| language: en | |
| license: apache-2.0 | |
| library_name: diffusers | |
| tags: [] | |
| datasets: CelebA | |
| metrics: [] | |
| <!-- This model card has been generated automatically according to the information the training script had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # diffusion_conditional | |
| ## Model description | |
| This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library | |
| on the `CelebA` dataset. | |
| ## Intended uses & limitations | |
| #### How to use | |
| ```python | |
| # TODO: add an example code snippet for running this diffusion pipeline | |
| ``` | |
| #### Limitations and bias | |
| [TODO: provide examples of latent issues and potential remediations] | |
| ## Training data | |
| [TODO: describe the data used to train the model] | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 0.0001 | |
| - train_batch_size: 32 | |
| - eval_batch_size: 16 | |
| - gradient_accumulation_steps: 1 | |
| - optimizer: AdamW with betas=(0.95, 0.999), weight_decay=1e-06 and epsilon=1e-08 | |
| - lr_scheduler: cosine | |
| - lr_warmup_steps: 500 | |
| - ema_inv_gamma: 1.0 | |
| - ema_inv_gamma: 0.75 | |
| - ema_inv_gamma: 0.9999 | |
| - mixed_precision: fp16 | |
| ### Training results | |
| 📈 [TensorBoard logs](https://huggingface.co/shalpin87/diffusion_conditional/tensorboard?#scalars) | |