| --- |
| 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](https://huggingface.co/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 |
| |
