Dream bench utilizes pydantic classes to manage configuration of your benchmarking run. You can view an example of it here
| key | explanation |
|---|---|
| path | path to the webdataset. |
| batch_size | the batch size for your image generation model. |
| key | explanation |
|---|---|
| entity | wandb entity to use |
| project | the wandb project name |
| name | (optional) name of the run |
| key | explanation |
|---|---|
| save_path | Path to save predicted outputs too. |
| metrics | A list of metrics to use in your run ["FID", "Aesthetic", "ClipScore"] |
| device | torch device ["cuda:?", "cpu"] to use for evaluation |
| clip_architecture | Clip architecture to use for evaluation ["ViT-L/14", "ViT-B/32"] |