Awesome

See my custom models at https://ntcai.xys and https://civitai.com/user/ntc

Based on 'Erasing Concepts from Diffusion Models' https://erasing.baulab.info

ConceptMod

Finetuning with words.

Allows manipulation of Stable Diffusion with it's own learned representations.

Example: 'vibrant colors++|boring--'

Will erase boring concept and exaggerate vibrant colors concept.

New - train or animate on runpod

Usage examples and training phrases available on civit:

https://civitai.com/tag/conceptmod?sort=Newest

New! Use conceptmod easily:

animate any lora: https://runpod.io/gsc?template=gp2czwaknt&ref=xf9c949d

train on a phrase: https://runpod.io/gsc?template=8y3jhbola2&ref=xf9c949d

See the readme on runpod for details on how to use these. Tag it with conceptmod if you release on civit.ai.

• animation: the community cloud is cheaper, 3070 is fine. Total costs ~ $0.05 per video
• train: requires 24 GB vram at least. Total costs ~ $5 per Lora

Concept modifications

• Exaggerate: To exaggerate a concept, use the "++" operator.

  Example: "alpaca++" exaggerates "alpaca".

• Erase: To reduce a concept, use the "--" operator.

  Example: "monochrome--" reduces "monochrome".

• Freeze: Freeze by using the "#" operator. This reduces movement of specified term during training steps.

  Example: "1woman#1woman" with "badword--" freezes the first phrase while deleting the badword.

  Note: "#" means resist changing the unconditional.

• Orthogonal: To make two concepts orthogonal, use the "%" operator.

  Example: "cat%dog" makes "cat" and "dog" orthogonal. untested term

  this term is unstable without regularizer. You will see NaN loss.

  Set the alpha negative to pull dog to cat. "cat%dog:-0.1" untested term

• Replace: To replace use the following syntax:

  "target~source"

  This evaluates to:

  f"{target}++:{2 * lambda_value}",
  f"{prefix}={target}:{4 * lambda_value}",
  f"{target}%{prefix}:-{lambda_value}"

lambda_value default is 0.1

• {random_prompt} : turns into a random prompt from https://huggingface.co/datasets/Gustavosta/Stable-Diffusion-Prompts

Example:

"final boss++:0.4|final boss%{random_prompt}:-0.1"

experimental

• Pixelwise l2 loss: For reducing overall movement

  "source^target"

  renders the images for each phrase and adds pixelwise l2 loss between the two. Minizes pixel level image changes for keywords.

• Write to Unconditional: To write a concept to the unconditional model, use the "=" operator after the concept.

  Example: "alpaca=" causes the system to treat "alpaca" as a default concept or a concept that should always be considered during content generation.

  untested term

• Blend: Blend by using the "%" operator with ":-1.0", which means in reverse.

  Example: "anime%hyperrealistic:-1.0" blends "anime" and "hyperrealistic".

  untested term

Prompt options

• "@"

  deprecated, does nothing

• Alpha: Add alpha to scale terms.

  Example: "=day time:0.75|=night time:0.25|=enchanted lake"

  untested term

Installation Guide

If you launched with runpad or the docker image (ntcai/conceptmod_train), skip to training as this is already done.

• To get started clone the following repository of Original Stable Diffusion Link
• Then download the files from our iccv-esd repository to stable-diffusion main directory of stable diffusion. This would replace the ldm folder of the original repo with our custom ldm directory
• Download the weights from here and move them to stable-diffusion/models/ldm/ (This will be ckpt_path variable in train-scripts/train-esd.py)
• [Only for training] To convert your trained models to diffusers download the diffusers Unet config from here (This will be diffusers_config_path variable in train-scripts/train-esd.py)

Dependencies

From https://civitai.com/user/Envy

Working on windows.

conda create --name conceptmod python=3.10
conda activate conceptmod
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
pip install pytorch_lightning==1.7.7
pip install omegaconf einops scipy scikit-image scikit-learn lmdb
pip install taming-transformers-rom1504 'git+https://github.com/openai/CLIP.git@main#egg=clip' image-reward safetensors datasets matplotlib diffusers kornia
conda install huggingface_hub

This assumes you've got a working anaconda environment set up.

Dependency issues

Please see this dockerfile for the list of dependencies you need:

https://github.com/ntc-ai/conceptmod/blob/main/docker/Dockerfile_train

Look for the pip install and python3 setup.py develop sections. Extracting a Lora from a checkpoint has different dependencies.

Training Guide

Checkout train_sequential.sh for an example.

Generating Images

To generate images from one of the custom models use the following instructions:

• To use eval-scripts/generate-images.py you would need a csv file with columns prompt, evaluation_seed and case_number. (Sample data in data/)
• To generate multiple images per prompt use the argument num_samples. It is default to 10.
• The path to model can be customised in the script.
• It is to be noted that the current version requires the model to be in saved in stable-diffusion/compvis-<based on hyperparameters>/diffusers-<based on hyperparameters>.pt
• python eval-scripts/generate-images.py --model_name='compvis-word_VanGogh-method_xattn-sg_3-ng_1-iter_1000-lr_1e-05' --prompts_path 'stable-diffusion/art_prompts.csv' --save_path 'evaluation_folder' --num_samples 10

Notes

mod_count is set to two conceptmods being trained in parallel. You can reduce it if needed. negative_guidance, start_guidance which are positive in the original repository, is negative in this one. See train_sequential.sh for usage example.

Citing our work

Cite the original, maybe gpt-4