get working version of gaussian diffusion with continuous time (only beta linear schedule for now, but will eventually contain alpha cosine schedule as well as parameterized, learned monotonic MLP)

README.md

@@ -34,7 +34,7 @@ diffusion = GaussianDiffusion(
     loss_type = 'l1'    # L1 or L2
 )
 
-training_images = torch.randn(8, 3, 128, 128) # your images need to be normalized from a range of -1 to +1
+training_images = torch.randn(8, 3, 128, 128) # images are normalized from 0 to 1
 loss = diffusion(training_images)
 loss.backward()
 # after a lot of training
@@ -108,3 +108,14 @@ Samples and model checkpoints will be logged to `./results` periodically
     url         = {https://proceedings.mlr.press/v139/nichol21a.html},
 }
 ```
+
+```bibtex
+@inproceedings{kingma2021on,
+    title       = {On Density Estimation with Diffusion Models},
+    author      = {Diederik P Kingma and Tim Salimans and Ben Poole and Jonathan Ho},
+    booktitle   = {Advances in Neural Information Processing Systems},
+    editor      = {A. Beygelzimer and Y. Dauphin and P. Liang and J. Wortman Vaughan},
+    year        = {2021},
+    url         = {https://openreview.net/forum?id=2LdBqxc1Yv}
+}
+```

denoising_diffusion_pytorch/__init__.py

@@ -1,4 +1,5 @@
 from denoising_diffusion_pytorch.denoising_diffusion_pytorch import GaussianDiffusion, Unet, Trainer
 
 from denoising_diffusion_pytorch.learned_gaussian_diffusion import LearnedGaussianDiffusion
+from denoising_diffusion_pytorch.continuous_time_gaussian_diffusion import ContinuousTimeGaussianDiffusion
 from denoising_diffusion_pytorch.weighted_objective_gaussian_diffusion import WeightedObjectiveGaussianDiffusion

denoising_diffusion_pytorch/continuous_time_gaussian_diffusion.py

@@ -0,0 +1,187 @@
+import torch
+from torch import sqrt
+from torch import nn, einsum
+import torch.nn.functional as F
+from torch.special import expm1
+
+from tqdm import tqdm
+from einops import rearrange, repeat
+
+# helpers
+
+def exists(val):
+    return val is not None
+
+def default(val, d):
+    if exists(val):
+        return val
+    return d() if callable(d) else d
+
+# normalization functions
+
+def normalize_to_neg_one_to_one(img):
+    return img * 2 - 1
+
+def unnormalize_to_zero_to_one(t):
+    return (t + 1) * 0.5
+
+# diffusion helpers
+
+def right_pad_dims_to(x, t):
+    padding_dims = x.ndim - t.ndim
+    if padding_dims <= 0:
+        return t
+    return t.view(*t.shape, *((1,) * padding_dims))
+
+# continuous schedules
+
+# equations are taken from https://openreview.net/attachment?id=2LdBqxc1Yv&name=supplementary_material
+# @crowsonkb Katherine's repository also helped here https://github.com/crowsonkb/v-diffusion-jax/blob/master/diffusion/utils.py
+
+# log(snr) that approximates the original linear schedule
+
+def beta_linear_log_snr(t):
+    return -torch.log(expm1(1e-4 + 10 * (t ** 2)))
+
+def alpha_cosine_log_snr(t):
+    raise NotImplementedError
+
+class learned_noise_schedule(nn.Module):
+    def __init__(self):
+        super().__init__()
+        raise NotImplementedError
+        # learned noise schedule, using learned monotonic MLP (weights kept positive) in the paper
+
+class ContinuousTimeGaussianDiffusion(nn.Module):
+    def __init__(
+        self,
+        denoise_fn,
+        *,
+        image_size,
+        channels = 3,
+        cond_scale = 500,
+        loss_type = 'l1',
+        noise_schedule = 'linear',
+        num_sample_steps = 500
+    ):
+        super().__init__()
+
+        self.denoise_fn = denoise_fn
+
+        # image dimensions
+
+        self.channels = channels
+        self.image_size = image_size
+
+        # continuous noise schedule related stuff
+
+        self.cond_scale = cond_scale # the log(snr) will be scaled by this value
+        self.loss_type = loss_type
+
+        if noise_schedule == 'linear':
+            self.log_snr = beta_linear_log_snr
+        else:
+            raise ValueError(f'unknown noise schedule {noise_schedule}')
+
+        # sampling
+
+        self.num_sample_steps = num_sample_steps
+
+    @property
+    def device(self):
+        return next(self.denoise_fn.parameters()).device
+
+    @property
+    def loss_fn(self):
+        if self.loss_type == 'l1':
+            return F.l1_loss
+        elif self.loss_type == 'l2':
+            return F.mse_loss
+        else:
+            raise ValueError(f'invalid loss type {self.loss_type}')
+
+    def p_mean_variance(self, x, time, time_next):
+        # 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
+
+        batch = x.shape[0]
+        batch_time = repeat(time, ' -> b', b = batch)
+
+        pred_noise = self.denoise_fn(x, batch_time * self.cond_scale)
+
+        log_snr = self.log_snr(time)
+        log_snr_next = self.log_snr(time_next)
+        c = -expm1(log_snr - log_snr_next)
+
+        squared_alpha, squared_alpha_next = log_snr.sigmoid(), log_snr_next.sigmoid()
+        squared_sigma, squared_sigma_next = (-log_snr).sigmoid(), (-log_snr_next).sigmoid()
+
+        model_mean = sqrt(squared_alpha_next / squared_alpha) * (x - c * sqrt(squared_sigma) * pred_noise)
+        posterior_variance = squared_sigma_next * c
+
+        return model_mean, posterior_variance
+
+    # sampling related functions
+
+    @torch.no_grad()
+    def p_sample(self, x, time, time_next):
+        batch, *_, device = *x.shape, x.device
+
+        model_mean, model_variance = self.p_mean_variance(x = x, time = time, time_next = time_next)
+        noise = torch.randn_like(x)
+        return model_mean + sqrt(model_variance) * noise
+
+    @torch.no_grad()
+    def p_sample_loop(self, shape):
+        batch = shape[0]
+
+        img = torch.randn(shape, device = self.device)
+        steps = torch.linspace(1., 0., self.num_sample_steps + 1, device = self.device)
+
+        for i in tqdm(range(self.num_sample_steps), desc = 'sampling loop time step', total = self.num_sample_steps):
+            times = steps[i]
+            times_next = steps[i + 1]
+            img = self.p_sample(img, times, times_next)
+
+        img = unnormalize_to_zero_to_one(img)
+        return img
+
+    @torch.no_grad()
+    def sample(self, batch_size = 16):
+        return self.p_sample_loop((batch_size, self.channels, self.image_size, self.image_size))
+
+    # training related functions - noise prediction
+
+    def q_sample(self, x_start, times, noise = None):
+        noise = default(noise, lambda: torch.randn_like(x_start))
+
+        log_snr = self.log_snr(times)
+
+        log_snr_padded = right_pad_dims_to(x_start, log_snr)
+        alpha, sigma = sqrt(log_snr_padded.sigmoid()), sqrt((-log_snr_padded).sigmoid())
+        x_noised =  x_start * alpha + noise * sigma
+
+        return x_noised, log_snr
+
+    def random_times(self, batch_size):
+        # times are now uniform from 0 to 1
+        return torch.zeros((batch_size,), device = self.device).float().uniform_(0, 1)
+
+    def p_losses(self, x_start, times, noise = None):
+        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 * self.cond_scale)
+        return self.loss_fn(model_out, noise)
+
+    def forward(self, img, *args, **kwargs):
+        b, c, h, w, device, img_size, = *img.shape, img.device, self.image_size
+        assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
+
+        times = self.random_times(b)
+        img = normalize_to_neg_one_to_one(img)
+        return self.p_losses(img, times, *args, **kwargs)

denoising_diffusion_pytorch/denoising_diffusion_pytorch.py

@@ -324,11 +324,6 @@ def extract(a, t, x_shape):
     out = a.gather(-1, t)
     return out.reshape(b, *((1,) * (len(x_shape) - 1)))
 
-def noise_like(shape, device, repeat=False):
-    repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
-    noise = lambda: torch.randn(shape, device=device)
-    return repeat_noise() if repeat else noise()
-
 def linear_beta_schedule(timesteps):
     scale = 1000 / timesteps
     beta_start = scale * 0.0001
@@ -444,10 +439,10 @@ class GaussianDiffusion(nn.Module):
         return model_mean, posterior_variance, posterior_log_variance
 
     @torch.no_grad()
-    def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
+    def p_sample(self, x, t, clip_denoised=True):
         b, *_, device = *x.shape, x.device
         model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
-        noise = noise_like(x.shape, device, repeat_noise)
+        noise = torch.randn_like(x)
         # no noise when t == 0
         nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
         return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise

setup.py

@@ -3,12 +3,13 @@ from setuptools import setup, find_packages
 setup(
   name = 'denoising-diffusion-pytorch',
   packages = find_packages(),
-  version = '0.16.1',
+  version = '0.16.3',
   license='MIT',
   description = 'Denoising Diffusion Probabilistic Models - Pytorch',
   author = 'Phil Wang',
   author_email = 'lucidrains@gmail.com',
   url = 'https://github.com/lucidrains/denoising-diffusion-pytorch',
+  long_description_content_type = 'text/markdown',
   keywords = [
     'artificial intelligence',
     'generative models'