EDM Diffusion Models

CHANGELOG.md

@@ -15,6 +15,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Added distributed FFT utility.
 - Added ruff as a linting tool.
 - Ported utilities from Modulus Launch to main package.
+- EDM diffusion models and recipes for training and sampling.
 
 ### Changed
 

examples/generative/diffusion/conf/config.yaml

@@ -0,0 +1,96 @@
+# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+hydra:
+  job:
+    chdir: True
+  run:
+    dir: ./outputs/
+
+
+# Main options
+outdir: ./results       # Where to save the results
+data: ./data            # Path to the dataset
+cond: true              # Train class-conditional model
+arch: ddpmpp            # Network architecture
+precond: edm            # Preconditioning & loss function
+dataset: 'cifar10'
+
+# Hyperparameters
+duration: 200           # Training duration
+batch: 128              # Total batch size
+batch_gpu: null         # Limit batch size per GPU
+cbase: null             # Channel multiplier
+cres: null              # Channels per resolution
+lr: 10e-4               # Learning rate
+ema: 0.5                # EMA half-life
+dropout: 0.13           # Dropout probability
+augment: null           # Augment probability
+xflip: false            # Enable dataset x-flips
+
+# Performance-related
+fp16: false             # Enable mixed-precision training
+ls: 1.0                 # Loss scaling
+bench: true             # enable cuDNN benchmarking
+cache: true             # Cache dataset in CPU memory
+workers: 1              # DataLoader worker processes
+fused_adam: false       # Whether to use fused Adam optimizer
+
+# I/O-related
+desc: null              # String to include in result dir name
+nosubdir: false         # If True, do not create a subdirectory for results
+tick: 50                # How often to print progress
+snap: 50                # How often to save snapshots
+dump: 500               # How often to dump state
+seed: null              # Random seed
+transfer: null          # Transfer learning from network pickle
+resume: null            # Resume from previous training state
+dry_run: false          # Print training options and exit
+
+# Generation-related
+ckpt_filename: checkpoint     # Checkpoint filename to be used for generation
+img_outdir: results_images    # Where to save the output images
+gen_seeds: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
+  19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,
+  39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58,
+  59, 60, 61, 62]             # Random seeds used for generation
+subdirs: true                 # Create subdirectory for every 1000 seeds
+class_idx: null               # Class label. Null is random
+max_batch_size: 64            # maximum batch size
+num_steps: 18                 # Number of sampling steps
+sigma_min: null               # Lowest noise level
+sigma_max: null               # Highest noise level
+rho: 7                        # Time step exponent
+s_churn: 0.                   # Stochasticity strength
+s_min: 0.                     # Stochasticity min noise level
+s_max: .inf                   # Stochasticity max noise level
+s_noise: 1.                   # Stochasticity noise inflation
+solver: heun                  # ODE solver [euler, heun]
+discretization: edm           # Time step discretization [vp, ve, iddpm, edm]
+schedule: linear              # noise schedule sigma(t) [vp, ve, linear]
+scaling: null                 # Signal scaling s(t) [vp, none]
+
+
+
+# # Weather-related
+# data_config: ?          # String to include the data config
+# task: ?                 # String to include the task
+# data_type: ?            # String to include the data type
+
+# # Regression
+# ckpt_unet: ?            # Checkpoint for the UNet to predict the mean
+
+
+
+

examples/generative/diffusion/conf/config_fid.yaml

@@ -0,0 +1,42 @@
+# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+hydra:
+  job:
+    chdir: True
+  run:
+    dir: ./outputs/
+
+# Main options 
+mode: calc                # calc: calculate FID for a given set of images
+                          # ref: Calculate dataset reference statistics needed by 'calc'
+
+ # FID options
+image_path: ./images      # Path to the images
+ref_path: ./ref           # Dataset reference statistics
+num_expected: 50000       # Number of images to use
+seed: 0                   # Random seed for selecting the images
+batch: 64                 # Maximum batch size
+
+# Reference statistics options
+dataset_path: ./data      # Path to the dataset
+dest_path: ./dest.npz     # Destination .npz file
+batch: 64                 # Maximum batch size
+
+
+
+
+
+
+

examples/generative/diffusion/dataset/__init__.py

@@ -0,0 +1,17 @@
+# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.anguage governing permissions and
+# limitations under the License.
+
+
+from .dataset import ImageFolderDataset