# 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).
  - 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.
  - `split_einsum` version is compatible with all compute unit options including Neural Engine.
  - `original` version is only compatible with `CPU & GPU` option.
  - Custom resolution versions are tagged accordingly.
  - The `vae-ft-mse-840000-ema-pruned.ckpt` VAE is embedded into the model.
  - This model was converted with a `vae-encoder` for use with `image2image`.
  - This model is `fp16`.
  - Descriptions are posted as-is from original model source.
  - Not all features and/or results may be available in `CoreML` format.
  - This model does not have the [unet split into chunks](https://github.com/apple/ml-stable-diffusion#-converting-models-to-core-ml).
  - This model does not include a `safety checker` (for NSFW content).<br>

# CyberRealistic 1.4:
Source(s): [CivitAI](https://civitai.com/models/15003/cyberrealistic)<br>

Introducing my versatile photorealistic model - the result of a rigorous testing process that blends various models to achieve the desired output. While I cannot recall all of the individual components used in its creation, I am immensely satisfied with the end result. This model incorporates several custom elements, adding an extra layer of uniqueness to its output.

One of the model's key strengths lies in its ability to effectively process textual inversions and LORA, providing accurate and detailed outputs. Additionally, the model requires minimal prompts, making it incredibly user-friendly and accessible.

VAE recommended (and already baked into the files here): sd-vae-ft-mse-original.

I have made some slight modifications in this version 1.4, which can be considered as an alternative version rather than a completely new one. It appears to be slightly more streamlined than the previous version (1.3). If you are happy with 1.3 stay with that version. 1.3 is still stable and feature rich.<br><br>

![image](https://imagecache.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/138ea49a-ef1e-4e93-ae52-0c7dd47de200/width=450/00018-1470091446.jpeg)

![image](https://imagecache.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/68e981ef-9253-4b45-2713-baa878297000/width=450/00034-1581758962.jpeg)

![image](https://imagecache.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/d27815e3-2b6d-47a9-e5a5-db912d78f000/width=450/00047-3802993121-512x768-topaz-enhance-faceai.jpeg)

![image](https://imagecache.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/7af6e59c-1354-4673-7b41-0f6f76a27c00/width=450/00081-2231000404-512x768-topaz-enhance-faceai.jpeg)
{"license": "creativeml-openrail-m", "tags": ["coreml", "stable-diffusion", "text-to-image"]}
coreml stable-diffusion text-to-image license:creativeml-openrail-m region:us
TAGS
#coreml #stable-diffusion #text-to-image #license-creativeml-openrail-m #region-us 

# Core ML Converted Model:

  - This model was converted to Core ML for use on Apple Silicon devices. Conversion instructions can be found here.
  - Provide the model to an app such as Mochi Diffusion Github / Discord to generate images.
  - 'split_einsum' version is compatible with all compute unit options including Neural Engine.
  - 'original' version is only compatible with 'CPU & GPU' option.
  - Custom resolution versions are tagged accordingly.
  - The 'URL' VAE is embedded into the model.
  - This model was converted with a 'vae-encoder' for use with 'image2image'.
  - This model is 'fp16'.
  - Descriptions are posted as-is from original model source.
  - Not all features and/or results may be available in 'CoreML' format.
  - This model does not have the unet split into chunks.
  - This model does not include a 'safety checker' (for NSFW content).<br>

# CyberRealistic 1.4:
Source(s): CivitAI<br>

Introducing my versatile photorealistic model - the result of a rigorous testing process that blends various models to achieve the desired output. While I cannot recall all of the individual components used in its creation, I am immensely satisfied with the end result. This model incorporates several custom elements, adding an extra layer of uniqueness to its output.

One of the model's key strengths lies in its ability to effectively process textual inversions and LORA, providing accurate and detailed outputs. Additionally, the model requires minimal prompts, making it incredibly user-friendly and accessible.

VAE recommended (and already baked into the files here): sd-vae-ft-mse-original.

I have made some slight modifications in this version 1.4, which can be considered as an alternative version rather than a completely new one. It appears to be slightly more streamlined than the previous version (1.3). If you are happy with 1.3 stay with that version. 1.3 is still stable and feature rich.<br><br>

!image

!image

!image

!image
# Core ML Converted Model:

  - This model was converted to Core ML for use on Apple Silicon devices. Conversion instructions can be found here.
  - Provide the model to an app such as Mochi Diffusion Github / Discord to generate images.
  - 'split_einsum' version is compatible with all compute unit options including Neural Engine.
  - 'original' version is only compatible with 'CPU & GPU' option.
  - Custom resolution versions are tagged accordingly.
  - The 'URL' VAE is embedded into the model.
  - This model was converted with a 'vae-encoder' for use with 'image2image'.
  - This model is 'fp16'.
  - Descriptions are posted as-is from original model source.
  - Not all features and/or results may be available in 'CoreML' format.
  - This model does not have the unet split into chunks.
  - This model does not include a 'safety checker' (for NSFW content).<br> # CyberRealistic 1.4:
Source(s): CivitAI<br>

Introducing my versatile photorealistic model - the result of a rigorous testing process that blends various models to achieve the desired output. While I cannot recall all of the individual components used in its creation, I am immensely satisfied with the end result. This model incorporates several custom elements, adding an extra layer of uniqueness to its output.

One of the model's key strengths lies in its ability to effectively process textual inversions and LORA, providing accurate and detailed outputs. Additionally, the model requires minimal prompts, making it incredibly user-friendly and accessible.

VAE recommended (and already baked into the files here): sd-vae-ft-mse-original.

I have made some slight modifications in this version 1.4, which can be considered as an alternative version rather than a completely new one. It appears to be slightly more streamlined than the previous version (1.3). If you are happy with 1.3 stay with that version. 1.3 is still stable and feature rich.<br><br>

!image

!image

!image

!image
TAGS
#coreml #stable-diffusion #text-to-image #license-creativeml-openrail-m #region-us 
 # Core ML Converted Model:

  - This model was converted to Core ML for use on Apple Silicon devices. Conversion instructions can be found here.
  - Provide the model to an app such as Mochi Diffusion Github / Discord to generate images.
  - 'split_einsum' version is compatible with all compute unit options including Neural Engine.
  - 'original' version is only compatible with 'CPU & GPU' option.
  - Custom resolution versions are tagged accordingly.
  - The 'URL' VAE is embedded into the model.
  - This model was converted with a 'vae-encoder' for use with 'image2image'.
  - This model is 'fp16'.
  - Descriptions are posted as-is from original model source.
  - Not all features and/or results may be available in 'CoreML' format.
  - This model does not have the unet split into chunks.
  - This model does not include a 'safety checker' (for NSFW content).<br> # CyberRealistic 1.4:
Source(s): CivitAI<br>

Introducing my versatile photorealistic model - the result of a rigorous testing process that blends various models to achieve the desired output. While I cannot recall all of the individual components used in its creation, I am immensely satisfied with the end result. This model incorporates several custom elements, adding an extra layer of uniqueness to its output.

One of the model's key strengths lies in its ability to effectively process textual inversions and LORA, providing accurate and detailed outputs. Additionally, the model requires minimal prompts, making it incredibly user-friendly and accessible.

VAE recommended (and already baked into the files here): sd-vae-ft-mse-original.

I have made some slight modifications in this version 1.4, which can be considered as an alternative version rather than a completely new one. It appears to be slightly more streamlined than the previous version (1.3). If you are happy with 1.3 stay with that version. 1.3 is still stable and feature rich.<br><br>

!image

!image

!image

!image
TAGS
#coreml #stable-diffusion #text-to-image #license-creativeml-openrail-m #region-us 
# Core ML Converted Model:

  - This model was converted to Core ML for use on Apple Silicon devices. Conversion instructions can be found here.
  - Provide the model to an app such as Mochi Diffusion Github / Discord to generate images.
  - 'split_einsum' version is compatible with all compute unit options including Neural Engine.
  - 'original' version is only compatible with 'CPU & GPU' option.
  - Custom resolution versions are tagged accordingly.
  - The 'URL' VAE is embedded into the model.
  - This model was converted with a 'vae-encoder' for use with 'image2image'.
  - This model is 'fp16'.
  - Descriptions are posted as-is from original model source.
  - Not all features and/or results may be available in 'CoreML' format.
  - This model does not have the unet split into chunks.
  - This model does not include a 'safety checker' (for NSFW content).<br># CyberRealistic 1.4:
Source(s): CivitAI<br>

Introducing my versatile photorealistic model - the result of a rigorous testing process that blends various models to achieve the desired output. While I cannot recall all of the individual components used in its creation, I am immensely satisfied with the end result. This model incorporates several custom elements, adding an extra layer of uniqueness to its output.

One of the model's key strengths lies in its ability to effectively process textual inversions and LORA, providing accurate and detailed outputs. Additionally, the model requires minimal prompts, making it incredibly user-friendly and accessible.

VAE recommended (and already baked into the files here): sd-vae-ft-mse-original.

I have made some slight modifications in this version 1.4, which can be considered as an alternative version rather than a completely new one. It appears to be slightly more streamlined than the previous version (1.3). If you are happy with 1.3 stay with that version. 1.3 is still stable and feature rich.<br><br>

!image

!image

!image

!image