This repository contains the implementation of Exponential-SNR Diffusion. This model aims to enhance the signal-to-noise ratio (SNR) exponentially over time to improve the performance of diffusion models.

To set up the environment for this project, follow these steps:

1. Create a Conda Environment:

   conda create -n diffusion python=3.8

2. Activate the Conda Environment:

   conda activate diffusion

• improved_diffusion/: Contains the source code for the Exponential-SNR Diffusion model.
• datasets/: Includes scripts to prepare datasets CIFAR-10.
• notebooks/: Jupyter notebooks for experimentation and visualization.
• scripts/: Shell and Python scripts for various utilities and tasks.

Besides, you can download trained checkpoints and sampled images from this address

1. Clone the Repository:

   git clone https://github.com/HoangAnAIRS/exponential-SNR-diffusion.git
   cd exponential-SNR-diffusion

2. Install Dependencies:

   Ensure you have activated the diffusion environment:

   conda activate diffusion

   Then, install the required packages:

   pip install -e . 

   pip install -r requirements.txt

3. Training the Model:

   To train the model, use the following script:

   CUDA_VISIBLE_DEVICES=1
   python3 image_train.py \
   --data_dir /home/admin/workspace/user/improved-diffusion/datasets/cifar_train \
   --image_size 32 \
   --num_channels 128 \
   --num_res_blocks 3 \
   --learn_sigma False \
   --dropout 0.3 \
   --diffusion_steps 1000 \
   --noise_schedule linear \
   --lr 1e-4 \
   --batch_size 128 \
   --schedule_sampler "early" \
   --log_dir /home/admin/workspace/user/improved-diffusion/logs_early_snr_clamp_5.0

   or get inside scripts folder, change configs in sample.sh file and run:

   bash train.sh

4. Sampling from the Model:

   To generate samples from the trained model, use the provided script:

   python3 image_sample.py \
   --image_size 32 \
   --num_channels 128 \
   --num_res_blocks 3 \
   --dropout 0.3 \
   --diffusion_steps 1000 \
   --noise_schedule linear \
   --timestep_respacing ddim250 \
   --use_ddim True \
   --device cuda:2 \
   --model_path /home/admin/workspace/user/improved-diffusion/logs_early_snr_x_start/ema_0.9999_080000.pt \
   --output_npz_dir /home/admin/workspace/user/improved-diffusion/sampled_images_early_snr_x_start_ddim250

   or get inside scripts folder, change configs in sample.sh file and run:

   bash sample.sh

5. Batch Sampling with Varying Models:

   To perform batch sampling using different models, use the following script:

   ./batch_sample_varies_model.sh

   This script loops through each model and generates samples, storing the results in the specified output directory.

6. Batch Sampling with Varying DDIM:

   To perform batch sampling with varying DDIM timesteps, use the following script:

   ./batch_sample_varies_ddim.sh

   This script loops through different timestep respacing values and generates samples for each, storing the results in the respective output directories.

For any questions or inquiries, please contact Hoang An AIRS.