replace resnets with convnext blocks

.gitignore

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

README.md

@@ -2,10 +2,14 @@
 
 ## 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> and then modified to use <a href="https://arxiv.org/abs/2201.03545">ConvNext</a> blocks instead of Resnets.
 
 <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
@@ -25,10 +29,9 @@ model = Unet(
 
 diffusion = GaussianDiffusion(
     model,
-    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?)
+    image_size = 128,
+    timesteps = 1000,   # number of steps
+    loss_type = 'l1'    # L1 or L2
 )
 
 training_images = torch.randn(8, 3, 128, 128)
@@ -36,7 +39,7 @@ loss = diffusion(training_images)
 loss.backward()
 # after a lot of training
 
-sampled_images = diffusion.sample(128, batch_size = 4)
+sampled_images = diffusion.sample(batch_size = 4)
 sampled_images.shape # (4, 3, 128, 128)
 ```
 
@@ -52,19 +55,17 @@ model = Unet(
 
 diffusion = GaussianDiffusion(
     model,
-    beta_start = 0.0001,
-    beta_end = 0.02,
-    num_diffusion_timesteps = 1000,   # number of steps
-    loss_type = 'l1'                  # L1 or L2
+    image_size = 128,
+    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 = 100000,         # total training steps
+    train_num_steps = 700000,         # total training steps
     gradient_accumulate_every = 2,    # gradient accumulation steps
     ema_decay = 0.995,                # exponential moving average decay
     fp16 = True                       # turn on mixed precision training with apex
@@ -73,17 +74,39 @@ trainer = Trainer(
 trainer.train()
 ```
 
-Todo: Command line tool for one-line training
+Samples and model checkpoints will be logged to `./results` periodically
 
 ## 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}
+    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}
 }
 ```

denoising_diffusion_pytorch/denoising_diffusion_pytorch.py

@@ -22,12 +22,6 @@ try:
 except:
     APEX_AVAILABLE = False
 
-# constants
-
-SAVE_AND_SAMPLE_EVERY = 1000
-UPDATE_EMA_EVERY = 10
-EXTS = ['jpg', 'png']
-
 # helpers functions
 
 def exists(x):
@@ -43,6 +37,14 @@ 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:
@@ -89,98 +91,119 @@ class SinusoidalPosEmb(nn.Module):
         emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
         return emb
 
-class Mish(nn.Module):
-    def forward(self, x):
-        return x * torch.tanh(F.softplus(x))
+def Upsample(dim):
+    return nn.ConvTranspose2d(dim, dim, 4, 2, 1)
 
-class Upsample(nn.Module):
-    def __init__(self, dim):
+def Downsample(dim):
+    return nn.Conv2d(dim, dim, 3, 2, 1)
+
+class LayerNorm(nn.Module):
+    def __init__(self, dim, eps = 1e-5):
         super().__init__()
-        self.conv = nn.ConvTranspose2d(dim, dim, 4, 2, 1)
+        self.eps = eps
+        self.g = nn.Parameter(torch.ones(1, dim, 1, 1))
+        self.b = nn.Parameter(torch.zeros(1, dim, 1, 1))
 
     def forward(self, x):
-        return self.conv(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
 
-class Downsample(nn.Module):
-    def __init__(self, dim):
+class PreNorm(nn.Module):
+    def __init__(self, dim, fn):
         super().__init__()
-        self.conv = nn.Conv2d(dim, dim, 3, 2, 1)
+        self.fn = fn
+        self.norm = LayerNorm(dim)
 
     def forward(self, 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
+        x = self.norm(x)
+        return self.fn(x)
 
 # building block modules
 
-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.GroupNorm(groups, dim_out),
-            Mish()
-        )
-    def forward(self, x):
-        return self.block(x)
+class ConvNextBlock(nn.Module):
+    """ https://arxiv.org/abs/2201.03545 """
 
-class ResnetBlock(nn.Module):
-    def __init__(self, dim, dim_out, *, time_emb_dim, groups = 8):
+    def __init__(self, dim, dim_out, *, time_emb_dim = None, mult = 2, norm = True):
         super().__init__()
         self.mlp = nn.Sequential(
-            Mish(),
-            nn.Linear(time_emb_dim, dim_out)
+            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, 1),
+            nn.GELU(),
+            LayerNorm(dim_out * mult),
+            nn.Conv2d(dim_out * mult, dim_out, 1)
         )
 
-        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):
-        h = self.block1(x)
-        h += self.mlp(time_emb)[:, :, None, None]
-        h = self.block2(h)
+    def forward(self, x, time_emb = None):
+        h = self.ds_conv(x)
+
+        if exists(self.mlp):
+            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 = 8, dim_head = 32):
+    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, 1, bias = False)
+        self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 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)
-        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)
+        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
+
+        k = k.softmax(dim = -1)
+        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)
 
 # model
 
 class Unet(nn.Module):
-    def __init__(self, dim, out_dim = None, dim_mults=(1, 2, 4, 8), groups = 8):
+    def __init__(
+        self,
+        dim,
+        out_dim = None,
+        dim_mults=(1, 2, 4, 8),
+        channels = 3,
+        with_time_emb = True
+    ):
         super().__init__()
-        dims = [3, *map(lambda m: dim * m, dim_mults)]
+        self.channels = channels
+
+        dims = [channels, *map(lambda m: dim * m, dim_mults)]
         in_out = list(zip(dims[:-1], dims[1:]))
 
-        self.time_pos_emb = SinusoidalPosEmb(dim)
-        self.mlp = nn.Sequential(
-            nn.Linear(dim, dim * 4),
-            Mish(),
-            nn.Linear(dim * 4, dim)
-        )
+        if with_time_emb:
+            time_dim = dim
+            self.time_mlp = nn.Sequential(
+                SinusoidalPosEmb(dim),
+                nn.Linear(dim, dim * 4),
+                nn.GELU(),
+                nn.Linear(dim * 4, dim)
+            )
+        else:
+            time_dim = None
+            self.time_mlp = None
 
         self.downs = nn.ModuleList([])
         self.ups = nn.ModuleList([])
@@ -190,42 +213,41 @@ class Unet(nn.Module):
             is_last = ind >= (num_resolutions - 1)
 
             self.downs.append(nn.ModuleList([
-                ResnetBlock(dim_in, dim_out, time_emb_dim = dim),
-                ResnetBlock(dim_out, dim_out, time_emb_dim = dim),
-                Residual(Rezero(LinearAttention(dim_out))),
+                ConvNextBlock(dim_in, dim_out, time_emb_dim = time_dim, norm = ind != 0),
+                ConvNextBlock(dim_out, dim_out, time_emb_dim = time_dim),
+                Residual(PreNorm(dim_out, LinearAttention(dim_out))),
                 Downsample(dim_out) if not is_last else nn.Identity()
             ]))
 
         mid_dim = dims[-1]
-        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)
+        self.mid_block1 = ConvNextBlock(mid_dim, mid_dim, time_emb_dim = time_dim)
+        self.mid_attn = Residual(PreNorm(mid_dim, LinearAttention(mid_dim)))
+        self.mid_block2 = ConvNextBlock(mid_dim, mid_dim, time_emb_dim = time_dim)
 
         for ind, (dim_in, dim_out) in enumerate(reversed(in_out[1:])):
             is_last = ind >= (num_resolutions - 1)
 
             self.ups.append(nn.ModuleList([
-                ResnetBlock(dim_out * 2, dim_in, time_emb_dim = dim),
-                ResnetBlock(dim_in, dim_in, time_emb_dim = dim),
-                Residual(Rezero(LinearAttention(dim_in))),
+                ConvNextBlock(dim_out * 2, dim_in, time_emb_dim = time_dim),
+                ConvNextBlock(dim_in, dim_in, time_emb_dim = time_dim),
+                Residual(PreNorm(dim_in, LinearAttention(dim_in))),
                 Upsample(dim_in) if not is_last else nn.Identity()
             ]))
 
-        out_dim = default(out_dim, 3)
+        out_dim = default(out_dim, channels)
         self.final_conv = nn.Sequential(
-            Block(dim, dim),
+            ConvNextBlock(dim, dim),
             nn.Conv2d(dim, out_dim, 1)
         )
 
     def forward(self, x, time):
-        t = self.time_pos_emb(time)
-        t = self.mlp(t)
+        t = self.time_mlp(time) if exists(self.time_mlp) else None
 
         h = []
 
-        for resnet, resnet2, attn, downsample in self.downs:
-            x = resnet(x, t)
-            x = resnet2(x, t)
+        for convnext, convnext2, attn, downsample in self.downs:
+            x = convnext(x, t)
+            x = convnext2(x, t)
             x = attn(x)
             h.append(x)
             x = downsample(x)
@@ -234,10 +256,10 @@ class Unet(nn.Module):
         x = self.mid_attn(x)
         x = self.mid_block2(x, t)
 
-        for resnet, resnet2, attn, upsample in self.ups:
+        for convnext, convnext2, attn, upsample in self.ups:
             x = torch.cat((x, h.pop()), dim=1)
-            x = resnet(x, t)
-            x = resnet2(x, t)
+            x = convnext(x, t)
+            x = convnext2(x, t)
             x = attn(x)
             x = upsample(x)
 
@@ -255,24 +277,47 @@ 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 = 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 np.clip(betas, a_min = 0, a_max = 0.999)
+
 class GaussianDiffusion(nn.Module):
-    def __init__(self, denoise_fn, beta_start=0.0001, beta_end=0.02, num_diffusion_timesteps=1000, loss_type='l1', betas = None):
+    def __init__(
+        self,
+        denoise_fn,
+        *,
+        image_size,
+        channels = 3,
+        timesteps = 1000,
+        loss_type = 'l1',
+        betas = None
+    ):
         super().__init__()
+        self.channels = channels
+        self.image_size = image_size
         self.denoise_fn = denoise_fn
 
         if exists(betas):
-            self.np_betas = betas.detach().cpu().numpy() if isinstance(betas, torch.Tensor) else betas
+            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
+            betas = cosine_beta_schedule(timesteps)
 
         alphas = 1. - betas
         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
+
         to_torch = partial(torch.tensor, dtype=torch.float32)
 
         self.register_buffer('betas', to_torch(betas))
@@ -348,8 +393,10 @@ class GaussianDiffusion(nn.Module):
         return img
 
     @torch.no_grad()
-    def sample(self, image_size, batch_size = 16):
-        return self.p_sample_loop((16, 3, image_size, image_size))
+    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))
 
     @torch.no_grad()
     def interpolate(self, x1, x2, t = None, lam = 0.5):
@@ -392,24 +439,26 @@ class GaussianDiffusion(nn.Module):
         return loss
 
     def forward(self, x, *args, **kwargs):
-        b, *_, device = *x.shape, x.device
+        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}'
         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):
+    def __init__(self, folder, image_size, exts = ['jpg', 'jpeg', 'png']):
         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.ToTensor(),
+            transforms.Lambda(lambda t: (t * 2) - 1)
         ])
 
     def __len__(self):
@@ -434,14 +483,23 @@ class Trainer(object):
         train_lr = 2e-5,
         train_num_steps = 100000,
         gradient_accumulate_every = 2,
-        fp16 = False
+        fp16 = False,
+        step_start_ema = 2000,
+        update_ema_every = 10,
+        save_and_sample_every = 1000,
+        results_folder = './results'
     ):
         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.image_size = image_size
+        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.gradient_accumulate_every = gradient_accumulate_every
         self.train_num_steps = train_num_steps
 
@@ -457,13 +515,16 @@ class Trainer(object):
         if fp16:
             (self.model, self.ema_model), self.opt = amp.initialize([self.model, self.ema_model], self.opt, opt_level='O1')
 
+        self.results_folder = Path(results_folder)
+        self.results_folder.mkdir(exist_ok = True)
+
         self.reset_parameters()
 
     def reset_parameters(self):
         self.ema_model.load_state_dict(self.model.state_dict())
 
     def step_ema(self):
-        if self.step < 2000:
+        if self.step < self.step_start_ema:
             self.reset_parameters()
             return
         self.ema.update_model_average(self.ema_model, self.model)
@@ -474,10 +535,10 @@ class Trainer(object):
             'model': self.model.state_dict(),
             'ema': self.ema_model.state_dict()
         }
-        torch.save(data, f'./model-{milestone}.pt')
+        torch.save(data, str(self.results_folder / f'model-{milestone}.pt'))
 
     def load(self, milestone):
-        data = torch.load(f'./model-{milestone}.pt')
+        data = torch.load(str(self.results_folder / f'model-{milestone}.pt'))
 
         self.step = data['step']
         self.model.load_state_dict(data['model'])
@@ -496,13 +557,16 @@ class Trainer(object):
             self.opt.step()
             self.opt.zero_grad()
 
-            if self.step % UPDATE_EMA_EVERY == 0:
+            if self.step % self.update_ema_every == 0:
                 self.step_ema()
 
-            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)
+            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)
                 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 = '0.2.4',
+  version = '0.7.0',
   license='MIT',
   description = 'Denoising Diffusion Probabilistic Models - Pytorch',
   author = 'Phil Wang',