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Model Details

Model Description

This repo includes every models we trained during the Jax Community event sprint, organized by Hugging Face. The folders {model} contains the Flax checkpoint and {model}_pt the Torch checkpoint.

  • Developed by: Baptiste Lemaire, Guillaume Thomas and Tom Dupuis from CEA-List
  • Model type: Canny Edge Maps conditionned Diffusion model
  • Language(s) (NLP): English


  • Fast low resolution image generation
  • Online data augmentation

See our gradio app for more information : UCDR-Net gradio

Training Details

Training Data

Training Procedure

We trained from scratch each one of our models. We kept the initial parameters, except for the Batch Size. You can find the training script in the following Event repo's folder


-Resize to 128 resolution -Canny Edge Map

Training parameters

The following table describes the differents hyperpa alt text

We stopped the coyo model a bit after it processed its first epoch. After running it, we discovered it performed pretty well even after only one epoch. So we deciced to keep it.

The last model has been trained with a custom DataLoader. The previous loads a batch containing 4 images from Bridge and 28 from Coyo. Therefore, we can't talk about epoch as the model processed coyo faster than bridge. We then trained the model according to steps and not epoch.


See UCDR-Net gradio

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

  • Hardware Type: TPU v4
  • Cloud Provider: Gcloud
  • Compute Region: us-central2-b
  • Carbon Emitted: [More Information Needed]
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Dataset used to train Baptlem/UCDR-Net_models

Space using Baptlem/UCDR-Net_models 1