metadata
license: other
tags:
- generated_from_trainer
- text generation
- stable diffusion
- midjourney
- text2image
- text to image
- prompt augment
- prompt engineering
thumbnail: https://i.imgur.com/DeKNHtC.jpg
datasets:
- pszemraj/text2image-prompts-multi
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
- text: Landscape of
example_title: landscape
parameters:
min_length: 16
max_length: 96
no_repeat_ngram_size: 1
do_sample: true
pszemraj/opt-350m-multiprompt
Generate/augment your prompt with a model trained on a large & diverse prompt dataset.
This model is a fine-tuned version of facebook/opt-350m on the pszemraj/text2image-prompts-multi dataset. It achieves the following results on the evaluation set:
- Loss: 1.6669
- eval steps per second: 16.21
- perplexity: 5.29
Example
The above example was created with DALL-E 2 but will of course work with any text2image model.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 16
- total_train_batch_size: 256
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.04
- num_epochs: 4.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.1677 | 1.0 | 990 | 2.0888 |
| 1.856 | 2.0 | 1980 | 1.8215 |
| 1.6864 | 3.0 | 2970 | 1.6935 |
| 1.6228 | 4.0 | 3960 | 1.6670 |
Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.6.1
- Tokenizers 0.13.1