---
license: apache-2.0
library_name: diffusers
tags:
- text-to-video
- dit
- diffusion-transformer
- education
- zulense
---

# 🧠 DiT (Diffusion Transformer) Fine-Tuning Experiments

**Core Backbone for the [Zulense Z1 Foundation Model](https://huggingface.co/zulense/z1)**

This repository hosts the **Diffusion Transformer (DiT)** checkpoints trained to generate educational video content. These models operate in the latent space of our [Causal VAE](https://huggingface.co/ProgramerSalar/causal_vae_checkpoint) and are responsible for the temporal consistency and logical flow of the generated math lectures.

## 📂 Model Ledger & Performance

We are releasing the training logs to demonstrate the optimization curve of the "Imagination Engine."

### 1. `finetune_2_pytorch_model.bin` (🌟 Production Candidate)
* **Role:** **The Z1 Foundation Backbone**
* **Status:** ✅ **Converged / High Fidelity**
* **Performance:**
    * This checkpoint represents our stable run. It successfully learned to align temporal attention with the "teacher's movement" and "blackboard writing" logic.
    * **Metrics:** Achieved target validation loss on the Class 5 & 8 Math dataset.
    * **Behavior:** Shows strong temporal coherence (objects don't disappear randomly) and adheres to the physics of writing on a board.
    * **Recommendation:** **Use this file** for all inference tasks related to Zulense Z1.

### 2. `finetune_1_pytorch_model.bin` (Experimental / Deprecated)
* **Role:** **Initial Warmup Run**
* **Status:** ⚠️ **Underfitted / High Noise**
* **Performance:**
    * This was an early checkpoint where the model struggled to decouple the background (classroom) from the foreground (teacher).
    * **Issues:** Resulted in "flickering" artifacts and poor text alignment.
    * **Archived:** Kept here for research comparison to show the impact of our improved data scheduling in `finetune_2`.

## 🏗️ Architecture Context

The Zulense Video Pipeline follows a two-stage generation process:
1.  **Stage 1 (VAE):** Compresses video into latents (See: `causal_vae_checkpoint`).
2.  **Stage 2 (DiT):** This model (`finetune_2`) acts as the denoising backbone, predicting the latent patches over time based on text prompts (e.g., *"Draw a triangle with 3 angles"*).

## 💻 Usage (Loading Weights)

```python
import torch

# Path to the best performing checkpoint
model_path = "finetune_2_pytorch_model.bin"

# Load weights (assuming standard DiT structure)
state_dict = torch.load(model_path, map_location="cpu")

print(f"✅ Loaded DiT Backbone: {model_path}")
print(f"Tensor keys found: {len(state_dict.keys())}")