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README.md
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license: creativeml-openrail-m
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---
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---
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license: creativeml-openrail-m
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tags:
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- coreml
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- stable-diffusion
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- text-to-image
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---
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# Core ML Converted Model:
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- This model was converted to [Core ML for use on Apple Silicon devices](https://github.com/apple/ml-stable-diffusion). Conversion instructions can be found [here](https://github.com/godly-devotion/MochiDiffusion/wiki/How-to-convert-ckpt-or-safetensors-files-to-Core-ML).
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- Provide the model to an app such as **Mochi Diffusion** [Github](https://github.com/godly-devotion/MochiDiffusion) / [Discord](https://discord.gg/x2kartzxGv) to generate images.
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- `split_einsum` version is compatible with all compute unit options including Neural Engine.
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- `original` version is only compatible with `CPU & GPU` option.
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- Custom resolution versions are tagged accordingly.
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- The `vae-ft-mse-840000-ema-pruned.ckpt` VAE is embedded into the model.
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- This model was converted with a `vae-encoder` for use with `image2image`.
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- This model is `fp16`.
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- Descriptions are posted as-is from original model source.
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- Not all features and/or results may be available in `CoreML` format.
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- This model does not have the [unet split into chunks](https://github.com/apple/ml-stable-diffusion#-converting-models-to-core-ml).
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- This model does not include a `safety checker` (for NSFW content).
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- This model can be used with ControlNet.
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<br>
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# inkpunkDiffusion-v2_cn:
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Source(s): [CivitAI](https://civitai.com/models/1087) / [Hugging Face](https://huggingface.co/Envvi/Inkpunk-Diffusion)<br>
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## Inkpunk Diffusion v2
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Finetuned Stable Diffusion model trained on Dreambooth.
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Vaguely inspired by Gorillaz, FLCL, and Yoji Shinkawa
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Use nvinkpunk in your prompts.
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### About Version 2
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Improvements:
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Excorsized the woman that seemed to be haunting the model and appearing in basically every prompt. (She still pops in here and there, I honestly don't know where she comes from.)
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Faces are better overall, and characters are more responsive to descriptive language in prompts. Try playing around with emotions/nationality/age/poses to get the best results.
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Hair anarchy has been toned down slightly and added more diverse hairstyles to the dataset to avoid everyone having tumbleweed hair. Again, descriptive language in prompts helps.
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Full body poses are also more diverse, though keep in mind that wide angle shots tend to lead to distorted faces that may need to be touched up with img2img/inpainting.<br><br>
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),_((detailed_face)),_((award_winning)),_(High_Detail),_Sharp,_8k,__trending_o.jpeg)
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