fix data not being normalized to range of -1 to 1

README.md

@@ -2,9 +2,7 @@
 
 ## Denoising Diffusion Probabilistic Model, in Pytorch
 
-Implementation of <a href="https://arxiv.org/abs/2006.11239">Denoising Diffusion Probabilistic Model</a> in Pytorch. It is a new approach to generative modeling that may <a href="https://ajolicoeur.wordpress.com/the-new-contender-to-gans-score-matching-with-langevin-sampling/">have the potential</a> to rival GANs. It uses denoising score matching to estimate the gradient of the data distribution, followed by Langevin sampling to sample from the true distribution.
-
-This implementation was transcribed from the official Tensorflow version <a href="https://github.com/hojonathanho/diffusion">here</a>
+Implementation of <a href="https://arxiv.org/abs/2006.11239">Denoising Diffusion Probabilistic Model</a> in Pytorch. It is a new approach to generative modeling that may <a href="https://ajolicoeur.wordpress.com/the-new-contender-to-gans-score-matching-with-langevin-sampling/">have the potential</a> to rival GANs. It uses denoising score matching to estimate the gradient of the data distribution, followed by Langevin sampling to sample from the true distribution. This implementation was transcribed from the official Tensorflow version <a href="https://github.com/hojonathanho/diffusion">here</a>. 
 
 <img src="./sample.png" width="500px"><img>
 
@@ -34,7 +32,7 @@ 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)
 loss = diffusion(training_images)
 loss.backward()
 # after a lot of training
@@ -68,7 +66,7 @@ trainer = Trainer(
     train_num_steps = 700000,         # total training steps
     gradient_accumulate_every = 2,    # gradient accumulation steps
     ema_decay = 0.995,                # exponential moving average decay
-    amp = True                        # turn on mixed precision
+    fp16 = True                       # turn on mixed precision training with apex
 )
 
 trainer.train()
@@ -79,32 +77,23 @@ Samples and model checkpoints will be logged to `./results` periodically
 ## Citations
 
 ```bibtex
-@inproceedings{NEURIPS2020_4c5bcfec,
-    author      = {Ho, Jonathan and Jain, Ajay and Abbeel, Pieter},
-    booktitle   = {Advances in Neural Information Processing Systems},
-    editor      = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
-    pages       = {6840--6851},
-    publisher   = {Curran Associates, Inc.},
-    title       = {Denoising Diffusion Probabilistic Models},
-    url         = {https://proceedings.neurips.cc/paper/2020/file/4c5bcfec8584af0d967f1ab10179ca4b-Paper.pdf},
-    volume      = {33},
-    year        = {2020}
+@misc{ho2020denoising,
+    title   = {Denoising Diffusion Probabilistic Models},
+    author  = {Jonathan Ho and Ajay Jain and Pieter Abbeel},
+    year    = {2020},
+    eprint  = {2006.11239},
+    archivePrefix = {arXiv},
+    primaryClass = {cs.LG}
 }
 ```
 
 ```bibtex
-@InProceedings{pmlr-v139-nichol21a,
-    title       = {Improved Denoising Diffusion Probabilistic Models},
-    author      = {Nichol, Alexander Quinn and Dhariwal, Prafulla},
-    booktitle   = {Proceedings of the 38th International Conference on Machine Learning},
-    pages       = {8162--8171},
-    year        = {2021},
-    editor      = {Meila, Marina and Zhang, Tong},
-    volume      = {139},
-    series      = {Proceedings of Machine Learning Research},
-    month       = {18--24 Jul},
-    publisher   = {PMLR},
-    pdf         = {http://proceedings.mlr.press/v139/nichol21a/nichol21a.pdf},
-    url         = {https://proceedings.mlr.press/v139/nichol21a.html},
+@inproceedings{anonymous2021improved,
+    title   = {Improved Denoising Diffusion Probabilistic Models},
+    author  = {Anonymous},
+    booktitle = {Submitted to International Conference on Learning Representations},
+    year    = {2021},
+    url     = {https://openreview.net/forum?id=-NEXDKk8gZ},
+    note    = {under review}
 }
 ```

denoising_diffusion_pytorch/__init__.py

@@ -1,2 +1 @@
 from denoising_diffusion_pytorch.denoising_diffusion_pytorch import GaussianDiffusion, Unet, Trainer
-from denoising_diffusion_pytorch.learned_gaussian_diffusion import LearnedGaussianDiffusion

denoising_diffusion_pytorch/denoising_diffusion_pytorch.py

@@ -7,16 +7,30 @@ from inspect import isfunction
 from functools import partial
 
 from torch.utils import data
-from torch.cuda.amp import autocast, GradScaler
-
 from pathlib import Path
 from torch.optim import Adam
 from torchvision import transforms, utils
 from PIL import Image
 
+import numpy as np
 from tqdm import tqdm
 from einops import rearrange
 
+try:
+    from apex import amp
+    APEX_AVAILABLE = True
+except:
+    APEX_AVAILABLE = False
+
+# constants
+
+SAVE_AND_SAMPLE_EVERY = 1000
+UPDATE_EMA_EVERY = 10
+EXTS = ['jpg', 'jpeg', 'png']
+
+RESULTS_FOLDER = Path('./results')
+RESULTS_FOLDER.mkdir(exist_ok = True)
+
 # helpers functions
 
 def exists(x):
@@ -40,11 +54,12 @@ def num_to_groups(num, divisor):
         arr.append(remainder)
     return arr
 
-def normalize_to_neg_one_to_one(img):
-    return img * 2 - 1
-
-def unnormalize_to_zero_to_one(t):
-    return (t + 1) * 0.5
+def loss_backwards(fp16, loss, optimizer, **kwargs):
+    if fp16:
+        with amp.scale_loss(loss, optimizer) as scaled_loss:
+            scaled_loss.backward(**kwargs)
+    else:
+        loss.backward(**kwargs)
 
 # small helper modules
 
@@ -85,33 +100,34 @@ class SinusoidalPosEmb(nn.Module):
         emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
         return emb
 
-def Upsample(dim):
-    return nn.ConvTranspose2d(dim, dim, 4, 2, 1)
+class Mish(nn.Module):
+    def forward(self, x):
+        return x * torch.tanh(F.softplus(x))
 
-def Downsample(dim):
-    return nn.Conv2d(dim, dim, 4, 2, 1)
-
-class LayerNorm(nn.Module):
-    def __init__(self, dim, eps = 1e-5):
+class Upsample(nn.Module):
+    def __init__(self, dim):
         super().__init__()
-        self.eps = eps
-        self.g = nn.Parameter(torch.ones(1, dim, 1, 1))
-        self.b = nn.Parameter(torch.zeros(1, dim, 1, 1))
+        self.conv = nn.ConvTranspose2d(dim, dim, 4, 2, 1)
 
     def forward(self, x):
-        var = torch.var(x, dim = 1, unbiased = False, keepdim = True)
-        mean = torch.mean(x, dim = 1, keepdim = True)
-        return (x - mean) / (var + self.eps).sqrt() * self.g + self.b
+        return self.conv(x)
 
-class PreNorm(nn.Module):
-    def __init__(self, dim, fn):
+class Downsample(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.conv = nn.Conv2d(dim, dim, 3, 2, 1)
+
+    def forward(self, x):
+        return self.conv(x)
+
+class Rezero(nn.Module):
+    def __init__(self, fn):
         super().__init__()
         self.fn = fn
-        self.norm = LayerNorm(dim)
+        self.g = nn.Parameter(torch.zeros(1))
 
     def forward(self, x):
-        x = self.norm(x)
-        return self.fn(x)
+        return self.fn(x) * self.g
 
 # building block modules
 
@@ -119,67 +135,34 @@ class Block(nn.Module):
     def __init__(self, dim, dim_out, groups = 8):
         super().__init__()
         self.block = nn.Sequential(
-            nn.Conv2d(dim, dim_out, 3, padding = 1),
+            nn.Conv2d(dim, dim_out, 3, padding=1),
             nn.GroupNorm(groups, dim_out),
-            nn.SiLU()
+            Mish()
         )
     def forward(self, x):
         return self.block(x)
 
 class ResnetBlock(nn.Module):
-    def __init__(self, dim, dim_out, *, time_emb_dim = None, groups = 8):
+    def __init__(self, dim, dim_out, *, time_emb_dim, groups = 8):
         super().__init__()
         self.mlp = nn.Sequential(
-            nn.SiLU(),
+            Mish(),
             nn.Linear(time_emb_dim, dim_out)
-        ) if exists(time_emb_dim) else None
+        )
 
-        self.block1 = Block(dim, dim_out, groups = groups)
-        self.block2 = Block(dim_out, dim_out, groups = groups)
+        self.block1 = Block(dim, dim_out)
+        self.block2 = Block(dim_out, dim_out)
         self.res_conv = nn.Conv2d(dim, dim_out, 1) if dim != dim_out else nn.Identity()
 
-    def forward(self, x, time_emb = None):
+    def forward(self, x, time_emb):
         h = self.block1(x)
-
-        if exists(self.mlp) and exists(time_emb):
-            time_emb = self.mlp(time_emb)
-            h = rearrange(time_emb, 'b c -> b c 1 1') + h
-
+        h += self.mlp(time_emb)[:, :, None, None]
         h = self.block2(h)
         return h + self.res_conv(x)
 
 class LinearAttention(nn.Module):
     def __init__(self, dim, heads = 4, dim_head = 32):
         super().__init__()
-        self.scale = dim_head ** -0.5
-        self.heads = heads
-        hidden_dim = dim_head * heads
-        self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False)
-
-        self.to_out = nn.Sequential(
-            nn.Conv2d(hidden_dim, dim, 1),
-            LayerNorm(dim)
-        )
-
-    def forward(self, x):
-        b, c, h, w = x.shape
-        qkv = self.to_qkv(x).chunk(3, dim = 1)
-        q, k, v = map(lambda t: rearrange(t, 'b (h c) x y -> b h c (x y)', h = self.heads), qkv)
-
-        q = q.softmax(dim = -2)
-        k = k.softmax(dim = -1)
-
-        q = q * self.scale
-        context = torch.einsum('b h d n, b h e n -> b h d e', k, v)
-
-        out = torch.einsum('b h d e, b h d n -> b h e n', context, q)
-        out = rearrange(out, 'b h c (x y) -> b (h c) x y', h = self.heads, x = h, y = w)
-        return self.to_out(out)
-
-class Attention(nn.Module):
-    def __init__(self, dim, heads = 4, dim_head = 32):
-        super().__init__()
-        self.scale = dim_head ** -0.5
         self.heads = heads
         hidden_dim = dim_head * heads
         self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False)
@@ -187,16 +170,12 @@ class Attention(nn.Module):
 
     def forward(self, x):
         b, c, h, w = x.shape
-        qkv = self.to_qkv(x).chunk(3, dim = 1)
-        q, k, v = map(lambda t: rearrange(t, 'b (h c) x y -> b h c (x y)', h = self.heads), qkv)
-        q = q * self.scale
-
-        sim = einsum('b h d i, b h d j -> b h i j', q, k)
-        sim = sim - sim.amax(dim = -1, keepdim = True).detach()
-        attn = sim.softmax(dim = -1)
-
-        out = einsum('b h i j, b h d j -> b h i d', attn, v)
-        out = rearrange(out, 'b h (x y) d -> b (h d) x y', x = h, y = w)
+        qkv = self.to_qkv(x)
+        q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3)
+        k = k.softmax(dim=-1)
+        context = torch.einsum('bhdn,bhen->bhde', k, v)
+        out = torch.einsum('bhde,bhdn->bhen', context, q)
+        out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w)
         return self.to_out(out)
 
 # model
@@ -205,43 +184,23 @@ class Unet(nn.Module):
     def __init__(
         self,
         dim,
-        init_dim = None,
         out_dim = None,
         dim_mults=(1, 2, 4, 8),
-        channels = 3,
-        with_time_emb = True,
-        resnet_block_groups = 8,
-        learned_variance = False
+        groups = 8,
+        channels = 3
     ):
         super().__init__()
-
-        # determine dimensions
-
         self.channels = channels
 
-        init_dim = default(init_dim, dim // 3 * 2)
-        self.init_conv = nn.Conv2d(channels, init_dim, 7, padding = 3)
-
-        dims = [init_dim, *map(lambda m: dim * m, dim_mults)]
+        dims = [channels, *map(lambda m: dim * m, dim_mults)]
         in_out = list(zip(dims[:-1], dims[1:]))
 
-        block_klass = partial(ResnetBlock, groups = resnet_block_groups)
-
-        # time embeddings
-
-        if with_time_emb:
-            time_dim = dim * 4
-            self.time_mlp = nn.Sequential(
-                SinusoidalPosEmb(dim),
-                nn.Linear(dim, time_dim),
-                nn.GELU(),
-                nn.Linear(time_dim, time_dim)
-            )
-        else:
-            time_dim = None
-            self.time_mlp = None
-
-        # layers
+        self.time_pos_emb = SinusoidalPosEmb(dim)
+        self.mlp = nn.Sequential(
+            nn.Linear(dim, dim * 4),
+            Mish(),
+            nn.Linear(dim * 4, dim)
+        )
 
         self.downs = nn.ModuleList([])
         self.ups = nn.ModuleList([])
@@ -251,45 +210,42 @@ class Unet(nn.Module):
             is_last = ind >= (num_resolutions - 1)
 
             self.downs.append(nn.ModuleList([
-                block_klass(dim_in, dim_out, time_emb_dim = time_dim),
-                block_klass(dim_out, dim_out, time_emb_dim = time_dim),
-                Residual(PreNorm(dim_out, LinearAttention(dim_out))),
+                ResnetBlock(dim_in, dim_out, time_emb_dim = dim),
+                ResnetBlock(dim_out, dim_out, time_emb_dim = dim),
+                Residual(Rezero(LinearAttention(dim_out))),
                 Downsample(dim_out) if not is_last else nn.Identity()
             ]))
 
         mid_dim = dims[-1]
-        self.mid_block1 = block_klass(mid_dim, mid_dim, time_emb_dim = time_dim)
-        self.mid_attn = Residual(PreNorm(mid_dim, Attention(mid_dim)))
-        self.mid_block2 = block_klass(mid_dim, mid_dim, time_emb_dim = time_dim)
+        self.mid_block1 = ResnetBlock(mid_dim, mid_dim, time_emb_dim = dim)
+        self.mid_attn = Residual(Rezero(LinearAttention(mid_dim)))
+        self.mid_block2 = ResnetBlock(mid_dim, mid_dim, time_emb_dim = dim)
 
         for ind, (dim_in, dim_out) in enumerate(reversed(in_out[1:])):
             is_last = ind >= (num_resolutions - 1)
 
             self.ups.append(nn.ModuleList([
-                block_klass(dim_out * 2, dim_in, time_emb_dim = time_dim),
-                block_klass(dim_in, dim_in, time_emb_dim = time_dim),
-                Residual(PreNorm(dim_in, LinearAttention(dim_in))),
+                ResnetBlock(dim_out * 2, dim_in, time_emb_dim = dim),
+                ResnetBlock(dim_in, dim_in, time_emb_dim = dim),
+                Residual(Rezero(LinearAttention(dim_in))),
                 Upsample(dim_in) if not is_last else nn.Identity()
             ]))
 
-        default_out_dim = channels * (1 if not learned_variance else 2)
-        self.out_dim = default(out_dim, default_out_dim)
-
+        out_dim = default(out_dim, channels)
         self.final_conv = nn.Sequential(
-            block_klass(dim, dim),
-            nn.Conv2d(dim, self.out_dim, 1)
+            Block(dim, dim),
+            nn.Conv2d(dim, out_dim, 1)
         )
 
     def forward(self, x, time):
-        x = self.init_conv(x)
-
-        t = self.time_mlp(time) if exists(self.time_mlp) else None
+        t = self.time_pos_emb(time)
+        t = self.mlp(t)
 
         h = []
 
-        for block1, block2, attn, downsample in self.downs:
-            x = block1(x, t)
-            x = block2(x, t)
+        for resnet, resnet2, attn, downsample in self.downs:
+            x = resnet(x, t)
+            x = resnet2(x, t)
             x = attn(x)
             h.append(x)
             x = downsample(x)
@@ -298,10 +254,10 @@ class Unet(nn.Module):
         x = self.mid_attn(x)
         x = self.mid_block2(x, t)
 
-        for block1, block2, attn, upsample in self.ups:
+        for resnet, resnet2, attn, upsample in self.ups:
             x = torch.cat((x, h.pop()), dim=1)
-            x = block1(x, t)
-            x = block2(x, t)
+            x = resnet(x, t)
+            x = resnet2(x, t)
             x = attn(x)
             x = upsample(x)
 
@@ -325,11 +281,11 @@ def cosine_beta_schedule(timesteps, s = 0.008):
     as proposed in https://openreview.net/forum?id=-NEXDKk8gZ
     """
     steps = timesteps + 1
-    x = torch.linspace(0, timesteps, steps, dtype = torch.float64)
-    alphas_cumprod = torch.cos(((x / timesteps) + s) / (1 + s) * torch.pi * 0.5) ** 2
+    x = np.linspace(0, steps, steps)
+    alphas_cumprod = np.cos(((x / steps) + s) / (1 + s) * np.pi * 0.5) ** 2
     alphas_cumprod = alphas_cumprod / alphas_cumprod[0]
     betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1])
-    return torch.clip(betas, 0, 0.999)
+    return np.clip(betas, a_min = 0, a_max = 0.999)
 
 class GaussianDiffusion(nn.Module):
     def __init__(
@@ -339,54 +295,50 @@ class GaussianDiffusion(nn.Module):
         image_size,
         channels = 3,
         timesteps = 1000,
-        loss_type = 'l1'
+        loss_type = 'l1',
+        betas = None
     ):
         super().__init__()
-        assert not (type(self) == GaussianDiffusion and denoise_fn.channels != denoise_fn.out_dim)
-
         self.channels = channels
         self.image_size = image_size
         self.denoise_fn = denoise_fn
 
-        betas = cosine_beta_schedule(timesteps)
+        if exists(betas):
+            betas = betas.detach().cpu().numpy() if isinstance(betas, torch.Tensor) else betas
+        else:
+            betas = cosine_beta_schedule(timesteps)
 
         alphas = 1. - betas
-        alphas_cumprod = torch.cumprod(alphas, axis=0)
-        alphas_cumprod_prev = F.pad(alphas_cumprod[:-1], (1, 0), value = 1.)
+        alphas_cumprod = np.cumprod(alphas, axis=0)
+        alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
 
         timesteps, = betas.shape
         self.num_timesteps = int(timesteps)
         self.loss_type = loss_type
 
-        # helper function to register buffer from float64 to float32
+        to_torch = partial(torch.tensor, dtype=torch.float32)
 
-        register_buffer = lambda name, val: self.register_buffer(name, val.to(torch.float32))
-
-        register_buffer('betas', betas)
-        register_buffer('alphas_cumprod', alphas_cumprod)
-        register_buffer('alphas_cumprod_prev', alphas_cumprod_prev)
+        self.register_buffer('betas', to_torch(betas))
+        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
+        self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
 
         # calculations for diffusion q(x_t | x_{t-1}) and others
-
-        register_buffer('sqrt_alphas_cumprod', torch.sqrt(alphas_cumprod))
-        register_buffer('sqrt_one_minus_alphas_cumprod', torch.sqrt(1. - alphas_cumprod))
-        register_buffer('log_one_minus_alphas_cumprod', torch.log(1. - alphas_cumprod))
-        register_buffer('sqrt_recip_alphas_cumprod', torch.sqrt(1. / alphas_cumprod))
-        register_buffer('sqrt_recipm1_alphas_cumprod', torch.sqrt(1. / alphas_cumprod - 1))
+        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
+        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
+        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
+        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
+        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
 
         # calculations for posterior q(x_{t-1} | x_t, x_0)
-
         posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod)
-
         # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
-
-        register_buffer('posterior_variance', posterior_variance)
-
+        self.register_buffer('posterior_variance', to_torch(posterior_variance))
         # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
-
-        register_buffer('posterior_log_variance_clipped', torch.log(posterior_variance.clamp(min =1e-20)))
-        register_buffer('posterior_mean_coef1', betas * torch.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))
-        register_buffer('posterior_mean_coef2', (1. - alphas_cumprod_prev) * torch.sqrt(alphas) / (1. - alphas_cumprod))
+        self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
+        self.register_buffer('posterior_mean_coef1', to_torch(
+            betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
+        self.register_buffer('posterior_mean_coef2', to_torch(
+            (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
 
     def q_mean_variance(self, x_start, t):
         mean = extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
@@ -468,15 +420,6 @@ class GaussianDiffusion(nn.Module):
             extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise
         )
 
-    @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_losses(self, x_start, t, noise = None):
         b, c, h, w = x_start.shape
         noise = default(noise, lambda: torch.randn_like(x_start))
@@ -484,7 +427,13 @@ class GaussianDiffusion(nn.Module):
         x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
         x_recon = self.denoise_fn(x_noisy, t)
 
-        loss = self.loss_fn(noise, x_recon)
+        if self.loss_type == 'l1':
+            loss = (noise - x_recon).abs().mean()
+        elif self.loss_type == 'l2':
+            loss = F.mse_loss(noise, x_recon)
+        else:
+            raise NotImplementedError()
+
         return loss
 
     def forward(self, x, *args, **kwargs):
@@ -496,18 +445,18 @@ 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):
         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.paths = [p for ext in EXTS for p in Path(f'{folder}').glob(f'**/*.{ext}')]
 
         self.transform = transforms.Compose([
             transforms.Resize(image_size),
             transforms.RandomHorizontalFlip(),
             transforms.CenterCrop(image_size),
             transforms.ToTensor(),
-            transforms.Lambda(normalize_to_neg_one_to_one)
+            transforms.Lambda(lambda t: (t * 2) - 1)
         ])
 
     def __len__(self):
@@ -532,20 +481,14 @@ class Trainer(object):
         train_lr = 2e-5,
         train_num_steps = 100000,
         gradient_accumulate_every = 2,
-        amp = False,
-        step_start_ema = 2000,
-        update_ema_every = 10,
-        save_and_sample_every = 1000,
-        results_folder = './results'
+        fp16 = False,
+        step_start_ema = 2000
     ):
         super().__init__()
         self.model = diffusion_model
         self.ema = EMA(ema_decay)
         self.ema_model = copy.deepcopy(self.model)
-        self.update_ema_every = update_ema_every
-
         self.step_start_ema = step_start_ema
-        self.save_and_sample_every = save_and_sample_every
 
         self.batch_size = train_batch_size
         self.image_size = diffusion_model.image_size
@@ -558,11 +501,11 @@ class Trainer(object):
 
         self.step = 0
 
-        self.amp = amp
-        self.scaler = GradScaler(enabled = amp)
+        assert not fp16 or fp16 and APEX_AVAILABLE, 'Apex must be installed in order for mixed precision training to be turned on'
 
-        self.results_folder = Path(results_folder)
-        self.results_folder.mkdir(exist_ok = True)
+        self.fp16 = fp16
+        if fp16:
+            (self.model, self.ema_model), self.opt = amp.initialize([self.model, self.ema_model], self.opt, opt_level='O1')
 
         self.reset_parameters()
 
@@ -579,46 +522,40 @@ class Trainer(object):
         data = {
             'step': self.step,
             'model': self.model.state_dict(),
-            'ema': self.ema_model.state_dict(),
-            'scaler': self.scaler.state_dict()
+            'ema': self.ema_model.state_dict()
         }
-        torch.save(data, str(self.results_folder / f'model-{milestone}.pt'))
+        torch.save(data, str(RESULTS_FOLDER / f'model-{milestone}.pt'))
 
     def load(self, milestone):
-        data = torch.load(str(self.results_folder / f'model-{milestone}.pt'))
+        data = torch.load(str(RESULTS_FOLDER / f'model-{milestone}.pt'))
 
         self.step = data['step']
         self.model.load_state_dict(data['model'])
         self.ema_model.load_state_dict(data['ema'])
-        self.scaler.load_state_dict(data['scaler'])
 
     def train(self):
+        backwards = partial(loss_backwards, self.fp16)
+
         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):
-                    loss = self.model(data)
-                    self.scaler.scale(loss / self.gradient_accumulate_every).backward()
-
+                loss = self.model(data)
                 print(f'{self.step}: {loss.item()}')
+                backwards(loss / self.gradient_accumulate_every, self.opt)
 
-            self.scaler.step(self.opt)
-            self.scaler.update()
+            self.opt.step()
             self.opt.zero_grad()
 
-            if self.step % self.update_ema_every == 0:
+            if self.step % UPDATE_EMA_EVERY == 0:
                 self.step_ema()
 
-            if self.step != 0 and self.step % self.save_and_sample_every == 0:
-                self.ema_model.eval()
-
-                milestone = self.step // self.save_and_sample_every
+            if self.step != 0 and self.step % SAVE_AND_SAMPLE_EVERY == 0:
+                milestone = self.step // SAVE_AND_SAMPLE_EVERY
                 batches = num_to_groups(36, self.batch_size)
                 all_images_list = list(map(lambda n: self.ema_model.sample(batch_size=n), batches))
                 all_images = torch.cat(all_images_list, dim=0)
-                all_images = unnormalize_to_zero_to_one(all_images)
-                utils.save_image(all_images, str(self.results_folder / f'sample-{milestone}.png'), nrow = 6)
+                all_images = (all_images * 0.5) + 1
+                utils.save_image(all_images, str(RESULTS_FOLDER / f'sample-{milestone}.png'), nrow = 6)
                 self.save(milestone)
 
             self.step += 1

denoising_diffusion_pytorch/learned_gaussian_diffusion.py

@@ -1,145 +0,0 @@
-import torch
-from math import pi, sqrt, log as ln
-from inspect import isfunction
-from torch import nn, einsum
-from einops import rearrange
-
-from denoising_diffusion_pytorch.denoising_diffusion_pytorch import GaussianDiffusion, extract, unnormalize_to_zero_to_one
-
-# constants
-
-NAT = 1. / ln(2)
-
-# helper functions
-
-def exists(x):
-    return x is not None
-
-def default(val, d):
-    if exists(val):
-        return val
-    return d() if isfunction(d) else d
-
-# tensor helpers
-
-def log(t, eps = 1e-12):
-    return torch.log(t.clamp(min = eps))
-
-def meanflat(x):
-    return x.mean(dim = tuple(range(1, len(x.shape))))
-
-def normal_kl(mean1, logvar1, mean2, logvar2):
-    """
-    KL divergence between normal distributions parameterized by mean and log-variance.
-    """
-    return 0.5 * (-1.0 + logvar2 - logvar1 + torch.exp(logvar1 - logvar2) + ((mean1 - mean2) ** 2) * torch.exp(-logvar2))
-
-def approx_standard_normal_cdf(x):
-    return 0.5 * (1.0 + torch.tanh(sqrt(2.0 / pi) * (x + 0.044715 * (x ** 3))))
-
-def discretized_gaussian_log_likelihood(x, *, means, log_scales, thres = 0.999):
-    assert x.shape == means.shape == log_scales.shape
-
-    centered_x = x - means
-    inv_stdv = torch.exp(-log_scales)
-    plus_in = inv_stdv * (centered_x + 1. / 255.)
-    cdf_plus = approx_standard_normal_cdf(plus_in)
-    min_in = inv_stdv * (centered_x - 1. / 255.)
-    cdf_min = approx_standard_normal_cdf(min_in)
-    log_cdf_plus = log(cdf_plus)
-    log_one_minus_cdf_min = log(1. - cdf_min)
-    cdf_delta = cdf_plus - cdf_min
-
-    log_probs = torch.where(x < -thres,
-        log_cdf_plus,
-        torch.where(x > thres,
-            log_one_minus_cdf_min,
-            log(cdf_delta)))
-
-    return log_probs
-
-# https://arxiv.org/abs/2102.09672
-
-# i thought the results were questionable, if one were to focus only on FID
-# but may as well get this in here for others to try, as GLIDE is using it (and DALL-E2 first stage of cascade)
-# gaussian diffusion for learned variance + hybrid eps simple + vb loss
-
-class LearnedGaussianDiffusion(GaussianDiffusion):
-    def __init__(
-        self,
-        denoise_fn,
-        vb_loss_weight = 0.001,  # lambda was 0.001 in the paper
-        *args,
-        **kwargs
-    ):
-        super().__init__(denoise_fn, *args, **kwargs)
-        assert denoise_fn.out_dim == (denoise_fn.channels * 2), 'dimension out of unet must be twice the number of channels for learned variance - you can also set the `learned_variance` keyword argument on the Unet to be `True`'
-        self.vb_loss_weight = vb_loss_weight
-
-    def q_posterior_mean_variance(self, x_start, x_t, t):
-        """
-        Compute the mean and variance of the diffusion posterior q(x_{t-1} | x_t, x_0)
-        """
-        posterior_mean = (
-            extract(self.posterior_mean_coef1, t, x_t.shape) * x_start +
-            extract(self.posterior_mean_coef2, t, x_t.shape) * x_t
-        )
-        posterior_variance = extract(self.posterior_variance, t, x_t.shape)
-        posterior_log_variance_clipped = extract(self.posterior_log_variance_clipped, t, x_t.shape)
-        return posterior_mean, posterior_variance, posterior_log_variance_clipped
-
-    def predict_xstart_from_xprev(self, x_t, t, xprev):
-        # (xprev - coef2*x_t) / coef1
-        return (
-            extract(1. / self.posterior_mean_coef1, t, x_t.shape) * xprev -
-            extract(self.posterior_mean_coef2 / self.posterior_mean_coef1, t, x_t.shape) * x_t
-        )
-
-    def p_mean_variance(self, *, x, t, clip_denoised, model_output = None):
-        model_output = default(model_output, lambda: self.denoise_fn(x, t))
-        pred_noise, var_interp_frac_unnormalized = model_output.chunk(2, dim = 1)
-
-        min_log = extract(self.posterior_log_variance_clipped, t, x.shape)
-        max_log = extract(torch.log(self.betas), t, x.shape)
-        var_interp_frac = unnormalize_to_zero_to_one(var_interp_frac_unnormalized)
-
-        model_log_variance = var_interp_frac * max_log + (1 - var_interp_frac) * min_log
-        model_variance = model_log_variance.exp()
-
-        x_start = self.predict_start_from_noise(x, t, pred_noise)
-        model_mean, _, _ = self.q_posterior(x_start, x, t)
-
-        return model_mean, model_variance, model_log_variance
-
-    def p_losses(self, x_start, t, noise = None, clip_denoised = False):
-        noise = default(noise, lambda: torch.randn_like(x_start))
-        x_t = self.q_sample(x_start = x_start, t = t, noise = noise)
-
-        # model output
-
-        model_output = self.denoise_fn(x_t, t)
-
-        # calculating kl loss for learned variance (interpolation)
-
-        true_mean, _, true_log_variance_clipped = self.q_posterior_mean_variance(x_start = x_start, x_t = x_t, t = t)
-        model_mean, _, model_log_variance = self.p_mean_variance(x = x_t, t = t, clip_denoised = clip_denoised, model_output = model_output)
-
-        # kl loss with detached model predicted mean, for stability reasons as in paper
-
-        kl = normal_kl(true_mean, true_log_variance_clipped, model_mean.detach(), model_log_variance)
-        kl = meanflat(kl) * NAT
-
-        decoder_nll = -discretized_gaussian_log_likelihood(x_start, means = model_mean, log_scales = 0.5 * model_log_variance)
-        decoder_nll = meanflat(decoder_nll) * NAT
-
-        # at the first timestep return the decoder NLL, otherwise return KL(q(x_{t-1}|x_t,x_0) || p(x_{t-1}|x_t))
-
-        vb_losses = torch.where(t == 0, decoder_nll, kl)
-
-        # simple loss - predicting noise, x0, or x_prev
-
-        pred_noise, _ = model_output.chunk(2, dim = 1)
-
-        simple_losses = self.loss_fn(pred_noise, noise)
-
-        return simple_losses + vb_losses.mean() * self.vb_loss_weight

setup.py

@@ -3,7 +3,7 @@ from setuptools import setup, find_packages
 setup(
   name = 'denoising-diffusion-pytorch',
   packages = find_packages(),
-  version = '0.14.0',
+  version = '0.6.4',
   license='MIT',
   description = 'Denoising Diffusion Probabilistic Models - Pytorch',
   author = 'Phil Wang',
@@ -15,6 +15,7 @@ setup(
   ],
   install_requires=[
     'einops',
+    'numpy',
     'pillow',
     'torch',
     'torchvision',