ILVR + ADM
This is the implementation of ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models (ICCV 2021 Oral).
This repository is heavily based on improved diffusion and guided diffusion. We use PyTorch-Resizer for resizing function.
Overview
ILVR is a learning-free method for controlling the generation of unconditional DDPMs. ILVR refines each generation step with low-frequency component of purturbed reference image. Our method enables various tasks (image translation, paint-to-image, editing with scribbles) with only a single model trained on a target dataset.
Download pre-trained models
Create a folder models/ and download model checkpoints into it. Here are the unconditional models trained on FFHQ and AFHQ-dog:
- 256×256 FFHQ: ffhq_10m.pt
- 256×256 AFHQ-dog: afhq_dog_4m.pt
These models have seen 10M and 4M images respectively. You may also try with models from guided diffusion.
ILVR Sampling
First, set PYTHONPATH variable to point to the root of the repository.
export PYTHONPATH=$PYTHONPATH:$(pwd)
Then, place your input image into a folder ref_imgs/.
Run the ilvr_sample.py script. Specify the folder where you want to save the output in --save_dir.
Here, we provide flags for sampling from above models. Feel free to change --down_N and --range_t to adapt downsampling factor and conditioning range from the paper.
Refer to improved diffusion for --timestep_respacing flag.
python scripts/ilvr_sample.py --attention_resolutions 16 --class_cond False --diffusion_steps 1000 --dropout 0.0 --image_size 256 --learn_sigma True --noise_schedule linear --num_channels 128 --num_head_channels 64 --num_res_blocks 1 --resblock_updown True --use_fp16 False --use_scale_shift_norm True --timestep_respacing 100 --model_path models/ffhq_10m.pt --base_samples ref_imgs/face --down_N 32 --range_t 20 --save_dir output
ILVR sampling is implemented in p_sample_loop_progressive of guided-diffusion/gaussian_diffusion.py
Results
These are samples generated with N=8 and 16:
These are cat-to-dog samples generated with N=32:
Note
This repo is re-implemention of our method on . Our initial implementation of the paper is based on denoising-diffusion-pytorch.
