95 lines
2.8 KiB
Markdown
95 lines
2.8 KiB
Markdown
|
## Install Python
|
||
|
|
|
||
|
|
Install Python 3.10 from here:
|
||
|
|
|
||
|
|
https://www.python.org/downloads/release/python-3109/
|
||
|
|
|
||
|
|
https://www.python.org/ftp/python/3.10.9/python-3.10.9-amd64.exe
|
||
|
|
|
||
|
|
Download and install Git from [git-scm.com](https://git-scm.com/).
|
||
|
|
|
||
|
|
or [Git for windows](https://gitforwindows.org/)
|
||
|
|
|
||
|
|
## Clone this repo
|
||
|
|
Clone the repo from normal command line then change into the directory:
|
||
|
|
|
||
|
|
git clone https://github.com/victorchall/EveryDream-trainer2
|
||
|
|
|
||
|
|
cd EveryDream-trainer2
|
||
|
|
|
||
|
|
## Download models
|
||
|
|
|
||
|
|
You need some sort of base model to start training. I suggest these two:
|
||
|
|
|
||
|
|
Stable Diffusion 1.5 with improved VAE:
|
||
|
|
|
||
|
|
https://huggingface.co/panopstor/EveryDream/blob/main/sd_v1-5_vae.ckpt
|
||
|
|
|
||
|
|
SD2.1 768:
|
||
|
|
|
||
|
|
https://huggingface.co/stabilityai/stable-diffusion-2-1/blob/main/v2-1_768-nonema-pruned.ckpt
|
||
|
|
|
||
|
|
You can use SD2.0 512 as well, but typically SD1.5 is going to be better.
|
||
|
|
|
||
|
|
https://huggingface.co/stabilityai/stable-diffusion-2-base/blob/main/512-base-ema.ckpt
|
||
|
|
|
||
|
|
Place these in the root folder of EveryDream2.
|
||
|
|
|
||
|
|
Run these commands *one time* to prepare them:
|
||
|
|
|
||
|
|
For SD1.x models, use this:
|
||
|
|
|
||
|
|
python utils/convert_original_stable_diffusion_to_diffusers.py --scheduler_type ddim ^
|
||
|
|
--original_config_file v1-inference.yaml ^
|
||
|
|
--image_size 768 ^
|
||
|
|
--checkpoint_path sd_v1-5_vae.ckpt ^
|
||
|
|
--prediction_type epsilon ^
|
||
|
|
--upcast_attn False ^
|
||
|
|
--pipeline_type FrozenOpenCLIPEmbedder ^
|
||
|
|
--dump_path "ckpt_cache/sd_v1-5_vae"
|
||
|
|
|
||
|
|
And the SD2.1 768 model:
|
||
|
|
|
||
|
|
python utils/convert_original_stable_diffusion_to_diffusers.py --scheduler_type ddim ^
|
||
|
|
--original_config_file v2-inference-v.yaml ^
|
||
|
|
--image_size 768 ^
|
||
|
|
--checkpoint_path v2-1_768-ema-pruned.ckpt ^
|
||
|
|
--prediction_type v_prediction ^
|
||
|
|
--upcast_attn False ^
|
||
|
|
--pipeline_type FrozenOpenCLIPEmbedder ^
|
||
|
|
--dump_path "ckpt_cache/v2-1_768-ema-pruned"
|
||
|
|
|
||
|
|
And finally the SD2.0 512 base model (generally not recommended base model):
|
||
|
|
|
||
|
|
python utils/convert_original_stable_diffusion_to_diffusers.py --scheduler_type ddim ^
|
||
|
|
--original_config_file v2-inference.yaml ^
|
||
|
|
--image_size 768 ^
|
||
|
|
--checkpoint_path 512-base-ema.ckpt ^
|
||
|
|
--prediction_type epsilon ^
|
||
|
|
--upcast_attn False ^
|
||
|
|
--pipeline_type FrozenOpenCLIPEmbedder ^
|
||
|
|
--dump_path "ckpt_cache/512-base-ema"
|
||
|
|
|
||
|
|
If you have other models, you need to know the base model that was used for them, in particular use the correct yaml (original_config_file) or it will not properly convert.
|
||
|
|
|
||
|
|
All of the above is one time. After running, you will use --resume_ckpt and just name the file without "ckpt_cache"
|
||
|
|
|
||
|
|
ex.
|
||
|
|
|
||
|
|
python train.py --resume_ckpt "sd_v1-5_vae" ...
|
||
|
|
python train.py --resume_ckpt "v2-1_768-ema-pruned" ...
|
||
|
|
python train.py --resume_ckpt "512-base-ema" ...
|
||
|
|
|
||
|
|
## Windows
|
||
|
|
|
||
|
|
|
||
|
|
Run windows_setup.bat to create your venv and install dependencies.
|
||
|
|
|
||
|
|
windows_setup.bat
|
||
|
|
|
||
|
|
|
||
|
|
## Linux, Linux containers, or WSL
|
||
|
|
|
||
|
|
TBD
|
||
|
|
|