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Sherwinroger002/
fashion_mnist_diffusion_class_conditional

Name: fashion_mnist_diffusion_class_conditional
Availability: InStock

safetensors

model_hub_mixin

pytorch_model_hub_mixin

text-to-image

zalando-datasets/fashion_mnist

mit

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🌀 Fashion-MNIST Diffusion (Class-Conditional UNet)

This repository hosts a class-conditional diffusion model trained on the Fashion-MNIST dataset.
The model uses a UNet-based architecture with timestep and label conditioning, implemented using PyTorch and integrated with the 🤗 Hugging Face Hub via PyTorchModelHubMixin.

🧩 Model Details

Property	Description
Architecture	Conditional UNet with residual and skip connections
Conditioning	Class embedding (10 Fashion-MNIST labels) + timestep embedding
Framework	PyTorch
Pipeline	`text-to-image` (adapted for diffusion)
License	MIT
Author	Sherwin Roger

The model learns to denoise images progressively, generating class-conditional samples (e.g., shoes, shirts, coats) from Gaussian noise through iterative diffusion steps.

🚀 Load from Hugging Face

You can directly load the model using huggingface_hub:

python

from huggingface_hub import hf_hub_download
import sys
import importlib.util

model_file = hf_hub_download(
    repo_id="Sherwinroger002/fashion_mnist_diffusion_class_conditional",
    filename="modeling.py"
)

spec = importlib.util.spec_from_file_location("modeling", model_file)
modeling = importlib.util.module_from_spec(spec)
sys.modules["modeling"] = modeling
spec.loader.exec_module(modeling)

model = modeling.EmbUnetModel.from_pretrained(
    "Sherwinroger002/fashion_mnist_diffusion_class_conditional"
)

image = modeling.generate("Sneaker", model)
modeling.show_image(image)

Sample output

Usage

This model can generate only these classes [ 'T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot' ] Pass the class name to make the model to generate the image

🪪 License

Released under the MIT License — free for commercial and research use.
If you use this model, please cite the repository and give credit to Sherwin Roger.

Citation

@software{fashion_mnist_diffusion_2025,
  author = {Sherwin Roger},
  title = {Fashion-MNIST Diffusion (Class-Conditional UNet)},
  year = {2025},
  url = {https://huggingface.co/Sherwinroger002/fashion_mnist_diffusion_class_conditional}
}