add convert_image_to keyword argument, for forcing images being loaded to be converted to some format, greyscale, rgb, rgba, whatever

README.md

@@ -80,6 +80,22 @@ trainer.train()
 
 Samples and model checkpoints will be logged to `./results` periodically
 
+## Multi-GPU Training
+
+The `Trainer` class is now equipped with <a href="https://huggingface.co/docs/accelerate/accelerator">🤗 Accelerator</a>. You can easily do multi-gpu training in two steps using their `accelerate` CLI
+
+At the project root directory, where the training script is, run
+
+```python
+$ accelerate config
+```
+
+Then, in the same directory
+
+```python
+$ accelerate launch train.py
+```
+
 ## Citations
 
 ```bibtex

denoising_diffusion_pytorch/denoising_diffusion_pytorch.py

@@ -6,25 +6,21 @@ import torch.nn.functional as F
 from inspect import isfunction
 from functools import partial
 
-from torch.utils import data
+from torch.utils.data import Dataset, DataLoader
 from multiprocessing import cpu_count
-from torch.cuda.amp import autocast, GradScaler
 
 from pathlib import Path
 from torch.optim import Adam
-from torchvision import transforms, utils
+from torchvision import transforms as T, utils
 from PIL import Image
 
 from einops import rearrange, reduce
 from einops.layers.torch import Rearrange
 
+from tqdm.auto import tqdm
 from ema_pytorch import EMA
 
-import sys
+from accelerate import Accelerator
-if 'ipykernel' in sys.modules:
-    from tqdm.notebook import tqdm
-else:
-    from tqdm import tqdm
 
 # helpers functions
 
@@ -41,6 +37,9 @@ def cycle(dl):
         for data in dl:
             yield data
 
+def has_int_squareroot(num):
+    return (math.sqrt(num) ** 2) == num
+
 def num_to_groups(num, divisor):
     groups = num // divisor
     remainder = num % divisor
@@ -49,6 +48,13 @@ def num_to_groups(num, divisor):
         arr.append(remainder)
     return arr
 
+def convert_image_to(img_type, image):
+    if image.mode != img_type:
+        return image.convert(img_type)
+    return image
+
+# normalization functions
+
 def normalize_to_neg_one_to_one(img):
     return img * 2 - 1
 
@@ -567,18 +573,28 @@ class GaussianDiffusion(nn.Module):
 
 # dataset classes
 
-class Dataset(data.Dataset):
+class Dataset(Dataset):
-    def __init__(self, folder, image_size, exts = ['jpg', 'jpeg', 'png'], augment_horizontal_flip = False):
+    def __init__(
+        self,
+        folder,
+        image_size,
+        exts = ['jpg', 'jpeg', 'png', 'tiff'],
+        augment_horizontal_flip = False,
+        convert_image_to = None
+    ):
         super().__init__()
         self.folder = folder
         self.image_size = image_size
         self.paths = [p for ext in exts for p in Path(f'{folder}').glob(f'**/*.{ext}')]
 
-        self.transform = transforms.Compose([
+        maybe_convert_fn = partial(convert_image_to, convert_image_to) if exists(convert_image_to) else nn.Identity()
-            transforms.Resize(image_size),
+
-            transforms.RandomHorizontalFlip() if augment_horizontal_flip else nn.Identity(),
+        self.transform = T.Compose([
-            transforms.CenterCrop(image_size),
+            T.Lambda(maybe_convert_fn),
-            transforms.ToTensor()
+            T.Resize(image_size),
+            T.RandomHorizontalFlip() if augment_horizontal_flip else nn.Identity(),
+            T.CenterCrop(image_size),
+            T.ToTensor()
         ])
 
     def __len__(self):
@@ -597,92 +613,135 @@ class Trainer(object):
         diffusion_model,
         folder,
         *,
-        ema_decay = 0.995,
+        train_batch_size = 16,
-        train_batch_size = 32,
+        gradient_accumulate_every = 1,
+        augment_horizontal_flip = True,
         train_lr = 1e-4,
         train_num_steps = 100000,
-        gradient_accumulate_every = 2,
-        amp = False,
-        step_start_ema = 2000,
         ema_update_every = 10,
+        ema_decay = 0.995,
         save_and_sample_every = 1000,
+        num_samples = 25,
         results_folder = './results',
-        augment_horizontal_flip = True
+        amp = False,
+        fp16 = False,
+        split_batches = True,
+        convert_image_to = None
     ):
         super().__init__()
-        self.image_size = diffusion_model.image_size
+
+        self.accelerator = Accelerator(
+            split_batches = split_batches,
+            mixed_precision = 'fp16' if fp16 else 'no'
+        )
+
+        self.accelerator.native_amp = amp
 
         self.model = diffusion_model
-        self.ema = EMA(diffusion_model, beta = ema_decay, update_every = ema_update_every)
 
-        self.step_start_ema = step_start_ema
+        assert has_int_squareroot(num_samples), 'number of samples must have an integer square root'
+        self.num_samples = num_samples
         self.save_and_sample_every = save_and_sample_every
 
         self.batch_size = train_batch_size
-        self.image_size = diffusion_model.image_size
         self.gradient_accumulate_every = gradient_accumulate_every
-        self.train_num_steps = train_num_steps
 
-        self.ds = Dataset(folder, self.image_size, augment_horizontal_flip = augment_horizontal_flip)
+        self.train_num_steps = train_num_steps
-        self.dl = cycle(data.DataLoader(self.ds, batch_size = train_batch_size, shuffle = True, pin_memory = True, num_workers = cpu_count()))
+        self.image_size = diffusion_model.image_size
+
+        # dataset and dataloader
+
+        self.ds = Dataset(folder, self.image_size, augment_horizontal_flip = augment_horizontal_flip, convert_image_to = convert_image_to)
+        dl = DataLoader(self.ds, batch_size = train_batch_size, shuffle = True, pin_memory = True, num_workers = cpu_count())
+
+        self.dl = cycle(dl)
+
+        # optimizer
+
         self.opt = Adam(diffusion_model.parameters(), lr = train_lr)
 
+        # for logging results in a folder periodically
+
+        if self.accelerator.is_main_process:
+            self.ema = EMA(diffusion_model, beta = ema_decay, update_every = ema_update_every)
+
+            self.results_folder = Path(results_folder)
+            self.results_folder.mkdir(exist_ok = True)
+
+        # step counter state
+
         self.step = 0
 
-        self.amp = amp
+        # prepare model, dataloader, optimizer with accelerator
-        self.scaler = GradScaler(enabled = amp)
 
-        self.results_folder = Path(results_folder)
+        self.model, self.dl, self.opt = self.accelerator.prepare(self.model, self.dl, self.opt)
-        self.results_folder.mkdir(exist_ok = True)
 
     def save(self, milestone):
+        if not self.accelerator.is_main_process:
+            return
+
         data = {
             'step': self.step,
-            'model': self.model.state_dict(),
+            'model': self.accelerator.get_state_dict(self.model),
             'ema': self.ema.state_dict(),
-            'scaler': self.scaler.state_dict()
+            'scaler': self.accelerator.scaler.state_dict() if exists(self.accelerator.scaler) else None
         }
+
         torch.save(data, str(self.results_folder / f'model-{milestone}.pt'))
 
     def load(self, milestone):
         data = torch.load(str(self.results_folder / f'model-{milestone}.pt'))
 
+        model = self.accelerator.unwrap_model(self.model)
+        model.load_state_dict(data['model'])
+
         self.step = data['step']
-        self.model.load_state_dict(data['model'])
         self.ema.load_state_dict(data['ema'])
-        self.scaler.load_state_dict(data['scaler'])
+
+        if exists(self.accelerator.scaler) and exists(data['scaler']):
+            self.accelerator.scaler.load_state_dict(data['scaler'])
 
     def train(self):
-        with tqdm(initial = self.step, total = self.train_num_steps) as pbar:
+        accelerator = self.accelerator
+        device = accelerator.device
+
+        with tqdm(initial = self.step, total = self.train_num_steps, disable = not accelerator.is_main_process) as pbar:
 
             while self.step < self.train_num_steps:
-                for i in range(self.gradient_accumulate_every):
-                    data = next(self.dl).cuda()
 
-                    with autocast(enabled = self.amp):
+                for _ in range(self.gradient_accumulate_every):
+                    data = next(self.dl).to(device)
+
+                    with self.accelerator.autocast():
                         loss = self.model(data)
-                        self.scaler.scale(loss / self.gradient_accumulate_every).backward()
+                        self.accelerator.backward(loss / self.gradient_accumulate_every)
 
-                    pbar.set_description(f'loss: {loss.item():.4f}')
+                pbar.set_description(f'loss: {loss.item():.4f}')
 
-                self.scaler.step(self.opt)
+                accelerator.wait_for_everyone()
-                self.scaler.update()
+
+                self.opt.step()
                 self.opt.zero_grad()
 
-                self.ema.update()
+                accelerator.wait_for_everyone()
 
-                if self.step != 0 and self.step % self.save_and_sample_every == 0:
+                if accelerator.is_main_process:
-                    self.ema.ema_model.eval()
+                    self.ema.to(device)
-                    with torch.no_grad():
+                    self.ema.update()
-                        milestone = self.step // self.save_and_sample_every
-                        batches = num_to_groups(36, self.batch_size)
-                        all_images_list = list(map(lambda n: self.ema.ema_model.sample(batch_size=n), batches))
 
-                    all_images = torch.cat(all_images_list, dim=0)
+                    if self.step != 0 and self.step % self.save_and_sample_every == 0:
-                    utils.save_image(all_images, str(self.results_folder / f'sample-{milestone}.png'), nrow = 6)
+                        self.ema.ema_model.eval()
-                    self.save(milestone)
+
+                        with torch.no_grad():
+                            milestone = self.step // self.save_and_sample_every
+                            batches = num_to_groups(self.num_samples, self.batch_size)
+                            all_images_list = list(map(lambda n: self.ema.ema_model.sample(batch_size=n), batches))
+
+                        all_images = torch.cat(all_images_list, dim = 0)
+                        utils.save_image(all_images, str(self.results_folder / f'sample-{milestone}.png'), nrow = int(math.sqrt(self.num_samples)))
+                        self.save(milestone)
 
                 self.step += 1
                 pbar.update(1)
 
-        print('training complete')
+        accelerator.print('training complete')

denoising_diffusion_pytorch/elucidated_diffusion.py

@@ -96,13 +96,30 @@ class ElucidatedDiffusion(nn.Module):
     def c_noise(self, sigma):
         return log(sigma) * 0.25
 
-    # noise distribution
+    # preconditioned network output
+    # equation (7) in the paper
 
-    def noise_distribution(self, batch_size):
+    def preconditioned_network_forward(self, noised_images, sigma, clamp = False):
-        return (self.P_mean + self.P_std * torch.randn((batch_size,), device = self.device)).exp()
+        batch, device = noised_images.shape[0], noised_images.device
 
-    def loss_weight(self, sigma):
+        if isinstance(sigma, float):
-        return (sigma ** 2 + self.sigma_data ** 2) * (sigma * self.sigma_data) ** -2
+            sigma = torch.full((batch,), sigma, device = device)
+
+        padded_sigma = rearrange(sigma, 'b -> b 1 1 1')
+
+        net_out = self.net(
+            self.c_in(padded_sigma) * noised_images,
+            self.c_noise(sigma)
+        )
+
+        out = self.c_skip(padded_sigma) * noised_images +  self.c_out(padded_sigma) * net_out
+
+        if clamp:
+            out = out.clamp(-1., 1.)
+
+        return out
+
+    # sampling
 
     # sample schedule
     # equation (5) in the paper
@@ -119,28 +136,8 @@ class ElucidatedDiffusion(nn.Module):
         sigmas = F.pad(sigmas, (0, 1), value = 0.) # last step is sigma value of 0.
         return sigmas
 
-    # preconditioned network output
-    # equation (7) in the paper
-
-    def preconditioned_network_forward(self, noised_images, sigma):
-        batch, device = noised_images.shape[0], noised_images.device
-
-        if isinstance(sigma, float):
-            sigma = torch.full((batch,), sigma, device = device)
-
-        padded_sigma = rearrange(sigma, 'b -> b 1 1 1')
-
-        net_out = self.net(
-            self.c_in(padded_sigma) * noised_images,
-            self.c_noise(sigma)
-        )
-
-        return self.c_skip(padded_sigma) * noised_images +  self.c_out(padded_sigma) * net_out
-
-    # sampling
-
     @torch.no_grad()
-    def sample(self, batch_size = 16, num_sample_steps = None):
+    def sample(self, batch_size = 16, num_sample_steps = None, clamp = True):
         num_sample_steps = default(num_sample_steps, self.num_sample_steps)
 
         shape = (batch_size, self.channels, self.image_size, self.image_size)
@@ -173,7 +170,7 @@ class ElucidatedDiffusion(nn.Module):
             sigma_hat = sigma + gamma * sigma
             images_hat = images + sqrt(sigma_hat ** 2 - sigma ** 2) * eps
 
-            model_output = self.preconditioned_network_forward(images_hat, sigma_hat)
+            model_output = self.preconditioned_network_forward(images_hat, sigma_hat, clamp = clamp)
             denoised_over_sigma = (images_hat - model_output) / sigma_hat
 
             images_next = images_hat + (sigma_next - sigma_hat) * denoised_over_sigma
@@ -181,7 +178,7 @@ class ElucidatedDiffusion(nn.Module):
             # second order correction, if not the last timestep
 
             if sigma_next != 0:
-                model_output_next = self.preconditioned_network_forward(images_next, sigma_next)
+                model_output_next = self.preconditioned_network_forward(images_next, sigma_next, clamp = clamp)
                 denoised_prime_over_sigma = (images_next - model_output_next) / sigma_next
                 images_next = images_hat + 0.5 * (sigma_next - sigma_hat) * (denoised_over_sigma + denoised_prime_over_sigma)
 
@@ -192,6 +189,12 @@ class ElucidatedDiffusion(nn.Module):
 
     # training
 
+    def loss_weight(self, sigma):
+        return (sigma ** 2 + self.sigma_data ** 2) * (sigma * self.sigma_data) ** -2
+
+    def noise_distribution(self, batch_size):
+        return (self.P_mean + self.P_std * torch.randn((batch_size,), device = self.device)).exp()
+
     def forward(self, images):
         batch_size, c, h, w, device, image_size, channels = *images.shape, images.device, self.image_size, self.channels
 

denoising_diffusion_pytorch/learned_gaussian_diffusion.py

@@ -22,7 +22,7 @@ def default(val, d):
 
 # tensor helpers
 
-def log(t, eps = 1e-12):
+def log(t, eps = 1e-15):
     return torch.log(t.clamp(min = eps))
 
 def meanflat(x):

setup.py

@@ -3,7 +3,7 @@ from setuptools import setup, find_packages
 setup(
   name = 'denoising-diffusion-pytorch',
   packages = find_packages(),
-  version = '0.23.2',
+  version = '0.24.2',
   license='MIT',
   description = 'Denoising Diffusion Probabilistic Models - Pytorch',
   author = 'Phil Wang',
@@ -15,6 +15,7 @@ setup(
     'generative models'
   ],
   install_requires=[
+    'accelerate',
     'einops',
     'ema-pytorch',
     'pillow',