Awesome

Installation

To install simply run

conda env create -f environment.yml

Training and Sampling

Training information and additional README can be found in each model subfolder.

1. Diffusion model for generating mask(s)

To change directory:

export OPENAI_LOGDIR={OUTPUT_FOLDER}

Model parameters:

MODEL_FLAGS="--image_size 256 --num_channels 128 --num_res_blocks 2 --num_heads 1 --learn_sigma True --use_scale_shift_norm False --attention_resolutions 16"
DIFFUSION_FLAGS="--diffusion_steps 1000 --noise_schedule linear --rescale_learned_sigmas False --rescale_timesteps False"
TRAIN_FLAGS="--lr 1e-4 --batch_size 2"

Training:

python improved-diffusion/scripts/image_train.py --data_dir ./improved-diffusion/datasets/Kvasir-SEG/masks $MODEL_FLAGS $DIFFUSION_FLAGS $TRAIN_FLAGS

Sampling:

python improved-diffusion/scripts/image_sample.py --num_samples {SAMPLES} --model_path {MODEL_CHECKPOINT.pt} --output {OUTPUT_TYPE} --postprocess {POSTPROCESS} $MODEL_FLAGS $DIFFUSION_FLAGS

2. Conditional diffusion model for generating polyp images

Training:

python latent-diffusion/main.py --base latent-diffusion/configs/latent-diffusion/kvasir-ldm-vq4-.yaml -t --gpus 0,

Sampling: We can generate polyp images based on the condition, based on:

• Existing dataset

python latent-diffusion/scripts/inference_dataset.py

• Generated mask, mask(s) needs to be placed inside latent-diffusion/data/samples/masks:

python latent-diffusion/scripts/inference_mask.py {IMAGE_NAME} --samples {SAMPLES}

Results are stored inside latent-diffusion/results

Pipeline

To render multiple polyps we can use {SAMPLES} to sample multiple mask(s) and use them to generate polyp(s):

export OPENAI_LOGDIR='latent-diffusion/results/masks/'

MODEL_FLAGS="--image_size 256 --num_channels 128 --num_res_blocks 2 --num_heads 1 --learn_sigma True --use_scale_shift_norm False --attention_resolutions 16"
DIFFUSION_FLAGS="--diffusion_steps 1000 --noise_schedule linear --rescale_learned_sigmas False --rescale_timesteps False"
TRAIN_FLAGS="--lr 1e-4 --batch_size 2"

python improved-diffusion/scripts/image_sample.py --num_samples {SAMPLES} --model_path {MODEL_CHECKPOINT.pt} --output png --postprocess {POSTPROCESS} $MODEL_FLAGS $DIFFUSION_FLAGS
python latent-diffusion/scripts/inference_pipe.py

Utils

a) Mask comparator

To visually inspect overlap between generated mask(s) and the training dataset that was used for training the diffusion model, we can use:

python improved-diffusion/scripts/image_compare.py {KVASIR_PATH} {MASK_IMAGE_PATH}

b) Segmentation model

Follow the instruction in ./segmentation_experiments

Contact details:

vajira@simula.no or ro.machacek0@gmail.com

Citation: