Official code repository for the paper: Free Hunch: Denoiser Covariance Estimation for Diffusion Models Without Extra Costs

This repository is the official implementation of the methods in the publication:

• Severi Rissanen, Markus Heinonen, and Arno Solin (2025). Free Hunch: Denoiser Covariance Estimation for Diffusion Models Without Extra Costs. In International Conference on Learning Representations (ICLR). [arXiv] [project page]

The main entry point is generate_conditional.py which supports multiple conditioning mechanisms used as baselines in the paper.

Installing Dependencies

We used Python 3.10.12. The core dependencies can be installed with:

bash pip install tqdm==4.66.1 pip install numpy==1.26.2 pip install scipy==1.11.4 pip install matplotlib==3.9.2 pip install scikit-image==1.3.2 pip install torch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 --index-url https://download.pytorch.org/whl/cu124 pip install lpips==0.1.4 pip install hydra-core==1.3.2 pip install click==8.1.7 pip install PyWavelets==1.7.0 pip install hdf5storage==0.1.19 pip install torch-dct==0.1.6

Preparing the Datasets

You can download the FFHQ dataset with the instructions from https://github.com/NVlabs/ffhq-dataset. For quicker experimentation, you can download a subset. After downloading the 1024x1024 version, you can use datasettool.py to rescale to 256x256: ```bash python datasettool.py convert --source "download_dir" --dest "data/ffhq" --resolution 256x256 --transform center-crop-dhariwal ```

For Imagenet, go to https://image-net.org/challenges/LSVRC/2012/2012-downloads.php and download the validation set (2.6GB). Then also process to 256x256 resolution with bash python dataset_tool.py convert --source "download_dir" --dest "data/imagenet" --resolution 256x256 --transform center-crop-dhariwal

We provide a set of 10 ImageNet images for quick experimentation.

Downloading models

You get the FFHQ model from DPS https://drive.google.com/drive/folders/1jElnRoFv7b31fG0v6pTSQkelbSX3xGZh?usp=sharing get the FFHQ model and download it to models/. The text file models/ffhq_10m_setup.txt contains the architecture setup needed for initialising the model.

For the Imagenet model, you can get it from the OpenAI guided diffusion repository link: https://openaipublic.blob.core.windows.net/diffusion/jul-2021/256x256diffusionuncond.pt. Place it in models/. The architecture setup is found in models/256x256_diffusion_uncond_setup.txt.

When generating samples for the inverse problem with generate_conditional.py, you need to set openaistatedictpath to `models/MODELNAME.ptandopenaisetuppathtomodels/256x256diffusionuncondsetup.txtormodels/ffhq10m_setup.txt`, respectively for Imagenet and FFHQ.

Getting the variances of frequencies in the DCT basis

To get the DCT variances of the dataset for the Free Hunch model, we have already included them in data/imagenet/dct_variance.pt. They are extracted from the Imagenet validation data, and we used the same variances for the FFHQ experiments. You can also run do_frequency_analysis.py to extract the variances from the data (after downloading the data).

Running experiments

Experiments are launched with generate_conditional.py. Below are example commands for the different methods. Replace gaussian_blur with motion_blur, inpainting, or super_resolution to tackle other inverse problems. Adjust noise_sigma to adjust the Gaussian noise standard deviation to match your experimental setup. Adjust num_steps to look at different step counts, and change solver to euler for the standard Euler solver. Set total_images to 3000 to match the results from the paper. The implementation for the "time" and "space" updates is found in conditioningutils/onlineupdate_bfgs.py, both for the memory-efficient representation and the dense matrix version.

For FFHQ, you need the choices: bash openai_state_dict_path=models/ffhq_10m.pt openai_setup_path=models/ffhq_10m_setup.txt dataset=ffhq dataset_path=data/ffhq

For Imagenet, you need: bash openai_state_dict_path=models/256x256_diffusion_uncond.pt openai_setup_path=models/256x256_diffusion_uncond_setup.txt dataset=imagenet dataset_path=data/imagenet

The results folder is chosen with the outdir argument. Below are some example choices.

DPS baseline

bash python3 generate_conditional.py \ --conditioning_mechanism=dps \ --cond_scaling=50 \ --S_churn=0 \ --num_steps=30 \ --solver=heun \ --max_batch_size=1 \ --total_images=10 \ --save_other_images=true \ --operator_name=gaussian_blur \ --noise_sigma=0.1 \ --num_other_images_to_save=5 \ --clip_x0_mean=true \ --scale_factor=4 \ --inpainting_type=random \ --inpainting_prob_lower=0.6 \ --inpainting_prob_upper=0.8 \ --dataset=imagenet \ --dataset_path=data/imagenet \ --openai_state_dict_path=models/256x256_diffusion_uncond.pt \ --openai_setup_path=models/256x256_diffusion_uncond_setup.txt \ --outdir=results/dps_gaussian_blur

PiGDM baseline

bash python3 generate_conditional.py \ --conditioning_mechanism=pigdm \ --cond_scaling=1 \ --S_churn=0 \ --num_steps=30 \ --solver=heun \ --max_batch_size=1 \ --total_images=10 \ --save_other_images=true \ --operator_name=gaussian_blur \ --noise_sigma=0.1 \ --num_other_images_to_save=5 \ --pigdm_posthoc_scaling=true \ --clip_x0_mean=true \ --scale_factor=4 \ --inpainting_type=random \ --inpainting_prob_lower=0.6 \ --inpainting_prob_upper=0.8 \ --dataset=imagenet \ --dataset_path=data/imagenet \ --openai_state_dict_path=models/256x256_diffusion_uncond.pt \ --openai_setup_path=models/256x256_diffusion_uncond_setup.txt \ --outdir=results/pigdm_gaussian_blur

Online covariance model

Without space updates: bash python3 generate_conditional.py \ --conditioning_mechanism=online_covariance \ --do_space_updates=false \ --use_analytical_score_time_update=true \ --project_to_diagonal=false \ --image_base_covariance=dct_diagonal \ --scale_factor=4.0 \ --cond_scaling=1 \ --S_churn=0 \ --num_steps=30 \ --solver=heun \ --max_batch_size=1 \ --total_images=10 \ --save_other_images=true \ --operator_name=gaussian_blur \ --noise_sigma=0.1 \ --num_other_images_to_save=5 \ --pigdm_posthoc_scaling=false \ --clip_x0_mean=false \ --scale_factor=4 \ --inpainting_type=random \ --inpainting_prob_lower=0.6 \ --inpainting_prob_upper=0.8 \ --dataset=imagenet \ --dataset_path=data/imagenet \ --openai_state_dict_path=models/256x256_diffusion_uncond.pt \ --openai_setup_path=models/256x256_diffusion_uncond_setup.txt \ --outdir=results/free-hunch-nospace_gaussian_blur

With space updates: bash python3 generate_conditional.py \ --conditioning_mechanism=online_covariance \ --do_space_updates=true \ --use_analytical_score_time_update=true \ --project_to_diagonal=false \ --image_base_covariance=dct_diagonal \ --space_step_update_lower_threshold=1000.0 \ --space_step_update_threshold=5.0 \ --scale_factor=4.0 \ --cond_scaling=1 \ --S_churn=0 \ --num_steps=30 \ --solver=heun \ --max_batch_size=1 \ --total_images=10 \ --save_other_images=true \ --operator_name=gaussian_blur \ --noise_sigma=0.1 \ --num_other_images_to_save=5 \ --pigdm_posthoc_scaling=false \ --clip_x0_mean=false \ --scale_factor=4 \ --inpainting_type=random \ --inpainting_prob_lower=0.6 \ --inpainting_prob_upper=0.8 \ --dataset=imagenet \ --dataset_path=data/imagenet \ --openai_state_dict_path=models/256x256_diffusion_uncond.pt \ --openai_setup_path=models/256x256_diffusion_uncond_setup.txt \ --outdir=results/free-hunch-spaceupdate_gaussian_blur

TMPD

bash python3 generate_conditional.py \ --conditioning_mechanism=tmpd \ --cond_scaling=1 \ --S_churn=0 \ --num_steps=30 \ --solver=heun \ --max_batch_size=1 \ --total_images=10 \ --save_other_images=true \ --operator_name=gaussian_blur \ --noise_sigma=0.1 \ --num_other_images_to_save=5 \ --pigdm_posthoc_scaling=false \ --clip_x0_mean=true \ --scale_factor=4 \ --inpainting_type=random \ --inpainting_prob_lower=0.6 \ --inpainting_prob_upper=0.8 \ --dataset=imagenet \ --dataset_path=data/imagenet \ --openai_state_dict_path=models/256x256_diffusion_uncond.pt \ --openai_setup_path=models/256x256_diffusion_uncond_setup.txt \ --outdir=results/tmpd_gaussian_blur

Peng (Convert)

bash python3 generate_conditional.py \ --conditioning_mechanism=peng_convert \ --cond_scaling=1 \ --S_churn=0 \ --num_steps=30 \ --solver=heun \ --max_batch_size=1 \ --total_images=10 \ --save_other_images=true \ --operator_name=gaussian_blur \ --noise_sigma=0.1 \ --num_other_images_to_save=5 \ --pigdm_posthoc_scaling=false \ --clip_x0_mean=true \ --scale_factor=4 \ --inpainting_type=random \ --inpainting_prob_lower=0.6 \ --inpainting_prob_upper=0.8 \ --dataset=imagenet \ --dataset_path=data/imagenet \ --openai_state_dict_path=models/256x256_diffusion_uncond.pt \ --openai_setup_path=models/256x256_diffusion_uncond_setup.txt \ --outdir=results/peng_convert_gaussian_blur

Peng (Analytic)

bash python3 generate_conditional.py \ --conditioning_mechanism=peng_analytic \ --cond_scaling=1 \ --S_churn=0 \ --num_steps=30 \ --solver=heun \ --max_batch_size=1 \ --total_images=10 \ --save_other_images=true \ --operator_name=gaussian_blur \ --noise_sigma=0.1 \ --num_other_images_to_save=5 \ --pigdm_posthoc_scaling=false \ --clip_x0_mean=true \ --scale_factor=4 \ --inpainting_type=random \ --inpainting_prob_lower=0.6 \ --inpainting_prob_upper=0.8 \ --dataset=imagenet \ --dataset_path=data/imagenet \ --openai_state_dict_path=models/256x256_diffusion_uncond.pt \ --openai_setup_path=models/256x256_diffusion_uncond_setup.txt \ --outdir=results/peng_analytic_gaussian_blur

DDNM

Here we show the super resolution task as an example, as DDNM is not designed to work well with the Gaussian Blur task we use as a baseline: bash python3 generate_conditional.py \ --conditioning_mechanism=ddnm \ --cond_scaling=1 \ --S_churn=0 \ --num_steps=30 \ --solver=heun \ --max_batch_size=1 \ --total_images=10 \ --save_other_images=true \ --operator_name=super_resolution \ --noise_sigma=0.1 \ --num_other_images_to_save=5 \ --pigdm_posthoc_scaling=false \ --clip_x0_mean=true \ --scale_factor=4 \ --inpainting_type=random \ --inpainting_prob_lower=0.6 \ --inpainting_prob_upper=0.8 \ --dataset=imagenet \ --dataset_path=data/imagenet \ --openai_state_dict_path=models/256x256_diffusion_uncond.pt \ --openai_setup_path=models/256x256_diffusion_uncond_setup.txt \ --outdir=results/ddnm_super_resolution

Toy data examples

We include some toy code for running experiments with low-dimensional Gaussian mixture data, without training models. These are useful for examining just the properties of the guidance methods themselves, excluding the training process. notebooks/figureexample.ipynb showcases a basic example of how to use the code, and notebooks/figure2.ipynb reproduces Fig. 2 of the paper.

Acknowledgements

The codebase is built on code from multiple other repositories, including https://github.com/NVlabs/edm2 for the EDM diffusion model formulation and some utility code, https://github.com/DPS2022/diffusion-posterior-sampling and https://github.com/yuanzhi-zhu/DiffPIR for measurement operators, https://github.com/xypeng9903/k-diffusion-inverse-problems and https://github.com/wyhuai/DDNM for the implementation of the different diffusion models. The analytic_variance folder contents come from https://github.com/xypeng9903/k-diffusion-inverse-problems.

Citation

If you want to cite the paper, you can use the following bibtex entry: bash @inproceedings{rissanen2024free, title={Free Hunch: Denoiser Covariance Estimation for Diffusion Models Without Extra Costs}, author={Rissanen, Severi and Heinonen, Markus and Solin, Arno}, booktitle={International Conference for Learning Representations}, year={2025} }

License

This software is provided under the MIT license.