Chattiori's Model Merger

This script merges stable-diffusion models with the settings of checkpoint merger at a user-defined ratio.
You can use ckpt and safetensors as checkpoint file.
This script isn't need to be loaded on GPU.

The mode is:

• "WS" for Weighted Sum
• "SIG" for Sigmoid Merge
• "GEO" for Geometric Merge
• "MAX" for Max Merge
• "AD" for Add Difference (requires model2)
• "sAD" for Smooth Add Difference (requires model2)
• "MD" for Multiply Difference (requires model2 and beta)
• "SIM" for Similarity Add Difference (requires model2 and beta)
• "TD" for Train Difference (requires model2)
• "TRS" for Triple Sum (requires model2 and beta)
• "TS" for Tensor Sum (requires beta)
• "ST" for Sum Twice (requires model2 and beta)
• "NoIn" for No Interporation
• "RM" for Read Metadata
• "DARE" for DARE Merge

The ratio works as follows:

• 0.5 is a 50/50 mix of model0 and model1
• 0.3 is a 70/30 mix with more influence from model0 than model1

Running it

If you aren't using Automatic's web UI or are comfortable with the command line, you can also run merge.py directly. Just like with the .bat method, I'd recommend creating a folder within your stable-diffusion installation's main folder. This script requires torch to be installed, which you most likely will have installed in a venv inside your stable-diffusion webui install.

• Requires pytorch safetensors
• Navigate to the merge folder in your terminal
• Activate the venv
  • For users of Automatic's Webui use
    • ..\venv\Scripts\activate
  • For users of sd-webui (formerly known as HLKY) you should just be able to do
    • conda activate ldm
• run merge.py with arguments
  • Form: python merge.py mode model_path model_0 model_1 --alpha 0.5 --output merged
  • Example: python merge.py "WS" "C:...\Model parent file path" "FILE A.ckpt" "FILE B.safetensors" --alpha 0.45 --vae "C:...\VAE.safetensors" --prune --save_half --output "MERGED"
    • Optional: --model_2 sets the tertiory model, if omitted
    • Optional: --alpha controls how much weight is put on the second model. Defaults to 0, if omitted
      Can be written in float value, Merge Block Weight type writing and Elemental Merge type writing.
    • Optional: --rand_alpha randomizes weight put on the second model, if omitted
      Need to be written in str like "MIN, MAX, SEED".
      If SEED is not setted, it will be completely random (generates seed).
      Or "MIN, MAX, SEED, [Elemental merge args]" if you want to specify.
      Check out Elemental Random for Elemental merge args.
    • Optional: --beta controls how much weight is put on the third model. Defaults to 0, if omitted
      Can be written in float value, Merge Block Weight type writing and Elemental Merge type writing.
    • Optional: --rand_beta randomizes weight put on the third model, if omitted
      Need to be written in str like "MIN, MAX, SEED".
      If SEED is not setted, it will be completely random (generates seed).
      Or "MIN, MAX, SEED, [Elemental merge args]" if you want to specify.
      Check out Elemental Random for Elemental merge args.
    • Optional: --vae sets the vae file by set the path, if omitted.
      If not, the vae stored inside the model will automatically discarded.
    • Optional: --m0_name determines the name that to write in the data for the model0, if omitted
    • Optional: --m1_name determines the name that to write in the data for the model1, if omitted
    • Optional: --m2_name determines the name that to write in the data for the model2, if omitted
    • Optional: --cosine0 determines to favor model0's structure with details from 1, if omitted
      Check out Calcmode by hako-mikan for the information.
    • Optional: --cosine1 determines to favor model1's structure with details from 0, if omitted
      Check out Calcmode by hako-mikan for the information.
    • Optional: --use_dif_10 determines to use the difference between model0 and model1 as model1, if omitted
    • Optional: --use_dif_20 determines to use the difference between model0 and model2 as model2, if omitted
    • Optional: --use_dif_21 determines to use the difference between model2 and model1 as model2, if omitted
    • Optional: --fine determines adjustment of details, if omitted
      Check out Elemental EN by hako-mikan for the information.
    • Optional: --save_half determines whether save the file as fp16, if omitted
    • Optional: --prune determines whether prune the model, if omitted
    • Optional: --keep_ema determines keep only ema while prune, if omitted
    • Optional: --save_safetensors determines whether save the file as safetensors, if omitted
    • Optional: --output is the filename of the merged file, without file extension. Defaults to "merged", if omitted
    • Optional: --functn determines whether add merge function names, if omitted
    • Optional: --delete_source determines whether to delete the source checkpoint files, not vae file, if omitted
    • Optional: --no_metadata saves the checkpoint without metadata, if omitted
    • Optional: --device is the device that's going to be used to merge the models. Unless you have a ton of VRAM, you should probably just ignore this. Defaults to 'cpu', if omitted.
      • For .ckpt files, required VRAM seems to be roughly equivalent to the size of (size of both models) * 1.15. Merging 2 models at 3.76GB resulted in rougly 8.6GB of VRAM usage on top of everything else going on.
      • For .safetensors, required VRAM seems to be roughly equivalent to the size of size of both models. Merging 2 models at 3.76GB resulted in rougly 7.5GB of VRAM usage on top of everything else going on.
      • If you have enough VRAM to merge on your GPU you can use --device "cuda:x" where x is the card corresponding to the output of nvidia-smi -L

For Colab users

• Install required scripts.
  !pip install torch safetensors for safetensors
  !pip install pytorch_lighting for ckpt

• Clone this repo.
  !cd /content/
!git clone https://github.com/Faildes/merge-models

• Make models file and vae file.
  !mkdir models
!mkdir vae

• After installing models and vaes to the right directory,
  Run merge.py.
  !cd /content/merge-models/
!python merge.py...

Potential Problems & Troubleshooting

• Depending on your operating system and specific installation of python you might need to replace python with py, python3, conda or something else entirely.

Credits

• AUTOMATIC1111 for overall designing.
• eyriewow for original merge-models.
• lopho and arenasys for Pruning system.
• idelairre for Geometric, Sigmoid and Max Sum.
• s1dlx for Multiply Difference and Similarity Add Difference.
• hako-mikan for Tensor Sum, Train Difference, Triple Sum, Sum twice, Smooth Add Difference, Finetuning, Cosine Merging and Elemental Merge.
• bbc-mc for Block Weighted Merge.
• martyn for DARE Merge.
• Eyriewow got the merging logic in merge.py from this post by r_Sh4d0w, who seems to have gotten it from mlfoundations/wise-ft