71 lines
2.8 KiB
Markdown
71 lines
2.8 KiB
Markdown
# Download and setup base models
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In order to train, you need a base model on which to train. This is a one-time setup to configure base models when you want to use a particular base.
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Make sure the trainer is installed properly first. See [SETUP.md](SETUP.md) for more details.
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When you finish you should see something like this, come back to reference this picture as you go through the steps below:
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 *(this picture is just an EXAMPLE)*
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## Download models
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You need some sort of base model to start training. I suggest these two:
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Stable Diffusion 1.5 with improved VAE:
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https://huggingface.co/panopstor/EveryDream/blob/main/sd_v1-5_vae.ckpt
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SD2.1 768:
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https://huggingface.co/stabilityai/stable-diffusion-2-1/blob/main/v2-1_768-nonema-pruned.ckpt
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You can use SD2.0 512 as well, but typically SD1.5 is going to be better.
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https://huggingface.co/stabilityai/stable-diffusion-2-base/blob/main/512-base-ema.ckpt
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Place these in the root folder of EveryDream2.
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Run these commands *one time* to prepare them. **It's very important to use the correct YAML!**
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For SD1.x models, use this (note it will spill a lot of warnings to the console, but its fine):
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python utils/convert_original_stable_diffusion_to_diffusers.py --scheduler_type ddim ^
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--original_config_file v1-inference.yaml ^
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--image_size 512 ^
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--checkpoint_path sd_v1-5_vae.ckpt ^
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--prediction_type epsilon ^
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--upcast_attn False ^
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--dump_path "ckpt_cache/sd_v1-5_vae"
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And the SD2.1 768 model (uses v2-v yaml and "v_prediction" prediction type):
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python utils/convert_original_stable_diffusion_to_diffusers.py --scheduler_type ddim ^
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--original_config_file v2-inference-v.yaml ^
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--image_size 768 ^
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--checkpoint_path v2-1_768-nonema-pruned.ckpt ^
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--prediction_type v_prediction ^
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--upcast_attn False ^
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--dump_path "ckpt_cache/v2-1_768-nonema-pruned"
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And finally the SD2.0 512 base model (generally not recommended base model):
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python utils/convert_original_stable_diffusion_to_diffusers.py --scheduler_type ddim ^
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--original_config_file v2-inference.yaml ^
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--image_size 512 ^
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--checkpoint_path 512-base-ema.ckpt ^
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--prediction_type epsilon ^
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--upcast_attn False ^
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--dump_path "ckpt_cache/512-base-ema"
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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.** Make sure to put some sort of name in the dump_path after "ckpt_cache/" so you can reference it later.
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All of the above is one time. After running, you will use --resume_ckpt and just name the file without "ckpt_cache/"
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ex.
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python train.py --resume_ckpt "sd_v1-5_vae" ...
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python train.py --resume_ckpt "v2-1_768-ema-pruned" ...
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python train.py --resume_ckpt "512-base-ema" ...
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