---
library_name: transformers
license: apache-2.0
datasets:
- isek-ai/danbooru-tags-2024
tags:
- trl
- sft
- optimum
- danbooru
inference: false
---
# Dart (Danbooru Tags Transformer) v2

This model is a fine-tuned Dart (Danbooru Tags Transformer) v2 base model that generates danbooru tags.

Demo: [🤗 Space with ZERO](https://huggingface.co/spaces/p1atdev/danbooru-tags-transformer-v2)

## Model variants

|Name|Architecture|Param size|Type|
|-|-|-|-|
|[v2-moe-sft](https://huggingface.co/p1atdev/dart-v2-moe-sft)|Mixtral|166m|SFT|
|[v2-moe-base](https://huggingface.co/p1atdev/dart-v2-moe-base)|Mixtral|166m|Pretrain|
|[v2-sft](https://huggingface.co/p1atdev/dart-v2-sft)|Mistral|114m|SFT|
|[v2-base](https://huggingface.co/p1atdev/dart-v2-base)|Mistral|114m|Pretrain|
|[v2-vectors](https://huggingface.co/p1atdev/dart-v2-vectors)|Embedding|-|Tag Embedding|

## Usage

### Using 🤗Transformers

```py
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
MODEL_NAME = "p1atdev/dart-v2-base"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, torch_dtype=torch.bfloat16)
prompt = (
    f"<|bos|>"
    f"<copyright>vocaloid</copyright>"
    f"<character>hatsune miku</character>"
    f"<|rating:general|><|aspect_ratio:tall|><|length:long|>"
    f"<general>1girl"
)
inputs = tokenizer(prompt, return_tensors="pt").input_ids
with torch.no_grad():
  outputs = model.generate(
    inputs,
    do_sample=True,
    temperature=1.0,
    top_p=1.0,
    top_k=100,
    max_new_tokens=128,
    num_beams=1,
  )
print(", ".join([tag for tag in tokenizer.batch_decode(outputs[0], skip_special_tokens=True) if tag.strip() != ""]))
```

### Using 📦`dartrs` library

> [!WARNING]
> This library is very experimental and there will be breaking changes in the future.

[📦`dartrs`](https://github.com/p1atdev/dartrs) is a [🤗`candle`](https://github.com/huggingface/candle) backend inference library for Dart v2 models.

```py
pip install -U dartrs
```

```py
from dartrs.dartrs import DartTokenizer
from dartrs.utils import get_generation_config
from dartrs.v2 import (
    compose_prompt,
    MistralModel,
    V2Model,
)
import time
import os
MODEL_NAME = "p1atdev/dart-v2-base"
model = MistralModel.from_pretrained(MODEL_NAME)
tokenizer = DartTokenizer.from_pretrained(MODEL_NAME)
config = get_generation_config(
    prompt=compose_prompt(
        copyright="vocaloid",
        character="hatsune miku",
        rating="general", # sfw, general, sensitive, nsfw, questionable, explicit
        aspect_ratio="tall", # ultra_wide, wide, square, tall, ultra_tall
        length="medium", # very_short, short, medium, long, very_long
        prompt="1girl, cat ears",
        do_completion=False
    ),
    tokenizer=tokenizer,
)
start = time.time()
output = model.generate(config)
end = time.time()
print(output)
print(f"Time taken: {end - start:.2f}s")
# cowboy shot, detached sleeves, empty eyes, green eyes, green hair, green necktie, hair in own mouth, hair ornament, letterboxed, light frown, long hair, long sleeves, looking to the side, necktie, parted lips, shirt, sleeveless, sleeveless shirt, twintails, wing collar
# Time taken: 0.26s
```

## Prompt Format

```py
prompt = (
    f"<|bos|>"
    f"<copyright>{copyright_tags_here}</copyright>"
    f"<character>{character_tags_here}</character>"
    f"<|rating:general|><|aspect_ratio:tall|><|length:long|>"
    f"<general>{general_tags_here}"
)
```

- Rating tag: `<|rating:sfw|>`, `<|rating:general|>`, `<|rating:sensitive|>`, `nsfw`, `<|rating:questionable|>`, `<|rating:explicit|>`
  - `sfw`: randomly generates tags in `general` or `sensitive` rating categories.
  - `general`: generates tags in `general` rating category.
  - `sensitive`: generates tags in `sensitive` rating category.
  - `nsfw`: randomly generates tags in `questionable` or `explicit` rating categories.
  - `questionable`: generates tags in `questionable` rating category.
  - `explicit`: generates tags in `explicit` rating category.

- Aspect ratio tag: `<|aspect_ratio:ultra_wide|>`, `<|aspect_ratio:wide|>`, `<|aspect_ratio:square|>`, `<|aspect_ratio:tall|>`, `<|aspect_ratio:ultra_tall|>`
  - `ultra_wide`: generates tags suits for extremely wide aspect ratio images. (~2:1)
  - `wide`: generates tags suits for wide aspect ratio images. (2:1~9:8)
  - `square`: generates tags suits for square aspect ratio images. (9:8~8:9)
  - `tall`: generates tags suits for tall aspect ratio images. (8:9~1:2)
  - `ultra_tall`: generates tags suits for extremely tall aspect ratio images. (1:2~)

- Length tag: `<|length:very_short|>`, `<|length:short|>`, `<|length:medium|>`, `<|length:long|>`, `<|length:very_long|>`
  - `very_short`: totally generates ~10 number of tags.
  - `short`: totally generates ~20 number of tags.
  - `medium`: totally generates ~30 number of tags.
  - `long`: totally generates ~40 number of tags.
  - `very_long`: totally generates 40~ number of tags.

## Model Details

### Model Description

- **Developed by:** Plat
- **Model type:** Causal language model
- **Language(s) (NLP):** Danbooru tags
- **License:** Apache-2.0
- **Demo:** Available on [🤗 Space](https://huggingface.co/spaces/p1atdev/danbooru-tags-transformer-v2)


## Training Details

### Training Data

This model was trained with:

- [isek-ai/danbooru-tags-2024](https://huggingface.co/datasets/isek-ai/danbooru-tags-2024/tree/202403-at20240423) with revision `202403-at20240423`: 7M size of danbooru tags dataset since 2005 to 2024/03/31.


### Training Procedure 

TODO

#### Preprocessing [optional]

[More Information Needed]


#### Training Hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 1024
- eval_batch_size: 256
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2048
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 5

## Evaluation

Evaluation has not been done yet and it needs to evaluate.


#### Model Architecture and Objective 

The architecture of this model is [Mistral](https://huggingface.co/docs/transformers/model_doc/mistral). See details in [config.json](./config.json).


### Compute Infrastructure

Private server.

#### Hardware

8x RTX A6000

#### Software

- Dataset processing: [🤗 Datasets](https://github.com/huggingface/datasets)
- Training: [🤗 Transformers](https://github.com/huggingface/transformers)
- SFT: [🤗 TRL](https://github.com/huggingface/trl)
- Inference library: [📦 dartrs](https://github.com/p1atdev/dartrs)
  - Backend: [🤗 candle](https://github.com/huggingface/candle)

## Related Projects

- [dart-v1](https://huggingface.co/p1atdev/dart-v1): The first version of the Dart model.
- [KBlueLeaf/DanTagGen](https://huggingface.co/collections/KBlueLeaf/dantaggen-65f82fa9335881a67573556b): The Aspect Ratio tag was inspired by this project.
- [furusu/danbooru-tag-similarity](https://huggingface.co/spaces/furusu/danbooru-tag-similarity): The idea of clustering tags and its training method was inspired by this project.