An implementation of SVDiff: Compact Parameter Space for Diffusion Fine-Tuning by using d🧨ffusers.
My summary tweet is found here.
left: LoRA, right: SVDiff
Compared with LoRA, the number of trainable parameters is 0.6 M less parameters and the file size is only <1MB (LoRA: 3.1MB)!!
$ pip install svdiff-pytorch
Or, manually
$ git clone https://github.com/mkshing/svdiff-pytorch
$ pip install -r requirements.txtThe following example script is for "Single-Subject Generation", which is a domain-tuning on a single object or concept (using 3-5 images). (See Section 4.1)
According to the paper, the learning rate for SVDiff needs to be 1000 times larger than the lr used for fine-tuning.
export MODEL_NAME="runwayml/stable-diffusion-v1-5"
export INSTANCE_DIR="path-to-instance-images"
export CLASS_DIR="path-to-class-images"
export OUTPUT_DIR="path-to-save-model"
accelerate launch train_svdiff.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir=$INSTANCE_DIR \
--class_data_dir=$CLASS_DIR \
--output_dir=$OUTPUT_DIR \
--with_prior_preservation --prior_loss_weight=1.0 \
--instance_prompt="photo of a sks dog" \
--class_prompt="photo of a dog" \
--resolution=512 \
--train_batch_size=1 \
--gradient_accumulation_steps=1 \
--learning_rate=5e-3 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--num_class_images=200 \
--max_train_steps=800from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
import torch
from svdiff_pytorch import load_unet_for_svdiff
pretrained_model_name_or_path = "runwayml/stable-diffusion-v1-5"
spectral_shifts_ckpt = "spectral_shifts.safetensors-path"
unet = load_unet_for_svdiff(pretrained_model_name_or_path, spectral_shifts_ckpt=spectral_shifts_ckpt, subfolder="unet")
# load pipe
pipe = StableDiffusionPipeline.from_pretrained(
pretrained_model_name_or_path,
unet=unet,
)
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe.to("cuda")
image = pipe("A picture of a sks dog in a bucket", num_inference_steps=25).images[0]You can use the following CLI too! Once it's done, you will see grid.png for the result.
python inference.py \
--pretrained_model_name_or_path="runwayml/stable-diffusion-v1-5" \
--spectral_shifts_ckpt="spectral_shifts.safetensors-path" \
--prompt="A picture of a sks dog in a bucket" \
--scheduler_type="dpm_solver++" \
--num_inference_steps=25 \
--num_images_per_prompt=2 You can also try SVDiff-pytorch in a UI with gradio. This demo supports both training and inference!
If you want to run it locally, run the following commands step by step.
$ git clone --recursive https://github.com/mkshing/svdiff-pytorch.git
$ cd scripts/gradio
$ pip install -r requirements.txt
$ export HF_TOKEN="YOUR_HF_TOKEN_HERE"
$ python app.py
You can adjust the strength of the weights by --spectral_shifts_scale
Here's a result for 0.8, 1.0, 1.2 (1.0 is the default).
By using ToMe for SD, the prior generation can be faster!
$ pip install tomesd
And, add --enable_tome_merging to your training arguments!
@misc{https://doi.org/10.48550/arXiv.2303.11305,
title = {SVDiff: Compact Parameter Space for Diffusion Fine-Tuning},
author = {Ligong Han and Yinxiao Li and Han Zhang and Peyman Milanfar and Dimitris Metaxas and Feng Yang},
year = {2023},
eprint = {2303.11305},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2303.11305}
}@misc{hu2021lora,
title = {LoRA: Low-Rank Adaptation of Large Language Models},
author = {Hu, Edward and Shen, Yelong and Wallis, Phil and Allen-Zhu, Zeyuan and Li, Yuanzhi and Wang, Lu and Chen, Weizhu},
year = {2021},
eprint = {2106.09685},
archivePrefix = {arXiv},
primaryClass = {cs.CL}
}@article{bolya2023tomesd,
title = {Token Merging for Fast Stable Diffusion},
author = {Bolya, Daniel and Hoffman, Judy},
journal = {arXiv},
url = {https://arxiv.org/abs/2303.17604},
year = {2023}
}- Training
- Inference
- Scaling spectral shifts
- Support multiple spectral shifts (Section 3.2)
- Cut-Mix-Unmix (Section 3.3)
- SVDiff + LoRA
