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pszemraj
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distilgpt2-multiprompt

Text Generation
Transformers
PyTorch
Safetensors
gpt2
text generation
stable diffusion
midjourney
text2image
text to image
prompt augment
prompt engineering
text-generation-inference
Inference Endpoints
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metadata
license: apache-2.0
tags:
  - text generation
  - stable diffusion
  - midjourney
  - text2image
  - text to image
  - prompt augment
  - prompt engineering
datasets:
  - pszemraj/text2image-multi-prompt
model-index:
  - name: distilgpt2-multiprompt-v2-fp
    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
  - text: Landscape of
    example_title: landscape
parameters:
  min_length: 16
  max_length: 96
  no_repeat_ngram_size: 1
  do_sample: true

distilgpt2-multiprompt

Generate/augment your prompt with a model trained on a large & diverse prompt dataset.

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