File size: 1,741 Bytes
b3bf2eb c588c3e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 | ---
datasets:
- shreenithi20/fmnist-8x8-latents
---
# 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. |