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.
vae tagged files have a vae embedded into the model.
Descriptions are posted as-is from original model source. Not all features and/or results may be available in CoreML format.
This model was converted with vae-encoder for i2i.

Note: Some models do not have the unet split into chunks.

miniSD:

Training details

Fine tuned from the stable-diffusion 1.4 checkpoint as follows:

22,000 steps fine-tuning only the attention layers of the unet, learn rate=1e-5, batch size=256
66,000 steps training the full unet, learn rate=5e-5, batch size=552
GPUs provided by Lambda GPU Cloud
Trained on LAION Improved Aesthetics 6plus.
Trained using https://github.com/justinpinkney/stable-diffusion, original checkpoint available here

License

This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies:

You can't use the model to deliberately produce nor share illegal or harmful outputs or content
The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license here