updated readme

Author: codinglabsong

README.md

@@ -1,14 +1,15 @@
 # Latent Diffusion MNIST Experiment
 
-Latent Diffusion MNIST Experiment is a Python project exploring the implementation of a latent diffusion model using a variational autoencoder (VAE) and a conditional U-Net. It currently targets the MNIST dataset, but the architecture and training pipeline are intentionally dataset-agnostic and can scale to larger and more complex image domains.
+Latent Diffusion MNIST Experiment is a Python project exploring the implementation of a latent diffusion model using a variational autoencoder (VAE) and a conditional U-Net. It currently targets the MNIST dataset, but the architecture and training pipeline are intentionally dataset-agnostic and can scale to larger and more complex image domains with minimal changes.
 
 ## Features
 - **Modular Architecture** – separate training routines for VAE and U-Net components built on top of Hugging Face Diffusers.
-- **Dataset Agnostic Pipeline** – although the repository demonstrates MNIST, the data loaders and model design can be extended to other datasets with minimal changes, retaining most of the training logic from this experiment.
+- **Dataset Agnostic Pipeline** – although the repository demonstrates MNIST, the data loaders and model design can be easily extended to other datasets, retaining most of the training logic from this experiment.
 - **Configurable Training** – hyperparameters (batch size, learning rates, epochs, etc.) are managed via `config.yaml` for reproducible experiments.
 - **Efficient & Stable Training** – leverages `Accelerate` for device management and distributed training, cosine learning rate schedules, and EMA tracking. Added gradient clipping.
 - **Visualization Utilities** – automatic saving of reconstruction and generation plots for monitoring model performance.
-- **Gradio App** – ready-to-deploy web app for interactive predictions. Hosted on [Huggingface Spaces](https://huggingface.co/spaces/codinglabsong/aging-gan).
+- **Gradio App** – *Work in Progress*
+<!-- - **Gradio App** – ready-to-deploy web app for interactive predictions. Hosted on [Huggingface Spaces](https://huggingface.co/spaces/codinglabsong/aging-gan). -->
 - **Developer Tools & CI** – Linting with ruff and black, unit tests with pytest, end‐to‐end smoke tests in GitHub Actions.
 
 ## Installation
@@ -61,7 +62,7 @@ The script will load the VAE and EMA-smoothed U-Net weights and produce images s
 ## Results
 ### Example Outputs
 
-*Placeholder for generated image examples.*
+*Work in Progress*
 
 ### Considerations for Improvements
 
@@ -70,8 +71,9 @@ The script will load the VAE and EMA-smoothed U-Net weights and produce images s
 - Integrate more advanced schedulers or guidance techniques.
 
 ## Running the Gradio Inference App
+*Work in Progress*
 
-This project includes an interactive Gradio app for making predictions with the trained model.
+<!-- This project includes an interactive Gradio app for making predictions with the trained model.
 
 1. **Obtain the Trained Model:**
     - Ensure that a trained model directory (`models/vae.pth` and `models/ema-unet.pth`) is available in the project root.
@@ -84,7 +86,7 @@ This project includes an interactive Gradio app for making predictions with the
     ```
     - Visit the printed URL (e.g., `http://127.0.0.1:7860`) to interact with the model.
 
-> You can also access the hosted demo on [Huggingface Spaces](https://huggingface.co/spaces/codinglabsong/aging-gan)
+> You can also access the hosted demo on [Huggingface Spaces](https://huggingface.co/spaces/codinglabsong/aging-gan) -->
 
 
 ## Testing

requirements.txt

@@ -1,4 +1,5 @@
 tqdm
 matplotlib
 diffusers[torch] 
-torchvision
+torchvision
+gradio

setup.py

@@ -10,5 +10,6 @@
         "matplotlib",
         "diffusers[torch]",
         "torchvision",
+        "gradio",
     ],
 )