---
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: 0.173
    - type: rouge-1
      value: 63.86
      name: Validation ROUGE-1
    - type: rouge-2
      value: 47.5195
      name: Validation ROUGE-2
    - type: rouge-l
      value: 62.0977
      name: Validation ROUGE-L
widget:
- text: masterpiece, 1girl, looking at viewer, sitting, tea, table, garden
  example_title: Prompt
pipeline_tag: text2text-generation
---
## T5-small-negative-prompt-generator
This model has been trained 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 includes negative embeddings (and thus will output them).

It achieves the following results on the evaluation set:
* Loss: 0.1730
* Rouge1: 63.8600
* Rouge2: 47.5195
* Rougel: 62.0977
* Rougelsum: 62.1006

The idea behind this is to automatically generate negative prompts that improve the end result according to the positive prompt input. This is still in its experimental stage, but it's already quite good. I believe it could be useful as a recommendation for new users who use stable-diffusion or similar.

The license is **cc-by-nc-4.0**. For commercial use rights, please [contact me](https://discord.com/users/859202914400075798).

## Usage

The length of the negative prompt is adjustable with the `max_new_tokens` parameter. Keep in mind that you'll need to adjust the samplers slightly to avoid repetition and improve the quality of the output.

```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",
    do_sample=True,
    max_new_tokens=50,
    repetition_penalty=1.2,
    no_repeat_ngram_size=2,
    temperature=0.9,
    top_p=0.92
)

print(generated_text)
# [{'generated_text': 'easynegative, badhandv4, (worst quality:2), (low quality lowres:1), blurry, text'}]
```
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.

## 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: 1
- 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).