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DG-Seg

Repository for "Diffusion-Generated Segmentation (DG-Seg)"

Paper: Preprint

Environment

Pytorch 1.12.1, Python 3.9

$ conda create --name diffgen python=3.9
$ conda activate diffgen
$ pip install ftfy regex matplotlib lpips kornia opencv-python color-matcher blobfile scikit-learn pytorch_msssim
$ pip install torch==1.12.1 torchvision==0.13.1
$ pip install git+https://github.com/openai/CLIP.git
$ (conda or pip) install mpi4py 

Model download

To generate images, please download the pre-trained diffusion model(s)

Full Model LINK ~2 GB

Cluster Models LINK ~40GB

download the model into ./checkpoints folder

Training Models

# Cluster Models

python ~/diffusion-gen/guided_diffusion/BOI_train.py \
    --data_dir ~/diffusion-gen/guided_diffusion/segmented-images \
    --cluster_file ~/diffusion-gen/clusters.pkl \
    --cluster_index 1 \
    --image_size 256 \
    --out_dir ~/diffusion-gen/checkpoints \
    --batch_size 1

# Full Model

python ~/diffusion-gen/guided_diffusion/image_train.py \
    --data_dir ~/diffusion-gen/guided_diffusion/segmented-images \
    --image_size 256 \
    --out_dir ~/diffusion-gen/checkpoints \
    --batch_size 1

--cluster_index may be changed to any cluster index from the cluster file

Sample images from cluster models

python ~/diffusion-gen/main.py \
    --cluster_path ~/diffusion-gen/clusters.pkl \
    --output_path ~/diffusion-gen/cluster_image_samples \
    --model_path ~/diffusion-gen/checkpoints/cluster_models/model110000.pt \
    --diff_iter 100 \
    --timestep_respacing 200 \
    --skip_timesteps 80 \
    --model_output_size 256 \
    --num_samples 1 \
    --batch_size 1 \
    --use_noise_aug_all \
    --use_colormatch \
    -fti -sty -inp -spi

The cluster path, output path, and model path can be changed to your own path.

Note the format of the model path: each model will be of the form cluster_{cluster index}_model_{model iteration}.pt. For example, cluster_0_model_110000.pt is the model for cluster 0 at iteration 110000. In the main.py file, we loop through all the cluster models with the given iteration number and generate samples for each cluster.

-fti selects a random image from the cluster to use as the target image for image-guided generation

-sty applies styling

-inp uses the inpainting technique

-spi instead of just sampling {num_samples} images, we sample {num_samples} images from each image

Sample images from full model

python ~/diffusion-gen/main.py \
    --data_dir ~/diffusion-gen/guided_diffusion/segmented-images/masked-images \
    --output_path ~/diffusion-gen/image_samples \
    --model_path ~/diffusion-gen/logs/256models/model200000.pt \
    --diff_iter 100 \
    --timestep_respacing 200 \
    --skip_timesteps 80 \
    --model_output_size 256 \
    --num_samples 1 \
    --batch_size 1 \
    --use_noise_aug_all \
    --use_colormatch \
    -fti -sty -inp -spi

Refer to above section for clarification on the arguments.

Our source code rely on Blended-diffusion, guided-diffusion, flexit, splicing vit, and DiffuseIT