Update README.md (#1)

- Update README.md (9bc7b53cff875062fdf508d31fae0a24d41cf69f)


Co-authored-by: Cho Yoorhim <yoorhim@users.noreply.huggingface.co>

README.md

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-# KOALA-700M Model Card
+# KOALA-700M Model Card
+
+## Model Discription
+KOALA, which stands for **KnOwledge-distillAtion in LAtent diffusion model**, marks a notable advancement in text-to-image (T2I) synthesis technology. This model is engineered to balance speed and performance effectively, making it ideal for resource-limited environments. By emphasizing self-attention in knowledge distillation, KOALA significantly enhances the accessibility and efficiency of high-quality text-to-image synthesis, particularly in settings with constrained resources. This approach represents a major leap forward in the field of T2I technology.
+
+## 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 Architecture
+
+## 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]
+```
+
+## 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}
+}
+```