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--- |
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datasets: |
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- kakaobrain/coyo-700m |
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language: |
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- en |
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--- |
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## Model Details |
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### Model Description |
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This repo includes every models we trained during the Jax Community event sprint, organized by Hugging Face. |
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The folders {model} contains the Flax checkpoint and {model}_pt the Torch checkpoint. |
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- **Developed by:** Baptiste Lemaire, Guillaume Thomas and Tom Dupuis from CEA-List |
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- **Model type:** Canny Edge Maps conditionned Diffusion model |
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- **Language(s) (NLP):** English |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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- Fast low resolution image generation |
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- Online data augmentation |
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See our gradio app for more information : [UCDR-Net gradio](https://huggingface.co/spaces/Baptlem/UCDR-Net) |
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## Training Details |
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### Training Data |
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* [Coyo-700M](https://github.com/kakaobrain/coyo-dataset) |
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* [Bridge](https://sites.google.com/view/bridgedata) |
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### Training Procedure |
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We trained from scratch each one of our models. We kept the initial parameters, except for the Batch Size. |
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You can find the training script in the following [Event repo's folder](https://github.com/huggingface/community-events/blob/main/jax-controlnet-sprint/training_scripts/train_controlnet_flax.py) |
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#### Preprocessing |
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-Resize to 128 resolution |
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-Canny Edge Map |
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#### Training parameters |
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The following table describes the differents hyperpa |
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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. |
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The last model has been trained with a custom DataLoader. The previous loads a batch containing 4 images from Bridge and 28 from Coyo. |
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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. |
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### Results |
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See [UCDR-Net gradio](https://huggingface.co/spaces/Baptlem/UCDR-Net) |
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## Environmental Impact |
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). |
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- **Hardware Type:** TPU v4 |
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- **Cloud Provider:** Gcloud |
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- **Compute Region:** us-central2-b |
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- **Carbon Emitted:** [More Information Needed] |
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