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yayoi_mix

yayoi_mix

yayoi_mix combines beautiful Asian features with realistic textures creating versatile images
Author:LoRA
Inclusion Time:23 Jan 2025
Downloads:1744
Pricing Model:Free
Size:1.99 GB
Version:v2.5
Introduction

This is a realistic merge model created using the following models:  Beautiful Realistic Asians (https://huggingface.co/BanKaiPls/AsianModel), XXMix_9 (https://civitai.com/models/47274/xxmix9), and Soda Mix (https://civitai.com/models/47507/soda-mix).  We thank the creators of these models.

This model is licensed under CreativeML Open RAIL-M.  Use of this model is subject to the terms and conditions of the constituent models.  The following are strictly prohibited:

Violent expressions

Child pornography

Sexual depiction of minors, including images depicting minors in swimwear, underwear, or similar attire.

Generating and publicly sharing images resembling real individuals without their explicit permission. (This does not apply to officially authorized LoRAs.)

We are not responsible for any issues arising from the use of this model or the images it generates.  Please use this model responsibly and at your own risk.  While some source models prohibit the sale of generated images, their licenses allow for different permissions when sharing merges; therefore, this restriction does not apply to this merged model.

Preview
FAQ

What to do if the model download fails?

Check whether the network connection is stable, try using a proxy or mirror source; confirm whether you need to log in to your account or provide an API key. If the path or version is wrong, the download will fail.

Why can't the model run in my framework?

Make sure you have installed the correct version of the framework, check the version of the dependent libraries required by the model, and update the relevant libraries or switch the supported framework version if necessary.

What to do if the model loads slowly?

Use a local cache model to avoid repeated downloads; or switch to a lighter model and optimize the storage path and reading method.

What to do if the model runs slowly?

Enable GPU or TPU acceleration, use batch data processing methods, or choose a lightweight model such as MobileNet to increase speed.

Why is there insufficient memory when running the model?

Try quantizing the model or using gradient checkpointing to reduce the memory requirements. You can also use distributed computing to spread the task across multiple devices.

What should I do if the model output is inaccurate?

Check whether the input data format is correct, whether the preprocessing method matching the model is in place, and if necessary, fine-tune the model to adapt to specific tasks.

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