README.md

---
license: apache-2.0
tags:
- vae
- video-generation
- education
- fine-tuning
- pytorch
---

# 🎓 Causal VAE Fine-Tuning Experiments (Indian Math Curriculum)

**Developing the "Imagination Engine" for [Zulense](https://huggingface.co/zulense)**

This repository contains experimental checkpoints for a **Causal VAE (Variational Autoencoder)** fine-tuned specifically on Indian educational content (NCERT Math). 

The goal of these experiments is to adapt standard video generation VAEs to better reconstruct "blackboard style" line art, diagrams, and text-heavy educational videos, which often suffer from artifacts in general-purpose models.

## 📂 Checkpoint Manifest

We are releasing two distinct checkpoints representing different stages of our training curriculum.

### 1. `FineTune_2_checkpoint.pth` (Recommended)
* **Target Domain:** **Class 5 Numeracy & Foundation**
* **Status:** ✅ **Improved Stability**
* **Experiment Notes:** * This run focused on simpler, foundational concepts (Class 5 curriculum) to stabilize the loss.
    * **Improvements:** Significantly reduced `kl_divergence` and reconstruction loss compared to the V1 baseline.
    * **Use Case:** Better at handling basic shapes and slower temporal movements typical in primary education teaching.

### 2. `checkpoint-0.pth` (Legacy / Research Artifact)
* **Target Domain:** **Class 8 Geometry & Algebra**
* **Status:** ⚠️ **Unstable / High Loss**
* **Experiment Notes:** * This was our initial attempt at modeling complex Class 8 geometry.
    * **Known Issues:** The model struggled with high-frequency details (text/grid lines), resulting in higher `vae_loss` and unstable KL divergence. 
    * **Why we kept it:** Retained for comparative analysis to show the difficulty jump between primary and middle school visual complexity.

## 🔬 Technical Context

Standard video VAEs are optimized for photorealism. Our experiments suggest that for **educational video synthesis**:
1.  **Text Preservation:** Standard VAEs struggle to reconstruct the sharp text found in math explanations.
2.  **Curriculum Learning:** Fine-tuning on simpler visual concepts (Class 5) before complex ones (Class 8) yields better convergence.

## 💻 Usage (PyTorch)

```python
import torch

# Load the Causal VAE checkpoint
checkpoint_path = "FineTune_2_checkpoint.pth" # Use the stable Class 5 checkpoint
state_dict = torch.load(checkpoint_path, map_location="cpu")

print(f"Loaded checkpoint: {checkpoint_path}")
# Note: This requires the specific Causal VAE architecture definition to load state_dict