shreenithi20/fmnist-t2i-diffusion

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
• 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

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.