InfinityGAN: Towards Infinite-Resolution Image Synthesis

"To infinity and beyond!"

Chieh Hubert Lin¹, Hsin-Ying Lee², Yen-Chi Cheng³, Sergey Tulyakov², Ming-Hsuan Yang^1,4
1UC Merced, ²Snap Research, ³CMU, ⁴Google Research

Abstract (click to view)

We present a novel framework, InfinityGAN, for arbitrary-sized image generation. The task is associated with several key challenges. First, scaling existing models to an arbitrarily large image size is resource-constrained, in terms of both computation and availability of large-field-of-view training data. InfinityGAN trains and infers in a seamless patch-by-patch manner with low computational resources. Second, large images should be locally and globally consistent, avoid repetitive patterns, and look realistic. To address these, InfinityGAN disentangles global appearances, local structures, and textures. With this formulation, we can generate images with spatial size and level of details not attainable before. Experimental evaluation validates that InfinityGAN generates images with superior realism compared to baselines and features parallelizable inference. Finally, we show several applications unlocked by our approach, such as spatial style fusion, multi-modal outpainting, and image inbetweening. All applications can be operated with arbitrary input and output sizes.

[Project Page] [Paper] [Supplementary]

(*These samples are downsampled, please access the raw images via [Google Drive](https://drive.google.com/drive/folders/1Ej3dgWVagitJR7FYtrlEWu3DSC2cOvJi?usp=sharing))

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How To Use

• A. Configure Environment
• B. Prepare Data
• C. Train Model
• D. Test Model
• E. Interactive Generation
• F. Evaluation
• G. Pretrained Models And Additional Materials

---

A. Configure Environment

Our repository works on Ubuntu. (One of our machine setups: Ubuntu + Python 3.8.5 + cudatoolkit 10.2)

Setup:

1. Create conda environement with conda env create --name pt16 --file meta_data/environment.yml. We only tested our pipeline on PyTorch 1.6. Please avoid using PyTorch 1.7 and 1.8 as we observe an awkward degradation in performance.
2. (Alternative) Directly install with conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.2 -c pytorch, conda install python-lmdb tqdm matplotlib imageio scikit-image scikit-learn scipy=1.5 and pip install tensorboardx==2.1 pyyaml==5.4.1 easydict.
3. Designate a directory where you are going to place all your lmdb dataset in env_config.py.

P.S. Theoretically this repository should be workable on Windows if you manage to run StyleGAN2, which requires extra efforts in dealing with cuda codes building with Visual Studio.

---

B. Prepare Data

Notes: We originally use "Flickr-Landscape (small)" in the V1 paper on Arxiv. We then update the results of all models to "Flickr-Landscape (large)" in the later versions of the paper. We only use the training split for all training and FID evaluation. Nevertheless, we still provide a validation set in "Flickr-Landscape (large)". Notice that the Flickr-Landscape dataset contains images at different sizes without aligning them in the lmdb, so you may add customized training augmentations if desired.

• Use prepared lmdb:

Dataset	Used in latest paper	# images	Minimum image size	All images same shape?	Size	Has holdout set?	Link
Flickr-Landscape (small)	X	50,000	1024	X	89G	X	(Google Drive)
Flickr-Landscape (large)	V	400,000	1024	X	786G	V	(Google Drive)
Flickr-Scenery	V	54,710	256	V	3.5G	V	(Will release via In&Out)
Places2-Scenery-Subset	V	56,431	256	V	3.2G	V	(Will release via In&Out)

• Construct your custom dataset
  1. Prepare a config similar to configs/dataset/flickr-landscape-small.yaml.
  2. Run (we only use training set)

     python prepare_data.py ./configs/dataset/flickr-landscape-small.yaml --train_only
     

  3. The lmdb will be constructed at the LMDB_ROOTS you specified in env_config.py.
  4. Remember to modify the data_params.dataset flag in your training config when you train the model.

---

C. Train Model

Our pipeline requires specifying CUDA_VISIBLE_DEVICES, and automatically switch to dataparallel if two or more GPUs are specified.

• InfinityGAN:
  

  CUDA_VISIBLE_DEVICES="0" python train.py ./configs/model/InfinityGAN.yaml
  

• StyleGAN2 + NCI:
  

  CUDA_VISIBLE_DEVICES="0" python train.py ./configs/model/StyleGAN2_NCI.yaml
  

• StyleGAN2 + NCI + FCG
  

  CUDA_VISIBLE_DEVICES="0" python train.py ./configs/model/StyleGAN2_NCI_FCG.yaml
  

Misc flags of train.py:

• --debug: With this flag, the training pipeline will run one iteration training, execute all logging and evaluation for one iteration, then quit without writing any thing to your logs. Sometimes you may just want to test your environment or config without writing any thing.
• --archive-mode: Our pipeline automatically backups your codes at ./logs/<exp_name>/codes/. You may run the model training within that folder by using this flag.

---

D. Test Model

Our pipeline requires specifying CUDA_VISIBLE_DEVICES, and automatically switch to dataparallel if two or more GPUs are specified.

Suppose with a model trained with a config ./configs/model/<OuO>.yaml, you want to generate images at HxW resolution. the testing configs are written as follow:

• Naive Generation
  Directly synthesize the whole image. O(H*W) memory allocation.
  

  CUDA_VISIBLE_DEVICES="0,1" python test.py \
   --model-config=./configs/model/<OuO>.yaml \
   --test-config=./configs/test/direct_gen_HxW.yaml
  

• Infinite Generation
  Sequentially generate patches. O(1) memory allocation.

  CUDA_VISIBLE_DEVICES="0,1" python test.py \
   --model-config=./configs/model/<OuO>.yaml \
   --test-config=./configs/test/infinite_gen_HxW.yaml
  

• Spatial Fusion Generation
  Spatially fuses multiple styles. Follows the "infinite generation" design.

  CUDA_VISIBLE_DEVICES="0,1" python test.py \
   --model-config=./configs/model/<OuO>.yaml \
   --test-config=./configs/test/fused_gen_HxW.yaml
  
  

• Inversion
  Please rememeber to update override_dataset_data_size and override_dataset_full_size if the inversion real image resolution is different from the training resolution.

  CUDA_VISIBLE_DEVICES="0" python test.py \
      --model-config="./configs/model/<OuO>.yaml" \
      --test-config="./test_configs/inversion_<???>.yaml"
  

• Outpainting Invert the latent variables, and outpaint the image.

  # Run inversion first
  CUDA_VISIBLE_DEVICES="0" python test.py \
      --model-config="./configs/model/<OuO>.yaml" \
      --test-config="./test_configs/inversion_256x256_L2R.yaml"
  
  # Then outpaint
  CUDA_VISIBLE_DEVICES="0" python test.py \
      --model-config="./configs/model/<OuO>.yaml" \
      --test-config="./test_configs/outpaint_with_fused_gen_256x256.yaml" \
      --inv-records="./logs/<OuO>/test/outpaint_with_fused_gen_256x256/stats/<id>.pkl" \
      --inv-placements=0.5,0.25
  

• Inbetweening Invert the latent variables, and outpaint the image.

  # Run inversion first
  CUDA_VISIBLE_DEVICES="0" python test.py \
      --model-config="./configs/model/<OuO>.yaml" \
      --test-config="./test_configs/inversion_IOF246_256x1280L_256x128.yaml"
  CUDA_VISIBLE_DEVICES="0" python test.py \
      --model-config="./configs/model/<OuO>.yaml" \
      --test-config="./test_configs/inversion_IOF246_256x1280R_256x128.yaml"
  
  # Then outpaint (the `inv-records` and `inv-placements` are ordered lists separated with `:`)
  CUDA_VISIBLE_DEVICES="0" python test.py \
      --model-config="./configs/model/<OuO>.yaml" \
      --test-config="./test_configs/inbetween_with_fused_gen_256x1280.yaml" \
      --inv-records="./logs/<OuO>/test/inversion_IOF246_256x1280L_256x128/stats/<id>.pkl:./logs/<OuO>/test/inversion_IOF246_256x1280R_256x128/stats/<id>.pkl" \
      --inv-placements=0.5,0.05:0.5,0.95
  

  P.S. As (i) the inversion area of the real image, (ii) the inversion area of the generated image, and (iii) the position of the inverted latents while outpainting can be different (as well as some further technical difficulties). Unfortunately, you need to invert the latent variables each time you change either the inversion area size, the position of the inversion area, or the outpainting target resolution.

Additional yaml configs for test.py:

• lowres_height: High-resolution images are hard to download from remote, we additionally save a low-resolution version of the images by aspect-ratio downsampling the images to the specified height.
• interactive: See below.
• parallel_batch_size: The "parallel batching" application mentioned in the paper.
  
  • Supports test_manager.infinte_generation and test_manager.fused_generation.
    
  • The implementation is at: test_manager.base_test_manager.py:maybe_parallel_inference().
    
  • Despite batch_size can be simultaneously supported, we make them mutually exclusive as the mixing use of these two batching strategies is not meaningful.

Additional args for test.py:

• --speed-benchmark: Collects GPU execution time (includes dataparallel scatter and collection time). Ignores the first-ten iterations.
• --calc-flops: Get the total FLOPs used in synthesizing a full image.

---

E. Interactive Generation

Set interactive: True in the config, or equivalently use --interactive in the command to test.py.

The interactive generation is supported for the following test_manager classes:

• (for infinite generation)
  test_manager.infinte_generation
• (for spatial fusion generation, outpainting, and inbetweening)
  test_manager.fused_generation

How to use:

• Selection: Left-click on the image two times to create a red selection bounding box that designates an area to re-sample. Right-click on the image two times to create a blue selection bounding box that designates an area to extract channel-wise statistics for the re-sampling mean and standard deviation (default is zero-mean and unit-variance if no blue boxes are selected).
• Sampling: Select the variables to resample. Only the spatially shaped latent variables (e.g., local latent and noises) can be regionally sampled with selection area.
• Undo/redo: Supports upto 100 steps of undo/redo. You may increase the value if you want and have a sufficient amout of CPU memory.

Note If you find the image is too large (such as 4096x4096 does not fit into your monitor at all), you can increase the self.fov_rescale to 2 or 4, which downsamples the image before displaying in the canvas (but you are still interacting with the image at its original image).

P.S. To quit the program, you need to close the interface window and kill (ctrl-c) the program in the terminal.

---

F. Evaluation

Evaluate ScaleInv FID

To test the model with x2 ScaleInv FID:

CUDA_VISIBLE_DEVICES="0,1" python eval_fids.py \
 ./configs/model/<exp_name>.yaml \
 --type=scaleinv \
 --scale=2 \
 --batch-size=2

Other arguments

• --ckpt: By default, we test the checkpoint at ./logs/<exp_name>/ckpt/best_fid.pth.tar. You may override the path with this argument if you want to test other checkpoints.
• --img-folder: You may use this in case you want to test with a folder with images.
• --type: We also implemented another FID schema spatial, which partitions the image into 16(=4x4) patches, extract Inception features for each patch, and concatenate them into a plain vector. This is much slower and consumes massive CPU memory. And the trend (FID v.s. scale) is similar to ScaleInv FID.
• --seq-inference: For InfinityGAN, due to the additional structure synthesizer, the model can OOM at higher resolution if generating the image at one-shot. You may use this flag to enable sequential inference (i.e., uses test_managers.infinite_generation.py). But this will slow down the inference due to some internal redundant computations.

Evaluate Outpainting

(This script is based on the codes from In&Out)

Please run the inversion first. It will store results (images and inverted variables) at ./logs/<exp_name>/test/<test_name>/, then you can evaluate with the following command:

CUDA_VISIBLE_DEVICES="0" python eval_outpaint_imgdir.py \
 --batch=48 \
 --size=256 \
 --real-dir=./logs/<exp_name>/test/<test_name>/imgs/real_gt/ \
 --fake-dir=./logs/<exp_name>/test/<test_name>/imgs/inv_cmp/

Note that this script only supports single GPU.

---

G. Pretrained Models and Additional Materials

Pretrained Models (Test Only)

You should structure the ./logs/ folder like this:

logs/ --+--- exp_name_A/ --- ckpt/ --- best_fid.pth.tar
        |
        +--- exp_name_B/ --- ckpt/ --- best_fid.pth.tar
        |
        +--- exp_name_C/ --- ckpt/ --- best_fid.pth.tar
        |
        (...)

You should be able to find corresponding config for each of the released model under ./configs/model/. You can run testing the model with:

CUDA_VISIBLE_DEVICES="0,1" python test.py \
     --model-config=./config/model/<exp_name>.yaml \
     --test-config=./configs/test/infinite_gen_1024x1024.yaml

The test script will auto-detect the checkpoint at ./logs/<exp_name>/ckpt/best_fid.pth.tar

Name	Dataset	Used in paper	Training full image size	Training patch size	Trained w/ #GPUs	Link
InfinityGAN	Flickr-Landscape (large)	V	197	101	1x TitanX	(Google Drive)
InfinityGAN-HR	Flickr-Landscape (large)	X	389	197	4x V100	(Google Drive)
InfinityGAN-UR	Flickr-Landscape (large)	X	1024	773	4x V100	(Google Drive)
InfinityGAN-IOF	InOut-Flickr-Scenery	V	197	101	1x TitanX	(Google Drive)
InfinityGAN-IOP	InOut-Places2-Scenery-subset	V	197	101	1x TitanX	(Google Drive)

Inversion results

Inverting a large set of samples requires a large amount of computation. In order to save your time and our earth (just a bit), we release the inversion results here.

The tar file (decompress with tar zxf <filename>.tar) contains following folders:

---+---inv_cmp/             : Compare (left-half) real and (right-half) reconstruction via inversion.
   |
   +---inv_comp_cropped/    : Composed (left-half) real and (right-half) outpainting via inversion.
   |
   +---inv_raw/             : The whole inverted image.
   |
   +---real_gt/             : The real data.

Note: You may notice that there is a cropped in the folder name. InfinityGAN actually inverts images slightly larger than the conditional image, then crop those area away in the end.

Known Issues

1. The performance on PyTorch 1.4/1.7 and PyTorch 1.6 are different. The root cause is unknown and still a misc event to us, so please use PyTorch 1.6 if possible.
2. Please do not use dataparallel on two different types of GPUs (e.g., data parallel with GTX1080 + GTX2080), one of the GPUs may generate gray or blank images.
3. OOM while training with a single GPU. PyTorch can sometimes raise OOM due to unfortunate memory allocations. Here are some tweaks that sometimes resolves the problem (if you indeed have only one GPU):
   • [Reminder] We use "TITAN X (Pascal)" with 12196 MB GPU memery in the single-GPU setup in our paper. We are not certain about the results on other GPUs with less memory.
   • Set torch.backends.cudnn.benchmark to False in train.py. Despite it was designed not to produce OOM, but somehow it sometimes unfortunately makes it.
   • Set calc_fid and calc_fid_ext2 to False. The evaluation allocates additional memory and uses different memory allocation pattern, which can mess up the PyTorch memory allocation schedule. You may directly use the last iteration, as all models converge well.
   • Set ext_mult_list to [2,] instead of [2, 4], which stops logging images generated at 4x testing resolution during training.
   • Reduce training batch_size smaller. However, it may influence the model performance.

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License

This repository borrows codes from different sources, please follow the user licenses from each of the source while using this project.

Notice that the code release aims to support the open-source culture in computer vision research community, facilitating the research efficiency and keeping the community open to new comers. The first author strongly discourage research groups that do not match such an open-source culture to use or read any piece of codes in this repository. Please keep the close-door culture bidirectional.

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Acknowledgement

The implementation heavily borrows from StyleGAN2-Pytorch, pytorch-fid and PerceptualSimilarity.

---

Citation

@inproceedings{
    lin2021infinity,
    title={Infinity{GAN}: Towards Infinite-Pixel Image Synthesis},
    author={Lin, Chieh Hubert and Cheng, Yen-Chi and Lee, Hsin-Ying and Tulyakov, Sergey and Yang, Ming-Hsuan},
    booktitle={International Conference on Learning Representations},
    year={2022},
    url={https://openreview.net/forum?id=ufGMqIM0a4b},
}