This is an unofficial code for DragDiffusion.
We show the DragDiffusion in a proof-of-concept way where we present the clean structured code of per-image optimization.
We hope the implementation of the principles helps.
The performances are not comparable with the paper's, and considering the performances, we do not include the GUI version yet.
conda env create -f environment.yml
conda activate diff
-
Put the image file in the
./finetune_data/and finetune the SD-v1.5 with LoRA.python dreambooth_lora.py --pretrained_model_name_or_path 'runwayml/stable-diffusion-v1-5' --instance_data_dir './finetune_data/' --instance_prompt 'xxy5syt00' --num_train_epochs 200 --checkpointing_steps 200 --output_dir 'lora-200' -
Latent optimization.
python run_drag.py
- Developed based on official version of DragGAN, unofficial version of DragGAN, and DIFT.