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
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). The dataset 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. 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.
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.