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README.md

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-# KOALA-700M-LLaVA-Caption Model Card
+---
+tags:
+- text-to-image
+- KOALA
+---
+
+<div align="center">
+  <img src="https://dl.dropboxusercontent.com/scl/fi/yosvi68jvyarbvymxc4hm/github_logo.png?rlkey=r9ouwcd7cqxjbvio43q9b3djd&dl=1" width="1024px" />
+</div>
+
+
+
+<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;
+</div>
+
+
+
+# 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.
+
+<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.
+
+<div align="center">
+  <img src="https://dl.dropboxusercontent.com/scl/fi/rjsqqgfney7be069y2yr7/teaser.png?rlkey=7lq0m90xpjcoqclzl4tieajpo&dl=1" width="1024px" />
+</div>
+
+
+## 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.
+
+<div align="center">
+  <img src="https://dl.dropboxusercontent.com/scl/fi/5ydeywgiyt1d3njw63dpk/arch.png?rlkey=1p6imbjs4lkmfpcxy153i1a2t&dl=1" width="1024px" />
+</div>
+
+### 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" />
+</div>
+
+
+
+
+
+## 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", 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}
+}
+```