metadata

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. 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.

Mo Di Diffusio:

Source(s): Hugging Face

Mo Di Diffusion

This is the fine-tuned Stable Diffusion 1.5 model trained on screenshots from a popular animation studio. Use the tokens modern disney style in your prompts for the effect.

If you enjoy my work, please consider supporting me

Videogame Characters rendered with the model: Animal Characters rendered with the model: Cars and Landscapes rendered with the model:

Prompt and settings for Lara Croft:

modern disney lara croft Steps: 50, Sampler: Euler a, CFG scale: 7, Seed: 3940025417, Size: 512x768

Prompt and settings for the Lion:

modern disney (baby lion) Negative prompt: person human Steps: 50, Sampler: Euler a, CFG scale: 7, Seed: 1355059992, Size: 512x512

This model was trained using the diffusers based dreambooth training by ShivamShrirao using prior-preservation loss and the train-text-encoder flag in 9.000 steps.

🧨 Diffusers

This model can be used just like any other Stable Diffusion model. For more information, please have a look at the Stable Diffusion.

You can also export the model to ONNX, MPS and/or FLAX/JAX.

from diffusers import StableDiffusionPipeline
import torch

model_id = "nitrosocke/mo-di-diffusion"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to("cuda")

prompt = "a magical princess with golden hair, modern disney style"
image = pipe(prompt).images[0]

image.save("./magical_princess.png")

Gradio & Colab

We also support a Gradio Web UI and Colab with Diffusers to run fine-tuned Stable Diffusion models:

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