This model is a fine-tuned version of distilgpt2 on the pszemraj/text2image-prompts-multi dataset. It achieves the following results on the evaluation set:

Loss: 2.0213
perplexity = 7.55

Intended uses & limitations

The model will generate augmentations that are biased towards the training data, i.e. what people already asked for in the SD/midjourney discords, etc. Creating a larger dataset was an attempt at mitigating this through more data from different datasets.

Training and evaluation data

See the pszemraj/text2image-prompts-multi dataset card for details. The dataset is a compilation of several text-to-image prompt datasets on huggingface :)

Training procedure

this was trained with several training rounds, 8 epochs in total on the train set.

Training hyperparameters (last training round)

The following hyperparameters were used during training:

learning_rate: 0.0006
train_batch_size: 16
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 8
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: 0.01
num_epochs: 2.0

Training results

Training Loss	Epoch	Step	Validation Loss
2.1637	1.0	965	2.0581
2.0885	2.0	1930	2.0213

Framework versions

Transformers 4.25.0.dev0
Pytorch 1.13.0+cu117
Datasets 2.6.1
Tokenizers 0.13.1

pszemraj/distilgpt2-multiprompt

distilgpt2-multiprompt