Update README.md

README.md

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----
-license: mit
----
+---
+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())}")