allow for turning off horizontal flip augmentation

README.md

@@ -121,3 +121,13 @@ Samples and model checkpoints will be logged to `./results` periodically
     url         = {https://openreview.net/forum?id=2LdBqxc1Yv}
 }
 ```
+
+```bibtex
+@article{Choi2022PerceptionPT,
+    title   = {Perception Prioritized Training of Diffusion Models},
+    author  = {Jooyoung Choi and Jungbeom Lee and Chaehun Shin and Sungwon Kim and Hyunwoo J. Kim and Sung-Hoon Yoon},
+    journal = {ArXiv},
+    year    = {2022},
+    volume  = {abs/2204.00227}
+}
+```

denoising_diffusion_pytorch/continuous_time_gaussian_diffusion.py

@@ -5,7 +5,7 @@ import torch.nn.functional as F
 from torch.special import expm1
 
 from tqdm import tqdm
-from einops import rearrange, repeat
+from einops import rearrange, repeat, reduce
 from einops.layers.torch import Rearrange
 
 # helpers
@@ -59,11 +59,14 @@ class MonotonicLinear(nn.Module):
 
 # log(snr) that approximates the original linear schedule
 
-def beta_linear_log_snr(t):
+def log(t, eps = 1e-20):
-    return -torch.log(expm1(1e-4 + 10 * (t ** 2)))
+    return torch.log(t.clamp(min = eps))
 
-def alpha_cosine_log_snr(t):
+def beta_linear_log_snr(t):
-    raise NotImplementedError
+    return -log(expm1(1e-4 + 10 * (t ** 2)))
+
+def alpha_cosine_log_snr(t, s = 0.008):
+    return -log((torch.cos((t + s) / (1 + s) * torch.pi * 0.5) ** -2) - 1, eps = 1e-5)
 
 class learned_noise_schedule(nn.Module):
     """ described in section H and then I.2 of the supplementary material for variational ddpm paper """
@@ -115,8 +118,11 @@ class ContinuousTimeGaussianDiffusion(nn.Module):
         loss_type = 'l1',
         noise_schedule = 'linear',
         num_sample_steps = 500,
+        clip_sample_denoised = True,
         learned_schedule_net_hidden_dim = 1024,
-        learned_noise_schedule_frac_gradient = 1.   # between 0 and 1, determines what percentage of gradients go back, so one can update the learned noise schedule more slowly
+        learned_noise_schedule_frac_gradient = 1.,   # between 0 and 1, determines what percentage of gradients go back, so one can update the learned noise schedule more slowly
+        p2_loss_weight_gamma = 0.,                   # p2 loss weight, from https://arxiv.org/abs/2204.00227 - 0 is equivalent to weight of 1 across time
+        p2_loss_weight_k = 1
     ):
         super().__init__()
         assert not denoise_fn.sinusoidal_cond_mlp
@@ -134,6 +140,8 @@ class ContinuousTimeGaussianDiffusion(nn.Module):
 
         if noise_schedule == 'linear':
             self.log_snr = beta_linear_log_snr
+        elif noise_schedule == 'cosine':
+            self.log_snr = alpha_cosine_log_snr
         elif noise_schedule == 'learned':
             log_snr_max, log_snr_min = [beta_linear_log_snr(torch.tensor([time])).item() for time in (0., 1.)]
 
@@ -149,6 +157,15 @@ class ContinuousTimeGaussianDiffusion(nn.Module):
         # sampling
 
         self.num_sample_steps = num_sample_steps
+        self.clip_sample_denoised = clip_sample_denoised
+
+        # p2 loss weight
+        # proposed https://arxiv.org/abs/2204.00227
+
+        assert p2_loss_weight_gamma <= 2, 'in paper, they noticed any gamma greater than 2 is harmful'
+
+        self.p2_loss_weight_gamma = p2_loss_weight_gamma  # recommended to be 0.5 or 1
+        self.p2_loss_weight_k = p2_loss_weight_k
 
     @property
     def device(self):
@@ -167,9 +184,6 @@ class ContinuousTimeGaussianDiffusion(nn.Module):
         # reviewer found an error in the equation in the paper (missing sigma)
         # following - https://openreview.net/forum?id=2LdBqxc1Yv&noteId=rIQgH0zKsRt
 
-        # todo - derive x_start from the posterior mean and do dynamic thresholding
-        # assumed that is what is going on in Imagen
-
         log_snr = self.log_snr(time)
         log_snr_next = self.log_snr(time_next)
         c = -expm1(log_snr - log_snr_next)
@@ -177,10 +191,21 @@ class ContinuousTimeGaussianDiffusion(nn.Module):
         squared_alpha, squared_alpha_next = log_snr.sigmoid(), log_snr_next.sigmoid()
         squared_sigma, squared_sigma_next = (-log_snr).sigmoid(), (-log_snr_next).sigmoid()
 
+        alpha, sigma, alpha_next = map(sqrt, (squared_alpha, squared_sigma, squared_alpha_next))
+
         batch_log_snr = repeat(log_snr, ' -> b', b = x.shape[0])
         pred_noise = self.denoise_fn(x, batch_log_snr)
 
-        model_mean = sqrt(squared_alpha_next / squared_alpha) * (x - c * sqrt(squared_sigma) * pred_noise)
+        if self.clip_sample_denoised:
+            x_start = (x - sigma * pred_noise) / alpha
+
+            # in Imagen, this was changed to dynamic thresholding
+            x_start.clamp_(-1., 1.)
+
+            model_mean = alpha_next * (x * (1 - c) / alpha + c * x_start)
+        else:
+            model_mean = alpha_next / alpha * (x - c * sigma * pred_noise)
+
         posterior_variance = squared_sigma_next * c
 
         return model_mean, posterior_variance
@@ -240,9 +265,17 @@ class ContinuousTimeGaussianDiffusion(nn.Module):
         noise = default(noise, lambda: torch.randn_like(x_start))
 
         x, log_snr = self.q_sample(x_start = x_start, times = times, noise = noise)
-
         model_out = self.denoise_fn(x, log_snr)
-        return self.loss_fn(model_out, noise)
+
+        losses = self.loss_fn(model_out, noise, reduction = 'none')
+        losses = reduce(losses, 'b ... -> b', 'mean')
+
+        if self.p2_loss_weight_gamma >= 0:
+            # following eq 8. in https://arxiv.org/abs/2204.00227
+            loss_weight = (self.p2_loss_weight_k + log_snr.exp()) ** -self.p2_loss_weight_gamma
+            losses = losses * loss_weight
+
+        return losses.mean()
 
     def forward(self, img, *args, **kwargs):
         b, c, h, w, device, img_size, = *img.shape, img.device, self.image_size

denoising_diffusion_pytorch/denoising_diffusion_pytorch.py

@@ -541,7 +541,7 @@ class GaussianDiffusion(nn.Module):
 # dataset classes
 
 class Dataset(data.Dataset):
-    def __init__(self, folder, image_size, exts = ['jpg', 'jpeg', 'png']):
+    def __init__(self, folder, image_size, exts = ['jpg', 'jpeg', 'png'], augment_horizontal_flip = False):
         super().__init__()
         self.folder = folder
         self.image_size = image_size
@@ -549,7 +549,7 @@ class Dataset(data.Dataset):
 
         self.transform = transforms.Compose([
             transforms.Resize(image_size),
-            transforms.RandomHorizontalFlip(),
+            transforms.RandomHorizontalFlip() if augment_horizontal_flip else nn.Identity(),
             transforms.CenterCrop(image_size),
             transforms.ToTensor()
         ])
@@ -580,7 +580,8 @@ class Trainer(object):
         step_start_ema = 2000,
         update_ema_every = 10,
         save_and_sample_every = 1000,
-        results_folder = './results'
+        results_folder = './results',
+        augment_horizontal_flip = True
     ):
         super().__init__()
         self.model = diffusion_model
@@ -596,7 +597,7 @@ class Trainer(object):
         self.gradient_accumulate_every = gradient_accumulate_every
         self.train_num_steps = train_num_steps
 
-        self.ds = Dataset(folder, image_size)
+        self.ds = Dataset(folder, image_size, augment_horizontal_flip = augment_horizontal_flip)
         self.dl = cycle(data.DataLoader(self.ds, batch_size = train_batch_size, shuffle=True, pin_memory=True))
         self.opt = Adam(diffusion_model.parameters(), lr=train_lr)
 

setup.py

@@ -3,7 +3,7 @@ from setuptools import setup, find_packages
 setup(
   name = 'denoising-diffusion-pytorch',
   packages = find_packages(),
-  version = '0.17.4',
+  version = '0.18.2',
   license='MIT',
   description = 'Denoising Diffusion Probabilistic Models - Pytorch',
   author = 'Phil Wang',