readme: ---
  license: creativeml-openrail-m
  tags:
  - coreml
  - stable-diffusion
  - text-to-image
  ---
  # Core ML Converted Model:
  
    - 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).<br>
    - 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.<br>
    - `split_einsum` version is compatible with all compute unit options including Neural Engine.<br>
    - `original` version is only compatible with CPU & GPU option.<br>
    
  # Note: Some models do not have the [unet split into chunks](https://github.com/apple/ml-stable-diffusion#-converting-models-to-core-ml).
  
  # Elldreth's Vivid Mix:
  Source(s): [CivitAI](https://civitai.com/models/2747/elldreths-vivid-mix)
  
  This mixed model is a combination of my all-time favorites AND new-found favorites, including a very popular anime model mixed with Zeipher's F222, Dreamlike, and H&A's awesome 3DKX_1.0b! Lastly, to top it off I used howder's jomad model. Every single model in this mix are great on their own. This mix allows you to take advantage of combined concepts and produces some great images.
  
  What's it good at?
  
  Realistic portraits
  
  Stylized characters
  
  Landscapes
  
  Fantasy
  
  Sci-Fi
  
  Anime
  
  Horror
  
  It's an all-around easy-to-prompt general purpose semi-realistic to realistic model that cranks out some really nice images. No trigger words required. All models were scanned prior to mixing and totally safe.
  
  So what's the difference between Vivid and all my other models?
  
  This model adds a lot more detail and realism to the images created with it and not just with portraits but landscapes as well. The other thing this model is better at is taking Textual Inversion embeddings. Lucid and Retro are both very resistant to TI Embeddings but Vivid is transformed very easily with a good embedding.
  
  What are you waiting for? Go get some great results from simple prompts.