fix docs

doc/INSTALL.md

@@ -1,24 +0,0 @@
-# Installation
-
-## Windows
-
-* Open a normal windows command prompt and run `windows_setup.bat` from the command line.  
-*Do **not** double click the file from Windows File Explorer*, you need the command window open.
-
-* While that is running, download the official xformers windows wheel from this URL: 
-https://github.com/facebookresearch/xformers/suites/9544395581/artifacts/454051141
-
-* Unzip the xformers file to the EveryDream2 folder
-
-* Check your command line window to make sure no errors occured.  If you have errors, please post them in the Discord and ask for assistance.
-
-* Once the command line is done with no errors, paste this command into the command prompt: 
-
-    `pip install xformers-0.0.15.dev0+303e613.d20221128-cp310-cp310-win_amd64.whl`
-
-* When you want to train in the future after closing the command line, run `activate_venv.bat` from the command line to activate the virtual environment again. (hint: you can type `a` then press tab, then press enter)
-
-## Next step
-
-Read the documentation to setup your base models from which you will train.
-[Base Model setup](doc/BASEMODELS.md)

doc/SETUP.md

@@ -17,78 +17,14 @@ Clone the repo from normal command line then change into the directory:
 
     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
+    windows_setup.cmd
 
-
-## Linux, Linux containers, or WSL
+## Linux, Linux containers, WSL, Runpod, etc
 
 TBD