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README.md

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+# Fashion MNIST Text-to-Image Diffusion Model
+
+A transformer-based diffusion model trained on Fashion MNIST latent representations for text-to-image generation.
+
+## Model Information
+
+- **Architecture**: Transformer-based diffusion model
+- **Input**: 8×8×4 VAE latents
+- **Conditioning**: Text embeddings (class labels)
+- **Training Steps**: 8,500
+- **Dataset**: [Fashion MNIST 8×8 Latents](https://huggingface.co/datasets/shreenithi20/fmnist-8x8-latents)
+- **Framework**: PyTorch
+
+## Checkpoints
+
+- `model-1000.safetensors`: Early training (1k steps)
+- `model-3000.safetensors`: Mid training (3k steps)  
+- `model-5000.safetensors`: Advanced training (5k steps)
+- `model-8500.safetensors`: Final model (8.5k steps) 
+
+## Usage
+
+```python
+from transformers import AutoConfig, AutoModel
+import torch
+
+# Load model
+model = AutoModel.from_pretrained("shreenithi20/fmnist-t2i-diffusion")
+model.eval()
+
+# Generate images
+with torch.no_grad():
+    generated_latents = model.generate(
+        text_embeddings=class_labels,
+        num_inference_steps=25,
+        guidance_scale=7.5
+    )
+```
+
+## Model Architecture
+
+- **Patch Size**: 1×1
+- **Embedding Dimension**: 384
+- **Transformer Layers**: 12
+- **Attention Heads**: 6
+- **Cross Attention Heads**: 4
+- **MLP Multiplier**: 4
+- **Timesteps**: Continuous (beta distribution)
+- **Beta Distribution**: a=1.0, b=2.5
+
+## Training Details
+
+- **Learning Rate**: 1e-3 (Constant)
+- **Batch Size**: 128
+- **Optimizer**: AdamW
+- **Mixed Precision**: Yes
+- **Gradient Accumulation**: 1
+
+## Results
+
+The model generates high-quality Fashion MNIST images conditioned on class labels, with 8×8 latent resolution that can be decoded to 64×64 pixel images.