Merge remote-tracking branch 'base/main' into palp/model-tests

.github/workflows/black.yml

@@ -0,0 +1,15 @@
+name: Run black
+on: [push, pull_request]
+
+jobs:
+ lint:
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v3
+ - name: Install venv
+ run: |
+ sudo apt-get -y install python3.10-venv
+ - uses: psf/black@stable
+ with:
+ options: "--check --verbose -l88"
+ src: "./sgm ./scripts ./main.py"

.github/workflows/test-build.yaml

@@ -0,0 +1,26 @@
+name: Build package
+
+on:
+ push:
+ pull_request:
+
+jobs:
+ build:
+ name: Build
+ runs-on: ubuntu-latest
+ strategy:
+ fail-fast: false
+ matrix:
+ python-version: ["3.8", "3.10"]
+ requirements-file: ["pt2", "pt13"]
+ steps:
+ - uses: actions/checkout@v2
+ - name: Set up Python ${{ matrix.python-version }}
+ uses: actions/setup-python@v2
+ with:
+ python-version: ${{ matrix.python-version }}
+ - name: Install dependencies
+ run: |
+ python -m pip install --upgrade pip
+ pip install -r requirements/${{ matrix.requirements-file }}.txt
+ pip install .

.gitignore

@@ -1,9 +1,14 @@
+# extensions
 *.egg-info
 *.py[cod]
+
+# envs
 .pt13
 .pt2
-.pt2_2
+
+# directories
 /checkpoints
 /dist
 /outputs
-build
+/build
+/src

README.md

@@ -59,10 +59,9 @@ This is assuming you have navigated to the `generative-models` root after clonin
 
 ```shell
 # install required packages from pypi
-python3 -m venv .pt1
-source .pt1/bin/activate
-pip3 install wheel
-pip3 install -r requirements_pt13.txt
+python3 -m venv .pt13
+source .pt13/bin/activate
+pip3 install -r requirements/pt13.txt
 ```
 
 **PyTorch 2.0** 
@@ -72,8 +71,20 @@ pip3 install -r requirements_pt13.txt
 # install required packages from pypi
 python3 -m venv .pt2
 source .pt2/bin/activate
-pip3 install wheel
-pip3 install -r requirements_pt2.txt
+pip3 install -r requirements/pt2.txt
+```
+
+
+#### 3. Install `sgm`
+
+```shell
+pip3 install .
+```
+
+#### 4. Install `sdata` for training
+
+```shell
+pip3 install -e git+https://github.com/Stability-AI/datapipelines.git@main#egg=sdata
 ```
 
 ## Packaging

main.py

@@ -12,22 +12,18 @@
 import torch
 import torchvision
 import wandb
-from PIL import Image
 from matplotlib import pyplot as plt
 from natsort import natsorted
 from omegaconf import OmegaConf
 from packaging import version
+from PIL import Image
 from pytorch_lightning import seed_everything
 from pytorch_lightning.callbacks import Callback
 from pytorch_lightning.loggers import WandbLogger
 from pytorch_lightning.trainer import Trainer
 from pytorch_lightning.utilities import rank_zero_only
 
-from sgm.util import (
- exists,
- instantiate_from_config,
- isheatmap,
-)
+from sgm.util import exists, instantiate_from_config, isheatmap
 
 MULTINODE_HACKS = True
 
@@ -910,11 +906,12 @@ def divein(*args, **kwargs):
  trainer.test(model, data)
  except RuntimeError as err:
  if MULTINODE_HACKS:
- import requests
  import datetime
  import os
  import socket
 
+ import requests
+
  device = os.environ.get("CUDA_VISIBLE_DEVICES", "?")
  hostname = socket.gethostname()
  ts = datetime.datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S")

requirements/pt13.txt

@@ -0,0 +1,40 @@
+black==23.7.0
+chardet>=5.1.0
+clip @ git+https://github.com/openai/CLIP.git
+einops>=0.6.1
+fairscale>=0.4.13
+fire>=0.5.0
+fsspec>=2023.6.0
+invisible-watermark>=0.2.0
+kornia==0.6.9
+matplotlib>=3.7.2
+natsort>=8.4.0
+numpy>=1.24.4
+omegaconf>=2.3.0
+onnx<=1.12.0
+open-clip-torch>=2.20.0
+opencv-python==4.6.0.66
+pandas>=2.0.3
+pillow>=9.5.0
+pudb>=2022.1.3
+pytorch-lightning==1.8.5
+pyyaml>=6.0.1
+scipy>=1.10.1
+streamlit>=1.25.0
+tensorboardx==2.5.1
+timm>=0.9.2
+tokenizers==0.12.1
+--extra-index-url https://download.pytorch.org/whl/cu117
+torch==1.13.1+cu117
+torchaudio==0.13.1
+torchdata==0.5.1
+torchmetrics>=1.0.1
+torchvision==0.14.1+cu117
+tqdm>=4.65.0
+transformers==4.19.1
+triton==2.0.0.post1
+urllib3<1.27,>=1.25.4
+wandb>=0.15.6
+webdataset>=0.2.33
+wheel>=0.41.0
+xformers==0.0.16

requirements/pt2.txt

@@ -0,0 +1,39 @@
+black==23.7.0
+chardet==5.1.0
+clip @ git+https://github.com/openai/CLIP.git
+einops>=0.6.1
+fairscale>=0.4.13
+fire>=0.5.0
+fsspec>=2023.6.0
+invisible-watermark>=0.2.0
+kornia==0.6.9
+matplotlib>=3.7.2
+natsort>=8.4.0
+ninja>=1.11.1
+numpy>=1.24.4
+omegaconf>=2.3.0
+open-clip-torch>=2.20.0
+opencv-python==4.6.0.66
+pandas>=2.0.3
+pillow>=9.5.0
+pudb>=2022.1.3
+pytorch-lightning==2.0.1
+pyyaml>=6.0.1
+scipy>=1.10.1
+streamlit>=0.73.1
+tensorboardx==2.6
+timm>=0.9.2
+tokenizers==0.12.1
+torch>=2.0.1
+torchaudio>=2.0.2
+torchdata==0.6.1
+torchmetrics>=1.0.1
+torchvision>=0.15.2
+tqdm>=4.65.0
+transformers==4.19.1
+triton==2.0.0
+urllib3<1.27,>=1.25.4
+wandb>=0.15.6
+webdataset>=0.2.33
+wheel>=0.41.0
+xformers>=0.0.20

scripts/demo/sampling.py

@@ -1,5 +1,6 @@
 import numpy as np
 from pytorch_lightning import seed_everything
+
 from scripts.demo.streamlit_helpers import *
 from scripts.util.detection.nsfw_and_watermark_dectection import DeepFloydDataFiltering
 from sgm.inference.helpers import (

scripts/demo/streamlit_helpers.py

@@ -8,11 +8,11 @@
 
 
 from sgm.modules.diffusionmodules.sampling import (
+ DPMPP2MSampler,
+ DPMPP2SAncestralSampler,
+ EulerAncestralSampler,
  EulerEDMSampler,
  HeunEDMSampler,
- EulerAncestralSampler,
- DPMPP2SAncestralSampler,
- DPMPP2MSampler,
  LinearMultistepSampler,
 )
 from sgm.inference.helpers import Img2ImgDiscretizationWrapper, embed_watermark

scripts/util/detection/nsfw_and_watermark_dectection.py

@@ -1,9 +1,10 @@
 import os
-import torch
+
+import clip
 import numpy as np
+import torch
 import torchvision.transforms as T
 from PIL import Image
-import clip
 
 RESOURCES_ROOT = "scripts/util/detection/"
 

sgm/__init__.py

@@ -1,5 +1,4 @@
-from .data import StableDataModuleFromConfig
 from .models import AutoencodingEngine, DiffusionEngine
-from .util import instantiate_from_config, get_configs_path
+from .util import get_configs_path, instantiate_from_config
 
-__version__ = "0.0.1"
+__version__ = "0.1.0"

sgm/data/cifar10.py

@@ -1,7 +1,7 @@
-import torchvision
 import pytorch_lightning as pl
-from torchvision import transforms
+import torchvision
 from torch.utils.data import DataLoader, Dataset
+from torchvision import transforms
 
 
 class CIFAR10DataDictWrapper(Dataset):

sgm/data/mnist.py

@@ -1,7 +1,7 @@
-import torchvision
 import pytorch_lightning as pl
-from torchvision import transforms
+import torchvision
 from torch.utils.data import DataLoader, Dataset
+from torchvision import transforms
 
 
 class MNISTDataDictWrapper(Dataset):

sgm/modules/autoencoding/losses/__init__.py

@@ -3,11 +3,11 @@
 import torch
 import torch.nn as nn
 from einops import rearrange
-from taming.modules.discriminator.model import NLayerDiscriminator, weights_init
-from taming.modules.losses.lpips import LPIPS
-from taming.modules.losses.vqperceptual import hinge_d_loss, vanilla_d_loss
 
 from ....util import default, instantiate_from_config
+from ..lpips.loss.lpips import LPIPS
+from ..lpips.model.model import NLayerDiscriminator, weights_init
+from ..lpips.vqperceptual import hinge_d_loss, vanilla_d_loss
 
 
 def adopt_weight(weight, global_step, threshold=0, value=0.0):

sgm/modules/autoencoding/lpips/loss/.gitignore

@@ -0,0 +1 @@
+vgg.pth