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  library_name: diffusers
 
 
 
 
 
 
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  ---
 
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
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- ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
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- This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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- ## Uses
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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- ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- [More Information Needed]
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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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.
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- ## How to Get Started with the Model
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  Use the code below to get started with the model.
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- [More Information Needed]
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- ## Training Details
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- ### Training Data
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- <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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- [More Information Needed]
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- ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
 
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- [More Information Needed]
 
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- ## Evaluation
 
 
 
 
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- <!-- This should link to a Dataset Card if possible. -->
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- [More Information Needed]
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- [More Information Needed]
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- [More Information Needed]
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- 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).
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### 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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- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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- **BibTeX:**
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- **APA:**
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- ## Glossary [optional]
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- <!-- 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 Needed]
 
 
 
 
 
 
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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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  library_name: diffusers
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+ license: apache-2.0
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+ language:
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+ - ja
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+ pipeline_tag: text-to-image
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+ tags:
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+ - stable-diffusion
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  ---
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+ # 🐟 EvoSDXL-JP-v1
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+ 🤗 [Models](https://huggingface.co/SakanaAI) | 📚 [Paper](https://arxiv.org/abs/2403.13187) | 📝 [Blog](https://sakana.ai/TODO) | 🐦 [Twitter](https://twitter.com/SakanaAILabs)
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+ **EvoSDXL-JP-v1** is an experimental education-purpose Japanese SDXL Lightning.
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+ This model was created using the Evolutionary Model Merge method.
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+ Please refer to our [report](https://arxiv.org/abs/2403.13187) and [blog](https://sakana.ai/TODO) for more details.
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+ This model was produced by merging the following models.
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+ We are grateful to the developers of the source models.
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+ - [SDXL](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
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+ - [Juggernaut-XL-v9](https://huggingface.co/RunDiffusion/Juggernaut-XL-v9)
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+ - [SDXL-DPO](https://huggingface.co/mhdang/dpo-sdxl-text2image-v1)
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+ - [JSDXL](https://huggingface.co/stabilityai/japanese-stable-diffusion-xl)
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+ - [SDXL-Lightning](https://huggingface.co/ByteDance/SDXL-Lightning)
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+ ## Usage
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  Use the code below to get started with the model.
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+ <details>
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+ <summary> Click to expand </summary>
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+ ```python
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+ from evosdxl_jp_v1 import load_evosdxl_jp
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+ prompt = "柴犬"
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+ pipe = load_evosdxl_jp(device="cuda")
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+ images = pipe(prompt, num_inference_steps=4, guidance_scale=0).images
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+ images[0].save("image.png")
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+ ```
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+ </details>
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+ ## Model Details
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ <!-- Provide a longer summary of what this model is. -->
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+ - **Developed by:** [Sakana AI](https://sakana.ai/)
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+ - **Model type:** Diffusion-based text-to-image generative model
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+ - **Language(s):** Japanese
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+ - **License:** [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
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+ - **Repository:** [SakanaAI/evolutionary-model-merge](https://github.com/SakanaAI/evolutionary-model-merge)
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+ - **Paper:** https://arxiv.org/abs/2403.13187
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+ - **Blog:** https://sakana.ai/TODO
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+ ## Uses
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+ This model is provided for research and development purposes only and should be considered as an experimental prototype.
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+ It is not intended for commercial use or deployment in mission-critical environments.
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+ Use of this model is at the user's own risk, and its performance and outcomes are not guaranteed.
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+ Sakana AI shall not be liable for any direct, indirect, special, incidental, or consequential damages, or any loss arising from the use of this model, regardless of the results obtained.
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+ Users must fully understand the risks associated with the use of this model and use it at their own discretion.
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+ ## Acknowledgement
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+ We would like to thank the developers of the source models for their contributions and for making their work available.
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+ ## Citation
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+ ```bibtex
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+ @misc{akiba2024evomodelmerge,
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+ title = {Evolutionary Optimization of Model Merging Recipes},
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+ author. = {Takuya Akiba and Makoto Shing and Yujin Tang and Qi Sun and David Ha},
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+ year = {2024},
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+ eprint = {2403.13187},
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+ archivePrefix = {arXiv},
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+ primaryClass = {cs.NE}
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+ }
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+ ```