
  <img src="https://dl.dropboxusercontent.com/scl/fi/u1az20y0zfww1l5lhbcyd/latency_gpu.svg?rlkey=vjn3gpkmywmp7jpilar4km7sd&dl=1" width="1024px" />
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## 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](https://huggingface.co/etri-vilab)
- Developer: [Youngwan Lee](https://youngwanlee.github.io/), [Kwanyong Park](https://pkyong95.github.io/), [Yoorhim Cho](https://ofzlo.github.io/), [Young-Ju Lee](https://scholar.google.com/citations?user=6goOQh8AAAAJ&hl=en), [Sung Ju Hwang](http://www.sungjuhwang.com/)
- Model Description: Latent Diffusion based text-to-image generative model. KOALA models uses the same text encoders as [SDXL-Base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) and only replace the denoising U-Net with the compressed U-Nets.
- Training data: [LAION-aesthetics-V2 6+](https://laion.ai/blog/laion-aesthetics/)
- Resources for more information: Check out [KOALA report on arXiv](https://arxiv.org/abs/2312.04005) and [project page](https://youngwanlee.github.io/KOALA/).




## Usage with 🤗[Diffusers library](https://github.com/huggingface/diffusers)
The inference code with denoising step 25
```python
import torch
from diffusers import StableDiffusionXLPipeline

pipe = StableDiffusionXLPipeline.from_pretrained("etri-vilab/koala-700m-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
```bibtex
@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}
}
```