---
license: cc-by-nc-4.0
datasets:
- AdamCodd/Civitai-8m-prompts
metrics:
- rouge
base_model: t5-small
model-index:
- name: t5-small-negative-prompt-generator
  results:
  - task:
      type: text-generation
      name: Text Generation
    metrics:
    - type: loss
      value: 0.14079
    - type: rouge-1
      value: 68.7527
      name: Validation ROUGE-1
    - type: rouge-2
      value: 53.8612
      name: Validation ROUGE-2
    - type: rouge-l
      value: 67.3497
      name: Validation ROUGE-L
widget:
- text: masterpiece, 1girl, looking at viewer, sitting, tea, table, garden
  example_title: Prompt
pipeline_tag: text2text-generation
inference: false
tags:
- art
---
## t5-small-negative-prompt-generator
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).

It achieves the following results on the evaluation set:
* Loss: 0.14079
* Rouge1: 68.7527
* Rouge2: 53.8612
* Rougel: 67.3497
* Rougelsum: 67.3552

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.

The license is **cc-by-nc-4.0**. For commercial use rights, please contact me (adamcoddml@gmail.com).

## Usage

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. 

```python
from transformers import pipeline

text2text_generator = pipeline("text2text-generation", model="AdamCodd/t5-small-negative-prompt-generator")

generated_text = text2text_generator(
    "masterpiece, 1girl, looking at viewer, sitting, tea, table, garden",
    max_new_tokens=50,
    repetition_penalty=1.2,
    no_repeat_ngram_size=2
)
print(generated_text)
# [{'generated_text': '(worst quality, low quality:1.4), EasyNegative'}]
```
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.

NB: The dataset includes negative embeddings, so they're present in the output as you can see.

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08
- Mixed precision
- num_epochs: 2
- weight_decay: 0.01

### Framework versions

- Transformers 4.36.2
- Datasets 2.16.1
- Tokenizers 0.15.0
- Evaluate 0.4.1

If you want to support me, you can [here](https://ko-fi.com/adamcodd).