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commonart-beta

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

Model Card for CommonArt

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

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

Uses

Direct Use

[More Information Needed]

Out-of-Scope Use

  • Generate misinfomation such as DeepFake.

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

Training Details

Training Data

We used these dataset to train the diffusion transformer:

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

  • Hardware Type: NVIDIA L4
  • Hours used: 20000
  • Cloud Provider: Google Cloud
  • Compute Region: Japan
  • Carbon Emitted: free

Technical Specifications

Model Architecture and Objective

Pixart-Σ based architecture

Compute Infrastructure

Google Cloud (Tokyo Region).

Hardware

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

Software

Pixart-Σ based code

Model Card Contact

AI Picasso, Inc.