allow for mixed precision training with fp16 flag

.gitignore

@@ -1,6 +1,3 @@
-# Generation results
-results/
-
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]

README.md

@@ -2,14 +2,10 @@
 
 ## 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> and then modified to use <a href="https://arxiv.org/abs/2201.03545">ConvNext</a> blocks instead of Resnets.
+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>
 
-[![PyPI version](https://badge.fury.io/py/denoising-diffusion-pytorch.svg)](https://badge.fury.io/py/denoising-diffusion-pytorch)
-
 ## Install
 
 ```bash
@@ -29,17 +25,18 @@ model = Unet(
 
 diffusion = GaussianDiffusion(
     model,
-    image_size = 128,
-    timesteps = 1000,   # number of steps
-    loss_type = 'l1'    # L1 or L2
+    beta_start = 0.0001,
+    beta_end = 0.02,
+    num_diffusion_timesteps = 1000,   # number of steps
+    loss_type = 'l1'                  # L1 or L2 (wavegrad paper claims l1 is better?)
 )
 
-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
 
-sampled_images = diffusion.sample(batch_size = 4)
+sampled_images = diffusion.sample(128, batch_size = 4)
 sampled_images.shape # (4, 3, 128, 128)
 ```
 
@@ -55,58 +52,38 @@ model = Unet(
 
 diffusion = GaussianDiffusion(
     model,
-    image_size = 128,
-    timesteps = 1000,   # number of steps
-    loss_type = 'l1'    # L1 or L2
+    beta_start = 0.0001,
+    beta_end = 0.02,
+    num_diffusion_timesteps = 1000,   # number of steps
+    loss_type = 'l1'                  # L1 or L2
 ).cuda()
 
 trainer = Trainer(
     diffusion,
     'path/to/your/images',
+    image_size = 128,
     train_batch_size = 32,
     train_lr = 2e-5,
-    train_num_steps = 700000,         # total training steps
+    train_num_steps = 100000,         # 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()
 ```
 
-Samples and model checkpoints will be logged to `./results` periodically
+Todo: Command line tool for one-line training
 
 ## Citations
 
 ```bibtex
 @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{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}
-}
-```
-
-```bibtex
-@misc{liu2022convnet,
-    title   = {A ConvNet for the 2020s},
-    author  = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie},
-    year    = {2022},
-    eprint  = {2201.03545},
-    archivePrefix = {arXiv},
-    primaryClass = {cs.CV}
+    title={Denoising Diffusion Probabilistic Models},
+    author={Jonathan Ho and Ajay Jain and Pieter Abbeel},
+    year={2020},
+    eprint={2006.11239},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
 }
 ```

denoising_diffusion_pytorch/denoising_diffusion_pytorch.py

@@ -7,16 +7,27 @@ 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', 'png']
+
 # helpers functions
 
 def exists(x):
@@ -32,13 +43,12 @@ def cycle(dl):
         for data in dl:
             yield data
 
-def num_to_groups(num, divisor):
-    groups = num // divisor
-    remainder = num % divisor
-    arr = [divisor] * groups
-    if remainder > 0:
-        arr.append(remainder)
-    return arr
+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
 
@@ -79,33 +89,33 @@ 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.fn = fn
-        self.norm = LayerNorm(dim)
+        self.conv = nn.Conv2d(dim, dim, 3, 2, 1)
 
     def forward(self, x):
-        x = self.norm(x)
-        return self.fn(x)
+        return self.conv(x)
+
+class Rezero(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.g = nn.Parameter(torch.zeros(1))
+
+    def forward(self, x):
+        return x * self.g
 
 # building block modules
 
@@ -113,167 +123,64 @@ 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)
         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 ConvNextBlock(nn.Module):
-    """ https://arxiv.org/abs/2201.03545 """
-
-    def __init__(self, dim, dim_out, *, time_emb_dim = None, mult = 2, norm = True):
-        super().__init__()
-        self.mlp = nn.Sequential(
-            nn.GELU(),
-            nn.Linear(time_emb_dim, dim)
-        ) if exists(time_emb_dim) else None
-
-        self.ds_conv = nn.Conv2d(dim, dim, 7, padding = 3, groups = dim)
-
-        self.net = nn.Sequential(
-            LayerNorm(dim) if norm else nn.Identity(),
-            nn.Conv2d(dim, dim_out * mult, 3, padding = 1),
-            nn.GELU(),
-            LayerNorm(dim_out * mult),
-            nn.Conv2d(dim_out * mult, dim_out, 3, padding = 1)
-        )
-
-        self.res_conv = nn.Conv2d(dim, dim_out, 1) if dim != dim_out else nn.Identity()
-
-    def forward(self, x, time_emb = None):
-        h = self.ds_conv(x)
-
-        if exists(self.mlp) and exists(time_emb):
-            assert exists(time_emb), 'time emb must be passed in'
-            condition = self.mlp(time_emb)
-            h = h + rearrange(condition, 'b c -> b c 1 1')
-
-        h = self.net(h)
-        return h + self.res_conv(x)
-
 class LinearAttention(nn.Module):
-    def __init__(self, dim, heads = 4, dim_head = 32):
+    def __init__(self, dim, heads = 8, 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)
+        self.to_qkv = nn.Conv2d(dim, hidden_dim, 1, bias = False)
         self.to_out = nn.Conv2d(hidden_dim, dim, 1)
 
     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)
+        q = q.softmax(dim=-2)
+        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
 
 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,
-        use_convnext = False,
-        resnet_block_groups = 8
-    ):
+    def __init__(self, dim, out_dim = None, dim_mults=(1, 2, 4, 8), groups = 8):
         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 = [3, *map(lambda m: dim * m, dim_mults)]
         in_out = list(zip(dims[:-1], dims[1:]))
 
-        # resnet or convnext
-
-        if use_convnext:
-            block_klass = ConvNextBlock
-        else:
-            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([])
@@ -283,43 +190,42 @@ 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()
             ]))
 
-        out_dim = default(out_dim, channels)
+        out_dim = default(out_dim, 3)
         self.final_conv = nn.Sequential(
-            block_klass(dim, dim),
+            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)
@@ -328,10 +234,10 @@ 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)
 
@@ -349,68 +255,47 @@ def noise_like(shape, device, repeat=False):
     noise = lambda: torch.randn(shape, device=device)
     return repeat_noise() if repeat else noise()
 
-def cosine_beta_schedule(timesteps, s = 0.008):
-    """
-    cosine schedule
-    as proposed in https://openreview.net/forum?id=-NEXDKk8gZ
-    """
-    steps = timesteps + 1
-    x = torch.linspace(0, timesteps, steps)
-    alphas_cumprod = torch.cos(((x / timesteps) + s) / (1 + s) * torch.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)
-
 class GaussianDiffusion(nn.Module):
-    def __init__(
-        self,
-        denoise_fn,
-        *,
-        image_size,
-        channels = 3,
-        timesteps = 1000,
-        loss_type = 'l1'
-    ):
+    def __init__(self, denoise_fn, beta_start=0.0001, beta_end=0.02, num_diffusion_timesteps=1000, loss_type='l1', betas = None):
         super().__init__()
-        self.channels = channels
-        self.image_size = image_size
         self.denoise_fn = denoise_fn
 
-        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.)
+        if exists(betas):
+            self.np_betas = betas.detach().cpu().numpy() if isinstance(betas, torch.Tensor) else betas
+        else:
+            self.np_betas = betas = np.linspace(beta_start, beta_end, num_diffusion_timesteps).astype(np.float64)
 
         timesteps, = betas.shape
         self.num_timesteps = int(timesteps)
         self.loss_type = loss_type
 
-        self.register_buffer('betas', betas)
-        self.register_buffer('alphas_cumprod', alphas_cumprod)
-        self.register_buffer('alphas_cumprod_prev', alphas_cumprod_prev)
+        alphas = 1. - betas
+        alphas_cumprod = np.cumprod(alphas, axis=0)
+        alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
+
+        to_torch = partial(torch.tensor, dtype=torch.float32)
+
+        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
-
-        self.register_buffer('sqrt_alphas_cumprod', torch.sqrt(alphas_cumprod))
-        self.register_buffer('sqrt_one_minus_alphas_cumprod', torch.sqrt(1. - alphas_cumprod))
-        self.register_buffer('log_one_minus_alphas_cumprod', torch.log(1. - alphas_cumprod))
-        self.register_buffer('sqrt_recip_alphas_cumprod', torch.sqrt(1. / alphas_cumprod))
-        self.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)
-
-        self.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
-
-        self.register_buffer('posterior_log_variance_clipped', torch.log(posterior_variance.clamp(min =1e-20)))
-        self.register_buffer('posterior_mean_coef1', betas * torch.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))
-        self.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
@@ -463,10 +348,8 @@ class GaussianDiffusion(nn.Module):
         return img
 
     @torch.no_grad()
-    def sample(self, batch_size = 16):
-        image_size = self.image_size
-        channels = self.channels
-        return self.p_sample_loop((batch_size, channels, image_size, image_size))
+    def sample(self, image_size, batch_size = 16):
+        return self.p_sample_loop((16, 3, image_size, image_size))
 
     @torch.no_grad()
     def interpolate(self, x1, x2, t = None, lam = 0.5):
@@ -509,26 +392,24 @@ class GaussianDiffusion(nn.Module):
         return loss
 
     def forward(self, x, *args, **kwargs):
-        b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
-        assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
+        b, *_, device = *x.shape, x.device
         t = torch.randint(0, self.num_timesteps, (b,), device=device).long()
         return self.p_losses(x, t, *args, **kwargs)
 
 # 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(lambda t: (t * 2) - 1)
+            transforms.ToTensor()
         ])
 
     def __len__(self):
@@ -553,23 +434,14 @@ 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
     ):
         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
+        self.image_size = image_size
         self.gradient_accumulate_every = gradient_accumulate_every
         self.train_num_steps = train_num_steps
 
@@ -579,11 +451,11 @@ 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()
 
@@ -591,7 +463,7 @@ class Trainer(object):
         self.ema_model.load_state_dict(self.model.state_dict())
 
     def step_ema(self):
-        if self.step < self.step_start_ema:
+        if self.step < 2000:
             self.reset_parameters()
             return
         self.ema.update_model_average(self.ema_model, self.model)
@@ -600,44 +472,37 @@ 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, f'./model-{milestone}.pt')
 
     def load(self, milestone):
-        data = torch.load(str(self.results_folder / f'model-{milestone}.pt'))
+        data = torch.load(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:
-                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_model.sample(batch_size=n), batches))
-                all_images = torch.cat(all_images_list, dim=0)
-                all_images = (all_images + 1) * 0.5
-                utils.save_image(all_images, str(self.results_folder / f'sample-{milestone}.png'), nrow = 6)
+            if self.step % SAVE_AND_SAMPLE_EVERY == 0:
+                milestone = self.step // SAVE_AND_SAMPLE_EVERY
+                all_images = self.ema_model.p_sample_loop((64, 3, self.image_size, self.image_size))
+                utils.save_image(all_images, f'./sample-{milestone}.png', nrow=8)
                 self.save(milestone)
 
             self.step += 1

setup.py

@@ -3,7 +3,7 @@ from setuptools import setup, find_packages
 setup(
   name = 'denoising-diffusion-pytorch',
   packages = find_packages(),
-  version = '0.11.0',
+  version = '0.2.3',
   license='MIT',
   description = 'Denoising Diffusion Probabilistic Models - Pytorch',
   author = 'Phil Wang',
@@ -15,8 +15,9 @@ setup(
   ],
   install_requires=[
     'einops',
+    'numpy',
     'pillow',
-    'torch',
+    'torch>=1.6',
     'torchvision',
     'tqdm'
   ],