Merge branch 'main' of https://github.com/huggingface/diffusers into main

examples/train_unconditional.py

@@ -9,14 +9,14 @@ from accelerate import Accelerator
 from datasets import load_dataset
 from diffusers import DDPM, DDPMScheduler, UNetModel
 from diffusers.hub_utils import init_git_repo, push_to_hub
-from diffusers.modeling_utils import unwrap_model
 from diffusers.optimization import get_scheduler
+from diffusers.training_utils import EMAModel
 from diffusers.utils import logging
 from torchvision.transforms import (
     CenterCrop,
     Compose,
     InterpolationMode,
-    Lambda,
+    Normalize,
     RandomHorizontalFlip,
     Resize,
     ToTensor,
@@ -48,7 +48,7 @@ def main(args):
             CenterCrop(args.resolution),
             RandomHorizontalFlip(),
             ToTensor(),
-            Lambda(lambda x: x * 2 - 1),
+            Normalize([0.5], [0.5]),
         ]
     )
     dataset = load_dataset(args.dataset, split="train")
@@ -71,6 +71,8 @@ def main(args):
         model, optimizer, train_dataloader, lr_scheduler
     )
 
+    ema_model = EMAModel(model, inv_gamma=1.0, power=3 / 4)
+
     if args.push_to_hub:
         repo = init_git_repo(args, at_init=True)
 
@@ -87,6 +89,7 @@ def main(args):
     logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
     logger.info(f"  Total optimization steps = {max_steps}")
 
+    global_step = 0
     for epoch in range(args.num_epochs):
         model.train()
         with tqdm(total=len(train_dataloader), unit="ba") as pbar:
@@ -117,19 +120,22 @@ def main(args):
                     torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
                     optimizer.step()
                     lr_scheduler.step()
+                    ema_model.step(model, global_step)
                     optimizer.zero_grad()
                 pbar.update(1)
-                pbar.set_postfix(loss=loss.detach().item(), lr=optimizer.param_groups[0]["lr"])
+                pbar.set_postfix(
+                    loss=loss.detach().item(), lr=optimizer.param_groups[0]["lr"], ema_decay=ema_model.decay
+                )
+                global_step += 1
 
-                optimizer.step()
-        if is_distributed:
-            torch.distributed.barrier()
+        accelerator.wait_for_everyone()
 
         # Generate a sample image for visual inspection
-        if args.local_rank in [-1, 0]:
-            model.eval()
+        if accelerator.is_main_process:
             with torch.no_grad():
-                pipeline = DDPM(unet=unwrap_model(model), noise_scheduler=noise_scheduler)
+                pipeline = DDPM(
+                    unet=accelerator.unwrap_model(ema_model.averaged_model), noise_scheduler=noise_scheduler
+                )
 
                 generator = torch.manual_seed(0)
                 # run pipeline in inference (sample random noise and denoise)
@@ -151,8 +157,7 @@ def main(args):
                 push_to_hub(args, pipeline, repo, commit_message=f"Epoch {epoch}", blocking=False)
             else:
                 pipeline.save_pretrained(args.output_dir)
-        if is_distributed:
-            torch.distributed.barrier()
+        accelerator.wait_for_everyone()
 
 
 if __name__ == "__main__":

src/diffusers/models/resnet.py

@@ -0,0 +1,278 @@
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+def avg_pool_nd(dims, *args, **kwargs):
+    """
+    Create a 1D, 2D, or 3D average pooling module.
+    """
+    if dims == 1:
+        return nn.AvgPool1d(*args, **kwargs)
+    elif dims == 2:
+        return nn.AvgPool2d(*args, **kwargs)
+    elif dims == 3:
+        return nn.AvgPool3d(*args, **kwargs)
+    raise ValueError(f"unsupported dimensions: {dims}")
+
+
+def conv_nd(dims, *args, **kwargs):
+    """
+    Create a 1D, 2D, or 3D convolution module.
+    """
+    if dims == 1:
+        return nn.Conv1d(*args, **kwargs)
+    elif dims == 2:
+        return nn.Conv2d(*args, **kwargs)
+    elif dims == 3:
+        return nn.Conv3d(*args, **kwargs)
+    raise ValueError(f"unsupported dimensions: {dims}")
+
+def conv_transpose_nd(dims, *args, **kwargs):
+    """
+    Create a 1D, 2D, or 3D convolution module.
+    """
+    if dims == 1:
+        return nn.ConvTranspose1d(*args, **kwargs)
+    elif dims == 2:
+        return nn.ConvTranspose2d(*args, **kwargs)
+    elif dims == 3:
+        return nn.ConvTranspose3d(*args, **kwargs)
+    raise ValueError(f"unsupported dimensions: {dims}")
+
+
+def Normalize(in_channels):
+    return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
+
+
+def nonlinearity(x, swish=1.0):
+    # swish
+    if swish == 1.0:
+        return F.silu(x)
+    else:
+        return x * F.sigmoid(x * float(swish))
+
+
+class Upsample(nn.Module):
+    """
+    An upsampling layer with an optional convolution.
+
+    :param channels: channels in the inputs and outputs.
+    :param use_conv: a bool determining if a convolution is applied.
+    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
+                 upsampling occurs in the inner-two dimensions.
+    """
+
+    def __init__(self, channels, use_conv, use_conv_transpose=False, dims=2, out_channels=None):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.dims = dims
+        self.use_conv_transpose = use_conv_transpose
+
+        if use_conv_transpose:
+            self.conv = conv_transpose_nd(dims, channels, out_channels, 4, 2, 1)
+        elif use_conv:
+            self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=1)
+
+    def forward(self, x):
+        assert x.shape[1] == self.channels
+        if self.use_conv_transpose:
+            return self.conv(x)
+        
+        if self.dims == 3:
+            x = F.interpolate(x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest")
+        else:
+            x = F.interpolate(x, scale_factor=2.0, mode="nearest")
+        
+        if self.use_conv:
+            x = self.conv(x)
+        
+        return x
+
+
+class Downsample(nn.Module):
+    """
+    A downsampling layer with an optional convolution.
+
+    :param channels: channels in the inputs and outputs.
+    :param use_conv: a bool determining if a convolution is applied.
+    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
+                 downsampling occurs in the inner-two dimensions.
+    """
+
+    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.dims = dims
+        self.padding = padding
+        stride = 2 if dims != 3 else (1, 2, 2)
+        if use_conv:
+            self.down = conv_nd(dims, self.channels, self.out_channels, 3, stride=stride, padding=padding)
+        else:
+            assert self.channels == self.out_channels
+            self.down = avg_pool_nd(dims, kernel_size=stride, stride=stride)
+
+    def forward(self, x):
+        assert x.shape[1] == self.channels
+        if self.use_conv and self.padding == 0 and self.dims == 2:
+            pad = (0, 1, 0, 1)
+            x = F.pad(x, pad, mode="constant", value=0)
+        return self.down(x)
+
+
+class UNetUpsample(nn.Module):
+    def __init__(self, in_channels, with_conv):
+        super().__init__()
+        self.with_conv = with_conv
+        if self.with_conv:
+            self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
+
+    def forward(self, x):
+        x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
+        if self.with_conv:
+            x = self.conv(x)
+        return x
+
+class GlideUpsample(nn.Module):
+    """
+    An upsampling layer with an optional convolution.
+
+    :param channels: channels in the inputs and outputs.
+    :param use_conv: a bool determining if a convolution is applied.
+    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
+                 upsampling occurs in the inner-two dimensions.
+    """
+
+    def __init__(self, channels, use_conv, dims=2, out_channels=None):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.dims = dims
+        if use_conv:
+            self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=1)
+
+    def forward(self, x):
+        assert x.shape[1] == self.channels
+        if self.dims == 3:
+            x = F.interpolate(x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest")
+        else:
+            x = F.interpolate(x, scale_factor=2, mode="nearest")
+        if self.use_conv:
+            x = self.conv(x)
+        return x
+
+
+class LDMUpsample(nn.Module):
+    """
+    An upsampling layer with an optional convolution.
+    :param channels: channels in the inputs and outputs.
+    :param use_conv: a bool determining if a convolution is applied.
+    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
+                 upsampling occurs in the inner-two dimensions.
+    """
+
+    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.dims = dims
+        if use_conv:
+            self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding)
+
+    def forward(self, x):
+        assert x.shape[1] == self.channels
+        if self.dims == 3:
+            x = F.interpolate(x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest")
+        else:
+            x = F.interpolate(x, scale_factor=2, mode="nearest")
+        if self.use_conv:
+            x = self.conv(x)
+        return x
+
+
+class GradTTSUpsample(torch.nn.Module):
+    def __init__(self, dim):
+        super(Upsample, self).__init__()
+        self.conv = torch.nn.ConvTranspose2d(dim, dim, 4, 2, 1)
+
+    def forward(self, x):
+        return self.conv(x)
+
+
+class Upsample1d(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.conv = nn.ConvTranspose1d(dim, dim, 4, 2, 1)
+
+    def forward(self, x):
+        return self.conv(x)
+
+
+# class ResnetBlock(nn.Module):
+#     def __init__(
+#         self,
+#         *,
+#         in_channels,
+#         out_channels=None,
+#         conv_shortcut=False,
+#         dropout,
+#         temb_channels=512,
+#         use_scale_shift_norm=False,
+#     ):
+#         super().__init__()
+#         self.in_channels = in_channels
+#         out_channels = in_channels if out_channels is None else out_channels
+#         self.out_channels = out_channels
+#         self.use_conv_shortcut = conv_shortcut
+#         self.use_scale_shift_norm = use_scale_shift_norm
+
+#         self.norm1 = Normalize(in_channels)
+#         self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
+
+#         temp_out_channles = 2 * out_channels if use_scale_shift_norm else out_channels
+#         self.temb_proj = torch.nn.Linear(temb_channels, temp_out_channles)
+
+#         self.norm2 = Normalize(out_channels)
+#         self.dropout = torch.nn.Dropout(dropout)
+#         self.conv2 = torch.nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
+#         if self.in_channels != self.out_channels:
+#             if self.use_conv_shortcut:
+#                 self.conv_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
+#             else:
+#                 self.nin_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
+
+#     def forward(self, x, temb):
+#         h = x
+#         h = self.norm1(h)
+#         h = nonlinearity(h)
+#         h = self.conv1(h)
+
+#         # TODO: check if this broadcasting works correctly for 1D and 3D
+#         temb = self.temb_proj(nonlinearity(temb))[:, :, None, None]
+
+#         if self.use_scale_shift_norm:
+#             out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
+#             scale, shift = torch.chunk(temb, 2, dim=1)
+#             h = self.norm2(h) * (1 + scale) + shift
+#             h = out_rest(h)
+#         else:
+#             h = h + temb
+#             h = self.norm2(h)
+#             h = nonlinearity(h)
+#             h = self.dropout(h)
+#             h = self.conv2(h)
+
+#         if self.in_channels != self.out_channels:
+#             if self.use_conv_shortcut:
+#                 x = self.conv_shortcut(x)
+#             else:
+#                 x = self.nin_shortcut(x)
+
+#         return x + h

src/diffusers/training_utils.py

@@ -0,0 +1,88 @@
+import copy
+
+import torch
+
+
+class EMAModel:
+    """
+    Exponential Moving Average of models weights
+    """
+
+    def __init__(
+        self,
+        model,
+        update_after_step=0,
+        inv_gamma=1.0,
+        power=2 / 3,
+        min_value=0.0,
+        max_value=0.9999,
+        device=None,
+    ):
+        """
+        @crowsonkb's notes on EMA Warmup:
+            If gamma=1 and power=1, implements a simple average. gamma=1, power=2/3 are
+            good values for models you plan to train for a million or more steps (reaches decay
+            factor 0.999 at 31.6K steps, 0.9999 at 1M steps), gamma=1, power=3/4 for models
+            you plan to train for less (reaches decay factor 0.999 at 10K steps, 0.9999 at
+            215.4k steps).
+        Args:
+            inv_gamma (float): Inverse multiplicative factor of EMA warmup. Default: 1.
+            power (float): Exponential factor of EMA warmup. Default: 2/3.
+            min_value (float): The minimum EMA decay rate. Default: 0.
+        """
+
+        self.averaged_model = copy.deepcopy(model)
+        self.averaged_model.requires_grad_(False)
+
+        self.update_after_step = update_after_step
+        self.inv_gamma = inv_gamma
+        self.power = power
+        self.min_value = min_value
+        self.max_value = max_value
+
+        if device is not None:
+            self.averaged_model = self.averaged_model.to(device=device)
+
+        self.decay = 0.0
+
+    def get_decay(self, optimization_step):
+        """
+        Compute the decay factor for the exponential moving average.
+        """
+        step = max(0, optimization_step - self.update_after_step - 1)
+        value = 1 - (1 + step / self.inv_gamma) ** -self.power
+
+        if step <= 0:
+            return 0.0
+
+        return max(self.min_value, min(value, self.max_value))
+
+    @torch.no_grad()
+    def step(self, new_model, optimization_step):
+        ema_state_dict = {}
+        ema_params = self.averaged_model.state_dict()
+
+        self.decay = self.get_decay(optimization_step)
+
+        for key, param in new_model.named_parameters():
+            if isinstance(param, dict):
+                continue
+            try:
+                ema_param = ema_params[key]
+            except KeyError:
+                ema_param = param.float().clone() if param.ndim == 1 else copy.deepcopy(param)
+                ema_params[key] = ema_param
+
+            if not param.requires_grad:
+                ema_params[key].copy_(param.to(dtype=ema_param.dtype).data)
+                ema_param = ema_params[key]
+            else:
+                ema_param.mul_(self.decay)
+                ema_param.add_(param.data.to(dtype=ema_param.dtype), alpha=1 - self.decay)
+
+            ema_state_dict[key] = ema_param
+
+        for key, param in new_model.named_buffers():
+            ema_state_dict[key] = param
+
+        self.averaged_model.load_state_dict(ema_state_dict, strict=False)