metadata
license: other
tags:
- generated_from_trainer
- stable diffusion
- diffusion
- text2image
- prompt augment
- prompt engineering
datasets:
- Gustavosta/Stable-Diffusion-Prompts
model-index:
- name: opt-350m-magicprompt-SD
results: []
widget:
- text: morning sun over Jakarta
example_title: morning sun
- text: 'WARNING: pip is'
example_title: pip
- text: sentient cheese
example_title: sentient cheese
- text: cheeps are
example_title: cheeps
- text: avocado armchair
example_title: creative prompt
parameters:
min_length: 16
max_length: 96
no_repeat_ngram_size: 1
do_sample: true
opt-350m-magicprompt-SD
Generate/augment your prompt, stable diffusion style.
This model is a fine-tuned version of facebook/opt-350m on the Gustavosta/Stable-Diffusion-Prompts dataset. It achieves the following results on the evaluation set:
- Loss: 1.2987
- eval_steps_per_second = 16.623
- perplexity = 3.6644
example
output (on DALL-E 2, but as words are words, works anywhere)
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: 8
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 32
- total_train_batch_size: 512
- 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 |
|---|---|---|---|
| 2.8568 | 0.95 | 16 | 2.5937 |
| 2.2487 | 1.95 | 32 | 2.1050 |
| 1.9011 | 2.95 | 48 | 1.8082 |
| 1.6837 | 3.95 | 64 | 1.6178 |
| 1.4887 | 4.95 | 80 | 1.4897 |
| 1.3812 | 5.95 | 96 | 1.4017 |
| 1.2944 | 6.95 | 112 | 1.3437 |
| 1.2574 | 7.95 | 128 | 1.3127 |
| 1.2325 | 8.95 | 144 | 1.3009 |
| 1.2223 | 9.95 | 160 | 1.2987 |
Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.6.1
- Tokenizers 0.13.1