Latent Ddpm Unet Ema Snowflakes

--- license: cc-by-4.0 tags: - diffusion - ddpm - unet - snowflakes - generative library_name: diffusers pipeline_tag: unconditional-image-generation --- # 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](https://huggingface.co/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) --- ## 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 --- ## 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) --- ## 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. --- ## Intended Use This model is intended for: - Research in generative modeling - Image synthesis experiments - Artistic and creative applications - Educational purposes related to diffusion models --- ## 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. --- ## Ethical Consideration...