save intermediate grad tts

@

+import string
+from abc import abstractmethod
+
+import numpy as np
+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, num_groups=32, eps=1e-6):
+    return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=eps, 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 TimestepBlock(nn.Module):
+    """
+    Any module where forward() takes timestep embeddings as a second argument.
+    """
+
+    @abstractmethod
+    def forward(self, x, emb):
+        """
+        Apply the module to `x` given `emb` timestep embeddings.
+        """
+
+
+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=False, 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, self.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=False, dims=2, out_channels=None, padding=1, name="conv"):
+        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)
+        self.name = name
+
+        if use_conv:
+            conv = conv_nd(dims, self.channels, self.out_channels, 3, stride=stride, padding=padding)
+        else:
+            assert self.channels == self.out_channels
+            conv = avg_pool_nd(dims, kernel_size=stride, stride=stride)
+
+        if name == "conv":
+            self.conv = conv
+        else:
+            self.op = conv
+
+    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)
+
+        if self.name == "conv":
+            return self.conv(x)
+        else:
+            return self.op(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)
+#
+#
+# TODO (patil-suraj): needs test
+# 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)
+
+
+# RESNETS
+
+# unet_glide.py & unet_ldm.py
+class ResBlock(TimestepBlock):
+    """
+    A residual block that can optionally change the number of channels.
+
+    :param channels: the number of input channels. :param emb_channels: the number of timestep embedding channels.
+    :param dropout: the rate of dropout. :param out_channels: if specified, the number of out channels. :param
+    use_conv: if True and out_channels is specified, use a spatial
+        convolution instead of a smaller 1x1 convolution to change the channels in the skip connection.
+    :param dims: determines if the signal is 1D, 2D, or 3D. :param use_checkpoint: if True, use gradient checkpointing
+    on this module. :param up: if True, use this block for upsampling. :param down: if True, use this block for
+    downsampling.
+    """
+
+    def __init__(
+        self,
+        channels,
+        emb_channels,
+        dropout,
+        out_channels=None,
+        use_conv=False,
+        use_scale_shift_norm=False,
+        dims=2,
+        use_checkpoint=False,
+        up=False,
+        down=False,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.emb_channels = emb_channels
+        self.dropout = dropout
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.use_checkpoint = use_checkpoint
+        self.use_scale_shift_norm = use_scale_shift_norm
+
+        self.in_layers = nn.Sequential(
+            normalization(channels, swish=1.0),
+            nn.Identity(),
+            conv_nd(dims, channels, self.out_channels, 3, padding=1),
+        )
+
+        self.updown = up or down
+
+        if up:
+            self.h_upd = Upsample(channels, use_conv=False, dims=dims)
+            self.x_upd = Upsample(channels, use_conv=False, dims=dims)
+        elif down:
+            self.h_upd = Downsample(channels, use_conv=False, dims=dims, padding=1, name="op")
+            self.x_upd = Downsample(channels, use_conv=False, dims=dims, padding=1, name="op")
+        else:
+            self.h_upd = self.x_upd = nn.Identity()
+
+        self.emb_layers = nn.Sequential(
+            nn.SiLU(),
+            linear(
+                emb_channels,
+                2 * self.out_channels if use_scale_shift_norm else self.out_channels,
+            ),
+        )
+        self.out_layers = nn.Sequential(
+            normalization(self.out_channels, swish=0.0 if use_scale_shift_norm else 1.0),
+            nn.SiLU() if use_scale_shift_norm else nn.Identity(),
+            nn.Dropout(p=dropout),
+            zero_module(conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)),
+        )
+
+        if self.out_channels == channels:
+            self.skip_connection = nn.Identity()
+        elif use_conv:
+            self.skip_connection = conv_nd(dims, channels, self.out_channels, 3, padding=1)
+        else:
+            self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
+
+    def forward(self, x, emb):
+        """
+        Apply the block to a Tensor, conditioned on a timestep embedding.
+
+        :param x: an [N x C x ...] Tensor of features. :param emb: an [N x emb_channels] Tensor of timestep embeddings.
+        :return: an [N x C x ...] Tensor of outputs.
+        """
+        if self.updown:
+            in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
+            h = in_rest(x)
+            h = self.h_upd(h)
+            x = self.x_upd(x)
+            h = in_conv(h)
+        else:
+            h = self.in_layers(x)
+        emb_out = self.emb_layers(emb).type(h.dtype)
+        while len(emb_out.shape) < len(h.shape):
+            emb_out = emb_out[..., None]
+        if self.use_scale_shift_norm:
+            out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
+            scale, shift = torch.chunk(emb_out, 2, dim=1)
+            h = out_norm(h) * (1 + scale) + shift
+            h = out_rest(h)
+        else:
+            h = h + emb_out
+            h = self.out_layers(h)
+        return self.skip_connection(x) + h
+
+
+# unet.py
+class OLD_ResnetBlock(nn.Module):
+    def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False, dropout, temb_channels=512):
+        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.norm1 = Normalize(in_channels)
+        self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
+        self.temb_proj = torch.nn.Linear(temb_channels, out_channels)
+        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)
+
+        h = h + self.temb_proj(nonlinearity(temb))[:, :, None, None]
+
+        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
+
+
+class ResnetBlock(nn.Module):
+    def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False, dropout, temb_channels=512, groups=32, pre_norm=True, eps=1e-6):
+        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.pre_norm = pre_norm
+
+        if self.pre_norm:
+            self.norm1 = Normalize(in_channels, num_groups=groups, eps=eps)
+        else:
+            self.norm1 = Normalize(out_channels, num_groups=groups, eps=eps)
+
+        self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
+        self.temb_proj = torch.nn.Linear(temb_channels, out_channels)
+        self.norm2 = Normalize(out_channels, num_groups=groups, eps=eps)
+        self.dropout = torch.nn.Dropout(dropout)
+        self.conv2 = torch.nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
+        self.nonlinearity = nonlinearity
+
+        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)
+
+#        num_groups = 8
+#        self.pre_norm = False
+#        eps = 1e-5
+#        self.nonlinearity = Mish()
+
+    def forward(self, x, temb, mask=None):
+        if mask is None:
+            mask = torch.ones_like(x)
+
+        h = x
+
+        h = h * mask
+        if self.pre_norm:
+            h = self.norm1(h)
+            h = self.nonlinearity(h)
+
+        h = self.conv1(h)
+
+        if not self.pre_norm:
+            h = self.norm1(h)
+            h = self.nonlinearity(h)
+            h = h * mask
+
+        h = h + self.temb_proj(self.nonlinearity(temb))[:, :, None, None]
+
+        if self.pre_norm:
+            h = self.norm2(h)
+            h = self.nonlinearity(h)
+
+        h = h * mask
+        h = self.dropout(h)
+        h = self.conv2(h)
+
+        if not self.pre_norm:
+            h = self.norm2(h)
+            h = self.nonlinearity(h)
+            h = h * mask
+
+        x = x * mask
+        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
+
+
+# unet_grad_tts.py
+class ResnetBlockGradTTS(torch.nn.Module):
+    def __init__(self, dim, dim_out, time_emb_dim, groups=8, eps=1e-6, overwrite=True, conv_shortcut=False, pre_norm=True):
+        super(ResnetBlockGradTTS, self).__init__()
+        self.mlp = torch.nn.Sequential(Mish(), torch.nn.Linear(time_emb_dim, dim_out))
+        self.pre_norm = pre_norm
+
+        self.block1 = Block(dim, dim_out, groups=groups)
+        self.block2 = Block(dim_out, dim_out, groups=groups)
+        if dim != dim_out:
+            self.res_conv = torch.nn.Conv2d(dim, dim_out, 1)
+        else:
+            self.res_conv = torch.nn.Identity()
+
+        self.overwrite = overwrite
+        if self.overwrite:
+            in_channels = dim
+            out_channels = dim_out
+            temb_channels = time_emb_dim
+
+            # To set via init
+            self.pre_norm = False
+            eps = 1e-5
+
+            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
+
+            if self.pre_norm:
+                self.norm1 = Normalize(in_channels, num_groups=groups, eps=eps)
+            else:
+                self.norm1 = Normalize(out_channels, num_groups=groups, eps=eps)
+
+            self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
+            self.temb_proj = torch.nn.Linear(temb_channels, out_channels)
+            self.norm2 = Normalize(out_channels, num_groups=groups, eps=eps)
+            dropout = 0.0
+            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)
+
+            self.nonlinearity = Mish()
+
+        self.is_overwritten = False
+
+    def set_weights(self):
+        self.conv1.weight.data = self.block1.block[0].weight.data
+        self.conv1.bias.data = self.block1.block[0].bias.data
+        self.norm1.weight.data = self.block1.block[1].weight.data
+        self.norm1.bias.data = self.block1.block[1].bias.data
+
+        self.conv2.weight.data = self.block2.block[0].weight.data
+        self.conv2.bias.data = self.block2.block[0].bias.data
+        self.norm2.weight.data = self.block2.block[1].weight.data
+        self.norm2.bias.data = self.block2.block[1].bias.data
+
+        self.temb_proj.weight.data = self.mlp[1].weight.data
+        self.temb_proj.bias.data = self.mlp[1].bias.data
+
+        if self.in_channels != self.out_channels:
+            self.nin_shortcut.weight.data = self.res_conv.weight.data
+            self.nin_shortcut.bias.data = self.res_conv.bias.data
+
+    def forward(self, x, mask, time_emb):
+        h = self.block1(x, mask)
+        h += self.mlp(time_emb).unsqueeze(-1).unsqueeze(-1)
+        h = self.block2(h, mask)
+        output = h + self.res_conv(x * mask)
+
+        output_2 = self.forward_2(x, time_emb, mask=mask)
+        return output
+
+    def forward_2(self, x, temb, mask=None):
+        if not self.is_overwritten:
+            self.set_weights()
+            self.is_overwritten = True
+
+        if mask is None:
+            mask = torch.ones_like(x)
+
+        h = x
+
+        h = h * mask
+        if self.pre_norm:
+            h = self.norm1(h)
+            h = self.nonlinearity(h)
+
+        h = self.conv1(h)
+
+        if not self.pre_norm:
+            h = self.norm1(h)
+            h = self.nonlinearity(h)
+        h = h * mask
+
+        h = h + self.temb_proj(self.nonlinearity(temb))[:, :, None, None]
+
+        h = h * mask
+        if self.pre_norm:
+            h = self.norm2(h)
+            h = self.nonlinearity(h)
+
+        h = self.dropout(h)
+        h = self.conv2(h)
+
+        if not self.pre_norm:
+            h = self.norm2(h)
+            h = self.nonlinearity(h)
+        h = h * mask
+
+        x = x * mask
+        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
+
+
+class Block(torch.nn.Module):
+    def __init__(self, dim, dim_out, groups=8):
+        super(Block, self).__init__()
+        self.block = torch.nn.Sequential(
+            torch.nn.Conv2d(dim, dim_out, 3, padding=1), torch.nn.GroupNorm(groups, dim_out), Mish()
+        )
+
+    def forward(self, x, mask):
+        output = self.block(x * mask)
+        return output * mask
+
+
+# unet_score_estimation.py
+class ResnetBlockBigGANpp(nn.Module):
+    def __init__(
+        self,
+        act,
+        in_ch,
+        out_ch=None,
+        temb_dim=None,
+        up=False,
+        down=False,
+        dropout=0.1,
+        fir=False,
+        fir_kernel=(1, 3, 3, 1),
+        skip_rescale=True,
+        init_scale=0.0,
+    ):
+        super().__init__()
+
+        out_ch = out_ch if out_ch else in_ch
+        self.GroupNorm_0 = nn.GroupNorm(num_groups=min(in_ch // 4, 32), num_channels=in_ch, eps=1e-6)
+        self.up = up
+        self.down = down
+        self.fir = fir
+        self.fir_kernel = fir_kernel
+
+        self.Conv_0 = conv3x3(in_ch, out_ch)
+        if temb_dim is not None:
+            self.Dense_0 = nn.Linear(temb_dim, out_ch)
+            self.Dense_0.weight.data = default_init()(self.Dense_0.weight.shape)
+            nn.init.zeros_(self.Dense_0.bias)
+
+        self.GroupNorm_1 = nn.GroupNorm(num_groups=min(out_ch // 4, 32), num_channels=out_ch, eps=1e-6)
+        self.Dropout_0 = nn.Dropout(dropout)
+        self.Conv_1 = conv3x3(out_ch, out_ch, init_scale=init_scale)
+        if in_ch != out_ch or up or down:
+            self.Conv_2 = conv1x1(in_ch, out_ch)
+
+        self.skip_rescale = skip_rescale
+        self.act = act
+        self.in_ch = in_ch
+        self.out_ch = out_ch
+
+    def forward(self, x, temb=None):
+        h = self.act(self.GroupNorm_0(x))
+
+        if self.up:
+            if self.fir:
+                h = upsample_2d(h, self.fir_kernel, factor=2)
+                x = upsample_2d(x, self.fir_kernel, factor=2)
+            else:
+                h = naive_upsample_2d(h, factor=2)
+                x = naive_upsample_2d(x, factor=2)
+        elif self.down:
+            if self.fir:
+                h = downsample_2d(h, self.fir_kernel, factor=2)
+                x = downsample_2d(x, self.fir_kernel, factor=2)
+            else:
+                h = naive_downsample_2d(h, factor=2)
+                x = naive_downsample_2d(x, factor=2)
+
+        h = self.Conv_0(h)
+        # Add bias to each feature map conditioned on the time embedding
+        if temb is not None:
+            h += self.Dense_0(self.act(temb))[:, :, None, None]
+        h = self.act(self.GroupNorm_1(h))
+        h = self.Dropout_0(h)
+        h = self.Conv_1(h)
+
+        if self.in_ch != self.out_ch or self.up or self.down:
+            x = self.Conv_2(x)
+
+        if not self.skip_rescale:
+            return x + h
+        else:
+            return (x + h) / np.sqrt(2.0)
+
+
+# unet_score_estimation.py
+class ResnetBlockDDPMpp(nn.Module):
+    """ResBlock adapted from DDPM."""
+
+    def __init__(
+        self,
+        act,
+        in_ch,
+        out_ch=None,
+        temb_dim=None,
+        conv_shortcut=False,
+        dropout=0.1,
+        skip_rescale=False,
+        init_scale=0.0,
+    ):
+        super().__init__()
+        out_ch = out_ch if out_ch else in_ch
+        self.GroupNorm_0 = nn.GroupNorm(num_groups=min(in_ch // 4, 32), num_channels=in_ch, eps=1e-6)
+        self.Conv_0 = conv3x3(in_ch, out_ch)
+        if temb_dim is not None:
+            self.Dense_0 = nn.Linear(temb_dim, out_ch)
+            self.Dense_0.weight.data = default_init()(self.Dense_0.weight.data.shape)
+            nn.init.zeros_(self.Dense_0.bias)
+        self.GroupNorm_1 = nn.GroupNorm(num_groups=min(out_ch // 4, 32), num_channels=out_ch, eps=1e-6)
+        self.Dropout_0 = nn.Dropout(dropout)
+        self.Conv_1 = conv3x3(out_ch, out_ch, init_scale=init_scale)
+        if in_ch != out_ch:
+            if conv_shortcut:
+                self.Conv_2 = conv3x3(in_ch, out_ch)
+            else:
+                self.NIN_0 = NIN(in_ch, out_ch)
+
+        self.skip_rescale = skip_rescale
+        self.act = act
+        self.out_ch = out_ch
+        self.conv_shortcut = conv_shortcut
+
+    def forward(self, x, temb=None):
+        h = self.act(self.GroupNorm_0(x))
+        h = self.Conv_0(h)
+        if temb is not None:
+            h += self.Dense_0(self.act(temb))[:, :, None, None]
+        h = self.act(self.GroupNorm_1(h))
+        h = self.Dropout_0(h)
+        h = self.Conv_1(h)
+        if x.shape[1] != self.out_ch:
+            if self.conv_shortcut:
+                x = self.Conv_2(x)
+            else:
+                x = self.NIN_0(x)
+        if not self.skip_rescale:
+            return x + h
+        else:
+            return (x + h) / np.sqrt(2.0)
+
+
+# unet_rl.py
+class ResidualTemporalBlock(nn.Module):
+    def __init__(self, inp_channels, out_channels, embed_dim, horizon, kernel_size=5):
+        super().__init__()
+
+        self.blocks = nn.ModuleList(
+            [
+                Conv1dBlock(inp_channels, out_channels, kernel_size),
+                Conv1dBlock(out_channels, out_channels, kernel_size),
+            ]
+        )
+
+        self.time_mlp = nn.Sequential(
+            nn.Mish(),
+            nn.Linear(embed_dim, out_channels),
+            RearrangeDim(),
+            #            Rearrange("batch t -> batch t 1"),
+        )
+
+        self.residual_conv = (
+            nn.Conv1d(inp_channels, out_channels, 1) if inp_channels != out_channels else nn.Identity()
+        )
+
+    def forward(self, x, t):
+        """
+        x : [ batch_size x inp_channels x horizon ] t : [ batch_size x embed_dim ] returns: out : [ batch_size x
+        out_channels x horizon ]
+        """
+        out = self.blocks[0](x) + self.time_mlp(t)
+        out = self.blocks[1](out)
+        return out + self.residual_conv(x)
+
+
+# HELPER Modules
+
+
+def normalization(channels, swish=0.0):
+    """
+    Make a standard normalization layer, with an optional swish activation.
+
+    :param channels: number of input channels. :return: an nn.Module for normalization.
+    """
+    return GroupNorm32(num_channels=channels, num_groups=32, swish=swish)
+
+
+class GroupNorm32(nn.GroupNorm):
+    def __init__(self, num_groups, num_channels, swish, eps=1e-5):
+        super().__init__(num_groups=num_groups, num_channels=num_channels, eps=eps)
+        self.swish = swish
+
+    def forward(self, x):
+        y = super().forward(x.float()).to(x.dtype)
+        if self.swish == 1.0:
+            y = F.silu(y)
+        elif self.swish:
+            y = y * F.sigmoid(y * float(self.swish))
+        return y
+
+
+def linear(*args, **kwargs):
+    """
+    Create a linear module.
+    """
+    return nn.Linear(*args, **kwargs)
+
+
+def zero_module(module):
+    """
+    Zero out the parameters of a module and return it.
+    """
+    for p in module.parameters():
+        p.detach().zero_()
+    return module
+
+
+class Mish(torch.nn.Module):
+    def forward(self, x):
+        return x * torch.tanh(torch.nn.functional.softplus(x))
+
+
+class Conv1dBlock(nn.Module):
+    """
+    Conv1d --> GroupNorm --> Mish
+    """
+
+    def __init__(self, inp_channels, out_channels, kernel_size, n_groups=8):
+        super().__init__()
+
+        self.block = nn.Sequential(
+            nn.Conv1d(inp_channels, out_channels, kernel_size, padding=kernel_size // 2),
+            RearrangeDim(),
+            #            Rearrange("batch channels horizon -> batch channels 1 horizon"),
+            nn.GroupNorm(n_groups, out_channels),
+            RearrangeDim(),
+            #            Rearrange("batch channels 1 horizon -> batch channels horizon"),
+            nn.Mish(),
+        )
+
+    def forward(self, x):
+        return self.block(x)
+
+
+class RearrangeDim(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, tensor):
+        if len(tensor.shape) == 2:
+            return tensor[:, :, None]
+        if len(tensor.shape) == 3:
+            return tensor[:, :, None, :]
+        elif len(tensor.shape) == 4:
+            return tensor[:, :, 0, :]
+        else:
+            raise ValueError(f"`len(tensor)`: {len(tensor)} has to be 2, 3 or 4.")
+
+
+def conv1x1(in_planes, out_planes, stride=1, bias=True, init_scale=1.0, padding=0):
+    """1x1 convolution with DDPM initialization."""
+    conv = nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, padding=padding, bias=bias)
+    conv.weight.data = default_init(init_scale)(conv.weight.data.shape)
+    nn.init.zeros_(conv.bias)
+    return conv
+
+
+def conv3x3(in_planes, out_planes, stride=1, bias=True, dilation=1, init_scale=1.0, padding=1):
+    """3x3 convolution with DDPM initialization."""
+    conv = nn.Conv2d(
+        in_planes, out_planes, kernel_size=3, stride=stride, padding=padding, dilation=dilation, bias=bias
+    )
+    conv.weight.data = default_init(init_scale)(conv.weight.data.shape)
+    nn.init.zeros_(conv.bias)
+    return conv
+
+
+def default_init(scale=1.0):
+    """The same initialization used in DDPM."""
+    scale = 1e-10 if scale == 0 else scale
+    return variance_scaling(scale, "fan_avg", "uniform")
+
+
+def variance_scaling(scale, mode, distribution, in_axis=1, out_axis=0, dtype=torch.float32, device="cpu"):
+    """Ported from JAX."""
+
+    def _compute_fans(shape, in_axis=1, out_axis=0):
+        receptive_field_size = np.prod(shape) / shape[in_axis] / shape[out_axis]
+        fan_in = shape[in_axis] * receptive_field_size
+        fan_out = shape[out_axis] * receptive_field_size
+        return fan_in, fan_out
+
+    def init(shape, dtype=dtype, device=device):
+        fan_in, fan_out = _compute_fans(shape, in_axis, out_axis)
+        if mode == "fan_in":
+            denominator = fan_in
+        elif mode == "fan_out":
+            denominator = fan_out
+        elif mode == "fan_avg":
+            denominator = (fan_in + fan_out) / 2
+        else:
+            raise ValueError("invalid mode for variance scaling initializer: {}".format(mode))
+        variance = scale / denominator
+        if distribution == "normal":
+            return torch.randn(*shape, dtype=dtype, device=device) * np.sqrt(variance)
+        elif distribution == "uniform":
+            return (torch.rand(*shape, dtype=dtype, device=device) * 2.0 - 1.0) * np.sqrt(3 * variance)
+        else:
+            raise ValueError("invalid distribution for variance scaling initializer")
+
+    return init
+
+
+def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
+    return upfirdn2d_native(input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1])
+
+
+def upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1):
+    _, channel, in_h, in_w = input.shape
+    input = input.reshape(-1, in_h, in_w, 1)
+
+    _, in_h, in_w, minor = input.shape
+    kernel_h, kernel_w = kernel.shape
+
+    out = input.view(-1, in_h, 1, in_w, 1, minor)
+    out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1])
+    out = out.view(-1, in_h * up_y, in_w * up_x, minor)
+
+    out = F.pad(out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)])
+    out = out[
+        :,
+        max(-pad_y0, 0) : out.shape[1] - max(-pad_y1, 0),
+        max(-pad_x0, 0) : out.shape[2] - max(-pad_x1, 0),
+        :,
+    ]
+
+    out = out.permute(0, 3, 1, 2)
+    out = out.reshape([-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1])
+    w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
+    out = F.conv2d(out, w)
+    out = out.reshape(
+        -1,
+        minor,
+        in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,
+        in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1,
+    )
+    out = out.permute(0, 2, 3, 1)
+    out = out[:, ::down_y, ::down_x, :]
+
+    out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
+    out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
+
+    return out.view(-1, channel, out_h, out_w)
+
+
+def upsample_2d(x, k=None, factor=2, gain=1):
+    r"""Upsample a batch of 2D images with the given filter.
+
+    Args:
+    Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]` and upsamples each image with the given
+    filter. The filter is normalized so that if the input pixels are constant, they will be scaled by the specified
+    `gain`. Pixels outside the image are assumed to be zero, and the filter is padded with zeros so that its shape is a:
+    multiple of the upsampling factor.
+        x: Input tensor of the shape `[N, C, H, W]` or `[N, H, W,
+          C]`.
+        k: FIR filter of the shape `[firH, firW]` or `[firN]`
+          (separable). The default is `[1] * factor`, which corresponds to nearest-neighbor upsampling.
+        factor: Integer upsampling factor (default: 2). gain: Scaling factor for signal magnitude (default: 1.0).
+
+    Returns:
+        Tensor of the shape `[N, C, H * factor, W * factor]`
+    """
+    assert isinstance(factor, int) and factor >= 1
+    if k is None:
+        k = [1] * factor
+    k = _setup_kernel(k) * (gain * (factor**2))
+    p = k.shape[0] - factor
+    return upfirdn2d(x, torch.tensor(k, device=x.device), up=factor, pad=((p + 1) // 2 + factor - 1, p // 2))
+
+
+def downsample_2d(x, k=None, factor=2, gain=1):
+    r"""Downsample a batch of 2D images with the given filter.
+
+    Args:
+    Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]` and downsamples each image with the
+    given filter. The filter is normalized so that if the input pixels are constant, they will be scaled by the
+    specified `gain`. Pixels outside the image are assumed to be zero, and the filter is padded with zeros so that its
+    shape is a multiple of the downsampling factor.
+        x: Input tensor of the shape `[N, C, H, W]` or `[N, H, W,
+          C]`.
+        k: FIR filter of the shape `[firH, firW]` or `[firN]`
+          (separable). The default is `[1] * factor`, which corresponds to average pooling.
+        factor: Integer downsampling factor (default: 2). gain: Scaling factor for signal magnitude (default: 1.0).
+
+    Returns:
+        Tensor of the shape `[N, C, H // factor, W // factor]`
+    """
+
+    assert isinstance(factor, int) and factor >= 1
+    if k is None:
+        k = [1] * factor
+    k = _setup_kernel(k) * gain
+    p = k.shape[0] - factor
+    return upfirdn2d(x, torch.tensor(k, device=x.device), down=factor, pad=((p + 1) // 2, p // 2))
+
+
+def naive_upsample_2d(x, factor=2):
+    _N, C, H, W = x.shape
+    x = torch.reshape(x, (-1, C, H, 1, W, 1))
+    x = x.repeat(1, 1, 1, factor, 1, factor)
+    return torch.reshape(x, (-1, C, H * factor, W * factor))
+
+
+def naive_downsample_2d(x, factor=2):
+    _N, C, H, W = x.shape
+    x = torch.reshape(x, (-1, C, H // factor, factor, W // factor, factor))
+    return torch.mean(x, dim=(3, 5))
+
+
+class NIN(nn.Module):
+    def __init__(self, in_dim, num_units, init_scale=0.1):
+        super().__init__()
+        self.W = nn.Parameter(default_init(scale=init_scale)((in_dim, num_units)), requires_grad=True)
+        self.b = nn.Parameter(torch.zeros(num_units), requires_grad=True)
+
+    def forward(self, x):
+        x = x.permute(0, 2, 3, 1)
+        y = contract_inner(x, self.W) + self.b
+        return y.permute(0, 3, 1, 2)
+
+
+def _setup_kernel(k):
+    k = np.asarray(k, dtype=np.float32)
+    if k.ndim == 1:
+        k = np.outer(k, k)
+    k /= np.sum(k)
+    assert k.ndim == 2
+    assert k.shape[0] == k.shape[1]
+    return k
+
+
+def contract_inner(x, y):
+    """tensordot(x, y, 1)."""
+    x_chars = list(string.ascii_lowercase[: len(x.shape)])
+    y_chars = list(string.ascii_lowercase[len(x.shape) : len(y.shape) + len(x.shape)])
+    y_chars[0] = x_chars[-1]  # first axis of y and last of x get summed
+    out_chars = x_chars[:-1] + y_chars[1:]
+    return _einsum(x_chars, y_chars, out_chars, x, y)
+
+
+def _einsum(a, b, c, x, y):
+    einsum_str = "{},{}->{}".format("".join(a), "".join(b), "".join(c))
+    return torch.einsum(einsum_str, x, y)

src/diffusers/models/resnet.py

@@ -46,8 +46,8 @@ def conv_transpose_nd(dims, *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 Normalize(in_channels, num_groups=32, eps=1e-6):
+    return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=eps, affine=True)
 
 
 def nonlinearity(x, swish=1.0):
@@ -341,7 +341,7 @@ class ResBlock(TimestepBlock):
 
 
 # unet.py
-class ResnetBlock(nn.Module):
+class OLD_ResnetBlock(nn.Module):
     def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False, dropout, temb_channels=512):
         super().__init__()
         self.in_channels = in_channels
@@ -383,11 +383,129 @@ class ResnetBlock(nn.Module):
         return x + h
 
 
+class ResnetBlock(nn.Module):
+    def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False, dropout=0.0, temb_channels=512, groups=32, pre_norm=True, eps=1e-6, non_linearity="swish", overwrite_for_grad_tts=False):
+        super().__init__()
+        self.pre_norm = pre_norm
+        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
+
+        if self.pre_norm:
+            self.norm1 = Normalize(in_channels, num_groups=groups, eps=eps)
+        else:
+            self.norm1 = Normalize(out_channels, num_groups=groups, eps=eps)
+
+        self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
+        self.temb_proj = torch.nn.Linear(temb_channels, out_channels)
+        self.norm2 = Normalize(out_channels, num_groups=groups, eps=eps)
+        self.dropout = torch.nn.Dropout(dropout)
+        self.conv2 = torch.nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
+        if non_linearity == "swish":
+            self.nonlinearity = nonlinearity
+        elif non_linearity == "mish":
+            self.nonlinearity = Mish()
+
+        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)
+
+        self.is_overwritten = False
+        self.overwrite_for_grad_tts = overwrite_for_grad_tts
+        if self.overwrite_for_grad_tts:
+            dim = in_channels
+            dim_out = out_channels
+            time_emb_dim = temb_channels
+            self.mlp = torch.nn.Sequential(Mish(), torch.nn.Linear(time_emb_dim, dim_out))
+            self.pre_norm = pre_norm
+
+            self.block1 = Block(dim, dim_out, groups=groups)
+            self.block2 = Block(dim_out, dim_out, groups=groups)
+            if dim != dim_out:
+                self.res_conv = torch.nn.Conv2d(dim, dim_out, 1)
+            else:
+                self.res_conv = torch.nn.Identity()
+
+#        num_groups = 8
+#        self.pre_norm = False
+#        eps = 1e-5
+#        non_linearity = "mish"
+
+    def set_weights_grad_tts(self):
+        self.conv1.weight.data = self.block1.block[0].weight.data
+        self.conv1.bias.data = self.block1.block[0].bias.data
+        self.norm1.weight.data = self.block1.block[1].weight.data
+        self.norm1.bias.data = self.block1.block[1].bias.data
+
+        self.conv2.weight.data = self.block2.block[0].weight.data
+        self.conv2.bias.data = self.block2.block[0].bias.data
+        self.norm2.weight.data = self.block2.block[1].weight.data
+        self.norm2.bias.data = self.block2.block[1].bias.data
+
+        self.temb_proj.weight.data = self.mlp[1].weight.data
+        self.temb_proj.bias.data = self.mlp[1].bias.data
+
+        if self.in_channels != self.out_channels:
+            self.nin_shortcut.weight.data = self.res_conv.weight.data
+            self.nin_shortcut.bias.data = self.res_conv.bias.data
+
+    def forward(self, x, temb, mask=None):
+        if not self.pre_norm:
+            temp = mask
+            mask = temb
+            temb = temp
+
+        if self.overwrite_for_grad_tts and not self.is_overwritten:
+            self.set_weights_grad_tts()
+            self.is_overwritten = True
+
+        h = x
+        h = h * mask if mask is not None else h
+        if self.pre_norm:
+            h = self.norm1(h)
+            h = self.nonlinearity(h)
+
+        h = self.conv1(h)
+
+        if not self.pre_norm:
+            h = self.norm1(h)
+            h = self.nonlinearity(h)
+        h = h * mask if mask is not None else h
+
+        h = h + self.temb_proj(self.nonlinearity(temb))[:, :, None, None]
+
+        h = h * mask if mask is not None else h
+        if self.pre_norm:
+            h = self.norm2(h)
+            h = self.nonlinearity(h)
+
+        h = self.dropout(h)
+        h = self.conv2(h)
+
+        if not self.pre_norm:
+            h = self.norm2(h)
+            h = self.nonlinearity(h)
+        h = h * mask if mask is not None else h
+
+        x = x * mask if mask is not None else x
+        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
+
+
 # unet_grad_tts.py
 class ResnetBlockGradTTS(torch.nn.Module):
-    def __init__(self, dim, dim_out, time_emb_dim, groups=8):
+    def __init__(self, dim, dim_out, time_emb_dim, groups=8, eps=1e-6, overwrite=True, conv_shortcut=False, pre_norm=True):
         super(ResnetBlockGradTTS, self).__init__()
         self.mlp = torch.nn.Sequential(Mish(), torch.nn.Linear(time_emb_dim, dim_out))
+        self.pre_norm = pre_norm
 
         self.block1 = Block(dim, dim_out, groups=groups)
         self.block2 = Block(dim_out, dim_out, groups=groups)
@@ -396,45 +514,126 @@ class ResnetBlockGradTTS(torch.nn.Module):
         else:
             self.res_conv = torch.nn.Identity()
 
+        self.overwrite = overwrite
+        if self.overwrite:
+            in_channels = dim
+            out_channels = dim_out
+            temb_channels = time_emb_dim
+
+            # To set via init
+            self.pre_norm = False
+            eps = 1e-5
+
+            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
+
+            if self.pre_norm:
+                self.norm1 = Normalize(in_channels, num_groups=groups, eps=eps)
+            else:
+                self.norm1 = Normalize(out_channels, num_groups=groups, eps=eps)
+
+            self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
+            self.temb_proj = torch.nn.Linear(temb_channels, out_channels)
+            self.norm2 = Normalize(out_channels, num_groups=groups, eps=eps)
+            dropout = 0.0
+            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)
+
+            self.nonlinearity = Mish()
+
+        self.is_overwritten = False
+
+    def set_weights(self):
+        self.conv1.weight.data = self.block1.block[0].weight.data
+        self.conv1.bias.data = self.block1.block[0].bias.data
+        self.norm1.weight.data = self.block1.block[1].weight.data
+        self.norm1.bias.data = self.block1.block[1].bias.data
+
+        self.conv2.weight.data = self.block2.block[0].weight.data
+        self.conv2.bias.data = self.block2.block[0].bias.data
+        self.norm2.weight.data = self.block2.block[1].weight.data
+        self.norm2.bias.data = self.block2.block[1].bias.data
+
+        self.temb_proj.weight.data = self.mlp[1].weight.data
+        self.temb_proj.bias.data = self.mlp[1].bias.data
+
+        if self.in_channels != self.out_channels:
+            self.nin_shortcut.weight.data = self.res_conv.weight.data
+            self.nin_shortcut.bias.data = self.res_conv.bias.data
+
     def forward(self, x, mask, time_emb):
         h = self.block1(x, mask)
         h += self.mlp(time_emb).unsqueeze(-1).unsqueeze(-1)
         h = self.block2(h, mask)
         output = h + self.res_conv(x * mask)
+
+        output = self.forward_2(x, time_emb, mask=mask)
         return output
 
+    def forward_2(self, x, temb, mask=None):
+        if not self.is_overwritten:
+            self.set_weights()
+            self.is_overwritten = True
 
-# unet_rl.py
-class ResidualTemporalBlock(nn.Module):
-    def __init__(self, inp_channels, out_channels, embed_dim, horizon, kernel_size=5):
-        super().__init__()
+        if mask is None:
+            mask = torch.ones_like(x)
 
-        self.blocks = nn.ModuleList(
-            [
-                Conv1dBlock(inp_channels, out_channels, kernel_size),
-                Conv1dBlock(out_channels, out_channels, kernel_size),
-            ]
-        )
+        h = x
 
-        self.time_mlp = nn.Sequential(
-            nn.Mish(),
-            nn.Linear(embed_dim, out_channels),
-            RearrangeDim(),
-            #            Rearrange("batch t -> batch t 1"),
-        )
+        h = h * mask
+        if self.pre_norm:
+            h = self.norm1(h)
+            h = self.nonlinearity(h)
 
-        self.residual_conv = (
-            nn.Conv1d(inp_channels, out_channels, 1) if inp_channels != out_channels else nn.Identity()
+        h = self.conv1(h)
+
+        if not self.pre_norm:
+            h = self.norm1(h)
+            h = self.nonlinearity(h)
+        h = h * mask
+
+        h = h + self.temb_proj(self.nonlinearity(temb))[:, :, None, None]
+
+        h = h * mask
+        if self.pre_norm:
+            h = self.norm2(h)
+            h = self.nonlinearity(h)
+
+        h = self.dropout(h)
+        h = self.conv2(h)
+
+        if not self.pre_norm:
+            h = self.norm2(h)
+            h = self.nonlinearity(h)
+        h = h * mask
+
+        x = x * mask
+        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
+
+
+class Block(torch.nn.Module):
+    def __init__(self, dim, dim_out, groups=8):
+        super(Block, self).__init__()
+        self.block = torch.nn.Sequential(
+            torch.nn.Conv2d(dim, dim_out, 3, padding=1), torch.nn.GroupNorm(groups, dim_out), Mish()
         )
 
-    def forward(self, x, t):
-        """
-        x : [ batch_size x inp_channels x horizon ] t : [ batch_size x embed_dim ] returns: out : [ batch_size x
-        out_channels x horizon ]
-        """
-        out = self.blocks[0](x) + self.time_mlp(t)
-        out = self.blocks[1](out)
-        return out + self.residual_conv(x)
+    def forward(self, x, mask):
+        output = self.block(x * mask)
+        return output * mask
 
 
 # unet_score_estimation.py
@@ -570,6 +769,39 @@ class ResnetBlockDDPMpp(nn.Module):
             return (x + h) / np.sqrt(2.0)
 
 
+# unet_rl.py
+class ResidualTemporalBlock(nn.Module):
+    def __init__(self, inp_channels, out_channels, embed_dim, horizon, kernel_size=5):
+        super().__init__()
+
+        self.blocks = nn.ModuleList(
+            [
+                Conv1dBlock(inp_channels, out_channels, kernel_size),
+                Conv1dBlock(out_channels, out_channels, kernel_size),
+            ]
+        )
+
+        self.time_mlp = nn.Sequential(
+            nn.Mish(),
+            nn.Linear(embed_dim, out_channels),
+            RearrangeDim(),
+            #            Rearrange("batch t -> batch t 1"),
+        )
+
+        self.residual_conv = (
+            nn.Conv1d(inp_channels, out_channels, 1) if inp_channels != out_channels else nn.Identity()
+        )
+
+    def forward(self, x, t):
+        """
+        x : [ batch_size x inp_channels x horizon ] t : [ batch_size x embed_dim ] returns: out : [ batch_size x
+        out_channels x horizon ]
+        """
+        out = self.blocks[0](x) + self.time_mlp(t)
+        out = self.blocks[1](out)
+        return out + self.residual_conv(x)
+
+
 # HELPER Modules
 
 
@@ -617,18 +849,6 @@ class Mish(torch.nn.Module):
         return x * torch.tanh(torch.nn.functional.softplus(x))
 
 
-class Block(torch.nn.Module):
-    def __init__(self, dim, dim_out, groups=8):
-        super(Block, self).__init__()
-        self.block = torch.nn.Sequential(
-            torch.nn.Conv2d(dim, dim_out, 3, padding=1), torch.nn.GroupNorm(groups, dim_out), Mish()
-        )
-
-    def forward(self, x, mask):
-        output = self.block(x * mask)
-        return output * mask
-
-
 class Conv1dBlock(nn.Module):
     """
     Conv1d --> GroupNorm --> Mish

src/diffusers/models/unet_grad_tts.py

@@ -5,6 +5,7 @@ from ..modeling_utils import ModelMixin
 from .attention import LinearAttention
 from .embeddings import get_timestep_embedding
 from .resnet import Downsample
+from .resnet import ResnetBlock as ResnetBlockNew
 from .resnet import ResnetBlockGradTTS as ResnetBlock
 from .resnet import Upsample
 
@@ -81,13 +82,20 @@ class UNetGradTTSModel(ModelMixin, ConfigMixin):
         self.ups = torch.nn.ModuleList([])
         num_resolutions = len(in_out)
 
+#        num_groups = 8
+#        self.pre_norm = False
+#        eps = 1e-5
+#        non_linearity = "mish"
+
         for ind, (dim_in, dim_out) in enumerate(in_out):
             is_last = ind >= (num_resolutions - 1)
             self.downs.append(
                 torch.nn.ModuleList(
                     [
-                        ResnetBlock(dim_in, dim_out, time_emb_dim=dim),
-                        ResnetBlock(dim_out, dim_out, time_emb_dim=dim),
+#                        ResnetBlock(dim_in, dim_out, time_emb_dim=dim),
+#                        ResnetBlock(dim_out, dim_out, time_emb_dim=dim),
+                        ResnetBlockNew(in_channels=dim_in, out_channels=dim_out, temb_channels=dim, groups=8, pre_norm=False, eps=1e-5, non_linearity="mish", overwrite_for_grad_tts=True),
+                        ResnetBlockNew(in_channels=dim_out, out_channels=dim_out, temb_channels=dim, groups=8, pre_norm=False, eps=1e-5, non_linearity="mish", overwrite_for_grad_tts=True),
                         Residual(Rezero(LinearAttention(dim_out))),
                         Downsample(dim_out, use_conv=True, padding=1) if not is_last else torch.nn.Identity(),
                     ]
@@ -95,16 +103,20 @@ class UNetGradTTSModel(ModelMixin, ConfigMixin):
             )
 
         mid_dim = dims[-1]
-        self.mid_block1 = ResnetBlock(mid_dim, mid_dim, time_emb_dim=dim)
+#        self.mid_block1 = ResnetBlock(mid_dim, mid_dim, time_emb_dim=dim)
+#        self.mid_block2 = ResnetBlock(mid_dim, mid_dim, time_emb_dim=dim)
+        self.mid_block1 = ResnetBlockNew(in_channels=mid_dim, out_channels=mid_dim, temb_channels=dim, groups=8, pre_norm=False, eps=1e-5, non_linearity="mish", overwrite_for_grad_tts=True)
         self.mid_attn = Residual(Rezero(LinearAttention(mid_dim)))
-        self.mid_block2 = ResnetBlock(mid_dim, mid_dim, time_emb_dim=dim)
+        self.mid_block2 = ResnetBlockNew(in_channels=mid_dim, out_channels=mid_dim, temb_channels=dim, groups=8, pre_norm=False, eps=1e-5, non_linearity="mish", overwrite_for_grad_tts=True)
 
         for ind, (dim_in, dim_out) in enumerate(reversed(in_out[1:])):
             self.ups.append(
                 torch.nn.ModuleList(
                     [
-                        ResnetBlock(dim_out * 2, dim_in, time_emb_dim=dim),
-                        ResnetBlock(dim_in, dim_in, time_emb_dim=dim),
+#                        ResnetBlock(dim_out * 2, dim_in, time_emb_dim=dim),
+#                        ResnetBlock(dim_in, dim_in, time_emb_dim=dim),
+                        ResnetBlockNew(in_channels=dim_out * 2, out_channels=dim_in, temb_channels=dim, groups=8, pre_norm=False, eps=1e-5, non_linearity="mish", overwrite_for_grad_tts=True),
+                        ResnetBlockNew(in_channels=dim_in, out_channels=dim_in, temb_channels=dim, groups=8, pre_norm=False, eps=1e-5, non_linearity="mish", overwrite_for_grad_tts=True),
                         Residual(Rezero(LinearAttention(dim_in))),
                         Upsample(dim_in, use_conv_transpose=True),
                     ]