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ContraCLIP: Interpretable GAN generation driven by pairs of contrasting sentences

Authors official PyTorch implementation of the ContraCLIP: Interpretable GAN generation driven by pairs of contrasting sentences. If you use this code for your research, please cite our paper.

ContraCLIP: Interpretable GAN generation driven by pairs of contrasting sentences<br> Christos Tzelepis, James Oldfield, Georgios Tzimiropoulos, and Ioannis Patras<br> https://arxiv.org/abs/2206.02104 <br>

Abstract: This work addresses the problem of discovering non-linear interpretable paths in the latent space of pre-trained GANs in a model-agnostic manner. In the proposed method, the discovery is driven by a set of pairs of natural language sentences with contrasting semantics, named semantic dipoles, that serve as the limits of the interpretation that we require by the trainable latent paths to encode. By using the pre-trained CLIP encoder, the sentences are projected into the vision-language space, where they serve as dipoles, and where RBF-based warping functions define a set of non-linear directional paths, one for each semantic dipole, allowing in this way traversals from one semantic pole to the other. By defining an objective that discovers paths in the latent space of GANs that generate changes along the desired paths in the vision-language embedding space, we provide an intuitive way of controlling the underlying generating factors and address some of the limitations of the state-of-the-art works, namely, that a) they are typically tailored to specific GAN architectures (i.e., StyleGAN), b) they disregard the relative position of the manipulated and the original image in the image embedding and the relative position of the image and the text embeddings, and c) they lead to abrupt image manipulations and quickly arrive at regions of low density and, thus, low image quality, providing limited control of the generative factors.

Overview

Installation

We recommend installing the required packages using python's native virtual environment as follows:

$ python -m venv contra-clip-venv
$ source contra-clip-venv/bin/activate
(contra-clip-venv) $ pip install --upgrade pip
(contra-clip-venv) $ pip install -r requirements.txt
(contra-clip-venv) $ pip install git+https://github.com/openai/CLIP.git
(contra-clip-venv) $ pip install --pre torch torchvision --extra-index-url https://download.pytorch.org/whl/nightly/cu113

For using the aforementioned virtual environment in a Jupyter Notebook, you need to manually add the kernel as follows:

(contra-clip-venv) $ python -m ipykernel install --user --name=contra-clip-venv

Prerequisite pre-trained models and pre-trained ContraCLIP models

Download the prerequisite pre-trained models (GAN generators and various pre-trained detectors, such as ArcFace, FairFace, etc), as well as (optionally) pre-trained ContraCLIP models (by passing -m or ----contraclip-models) as follows:

(contra-clip-venv) $ python download.py -m

This will create a directory models/pretrained with the following sub-directories (~3.3 GiB):

./models/pretrained/
├── genforce
│   ├── pggan_car256.pth
│   ├── pggan_celebahq1024.pth
│   ├── pggan_church256.pth
│   ├── stylegan2_afhqcat512.pth
│   ├── stylegan2_afhqdog512.pth
│   ├── stylegan2_car512.pth
│   ├── stylegan2_church256.pth
│   └── stylegan2_ffhq1024.pth
├── arcface
│   └── model_ir_se50.pth
├── au_detector
│   └── disfa_adaptation_f0.pth
├── celeba_attributes
│   └── eval_predictor.pth.tar
├── fairface
│   ├── fairface_alldata_4race_20191111.pt
│   └── res34_fair_align_multi_7_20190809.pt
├── hopenet
│   ├── hopenet_alpha1.pkl
│   ├── hopenet_alpha2.pkl
│   └── hopenet_robust_alpha1.pkl
└── sfd
    └── s3fd-619a316812.pth

as well as, a directory experiments/complete/ (if not already created by the user upon an experiment's completion) for downloading the ContraCLIP pre-trained models with the following sub-directories (~160 MiB):

.experiments/complete/
├── ContraCLIP_pggan_celebahq1024-Z-K9-D64-lss_beta_0.5-eps0.1_0.2-nonlinear_css_beta_0.5-contrastive_0.07-20000-attributes
├── ContraCLIP_pggan_celebahq1024-Z-K9-D64-lss_beta_0.5-eps0.1_0.2-nonlinear_css_beta_0.5-cossim-20000-attributes
├── ContraCLIP_stylegan2_afhqcat512-W+-K3-D64-lss_beta_0.5-eps0.1_0.2-nonlinear_css_beta_0.5-contrastive_0.07-20000-cats
├── ContraCLIP_stylegan2_afhqdog512-W+-K4-D64-lss_beta_0.5-eps0.1_0.2-nonlinear_css_beta_0.5-contrastive_0.07-20000-dogs
├── ContraCLIP_stylegan2_car512-W+-K3-D64-lss_beta_0.5-eps0.1_0.2-nonlinear_css_beta_0.5-contrastive_0.07-20000-cars
├── ContraCLIP_stylegan2_ffhq1024-W+-K21-D64-lss_beta_0.5-eps0.1_0.2-nonlinear_css_beta_0.5-contrastive_0.07-20000-expressions
├── ContraCLIP_stylegan2_ffhq1024-W+-K21-D64-lss_beta_0.5-eps0.1_0.2-nonlinear_css_beta_0.5-cossim-20000-expressions
├── ContraCLIP_stylegan2_ffhq1024-W+-K3-D64-lss_beta_0.5-eps0.1_0.2-nonlinear_css_beta_0.5-contrastive_0.07-20000-complex
├── ContraCLIP_stylegan2_ffhq1024-W+-K3-D64-lss_beta_0.5-eps0.1_0.2-nonlinear_css_beta_0.5-contrastive_0.07-20000-expressions3
├── ContraCLIP_stylegan2_ffhq1024-W+-K3-D64-lss_beta_0.5-eps0.1_0.2-nonlinear_css_beta_0.5-cossim-20000-complex
├── ContraCLIP_stylegan2_ffhq1024-W+-K3-D64-lss_beta_0.5-eps0.1_0.2-nonlinear_css_beta_0.5-cossim-20000-expressions3
├── ContraCLIP_stylegan2_ffhq1024-W+-K9-D64-lss_beta_0.5-eps0.1_0.2-nonlinear_css_beta_0.5-contrastive_0.07-20000-attributes
└── ContraCLIP_stylegan2_ffhq1024-W+-K9-D64-lss_beta_0.5-eps0.1_0.2-nonlinear_css_beta_0.5-cossim-20000-attributes

We note that the pre-trained detectors (such as ArcFace) are used only during the evaluation stage (no ID preserving loss is imposed during training).

Training

For training a ContraCLIP model you need to use train.py (check its basic usage by running python train.py -h). For example, in order to train a ContraCLIP model for the corpus of contrasting sentences called "expressions3" (defined in lib/config.py) on the StyleGAN2 pre-trained (on FFHQ) generator (in its W latent space with a truncation parameter equal to 0.7), the following command:

(contra-clip-venv) $ python train.py --gan=stylegan2_ffhq1024 --truncation=0.7 --stylegan-space=W --corpus=expressions3 --num-latent-support-dipoles=128 --loss=contrastive --temperature=0.5 --beta=0.75 --min-shift-magnitude=0.1 --max-shift-magnitude=0.2 --batch-size=3 --max-iter=120000 --log-freq=10--ckp-freq=100

In the example above, the batch size is set to 3 and the training will be conducted for 120000 iterations. Minimum and maximum shift magnitudes are set to 0.1 and 0.2, respectively, and the number of support dipoles for each latent path is set to 128 (please see the WarpedGANSpace for more details). Moreover, contrastive loss is being used with a temperature parameter equal to 0.5. The beta parameter of the CLIP text space RBF dipoles is set to 0.75. A set of auxiliary training scripts (for the results reported in the paper) can be found under scripts/train/.

The training script will create a directory with the following name format:

ContraCLIP_<gan_type>-<latent_space>-K<num_of_paths>-D<num_latent_support_sets>-eps<min_shift_magnitude>_<max_shift_magnitude>-<linear|nonlinear>_beta-<beta>-contrastive_<temperature>-<corpus>

For instance, ContraCLIP_stylegan2_ffhq1024-W-K3-D128-eps0.1_0.2-nonlinear_beta-0.75-contrastive_0.5-expressions3, under experiments/wip/ while training is in progress, which after training completion, will be copied under experiments/complete/. This directory has the following structure:

├── models/
├── args.json
├── stats.json
└── command.sh

where models/ contains the weights for the latent support sets (latent_support_sets.pt). While training is in progress (i.e., while this directory is found under experiments/wip/), the corresponding models/ directory contains a checkpoint file (checkpoint.pt) containing the last iteration, and the weights for the latent support sets, so as to resume training. Re-run the same command, and if the last iteration is less than the given maximum number of iterations, training will resume from the last iteration. This directory will be referred to as EXP_DIR for the rest of this document.

Evaluation

As soon as a ContraCLIP model is trained, the corresponding experiment's directory (i.e., EXP_DIR) can be found under experiments/complete/. In order to evaluate the model, we can generate image sequences across the discovered latent paths (for the given pairs of contrasting sentences). For doing so, we need to create a pool of latent codes/images for the corresponding GAN type. This can be done using sample_gan.py. The pool of latent codes/images will be stored under experiments/latent_codes/<gan_type>/. We will be referring to it as POOL for the rest of this document.

For example, the following command will create a pool named stylegan2_ffhq1024-4 under experiments/latent_codes/stylegan2_ffhq1024/:

(contra-clip-venv) $ python sample_gan.py -v --gan-type=stylegan2_ffhq1024 --stylegan-space=W --truncation=0.7 --num-samples=4

Latent space traversals can then be calculated using the script traverse_latent_space.py (please check its basic usage by running traverse_latent_space.py -h) for a given model and a given POOL. Upon completion, results (i.e., latent traversals) will be stored under the following directory:

experiments/complete/EXP_DIR/results/POOL/<2*shift_steps>_<eps>_<total_length>,

where eps, shift_steps, and total_length denote respectively the shift magnitude (of a single step on the path), the number of such steps, and the total traversal length. A set of auxiliary evaluation scripts (for the results reported in the paper) can be found under scripts/eval/.

Citation

@misc{tzelepis2022contraclip,
    author = {Tzelepis, Christos and James, Oldfield and Tzimiropoulos, Georgios and Patras, Ioannis},
    title = {{ContraCLIP}: Interpretable {GAN} generation driven by pairs of contrasting sentences},
    year={2022},
    eprint={2206.02104},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}