gpt-neo-125M-magicprompt-SD
Generate/augment your prompt, stable diffusion style.
This model is a fine-tuned version of EleutherAI/gpt-neo-125M on the Gustavosta/Stable-Diffusion-Prompts dataset. It achieves the following results on the evaluation set:
- Loss: 1.8875
- perplexity: 6.6028
Training and evaluation data
refer to the Gustavosta/Stable-Diffusion-Prompts dataset.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 10.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 3.2189 | 0.99 | 33 | 3.0051 |
| 2.5466 | 1.99 | 66 | 2.5215 |
| 2.2791 | 2.99 | 99 | 2.2881 |
| 2.107 | 3.99 | 132 | 2.1322 |
| 1.9458 | 4.99 | 165 | 2.0270 |
| 1.8664 | 5.99 | 198 | 1.9580 |
| 1.8083 | 6.99 | 231 | 1.9177 |
| 1.7631 | 7.99 | 264 | 1.8964 |
| 1.7369 | 8.99 | 297 | 1.8885 |
| 1.766 | 9.99 | 330 | 1.8875 |
Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.6.1
- Tokenizers 0.13.1
