latent-ddpm-unet-ema-snowflakes

Unconditional DDPM U-Net for Snowflake Image Generation

Model Description

This model is an unconditional DDPM U-Net (EMA) trained from scratch to generate images of snowflakes.

The model operates on latent representations produced by a pre-trained Variational Autoencoder (VAE) (stabilityai/sd-vae-ft-mse) and learns the visual structure of real snowflakes from macro photography, enabling the generation of novel, synthetic snowflake images that resemble real crystalline forms.

The VAE used during training and inference is not part of this model.

• Model type: DDPM U-Net (EMA)
• Training framework: PyTorch / 🤗 Diffusers
• Latent space: VAE latents
• Input: Gaussian noise
• Output: Snowflake images (after VAE decoding, 512×512 resolution)

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Downstream Usage

This model is used in the open-source application Snowflakeizer, which applies the model for snowflake image generation.

• Application: Snowflakeizer
• Source code: https://github.com/dledwon/Snowflakeizer

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Training Data

The model was trained on "Macro photos of real snowflakes" by Alexey Kljatov.

• Author: Alexey Kljatov
• Description: Macro photographs of real snowflakes
• Source:
  https://www.flickr.com/photos/chaoticmind75/albums/72157702326145532/
• License: Creative Commons Attribution (CC BY)

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Licensing

• Training data:
  Creative Commons Attribution (CC BY) — © Alexey Kljatov
• Model weights:
  Creative Commons Attribution 4.0 International (CC BY 4.0)

When using this model, please ensure proper attribution to the original dataset author as required by the CC BY license.

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Intended Use

This model is intended for:

• Research in generative modeling
• Image synthesis experiments
• Artistic and creative applications
• Educational purposes related to diffusion models

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Limitations

• The model was trained on a single-domain dataset (snowflake macro photography).
• Generated images may resemble real snowflakes but do not correspond to physically accurate crystal growth processes.
• The model may produce artifacts or unrealistic structures.

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Ethical Considerations

• The training data consists of non-sensitive macro photographs of natural objects.
• There are no known privacy, biometric, or identity-related concerns.
• Generated images should not be presented as real scientific observations.

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