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
inference: false
tags:
- art
t5-small-negative-prompt-generator
This model t5-small has been finetuned on a subset of the 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: 0.1730
- 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. 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.
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. The dataset includes negative embeddings, so they'll be present in the output.
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.