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Repository: openai/guided-diffusion
Branch: main
Commit: 22e0df818350
Files: 30
Total size: 211.6 KB
Directory structure:
gitextract_l679qbmt/
├── .gitignore
├── LICENSE
├── README.md
├── datasets/
│ ├── README.md
│ └── lsun_bedroom.py
├── evaluations/
│ ├── README.md
│ ├── evaluator.py
│ └── requirements.txt
├── guided_diffusion/
│ ├── __init__.py
│ ├── dist_util.py
│ ├── fp16_util.py
│ ├── gaussian_diffusion.py
│ ├── image_datasets.py
│ ├── logger.py
│ ├── losses.py
│ ├── nn.py
│ ├── resample.py
│ ├── respace.py
│ ├── script_util.py
│ ├── train_util.py
│ └── unet.py
├── model-card.md
├── scripts/
│ ├── classifier_sample.py
│ ├── classifier_train.py
│ ├── image_nll.py
│ ├── image_sample.py
│ ├── image_train.py
│ ├── super_res_sample.py
│ └── super_res_train.py
└── setup.py
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FILE CONTENTS
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FILE: .gitignore
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.DS_Store
__pycache__/
classify_image_graph_def.pb
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FILE: LICENSE
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MIT License
Copyright (c) 2021 OpenAI
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
================================================
FILE: README.md
================================================
# guided-diffusion
This is the codebase for [Diffusion Models Beat GANS on Image Synthesis](http://arxiv.org/abs/2105.05233).
This repository is based on [openai/improved-diffusion](https://github.com/openai/improved-diffusion), with modifications for classifier conditioning and architecture improvements.
# Download pre-trained models
We have released checkpoints for the main models in the paper. Before using these models, please review the corresponding [model card](model-card.md) to understand the intended use and limitations of these models.
Here are the download links for each model checkpoint:
* 64x64 classifier: [64x64_classifier.pt](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/64x64_classifier.pt)
* 64x64 diffusion: [64x64_diffusion.pt](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/64x64_diffusion.pt)
* 128x128 classifier: [128x128_classifier.pt](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/128x128_classifier.pt)
* 128x128 diffusion: [128x128_diffusion.pt](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/128x128_diffusion.pt)
* 256x256 classifier: [256x256_classifier.pt](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/256x256_classifier.pt)
* 256x256 diffusion: [256x256_diffusion.pt](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/256x256_diffusion.pt)
* 256x256 diffusion (not class conditional): [256x256_diffusion_uncond.pt](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/256x256_diffusion_uncond.pt)
* 512x512 classifier: [512x512_classifier.pt](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/512x512_classifier.pt)
* 512x512 diffusion: [512x512_diffusion.pt](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/512x512_diffusion.pt)
* 64x64 -> 256x256 upsampler: [64_256_upsampler.pt](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/64_256_upsampler.pt)
* 128x128 -> 512x512 upsampler: [128_512_upsampler.pt](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/128_512_upsampler.pt)
* LSUN bedroom: [lsun_bedroom.pt](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/lsun_bedroom.pt)
* LSUN cat: [lsun_cat.pt](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/lsun_cat.pt)
* LSUN horse: [lsun_horse.pt](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/lsun_horse.pt)
* LSUN horse (no dropout): [lsun_horse_nodropout.pt](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/lsun_horse_nodropout.pt)
# Sampling from pre-trained models
To sample from these models, you can use the `classifier_sample.py`, `image_sample.py`, and `super_res_sample.py` scripts.
Here, we provide flags for sampling from all of these models.
We assume that you have downloaded the relevant model checkpoints into a folder called `models/`.
For these examples, we will generate 100 samples with batch size 4. Feel free to change these values.
```
SAMPLE_FLAGS="--batch_size 4 --num_samples 100 --timestep_respacing 250"
```
## Classifier guidance
Note for these sampling runs that you can set `--classifier_scale 0` to sample from the base diffusion model.
You may also use the `image_sample.py` script instead of `classifier_sample.py` in that case.
* 64x64 model:
```
MODEL_FLAGS="--attention_resolutions 32,16,8 --class_cond True --diffusion_steps 1000 --dropout 0.1 --image_size 64 --learn_sigma True --noise_schedule cosine --num_channels 192 --num_head_channels 64 --num_res_blocks 3 --resblock_updown True --use_new_attention_order True --use_fp16 True --use_scale_shift_norm True"
python classifier_sample.py $MODEL_FLAGS --classifier_scale 1.0 --classifier_path models/64x64_classifier.pt --classifier_depth 4 --model_path models/64x64_diffusion.pt $SAMPLE_FLAGS
```
* 128x128 model:
```
MODEL_FLAGS="--attention_resolutions 32,16,8 --class_cond True --diffusion_steps 1000 --image_size 128 --learn_sigma True --noise_schedule linear --num_channels 256 --num_heads 4 --num_res_blocks 2 --resblock_updown True --use_fp16 True --use_scale_shift_norm True"
python classifier_sample.py $MODEL_FLAGS --classifier_scale 0.5 --classifier_path models/128x128_classifier.pt --model_path models/128x128_diffusion.pt $SAMPLE_FLAGS
```
* 256x256 model:
```
MODEL_FLAGS="--attention_resolutions 32,16,8 --class_cond True --diffusion_steps 1000 --image_size 256 --learn_sigma True --noise_schedule linear --num_channels 256 --num_head_channels 64 --num_res_blocks 2 --resblock_updown True --use_fp16 True --use_scale_shift_norm True"
python classifier_sample.py $MODEL_FLAGS --classifier_scale 1.0 --classifier_path models/256x256_classifier.pt --model_path models/256x256_diffusion.pt $SAMPLE_FLAGS
```
* 256x256 model (unconditional):
```
MODEL_FLAGS="--attention_resolutions 32,16,8 --class_cond False --diffusion_steps 1000 --image_size 256 --learn_sigma True --noise_schedule linear --num_channels 256 --num_head_channels 64 --num_res_blocks 2 --resblock_updown True --use_fp16 True --use_scale_shift_norm True"
python classifier_sample.py $MODEL_FLAGS --classifier_scale 10.0 --classifier_path models/256x256_classifier.pt --model_path models/256x256_diffusion_uncond.pt $SAMPLE_FLAGS
```
* 512x512 model:
```
MODEL_FLAGS="--attention_resolutions 32,16,8 --class_cond True --diffusion_steps 1000 --image_size 512 --learn_sigma True --noise_schedule linear --num_channels 256 --num_head_channels 64 --num_res_blocks 2 --resblock_updown True --use_fp16 False --use_scale_shift_norm True"
python classifier_sample.py $MODEL_FLAGS --classifier_scale 4.0 --classifier_path models/512x512_classifier.pt --model_path models/512x512_diffusion.pt $SAMPLE_FLAGS
```
## Upsampling
For these runs, we assume you have some base samples in a file `64_samples.npz` or `128_samples.npz` for the two respective models.
* 64 -> 256:
```
MODEL_FLAGS="--attention_resolutions 32,16,8 --class_cond True --diffusion_steps 1000 --large_size 256 --small_size 64 --learn_sigma True --noise_schedule linear --num_channels 192 --num_heads 4 --num_res_blocks 2 --resblock_updown True --use_fp16 True --use_scale_shift_norm True"
python super_res_sample.py $MODEL_FLAGS --model_path models/64_256_upsampler.pt --base_samples 64_samples.npz $SAMPLE_FLAGS
```
* 128 -> 512:
```
MODEL_FLAGS="--attention_resolutions 32,16 --class_cond True --diffusion_steps 1000 --large_size 512 --small_size 128 --learn_sigma True --noise_schedule linear --num_channels 192 --num_head_channels 64 --num_res_blocks 2 --resblock_updown True --use_fp16 True --use_scale_shift_norm True"
python super_res_sample.py $MODEL_FLAGS --model_path models/128_512_upsampler.pt $SAMPLE_FLAGS --base_samples 128_samples.npz
```
## LSUN models
These models are class-unconditional and correspond to a single LSUN class. Here, we show how to sample from `lsun_bedroom.pt`, but the other two LSUN checkpoints should work as well:
```
MODEL_FLAGS="--attention_resolutions 32,16,8 --class_cond False --diffusion_steps 1000 --dropout 0.1 --image_size 256 --learn_sigma True --noise_schedule linear --num_channels 256 --num_head_channels 64 --num_res_blocks 2 --resblock_updown True --use_fp16 True --use_scale_shift_norm True"
python image_sample.py $MODEL_FLAGS --model_path models/lsun_bedroom.pt $SAMPLE_FLAGS
```
You can sample from `lsun_horse_nodropout.pt` by changing the dropout flag:
```
MODEL_FLAGS="--attention_resolutions 32,16,8 --class_cond False --diffusion_steps 1000 --dropout 0.0 --image_size 256 --learn_sigma True --noise_schedule linear --num_channels 256 --num_head_channels 64 --num_res_blocks 2 --resblock_updown True --use_fp16 True --use_scale_shift_norm True"
python image_sample.py $MODEL_FLAGS --model_path models/lsun_horse_nodropout.pt $SAMPLE_FLAGS
```
Note that for these models, the best samples result from using 1000 timesteps:
```
SAMPLE_FLAGS="--batch_size 4 --num_samples 100 --timestep_respacing 1000"
```
# Results
This table summarizes our ImageNet results for pure guided diffusion models:
| Dataset | FID | Precision | Recall |
|------------------|------|-----------|--------|
| ImageNet 64x64 | 2.07 | 0.74 | 0.63 |
| ImageNet 128x128 | 2.97 | 0.78 | 0.59 |
| ImageNet 256x256 | 4.59 | 0.82 | 0.52 |
| ImageNet 512x512 | 7.72 | 0.87 | 0.42 |
This table shows the best results for high resolutions when using upsampling and guidance together:
| Dataset | FID | Precision | Recall |
|------------------|------|-----------|--------|
| ImageNet 256x256 | 3.94 | 0.83 | 0.53 |
| ImageNet 512x512 | 3.85 | 0.84 | 0.53 |
Finally, here are the unguided results on individual LSUN classes:
| Dataset | FID | Precision | Recall |
|--------------|------|-----------|--------|
| LSUN Bedroom | 1.90 | 0.66 | 0.51 |
| LSUN Cat | 5.57 | 0.63 | 0.52 |
| LSUN Horse | 2.57 | 0.71 | 0.55 |
# Training models
Training diffusion models is described in the [parent repository](https://github.com/openai/improved-diffusion). Training a classifier is similar. We assume you have put training hyperparameters into a `TRAIN_FLAGS` variable, and classifier hyperparameters into a `CLASSIFIER_FLAGS` variable. Then you can run:
```
mpiexec -n N python scripts/classifier_train.py --data_dir path/to/imagenet $TRAIN_FLAGS $CLASSIFIER_FLAGS
```
Make sure to divide the batch size in `TRAIN_FLAGS` by the number of MPI processes you are using.
Here are flags for training the 128x128 classifier. You can modify these for training classifiers at other resolutions:
```sh
TRAIN_FLAGS="--iterations 300000 --anneal_lr True --batch_size 256 --lr 3e-4 --save_interval 10000 --weight_decay 0.05"
CLASSIFIER_FLAGS="--image_size 128 --classifier_attention_resolutions 32,16,8 --classifier_depth 2 --classifier_width 128 --classifier_pool attention --classifier_resblock_updown True --classifier_use_scale_shift_norm True"
```
For sampling from a 128x128 classifier-guided model, 25 step DDIM:
```sh
MODEL_FLAGS="--attention_resolutions 32,16,8 --class_cond True --image_size 128 --learn_sigma True --num_channels 256 --num_heads 4 --num_res_blocks 2 --resblock_updown True --use_fp16 True --use_scale_shift_norm True"
CLASSIFIER_FLAGS="--image_size 128 --classifier_attention_resolutions 32,16,8 --classifier_depth 2 --classifier_width 128 --classifier_pool attention --classifier_resblock_updown True --classifier_use_scale_shift_norm True --classifier_scale 1.0 --classifier_use_fp16 True"
SAMPLE_FLAGS="--batch_size 4 --num_samples 50000 --timestep_respacing ddim25 --use_ddim True"
mpiexec -n N python scripts/classifier_sample.py \
--model_path /path/to/model.pt \
--classifier_path path/to/classifier.pt \
$MODEL_FLAGS $CLASSIFIER_FLAGS $SAMPLE_FLAGS
```
To sample for 250 timesteps without DDIM, replace `--timestep_respacing ddim25` to `--timestep_respacing 250`, and replace `--use_ddim True` with `--use_ddim False`.
================================================
FILE: datasets/README.md
================================================
# Downloading datasets
This directory includes instructions and scripts for downloading ImageNet and LSUN bedrooms for use in this codebase.
## Class-conditional ImageNet
For our class-conditional models, we use the official ILSVRC2012 dataset with manual center cropping and downsampling. To obtain this dataset, navigate to [this page on image-net.org](http://www.image-net.org/challenges/LSVRC/2012/downloads) and sign in (or create an account if you do not already have one). Then click on the link reading "Training images (Task 1 & 2)". This is a 138GB tar file containing 1000 sub-tar files, one per class.
Once the file is downloaded, extract it and look inside. You should see 1000 `.tar` files. You need to extract each of these, which may be impractical to do by hand on your operating system. To automate the process on a Unix-based system, you can `cd` into the directory and run this short shell script:
```
for file in *.tar; do tar xf "$file"; rm "$file"; done
```
This will extract and remove each tar file in turn.
Once all of the images have been extracted, the resulting directory should be usable as a data directory (the `--data_dir` argument for the training script). The filenames should all start with WNID (class ids) followed by underscores, like `n01440764_2708.JPEG`. Conveniently (but not by accident) this is how the automated data-loader expects to discover class labels.
## LSUN bedroom
To download and pre-process LSUN bedroom, clone [fyu/lsun](https://github.com/fyu/lsun) on GitHub and run their download script `python3 download.py bedroom`. The result will be an "lmdb" database named like `bedroom_train_lmdb`. You can pass this to our [lsun_bedroom.py](lsun_bedroom.py) script like so:
```
python lsun_bedroom.py bedroom_train_lmdb lsun_train_output_dir
```
This creates a directory called `lsun_train_output_dir`. This directory can be passed to the training scripts via the `--data_dir` argument.
================================================
FILE: datasets/lsun_bedroom.py
================================================
"""
Convert an LSUN lmdb database into a directory of images.
"""
import argparse
import io
import os
from PIL import Image
import lmdb
import numpy as np
def read_images(lmdb_path, image_size):
env = lmdb.open(lmdb_path, map_size=1099511627776, max_readers=100, readonly=True)
with env.begin(write=False) as transaction:
cursor = transaction.cursor()
for _, webp_data in cursor:
img = Image.open(io.BytesIO(webp_data))
width, height = img.size
scale = image_size / min(width, height)
img = img.resize(
(int(round(scale * width)), int(round(scale * height))),
resample=Image.BOX,
)
arr = np.array(img)
h, w, _ = arr.shape
h_off = (h - image_size) // 2
w_off = (w - image_size) // 2
arr = arr[h_off : h_off + image_size, w_off : w_off + image_size]
yield arr
def dump_images(out_dir, images, prefix):
if not os.path.exists(out_dir):
os.mkdir(out_dir)
for i, img in enumerate(images):
Image.fromarray(img).save(os.path.join(out_dir, f"{prefix}_{i:07d}.png"))
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--image-size", help="new image size", type=int, default=256)
parser.add_argument("--prefix", help="class name", type=str, default="bedroom")
parser.add_argument("lmdb_path", help="path to an LSUN lmdb database")
parser.add_argument("out_dir", help="path to output directory")
args = parser.parse_args()
images = read_images(args.lmdb_path, args.image_size)
dump_images(args.out_dir, images, args.prefix)
if __name__ == "__main__":
main()
================================================
FILE: evaluations/README.md
================================================
# Evaluations
To compare different generative models, we use FID, sFID, Precision, Recall, and Inception Score. These metrics can all be calculated using batches of samples, which we store in `.npz` (numpy) files.
# Download batches
We provide pre-computed sample batches for the reference datasets, our diffusion models, and several baselines we compare against. These are all stored in `.npz` format.
Reference dataset batches contain pre-computed statistics over the whole dataset, as well as 10,000 images for computing Precision and Recall. All other batches contain 50,000 images which can be used to compute statistics and Precision/Recall.
Here are links to download all of the sample and reference batches:
* LSUN
* LSUN bedroom: [reference batch](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/lsun/bedroom/VIRTUAL_lsun_bedroom256.npz)
* [ADM (dropout)](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/lsun/bedroom/admnet_dropout_lsun_bedroom.npz)
* [DDPM](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/lsun/bedroom/ddpm_lsun_bedroom.npz)
* [IDDPM](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/lsun/bedroom/iddpm_lsun_bedroom.npz)
* [StyleGAN](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/lsun/bedroom/stylegan_lsun_bedroom.npz)
* LSUN cat: [reference batch](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/lsun/cat/VIRTUAL_lsun_cat256.npz)
* [ADM (dropout)](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/lsun/cat/admnet_dropout_lsun_cat.npz)
* [StyleGAN2](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/lsun/cat/stylegan2_lsun_cat.npz)
* LSUN horse: [reference batch](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/lsun/horse/VIRTUAL_lsun_horse256.npz)
* [ADM (dropout)](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/lsun/horse/admnet_dropout_lsun_horse.npz)
* [ADM](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/lsun/horse/admnet_lsun_horse.npz)
* ImageNet
* ImageNet 64x64: [reference batch](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/64/VIRTUAL_imagenet64_labeled.npz)
* [ADM](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/64/admnet_imagenet64.npz)
* [IDDPM](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/64/iddpm_imagenet64.npz)
* [BigGAN](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/64/biggan_deep_imagenet64.npz)
* ImageNet 128x128: [reference batch](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/128/VIRTUAL_imagenet128_labeled.npz)
* [ADM](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/128/admnet_imagenet128.npz)
* [ADM-G](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/128/admnet_guided_imagenet128.npz)
* [ADM-G, 25 steps](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/128/admnet_guided_25step_imagenet128.npz)
* [BigGAN-deep (trunc=1.0)](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/128/biggan_deep_trunc1_imagenet128.npz)
* ImageNet 256x256: [reference batch](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/256/VIRTUAL_imagenet256_labeled.npz)
* [ADM](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/256/admnet_imagenet256.npz)
* [ADM-G](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/256/admnet_guided_imagenet256.npz)
* [ADM-G, 25 step](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/256/admnet_guided_25step_imagenet256.npz)
* [ADM-G + ADM-U](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/256/admnet_guided_upsampled_imagenet256.npz)
* [ADM-U](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/256/admnet_upsampled_imagenet256.npz)
* [BigGAN-deep (trunc=1.0)](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/256/biggan_deep_trunc1_imagenet256.npz)
* ImageNet 512x512: [reference batch](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/512/VIRTUAL_imagenet512.npz)
* [ADM](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/512/admnet_imagenet512.npz)
* [ADM-G](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/512/admnet_guided_imagenet512.npz)
* [ADM-G, 25 step](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/512/admnet_guided_25step_imagenet512.npz)
* [ADM-G + ADM-U](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/512/admnet_guided_upsampled_imagenet512.npz)
* [ADM-U](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/512/admnet_upsampled_imagenet512.npz)
* [BigGAN-deep (trunc=1.0)](https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/512/biggan_deep_trunc1_imagenet512.npz)
# Run evaluations
First, generate or download a batch of samples and download the corresponding reference batch for the given dataset. For this example, we'll use ImageNet 256x256, so the refernce batch is `VIRTUAL_imagenet256_labeled.npz` and we can use the sample batch `admnet_guided_upsampled_imagenet256.npz`.
Next, run the `evaluator.py` script. The requirements of this script can be found in [requirements.txt](requirements.txt). Pass two arguments to the script: the reference batch and the sample batch. The script will download the InceptionV3 model used for evaluations into the current working directory (if it is not already present). This file is roughly 100MB.
The output of the script will look something like this, where the first `...` is a bunch of verbose TensorFlow logging:
```
$ python evaluator.py VIRTUAL_imagenet256_labeled.npz admnet_guided_upsampled_imagenet256.npz
...
computing reference batch activations...
computing/reading reference batch statistics...
computing sample batch activations...
computing/reading sample batch statistics...
Computing evaluations...
Inception Score: 215.8370361328125
FID: 3.9425574129223264
sFID: 6.140433703346162
Precision: 0.8265
Recall: 0.5309
```
================================================
FILE: evaluations/evaluator.py
================================================
import argparse
import io
import os
import random
import warnings
import zipfile
from abc import ABC, abstractmethod
from contextlib import contextmanager
from functools import partial
from multiprocessing import cpu_count
from multiprocessing.pool import ThreadPool
from typing import Iterable, Optional, Tuple
import numpy as np
import requests
import tensorflow.compat.v1 as tf
from scipy import linalg
from tqdm.auto import tqdm
INCEPTION_V3_URL = "https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/classify_image_graph_def.pb"
INCEPTION_V3_PATH = "classify_image_graph_def.pb"
FID_POOL_NAME = "pool_3:0"
FID_SPATIAL_NAME = "mixed_6/conv:0"
def main():
parser = argparse.ArgumentParser()
parser.add_argument("ref_batch", help="path to reference batch npz file")
parser.add_argument("sample_batch", help="path to sample batch npz file")
args = parser.parse_args()
config = tf.ConfigProto(
allow_soft_placement=True # allows DecodeJpeg to run on CPU in Inception graph
)
config.gpu_options.allow_growth = True
evaluator = Evaluator(tf.Session(config=config))
print("warming up TensorFlow...")
# This will cause TF to print a bunch of verbose stuff now rather
# than after the next print(), to help prevent confusion.
evaluator.warmup()
print("computing reference batch activations...")
ref_acts = evaluator.read_activations(args.ref_batch)
print("computing/reading reference batch statistics...")
ref_stats, ref_stats_spatial = evaluator.read_statistics(args.ref_batch, ref_acts)
print("computing sample batch activations...")
sample_acts = evaluator.read_activations(args.sample_batch)
print("computing/reading sample batch statistics...")
sample_stats, sample_stats_spatial = evaluator.read_statistics(args.sample_batch, sample_acts)
print("Computing evaluations...")
print("Inception Score:", evaluator.compute_inception_score(sample_acts[0]))
print("FID:", sample_stats.frechet_distance(ref_stats))
print("sFID:", sample_stats_spatial.frechet_distance(ref_stats_spatial))
prec, recall = evaluator.compute_prec_recall(ref_acts[0], sample_acts[0])
print("Precision:", prec)
print("Recall:", recall)
class InvalidFIDException(Exception):
pass
class FIDStatistics:
def __init__(self, mu: np.ndarray, sigma: np.ndarray):
self.mu = mu
self.sigma = sigma
def frechet_distance(self, other, eps=1e-6):
"""
Compute the Frechet distance between two sets of statistics.
"""
# https://github.com/bioinf-jku/TTUR/blob/73ab375cdf952a12686d9aa7978567771084da42/fid.py#L132
mu1, sigma1 = self.mu, self.sigma
mu2, sigma2 = other.mu, other.sigma
mu1 = np.atleast_1d(mu1)
mu2 = np.atleast_1d(mu2)
sigma1 = np.atleast_2d(sigma1)
sigma2 = np.atleast_2d(sigma2)
assert (
mu1.shape == mu2.shape
), f"Training and test mean vectors have different lengths: {mu1.shape}, {mu2.shape}"
assert (
sigma1.shape == sigma2.shape
), f"Training and test covariances have different dimensions: {sigma1.shape}, {sigma2.shape}"
diff = mu1 - mu2
# product might be almost singular
covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False)
if not np.isfinite(covmean).all():
msg = (
"fid calculation produces singular product; adding %s to diagonal of cov estimates"
% eps
)
warnings.warn(msg)
offset = np.eye(sigma1.shape[0]) * eps
covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset))
# numerical error might give slight imaginary component
if np.iscomplexobj(covmean):
if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):
m = np.max(np.abs(covmean.imag))
raise ValueError("Imaginary component {}".format(m))
covmean = covmean.real
tr_covmean = np.trace(covmean)
return diff.dot(diff) + np.trace(sigma1) + np.trace(sigma2) - 2 * tr_covmean
class Evaluator:
def __init__(
self,
session,
batch_size=64,
softmax_batch_size=512,
):
self.sess = session
self.batch_size = batch_size
self.softmax_batch_size = softmax_batch_size
self.manifold_estimator = ManifoldEstimator(session)
with self.sess.graph.as_default():
self.image_input = tf.placeholder(tf.float32, shape=[None, None, None, 3])
self.softmax_input = tf.placeholder(tf.float32, shape=[None, 2048])
self.pool_features, self.spatial_features = _create_feature_graph(self.image_input)
self.softmax = _create_softmax_graph(self.softmax_input)
def warmup(self):
self.compute_activations(np.zeros([1, 8, 64, 64, 3]))
def read_activations(self, npz_path: str) -> Tuple[np.ndarray, np.ndarray]:
with open_npz_array(npz_path, "arr_0") as reader:
return self.compute_activations(reader.read_batches(self.batch_size))
def compute_activations(self, batches: Iterable[np.ndarray]) -> Tuple[np.ndarray, np.ndarray]:
"""
Compute image features for downstream evals.
:param batches: a iterator over NHWC numpy arrays in [0, 255].
:return: a tuple of numpy arrays of shape [N x X], where X is a feature
dimension. The tuple is (pool_3, spatial).
"""
preds = []
spatial_preds = []
for batch in tqdm(batches):
batch = batch.astype(np.float32)
pred, spatial_pred = self.sess.run(
[self.pool_features, self.spatial_features], {self.image_input: batch}
)
preds.append(pred.reshape([pred.shape[0], -1]))
spatial_preds.append(spatial_pred.reshape([spatial_pred.shape[0], -1]))
return (
np.concatenate(preds, axis=0),
np.concatenate(spatial_preds, axis=0),
)
def read_statistics(
self, npz_path: str, activations: Tuple[np.ndarray, np.ndarray]
) -> Tuple[FIDStatistics, FIDStatistics]:
obj = np.load(npz_path)
if "mu" in list(obj.keys()):
return FIDStatistics(obj["mu"], obj["sigma"]), FIDStatistics(
obj["mu_s"], obj["sigma_s"]
)
return tuple(self.compute_statistics(x) for x in activations)
def compute_statistics(self, activations: np.ndarray) -> FIDStatistics:
mu = np.mean(activations, axis=0)
sigma = np.cov(activations, rowvar=False)
return FIDStatistics(mu, sigma)
def compute_inception_score(self, activations: np.ndarray, split_size: int = 5000) -> float:
softmax_out = []
for i in range(0, len(activations), self.softmax_batch_size):
acts = activations[i : i + self.softmax_batch_size]
softmax_out.append(self.sess.run(self.softmax, feed_dict={self.softmax_input: acts}))
preds = np.concatenate(softmax_out, axis=0)
# https://github.com/openai/improved-gan/blob/4f5d1ec5c16a7eceb206f42bfc652693601e1d5c/inception_score/model.py#L46
scores = []
for i in range(0, len(preds), split_size):
part = preds[i : i + split_size]
kl = part * (np.log(part) - np.log(np.expand_dims(np.mean(part, 0), 0)))
kl = np.mean(np.sum(kl, 1))
scores.append(np.exp(kl))
return float(np.mean(scores))
def compute_prec_recall(
self, activations_ref: np.ndarray, activations_sample: np.ndarray
) -> Tuple[float, float]:
radii_1 = self.manifold_estimator.manifold_radii(activations_ref)
radii_2 = self.manifold_estimator.manifold_radii(activations_sample)
pr = self.manifold_estimator.evaluate_pr(
activations_ref, radii_1, activations_sample, radii_2
)
return (float(pr[0][0]), float(pr[1][0]))
class ManifoldEstimator:
"""
A helper for comparing manifolds of feature vectors.
Adapted from https://github.com/kynkaat/improved-precision-and-recall-metric/blob/f60f25e5ad933a79135c783fcda53de30f42c9b9/precision_recall.py#L57
"""
def __init__(
self,
session,
row_batch_size=10000,
col_batch_size=10000,
nhood_sizes=(3,),
clamp_to_percentile=None,
eps=1e-5,
):
"""
Estimate the manifold of given feature vectors.
:param session: the TensorFlow session.
:param row_batch_size: row batch size to compute pairwise distances
(parameter to trade-off between memory usage and performance).
:param col_batch_size: column batch size to compute pairwise distances.
:param nhood_sizes: number of neighbors used to estimate the manifold.
:param clamp_to_percentile: prune hyperspheres that have radius larger than
the given percentile.
:param eps: small number for numerical stability.
"""
self.distance_block = DistanceBlock(session)
self.row_batch_size = row_batch_size
self.col_batch_size = col_batch_size
self.nhood_sizes = nhood_sizes
self.num_nhoods = len(nhood_sizes)
self.clamp_to_percentile = clamp_to_percentile
self.eps = eps
def warmup(self):
feats, radii = (
np.zeros([1, 2048], dtype=np.float32),
np.zeros([1, 1], dtype=np.float32),
)
self.evaluate_pr(feats, radii, feats, radii)
def manifold_radii(self, features: np.ndarray) -> np.ndarray:
num_images = len(features)
# Estimate manifold of features by calculating distances to k-NN of each sample.
radii = np.zeros([num_images, self.num_nhoods], dtype=np.float32)
distance_batch = np.zeros([self.row_batch_size, num_images], dtype=np.float32)
seq = np.arange(max(self.nhood_sizes) + 1, dtype=np.int32)
for begin1 in range(0, num_images, self.row_batch_size):
end1 = min(begin1 + self.row_batch_size, num_images)
row_batch = features[begin1:end1]
for begin2 in range(0, num_images, self.col_batch_size):
end2 = min(begin2 + self.col_batch_size, num_images)
col_batch = features[begin2:end2]
# Compute distances between batches.
distance_batch[
0 : end1 - begin1, begin2:end2
] = self.distance_block.pairwise_distances(row_batch, col_batch)
# Find the k-nearest neighbor from the current batch.
radii[begin1:end1, :] = np.concatenate(
[
x[:, self.nhood_sizes]
for x in _numpy_partition(distance_batch[0 : end1 - begin1, :], seq, axis=1)
],
axis=0,
)
if self.clamp_to_percentile is not None:
max_distances = np.percentile(radii, self.clamp_to_percentile, axis=0)
radii[radii > max_distances] = 0
return radii
def evaluate(self, features: np.ndarray, radii: np.ndarray, eval_features: np.ndarray):
"""
Evaluate if new feature vectors are at the manifold.
"""
num_eval_images = eval_features.shape[0]
num_ref_images = radii.shape[0]
distance_batch = np.zeros([self.row_batch_size, num_ref_images], dtype=np.float32)
batch_predictions = np.zeros([num_eval_images, self.num_nhoods], dtype=np.int32)
max_realism_score = np.zeros([num_eval_images], dtype=np.float32)
nearest_indices = np.zeros([num_eval_images], dtype=np.int32)
for begin1 in range(0, num_eval_images, self.row_batch_size):
end1 = min(begin1 + self.row_batch_size, num_eval_images)
feature_batch = eval_features[begin1:end1]
for begin2 in range(0, num_ref_images, self.col_batch_size):
end2 = min(begin2 + self.col_batch_size, num_ref_images)
ref_batch = features[begin2:end2]
distance_batch[
0 : end1 - begin1, begin2:end2
] = self.distance_block.pairwise_distances(feature_batch, ref_batch)
# From the minibatch of new feature vectors, determine if they are in the estimated manifold.
# If a feature vector is inside a hypersphere of some reference sample, then
# the new sample lies at the estimated manifold.
# The radii of the hyperspheres are determined from distances of neighborhood size k.
samples_in_manifold = distance_batch[0 : end1 - begin1, :, None] <= radii
batch_predictions[begin1:end1] = np.any(samples_in_manifold, axis=1).astype(np.int32)
max_realism_score[begin1:end1] = np.max(
radii[:, 0] / (distance_batch[0 : end1 - begin1, :] + self.eps), axis=1
)
nearest_indices[begin1:end1] = np.argmin(distance_batch[0 : end1 - begin1, :], axis=1)
return {
"fraction": float(np.mean(batch_predictions)),
"batch_predictions": batch_predictions,
"max_realisim_score": max_realism_score,
"nearest_indices": nearest_indices,
}
def evaluate_pr(
self,
features_1: np.ndarray,
radii_1: np.ndarray,
features_2: np.ndarray,
radii_2: np.ndarray,
) -> Tuple[np.ndarray, np.ndarray]:
"""
Evaluate precision and recall efficiently.
:param features_1: [N1 x D] feature vectors for reference batch.
:param radii_1: [N1 x K1] radii for reference vectors.
:param features_2: [N2 x D] feature vectors for the other batch.
:param radii_2: [N x K2] radii for other vectors.
:return: a tuple of arrays for (precision, recall):
- precision: an np.ndarray of length K1
- recall: an np.ndarray of length K2
"""
features_1_status = np.zeros([len(features_1), radii_2.shape[1]], dtype=np.bool)
features_2_status = np.zeros([len(features_2), radii_1.shape[1]], dtype=np.bool)
for begin_1 in range(0, len(features_1), self.row_batch_size):
end_1 = begin_1 + self.row_batch_size
batch_1 = features_1[begin_1:end_1]
for begin_2 in range(0, len(features_2), self.col_batch_size):
end_2 = begin_2 + self.col_batch_size
batch_2 = features_2[begin_2:end_2]
batch_1_in, batch_2_in = self.distance_block.less_thans(
batch_1, radii_1[begin_1:end_1], batch_2, radii_2[begin_2:end_2]
)
features_1_status[begin_1:end_1] |= batch_1_in
features_2_status[begin_2:end_2] |= batch_2_in
return (
np.mean(features_2_status.astype(np.float64), axis=0),
np.mean(features_1_status.astype(np.float64), axis=0),
)
class DistanceBlock:
"""
Calculate pairwise distances between vectors.
Adapted from https://github.com/kynkaat/improved-precision-and-recall-metric/blob/f60f25e5ad933a79135c783fcda53de30f42c9b9/precision_recall.py#L34
"""
def __init__(self, session):
self.session = session
# Initialize TF graph to calculate pairwise distances.
with session.graph.as_default():
self._features_batch1 = tf.placeholder(tf.float32, shape=[None, None])
self._features_batch2 = tf.placeholder(tf.float32, shape=[None, None])
distance_block_16 = _batch_pairwise_distances(
tf.cast(self._features_batch1, tf.float16),
tf.cast(self._features_batch2, tf.float16),
)
self.distance_block = tf.cond(
tf.reduce_all(tf.math.is_finite(distance_block_16)),
lambda: tf.cast(distance_block_16, tf.float32),
lambda: _batch_pairwise_distances(self._features_batch1, self._features_batch2),
)
# Extra logic for less thans.
self._radii1 = tf.placeholder(tf.float32, shape=[None, None])
self._radii2 = tf.placeholder(tf.float32, shape=[None, None])
dist32 = tf.cast(self.distance_block, tf.float32)[..., None]
self._batch_1_in = tf.math.reduce_any(dist32 <= self._radii2, axis=1)
self._batch_2_in = tf.math.reduce_any(dist32 <= self._radii1[:, None], axis=0)
def pairwise_distances(self, U, V):
"""
Evaluate pairwise distances between two batches of feature vectors.
"""
return self.session.run(
self.distance_block,
feed_dict={self._features_batch1: U, self._features_batch2: V},
)
def less_thans(self, batch_1, radii_1, batch_2, radii_2):
return self.session.run(
[self._batch_1_in, self._batch_2_in],
feed_dict={
self._features_batch1: batch_1,
self._features_batch2: batch_2,
self._radii1: radii_1,
self._radii2: radii_2,
},
)
def _batch_pairwise_distances(U, V):
"""
Compute pairwise distances between two batches of feature vectors.
"""
with tf.variable_scope("pairwise_dist_block"):
# Squared norms of each row in U and V.
norm_u = tf.reduce_sum(tf.square(U), 1)
norm_v = tf.reduce_sum(tf.square(V), 1)
# norm_u as a column and norm_v as a row vectors.
norm_u = tf.reshape(norm_u, [-1, 1])
norm_v = tf.reshape(norm_v, [1, -1])
# Pairwise squared Euclidean distances.
D = tf.maximum(norm_u - 2 * tf.matmul(U, V, False, True) + norm_v, 0.0)
return D
class NpzArrayReader(ABC):
@abstractmethod
def read_batch(self, batch_size: int) -> Optional[np.ndarray]:
pass
@abstractmethod
def remaining(self) -> int:
pass
def read_batches(self, batch_size: int) -> Iterable[np.ndarray]:
def gen_fn():
while True:
batch = self.read_batch(batch_size)
if batch is None:
break
yield batch
rem = self.remaining()
num_batches = rem // batch_size + int(rem % batch_size != 0)
return BatchIterator(gen_fn, num_batches)
class BatchIterator:
def __init__(self, gen_fn, length):
self.gen_fn = gen_fn
self.length = length
def __len__(self):
return self.length
def __iter__(self):
return self.gen_fn()
class StreamingNpzArrayReader(NpzArrayReader):
def __init__(self, arr_f, shape, dtype):
self.arr_f = arr_f
self.shape = shape
self.dtype = dtype
self.idx = 0
def read_batch(self, batch_size: int) -> Optional[np.ndarray]:
if self.idx >= self.shape[0]:
return None
bs = min(batch_size, self.shape[0] - self.idx)
self.idx += bs
if self.dtype.itemsize == 0:
return np.ndarray([bs, *self.shape[1:]], dtype=self.dtype)
read_count = bs * np.prod(self.shape[1:])
read_size = int(read_count * self.dtype.itemsize)
data = _read_bytes(self.arr_f, read_size, "array data")
return np.frombuffer(data, dtype=self.dtype).reshape([bs, *self.shape[1:]])
def remaining(self) -> int:
return max(0, self.shape[0] - self.idx)
class MemoryNpzArrayReader(NpzArrayReader):
def __init__(self, arr):
self.arr = arr
self.idx = 0
@classmethod
def load(cls, path: str, arr_name: str):
with open(path, "rb") as f:
arr = np.load(f)[arr_name]
return cls(arr)
def read_batch(self, batch_size: int) -> Optional[np.ndarray]:
if self.idx >= self.arr.shape[0]:
return None
res = self.arr[self.idx : self.idx + batch_size]
self.idx += batch_size
return res
def remaining(self) -> int:
return max(0, self.arr.shape[0] - self.idx)
@contextmanager
def open_npz_array(path: str, arr_name: str) -> NpzArrayReader:
with _open_npy_file(path, arr_name) as arr_f:
version = np.lib.format.read_magic(arr_f)
if version == (1, 0):
header = np.lib.format.read_array_header_1_0(arr_f)
elif version == (2, 0):
header = np.lib.format.read_array_header_2_0(arr_f)
else:
yield MemoryNpzArrayReader.load(path, arr_name)
return
shape, fortran, dtype = header
if fortran or dtype.hasobject:
yield MemoryNpzArrayReader.load(path, arr_name)
else:
yield StreamingNpzArrayReader(arr_f, shape, dtype)
def _read_bytes(fp, size, error_template="ran out of data"):
"""
Copied from: https://github.com/numpy/numpy/blob/fb215c76967739268de71aa4bda55dd1b062bc2e/numpy/lib/format.py#L788-L886
Read from file-like object until size bytes are read.
Raises ValueError if not EOF is encountered before size bytes are read.
Non-blocking objects only supported if they derive from io objects.
Required as e.g. ZipExtFile in python 2.6 can return less data than
requested.
"""
data = bytes()
while True:
# io files (default in python3) return None or raise on
# would-block, python2 file will truncate, probably nothing can be
# done about that. note that regular files can't be non-blocking
try:
r = fp.read(size - len(data))
data += r
if len(r) == 0 or len(data) == size:
break
except io.BlockingIOError:
pass
if len(data) != size:
msg = "EOF: reading %s, expected %d bytes got %d"
raise ValueError(msg % (error_template, size, len(data)))
else:
return data
@contextmanager
def _open_npy_file(path: str, arr_name: str):
with open(path, "rb") as f:
with zipfile.ZipFile(f, "r") as zip_f:
if f"{arr_name}.npy" not in zip_f.namelist():
raise ValueError(f"missing {arr_name} in npz file")
with zip_f.open(f"{arr_name}.npy", "r") as arr_f:
yield arr_f
def _download_inception_model():
if os.path.exists(INCEPTION_V3_PATH):
return
print("downloading InceptionV3 model...")
with requests.get(INCEPTION_V3_URL, stream=True) as r:
r.raise_for_status()
tmp_path = INCEPTION_V3_PATH + ".tmp"
with open(tmp_path, "wb") as f:
for chunk in tqdm(r.iter_content(chunk_size=8192)):
f.write(chunk)
os.rename(tmp_path, INCEPTION_V3_PATH)
def _create_feature_graph(input_batch):
_download_inception_model()
prefix = f"{random.randrange(2**32)}_{random.randrange(2**32)}"
with open(INCEPTION_V3_PATH, "rb") as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
pool3, spatial = tf.import_graph_def(
graph_def,
input_map={f"ExpandDims:0": input_batch},
return_elements=[FID_POOL_NAME, FID_SPATIAL_NAME],
name=prefix,
)
_update_shapes(pool3)
spatial = spatial[..., :7]
return pool3, spatial
def _create_softmax_graph(input_batch):
_download_inception_model()
prefix = f"{random.randrange(2**32)}_{random.randrange(2**32)}"
with open(INCEPTION_V3_PATH, "rb") as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
(matmul,) = tf.import_graph_def(
graph_def, return_elements=[f"softmax/logits/MatMul"], name=prefix
)
w = matmul.inputs[1]
logits = tf.matmul(input_batch, w)
return tf.nn.softmax(logits)
def _update_shapes(pool3):
# https://github.com/bioinf-jku/TTUR/blob/73ab375cdf952a12686d9aa7978567771084da42/fid.py#L50-L63
ops = pool3.graph.get_operations()
for op in ops:
for o in op.outputs:
shape = o.get_shape()
if shape._dims is not None: # pylint: disable=protected-access
# shape = [s.value for s in shape] TF 1.x
shape = [s for s in shape] # TF 2.x
new_shape = []
for j, s in enumerate(shape):
if s == 1 and j == 0:
new_shape.append(None)
else:
new_shape.append(s)
o.__dict__["_shape_val"] = tf.TensorShape(new_shape)
return pool3
def _numpy_partition(arr, kth, **kwargs):
num_workers = min(cpu_count(), len(arr))
chunk_size = len(arr) // num_workers
extra = len(arr) % num_workers
start_idx = 0
batches = []
for i in range(num_workers):
size = chunk_size + (1 if i < extra else 0)
batches.append(arr[start_idx : start_idx + size])
start_idx += size
with ThreadPool(num_workers) as pool:
return list(pool.map(partial(np.partition, kth=kth, **kwargs), batches))
if __name__ == "__main__":
main()
================================================
FILE: evaluations/requirements.txt
================================================
tensorflow-gpu>=2.0
scipy
requests
tqdm
================================================
FILE: guided_diffusion/__init__.py
================================================
"""
Codebase for "Improved Denoising Diffusion Probabilistic Models".
"""
================================================
FILE: guided_diffusion/dist_util.py
================================================
"""
Helpers for distributed training.
"""
import io
import os
import socket
import blobfile as bf
from mpi4py import MPI
import torch as th
import torch.distributed as dist
# Change this to reflect your cluster layout.
# The GPU for a given rank is (rank % GPUS_PER_NODE).
GPUS_PER_NODE = 8
SETUP_RETRY_COUNT = 3
def setup_dist():
"""
Setup a distributed process group.
"""
if dist.is_initialized():
return
os.environ["CUDA_VISIBLE_DEVICES"] = f"{MPI.COMM_WORLD.Get_rank() % GPUS_PER_NODE}"
comm = MPI.COMM_WORLD
backend = "gloo" if not th.cuda.is_available() else "nccl"
if backend == "gloo":
hostname = "localhost"
else:
hostname = socket.gethostbyname(socket.getfqdn())
os.environ["MASTER_ADDR"] = comm.bcast(hostname, root=0)
os.environ["RANK"] = str(comm.rank)
os.environ["WORLD_SIZE"] = str(comm.size)
port = comm.bcast(_find_free_port(), root=0)
os.environ["MASTER_PORT"] = str(port)
dist.init_process_group(backend=backend, init_method="env://")
def dev():
"""
Get the device to use for torch.distributed.
"""
if th.cuda.is_available():
return th.device(f"cuda")
return th.device("cpu")
def load_state_dict(path, **kwargs):
"""
Load a PyTorch file without redundant fetches across MPI ranks.
"""
chunk_size = 2 ** 30 # MPI has a relatively small size limit
if MPI.COMM_WORLD.Get_rank() == 0:
with bf.BlobFile(path, "rb") as f:
data = f.read()
num_chunks = len(data) // chunk_size
if len(data) % chunk_size:
num_chunks += 1
MPI.COMM_WORLD.bcast(num_chunks)
for i in range(0, len(data), chunk_size):
MPI.COMM_WORLD.bcast(data[i : i + chunk_size])
else:
num_chunks = MPI.COMM_WORLD.bcast(None)
data = bytes()
for _ in range(num_chunks):
data += MPI.COMM_WORLD.bcast(None)
return th.load(io.BytesIO(data), **kwargs)
def sync_params(params):
"""
Synchronize a sequence of Tensors across ranks from rank 0.
"""
for p in params:
with th.no_grad():
dist.broadcast(p, 0)
def _find_free_port():
try:
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.bind(("", 0))
s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
return s.getsockname()[1]
finally:
s.close()
================================================
FILE: guided_diffusion/fp16_util.py
================================================
"""
Helpers to train with 16-bit precision.
"""
import numpy as np
import torch as th
import torch.nn as nn
from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
from . import logger
INITIAL_LOG_LOSS_SCALE = 20.0
def convert_module_to_f16(l):
"""
Convert primitive modules to float16.
"""
if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Conv3d)):
l.weight.data = l.weight.data.half()
if l.bias is not None:
l.bias.data = l.bias.data.half()
def convert_module_to_f32(l):
"""
Convert primitive modules to float32, undoing convert_module_to_f16().
"""
if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Conv3d)):
l.weight.data = l.weight.data.float()
if l.bias is not None:
l.bias.data = l.bias.data.float()
def make_master_params(param_groups_and_shapes):
"""
Copy model parameters into a (differently-shaped) list of full-precision
parameters.
"""
master_params = []
for param_group, shape in param_groups_and_shapes:
master_param = nn.Parameter(
_flatten_dense_tensors(
[param.detach().float() for (_, param) in param_group]
).view(shape)
)
master_param.requires_grad = True
master_params.append(master_param)
return master_params
def model_grads_to_master_grads(param_groups_and_shapes, master_params):
"""
Copy the gradients from the model parameters into the master parameters
from make_master_params().
"""
for master_param, (param_group, shape) in zip(
master_params, param_groups_and_shapes
):
master_param.grad = _flatten_dense_tensors(
[param_grad_or_zeros(param) for (_, param) in param_group]
).view(shape)
def master_params_to_model_params(param_groups_and_shapes, master_params):
"""
Copy the master parameter data back into the model parameters.
"""
# Without copying to a list, if a generator is passed, this will
# silently not copy any parameters.
for master_param, (param_group, _) in zip(master_params, param_groups_and_shapes):
for (_, param), unflat_master_param in zip(
param_group, unflatten_master_params(param_group, master_param.view(-1))
):
param.detach().copy_(unflat_master_param)
def unflatten_master_params(param_group, master_param):
return _unflatten_dense_tensors(master_param, [param for (_, param) in param_group])
def get_param_groups_and_shapes(named_model_params):
named_model_params = list(named_model_params)
scalar_vector_named_params = (
[(n, p) for (n, p) in named_model_params if p.ndim <= 1],
(-1),
)
matrix_named_params = (
[(n, p) for (n, p) in named_model_params if p.ndim > 1],
(1, -1),
)
return [scalar_vector_named_params, matrix_named_params]
def master_params_to_state_dict(
model, param_groups_and_shapes, master_params, use_fp16
):
if use_fp16:
state_dict = model.state_dict()
for master_param, (param_group, _) in zip(
master_params, param_groups_and_shapes
):
for (name, _), unflat_master_param in zip(
param_group, unflatten_master_params(param_group, master_param.view(-1))
):
assert name in state_dict
state_dict[name] = unflat_master_param
else:
state_dict = model.state_dict()
for i, (name, _value) in enumerate(model.named_parameters()):
assert name in state_dict
state_dict[name] = master_params[i]
return state_dict
def state_dict_to_master_params(model, state_dict, use_fp16):
if use_fp16:
named_model_params = [
(name, state_dict[name]) for name, _ in model.named_parameters()
]
param_groups_and_shapes = get_param_groups_and_shapes(named_model_params)
master_params = make_master_params(param_groups_and_shapes)
else:
master_params = [state_dict[name] for name, _ in model.named_parameters()]
return master_params
def zero_master_grads(master_params):
for param in master_params:
param.grad = None
def zero_grad(model_params):
for param in model_params:
# Taken from https://pytorch.org/docs/stable/_modules/torch/optim/optimizer.html#Optimizer.add_param_group
if param.grad is not None:
param.grad.detach_()
param.grad.zero_()
def param_grad_or_zeros(param):
if param.grad is not None:
return param.grad.data.detach()
else:
return th.zeros_like(param)
class MixedPrecisionTrainer:
def __init__(
self,
*,
model,
use_fp16=False,
fp16_scale_growth=1e-3,
initial_lg_loss_scale=INITIAL_LOG_LOSS_SCALE,
):
self.model = model
self.use_fp16 = use_fp16
self.fp16_scale_growth = fp16_scale_growth
self.model_params = list(self.model.parameters())
self.master_params = self.model_params
self.param_groups_and_shapes = None
self.lg_loss_scale = initial_lg_loss_scale
if self.use_fp16:
self.param_groups_and_shapes = get_param_groups_and_shapes(
self.model.named_parameters()
)
self.master_params = make_master_params(self.param_groups_and_shapes)
self.model.convert_to_fp16()
def zero_grad(self):
zero_grad(self.model_params)
def backward(self, loss: th.Tensor):
if self.use_fp16:
loss_scale = 2 ** self.lg_loss_scale
(loss * loss_scale).backward()
else:
loss.backward()
def optimize(self, opt: th.optim.Optimizer):
if self.use_fp16:
return self._optimize_fp16(opt)
else:
return self._optimize_normal(opt)
def _optimize_fp16(self, opt: th.optim.Optimizer):
logger.logkv_mean("lg_loss_scale", self.lg_loss_scale)
model_grads_to_master_grads(self.param_groups_and_shapes, self.master_params)
grad_norm, param_norm = self._compute_norms(grad_scale=2 ** self.lg_loss_scale)
if check_overflow(grad_norm):
self.lg_loss_scale -= 1
logger.log(f"Found NaN, decreased lg_loss_scale to {self.lg_loss_scale}")
zero_master_grads(self.master_params)
return False
logger.logkv_mean("grad_norm", grad_norm)
logger.logkv_mean("param_norm", param_norm)
for p in self.master_params:
p.grad.mul_(1.0 / (2 ** self.lg_loss_scale))
opt.step()
zero_master_grads(self.master_params)
master_params_to_model_params(self.param_groups_and_shapes, self.master_params)
self.lg_loss_scale += self.fp16_scale_growth
return True
def _optimize_normal(self, opt: th.optim.Optimizer):
grad_norm, param_norm = self._compute_norms()
logger.logkv_mean("grad_norm", grad_norm)
logger.logkv_mean("param_norm", param_norm)
opt.step()
return True
def _compute_norms(self, grad_scale=1.0):
grad_norm = 0.0
param_norm = 0.0
for p in self.master_params:
with th.no_grad():
param_norm += th.norm(p, p=2, dtype=th.float32).item() ** 2
if p.grad is not None:
grad_norm += th.norm(p.grad, p=2, dtype=th.float32).item() ** 2
return np.sqrt(grad_norm) / grad_scale, np.sqrt(param_norm)
def master_params_to_state_dict(self, master_params):
return master_params_to_state_dict(
self.model, self.param_groups_and_shapes, master_params, self.use_fp16
)
def state_dict_to_master_params(self, state_dict):
return state_dict_to_master_params(self.model, state_dict, self.use_fp16)
def check_overflow(value):
return (value == float("inf")) or (value == -float("inf")) or (value != value)
================================================
FILE: guided_diffusion/gaussian_diffusion.py
================================================
"""
This code started out as a PyTorch port of Ho et al's diffusion models:
https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/diffusion_utils_2.py
Docstrings have been added, as well as DDIM sampling and a new collection of beta schedules.
"""
import enum
import math
import numpy as np
import torch as th
from .nn import mean_flat
from .losses import normal_kl, discretized_gaussian_log_likelihood
def get_named_beta_schedule(schedule_name, num_diffusion_timesteps):
"""
Get a pre-defined beta schedule for the given name.
The beta schedule library consists of beta schedules which remain similar
in the limit of num_diffusion_timesteps.
Beta schedules may be added, but should not be removed or changed once
they are committed to maintain backwards compatibility.
"""
if schedule_name == "linear":
# Linear schedule from Ho et al, extended to work for any number of
# diffusion steps.
scale = 1000 / num_diffusion_timesteps
beta_start = scale * 0.0001
beta_end = scale * 0.02
return np.linspace(
beta_start, beta_end, num_diffusion_timesteps, dtype=np.float64
)
elif schedule_name == "cosine":
return betas_for_alpha_bar(
num_diffusion_timesteps,
lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2,
)
else:
raise NotImplementedError(f"unknown beta schedule: {schedule_name}")
def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
"""
Create a beta schedule that discretizes the given alpha_t_bar function,
which defines the cumulative product of (1-beta) over time from t = [0,1].
:param num_diffusion_timesteps: the number of betas to produce.
:param alpha_bar: a lambda that takes an argument t from 0 to 1 and
produces the cumulative product of (1-beta) up to that
part of the diffusion process.
:param max_beta: the maximum beta to use; use values lower than 1 to
prevent singularities.
"""
betas = []
for i in range(num_diffusion_timesteps):
t1 = i / num_diffusion_timesteps
t2 = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
return np.array(betas)
class ModelMeanType(enum.Enum):
"""
Which type of output the model predicts.
"""
PREVIOUS_X = enum.auto() # the model predicts x_{t-1}
START_X = enum.auto() # the model predicts x_0
EPSILON = enum.auto() # the model predicts epsilon
class ModelVarType(enum.Enum):
"""
What is used as the model's output variance.
The LEARNED_RANGE option has been added to allow the model to predict
values between FIXED_SMALL and FIXED_LARGE, making its job easier.
"""
LEARNED = enum.auto()
FIXED_SMALL = enum.auto()
FIXED_LARGE = enum.auto()
LEARNED_RANGE = enum.auto()
class LossType(enum.Enum):
MSE = enum.auto() # use raw MSE loss (and KL when learning variances)
RESCALED_MSE = (
enum.auto()
) # use raw MSE loss (with RESCALED_KL when learning variances)
KL = enum.auto() # use the variational lower-bound
RESCALED_KL = enum.auto() # like KL, but rescale to estimate the full VLB
def is_vb(self):
return self == LossType.KL or self == LossType.RESCALED_KL
class GaussianDiffusion:
"""
Utilities for training and sampling diffusion models.
Ported directly from here, and then adapted over time to further experimentation.
https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/diffusion_utils_2.py#L42
:param betas: a 1-D numpy array of betas for each diffusion timestep,
starting at T and going to 1.
:param model_mean_type: a ModelMeanType determining what the model outputs.
:param model_var_type: a ModelVarType determining how variance is output.
:param loss_type: a LossType determining the loss function to use.
:param rescale_timesteps: if True, pass floating point timesteps into the
model so that they are always scaled like in the
original paper (0 to 1000).
"""
def __init__(
self,
*,
betas,
model_mean_type,
model_var_type,
loss_type,
rescale_timesteps=False,
):
self.model_mean_type = model_mean_type
self.model_var_type = model_var_type
self.loss_type = loss_type
self.rescale_timesteps = rescale_timesteps
# Use float64 for accuracy.
betas = np.array(betas, dtype=np.float64)
self.betas = betas
assert len(betas.shape) == 1, "betas must be 1-D"
assert (betas > 0).all() and (betas <= 1).all()
self.num_timesteps = int(betas.shape[0])
alphas = 1.0 - betas
self.alphas_cumprod = np.cumprod(alphas, axis=0)
self.alphas_cumprod_prev = np.append(1.0, self.alphas_cumprod[:-1])
self.alphas_cumprod_next = np.append(self.alphas_cumprod[1:], 0.0)
assert self.alphas_cumprod_prev.shape == (self.num_timesteps,)
# calculations for diffusion q(x_t | x_{t-1}) and others
self.sqrt_alphas_cumprod = np.sqrt(self.alphas_cumprod)
self.sqrt_one_minus_alphas_cumprod = np.sqrt(1.0 - self.alphas_cumprod)
self.log_one_minus_alphas_cumprod = np.log(1.0 - self.alphas_cumprod)
self.sqrt_recip_alphas_cumprod = np.sqrt(1.0 / self.alphas_cumprod)
self.sqrt_recipm1_alphas_cumprod = np.sqrt(1.0 / self.alphas_cumprod - 1)
# calculations for posterior q(x_{t-1} | x_t, x_0)
self.posterior_variance = (
betas * (1.0 - self.alphas_cumprod_prev) / (1.0 - self.alphas_cumprod)
)
# log calculation clipped because the posterior variance is 0 at the
# beginning of the diffusion chain.
self.posterior_log_variance_clipped = np.log(
np.append(self.posterior_variance[1], self.posterior_variance[1:])
)
self.posterior_mean_coef1 = (
betas * np.sqrt(self.alphas_cumprod_prev) / (1.0 - self.alphas_cumprod)
)
self.posterior_mean_coef2 = (
(1.0 - self.alphas_cumprod_prev)
* np.sqrt(alphas)
/ (1.0 - self.alphas_cumprod)
)
def q_mean_variance(self, x_start, t):
"""
Get the distribution q(x_t | x_0).
:param x_start: the [N x C x ...] tensor of noiseless inputs.
:param t: the number of diffusion steps (minus 1). Here, 0 means one step.
:return: A tuple (mean, variance, log_variance), all of x_start's shape.
"""
mean = (
_extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
)
variance = _extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
log_variance = _extract_into_tensor(
self.log_one_minus_alphas_cumprod, t, x_start.shape
)
return mean, variance, log_variance
def q_sample(self, x_start, t, noise=None):
"""
Diffuse the data for a given number of diffusion steps.
In other words, sample from q(x_t | x_0).
:param x_start: the initial data batch.
:param t: the number of diffusion steps (minus 1). Here, 0 means one step.
:param noise: if specified, the split-out normal noise.
:return: A noisy version of x_start.
"""
if noise is None:
noise = th.randn_like(x_start)
assert noise.shape == x_start.shape
return (
_extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
+ _extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape)
* noise
)
def q_posterior_mean_variance(self, x_start, x_t, t):
"""
Compute the mean and variance of the diffusion posterior:
q(x_{t-1} | x_t, x_0)
"""
assert x_start.shape == x_t.shape
posterior_mean = (
_extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start
+ _extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
)
posterior_variance = _extract_into_tensor(self.posterior_variance, t, x_t.shape)
posterior_log_variance_clipped = _extract_into_tensor(
self.posterior_log_variance_clipped, t, x_t.shape
)
assert (
posterior_mean.shape[0]
== posterior_variance.shape[0]
== posterior_log_variance_clipped.shape[0]
== x_start.shape[0]
)
return posterior_mean, posterior_variance, posterior_log_variance_clipped
def p_mean_variance(
self, model, x, t, clip_denoised=True, denoised_fn=None, model_kwargs=None
):
"""
Apply the model to get p(x_{t-1} | x_t), as well as a prediction of
the initial x, x_0.
:param model: the model, which takes a signal and a batch of timesteps
as input.
:param x: the [N x C x ...] tensor at time t.
:param t: a 1-D Tensor of timesteps.
:param clip_denoised: if True, clip the denoised signal into [-1, 1].
:param denoised_fn: if not None, a function which applies to the
x_start prediction before it is used to sample. Applies before
clip_denoised.
:param model_kwargs: if not None, a dict of extra keyword arguments to
pass to the model. This can be used for conditioning.
:return: a dict with the following keys:
- 'mean': the model mean output.
- 'variance': the model variance output.
- 'log_variance': the log of 'variance'.
- 'pred_xstart': the prediction for x_0.
"""
if model_kwargs is None:
model_kwargs = {}
B, C = x.shape[:2]
assert t.shape == (B,)
model_output = model(x, self._scale_timesteps(t), **model_kwargs)
if self.model_var_type in [ModelVarType.LEARNED, ModelVarType.LEARNED_RANGE]:
assert model_output.shape == (B, C * 2, *x.shape[2:])
model_output, model_var_values = th.split(model_output, C, dim=1)
if self.model_var_type == ModelVarType.LEARNED:
model_log_variance = model_var_values
model_variance = th.exp(model_log_variance)
else:
min_log = _extract_into_tensor(
self.posterior_log_variance_clipped, t, x.shape
)
max_log = _extract_into_tensor(np.log(self.betas), t, x.shape)
# The model_var_values is [-1, 1] for [min_var, max_var].
frac = (model_var_values + 1) / 2
model_log_variance = frac * max_log + (1 - frac) * min_log
model_variance = th.exp(model_log_variance)
else:
model_variance, model_log_variance = {
# for fixedlarge, we set the initial (log-)variance like so
# to get a better decoder log likelihood.
ModelVarType.FIXED_LARGE: (
np.append(self.posterior_variance[1], self.betas[1:]),
np.log(np.append(self.posterior_variance[1], self.betas[1:])),
),
ModelVarType.FIXED_SMALL: (
self.posterior_variance,
self.posterior_log_variance_clipped,
),
}[self.model_var_type]
model_variance = _extract_into_tensor(model_variance, t, x.shape)
model_log_variance = _extract_into_tensor(model_log_variance, t, x.shape)
def process_xstart(x):
if denoised_fn is not None:
x = denoised_fn(x)
if clip_denoised:
return x.clamp(-1, 1)
return x
if self.model_mean_type == ModelMeanType.PREVIOUS_X:
pred_xstart = process_xstart(
self._predict_xstart_from_xprev(x_t=x, t=t, xprev=model_output)
)
model_mean = model_output
elif self.model_mean_type in [ModelMeanType.START_X, ModelMeanType.EPSILON]:
if self.model_mean_type == ModelMeanType.START_X:
pred_xstart = process_xstart(model_output)
else:
pred_xstart = process_xstart(
self._predict_xstart_from_eps(x_t=x, t=t, eps=model_output)
)
model_mean, _, _ = self.q_posterior_mean_variance(
x_start=pred_xstart, x_t=x, t=t
)
else:
raise NotImplementedError(self.model_mean_type)
assert (
model_mean.shape == model_log_variance.shape == pred_xstart.shape == x.shape
)
return {
"mean": model_mean,
"variance": model_variance,
"log_variance": model_log_variance,
"pred_xstart": pred_xstart,
}
def _predict_xstart_from_eps(self, x_t, t, eps):
assert x_t.shape == eps.shape
return (
_extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t
- _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * eps
)
def _predict_xstart_from_xprev(self, x_t, t, xprev):
assert x_t.shape == xprev.shape
return ( # (xprev - coef2*x_t) / coef1
_extract_into_tensor(1.0 / self.posterior_mean_coef1, t, x_t.shape) * xprev
- _extract_into_tensor(
self.posterior_mean_coef2 / self.posterior_mean_coef1, t, x_t.shape
)
* x_t
)
def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
return (
_extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t
- pred_xstart
) / _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
def _scale_timesteps(self, t):
if self.rescale_timesteps:
return t.float() * (1000.0 / self.num_timesteps)
return t
def condition_mean(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
"""
Compute the mean for the previous step, given a function cond_fn that
computes the gradient of a conditional log probability with respect to
x. In particular, cond_fn computes grad(log(p(y|x))), and we want to
condition on y.
This uses the conditioning strategy from Sohl-Dickstein et al. (2015).
"""
gradient = cond_fn(x, self._scale_timesteps(t), **model_kwargs)
new_mean = (
p_mean_var["mean"].float() + p_mean_var["variance"] * gradient.float()
)
return new_mean
def condition_score(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
"""
Compute what the p_mean_variance output would have been, should the
model's score function be conditioned by cond_fn.
See condition_mean() for details on cond_fn.
Unlike condition_mean(), this instead uses the conditioning strategy
from Song et al (2020).
"""
alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape)
eps = self._predict_eps_from_xstart(x, t, p_mean_var["pred_xstart"])
eps = eps - (1 - alpha_bar).sqrt() * cond_fn(
x, self._scale_timesteps(t), **model_kwargs
)
out = p_mean_var.copy()
out["pred_xstart"] = self._predict_xstart_from_eps(x, t, eps)
out["mean"], _, _ = self.q_posterior_mean_variance(
x_start=out["pred_xstart"], x_t=x, t=t
)
return out
def p_sample(
self,
model,
x,
t,
clip_denoised=True,
denoised_fn=None,
cond_fn=None,
model_kwargs=None,
):
"""
Sample x_{t-1} from the model at the given timestep.
:param model: the model to sample from.
:param x: the current tensor at x_{t-1}.
:param t: the value of t, starting at 0 for the first diffusion step.
:param clip_denoised: if True, clip the x_start prediction to [-1, 1].
:param denoised_fn: if not None, a function which applies to the
x_start prediction before it is used to sample.
:param cond_fn: if not None, this is a gradient function that acts
similarly to the model.
:param model_kwargs: if not None, a dict of extra keyword arguments to
pass to the model. This can be used for conditioning.
:return: a dict containing the following keys:
- 'sample': a random sample from the model.
- 'pred_xstart': a prediction of x_0.
"""
out = self.p_mean_variance(
model,
x,
t,
clip_denoised=clip_denoised,
denoised_fn=denoised_fn,
model_kwargs=model_kwargs,
)
noise = th.randn_like(x)
nonzero_mask = (
(t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
) # no noise when t == 0
if cond_fn is not None:
out["mean"] = self.condition_mean(
cond_fn, out, x, t, model_kwargs=model_kwargs
)
sample = out["mean"] + nonzero_mask * th.exp(0.5 * out["log_variance"]) * noise
return {"sample": sample, "pred_xstart": out["pred_xstart"]}
def p_sample_loop(
self,
model,
shape,
noise=None,
clip_denoised=True,
denoised_fn=None,
cond_fn=None,
model_kwargs=None,
device=None,
progress=False,
):
"""
Generate samples from the model.
:param model: the model module.
:param shape: the shape of the samples, (N, C, H, W).
:param noise: if specified, the noise from the encoder to sample.
Should be of the same shape as `shape`.
:param clip_denoised: if True, clip x_start predictions to [-1, 1].
:param denoised_fn: if not None, a function which applies to the
x_start prediction before it is used to sample.
:param cond_fn: if not None, this is a gradient function that acts
similarly to the model.
:param model_kwargs: if not None, a dict of extra keyword arguments to
pass to the model. This can be used for conditioning.
:param device: if specified, the device to create the samples on.
If not specified, use a model parameter's device.
:param progress: if True, show a tqdm progress bar.
:return: a non-differentiable batch of samples.
"""
final = None
for sample in self.p_sample_loop_progressive(
model,
shape,
noise=noise,
clip_denoised=clip_denoised,
denoised_fn=denoised_fn,
cond_fn=cond_fn,
model_kwargs=model_kwargs,
device=device,
progress=progress,
):
final = sample
return final["sample"]
def p_sample_loop_progressive(
self,
model,
shape,
noise=None,
clip_denoised=True,
denoised_fn=None,
cond_fn=None,
model_kwargs=None,
device=None,
progress=False,
):
"""
Generate samples from the model and yield intermediate samples from
each timestep of diffusion.
Arguments are the same as p_sample_loop().
Returns a generator over dicts, where each dict is the return value of
p_sample().
"""
if device is None:
device = next(model.parameters()).device
assert isinstance(shape, (tuple, list))
if noise is not None:
img = noise
else:
img = th.randn(*shape, device=device)
indices = list(range(self.num_timesteps))[::-1]
if progress:
# Lazy import so that we don't depend on tqdm.
from tqdm.auto import tqdm
indices = tqdm(indices)
for i in indices:
t = th.tensor([i] * shape[0], device=device)
with th.no_grad():
out = self.p_sample(
model,
img,
t,
clip_denoised=clip_denoised,
denoised_fn=denoised_fn,
cond_fn=cond_fn,
model_kwargs=model_kwargs,
)
yield out
img = out["sample"]
def ddim_sample(
self,
model,
x,
t,
clip_denoised=True,
denoised_fn=None,
cond_fn=None,
model_kwargs=None,
eta=0.0,
):
"""
Sample x_{t-1} from the model using DDIM.
Same usage as p_sample().
"""
out = self.p_mean_variance(
model,
x,
t,
clip_denoised=clip_denoised,
denoised_fn=denoised_fn,
model_kwargs=model_kwargs,
)
if cond_fn is not None:
out = self.condition_score(cond_fn, out, x, t, model_kwargs=model_kwargs)
# Usually our model outputs epsilon, but we re-derive it
# in case we used x_start or x_prev prediction.
eps = self._predict_eps_from_xstart(x, t, out["pred_xstart"])
alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape)
alpha_bar_prev = _extract_into_tensor(self.alphas_cumprod_prev, t, x.shape)
sigma = (
eta
* th.sqrt((1 - alpha_bar_prev) / (1 - alpha_bar))
* th.sqrt(1 - alpha_bar / alpha_bar_prev)
)
# Equation 12.
noise = th.randn_like(x)
mean_pred = (
out["pred_xstart"] * th.sqrt(alpha_bar_prev)
+ th.sqrt(1 - alpha_bar_prev - sigma ** 2) * eps
)
nonzero_mask = (
(t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
) # no noise when t == 0
sample = mean_pred + nonzero_mask * sigma * noise
return {"sample": sample, "pred_xstart": out["pred_xstart"]}
def ddim_reverse_sample(
self,
model,
x,
t,
clip_denoised=True,
denoised_fn=None,
model_kwargs=None,
eta=0.0,
):
"""
Sample x_{t+1} from the model using DDIM reverse ODE.
"""
assert eta == 0.0, "Reverse ODE only for deterministic path"
out = self.p_mean_variance(
model,
x,
t,
clip_denoised=clip_denoised,
denoised_fn=denoised_fn,
model_kwargs=model_kwargs,
)
# Usually our model outputs epsilon, but we re-derive it
# in case we used x_start or x_prev prediction.
eps = (
_extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x.shape) * x
- out["pred_xstart"]
) / _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x.shape)
alpha_bar_next = _extract_into_tensor(self.alphas_cumprod_next, t, x.shape)
# Equation 12. reversed
mean_pred = (
out["pred_xstart"] * th.sqrt(alpha_bar_next)
+ th.sqrt(1 - alpha_bar_next) * eps
)
return {"sample": mean_pred, "pred_xstart": out["pred_xstart"]}
def ddim_sample_loop(
self,
model,
shape,
noise=None,
clip_denoised=True,
denoised_fn=None,
cond_fn=None,
model_kwargs=None,
device=None,
progress=False,
eta=0.0,
):
"""
Generate samples from the model using DDIM.
Same usage as p_sample_loop().
"""
final = None
for sample in self.ddim_sample_loop_progressive(
model,
shape,
noise=noise,
clip_denoised=clip_denoised,
denoised_fn=denoised_fn,
cond_fn=cond_fn,
model_kwargs=model_kwargs,
device=device,
progress=progress,
eta=eta,
):
final = sample
return final["sample"]
def ddim_sample_loop_progressive(
self,
model,
shape,
noise=None,
clip_denoised=True,
denoised_fn=None,
cond_fn=None,
model_kwargs=None,
device=None,
progress=False,
eta=0.0,
):
"""
Use DDIM to sample from the model and yield intermediate samples from
each timestep of DDIM.
Same usage as p_sample_loop_progressive().
"""
if device is None:
device = next(model.parameters()).device
assert isinstance(shape, (tuple, list))
if noise is not None:
img = noise
else:
img = th.randn(*shape, device=device)
indices = list(range(self.num_timesteps))[::-1]
if progress:
# Lazy import so that we don't depend on tqdm.
from tqdm.auto import tqdm
indices = tqdm(indices)
for i in indices:
t = th.tensor([i] * shape[0], device=device)
with th.no_grad():
out = self.ddim_sample(
model,
img,
t,
clip_denoised=clip_denoised,
denoised_fn=denoised_fn,
cond_fn=cond_fn,
model_kwargs=model_kwargs,
eta=eta,
)
yield out
img = out["sample"]
def _vb_terms_bpd(
self, model, x_start, x_t, t, clip_denoised=True, model_kwargs=None
):
"""
Get a term for the variational lower-bound.
The resulting units are bits (rather than nats, as one might expect).
This allows for comparison to other papers.
:return: a dict with the following keys:
- 'output': a shape [N] tensor of NLLs or KLs.
- 'pred_xstart': the x_0 predictions.
"""
true_mean, _, true_log_variance_clipped = self.q_posterior_mean_variance(
x_start=x_start, x_t=x_t, t=t
)
out = self.p_mean_variance(
model, x_t, t, clip_denoised=clip_denoised, model_kwargs=model_kwargs
)
kl = normal_kl(
true_mean, true_log_variance_clipped, out["mean"], out["log_variance"]
)
kl = mean_flat(kl) / np.log(2.0)
decoder_nll = -discretized_gaussian_log_likelihood(
x_start, means=out["mean"], log_scales=0.5 * out["log_variance"]
)
assert decoder_nll.shape == x_start.shape
decoder_nll = mean_flat(decoder_nll) / np.log(2.0)
# At the first timestep return the decoder NLL,
# otherwise return KL(q(x_{t-1}|x_t,x_0) || p(x_{t-1}|x_t))
output = th.where((t == 0), decoder_nll, kl)
return {"output": output, "pred_xstart": out["pred_xstart"]}
def training_losses(self, model, x_start, t, model_kwargs=None, noise=None):
"""
Compute training losses for a single timestep.
:param model: the model to evaluate loss on.
:param x_start: the [N x C x ...] tensor of inputs.
:param t: a batch of timestep indices.
:param model_kwargs: if not None, a dict of extra keyword arguments to
pass to the model. This can be used for conditioning.
:param noise: if specified, the specific Gaussian noise to try to remove.
:return: a dict with the key "loss" containing a tensor of shape [N].
Some mean or variance settings may also have other keys.
"""
if model_kwargs is None:
model_kwargs = {}
if noise is None:
noise = th.randn_like(x_start)
x_t = self.q_sample(x_start, t, noise=noise)
terms = {}
if self.loss_type == LossType.KL or self.loss_type == LossType.RESCALED_KL:
terms["loss"] = self._vb_terms_bpd(
model=model,
x_start=x_start,
x_t=x_t,
t=t,
clip_denoised=False,
model_kwargs=model_kwargs,
)["output"]
if self.loss_type == LossType.RESCALED_KL:
terms["loss"] *= self.num_timesteps
elif self.loss_type == LossType.MSE or self.loss_type == LossType.RESCALED_MSE:
model_output = model(x_t, self._scale_timesteps(t), **model_kwargs)
if self.model_var_type in [
ModelVarType.LEARNED,
ModelVarType.LEARNED_RANGE,
]:
B, C = x_t.shape[:2]
assert model_output.shape == (B, C * 2, *x_t.shape[2:])
model_output, model_var_values = th.split(model_output, C, dim=1)
# Learn the variance using the variational bound, but don't let
# it affect our mean prediction.
frozen_out = th.cat([model_output.detach(), model_var_values], dim=1)
terms["vb"] = self._vb_terms_bpd(
model=lambda *args, r=frozen_out: r,
x_start=x_start,
x_t=x_t,
t=t,
clip_denoised=False,
)["output"]
if self.loss_type == LossType.RESCALED_MSE:
# Divide by 1000 for equivalence with initial implementation.
# Without a factor of 1/1000, the VB term hurts the MSE term.
terms["vb"] *= self.num_timesteps / 1000.0
target = {
ModelMeanType.PREVIOUS_X: self.q_posterior_mean_variance(
x_start=x_start, x_t=x_t, t=t
)[0],
ModelMeanType.START_X: x_start,
ModelMeanType.EPSILON: noise,
}[self.model_mean_type]
assert model_output.shape == target.shape == x_start.shape
terms["mse"] = mean_flat((target - model_output) ** 2)
if "vb" in terms:
terms["loss"] = terms["mse"] + terms["vb"]
else:
terms["loss"] = terms["mse"]
else:
raise NotImplementedError(self.loss_type)
return terms
def _prior_bpd(self, x_start):
"""
Get the prior KL term for the variational lower-bound, measured in
bits-per-dim.
This term can't be optimized, as it only depends on the encoder.
:param x_start: the [N x C x ...] tensor of inputs.
:return: a batch of [N] KL values (in bits), one per batch element.
"""
batch_size = x_start.shape[0]
t = th.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
kl_prior = normal_kl(
mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0
)
return mean_flat(kl_prior) / np.log(2.0)
def calc_bpd_loop(self, model, x_start, clip_denoised=True, model_kwargs=None):
"""
Compute the entire variational lower-bound, measured in bits-per-dim,
as well as other related quantities.
:param model: the model to evaluate loss on.
:param x_start: the [N x C x ...] tensor of inputs.
:param clip_denoised: if True, clip denoised samples.
:param model_kwargs: if not None, a dict of extra keyword arguments to
pass to the model. This can be used for conditioning.
:return: a dict containing the following keys:
- total_bpd: the total variational lower-bound, per batch element.
- prior_bpd: the prior term in the lower-bound.
- vb: an [N x T] tensor of terms in the lower-bound.
- xstart_mse: an [N x T] tensor of x_0 MSEs for each timestep.
- mse: an [N x T] tensor of epsilon MSEs for each timestep.
"""
device = x_start.device
batch_size = x_start.shape[0]
vb = []
xstart_mse = []
mse = []
for t in list(range(self.num_timesteps))[::-1]:
t_batch = th.tensor([t] * batch_size, device=device)
noise = th.randn_like(x_start)
x_t = self.q_sample(x_start=x_start, t=t_batch, noise=noise)
# Calculate VLB term at the current timestep
with th.no_grad():
out = self._vb_terms_bpd(
model,
x_start=x_start,
x_t=x_t,
t=t_batch,
clip_denoised=clip_denoised,
model_kwargs=model_kwargs,
)
vb.append(out["output"])
xstart_mse.append(mean_flat((out["pred_xstart"] - x_start) ** 2))
eps = self._predict_eps_from_xstart(x_t, t_batch, out["pred_xstart"])
mse.append(mean_flat((eps - noise) ** 2))
vb = th.stack(vb, dim=1)
xstart_mse = th.stack(xstart_mse, dim=1)
mse = th.stack(mse, dim=1)
prior_bpd = self._prior_bpd(x_start)
total_bpd = vb.sum(dim=1) + prior_bpd
return {
"total_bpd": total_bpd,
"prior_bpd": prior_bpd,
"vb": vb,
"xstart_mse": xstart_mse,
"mse": mse,
}
def _extract_into_tensor(arr, timesteps, broadcast_shape):
"""
Extract values from a 1-D numpy array for a batch of indices.
:param arr: the 1-D numpy array.
:param timesteps: a tensor of indices into the array to extract.
:param broadcast_shape: a larger shape of K dimensions with the batch
dimension equal to the length of timesteps.
:return: a tensor of shape [batch_size, 1, ...] where the shape has K dims.
"""
res = th.from_numpy(arr).to(device=timesteps.device)[timesteps].float()
while len(res.shape) < len(broadcast_shape):
res = res[..., None]
return res.expand(broadcast_shape)
================================================
FILE: guided_diffusion/image_datasets.py
================================================
import math
import random
from PIL import Image
import blobfile as bf
from mpi4py import MPI
import numpy as np
from torch.utils.data import DataLoader, Dataset
def load_data(
*,
data_dir,
batch_size,
image_size,
class_cond=False,
deterministic=False,
random_crop=False,
random_flip=True,
):
"""
For a dataset, create a generator over (images, kwargs) pairs.
Each images is an NCHW float tensor, and the kwargs dict contains zero or
more keys, each of which map to a batched Tensor of their own.
The kwargs dict can be used for class labels, in which case the key is "y"
and the values are integer tensors of class labels.
:param data_dir: a dataset directory.
:param batch_size: the batch size of each returned pair.
:param image_size: the size to which images are resized.
:param class_cond: if True, include a "y" key in returned dicts for class
label. If classes are not available and this is true, an
exception will be raised.
:param deterministic: if True, yield results in a deterministic order.
:param random_crop: if True, randomly crop the images for augmentation.
:param random_flip: if True, randomly flip the images for augmentation.
"""
if not data_dir:
raise ValueError("unspecified data directory")
all_files = _list_image_files_recursively(data_dir)
classes = None
if class_cond:
# Assume classes are the first part of the filename,
# before an underscore.
class_names = [bf.basename(path).split("_")[0] for path in all_files]
sorted_classes = {x: i for i, x in enumerate(sorted(set(class_names)))}
classes = [sorted_classes[x] for x in class_names]
dataset = ImageDataset(
image_size,
all_files,
classes=classes,
shard=MPI.COMM_WORLD.Get_rank(),
num_shards=MPI.COMM_WORLD.Get_size(),
random_crop=random_crop,
random_flip=random_flip,
)
if deterministic:
loader = DataLoader(
dataset, batch_size=batch_size, shuffle=False, num_workers=1, drop_last=True
)
else:
loader = DataLoader(
dataset, batch_size=batch_size, shuffle=True, num_workers=1, drop_last=True
)
while True:
yield from loader
def _list_image_files_recursively(data_dir):
results = []
for entry in sorted(bf.listdir(data_dir)):
full_path = bf.join(data_dir, entry)
ext = entry.split(".")[-1]
if "." in entry and ext.lower() in ["jpg", "jpeg", "png", "gif"]:
results.append(full_path)
elif bf.isdir(full_path):
results.extend(_list_image_files_recursively(full_path))
return results
class ImageDataset(Dataset):
def __init__(
self,
resolution,
image_paths,
classes=None,
shard=0,
num_shards=1,
random_crop=False,
random_flip=True,
):
super().__init__()
self.resolution = resolution
self.local_images = image_paths[shard:][::num_shards]
self.local_classes = None if classes is None else classes[shard:][::num_shards]
self.random_crop = random_crop
self.random_flip = random_flip
def __len__(self):
return len(self.local_images)
def __getitem__(self, idx):
path = self.local_images[idx]
with bf.BlobFile(path, "rb") as f:
pil_image = Image.open(f)
pil_image.load()
pil_image = pil_image.convert("RGB")
if self.random_crop:
arr = random_crop_arr(pil_image, self.resolution)
else:
arr = center_crop_arr(pil_image, self.resolution)
if self.random_flip and random.random() < 0.5:
arr = arr[:, ::-1]
arr = arr.astype(np.float32) / 127.5 - 1
out_dict = {}
if self.local_classes is not None:
out_dict["y"] = np.array(self.local_classes[idx], dtype=np.int64)
return np.transpose(arr, [2, 0, 1]), out_dict
def center_crop_arr(pil_image, image_size):
# We are not on a new enough PIL to support the `reducing_gap`
# argument, which uses BOX downsampling at powers of two first.
# Thus, we do it by hand to improve downsample quality.
while min(*pil_image.size) >= 2 * image_size:
pil_image = pil_image.resize(
tuple(x // 2 for x in pil_image.size), resample=Image.BOX
)
scale = image_size / min(*pil_image.size)
pil_image = pil_image.resize(
tuple(round(x * scale) for x in pil_image.size), resample=Image.BICUBIC
)
arr = np.array(pil_image)
crop_y = (arr.shape[0] - image_size) // 2
crop_x = (arr.shape[1] - image_size) // 2
return arr[crop_y : crop_y + image_size, crop_x : crop_x + image_size]
def random_crop_arr(pil_image, image_size, min_crop_frac=0.8, max_crop_frac=1.0):
min_smaller_dim_size = math.ceil(image_size / max_crop_frac)
max_smaller_dim_size = math.ceil(image_size / min_crop_frac)
smaller_dim_size = random.randrange(min_smaller_dim_size, max_smaller_dim_size + 1)
# We are not on a new enough PIL to support the `reducing_gap`
# argument, which uses BOX downsampling at powers of two first.
# Thus, we do it by hand to improve downsample quality.
while min(*pil_image.size) >= 2 * smaller_dim_size:
pil_image = pil_image.resize(
tuple(x // 2 for x in pil_image.size), resample=Image.BOX
)
scale = smaller_dim_size / min(*pil_image.size)
pil_image = pil_image.resize(
tuple(round(x * scale) for x in pil_image.size), resample=Image.BICUBIC
)
arr = np.array(pil_image)
crop_y = random.randrange(arr.shape[0] - image_size + 1)
crop_x = random.randrange(arr.shape[1] - image_size + 1)
return arr[crop_y : crop_y + image_size, crop_x : crop_x + image_size]
================================================
FILE: guided_diffusion/logger.py
================================================
"""
Logger copied from OpenAI baselines to avoid extra RL-based dependencies:
https://github.com/openai/baselines/blob/ea25b9e8b234e6ee1bca43083f8f3cf974143998/baselines/logger.py
"""
import os
import sys
import shutil
import os.path as osp
import json
import time
import datetime
import tempfile
import warnings
from collections import defaultdict
from contextlib import contextmanager
DEBUG = 10
INFO = 20
WARN = 30
ERROR = 40
DISABLED = 50
class KVWriter(object):
def writekvs(self, kvs):
raise NotImplementedError
class SeqWriter(object):
def writeseq(self, seq):
raise NotImplementedError
class HumanOutputFormat(KVWriter, SeqWriter):
def __init__(self, filename_or_file):
if isinstance(filename_or_file, str):
self.file = open(filename_or_file, "wt")
self.own_file = True
else:
assert hasattr(filename_or_file, "read"), (
"expected file or str, got %s" % filename_or_file
)
self.file = filename_or_file
self.own_file = False
def writekvs(self, kvs):
# Create strings for printing
key2str = {}
for (key, val) in sorted(kvs.items()):
if hasattr(val, "__float__"):
valstr = "%-8.3g" % val
else:
valstr = str(val)
key2str[self._truncate(key)] = self._truncate(valstr)
# Find max widths
if len(key2str) == 0:
print("WARNING: tried to write empty key-value dict")
return
else:
keywidth = max(map(len, key2str.keys()))
valwidth = max(map(len, key2str.values()))
# Write out the data
dashes = "-" * (keywidth + valwidth + 7)
lines = [dashes]
for (key, val) in sorted(key2str.items(), key=lambda kv: kv[0].lower()):
lines.append(
"| %s%s | %s%s |"
% (key, " " * (keywidth - len(key)), val, " " * (valwidth - len(val)))
)
lines.append(dashes)
self.file.write("\n".join(lines) + "\n")
# Flush the output to the file
self.file.flush()
def _truncate(self, s):
maxlen = 30
return s[: maxlen - 3] + "..." if len(s) > maxlen else s
def writeseq(self, seq):
seq = list(seq)
for (i, elem) in enumerate(seq):
self.file.write(elem)
if i < len(seq) - 1: # add space unless this is the last one
self.file.write(" ")
self.file.write("\n")
self.file.flush()
def close(self):
if self.own_file:
self.file.close()
class JSONOutputFormat(KVWriter):
def __init__(self, filename):
self.file = open(filename, "wt")
def writekvs(self, kvs):
for k, v in sorted(kvs.items()):
if hasattr(v, "dtype"):
kvs[k] = float(v)
self.file.write(json.dumps(kvs) + "\n")
self.file.flush()
def close(self):
self.file.close()
class CSVOutputFormat(KVWriter):
def __init__(self, filename):
self.file = open(filename, "w+t")
self.keys = []
self.sep = ","
def writekvs(self, kvs):
# Add our current row to the history
extra_keys = list(kvs.keys() - self.keys)
extra_keys.sort()
if extra_keys:
self.keys.extend(extra_keys)
self.file.seek(0)
lines = self.file.readlines()
self.file.seek(0)
for (i, k) in enumerate(self.keys):
if i > 0:
self.file.write(",")
self.file.write(k)
self.file.write("\n")
for line in lines[1:]:
self.file.write(line[:-1])
self.file.write(self.sep * len(extra_keys))
self.file.write("\n")
for (i, k) in enumerate(self.keys):
if i > 0:
self.file.write(",")
v = kvs.get(k)
if v is not None:
self.file.write(str(v))
self.file.write("\n")
self.file.flush()
def close(self):
self.file.close()
class TensorBoardOutputFormat(KVWriter):
"""
Dumps key/value pairs into TensorBoard's numeric format.
"""
def __init__(self, dir):
os.makedirs(dir, exist_ok=True)
self.dir = dir
self.step = 1
prefix = "events"
path = osp.join(osp.abspath(dir), prefix)
import tensorflow as tf
from tensorflow.python import pywrap_tensorflow
from tensorflow.core.util import event_pb2
from tensorflow.python.util import compat
self.tf = tf
self.event_pb2 = event_pb2
self.pywrap_tensorflow = pywrap_tensorflow
self.writer = pywrap_tensorflow.EventsWriter(compat.as_bytes(path))
def writekvs(self, kvs):
def summary_val(k, v):
kwargs = {"tag": k, "simple_value": float(v)}
return self.tf.Summary.Value(**kwargs)
summary = self.tf.Summary(value=[summary_val(k, v) for k, v in kvs.items()])
event = self.event_pb2.Event(wall_time=time.time(), summary=summary)
event.step = (
self.step
) # is there any reason why you'd want to specify the step?
self.writer.WriteEvent(event)
self.writer.Flush()
self.step += 1
def close(self):
if self.writer:
self.writer.Close()
self.writer = None
def make_output_format(format, ev_dir, log_suffix=""):
os.makedirs(ev_dir, exist_ok=True)
if format == "stdout":
return HumanOutputFormat(sys.stdout)
elif format == "log":
return HumanOutputFormat(osp.join(ev_dir, "log%s.txt" % log_suffix))
elif format == "json":
return JSONOutputFormat(osp.join(ev_dir, "progress%s.json" % log_suffix))
elif format == "csv":
return CSVOutputFormat(osp.join(ev_dir, "progress%s.csv" % log_suffix))
elif format == "tensorboard":
return TensorBoardOutputFormat(osp.join(ev_dir, "tb%s" % log_suffix))
else:
raise ValueError("Unknown format specified: %s" % (format,))
# ================================================================
# API
# ================================================================
def logkv(key, val):
"""
Log a value of some diagnostic
Call this once for each diagnostic quantity, each iteration
If called many times, last value will be used.
"""
get_current().logkv(key, val)
def logkv_mean(key, val):
"""
The same as logkv(), but if called many times, values averaged.
"""
get_current().logkv_mean(key, val)
def logkvs(d):
"""
Log a dictionary of key-value pairs
"""
for (k, v) in d.items():
logkv(k, v)
def dumpkvs():
"""
Write all of the diagnostics from the current iteration
"""
return get_current().dumpkvs()
def getkvs():
return get_current().name2val
def log(*args, level=INFO):
"""
Write the sequence of args, with no separators, to the console and output files (if you've configured an output file).
"""
get_current().log(*args, level=level)
def debug(*args):
log(*args, level=DEBUG)
def info(*args):
log(*args, level=INFO)
def warn(*args):
log(*args, level=WARN)
def error(*args):
log(*args, level=ERROR)
def set_level(level):
"""
Set logging threshold on current logger.
"""
get_current().set_level(level)
def set_comm(comm):
get_current().set_comm(comm)
def get_dir():
"""
Get directory that log files are being written to.
will be None if there is no output directory (i.e., if you didn't call start)
"""
return get_current().get_dir()
record_tabular = logkv
dump_tabular = dumpkvs
@contextmanager
def profile_kv(scopename):
logkey = "wait_" + scopename
tstart = time.time()
try:
yield
finally:
get_current().name2val[logkey] += time.time() - tstart
def profile(n):
"""
Usage:
@profile("my_func")
def my_func(): code
"""
def decorator_with_name(func):
def func_wrapper(*args, **kwargs):
with profile_kv(n):
return func(*args, **kwargs)
return func_wrapper
return decorator_with_name
# ================================================================
# Backend
# ================================================================
def get_current():
if Logger.CURRENT is None:
_configure_default_logger()
return Logger.CURRENT
class Logger(object):
DEFAULT = None # A logger with no output files. (See right below class definition)
# So that you can still log to the terminal without setting up any output files
CURRENT = None # Current logger being used by the free functions above
def __init__(self, dir, output_formats, comm=None):
self.name2val = defaultdict(float) # values this iteration
self.name2cnt = defaultdict(int)
self.level = INFO
self.dir = dir
self.output_formats = output_formats
self.comm = comm
# Logging API, forwarded
# ----------------------------------------
def logkv(self, key, val):
self.name2val[key] = val
def logkv_mean(self, key, val):
oldval, cnt = self.name2val[key], self.name2cnt[key]
self.name2val[key] = oldval * cnt / (cnt + 1) + val / (cnt + 1)
self.name2cnt[key] = cnt + 1
def dumpkvs(self):
if self.comm is None:
d = self.name2val
else:
d = mpi_weighted_mean(
self.comm,
{
name: (val, self.name2cnt.get(name, 1))
for (name, val) in self.name2val.items()
},
)
if self.comm.rank != 0:
d["dummy"] = 1 # so we don't get a warning about empty dict
out = d.copy() # Return the dict for unit testing purposes
for fmt in self.output_formats:
if isinstance(fmt, KVWriter):
fmt.writekvs(d)
self.name2val.clear()
self.name2cnt.clear()
return out
def log(self, *args, level=INFO):
if self.level <= level:
self._do_log(args)
# Configuration
# ----------------------------------------
def set_level(self, level):
self.level = level
def set_comm(self, comm):
self.comm = comm
def get_dir(self):
return self.dir
def close(self):
for fmt in self.output_formats:
fmt.close()
# Misc
# ----------------------------------------
def _do_log(self, args):
for fmt in self.output_formats:
if isinstance(fmt, SeqWriter):
fmt.writeseq(map(str, args))
def get_rank_without_mpi_import():
# check environment variables here instead of importing mpi4py
# to avoid calling MPI_Init() when this module is imported
for varname in ["PMI_RANK", "OMPI_COMM_WORLD_RANK"]:
if varname in os.environ:
return int(os.environ[varname])
return 0
def mpi_weighted_mean(comm, local_name2valcount):
"""
Copied from: https://github.com/openai/baselines/blob/ea25b9e8b234e6ee1bca43083f8f3cf974143998/baselines/common/mpi_util.py#L110
Perform a weighted average over dicts that are each on a different node
Input: local_name2valcount: dict mapping key -> (value, count)
Returns: key -> mean
"""
all_name2valcount = comm.gather(local_name2valcount)
if comm.rank == 0:
name2sum = defaultdict(float)
name2count = defaultdict(float)
for n2vc in all_name2valcount:
for (name, (val, count)) in n2vc.items():
try:
val = float(val)
except ValueError:
if comm.rank == 0:
warnings.warn(
"WARNING: tried to compute mean on non-float {}={}".format(
name, val
)
)
else:
name2sum[name] += val * count
name2count[name] += count
return {name: name2sum[name] / name2count[name] for name in name2sum}
else:
return {}
def configure(dir=None, format_strs=None, comm=None, log_suffix=""):
"""
If comm is provided, average all numerical stats across that comm
"""
if dir is None:
dir = os.getenv("OPENAI_LOGDIR")
if dir is None:
dir = osp.join(
tempfile.gettempdir(),
datetime.datetime.now().strftime("openai-%Y-%m-%d-%H-%M-%S-%f"),
)
assert isinstance(dir, str)
dir = os.path.expanduser(dir)
os.makedirs(os.path.expanduser(dir), exist_ok=True)
rank = get_rank_without_mpi_import()
if rank > 0:
log_suffix = log_suffix + "-rank%03i" % rank
if format_strs is None:
if rank == 0:
format_strs = os.getenv("OPENAI_LOG_FORMAT", "stdout,log,csv").split(",")
else:
format_strs = os.getenv("OPENAI_LOG_FORMAT_MPI", "log").split(",")
format_strs = filter(None, format_strs)
output_formats = [make_output_format(f, dir, log_suffix) for f in format_strs]
Logger.CURRENT = Logger(dir=dir, output_formats=output_formats, comm=comm)
if output_formats:
log("Logging to %s" % dir)
def _configure_default_logger():
configure()
Logger.DEFAULT = Logger.CURRENT
def reset():
if Logger.CURRENT is not Logger.DEFAULT:
Logger.CURRENT.close()
Logger.CURRENT = Logger.DEFAULT
log("Reset logger")
@contextmanager
def scoped_configure(dir=None, format_strs=None, comm=None):
prevlogger = Logger.CURRENT
configure(dir=dir, format_strs=format_strs, comm=comm)
try:
yield
finally:
Logger.CURRENT.close()
Logger.CURRENT = prevlogger
================================================
FILE: guided_diffusion/losses.py
================================================
"""
Helpers for various likelihood-based losses. These are ported from the original
Ho et al. diffusion models codebase:
https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/utils.py
"""
import numpy as np
import torch as th
def normal_kl(mean1, logvar1, mean2, logvar2):
"""
Compute the KL divergence between two gaussians.
Shapes are automatically broadcasted, so batches can be compared to
scalars, among other use cases.
"""
tensor = None
for obj in (mean1, logvar1, mean2, logvar2):
if isinstance(obj, th.Tensor):
tensor = obj
break
assert tensor is not None, "at least one argument must be a Tensor"
# Force variances to be Tensors. Broadcasting helps convert scalars to
# Tensors, but it does not work for th.exp().
logvar1, logvar2 = [
x if isinstance(x, th.Tensor) else th.tensor(x).to(tensor)
for x in (logvar1, logvar2)
]
return 0.5 * (
-1.0
+ logvar2
- logvar1
+ th.exp(logvar1 - logvar2)
+ ((mean1 - mean2) ** 2) * th.exp(-logvar2)
)
def approx_standard_normal_cdf(x):
"""
A fast approximation of the cumulative distribution function of the
standard normal.
"""
return 0.5 * (1.0 + th.tanh(np.sqrt(2.0 / np.pi) * (x + 0.044715 * th.pow(x, 3))))
def discretized_gaussian_log_likelihood(x, *, means, log_scales):
"""
Compute the log-likelihood of a Gaussian distribution discretizing to a
given image.
:param x: the target images. It is assumed that this was uint8 values,
rescaled to the range [-1, 1].
:param means: the Gaussian mean Tensor.
:param log_scales: the Gaussian log stddev Tensor.
:return: a tensor like x of log probabilities (in nats).
"""
assert x.shape == means.shape == log_scales.shape
centered_x = x - means
inv_stdv = th.exp(-log_scales)
plus_in = inv_stdv * (centered_x + 1.0 / 255.0)
cdf_plus = approx_standard_normal_cdf(plus_in)
min_in = inv_stdv * (centered_x - 1.0 / 255.0)
cdf_min = approx_standard_normal_cdf(min_in)
log_cdf_plus = th.log(cdf_plus.clamp(min=1e-12))
log_one_minus_cdf_min = th.log((1.0 - cdf_min).clamp(min=1e-12))
cdf_delta = cdf_plus - cdf_min
log_probs = th.where(
x < -0.999,
log_cdf_plus,
th.where(x > 0.999, log_one_minus_cdf_min, th.log(cdf_delta.clamp(min=1e-12))),
)
assert log_probs.shape == x.shape
return log_probs
================================================
FILE: guided_diffusion/nn.py
================================================
"""
Various utilities for neural networks.
"""
import math
import torch as th
import torch.nn as nn
# PyTorch 1.7 has SiLU, but we support PyTorch 1.5.
class SiLU(nn.Module):
def forward(self, x):
return x * th.sigmoid(x)
class GroupNorm32(nn.GroupNorm):
def forward(self, x):
return super().forward(x.float()).type(x.dtype)
def conv_nd(dims, *args, **kwargs):
"""
Create a 1D, 2D, or 3D convolution module.
"""
if dims == 1:
return nn.Conv1d(*args, **kwargs)
elif dims == 2:
return nn.Conv2d(*args, **kwargs)
elif dims == 3:
return nn.Conv3d(*args, **kwargs)
raise ValueError(f"unsupported dimensions: {dims}")
def linear(*args, **kwargs):
"""
Create a linear module.
"""
return nn.Linear(*args, **kwargs)
def avg_pool_nd(dims, *args, **kwargs):
"""
Create a 1D, 2D, or 3D average pooling module.
"""
if dims == 1:
return nn.AvgPool1d(*args, **kwargs)
elif dims == 2:
return nn.AvgPool2d(*args, **kwargs)
elif dims == 3:
return nn.AvgPool3d(*args, **kwargs)
raise ValueError(f"unsupported dimensions: {dims}")
def update_ema(target_params, source_params, rate=0.99):
"""
Update target parameters to be closer to those of source parameters using
an exponential moving average.
:param target_params: the target parameter sequence.
:param source_params: the source parameter sequence.
:param rate: the EMA rate (closer to 1 means slower).
"""
for targ, src in zip(target_params, source_params):
targ.detach().mul_(rate).add_(src, alpha=1 - rate)
def zero_module(module):
"""
Zero out the parameters of a module and return it.
"""
for p in module.parameters():
p.detach().zero_()
return module
def scale_module(module, scale):
"""
Scale the parameters of a module and return it.
"""
for p in module.parameters():
p.detach().mul_(scale)
return module
def mean_flat(tensor):
"""
Take the mean over all non-batch dimensions.
"""
return tensor.mean(dim=list(range(1, len(tensor.shape))))
def normalization(channels):
"""
Make a standard normalization layer.
:param channels: number of input channels.
:return: an nn.Module for normalization.
"""
return GroupNorm32(32, channels)
def timestep_embedding(timesteps, dim, max_period=10000):
"""
Create sinusoidal timestep embeddings.
:param timesteps: a 1-D Tensor of N indices, one per batch element.
These may be fractional.
:param dim: the dimension of the output.
:param max_period: controls the minimum frequency of the embeddings.
:return: an [N x dim] Tensor of positional embeddings.
"""
half = dim // 2
freqs = th.exp(
-math.log(max_period) * th.arange(start=0, end=half, dtype=th.float32) / half
).to(device=timesteps.device)
args = timesteps[:, None].float() * freqs[None]
embedding = th.cat([th.cos(args), th.sin(args)], dim=-1)
if dim % 2:
embedding = th.cat([embedding, th.zeros_like(embedding[:, :1])], dim=-1)
return embedding
def checkpoint(func, inputs, params, flag):
"""
Evaluate a function without caching intermediate activations, allowing for
reduced memory at the expense of extra compute in the backward pass.
:param func: the function to evaluate.
:param inputs: the argument sequence to pass to `func`.
:param params: a sequence of parameters `func` depends on but does not
explicitly take as arguments.
:param flag: if False, disable gradient checkpointing.
"""
if flag:
args = tuple(inputs) + tuple(params)
return CheckpointFunction.apply(func, len(inputs), *args)
else:
return func(*inputs)
class CheckpointFunction(th.autograd.Function):
@staticmethod
def forward(ctx, run_function, length, *args):
ctx.run_function = run_function
ctx.input_tensors = list(args[:length])
ctx.input_params = list(args[length:])
with th.no_grad():
output_tensors = ctx.run_function(*ctx.input_tensors)
return output_tensors
@staticmethod
def backward(ctx, *output_grads):
ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
with th.enable_grad():
# Fixes a bug where the first op in run_function modifies the
# Tensor storage in place, which is not allowed for detach()'d
# Tensors.
shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
output_tensors = ctx.run_function(*shallow_copies)
input_grads = th.autograd.grad(
output_tensors,
ctx.input_tensors + ctx.input_params,
output_grads,
allow_unused=True,
)
del ctx.input_tensors
del ctx.input_params
del output_tensors
return (None, None) + input_grads
================================================
FILE: guided_diffusion/resample.py
================================================
from abc import ABC, abstractmethod
import numpy as np
import torch as th
import torch.distributed as dist
def create_named_schedule_sampler(name, diffusion):
"""
Create a ScheduleSampler from a library of pre-defined samplers.
:param name: the name of the sampler.
:param diffusion: the diffusion object to sample for.
"""
if name == "uniform":
return UniformSampler(diffusion)
elif name == "loss-second-moment":
return LossSecondMomentResampler(diffusion)
else:
raise NotImplementedError(f"unknown schedule sampler: {name}")
class ScheduleSampler(ABC):
"""
A distribution over timesteps in the diffusion process, intended to reduce
variance of the objective.
By default, samplers perform unbiased importance sampling, in which the
objective's mean is unchanged.
However, subclasses may override sample() to change how the resampled
terms are reweighted, allowing for actual changes in the objective.
"""
@abstractmethod
def weights(self):
"""
Get a numpy array of weights, one per diffusion step.
The weights needn't be normalized, but must be positive.
"""
def sample(self, batch_size, device):
"""
Importance-sample timesteps for a batch.
:param batch_size: the number of timesteps.
:param device: the torch device to save to.
:return: a tuple (timesteps, weights):
- timesteps: a tensor of timestep indices.
- weights: a tensor of weights to scale the resulting losses.
"""
w = self.weights()
p = w / np.sum(w)
indices_np = np.random.choice(len(p), size=(batch_size,), p=p)
indices = th.from_numpy(indices_np).long().to(device)
weights_np = 1 / (len(p) * p[indices_np])
weights = th.from_numpy(weights_np).float().to(device)
return indices, weights
class UniformSampler(ScheduleSampler):
def __init__(self, diffusion):
self.diffusion = diffusion
self._weights = np.ones([diffusion.num_timesteps])
def weights(self):
return self._weights
class LossAwareSampler(ScheduleSampler):
def update_with_local_losses(self, local_ts, local_losses):
"""
Update the reweighting using losses from a model.
Call this method from each rank with a batch of timesteps and the
corresponding losses for each of those timesteps.
This method will perform synchronization to make sure all of the ranks
maintain the exact same reweighting.
:param local_ts: an integer Tensor of timesteps.
:param local_losses: a 1D Tensor of losses.
"""
batch_sizes = [
th.tensor([0], dtype=th.int32, device=local_ts.device)
for _ in range(dist.get_world_size())
]
dist.all_gather(
batch_sizes,
th.tensor([len(local_ts)], dtype=th.int32, device=local_ts.device),
)
# Pad all_gather batches to be the maximum batch size.
batch_sizes = [x.item() for x in batch_sizes]
max_bs = max(batch_sizes)
timestep_batches = [th.zeros(max_bs).to(local_ts) for bs in batch_sizes]
loss_batches = [th.zeros(max_bs).to(local_losses) for bs in batch_sizes]
dist.all_gather(timestep_batches, local_ts)
dist.all_gather(loss_batches, local_losses)
timesteps = [
x.item() for y, bs in zip(timestep_batches, batch_sizes) for x in y[:bs]
]
losses = [x.item() for y, bs in zip(loss_batches, batch_sizes) for x in y[:bs]]
self.update_with_all_losses(timesteps, losses)
@abstractmethod
def update_with_all_losses(self, ts, losses):
"""
Update the reweighting using losses from a model.
Sub-classes should override this method to update the reweighting
using losses from the model.
This method directly updates the reweighting without synchronizing
between workers. It is called by update_with_local_losses from all
ranks with identical arguments. Thus, it should have deterministic
behavior to maintain state across workers.
:param ts: a list of int timesteps.
:param losses: a list of float losses, one per timestep.
"""
class LossSecondMomentResampler(LossAwareSampler):
def __init__(self, diffusion, history_per_term=10, uniform_prob=0.001):
self.diffusion = diffusion
self.history_per_term = history_per_term
self.uniform_prob = uniform_prob
self._loss_history = np.zeros(
[diffusion.num_timesteps, history_per_term], dtype=np.float64
)
self._loss_counts = np.zeros([diffusion.num_timesteps], dtype=np.int)
def weights(self):
if not self._warmed_up():
return np.ones([self.diffusion.num_timesteps], dtype=np.float64)
weights = np.sqrt(np.mean(self._loss_history ** 2, axis=-1))
weights /= np.sum(weights)
weights *= 1 - self.uniform_prob
weights += self.uniform_prob / len(weights)
return weights
def update_with_all_losses(self, ts, losses):
for t, loss in zip(ts, losses):
if self._loss_counts[t] == self.history_per_term:
# Shift out the oldest loss term.
self._loss_history[t, :-1] = self._loss_history[t, 1:]
self._loss_history[t, -1] = loss
else:
self._loss_history[t, self._loss_counts[t]] = loss
self._loss_counts[t] += 1
def _warmed_up(self):
return (self._loss_counts == self.history_per_term).all()
================================================
FILE: guided_diffusion/respace.py
================================================
import numpy as np
import torch as th
from .gaussian_diffusion import GaussianDiffusion
def space_timesteps(num_timesteps, section_counts):
"""
Create a list of timesteps to use from an original diffusion process,
given the number of timesteps we want to take from equally-sized portions
of the original process.
For example, if there's 300 timesteps and the section counts are [10,15,20]
then the first 100 timesteps are strided to be 10 timesteps, the second 100
are strided to be 15 timesteps, and the final 100 are strided to be 20.
If the stride is a string starting with "ddim", then the fixed striding
from the DDIM paper is used, and only one section is allowed.
:param num_timesteps: the number of diffusion steps in the original
process to divide up.
:param section_counts: either a list of numbers, or a string containing
comma-separated numbers, indicating the step count
per section. As a special case, use "ddimN" where N
is a number of steps to use the striding from the
DDIM paper.
:return: a set of diffusion steps from the original process to use.
"""
if isinstance(section_counts, str):
if section_counts.startswith("ddim"):
desired_count = int(section_counts[len("ddim") :])
for i in range(1, num_timesteps):
if len(range(0, num_timesteps, i)) == desired_count:
return set(range(0, num_timesteps, i))
raise ValueError(
f"cannot create exactly {num_timesteps} steps with an integer stride"
)
section_counts = [int(x) for x in section_counts.split(",")]
size_per = num_timesteps // len(section_counts)
extra = num_timesteps % len(section_counts)
start_idx = 0
all_steps = []
for i, section_count in enumerate(section_counts):
size = size_per + (1 if i < extra else 0)
if size < section_count:
raise ValueError(
f"cannot divide section of {size} steps into {section_count}"
)
if section_count <= 1:
frac_stride = 1
else:
frac_stride = (size - 1) / (section_count - 1)
cur_idx = 0.0
taken_steps = []
for _ in range(section_count):
taken_steps.append(start_idx + round(cur_idx))
cur_idx += frac_stride
all_steps += taken_steps
start_idx += size
return set(all_steps)
class SpacedDiffusion(GaussianDiffusion):
"""
A diffusion process which can skip steps in a base diffusion process.
:param use_timesteps: a collection (sequence or set) of timesteps from the
original diffusion process to retain.
:param kwargs: the kwargs to create the base diffusion process.
"""
def __init__(self, use_timesteps, **kwargs):
self.use_timesteps = set(use_timesteps)
self.timestep_map = []
self.original_num_steps = len(kwargs["betas"])
base_diffusion = GaussianDiffusion(**kwargs) # pylint: disable=missing-kwoa
last_alpha_cumprod = 1.0
new_betas = []
for i, alpha_cumprod in enumerate(base_diffusion.alphas_cumprod):
if i in self.use_timesteps:
new_betas.append(1 - alpha_cumprod / last_alpha_cumprod)
last_alpha_cumprod = alpha_cumprod
self.timestep_map.append(i)
kwargs["betas"] = np.array(new_betas)
super().__init__(**kwargs)
def p_mean_variance(
self, model, *args, **kwargs
): # pylint: disable=signature-differs
return super().p_mean_variance(self._wrap_model(model), *args, **kwargs)
def training_losses(
self, model, *args, **kwargs
): # pylint: disable=signature-differs
return super().training_losses(self._wrap_model(model), *args, **kwargs)
def condition_mean(self, cond_fn, *args, **kwargs):
return super().condition_mean(self._wrap_model(cond_fn), *args, **kwargs)
def condition_score(self, cond_fn, *args, **kwargs):
return super().condition_score(self._wrap_model(cond_fn), *args, **kwargs)
def _wrap_model(self, model):
if isinstance(model, _WrappedModel):
return model
return _WrappedModel(
model, self.timestep_map, self.rescale_timesteps, self.original_num_steps
)
def _scale_timesteps(self, t):
# Scaling is done by the wrapped model.
return t
class _WrappedModel:
def __init__(self, model, timestep_map, rescale_timesteps, original_num_steps):
self.model = model
self.timestep_map = timestep_map
self.rescale_timesteps = rescale_timesteps
self.original_num_steps = original_num_steps
def __call__(self, x, ts, **kwargs):
map_tensor = th.tensor(self.timestep_map, device=ts.device, dtype=ts.dtype)
new_ts = map_tensor[ts]
if self.rescale_timesteps:
new_ts = new_ts.float() * (1000.0 / self.original_num_steps)
return self.model(x, new_ts, **kwargs)
================================================
FILE: guided_diffusion/script_util.py
================================================
import argparse
import inspect
from . import gaussian_diffusion as gd
from .respace import SpacedDiffusion, space_timesteps
from .unet import SuperResModel, UNetModel, EncoderUNetModel
NUM_CLASSES = 1000
def diffusion_defaults():
"""
Defaults for image and classifier training.
"""
return dict(
learn_sigma=False,
diffusion_steps=1000,
noise_schedule="linear",
timestep_respacing="",
use_kl=False,
predict_xstart=False,
rescale_timesteps=False,
rescale_learned_sigmas=False,
)
def classifier_defaults():
"""
Defaults for classifier models.
"""
return dict(
image_size=64,
classifier_use_fp16=False,
classifier_width=128,
classifier_depth=2,
classifier_attention_resolutions="32,16,8", # 16
classifier_use_scale_shift_norm=True, # False
classifier_resblock_updown=True, # False
classifier_pool="attention",
)
def model_and_diffusion_defaults():
"""
Defaults for image training.
"""
res = dict(
image_size=64,
num_channels=128,
num_res_blocks=2,
num_heads=4,
num_heads_upsample=-1,
num_head_channels=-1,
attention_resolutions="16,8",
channel_mult="",
dropout=0.0,
class_cond=False,
use_checkpoint=False,
use_scale_shift_norm=True,
resblock_updown=False,
use_fp16=False,
use_new_attention_order=False,
)
res.update(diffusion_defaults())
return res
def classifier_and_diffusion_defaults():
res = classifier_defaults()
res.update(diffusion_defaults())
return res
def create_model_and_diffusion(
image_size,
class_cond,
learn_sigma,
num_channels,
num_res_blocks,
channel_mult,
num_heads,
num_head_channels,
num_heads_upsample,
attention_resolutions,
dropout,
diffusion_steps,
noise_schedule,
timestep_respacing,
use_kl,
predict_xstart,
rescale_timesteps,
rescale_learned_sigmas,
use_checkpoint,
use_scale_shift_norm,
resblock_updown,
use_fp16,
use_new_attention_order,
):
model = create_model(
image_size,
num_channels,
num_res_blocks,
channel_mult=channel_mult,
learn_sigma=learn_sigma,
class_cond=class_cond,
use_checkpoint=use_checkpoint,
attention_resolutions=attention_resolutions,
num_heads=num_heads,
num_head_channels=num_head_channels,
num_heads_upsample=num_heads_upsample,
use_scale_shift_norm=use_scale_shift_norm,
dropout=dropout,
resblock_updown=resblock_updown,
use_fp16=use_fp16,
use_new_attention_order=use_new_attention_order,
)
diffusion = create_gaussian_diffusion(
steps=diffusion_steps,
learn_sigma=learn_sigma,
noise_schedule=noise_schedule,
use_kl=use_kl,
predict_xstart=predict_xstart,
rescale_timesteps=rescale_timesteps,
rescale_learned_sigmas=rescale_learned_sigmas,
timestep_respacing=timestep_respacing,
)
return model, diffusion
def create_model(
image_size,
num_channels,
num_res_blocks,
channel_mult="",
learn_sigma=False,
class_cond=False,
use_checkpoint=False,
attention_resolutions="16",
num_heads=1,
num_head_channels=-1,
num_heads_upsample=-1,
use_scale_shift_norm=False,
dropout=0,
resblock_updown=False,
use_fp16=False,
use_new_attention_order=False,
):
if channel_mult == "":
if image_size == 512:
channel_mult = (0.5, 1, 1, 2, 2, 4, 4)
elif image_size == 256:
channel_mult = (1, 1, 2, 2, 4, 4)
elif image_size == 128:
channel_mult = (1, 1, 2, 3, 4)
elif image_size == 64:
channel_mult = (1, 2, 3, 4)
else:
raise ValueError(f"unsupported image size: {image_size}")
else:
channel_mult = tuple(int(ch_mult) for ch_mult in channel_mult.split(","))
attention_ds = []
for res in attention_resolutions.split(","):
attention_ds.append(image_size // int(res))
return UNetModel(
image_size=image_size,
in_channels=3,
model_channels=num_channels,
out_channels=(3 if not learn_sigma else 6),
num_res_blocks=num_res_blocks,
attention_resolutions=tuple(attention_ds),
dropout=dropout,
channel_mult=channel_mult,
num_classes=(NUM_CLASSES if class_cond else None),
use_checkpoint=use_checkpoint,
use_fp16=use_fp16,
num_heads=num_heads,
num_head_channels=num_head_channels,
num_heads_upsample=num_heads_upsample,
use_scale_shift_norm=use_scale_shift_norm,
resblock_updown=resblock_updown,
use_new_attention_order=use_new_attention_order,
)
def create_classifier_and_diffusion(
image_size,
classifier_use_fp16,
classifier_width,
classifier_depth,
classifier_attention_resolutions,
classifier_use_scale_shift_norm,
classifier_resblock_updown,
classifier_pool,
learn_sigma,
diffusion_steps,
noise_schedule,
timestep_respacing,
use_kl,
predict_xstart,
rescale_timesteps,
rescale_learned_sigmas,
):
classifier = create_classifier(
image_size,
classifier_use_fp16,
classifier_width,
classifier_depth,
classifier_attention_resolutions,
classifier_use_scale_shift_norm,
classifier_resblock_updown,
classifier_pool,
)
diffusion = create_gaussian_diffusion(
steps=diffusion_steps,
learn_sigma=learn_sigma,
noise_schedule=noise_schedule,
use_kl=use_kl,
predict_xstart=predict_xstart,
rescale_timesteps=rescale_timesteps,
rescale_learned_sigmas=rescale_learned_sigmas,
timestep_respacing=timestep_respacing,
)
return classifier, diffusion
def create_classifier(
image_size,
classifier_use_fp16,
classifier_width,
classifier_depth,
classifier_attention_resolutions,
classifier_use_scale_shift_norm,
classifier_resblock_updown,
classifier_pool,
):
if image_size == 512:
channel_mult = (0.5, 1, 1, 2, 2, 4, 4)
elif image_size == 256:
channel_mult = (1, 1, 2, 2, 4, 4)
elif image_size == 128:
channel_mult = (1, 1, 2, 3, 4)
elif image_size == 64:
channel_mult = (1, 2, 3, 4)
else:
raise ValueError(f"unsupported image size: {image_size}")
attention_ds = []
for res in classifier_attention_resolutions.split(","):
attention_ds.append(image_size // int(res))
return EncoderUNetModel(
image_size=image_size,
in_channels=3,
model_channels=classifier_width,
out_channels=1000,
num_res_blocks=classifier_depth,
attention_resolutions=tuple(attention_ds),
channel_mult=channel_mult,
use_fp16=classifier_use_fp16,
num_head_channels=64,
use_scale_shift_norm=classifier_use_scale_shift_norm,
resblock_updown=classifier_resblock_updown,
pool=classifier_pool,
)
def sr_model_and_diffusion_defaults():
res = model_and_diffusion_defaults()
res["large_size"] = 256
res["small_size"] = 64
arg_names = inspect.getfullargspec(sr_create_model_and_diffusion)[0]
for k in res.copy().keys():
if k not in arg_names:
del res[k]
return res
def sr_create_model_and_diffusion(
large_size,
small_size,
class_cond,
learn_sigma,
num_channels,
num_res_blocks,
num_heads,
num_head_channels,
num_heads_upsample,
attention_resolutions,
dropout,
diffusion_steps,
noise_schedule,
timestep_respacing,
use_kl,
predict_xstart,
rescale_timesteps,
rescale_learned_sigmas,
use_checkpoint,
use_scale_shift_norm,
resblock_updown,
use_fp16,
):
model = sr_create_model(
large_size,
small_size,
num_channels,
num_res_blocks,
learn_sigma=learn_sigma,
class_cond=class_cond,
use_checkpoint=use_checkpoint,
attention_resolutions=attention_resolutions,
num_heads=num_heads,
num_head_channels=num_head_channels,
num_heads_upsample=num_heads_upsample,
use_scale_shift_norm=use_scale_shift_norm,
dropout=dropout,
resblock_updown=resblock_updown,
use_fp16=use_fp16,
)
diffusion = create_gaussian_diffusion(
steps=diffusion_steps,
learn_sigma=learn_sigma,
noise_schedule=noise_schedule,
use_kl=use_kl,
predict_xstart=predict_xstart,
rescale_timesteps=rescale_timesteps,
rescale_learned_sigmas=rescale_learned_sigmas,
timestep_respacing=timestep_respacing,
)
return model, diffusion
def sr_create_model(
large_size,
small_size,
num_channels,
num_res_blocks,
learn_sigma,
class_cond,
use_checkpoint,
attention_resolutions,
num_heads,
num_head_channels,
num_heads_upsample,
use_scale_shift_norm,
dropout,
resblock_updown,
use_fp16,
):
_ = small_size # hack to prevent unused variable
if large_size == 512:
channel_mult = (1, 1, 2, 2, 4, 4)
elif large_size == 256:
channel_mult = (1, 1, 2, 2, 4, 4)
elif large_size == 64:
channel_mult = (1, 2, 3, 4)
else:
raise ValueError(f"unsupported large size: {large_size}")
attention_ds = []
for res in attention_resolutions.split(","):
attention_ds.append(large_size // int(res))
return SuperResModel(
image_size=large_size,
in_channels=3,
model_channels=num_channels,
out_channels=(3 if not learn_sigma else 6),
num_res_blocks=num_res_blocks,
attention_resolutions=tuple(attention_ds),
dropout=dropout,
channel_mult=channel_mult,
num_classes=(NUM_CLASSES if class_cond else None),
use_checkpoint=use_checkpoint,
num_heads=num_heads,
num_head_channels=num_head_channels,
num_heads_upsample=num_heads_upsample,
use_scale_shift_norm=use_scale_shift_norm,
resblock_updown=resblock_updown,
use_fp16=use_fp16,
)
def create_gaussian_diffusion(
*,
steps=1000,
learn_sigma=False,
sigma_small=False,
noise_schedule="linear",
use_kl=False,
predict_xstart=False,
rescale_timesteps=False,
rescale_learned_sigmas=False,
timestep_respacing="",
):
betas = gd.get_named_beta_schedule(noise_schedule, steps)
if use_kl:
loss_type = gd.LossType.RESCALED_KL
elif rescale_learned_sigmas:
loss_type = gd.LossType.RESCALED_MSE
else:
loss_type = gd.LossType.MSE
if not timestep_respacing:
timestep_respacing = [steps]
return SpacedDiffusion(
use_timesteps=space_timesteps(steps, timestep_respacing),
betas=betas,
model_mean_type=(
gd.ModelMeanType.EPSILON if not predict_xstart else gd.ModelMeanType.START_X
),
model_var_type=(
(
gd.ModelVarType.FIXED_LARGE
if not sigma_small
else gd.ModelVarType.FIXED_SMALL
)
if not learn_sigma
else gd.ModelVarType.LEARNED_RANGE
),
loss_type=loss_type,
rescale_timesteps=rescale_timesteps,
)
def add_dict_to_argparser(parser, default_dict):
for k, v in default_dict.items():
v_type = type(v)
if v is None:
v_type = str
elif isinstance(v, bool):
v_type = str2bool
parser.add_argument(f"--{k}", default=v, type=v_type)
def args_to_dict(args, keys):
return {k: getattr(args, k) for k in keys}
def str2bool(v):
"""
https://stackoverflow.com/questions/15008758/parsing-boolean-values-with-argparse
"""
if isinstance(v, bool):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise argparse.ArgumentTypeError("boolean value expected")
================================================
FILE: guided_diffusion/train_util.py
================================================
import copy
import functools
import os
import blobfile as bf
import torch as th
import torch.distributed as dist
from torch.nn.parallel.distributed import DistributedDataParallel as DDP
from torch.optim import AdamW
from . import dist_util, logger
from .fp16_util import MixedPrecisionTrainer
from .nn import update_ema
from .resample import LossAwareSampler, UniformSampler
# For ImageNet experiments, this was a good default value.
# We found that the lg_loss_scale quickly climbed to
# 20-21 within the first ~1K steps of training.
INITIAL_LOG_LOSS_SCALE = 20.0
class TrainLoop:
def __init__(
self,
*,
model,
diffusion,
data,
batch_size,
microbatch,
lr,
ema_rate,
log_interval,
save_interval,
resume_checkpoint,
use_fp16=False,
fp16_scale_growth=1e-3,
schedule_sampler=None,
weight_decay=0.0,
lr_anneal_steps=0,
):
self.model = model
self.diffusion = diffusion
self.data = data
self.batch_size = batch_size
self.microbatch = microbatch if microbatch > 0 else batch_size
self.lr = lr
self.ema_rate = (
[ema_rate]
if isinstance(ema_rate, float)
else [float(x) for x in ema_rate.split(",")]
)
self.log_interval = log_interval
self.save_interval = save_interval
self.resume_checkpoint = resume_checkpoint
self.use_fp16 = use_fp16
self.fp16_scale_growth = fp16_scale_growth
self.schedule_sampler = schedule_sampler or UniformSampler(diffusion)
self.weight_decay = weight_decay
self.lr_anneal_steps = lr_anneal_steps
self.step = 0
self.resume_step = 0
self.global_batch = self.batch_size * dist.get_world_size()
self.sync_cuda = th.cuda.is_available()
self._load_and_sync_parameters()
self.mp_trainer = MixedPrecisionTrainer(
model=self.model,
use_fp16=self.use_fp16,
fp16_scale_growth=fp16_scale_growth,
)
self.opt = AdamW(
self.mp_trainer.master_params, lr=self.lr, weight_decay=self.weight_decay
)
if self.resume_step:
self._load_optimizer_state()
# Model was resumed, either due to a restart or a checkpoint
# being specified at the command line.
self.ema_params = [
self._load_ema_parameters(rate) for rate in self.ema_rate
]
else:
self.ema_params = [
copy.deepcopy(self.mp_trainer.master_params)
for _ in range(len(self.ema_rate))
]
if th.cuda.is_available():
self.use_ddp = True
self.ddp_model = DDP(
self.model,
device_ids=[dist_util.dev()],
output_device=dist_util.dev(),
broadcast_buffers=False,
bucket_cap_mb=128,
find_unused_parameters=False,
)
else:
if dist.get_world_size() > 1:
logger.warn(
"Distributed training requires CUDA. "
"Gradients will not be synchronized properly!"
)
self.use_ddp = False
self.ddp_model = self.model
def _load_and_sync_parameters(self):
resume_checkpoint = find_resume_checkpoint() or self.resume_checkpoint
if resume_checkpoint:
self.resume_step = parse_resume_step_from_filename(resume_checkpoint)
if dist.get_rank() == 0:
logger.log(f"loading model from checkpoint: {resume_checkpoint}...")
self.model.load_state_dict(
dist_util.load_state_dict(
resume_checkpoint, map_location=dist_util.dev()
)
)
dist_util.sync_params(self.model.parameters())
def _load_ema_parameters(self, rate):
ema_params = copy.deepcopy(self.mp_trainer.master_params)
main_checkpoint = find_resume_checkpoint() or self.resume_checkpoint
ema_checkpoint = find_ema_checkpoint(main_checkpoint, self.resume_step, rate)
if ema_checkpoint:
if dist.get_rank() == 0:
logger.log(f"loading EMA from checkpoint: {ema_checkpoint}...")
state_dict = dist_util.load_state_dict(
ema_checkpoint, map_location=dist_util.dev()
)
ema_params = self.mp_trainer.state_dict_to_master_params(state_dict)
dist_util.sync_params(ema_params)
return ema_params
def _load_optimizer_state(self):
main_checkpoint = find_resume_checkpoint() or self.resume_checkpoint
opt_checkpoint = bf.join(
bf.dirname(main_checkpoint), f"opt{self.resume_step:06}.pt"
)
if bf.exists(opt_checkpoint):
logger.log(f"loading optimizer state from checkpoint: {opt_checkpoint}")
state_dict = dist_util.load_state_dict(
opt_checkpoint, map_location=dist_util.dev()
)
self.opt.load_state_dict(state_dict)
def run_loop(self):
while (
not self.lr_anneal_steps
or self.step + self.resume_step < self.lr_anneal_steps
):
batch, cond = next(self.data)
self.run_step(batch, cond)
if self.step % self.log_interval == 0:
logger.dumpkvs()
if self.step % self.save_interval == 0:
self.save()
# Run for a finite amount of time in integration tests.
if os.environ.get("DIFFUSION_TRAINING_TEST", "") and self.step > 0:
return
self.step += 1
# Save the last checkpoint if it wasn't already saved.
if (self.step - 1) % self.save_interval != 0:
self.save()
def run_step(self, batch, cond):
self.forward_backward(batch, cond)
took_step = self.mp_trainer.optimize(self.opt)
if took_step:
self._update_ema()
self._anneal_lr()
self.log_step()
def forward_backward(self, batch, cond):
self.mp_trainer.zero_grad()
for i in range(0, batch.shape[0], self.microbatch):
micro = batch[i : i + self.microbatch].to(dist_util.dev())
micro_cond = {
k: v[i : i + self.microbatch].to(dist_util.dev())
for k, v in cond.items()
}
last_batch = (i + self.microbatch) >= batch.shape[0]
t, weights = self.schedule_sampler.sample(micro.shape[0], dist_util.dev())
compute_losses = functools.partial(
self.diffusion.training_losses,
self.ddp_model,
micro,
t,
model_kwargs=micro_cond,
)
if last_batch or not self.use_ddp:
losses = compute_losses()
else:
with self.ddp_model.no_sync():
losses = compute_losses()
if isinstance(self.schedule_sampler, LossAwareSampler):
self.schedule_sampler.update_with_local_losses(
t, losses["loss"].detach()
)
loss = (losses["loss"] * weights).mean()
log_loss_dict(
self.diffusion, t, {k: v * weights for k, v in losses.items()}
)
self.mp_trainer.backward(loss)
def _update_ema(self):
for rate, params in zip(self.ema_rate, self.ema_params):
update_ema(params, self.mp_trainer.master_params, rate=rate)
def _anneal_lr(self):
if not self.lr_anneal_steps:
return
frac_done = (self.step + self.resume_step) / self.lr_anneal_steps
lr = self.lr * (1 - frac_done)
for param_group in self.opt.param_groups:
param_group["lr"] = lr
def log_step(self):
logger.logkv("step", self.step + self.resume_step)
logger.logkv("samples", (self.step + self.resume_step + 1) * self.global_batch)
def save(self):
def save_checkpoint(rate, params):
state_dict = self.mp_trainer.master_params_to_state_dict(params)
if dist.get_rank() == 0:
logger.log(f"saving model {rate}...")
if not rate:
filename = f"model{(self.step+self.resume_step):06d}.pt"
else:
filename = f"ema_{rate}_{(self.step+self.resume_step):06d}.pt"
with bf.BlobFile(bf.join(get_blob_logdir(), filename), "wb") as f:
th.save(state_dict, f)
save_checkpoint(0, self.mp_trainer.master_params)
for rate, params in zip(self.ema_rate, self.ema_params):
save_checkpoint(rate, params)
if dist.get_rank() == 0:
with bf.BlobFile(
bf.join(get_blob_logdir(), f"opt{(self.step+self.resume_step):06d}.pt"),
"wb",
) as f:
th.save(self.opt.state_dict(), f)
dist.barrier()
def parse_resume_step_from_filename(filename):
"""
Parse filenames of the form path/to/modelNNNNNN.pt, where NNNNNN is the
checkpoint's number of steps.
"""
split = filename.split("model")
if len(split) < 2:
return 0
split1 = split[-1].split(".")[0]
try:
return int(split1)
except ValueError:
return 0
def get_blob_logdir():
# You can change this to be a separate path to save checkpoints to
# a blobstore or some external drive.
return logger.get_dir()
def find_resume_checkpoint():
# On your infrastructure, you may want to override this to automatically
# discover the latest checkpoint on your blob storage, etc.
return None
def find_ema_checkpoint(main_checkpoint, step, rate):
if main_checkpoint is None:
return None
filename = f"ema_{rate}_{(step):06d}.pt"
path = bf.join(bf.dirname(main_checkpoint), filename)
if bf.exists(path):
return path
return None
def log_loss_dict(diffusion, ts, losses):
for key, values in losses.items():
logger.logkv_mean(key, values.mean().item())
# Log the quantiles (four quartiles, in particular).
for sub_t, sub_loss in zip(ts.cpu().numpy(), values.detach().cpu().numpy()):
quartile = int(4 * sub_t / diffusion.num_timesteps)
logger.logkv_mean(f"{key}_q{quartile}", sub_loss)
================================================
FILE: guided_diffusion/unet.py
================================================
from abc import abstractmethod
import math
import numpy as np
import torch as th
import torch.nn as nn
import torch.nn.functional as F
from .fp16_util import convert_module_to_f16, convert_module_to_f32
from .nn import (
checkpoint,
conv_nd,
linear,
avg_pool_nd,
zero_module,
normalization,
timestep_embedding,
)
class AttentionPool2d(nn.Module):
"""
Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py
"""
def __init__(
self,
spacial_dim: int,
embed_dim: int,
num_heads_channels: int,
output_dim: int = None,
):
super().__init__()
self.positional_embedding = nn.Parameter(
th.randn(embed_dim, spacial_dim ** 2 + 1) / embed_dim ** 0.5
)
self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1)
self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1)
self.num_heads = embed_dim // num_heads_channels
self.attention = QKVAttention(self.num_heads)
def forward(self, x):
b, c, *_spatial = x.shape
x = x.reshape(b, c, -1) # NC(HW)
x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1) # NC(HW+1)
x = x + self.positional_embedding[None, :, :].to(x.dtype) # NC(HW+1)
x = self.qkv_proj(x)
x = self.attention(x)
x = self.c_proj(x)
return x[:, :, 0]
class TimestepBlock(nn.Module):
"""
Any module where forward() takes timestep embeddings as a second argument.
"""
@abstractmethod
def forward(self, x, emb):
"""
Apply the module to `x` given `emb` timestep embeddings.
"""
class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
"""
A sequential module that passes timestep embeddings to the children that
support it as an extra input.
"""
def forward(self, x, emb):
for layer in self:
if isinstance(layer, TimestepBlock):
x = layer(x, emb)
else:
x = layer(x)
return x
class Upsample(nn.Module):
"""
An upsampling layer with an optional convolution.
:param channels: channels in the inputs and outputs.
:param use_conv: a bool determining if a convolution is applied.
:param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
upsampling occurs in the inner-two dimensions.
"""
def __init__(self, channels, use_conv, dims=2, out_channels=None):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.dims = dims
if use_conv:
self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=1)
def forward(self, x):
assert x.shape[1] == self.channels
if self.dims == 3:
x = F.interpolate(
x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
)
else:
x = F.interpolate(x, scale_factor=2, mode="nearest")
if self.use_conv:
x = self.conv(x)
return x
class Downsample(nn.Module):
"""
A downsampling layer with an optional convolution.
:param channels: channels in the inputs and outputs.
:param use_conv: a bool determining if a convolution is applied.
:param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
downsampling occurs in the inner-two dimensions.
"""
def __init__(self, channels, use_conv, dims=2, out_channels=None):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.dims = dims
stride = 2 if dims != 3 else (1, 2, 2)
if use_conv:
self.op = conv_nd(
dims, self.channels, self.out_channels, 3, stride=stride, padding=1
)
else:
assert self.channels == self.out_channels
self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
def forward(self, x):
assert x.shape[1] == self.channels
return self.op(x)
class ResBlock(TimestepBlock):
"""
A residual block that can optionally change the number of channels.
:param channels: the number of input channels.
:param emb_channels: the number of timestep embedding channels.
:param dropout: the rate of dropout.
:param out_channels: if specified, the number of out channels.
:param use_conv: if True and out_channels is specified, use a spatial
convolution instead of a smaller 1x1 convolution to change the
channels in the skip connection.
:param dims: determines if the signal is 1D, 2D, or 3D.
:param use_checkpoint: if True, use gradient checkpointing on this module.
:param up: if True, use this block for upsampling.
:param down: if True, use this block for downsampling.
"""
def __init__(
self,
channels,
emb_channels,
dropout,
out_channels=None,
use_conv=False,
use_scale_shift_norm=False,
dims=2,
use_checkpoint=False,
up=False,
down=False,
):
super().__init__()
self.channels = channels
self.emb_channels = emb_channels
self.dropout = dropout
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.use_checkpoint = use_checkpoint
self.use_scale_shift_norm = use_scale_shift_norm
self.in_layers = nn.Sequential(
normalization(channels),
nn.SiLU(),
conv_nd(dims, channels, self.out_channels, 3, padding=1),
)
self.updown = up or down
if up:
self.h_upd = Upsample(channels, False, dims)
self.x_upd = Upsample(channels, False, dims)
elif down:
self.h_upd = Downsample(channels, False, dims)
self.x_upd = Downsample(channels, False, dims)
else:
self.h_upd = self.x_upd = nn.Identity()
self.emb_layers = nn.Sequential(
nn.SiLU(),
linear(
emb_channels,
2 * self.out_channels if use_scale_shift_norm else self.out_channels,
),
)
self.out_layers = nn.Sequential(
normalization(self.out_channels),
nn.SiLU(),
nn.Dropout(p=dropout),
zero_module(
conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
),
)
if self.out_channels == channels:
self.skip_connection = nn.Identity()
elif use_conv:
self.skip_connection = conv_nd(
dims, channels, self.out_channels, 3, padding=1
)
else:
self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
def forward(self, x, emb):
"""
Apply the block to a Tensor, conditioned on a timestep embedding.
:param x: an [N x C x ...] Tensor of features.
:param emb: an [N x emb_channels] Tensor of timestep embeddings.
:return: an [N x C x ...] Tensor of outputs.
"""
return checkpoint(
self._forward, (x, emb), self.parameters(), self.use_checkpoint
)
def _forward(self, x, emb):
if self.updown:
in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
h = in_rest(x)
h = self.h_upd(h)
x = self.x_upd(x)
h = in_conv(h)
else:
h = self.in_layers(x)
emb_out = self.emb_layers(emb).type(h.dtype)
while len(emb_out.shape) < len(h.shape):
emb_out = emb_out[..., None]
if self.use_scale_shift_norm:
out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
scale, shift = th.chunk(emb_out, 2, dim=1)
h = out_norm(h) * (1 + scale) + shift
h = out_rest(h)
else:
h = h + emb_out
h = self.out_layers(h)
return self.skip_connection(x) + h
class AttentionBlock(nn.Module):
"""
An attention block that allows spatial positions to attend to each other.
Originally ported from here, but adapted to the N-d case.
https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
"""
def __init__(
self,
channels,
num_heads=1,
num_head_channels=-1,
use_checkpoint=False,
use_new_attention_order=False,
):
super().__init__()
self.channels = channels
if num_head_channels == -1:
self.num_heads = num_heads
else:
assert (
channels % num_head_channels == 0
), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
self.num_heads = channels // num_head_channels
self.use_checkpoint = use_checkpoint
self.norm = normalization(channels)
self.qkv = conv_nd(1, channels, channels * 3, 1)
if use_new_attention_order:
# split qkv before split heads
self.attention = QKVAttention(self.num_heads)
else:
# split heads before split qkv
self.attention = QKVAttentionLegacy(self.num_heads)
self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
def forward(self, x):
return checkpoint(self._forward, (x,), self.parameters(), True)
def _forward(self, x):
b, c, *spatial = x.shape
x = x.reshape(b, c, -1)
qkv = self.qkv(self.norm(x))
h = self.attention(qkv)
h = self.proj_out(h)
return (x + h).reshape(b, c, *spatial)
def count_flops_attn(model, _x, y):
"""
A counter for the `thop` package to count the operations in an
attention operation.
Meant to be used like:
macs, params = thop.profile(
model,
inputs=(inputs, timestamps),
custom_ops={QKVAttention: QKVAttention.count_flops},
)
"""
b, c, *spatial = y[0].shape
num_spatial = int(np.prod(spatial))
# We perform two matmuls with the same number of ops.
# The first computes the weight matrix, the second computes
# the combination of the value vectors.
matmul_ops = 2 * b * (num_spatial ** 2) * c
model.total_ops += th.DoubleTensor([matmul_ops])
class QKVAttentionLegacy(nn.Module):
"""
A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
"""
def __init__(self, n_heads):
super().__init__()
self.n_heads = n_heads
def forward(self, qkv):
"""
Apply QKV attention.
:param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
:return: an [N x (H * C) x T] tensor after attention.
"""
bs, width, length = qkv.shape
assert width % (3 * self.n_heads) == 0
ch = width // (3 * self.n_heads)
q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
scale = 1 / math.sqrt(math.sqrt(ch))
weight = th.einsum(
"bct,bcs->bts", q * scale, k * scale
) # More stable with f16 than dividing afterwards
weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
a = th.einsum("bts,bcs->bct", weight, v)
return a.reshape(bs, -1, length)
@staticmethod
def count_flops(model, _x, y):
return count_flops_attn(model, _x, y)
class QKVAttention(nn.Module):
"""
A module which performs QKV attention and splits in a different order.
"""
def __init__(self, n_heads):
super().__init__()
self.n_heads = n_heads
def forward(self, qkv):
"""
Apply QKV attention.
:param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
:return: an [N x (H * C) x T] tensor after attention.
"""
bs, width, length = qkv.shape
assert width % (3 * self.n_heads) == 0
ch = width // (3 * self.n_heads)
q, k, v = qkv.chunk(3, dim=1)
scale = 1 / math.sqrt(math.sqrt(ch))
weight = th.einsum(
"bct,bcs->bts",
(q * scale).view(bs * self.n_heads, ch, length),
(k * scale).view(bs * self.n_heads, ch, length),
) # More stable with f16 than dividing afterwards
weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length))
return a.reshape(bs, -1, length)
@staticmethod
def count_flops(model, _x, y):
return count_flops_attn(model, _x, y)
class UNetModel(nn.Module):
"""
The full UNet model with attention and timestep embedding.
:param in_channels: channels in the input Tensor.
:param model_channels: base channel count for the model.
:param out_channels: channels in the output Tensor.
:param num_res_blocks: number of residual blocks per downsample.
:param attention_resolutions: a collection of downsample rates at which
attention will take place. May be a set, list, or tuple.
For example, if this contains 4, then at 4x downsampling, attention
will be used.
:param dropout: the dropout probability.
:param channel_mult: channel multiplier for each level of the UNet.
:param conv_resample: if True, use learned convolutions for upsampling and
downsampling.
:param dims: determines if the signal is 1D, 2D, or 3D.
:param num_classes: if specified (as an int), then this model will be
class-conditional with `num_classes` classes.
:param use_checkpoint: use gradient checkpointing to reduce memory usage.
:param num_heads: the number of attention heads in each attention layer.
:param num_heads_channels: if specified, ignore num_heads and instead use
a fixed channel width per attention head.
:param num_heads_upsample: works with num_heads to set a different number
of heads for upsampling. Deprecated.
:param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
:param resblock_updown: use residual blocks for up/downsampling.
:param use_new_attention_order: use a different attention pattern for potentially
increased efficiency.
"""
def __init__(
self,
image_size,
in_channels,
model_channels,
out_channels,
num_res_blocks,
attention_resolutions,
dropout=0,
channel_mult=(1, 2, 4, 8),
conv_resample=True,
dims=2,
num_classes=None,
use_checkpoint=False,
use_fp16=False,
num_heads=1,
num_head_channels=-1,
num_heads_upsample=-1,
use_scale_shift_norm=False,
resblock_updown=False,
use_new_attention_order=False,
):
super().__init__()
if num_heads_upsample == -1:
num_heads_upsample = num_heads
self.image_size = image_size
self.in_channels = in_channels
self.model_channels = model_channels
self.out_channels = out_channels
self.num_res_blocks = num_res_blocks
self.attention_resolutions = attention_resolutions
self.dropout = dropout
self.channel_mult = channel_mult
self.conv_resample = conv_resample
self.num_classes = num_classes
self.use_checkpoint = use_checkpoint
self.dtype = th.float16 if use_fp16 else th.float32
self.num_heads = num_heads
self.num_head_channels = num_head_channels
self.num_heads_upsample = num_heads_upsample
time_embed_dim = model_channels * 4
self.time_embed = nn.Sequential(
linear(model_channels, time_embed_dim),
nn.SiLU(),
linear(time_embed_dim, time_embed_dim),
)
if self.num_classes is not None:
self.label_emb = nn.Embedding(num_classes, time_embed_dim)
ch = input_ch = int(channel_mult[0] * model_channels)
self.input_blocks = nn.ModuleList(
[TimestepEmbedSequential(conv_nd(dims, in_channels, ch, 3, padding=1))]
)
self._feature_size = ch
input_block_chans = [ch]
ds = 1
for level, mult in enumerate(channel_mult):
for _ in range(num_res_blocks):
layers = [
ResBlock(
ch,
time_embed_dim,
dropout,
out_channels=int(mult * model_channels),
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
)
]
ch = int(mult * model_channels)
if ds in attention_resolutions:
layers.append(
AttentionBlock(
ch,
use_checkpoint=use_checkpoint,
num_heads=num_heads,
num_head_channels=num_head_channels,
use_new_attention_order=use_new_attention_order,
)
)
self.input_blocks.append(TimestepEmbedSequential(*layers))
self._feature_size += ch
input_block_chans.append(ch)
if level != len(channel_mult) - 1:
out_ch = ch
self.input_blocks.append(
TimestepEmbedSequential(
ResBlock(
ch,
time_embed_dim,
dropout,
out_channels=out_ch,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
down=True,
)
if resblock_updown
else Downsample(
ch, conv_resample, dims=dims, out_channels=out_ch
)
)
)
ch = out_ch
input_block_chans.append(ch)
ds *= 2
self._feature_size += ch
self.middle_block = TimestepEmbedSequential(
ResBlock(
ch,
time_embed_dim,
dropout,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
),
AttentionBlock(
ch,
use_checkpoint=use_checkpoint,
num_heads=num_heads,
num_head_channels=num_head_channels,
use_new_attention_order=use_new_attention_order,
),
ResBlock(
ch,
time_embed_dim,
dropout,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
),
)
self._feature_size += ch
self.output_blocks = nn.ModuleList([])
for level, mult in list(enumerate(channel_mult))[::-1]:
for i in range(num_res_blocks + 1):
ich = input_block_chans.pop()
layers = [
ResBlock(
ch + ich,
time_embed_dim,
dropout,
out_channels=int(model_channels * mult),
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
)
]
ch = int(model_channels * mult)
if ds in attention_resolutions:
layers.append(
AttentionBlock(
ch,
use_checkpoint=use_checkpoint,
num_heads=num_heads_upsample,
num_head_channels=num_head_channels,
use_new_attention_order=use_new_attention_order,
)
)
if level and i == num_res_blocks:
out_ch = ch
layers.append(
ResBlock(
ch,
time_embed_dim,
dropout,
out_channels=out_ch,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
up=True,
)
if resblock_updown
else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
)
ds //= 2
self.output_blocks.append(TimestepEmbedSequential(*layers))
self._feature_size += ch
self.out = nn.Sequential(
normalization(ch),
nn.SiLU(),
zero_module(conv_nd(dims, input_ch, out_channels, 3, padding=1)),
)
def convert_to_fp16(self):
"""
Convert the torso of the model to float16.
"""
self.input_blocks.apply(convert_module_to_f16)
self.middle_block.apply(convert_module_to_f16)
self.output_blocks.apply(convert_module_to_f16)
def convert_to_fp32(self):
"""
Convert the torso of the model to float32.
"""
self.input_blocks.apply(convert_module_to_f32)
self.middle_block.apply(convert_module_to_f32)
self.output_blocks.apply(convert_module_to_f32)
def forward(self, x, timesteps, y=None):
"""
Apply the model to an input batch.
:param x: an [N x C x ...] Tensor of inputs.
:param timesteps: a 1-D batch of timesteps.
:param y: an [N] Tensor of labels, if class-conditional.
:return: an [N x C x ...] Tensor of outputs.
"""
assert (y is not None) == (
self.num_classes is not None
), "must specify y if and only if the model is class-conditional"
hs = []
emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
if self.num_classes is not None:
assert y.shape == (x.shape[0],)
emb = emb + self.label_emb(y)
h = x.type(self.dtype)
for module in self.input_blocks:
h = module(h, emb)
hs.append(h)
h = self.middle_block(h, emb)
for module in self.output_blocks:
h = th.cat([h, hs.pop()], dim=1)
h = module(h, emb)
h = h.type(x.dtype)
return self.out(h)
class SuperResModel(UNetModel):
"""
A UNetModel that performs super-resolution.
Expects an extra kwarg `low_res` to condition on a low-resolution image.
"""
def __init__(self, image_size, in_channels, *args, **kwargs):
super().__init__(image_size, in_channels * 2, *args, **kwargs)
def forward(self, x, timesteps, low_res=None, **kwargs):
_, _, new_height, new_width = x.shape
upsampled = F.interpolate(low_res, (new_height, new_width), mode="bilinear")
x = th.cat([x, upsampled], dim=1)
return super().forward(x, timesteps, **kwargs)
class EncoderUNetModel(nn.Module):
"""
The half UNet model with attention and timestep embedding.
For usage, see UNet.
"""
def __init__(
self,
image_size,
in_channels,
model_channels,
out_channels,
num_res_blocks,
attention_resolutions,
dropout=0,
channel_mult=(1, 2, 4, 8),
conv_resample=True,
dims=2,
use_checkpoint=False,
use_fp16=False,
num_heads=1,
num_head_channels=-1,
num_heads_upsample=-1,
use_scale_shift_norm=False,
resblock_updown=False,
use_new_attention_order=False,
pool="adaptive",
):
super().__init__()
if num_heads_upsample == -1:
num_heads_upsample = num_heads
self.in_channels = in_channels
self.model_channels = model_channels
self.out_channels = out_channels
self.num_res_blocks = num_res_blocks
self.attention_resolutions = attention_resolutions
self.dropout = dropout
self.channel_mult = channel_mult
self.conv_resample = conv_resample
self.use_checkpoint = use_checkpoint
self.dtype = th.float16 if use_fp16 else th.float32
self.num_heads = num_heads
self.num_head_channels = num_head_channels
self.num_heads_upsample = num_heads_upsample
time_embed_dim = model_channels * 4
self.time_embed = nn.Sequential(
linear(model_channels, time_embed_dim),
nn.SiLU(),
linear(time_embed_dim, time_embed_dim),
)
ch = int(channel_mult[0] * model_channels)
self.input_blocks = nn.ModuleList(
[TimestepEmbedSequential(conv_nd(dims, in_channels, ch, 3, padding=1))]
)
self._feature_size = ch
input_block_chans = [ch]
ds = 1
for level, mult in enumerate(channel_mult):
for _ in range(num_res_blocks):
layers = [
ResBlock(
ch,
time_embed_dim,
dropout,
out_channels=int(mult * model_channels),
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
)
]
ch = int(mult * model_channels)
if ds in attention_resolutions:
layers.append(
AttentionBlock(
ch,
use_checkpoint=use_checkpoint,
num_heads=num_heads,
num_head_channels=num_head_channels,
use_new_attention_order=use_new_attention_order,
)
)
self.input_blocks.append(TimestepEmbedSequential(*layers))
self._feature_size += ch
input_block_chans.append(ch)
if level != len(channel_mult) - 1:
out_ch = ch
self.input_blocks.append(
TimestepEmbedSequential(
ResBlock(
ch,
time_embed_dim,
dropout,
out_channels=out_ch,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
down=True,
)
if resblock_updown
else Downsample(
ch, conv_resample, dims=dims, out_channels=out_ch
)
)
)
ch = out_ch
input_block_chans.append(ch)
ds *= 2
self._feature_size += ch
self.middle_block = TimestepEmbedSequential(
ResBlock(
ch,
time_embed_dim,
dropout,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
),
AttentionBlock(
ch,
use_checkpoint=use_checkpoint,
num_heads=num_heads,
num_head_channels=num_head_channels,
use_new_attention_order=use_new_attention_order,
),
ResBlock(
ch,
time_embed_dim,
dropout,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
),
)
self._feature_size += ch
self.pool = pool
if pool == "adaptive":
self.out = nn.Sequential(
normalization(ch),
nn.SiLU(),
nn.AdaptiveAvgPool2d((1, 1)),
zero_module(conv_nd(dims, ch, out_channels, 1)),
nn.Flatten(),
)
elif pool == "attention":
assert num_head_channels != -1
self.out = nn.Sequential(
normalization(ch),
nn.SiLU(),
AttentionPool2d(
(image_size // ds), ch, num_head_channels, out_channels
),
)
elif pool == "spatial":
self.out = nn.Sequential(
nn.Linear(self._feature_size, 2048),
nn.ReLU(),
nn.Linear(2048, self.out_channels),
)
elif pool == "spatial_v2":
self.out = nn.Sequential(
nn.Linear(self._feature_size, 2048),
normalization(2048),
nn.SiLU(),
nn.Linear(2048, self.out_channels),
)
else:
raise NotImplementedError(f"Unexpected {pool} pooling")
def convert_to_fp16(self):
"""
Convert the torso of the model to float16.
"""
self.input_blocks.apply(convert_module_to_f16)
self.middle_block.apply(convert_module_to_f16)
def convert_to_fp32(self):
"""
Convert the torso of the model to float32.
"""
self.input_blocks.apply(convert_module_to_f32)
self.middle_block.apply(convert_module_to_f32)
def forward(self, x, timesteps):
"""
Apply the model to an input batch.
:param x: an [N x C x ...] Tensor of inputs.
:param timesteps: a 1-D batch of timesteps.
:return: an [N x K] Tensor of outputs.
"""
emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
results = []
h = x.type(self.dtype)
for module in self.input_blocks:
h = module(h, emb)
if self.pool.startswith("spatial"):
results.append(h.type(x.dtype).mean(dim=(2, 3)))
h = self.middle_block(h, emb)
if self.pool.startswith("spatial"):
results.append(h.type(x.dtype).mean(dim=(2, 3)))
h = th.cat(results, axis=-1)
return self.out(h)
else:
h = h.type(x.dtype)
return self.out(h)
================================================
FILE: model-card.md
================================================
# Overview
These are diffusion models and noised image classifiers described in the paper [Diffusion Models Beat GANs on Image Synthesis](https://arxiv.org/abs/2105.05233).
Included in this release are the following models:
* Noisy ImageNet classifiers at resolutions 64x64, 128x128, 256x256, 512x512
* A class-unconditional ImageNet diffusion model at resolution 256x256
* Class conditional ImageNet diffusion models at 64x64, 128x128, 256x256, 512x512 resolutions
* Class-conditional ImageNet upsampling diffusion models: 64x64->256x256, 128x128->512x512
* Diffusion models trained on three LSUN classes at 256x256 resolution: cat, horse, bedroom
# Datasets
All of the models we are releasing were either trained on the [ILSVRC 2012 subset of ImageNet](http://www.image-net.org/challenges/LSVRC/2012/) or on single classes of [LSUN](https://arxiv.org/abs/1506.03365).
Here, we describe characteristics of these datasets which impact model behavior:
**LSUN**: This dataset was collected in 2015 using a combination of human labeling (from Amazon Mechanical Turk) and automated data labeling.
* Each of the three classes we consider contain over a million images.
* The dataset creators found that the label accuracy was roughly 90% across the entire LSUN dataset when measured by trained experts.
* Images are scraped from the internet, and LSUN cat images in particular tend to often follow a “meme” format.
* We found that there are occasionally humans in these photos, including faces, especially within the cat class.
**ILSVRC 2012 subset of ImageNet**: This dataset was curated in 2012 and consists of roughly one million images, each belonging to one of 1000 classes.
* A large portion of the classes in this dataset are animals, plants, and other naturally-occurring objects.
* Many images contain humans, although usually these humans aren’t reflected by the class label (e.g. the class “Tench, tinca tinca” contains many photos of people holding fish).
# Performance
These models are intended to generate samples consistent with their training distributions.
This has been measured in terms of FID, Precision, and Recall.
These metrics all rely on the representations of a [pre-trained Inception-V3 model](https://arxiv.org/abs/1512.00567),
which was trained on ImageNet, and so is likely to focus more on the ImageNet classes (such as animals) than on other visual features (such as human faces).
Qualitatively, the samples produced by these models often look highly realistic, especially when a diffusion model is combined with a noisy classifier.
# Intended Use
These models are intended to be used for research purposes only.
In particular, they can be used as a baseline for generative modeling research, or as a starting point to build off of for such research.
These models are not intended to be commercially deployed.
Additionally, they are not intended to be used to create propaganda or offensive imagery.
Before releasing these models, we probed their ability to ease the creation of targeted imagery, since doing so could be potentially harmful.
We did this either by fine-tuning our ImageNet models on a target LSUN class, or through classifier guidance with publicly available [CLIP models](https://github.com/openai/CLIP).
* To probe fine-tuning capabilities, we restricted our compute budget to roughly $100 and tried both standard fine-tuning,
and a diffusion-specific approach where we train a specialized classifier for the LSUN class. The resulting FIDs were significantly worse than publicly available GAN models, indicating that fine-tuning an ImageNet diffusion model does not significantly lower the cost of image generation.
* To probe guidance with CLIP, we tried two approaches for using pre-trained CLIP models for classifier guidance. Either we fed the noised image to CLIP directly and used its gradients, or we fed the diffusion model's denoised prediction to the CLIP model and differentiated through the whole process. In both cases, we found that it was difficult to recover information from the CLIP model, indicating that these diffusion models are unlikely to make it significantly easier to extract knowledge from CLIP compared to existing GAN models.
# Limitations
These models sometimes produce highly unrealistic outputs, particularly when generating images containing human faces.
This may stem from ImageNet's emphasis on non-human objects.
While classifier guidance can improve sample quality, it reduces diversity, resulting in some modes of the data distribution being underrepresented.
This can potentially amplify existing biases in the training dataset such as gender and racial biases.
Because ImageNet and LSUN contain images from the internet, they include photos of real people, and the model may have memorized some of the information contained in these photos.
However, these images are already publicly available, and existing generative models trained on ImageNet have not demonstrated significant leakage of this information.
================================================
FILE: scripts/classifier_sample.py
================================================
"""
Like image_sample.py, but use a noisy image classifier to guide the sampling
process towards more realistic images.
"""
import argparse
import os
import numpy as np
import torch as th
import torch.distributed as dist
import torch.nn.functional as F
from guided_diffusion import dist_util, logger
from guided_diffusion.script_util import (
NUM_CLASSES,
model_and_diffusion_defaults,
classifier_defaults,
create_model_and_diffusion,
create_classifier,
add_dict_to_argparser,
args_to_dict,
)
def main():
args = create_argparser().parse_args()
dist_util.setup_dist()
logger.configure()
logger.log("creating model and diffusion...")
model, diffusion = create_model_and_diffusion(
**args_to_dict(args, model_and_diffusion_defaults().keys())
)
model.load_state_dict(
dist_util.load_state_dict(args.model_path, map_location="cpu")
)
model.to(dist_util.dev())
if args.use_fp16:
model.convert_to_fp16()
model.eval()
logger.log("loading classifier...")
classifier = create_classifier(**args_to_dict(args, classifier_defaults().keys()))
classifier.load_state_dict(
dist_util.load_state_dict(args.classifier_path, map_location="cpu")
)
classifier.to(dist_util.dev())
if args.classifier_use_fp16:
classifier.convert_to_fp16()
classifier.eval()
def cond_fn(x, t, y=None):
assert y is not None
with th.enable_grad():
x_in = x.detach().requires_grad_(True)
logits = classifier(x_in, t)
log_probs = F.log_softmax(logits, dim=-1)
selected = log_probs[range(len(logits)), y.view(-1)]
return th.autograd.grad(selected.sum(), x_in)[0] * args.classifier_scale
def model_fn(x, t, y=None):
assert y is not None
return model(x, t, y if args.class_cond else None)
logger.log("sampling...")
all_images = []
all_labels = []
while len(all_images) * args.batch_size < args.num_samples:
model_kwargs = {}
classes = th.randint(
low=0, high=NUM_CLASSES, size=(args.batch_size,), device=dist_util.dev()
)
model_kwargs["y"] = classes
sample_fn = (
diffusion.p_sample_loop if not args.use_ddim else diffusion.ddim_sample_loop
)
sample = sample_fn(
model_fn,
(args.batch_size, 3, args.image_size, args.image_size),
clip_denoised=args.clip_denoised,
model_kwargs=model_kwargs,
cond_fn=cond_fn,
device=dist_util.dev(),
)
sample = ((sample + 1) * 127.5).clamp(0, 255).to(th.uint8)
sample = sample.permute(0, 2, 3, 1)
sample = sample.contiguous()
gathered_samples = [th.zeros_like(sample) for _ in range(dist.get_world_size())]
dist.all_gather(gathered_samples, sample) # gather not supported with NCCL
all_images.extend([sample.cpu().numpy() for sample in gathered_samples])
gathered_labels = [th.zeros_like(classes) for _ in range(dist.get_world_size())]
dist.all_gather(gathered_labels, classes)
all_labels.extend([labels.cpu().numpy() for labels in gathered_labels])
logger.log(f"created {len(all_images) * args.batch_size} samples")
arr = np.concatenate(all_images, axis=0)
arr = arr[: args.num_samples]
label_arr = np.concatenate(all_labels, axis=0)
label_arr = label_arr[: args.num_samples]
if dist.get_rank() == 0:
shape_str = "x".join([str(x) for x in arr.shape])
out_path = os.path.join(logger.get_dir(), f"samples_{shape_str}.npz")
logger.log(f"saving to {out_path}")
np.savez(out_path, arr, label_arr)
dist.barrier()
logger.log("sampling complete")
def create_argparser():
defaults = dict(
clip_denoised=True,
num_samples=10000,
batch_size=16,
use_ddim=False,
model_path="",
classifier_path="",
classifier_scale=1.0,
)
defaults.update(model_and_diffusion_defaults())
defaults.update(classifier_defaults())
parser = argparse.ArgumentParser()
add_dict_to_argparser(parser, defaults)
return parser
if __name__ == "__main__":
main()
================================================
FILE: scripts/classifier_train.py
================================================
"""
Train a noised image classifier on ImageNet.
"""
import argparse
import os
import blobfile as bf
import torch as th
import torch.distributed as dist
import torch.nn.functional as F
from torch.nn.parallel.distributed import DistributedDataParallel as DDP
from torch.optim import AdamW
from guided_diffusion import dist_util, logger
from guided_diffusion.fp16_util import MixedPrecisionTrainer
from guided_diffusion.image_datasets import load_data
from guided_diffusion.resample import create_named_schedule_sampler
from guided_diffusion.script_util import (
add_dict_to_argparser,
args_to_dict,
classifier_and_diffusion_defaults,
create_classifier_and_diffusion,
)
from guided_diffusion.train_util import parse_resume_step_from_filename, log_loss_dict
def main():
args = create_argparser().parse_args()
dist_util.setup_dist()
logger.configure()
logger.log("creating model and diffusion...")
model, diffusion = create_classifier_and_diffusion(
**args_to_dict(args, classifier_and_diffusion_defaults().keys())
)
model.to(dist_util.dev())
if args.noised:
schedule_sampler = create_named_schedule_sampler(
args.schedule_sampler, diffusion
)
resume_step = 0
if args.resume_checkpoint:
resume_step = parse_resume_step_from_filename(args.resume_checkpoint)
if dist.get_rank() == 0:
logger.log(
f"loading model from checkpoint: {args.resume_checkpoint}... at {resume_step} step"
)
model.load_state_dict(
dist_util.load_state_dict(
args.resume_checkpoint, map_location=dist_util.dev()
)
)
# Needed for creating correct EMAs and fp16 parameters.
dist_util.sync_params(model.parameters())
mp_trainer = MixedPrecisionTrainer(
model=model, use_fp16=args.classifier_use_fp16, initial_lg_loss_scale=16.0
)
model = DDP(
model,
device_ids=[dist_util.dev()],
output_device=dist_util.dev(),
broadcast_buffers=False,
bucket_cap_mb=128,
find_unused_parameters=False,
)
logger.log("creating data loader...")
data = load_data(
data_dir=args.data_dir,
batch_size=args.batch_size,
image_size=args.image_size,
class_cond=True,
random_crop=True,
)
if args.val_data_dir:
val_data = load_data(
data_dir=args.val_data_dir,
batch_size=args.batch_size,
image_size=args.image_size,
class_cond=True,
)
else:
val_data = None
logger.log(f"crea
gitextract_l679qbmt/ ├── .gitignore ├── LICENSE ├── README.md ├── datasets/ │ ├── README.md │ └── lsun_bedroom.py ├── evaluations/ │ ├── README.md │ ├── evaluator.py │ └── requirements.txt ├── guided_diffusion/ │ ├── __init__.py │ ├── dist_util.py │ ├── fp16_util.py │ ├── gaussian_diffusion.py │ ├── image_datasets.py │ ├── logger.py │ ├── losses.py │ ├── nn.py │ ├── resample.py │ ├── respace.py │ ├── script_util.py │ ├── train_util.py │ └── unet.py ├── model-card.md ├── scripts/ │ ├── classifier_sample.py │ ├── classifier_train.py │ ├── image_nll.py │ ├── image_sample.py │ ├── image_train.py │ ├── super_res_sample.py │ └── super_res_train.py └── setup.py
SYMBOL INDEX (318 symbols across 21 files)
FILE: datasets/lsun_bedroom.py
function read_images (line 14) | def read_images(lmdb_path, image_size):
function dump_images (line 34) | def dump_images(out_dir, images, prefix):
function main (line 41) | def main():
FILE: evaluations/evaluator.py
function main (line 27) | def main():
class InvalidFIDException (line 63) | class InvalidFIDException(Exception):
class FIDStatistics (line 67) | class FIDStatistics:
method __init__ (line 68) | def __init__(self, mu: np.ndarray, sigma: np.ndarray):
method frechet_distance (line 72) | def frechet_distance(self, other, eps=1e-6):
class Evaluator (line 118) | class Evaluator:
method __init__ (line 119) | def __init__(
method warmup (line 135) | def warmup(self):
method read_activations (line 138) | def read_activations(self, npz_path: str) -> Tuple[np.ndarray, np.ndar...
method compute_activations (line 142) | def compute_activations(self, batches: Iterable[np.ndarray]) -> Tuple[...
method read_statistics (line 164) | def read_statistics(
method compute_statistics (line 174) | def compute_statistics(self, activations: np.ndarray) -> FIDStatistics:
method compute_inception_score (line 179) | def compute_inception_score(self, activations: np.ndarray, split_size:...
method compute_prec_recall (line 194) | def compute_prec_recall(
class ManifoldEstimator (line 205) | class ManifoldEstimator:
method __init__ (line 212) | def __init__(
method warmup (line 241) | def warmup(self):
method manifold_radii (line 248) | def manifold_radii(self, features: np.ndarray) -> np.ndarray:
method evaluate (line 283) | def evaluate(self, features: np.ndarray, radii: np.ndarray, eval_featu...
method evaluate_pr (line 325) | def evaluate_pr(
class DistanceBlock (line 362) | class DistanceBlock:
method __init__ (line 369) | def __init__(self, session):
method pairwise_distances (line 393) | def pairwise_distances(self, U, V):
method less_thans (line 402) | def less_thans(self, batch_1, radii_1, batch_2, radii_2):
function _batch_pairwise_distances (line 414) | def _batch_pairwise_distances(U, V):
class NpzArrayReader (line 433) | class NpzArrayReader(ABC):
method read_batch (line 435) | def read_batch(self, batch_size: int) -> Optional[np.ndarray]:
method remaining (line 439) | def remaining(self) -> int:
method read_batches (line 442) | def read_batches(self, batch_size: int) -> Iterable[np.ndarray]:
class BatchIterator (line 455) | class BatchIterator:
method __init__ (line 456) | def __init__(self, gen_fn, length):
method __len__ (line 460) | def __len__(self):
method __iter__ (line 463) | def __iter__(self):
class StreamingNpzArrayReader (line 467) | class StreamingNpzArrayReader(NpzArrayReader):
method __init__ (line 468) | def __init__(self, arr_f, shape, dtype):
method read_batch (line 474) | def read_batch(self, batch_size: int) -> Optional[np.ndarray]:
method remaining (line 489) | def remaining(self) -> int:
class MemoryNpzArrayReader (line 493) | class MemoryNpzArrayReader(NpzArrayReader):
method __init__ (line 494) | def __init__(self, arr):
method load (line 499) | def load(cls, path: str, arr_name: str):
method read_batch (line 504) | def read_batch(self, batch_size: int) -> Optional[np.ndarray]:
method remaining (line 512) | def remaining(self) -> int:
function open_npz_array (line 517) | def open_npz_array(path: str, arr_name: str) -> NpzArrayReader:
function _read_bytes (line 534) | def _read_bytes(fp, size, error_template="ran out of data"):
function _open_npy_file (line 564) | def _open_npy_file(path: str, arr_name: str):
function _download_inception_model (line 573) | def _download_inception_model():
function _create_feature_graph (line 586) | def _create_feature_graph(input_batch):
function _create_softmax_graph (line 603) | def _create_softmax_graph(input_batch):
function _update_shapes (line 617) | def _update_shapes(pool3):
function _numpy_partition (line 636) | def _numpy_partition(arr, kth, **kwargs):
FILE: guided_diffusion/dist_util.py
function setup_dist (line 21) | def setup_dist():
function dev (line 45) | def dev():
function load_state_dict (line 54) | def load_state_dict(path, **kwargs):
function sync_params (line 77) | def sync_params(params):
function _find_free_port (line 86) | def _find_free_port():
FILE: guided_diffusion/fp16_util.py
function convert_module_to_f16 (line 15) | def convert_module_to_f16(l):
function convert_module_to_f32 (line 25) | def convert_module_to_f32(l):
function make_master_params (line 35) | def make_master_params(param_groups_and_shapes):
function model_grads_to_master_grads (line 52) | def model_grads_to_master_grads(param_groups_and_shapes, master_params):
function master_params_to_model_params (line 65) | def master_params_to_model_params(param_groups_and_shapes, master_params):
function unflatten_master_params (line 78) | def unflatten_master_params(param_group, master_param):
function get_param_groups_and_shapes (line 82) | def get_param_groups_and_shapes(named_model_params):
function master_params_to_state_dict (line 95) | def master_params_to_state_dict(
function state_dict_to_master_params (line 116) | def state_dict_to_master_params(model, state_dict, use_fp16):
function zero_master_grads (line 128) | def zero_master_grads(master_params):
function zero_grad (line 133) | def zero_grad(model_params):
function param_grad_or_zeros (line 141) | def param_grad_or_zeros(param):
class MixedPrecisionTrainer (line 148) | class MixedPrecisionTrainer:
method __init__ (line 149) | def __init__(
method zero_grad (line 173) | def zero_grad(self):
method backward (line 176) | def backward(self, loss: th.Tensor):
method optimize (line 183) | def optimize(self, opt: th.optim.Optimizer):
method _optimize_fp16 (line 189) | def _optimize_fp16(self, opt: th.optim.Optimizer):
method _optimize_normal (line 210) | def _optimize_normal(self, opt: th.optim.Optimizer):
method _compute_norms (line 217) | def _compute_norms(self, grad_scale=1.0):
method master_params_to_state_dict (line 227) | def master_params_to_state_dict(self, master_params):
method state_dict_to_master_params (line 232) | def state_dict_to_master_params(self, state_dict):
function check_overflow (line 236) | def check_overflow(value):
FILE: guided_diffusion/gaussian_diffusion.py
function get_named_beta_schedule (line 18) | def get_named_beta_schedule(schedule_name, num_diffusion_timesteps):
function betas_for_alpha_bar (line 45) | def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.9...
class ModelMeanType (line 65) | class ModelMeanType(enum.Enum):
class ModelVarType (line 75) | class ModelVarType(enum.Enum):
class LossType (line 89) | class LossType(enum.Enum):
method is_vb (line 97) | def is_vb(self):
class GaussianDiffusion (line 101) | class GaussianDiffusion:
method __init__ (line 118) | def __init__(
method q_mean_variance (line 171) | def q_mean_variance(self, x_start, t):
method q_sample (line 188) | def q_sample(self, x_start, t, noise=None):
method q_posterior_mean_variance (line 208) | def q_posterior_mean_variance(self, x_start, x_t, t):
method p_mean_variance (line 232) | def p_mean_variance(
method _predict_xstart_from_eps (line 328) | def _predict_xstart_from_eps(self, x_t, t, eps):
method _predict_xstart_from_xprev (line 335) | def _predict_xstart_from_xprev(self, x_t, t, xprev):
method _predict_eps_from_xstart (line 345) | def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
method _scale_timesteps (line 351) | def _scale_timesteps(self, t):
method condition_mean (line 356) | def condition_mean(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
method condition_score (line 371) | def condition_score(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
method p_sample (line 395) | def p_sample(
method p_sample_loop (line 441) | def p_sample_loop(
method p_sample_loop_progressive (line 487) | def p_sample_loop_progressive(
method ddim_sample (line 537) | def ddim_sample(
method ddim_reverse_sample (line 587) | def ddim_reverse_sample(
method ddim_sample_loop (line 625) | def ddim_sample_loop(
method ddim_sample_loop_progressive (line 659) | def ddim_sample_loop_progressive(
method _vb_terms_bpd (line 709) | def _vb_terms_bpd(
method training_losses (line 744) | def training_losses(self, model, x_start, t, model_kwargs=None, noise=...
method _prior_bpd (line 819) | def _prior_bpd(self, x_start):
method calc_bpd_loop (line 837) | def calc_bpd_loop(self, model, x_start, clip_denoised=True, model_kwar...
function _extract_into_tensor (line 895) | def _extract_into_tensor(arr, timesteps, broadcast_shape):
FILE: guided_diffusion/image_datasets.py
function load_data (line 11) | def load_data(
function _list_image_files_recursively (line 70) | def _list_image_files_recursively(data_dir):
class ImageDataset (line 82) | class ImageDataset(Dataset):
method __init__ (line 83) | def __init__(
method __len__ (line 100) | def __len__(self):
method __getitem__ (line 103) | def __getitem__(self, idx):
function center_crop_arr (line 126) | def center_crop_arr(pil_image, image_size):
function random_crop_arr (line 146) | def random_crop_arr(pil_image, image_size, min_crop_frac=0.8, max_crop_f...
FILE: guided_diffusion/logger.py
class KVWriter (line 26) | class KVWriter(object):
method writekvs (line 27) | def writekvs(self, kvs):
class SeqWriter (line 31) | class SeqWriter(object):
method writeseq (line 32) | def writeseq(self, seq):
class HumanOutputFormat (line 36) | class HumanOutputFormat(KVWriter, SeqWriter):
method __init__ (line 37) | def __init__(self, filename_or_file):
method writekvs (line 48) | def writekvs(self, kvs):
method _truncate (line 80) | def _truncate(self, s):
method writeseq (line 84) | def writeseq(self, seq):
method close (line 93) | def close(self):
class JSONOutputFormat (line 98) | class JSONOutputFormat(KVWriter):
method __init__ (line 99) | def __init__(self, filename):
method writekvs (line 102) | def writekvs(self, kvs):
method close (line 109) | def close(self):
class CSVOutputFormat (line 113) | class CSVOutputFormat(KVWriter):
method __init__ (line 114) | def __init__(self, filename):
method writekvs (line 119) | def writekvs(self, kvs):
method close (line 146) | def close(self):
class TensorBoardOutputFormat (line 150) | class TensorBoardOutputFormat(KVWriter):
method __init__ (line 155) | def __init__(self, dir):
method writekvs (line 171) | def writekvs(self, kvs):
method close (line 185) | def close(self):
function make_output_format (line 191) | def make_output_format(format, ev_dir, log_suffix=""):
function logkv (line 212) | def logkv(key, val):
function logkv_mean (line 221) | def logkv_mean(key, val):
function logkvs (line 228) | def logkvs(d):
function dumpkvs (line 236) | def dumpkvs():
function getkvs (line 243) | def getkvs():
function log (line 247) | def log(*args, level=INFO):
function debug (line 254) | def debug(*args):
function info (line 258) | def info(*args):
function warn (line 262) | def warn(*args):
function error (line 266) | def error(*args):
function set_level (line 270) | def set_level(level):
function set_comm (line 277) | def set_comm(comm):
function get_dir (line 281) | def get_dir():
function profile_kv (line 294) | def profile_kv(scopename):
function profile (line 303) | def profile(n):
function get_current (line 325) | def get_current():
class Logger (line 332) | class Logger(object):
method __init__ (line 337) | def __init__(self, dir, output_formats, comm=None):
method logkv (line 347) | def logkv(self, key, val):
method logkv_mean (line 350) | def logkv_mean(self, key, val):
method dumpkvs (line 355) | def dumpkvs(self):
method log (line 376) | def log(self, *args, level=INFO):
method set_level (line 382) | def set_level(self, level):
method set_comm (line 385) | def set_comm(self, comm):
method get_dir (line 388) | def get_dir(self):
method close (line 391) | def close(self):
method _do_log (line 397) | def _do_log(self, args):
function get_rank_without_mpi_import (line 403) | def get_rank_without_mpi_import():
function mpi_weighted_mean (line 412) | def mpi_weighted_mean(comm, local_name2valcount):
function configure (line 442) | def configure(dir=None, format_strs=None, comm=None, log_suffix=""):
function _configure_default_logger (line 474) | def _configure_default_logger():
function reset (line 479) | def reset():
function scoped_configure (line 487) | def scoped_configure(dir=None, format_strs=None, comm=None):
FILE: guided_diffusion/losses.py
function normal_kl (line 12) | def normal_kl(mean1, logvar1, mean2, logvar2):
function approx_standard_normal_cdf (line 42) | def approx_standard_normal_cdf(x):
function discretized_gaussian_log_likelihood (line 50) | def discretized_gaussian_log_likelihood(x, *, means, log_scales):
FILE: guided_diffusion/nn.py
class SiLU (line 12) | class SiLU(nn.Module):
method forward (line 13) | def forward(self, x):
class GroupNorm32 (line 17) | class GroupNorm32(nn.GroupNorm):
method forward (line 18) | def forward(self, x):
function conv_nd (line 22) | def conv_nd(dims, *args, **kwargs):
function linear (line 35) | def linear(*args, **kwargs):
function avg_pool_nd (line 42) | def avg_pool_nd(dims, *args, **kwargs):
function update_ema (line 55) | def update_ema(target_params, source_params, rate=0.99):
function zero_module (line 68) | def zero_module(module):
function scale_module (line 77) | def scale_module(module, scale):
function mean_flat (line 86) | def mean_flat(tensor):
function normalization (line 93) | def normalization(channels):
function timestep_embedding (line 103) | def timestep_embedding(timesteps, dim, max_period=10000):
function checkpoint (line 124) | def checkpoint(func, inputs, params, flag):
class CheckpointFunction (line 142) | class CheckpointFunction(th.autograd.Function):
method forward (line 144) | def forward(ctx, run_function, length, *args):
method backward (line 153) | def backward(ctx, *output_grads):
FILE: guided_diffusion/resample.py
function create_named_schedule_sampler (line 8) | def create_named_schedule_sampler(name, diffusion):
class ScheduleSampler (line 23) | class ScheduleSampler(ABC):
method weights (line 35) | def weights(self):
method sample (line 42) | def sample(self, batch_size, device):
class UniformSampler (line 61) | class UniformSampler(ScheduleSampler):
method __init__ (line 62) | def __init__(self, diffusion):
method weights (line 66) | def weights(self):
class LossAwareSampler (line 70) | class LossAwareSampler(ScheduleSampler):
method update_with_local_losses (line 71) | def update_with_local_losses(self, local_ts, local_losses):
method update_with_all_losses (line 107) | def update_with_all_losses(self, ts, losses):
class LossSecondMomentResampler (line 124) | class LossSecondMomentResampler(LossAwareSampler):
method __init__ (line 125) | def __init__(self, diffusion, history_per_term=10, uniform_prob=0.001):
method weights (line 134) | def weights(self):
method update_with_all_losses (line 143) | def update_with_all_losses(self, ts, losses):
method _warmed_up (line 153) | def _warmed_up(self):
FILE: guided_diffusion/respace.py
function space_timesteps (line 7) | def space_timesteps(num_timesteps, section_counts):
class SpacedDiffusion (line 63) | class SpacedDiffusion(GaussianDiffusion):
method __init__ (line 72) | def __init__(self, use_timesteps, **kwargs):
method p_mean_variance (line 88) | def p_mean_variance(
method training_losses (line 93) | def training_losses(
method condition_mean (line 98) | def condition_mean(self, cond_fn, *args, **kwargs):
method condition_score (line 101) | def condition_score(self, cond_fn, *args, **kwargs):
method _wrap_model (line 104) | def _wrap_model(self, model):
method _scale_timesteps (line 111) | def _scale_timesteps(self, t):
class _WrappedModel (line 116) | class _WrappedModel:
method __init__ (line 117) | def __init__(self, model, timestep_map, rescale_timesteps, original_nu...
method __call__ (line 123) | def __call__(self, x, ts, **kwargs):
FILE: guided_diffusion/script_util.py
function diffusion_defaults (line 11) | def diffusion_defaults():
function classifier_defaults (line 27) | def classifier_defaults():
function model_and_diffusion_defaults (line 43) | def model_and_diffusion_defaults():
function classifier_and_diffusion_defaults (line 68) | def classifier_and_diffusion_defaults():
function create_model_and_diffusion (line 74) | def create_model_and_diffusion(
function create_model (line 130) | def create_model(
function create_classifier_and_diffusion (line 187) | def create_classifier_and_diffusion(
function create_classifier (line 228) | def create_classifier(
function sr_model_and_diffusion_defaults (line 269) | def sr_model_and_diffusion_defaults():
function sr_create_model_and_diffusion (line 280) | def sr_create_model_and_diffusion(
function sr_create_model (line 334) | def sr_create_model(
function create_gaussian_diffusion (line 386) | def create_gaussian_diffusion(
function add_dict_to_argparser (line 427) | def add_dict_to_argparser(parser, default_dict):
function args_to_dict (line 437) | def args_to_dict(args, keys):
function str2bool (line 441) | def str2bool(v):
FILE: guided_diffusion/train_util.py
class TrainLoop (line 22) | class TrainLoop:
method __init__ (line 23) | def __init__(
method _load_and_sync_parameters (line 110) | def _load_and_sync_parameters(self):
method _load_ema_parameters (line 125) | def _load_ema_parameters(self, rate):
method _load_optimizer_state (line 141) | def _load_optimizer_state(self):
method run_loop (line 153) | def run_loop(self):
method run_step (line 172) | def run_step(self, batch, cond):
method forward_backward (line 180) | def forward_backward(self, batch, cond):
method _update_ema (line 216) | def _update_ema(self):
method _anneal_lr (line 220) | def _anneal_lr(self):
method log_step (line 228) | def log_step(self):
method save (line 232) | def save(self):
function parse_resume_step_from_filename (line 258) | def parse_resume_step_from_filename(filename):
function get_blob_logdir (line 273) | def get_blob_logdir():
function find_resume_checkpoint (line 279) | def find_resume_checkpoint():
function find_ema_checkpoint (line 285) | def find_ema_checkpoint(main_checkpoint, step, rate):
function log_loss_dict (line 295) | def log_loss_dict(diffusion, ts, losses):
FILE: guided_diffusion/unet.py
class AttentionPool2d (line 22) | class AttentionPool2d(nn.Module):
method __init__ (line 27) | def __init__(
method forward (line 43) | def forward(self, x):
class TimestepBlock (line 54) | class TimestepBlock(nn.Module):
method forward (line 60) | def forward(self, x, emb):
class TimestepEmbedSequential (line 66) | class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
method forward (line 72) | def forward(self, x, emb):
class Upsample (line 81) | class Upsample(nn.Module):
method __init__ (line 91) | def __init__(self, channels, use_conv, dims=2, out_channels=None):
method forward (line 100) | def forward(self, x):
class Downsample (line 113) | class Downsample(nn.Module):
method __init__ (line 123) | def __init__(self, channels, use_conv, dims=2, out_channels=None):
method forward (line 138) | def forward(self, x):
class ResBlock (line 143) | class ResBlock(TimestepBlock):
method __init__ (line 160) | def __init__(
method forward (line 224) | def forward(self, x, emb):
method _forward (line 236) | def _forward(self, x, emb):
class AttentionBlock (line 259) | class AttentionBlock(nn.Module):
method __init__ (line 267) | def __init__(
method forward (line 296) | def forward(self, x):
method _forward (line 299) | def _forward(self, x):
function count_flops_attn (line 308) | def count_flops_attn(model, _x, y):
class QKVAttentionLegacy (line 328) | class QKVAttentionLegacy(nn.Module):
method __init__ (line 333) | def __init__(self, n_heads):
method forward (line 337) | def forward(self, qkv):
method count_flops (line 357) | def count_flops(model, _x, y):
class QKVAttention (line 361) | class QKVAttention(nn.Module):
method __init__ (line 366) | def __init__(self, n_heads):
method forward (line 370) | def forward(self, qkv):
method count_flops (line 392) | def count_flops(model, _x, y):
class UNetModel (line 396) | class UNetModel(nn.Module):
method __init__ (line 427) | def __init__(
method convert_to_fp16 (line 618) | def convert_to_fp16(self):
method convert_to_fp32 (line 626) | def convert_to_fp32(self):
method forward (line 634) | def forward(self, x, timesteps, y=None):
class SuperResModel (line 666) | class SuperResModel(UNetModel):
method __init__ (line 673) | def __init__(self, image_size, in_channels, *args, **kwargs):
method forward (line 676) | def forward(self, x, timesteps, low_res=None, **kwargs):
class EncoderUNetModel (line 683) | class EncoderUNetModel(nn.Module):
method __init__ (line 690) | def __init__(
method convert_to_fp16 (line 857) | def convert_to_fp16(self):
method convert_to_fp32 (line 864) | def convert_to_fp32(self):
method forward (line 871) | def forward(self, x, timesteps):
FILE: scripts/classifier_sample.py
function main (line 26) | def main():
function create_argparser (line 113) | def create_argparser():
FILE: scripts/classifier_train.py
function main (line 28) | def main():
function set_annealed_lr (line 170) | def set_annealed_lr(opt, base_lr, frac_done):
function save_model (line 176) | def save_model(mp_trainer, opt, step):
function compute_top_k (line 185) | def compute_top_k(logits, labels, k, reduction="mean"):
function split_microbatches (line 193) | def split_microbatches(microbatch, *args):
function create_argparser (line 202) | def create_argparser():
FILE: scripts/image_nll.py
function main (line 21) | def main():
function run_bpd_evaluation (line 50) | def run_bpd_evaluation(model, diffusion, data, num_samples, clip_denoised):
function create_argparser (line 85) | def create_argparser():
FILE: scripts/image_sample.py
function main (line 23) | def main():
function create_argparser (line 93) | def create_argparser():
FILE: scripts/image_train.py
function main (line 19) | def main():
function create_argparser (line 60) | def create_argparser():
FILE: scripts/super_res_sample.py
function main (line 23) | def main():
function load_data_for_worker (line 77) | def load_data_for_worker(base_samples, batch_size, class_cond):
function create_argparser (line 103) | def create_argparser():
FILE: scripts/super_res_train.py
function main (line 21) | def main():
function load_superres_data (line 63) | def load_superres_data(data_dir, batch_size, large_size, small_size, cla...
function create_argparser (line 75) | def create_argparser():
Condensed preview — 30 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (226K chars).
[
{
"path": ".gitignore",
"chars": 51,
"preview": ".DS_Store\n__pycache__/\nclassify_image_graph_def.pb\n"
},
{
"path": "LICENSE",
"chars": 1062,
"preview": "MIT License\n\nCopyright (c) 2021 OpenAI\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof "
},
{
"path": "README.md",
"chars": 10972,
"preview": "# guided-diffusion\n\nThis is the codebase for [Diffusion Models Beat GANS on Image Synthesis](http://arxiv.org/abs/2105.0"
},
{
"path": "datasets/README.md",
"chars": 1950,
"preview": "# Downloading datasets\n\nThis directory includes instructions and scripts for downloading ImageNet and LSUN bedrooms for "
},
{
"path": "datasets/lsun_bedroom.py",
"chars": 1722,
"preview": "\"\"\"\nConvert an LSUN lmdb database into a directory of images.\n\"\"\"\n\nimport argparse\nimport io\nimport os\n\nfrom PIL import "
},
{
"path": "evaluations/README.md",
"chars": 6719,
"preview": "# Evaluations\n\nTo compare different generative models, we use FID, sFID, Precision, Recall, and Inception Score. These m"
},
{
"path": "evaluations/evaluator.py",
"chars": 25057,
"preview": "import argparse\nimport io\nimport os\nimport random\nimport warnings\nimport zipfile\nfrom abc import ABC, abstractmethod\nfro"
},
{
"path": "evaluations/requirements.txt",
"chars": 39,
"preview": "tensorflow-gpu>=2.0\nscipy\nrequests\ntqdm"
},
{
"path": "guided_diffusion/__init__.py",
"chars": 74,
"preview": "\"\"\"\nCodebase for \"Improved Denoising Diffusion Probabilistic Models\".\n\"\"\"\n"
},
{
"path": "guided_diffusion/dist_util.py",
"chars": 2424,
"preview": "\"\"\"\nHelpers for distributed training.\n\"\"\"\n\nimport io\nimport os\nimport socket\n\nimport blobfile as bf\nfrom mpi4py import M"
},
{
"path": "guided_diffusion/fp16_util.py",
"chars": 7962,
"preview": "\"\"\"\nHelpers to train with 16-bit precision.\n\"\"\"\n\nimport numpy as np\nimport torch as th\nimport torch.nn as nn\nfrom torch."
},
{
"path": "guided_diffusion/gaussian_diffusion.py",
"chars": 34335,
"preview": "\"\"\"\nThis code started out as a PyTorch port of Ho et al's diffusion models:\nhttps://github.com/hojonathanho/diffusion/bl"
},
{
"path": "guided_diffusion/image_datasets.py",
"chars": 5930,
"preview": "import math\nimport random\n\nfrom PIL import Image\nimport blobfile as bf\nfrom mpi4py import MPI\nimport numpy as np\nfrom to"
},
{
"path": "guided_diffusion/logger.py",
"chars": 13979,
"preview": "\"\"\"\nLogger copied from OpenAI baselines to avoid extra RL-based dependencies:\nhttps://github.com/openai/baselines/blob/e"
},
{
"path": "guided_diffusion/losses.py",
"chars": 2534,
"preview": "\"\"\"\nHelpers for various likelihood-based losses. These are ported from the original\nHo et al. diffusion models codebase:"
},
{
"path": "guided_diffusion/nn.py",
"chars": 5020,
"preview": "\"\"\"\nVarious utilities for neural networks.\n\"\"\"\n\nimport math\n\nimport torch as th\nimport torch.nn as nn\n\n\n# PyTorch 1.7 ha"
},
{
"path": "guided_diffusion/resample.py",
"chars": 5689,
"preview": "from abc import ABC, abstractmethod\n\nimport numpy as np\nimport torch as th\nimport torch.distributed as dist\n\n\ndef create"
},
{
"path": "guided_diffusion/respace.py",
"chars": 5193,
"preview": "import numpy as np\nimport torch as th\n\nfrom .gaussian_diffusion import GaussianDiffusion\n\n\ndef space_timesteps(num_times"
},
{
"path": "guided_diffusion/script_util.py",
"chars": 12349,
"preview": "import argparse\nimport inspect\n\nfrom . import gaussian_diffusion as gd\nfrom .respace import SpacedDiffusion, space_times"
},
{
"path": "guided_diffusion/train_util.py",
"chars": 10604,
"preview": "import copy\nimport functools\nimport os\n\nimport blobfile as bf\nimport torch as th\nimport torch.distributed as dist\nfrom t"
},
{
"path": "guided_diffusion/unet.py",
"chars": 31233,
"preview": "from abc import abstractmethod\n\nimport math\n\nimport numpy as np\nimport torch as th\nimport torch.nn as nn\nimport torch.nn"
},
{
"path": "model-card.md",
"chars": 5026,
"preview": "# Overview\n\nThese are diffusion models and noised image classifiers described in the paper [Diffusion Models Beat GANs o"
},
{
"path": "scripts/classifier_sample.py",
"chars": 4266,
"preview": "\"\"\"\nLike image_sample.py, but use a noisy image classifier to guide the sampling\nprocess towards more realistic images.\n"
},
{
"path": "scripts/classifier_train.py",
"chars": 7313,
"preview": "\"\"\"\nTrain a noised image classifier on ImageNet.\n\"\"\"\n\nimport argparse\nimport os\n\nimport blobfile as bf\nimport torch as t"
},
{
"path": "scripts/image_nll.py",
"chars": 2934,
"preview": "\"\"\"\nApproximate the bits/dimension for an image model.\n\"\"\"\n\nimport argparse\nimport os\n\nimport numpy as np\nimport torch.d"
},
{
"path": "scripts/image_sample.py",
"chars": 3398,
"preview": "\"\"\"\nGenerate a large batch of image samples from a model and save them as a large\nnumpy array. This can be used to produ"
},
{
"path": "scripts/image_train.py",
"chars": 2298,
"preview": "\"\"\"\nTrain a diffusion model on images.\n\"\"\"\n\nimport argparse\n\nfrom guided_diffusion import dist_util, logger\nfrom guided_"
},
{
"path": "scripts/super_res_sample.py",
"chars": 3725,
"preview": "\"\"\"\nGenerate a large batch of samples from a super resolution model, given a batch\nof samples from a regular model from "
},
{
"path": "scripts/super_res_train.py",
"chars": 2695,
"preview": "\"\"\"\nTrain a super-resolution model.\n\"\"\"\n\nimport argparse\n\nimport torch.nn.functional as F\n\nfrom guided_diffusion import "
},
{
"path": "setup.py",
"chars": 164,
"preview": "from setuptools import setup\n\nsetup(\n name=\"guided-diffusion\",\n py_modules=[\"guided_diffusion\"],\n install_requi"
}
]
About this extraction
This page contains the full source code of the openai/guided-diffusion GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 30 files (211.6 KB), approximately 52.2k tokens, and a symbol index with 318 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.