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--- |
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license: cc-by-nc-4.0 |
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datasets: |
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- AdamCodd/Civitai-8m-prompts |
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metrics: |
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- rouge |
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base_model: t5-small |
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model-index: |
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- name: t5-small-negative-prompt-generator |
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results: |
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- task: |
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type: text-generation |
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name: Text Generation |
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metrics: |
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- type: loss |
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value: 0.14079 |
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- type: rouge-1 |
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value: 68.7527 |
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name: Validation ROUGE-1 |
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- type: rouge-2 |
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value: 53.8612 |
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name: Validation ROUGE-2 |
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- type: rouge-l |
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value: 67.3497 |
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name: Validation ROUGE-L |
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widget: |
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- text: masterpiece, 1girl, looking at viewer, sitting, tea, table, garden |
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example_title: Prompt |
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pipeline_tag: text2text-generation |
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inference: false |
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tags: |
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- art |
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extra_gated_prompt: "To get access to this model, send an email to [email protected] and provide a brief description of your project or application. Requests without this information will not be considered, and access will not be granted under any circumstances." |
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extra_gated_fields: |
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Company/University: text |
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Country: country |
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--- |
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## t5-small-negative-prompt-generator |
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This model [t5-small](https://huggingface.co/google-t5/t5-small) has been finetuned on a subset of the [AdamCodd/Civitai-8m-prompts](https://huggingface.co/datasets/AdamCodd/Civitai-8m-prompts) dataset (~800K prompts) focused on the top 10% prompts according to Civitai's positive engagement ("stats" field in the dataset). |
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It achieves the following results on the evaluation set: |
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* Loss: 0.14079 |
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* Rouge1: 68.7527 |
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* Rouge2: 53.8612 |
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* Rougel: 67.3497 |
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* Rougelsum: 67.3552 |
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The idea behind this is to automatically generate negative prompts that improve the end result according to the positive prompt input. I believe it could be useful to display suggestions for new users who use stable-diffusion or similar. |
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The license is **cc-by-nc-4.0**. For commercial use rights, please contact me ([email protected]). |
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## Usage |
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The length of the negative prompt is adjustable with the `max_new_tokens` parameter. The `repetition_penalty` and `no_repeat_ngram_size` are both needed as it'll start to repeat itself very quickly without it. You can use `temperature` and `top_k` to improve the creativity of the outputs. |
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```python |
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from transformers import pipeline |
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text2text_generator = pipeline("text2text-generation", model="AdamCodd/t5-small-negative-prompt-generator") |
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generated_text = text2text_generator( |
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"masterpiece, 1girl, looking at viewer, sitting, tea, table, garden", |
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max_new_tokens=50, |
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repetition_penalty=1.2, |
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no_repeat_ngram_size=2 |
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) |
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print(generated_text) |
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# [{'generated_text': '(worst quality, low quality:1.4), EasyNegative'}] |
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``` |
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This model has been trained exclusively on stable-diffusion prompts (SD1.4, SD1.5, SD2.1, SDXL...) so it might not work as well on non-stable-diffusion models. |
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NB: The dataset includes negative embeddings, so they're present in the output as you can see. |
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## Training and evaluation data |
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More information needed |
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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: 3e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08 |
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- Mixed precision |
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- num_epochs: 2 |
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- weight_decay: 0.01 |
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### Framework versions |
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- Transformers 4.36.2 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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- Evaluate 0.4.1 |
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If you want to support me, you can [here](https://ko-fi.com/adamcodd). |
