Pastel Mix on Rplicate

This model use andite/pastel-mix better-vae version and diffusers with three pipelines to generate images.

How it works

This implementation is targeted to make results similar to the pastel-mix demo generated by Stable Diffusion WebUI.

It uses three pipelines:

• First Pass - use the prompt to generate a base image to add details
• Second Pass - upscale with Latent mode
• Third Pass - use the image to image and add more details from the upscaled image

There have many limitations for now because diffusers didn't support all Stable Diffusion WebUI features.

Development

Prepare Batter VAE model

By default andite/pastel model didn't provide the diffusers model with the better-vae version, you have to convert it yourself.

Get diffusers and downlaod pastelmix-better-vae.safetensors from HuggingFaces

git clone git@github.com:huggingface/diffusers.git

Install required python packages

pip3 install diffusers omegaconf transformers safetensors

Convert the model

python3 ./diffusers/scripts/convert_original_stable_diffusion_to_diffusers.py --dump_path ./pastel-mix-better-vae --scheduler_type ddim --from_safetensors --checkpoint_path ./pastelmix-better-vae.safetensors

After your model is dumped, follow HuggingFace Guide to upload it.

If your plan to work on the local machine with GPU, the upload is optional.

Prepare Environment

Before starting, make sure you have docker is installed.

make setup

It will install cog and huggingface-cli for future works.

Run huggingface-cli login if you are working on a remote machine.

And run cog login to make sure you can upload the model.

Testing

You can use cog predict or predefined command make predict to test it works correctly.