---
datasets:
- common-canvas/commoncatalog-cc-by
- madebyollin/megalith-10m
- madebyollin/soa-full
- alfredplpl/artbench-pd-256x256
language:
- ja
- en
library_name: diffusers
license: apache-2.0
pipeline_tag: text-to-image
tags:
- art
---

# Model Card for CommonArt β

![eyecatch](eyecatch.jpg)

This is a text-to-image model learning from CC-BY-4.0, CC-0 or CC-0 like images.

## Updates
- 2024/09/21: Update the model. (30000 L4 GPU hours)
- 2024/09/09: Release this model. (20000 L4 GPU hours)

## Model Details

### Model Description

At AI Picasso, we develop AI technology through active dialogue with creators, aiming for mutual understanding and cooperation. 
We strive to solve challenges faced by creators and grow together. 
One of these challenges is that some creators and fans want to use image generation but can't, likely due to the lack of permission to use certain images for training.
To address this issue, we have developed CommonArt β. As it's still in beta, its capabilities are limited. 
However, its structure is expected to be the same as the final version.

#### Features of CommonArt β

- Principally uses images with obtained learning permissions
- Understands both Japanese and English text inputs directly
- Minimizes the risk of exact reproduction of training images
- Utilizes cutting-edge technology for high quality and efficiency

### Misc.

- **Developed by:** alfredplpl
- **Funded by:** AI Picasso, Inc.
- **Shared by:** AI Picasso, Inc.
- **Model type:** Diffusion Transformer based architecture
- **Language(s) (NLP):** Japanese, English
- **License:** Apache-2.0

### Model Sources

- **Repository:** [Github](https://github.com/PixArt-alpha/PixArt-sigma)
- **Paper :** [PIXART-δ](https://arxiv.org/abs/2401.05252)

## How to Get Started with the Model

- diffusers for 16GB+ VRAM GPU
  
1. Install libraries.

```bash
pip install transformers diffusers
```

2. Run the following script

```python
import torch
from diffusers import Transformer2DModel, PixArtSigmaPipeline, AutoencoderKL, DPMSolverMultistepScheduler
from transformers import AutoModelForCausalLM, AutoTokenizer

# Prompts
prompt = "カラフルなお花畑。赤、青、黄、紫、ピンクなどの色とりどりの花に溢れている。"
neg_prompt=""

# Settings
device = "cuda"
weight_dtype = torch.float32
weight_dtype_te = torch.bfloat16
generator = torch.Generator().manual_seed(44)

# Load text encoder
tokenizer = AutoTokenizer.from_pretrained("cyberagent/calm2-7b")
text_encoder =  AutoModelForCausalLM.from_pretrained(
    "cyberagent/calm2-7b",
    torch_dtype=weight_dtype_te,
    device_map=device
)

# Get text embeddings
with torch.no_grad():
    pos_ids = tokenizer(
        prompt, max_length=512, padding="max_length", truncation=True, return_tensors="pt",
    ).to(device)
    pos_emb = text_encoder(pos_ids.input_ids, output_hidden_states=True, attention_mask=pos_ids.attention_mask)
    pos_emb = pos_emb.hidden_states[-1]
    neg_ids = tokenizer(
        neg_prompt, max_length=512, padding="max_length", truncation=True, return_tensors="pt",
    ).to(device)
    neg_emb = text_encoder(neg_ids.input_ids, output_hidden_states=True, attention_mask=neg_ids.attention_mask)
    neg_emb = neg_emb.hidden_states[-1]

# Important
del text_encoder

# load models
transformer = Transformer2DModel.from_pretrained(
    "aipicasso/commonart-beta",
    torch_dtype=weight_dtype
)
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=weight_dtype)
scheduler=DPMSolverMultistepScheduler()

pipe = PixArtSigmaPipeline(
    vae=vae,
    tokenizer=None,
    text_encoder=None,
    transformer=transformer,
    scheduler=scheduler
)

pipe.to(device)

# Generate Image
with torch.no_grad():
    image = pipe(
        negative_prompt=None,
        prompt_embeds=pos_emb,
        negative_prompt_embeds=neg_emb,
        prompt_attention_mask=pos_ids.attention_mask,
        negative_prompt_attention_mask=neg_ids.attention_mask,
        max_sequence_length=512,
        width=512,
        height=512,
        num_inference_steps=20,
        generator=generator,
        guidance_scale=4.5).images[0]
image.save("flowers.png")

```

- diffusers for 8GB VRAM GPU
1. Install libraries.

```bash
pip install transformers diffusers quanto
```

2. Run the following script

```python
import torch
from diffusers import Transformer2DModel, PixArtSigmaPipeline, AutoencoderKL, DPMSolverMultistepScheduler
from transformers import AutoModelForCausalLM, AutoTokenizer, QuantoConfig

# Prompts
prompt = "カラフルなお花畑。赤、青、黄、紫、ピンクなどの色とりどりの花に溢れている。"
neg_prompt=""

# Settings
device = "cuda"
weight_dtype = torch.bfloat16
weight_dtype_te = torch.bfloat16
generator = torch.Generator().manual_seed(44)

# Load text encoder
tokenizer = AutoTokenizer.from_pretrained("cyberagent/calm2-7b")
quantization_config = QuantoConfig(weights="int8")
text_encoder =  AutoModelForCausalLM.from_pretrained(
    "cyberagent/calm2-7b",
    quantization_config=quantization_config,
    torch_dtype=weight_dtype_te,
    device_map=device
)

# Get text embeddings
with torch.no_grad():
    pos_ids = tokenizer(
        prompt, max_length=512, padding="max_length", truncation=True, return_tensors="pt",
    ).to(device)
    pos_emb = text_encoder(pos_ids.input_ids, output_hidden_states=True, attention_mask=pos_ids.attention_mask)
    pos_emb = pos_emb.hidden_states[-1]
    neg_ids = tokenizer(
        neg_prompt, max_length=512, padding="max_length", truncation=True, return_tensors="pt",
    ).to(device)
    neg_emb = text_encoder(neg_ids.input_ids, output_hidden_states=True, attention_mask=neg_ids.attention_mask)
    neg_emb = neg_emb.hidden_states[-1]

# Important
del text_encoder

# load models
transformer = Transformer2DModel.from_pretrained(
    "aipicasso/commonart-beta",
    torch_dtype=weight_dtype
)
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=weight_dtype)
scheduler=DPMSolverMultistepScheduler()

pipe = PixArtSigmaPipeline(
    vae=vae,
    tokenizer=None,
    text_encoder=None,
    transformer=transformer,
    scheduler=scheduler
)

pipe.to(device)

# Generate Image
with torch.no_grad():
    image = pipe(
        negative_prompt=None,
        prompt_embeds=pos_emb,
        negative_prompt_embeds=neg_emb,
        prompt_attention_mask=pos_ids.attention_mask,
        negative_prompt_attention_mask=neg_ids.attention_mask,
        max_sequence_length=512,
        width=512,
        height=512,
        num_inference_steps=20,
        generator=generator,
        guidance_scale=4.5).images[0]
image.save("flowers.png")
```

## Uses

### Direct Use

- Assistance in creating illustrations, manga, and anime
  - For both commercial and non-commercial purposes
  - Communication with creators when making requests
- Commercial provision of image generation services
  - Please be cautious when handling generated content
- Self-expression
  - Using this AI to express "your" uniqueness
- Research and development
  - Fine-tuning (also known as additional training) such as LoRA
  - Merging with other models
  - Examining the performance of this model using metrics like FID
- Education
  - Graduation projects for art school or vocational school students
  - University students' graduation theses or project assignments
  - Teachers demonstrating the current state of image generation AI
- Uses described in the Hugging Face Community
  - Please ask questions in Japanese or English

### Out-of-Scope Use

- Generate misinfomation such as DeepFake.

## Bias, Risks, and Limitations

See Yahoo Flickr Creative Commons 100M dataset for more information. The information was collected circa 2014 and known to have a bias towards internet connected Western countries. Some areas such as the global south lack representation.

## Training Details

### Training Data
We used these dataset to train the diffusion transformer:

- [CommonCatalog-cc-by](https://huggingface.co/datasets/common-canvas/commoncatalog-cc-by)
- [Megalith-10M](https://huggingface.co/datasets/madebyollin/megalith-10m)
- [Smithonian Open Access](https://huggingface.co/datasets/madebyollin/soa-full)
- [ArtBench (CC-0 only) ](https://huggingface.co/datasets/alfredplpl/artbench-pd-256x256)


## Environmental Impact

- **Hardware Type:** NVIDIA L4
- **Hours used:** 30000
- **Cloud Provider:** Google Cloud
- **Compute Region:** Japan
- **Carbon Emitted:** free

## Technical Specifications

### Model Architecture and Objective

[Pixart-Σ based architecture](https://github.com/PixArt-alpha/PixArt-sigma)

### Compute Infrastructure

Google Cloud (Tokyo Region).

#### Hardware

We used NVIDIA L4x8 instance 4 nodes. (Total: L4x32)

#### Software

[Pixart-Σ based code](https://github.com/PixArt-alpha/PixArt-sigma)

## Model Card Contact

- support@aipicasso.app

# Acknowledgement
We approciate the image providers.
So, we are **standing on the shoulders of giants**.