Update README.md

README.md

@@ -6,9 +6,7 @@ datasets:
 
 # TinyDiT
 
-TinyDiT is an **85 million parameter unconditional image generation model** trained on **21,000+ anime face images**. The model is designed to be lightweight, efficient, and fast while still producing visually appealing anime-style face generations.
-
-The project explores compact diffusion transformer architectures capable of generating high-quality images with relatively low computational requirements.
+TinyDiT is an **85 million parameter unconditional image generation model** trained on **21,000+ anime face images**.
 
 ## Model Details
 
@@ -26,29 +24,19 @@ TinyDiT was trained on a curated anime face dataset containing over 21k images.
 
 **Dataset Repository:** `YOUR_DATASET_REPO_ID`
 
-Replace the placeholder above with your actual Hugging Face dataset repository ID.
-
 ## VAE
 
-The model uses a compact **13M parameter Variational Autoencoder (VAE)** for latent-space encoding and decoding. This significantly reduces training cost and improves inference efficiency.
-
-## Features
+The model uses a compact **13M parameter Variational Autoencoder (VAE)** for latent-space encoding and decoding. 
 
-* Compact 85M parameter architecture
-* Fast and lightweight image generation
-* Anime-style face synthesis
-* Efficient latent diffusion training
-* Suitable for low-resource GPUs and experimentation
 
 ## Example Generated Image
 
 Below is a sample image generated by TinyDiT:
 
 <p align="center">
-  <img src="generated_sample.png" width="256"/>
+  <img src="sample.png" width="256"/>
 </p>
 
-The model produces soft anime-style portraits with coherent facial structure and color consistency despite its relatively small size.
 
 ## Usage
 
@@ -63,37 +51,14 @@ image = pipe().images[0]
 image.save("tinydit_sample.png")
 ```
 
-## Training
-
-TinyDiT was trained using latent diffusion techniques on anime face images with a lightweight transformer backbone.
-
-### Training Highlights
-
-* 21k+ anime face dataset
-* Latent-space diffusion training
-* Compact transformer architecture
-* Memory-efficient VAE
-* Optimized for smaller GPUs
 
 ## Limitations
 
 * Trained only on anime face data
 * Unconditional generation only
 * Limited diversity compared to larger diffusion models
-* Lower image sharpness at higher resolutions
 * May occasionally generate blurry or distorted outputs
 
-## Future Improvements
-
-* Text-conditioned generation
-* Larger and more diverse datasets
-* Higher-resolution image synthesis
-* Improved sampling methods
-* Better facial detail consistency
-
-## License
-
-Please specify the appropriate license for this repository.
 
 ## Acknowledgements