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--- |
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language: en |
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license: apache-2.0 |
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library_name: diffusers |
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tags: |
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- stable-diffusion |
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- stable-diffusion-diffusers |
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- text-to-image |
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datasets: YaYaB/onepiece-blip-captions |
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metrics: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the training script had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# sd-onepiece-diffusers4 |
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## Model description |
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This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library |
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on the `YaYaB/onepiece-blip-captions` dataset. |
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## Intended uses & limitations |
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#### How to use |
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```python |
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# TODO: add an example code snippet for running this diffusion pipeline |
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``` |
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#### Limitations and bias |
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[TODO: provide examples of latent issues and potential remediations] |
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## Training data |
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[TODO: describe the data used to train the model] |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- gradient_accumulation_steps: 4 |
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- optimizer: AdamW with betas=(0.9, 0.999), weight_decay=0.01 and epsilon=1e-08 |
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- lr_scheduler: constant |
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- lr_warmup_steps: 500 |
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- ema_inv_gamma: None |
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- ema_inv_gamma: None |
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- ema_inv_gamma: None |
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- mixed_precision: fp16 |
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### Training results |
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📈 [TensorBoard logs](https://huggingface.co/YaYaB/sd-onepiece-diffusers4/tensorboard?#scalars) |
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