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shreenithi20/
fmnist-t2i-diffusion

Name: fmnist-t2i-diffusion
Availability: InStock

fmnist_t2i_diffusion

shreenithi20/fmnist-8x8-latents

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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
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.