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--- |
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license: other |
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tags: |
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- generated_from_trainer |
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- text generation |
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- stable diffusion |
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- midjourney |
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- text2image |
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- text to image |
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- prompt augment |
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- prompt engineering |
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thumbnail: https://i.imgur.com/DeKNHtC.jpg |
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datasets: |
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- pszemraj/text2image-multi-prompt |
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widget: |
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- text: "morning sun over Jakarta" |
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example_title: "morning sun" |
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- text: "WARNING: pip is" |
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example_title: "pip" |
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- text: "sentient cheese" |
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example_title: "sentient cheese" |
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- text: "cheeps are" |
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example_title: "cheeps" |
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- text: "avocado armchair" |
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example_title: "creative prompt" |
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- text: "Landscape of" |
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example_title: "landscape" |
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parameters: |
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min_length: 16 |
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max_length: 96 |
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no_repeat_ngram_size: 1 |
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do_sample: True |
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--- |
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# pszemraj/opt-350m-multiprompt |
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<a href="https://colab.research.google.com/gist/pszemraj/bdd1238ee4b8330aeec6774a16f9a677/opt-350m-multiprompt-demo.ipynb"> |
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<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> |
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</a> |
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Generate/augment your prompt with a model trained on a large & diverse prompt dataset. |
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This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on the pszemraj/text2image-prompts-multi dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.6669 |
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- eval steps per second: 16.21 |
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- perplexity: 5.29 |
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## Example |
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<br> |
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_The above example was created with [DALL-E 2](https://labs.openai.com/sc/YbiY2kkuQeODzHNwUHn4D5RN) but will of course work with any text2image model._ |
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## Intended uses & limitations |
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- 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. |
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## Training and evaluation data |
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See the `pszemraj/text2image-prompts-multi` dataset card for details. The dataset is a compilation of several text-to-image prompt datasets on huggingface :) |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0002 |
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- train_batch_size: 8 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 2 |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 256 |
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- total_eval_batch_size: 8 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.04 |
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- num_epochs: 4.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 2.1677 | 1.0 | 990 | 2.0888 | |
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| 1.856 | 2.0 | 1980 | 1.8215 | |
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| 1.6864 | 3.0 | 2970 | 1.6935 | |
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| 1.6228 | 4.0 | 3960 | 1.6670 | |
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### Framework versions |
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- Transformers 4.25.0.dev0 |
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- Pytorch 1.13.0+cu117 |
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- Datasets 2.6.1 |
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- Tokenizers 0.13.1 |
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