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---
license: apache-2.0
language:
- ja
- en
pipeline_tag: text-to-image
library_name: diffusers
tags:
- art
---
# 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
- Uses the standard Apache-2.0 license for the model
- 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 [optional]
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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
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### Direct Use
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[More Information Needed]
### Downstream Use [optional]
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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
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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
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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#### Factors
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#### Metrics
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### Results
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#### Summary
## Model Examination [optional]
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
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### Compute Infrastructure
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#### Hardware
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#### Software
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## Citation [optional]
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**BibTeX:**
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**APA:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
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