up

@

+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)
+
+
+# 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_score_estimation.py
+class ResnetBlockBigGANppNew(nn.Module):
+    def __init__(
+        self,
+        act,
+        in_ch,
+        out_ch=None,
+        temb_dim=None,
+        up=False,
+        down=False,
+        dropout=0.1,
+        fir_kernel=(1, 3, 3, 1),
+        skip_rescale=True,
+        init_scale=0.0,
+        overwrite=True,
+    ):
+        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_kernel = fir_kernel
+
+        self.Conv_0 = conv2d(in_ch, out_ch, kernel_size=3, padding=1)
+        if temb_dim is not None:
+            self.Dense_0 = nn.Linear(temb_dim, out_ch)
+            self.Dense_0.weight.data = variance_scaling()(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 = conv2d(out_ch, out_ch, init_scale=init_scale, kernel_size=3, padding=1)
+        if in_ch != out_ch or up or down:
+            # 1x1 convolution with DDPM initialization.
+            self.Conv_2 = conv2d(in_ch, out_ch, kernel_size=1, padding=0)
+
+        self.skip_rescale = skip_rescale
+        self.act = act
+        self.in_ch = in_ch
+        self.out_ch = out_ch
+
+        if self.overwrite:
+            in_channels = in_ch
+            out_channels = out_ch
+            groups = min(in_ch // 4, 32)
+            eps = 1e-6
+            self.pre_norm = True
+            temb_channels = temb_dim
+            non_linearity = "silu"
+            time_embedding_norm = "default"
+
+            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)
+
+            if time_embedding_norm == "default":
+                self.temb_proj = torch.nn.Linear(temb_channels, out_channels)
+            elif time_embedding_norm == "scale_shift":
+                self.temb_proj = torch.nn.Linear(temb_channels, 2 * 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()
+            elif non_linearity == "silu":
+                self.nonlinearity = nn.SiLU()
+
+            if up:
+                self.h_upd = Upsample(in_channels, use_conv=False, dims=2)
+                self.x_upd = Upsample(in_channels, use_conv=False, dims=2)
+            elif down:
+                self.h_upd = Downsample(in_channels, use_conv=False, dims=2, padding=1, name="op")
+                self.x_upd = Downsample(in_channels, use_conv=False, dims=2, padding=1, name="op")
+
+            if self.in_channels != self.out_channels:
+                self.nin_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
+
+    def forward(self, x, temb=None):
+        h = self.act(self.GroupNorm_0(x))
+
+        if self.up:
+            h = upsample_2d(h, self.fir_kernel, factor=2)
+            x = upsample_2d(x, self.fir_kernel, factor=2)
+        elif self.down:
+            h = downsample_2d(h, self.fir_kernel, factor=2)
+            x = downsample_2d(x, self.fir_kernel, 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)
+
+    def forward_2(self, x, temb, mask=1.0):
+        # TODO(Patrick) eventually this class should be split into multiple classes
+        # too many if else statements
+        if self.overwrite_for_grad_tts and not self.is_overwritten:
+            self.set_weights_grad_tts()
+            self.is_overwritten = True
+        elif self.overwrite_for_ldm and not self.is_overwritten:
+            self.set_weights_ldm()
+            self.is_overwritten = True
+
+        h = x
+        h = h * mask
+        if self.pre_norm:
+            h = self.norm1(h)
+            h = self.nonlinearity(h)
+
+        if self.up or self.down:
+            x = self.x_upd(x)
+            h = self.h_upd(h)
+
+        h = self.conv1(h)
+
+        if not self.pre_norm:
+            h = self.norm1(h)
+            h = self.nonlinearity(h)
+        h = h * mask
+
+        temb = self.temb_proj(self.nonlinearity(temb))[:, :, None, None]
+
+        if self.time_embedding_norm == "scale_shift":
+            scale, shift = torch.chunk(temb, 2, dim=1)
+
+            h = self.norm2(h)
+            h = h + h * scale + shift
+            h = self.nonlinearity(h)
+        elif self.time_embedding_norm == "default":
+            h = h + temb
+            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:
+            x = self.nin_shortcut(x)
+
+        return x + h
+
+
+
+# unet.py, unet_grad_tts.py, unet_ldm.py, unet_glide.py
+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",
+        time_embedding_norm="default",
+        up=False,
+        down=False,
+        overwrite_for_grad_tts=False,
+        overwrite_for_ldm=False,
+        overwrite_for_glide=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
+        self.time_embedding_norm = time_embedding_norm
+        self.up = up
+        self.down = down
+
+        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)
+
+        if time_embedding_norm == "default":
+            self.temb_proj = torch.nn.Linear(temb_channels, out_channels)
+        elif time_embedding_norm == "scale_shift":
+            self.temb_proj = torch.nn.Linear(temb_channels, 2 * 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()
+        elif non_linearity == "silu":
+            self.nonlinearity = nn.SiLU()
+
+        if up:
+            self.h_upd = Upsample(in_channels, use_conv=False, dims=2)
+            self.x_upd = Upsample(in_channels, use_conv=False, dims=2)
+        elif down:
+            self.h_upd = Downsample(in_channels, use_conv=False, dims=2, padding=1, name="op")
+            self.x_upd = Downsample(in_channels, use_conv=False, dims=2, padding=1, name="op")
+
+        if self.in_channels != self.out_channels:
+            self.nin_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
+
+        # TODO(SURAJ, PATRICK): ALL OF THE FOLLOWING OF THE INIT METHOD CAN BE DELETED ONCE WEIGHTS ARE CONVERTED
+        self.is_overwritten = False
+        self.overwrite_for_glide = overwrite_for_glide
+        self.overwrite_for_grad_tts = overwrite_for_grad_tts
+        self.overwrite_for_ldm = overwrite_for_ldm or overwrite_for_glide
+        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()
+        elif self.overwrite_for_ldm:
+            dims = 2
+            channels = in_channels
+            emb_channels = temb_channels
+            use_scale_shift_norm = False
+
+            self.in_layers = nn.Sequential(
+                normalization(channels, swish=1.0),
+                nn.Identity(),
+                conv_nd(dims, channels, self.out_channels, 3, padding=1),
+            )
+            self.emb_layers = nn.Sequential(
+                nn.SiLU(),
+                linear(
+                    emb_channels,
+                    2 * self.out_channels if self.time_embedding_norm == "scale_shift" 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 == in_channels:
+                self.skip_connection = nn.Identity()
+            else:
+                self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
+
+    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 set_weights_ldm(self):
+        self.norm1.weight.data = self.in_layers[0].weight.data
+        self.norm1.bias.data = self.in_layers[0].bias.data
+
+        self.conv1.weight.data = self.in_layers[-1].weight.data
+        self.conv1.bias.data = self.in_layers[-1].bias.data
+
+        self.temb_proj.weight.data = self.emb_layers[-1].weight.data
+        self.temb_proj.bias.data = self.emb_layers[-1].bias.data
+
+        self.norm2.weight.data = self.out_layers[0].weight.data
+        self.norm2.bias.data = self.out_layers[0].bias.data
+
+        self.conv2.weight.data = self.out_layers[-1].weight.data
+        self.conv2.bias.data = self.out_layers[-1].bias.data
+
+        if self.in_channels != self.out_channels:
+            self.nin_shortcut.weight.data = self.skip_connection.weight.data
+            self.nin_shortcut.bias.data = self.skip_connection.bias.data
+
+    def forward(self, x, temb, mask=1.0):
+        # TODO(Patrick) eventually this class should be split into multiple classes
+        # too many if else statements
+        if self.overwrite_for_grad_tts and not self.is_overwritten:
+            self.set_weights_grad_tts()
+            self.is_overwritten = True
+        elif self.overwrite_for_ldm and not self.is_overwritten:
+            self.set_weights_ldm()
+            self.is_overwritten = True
+
+        h = x
+        h = h * mask
+        if self.pre_norm:
+            h = self.norm1(h)
+            h = self.nonlinearity(h)
+
+        if self.up or self.down:
+            x = self.x_upd(x)
+            h = self.h_upd(h)
+
+        h = self.conv1(h)
+
+        if not self.pre_norm:
+            h = self.norm1(h)
+            h = self.nonlinearity(h)
+        h = h * mask
+
+        temb = self.temb_proj(self.nonlinearity(temb))[:, :, None, None]
+
+        if self.time_embedding_norm == "scale_shift":
+            scale, shift = torch.chunk(temb, 2, dim=1)
+
+            h = self.norm2(h)
+            h = h + h * scale + shift
+            h = self.nonlinearity(h)
+        elif self.time_embedding_norm == "default":
+            h = h + temb
+            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:
+            x = self.nin_shortcut(x)
+
+        return x + h
+
+
+# TODO(Patrick) - just there to convert the weights; can delete afterward
+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()
+        )
+
+
+# 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_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_kernel = fir_kernel
+
+        self.Conv_0 = conv2d(in_ch, out_ch, kernel_size=3, padding=1)
+        if temb_dim is not None:
+            self.Dense_0 = nn.Linear(temb_dim, out_ch)
+            self.Dense_0.weight.data = variance_scaling()(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 = conv2d(out_ch, out_ch, init_scale=init_scale, kernel_size=3, padding=1)
+        if in_ch != out_ch or up or down:
+            # 1x1 convolution with DDPM initialization.
+            self.Conv_2 = conv2d(in_ch, out_ch, kernel_size=1, padding=0)
+
+        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:
+            h = upsample_2d(h, self.fir_kernel, factor=2)
+            x = upsample_2d(x, self.fir_kernel, factor=2)
+        elif self.down:
+            h = downsample_2d(h, self.fir_kernel, factor=2)
+            x = downsample_2d(x, self.fir_kernel, 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_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 conv2d(in_planes, out_planes, kernel_size=3, stride=1, bias=True, init_scale=1.0, padding=1):
+    """nXn convolution with DDPM initialization."""
+    conv = nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, bias=bias)
+    conv.weight.data = variance_scaling(init_scale)(conv.weight.data.shape)
+    nn.init.zeros_(conv.bias)
+    return conv
+
+
+def variance_scaling(scale=1.0, in_axis=1, out_axis=0, dtype=torch.float32, device="cpu"):
+    """Ported from JAX."""
+    scale = 1e-10 if scale == 0 else scale
+
+    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)
+        denominator = (fan_in + fan_out) / 2
+        variance = scale / denominator
+        return (torch.rand(*shape, dtype=dtype, device=device) * 2.0 - 1.0) * np.sqrt(3 * variance)
+
+    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 _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

G

+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)
+
+
+# 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_score_estimation.py
+class ResnetBlockBigGANppNew(nn.Module):
+    def __init__(
+        self,
+        act,
+        in_ch,
+        out_ch=None,
+        temb_dim=None,
+        up=False,
+        down=False,
+        dropout=0.1,
+        fir_kernel=(1, 3, 3, 1),
+        skip_rescale=True,
+        init_scale=0.0,
+        overwrite=True,
+    ):
+        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_kernel = fir_kernel
+
+        self.Conv_0 = conv2d(in_ch, out_ch, kernel_size=3, padding=1)
+        if temb_dim is not None:
+            self.Dense_0 = nn.Linear(temb_dim, out_ch)
+            self.Dense_0.weight.data = variance_scaling()(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 = conv2d(out_ch, out_ch, init_scale=init_scale, kernel_size=3, padding=1)
+        if in_ch != out_ch or up or down:
+            # 1x1 convolution with DDPM initialization.
+            self.Conv_2 = conv2d(in_ch, out_ch, kernel_size=1, padding=0)
+
+        self.skip_rescale = skip_rescale
+        self.act = act
+        self.in_ch = in_ch
+        self.out_ch = out_ch
+
+        self.is_overwritten = False
+        self.overwrite = overwrite
+        if overwrite:
+            self.in_channels = in_channels = in_ch
+            self.out_channels = out_channels = out_ch
+            groups = min(in_ch // 4, 32)
+            eps = 1e-6
+            self.pre_norm = True
+            temb_channels = temb_dim
+            non_linearity = "silu"
+            self.time_embedding_norm = time_embedding_norm = "default"
+
+            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)
+
+            if time_embedding_norm == "default":
+                self.temb_proj = torch.nn.Linear(temb_channels, out_channels)
+            elif time_embedding_norm == "scale_shift":
+                self.temb_proj = torch.nn.Linear(temb_channels, 2 * 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()
+            elif non_linearity == "silu":
+                self.nonlinearity = nn.SiLU()
+
+            if up:
+                self.h_upd = Upsample(in_channels, use_conv=False, dims=2)
+                self.x_upd = Upsample(in_channels, use_conv=False, dims=2)
+            elif down:
+                self.h_upd = Downsample(in_channels, use_conv=False, dims=2, padding=1, name="op")
+                self.x_upd = Downsample(in_channels, use_conv=False, dims=2, padding=1, name="op")
+
+            if self.in_channels != self.out_channels:
+                self.nin_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
+
+    def set_weights(self):
+        self.conv1.weight.data = self.Conv_0.weight.data
+        self.conv1.bias.data = self.Conv_0.bias.data
+        self.norm1.weight.data = self.GroupNorm_0.weight.data
+        self.norm1.bias.data = self.GroupNorm_0.bias.data
+
+        self.conv2.weight.data = self.Conv_1.weight.data
+        self.conv2.bias.data = self.Conv_1.bias.data
+        self.norm2.weight.data = self.GroupNorm_1.weight.data
+        self.norm2.bias.data = self.GroupNorm_1.bias.data
+
+        self.temb_proj.weight.data = self.Dense_0.weight.data
+        self.temb_proj.bias.data = self.Dense_0.bias.data
+
+        if self.in_channels != self.out_channels:
+            self.nin_shortcut.weight.data = self.Conv_2.weight.data
+            self.nin_shortcut.bias.data = self.Conv_2.bias.data
+
+    def set_weights_ldm(self):
+        import ipdb; ipdb.set_trace()
+
+    def forward(self, x, temb=None):
+        if self.overwrite and not self.is_overwritten:
+            self.set_weights()
+            self.is_overwritten = True
+
+        orig_x = x
+        h = self.act(self.GroupNorm_0(x))
+
+        if self.up:
+            h = upsample_2d(h, self.fir_kernel, factor=2)
+            x = upsample_2d(x, self.fir_kernel, factor=2)
+        elif self.down:
+            h = downsample_2d(h, self.fir_kernel, factor=2)
+            x = downsample_2d(x, self.fir_kernel, 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:
+            result = x + h
+        else:
+            result = (x + h) / np.sqrt(2.0)
+
+        result_2 = self.forward_2(orig_x, temb)
+        print("Diff", (result - result_2).abs().sum())
+        import ipdb; ipdb.set_trace()
+
+        return result
+
+    def forward_2(self, x, temb, mask=1.0):
+        h = x
+        h = h * mask
+        if self.pre_norm:
+            h = self.norm1(h)
+            h = self.nonlinearity(h)
+
+        if self.up or self.down:
+            x = self.x_upd(x)
+            h = self.h_upd(h)
+
+        h = self.conv1(h)
+
+        if not self.pre_norm:
+            h = self.norm1(h)
+            h = self.nonlinearity(h)
+        h = h * mask
+
+        temb = self.temb_proj(self.nonlinearity(temb))[:, :, None, None]
+
+        if self.time_embedding_norm == "scale_shift":
+            scale, shift = torch.chunk(temb, 2, dim=1)
+
+            h = self.norm2(h)
+            h = h + h * scale + shift
+            h = self.nonlinearity(h)
+        elif self.time_embedding_norm == "default":
+            h = h + temb
+            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:
+            x = self.nin_shortcut(x)
+
+        return x + h
+
+
+# unet.py, unet_grad_tts.py, unet_ldm.py, unet_glide.py
+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",
+        time_embedding_norm="default",
+        up=False,
+        down=False,
+        overwrite_for_grad_tts=False,
+        overwrite_for_ldm=False,
+        overwrite_for_glide=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
+        self.time_embedding_norm = time_embedding_norm
+        self.up = up
+        self.down = down
+
+        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)
+
+        if time_embedding_norm == "default":
+            self.temb_proj = torch.nn.Linear(temb_channels, out_channels)
+        elif time_embedding_norm == "scale_shift":
+            self.temb_proj = torch.nn.Linear(temb_channels, 2 * 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()
+        elif non_linearity == "silu":
+            self.nonlinearity = nn.SiLU()
+
+        if up:
+            self.h_upd = Upsample(in_channels, use_conv=False, dims=2)
+            self.x_upd = Upsample(in_channels, use_conv=False, dims=2)
+        elif down:
+            self.h_upd = Downsample(in_channels, use_conv=False, dims=2, padding=1, name="op")
+            self.x_upd = Downsample(in_channels, use_conv=False, dims=2, padding=1, name="op")
+
+        if self.in_channels != self.out_channels:
+            self.nin_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
+
+        # TODO(SURAJ, PATRICK): ALL OF THE FOLLOWING OF THE INIT METHOD CAN BE DELETED ONCE WEIGHTS ARE CONVERTED
+        self.is_overwritten = False
+        self.overwrite_for_glide = overwrite_for_glide
+        self.overwrite_for_grad_tts = overwrite_for_grad_tts
+        self.overwrite_for_ldm = overwrite_for_ldm or overwrite_for_glide
+        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()
+        elif self.overwrite_for_ldm:
+            dims = 2
+            channels = in_channels
+            emb_channels = temb_channels
+            use_scale_shift_norm = False
+
+            self.in_layers = nn.Sequential(
+                normalization(channels, swish=1.0),
+                nn.Identity(),
+                conv_nd(dims, channels, self.out_channels, 3, padding=1),
+            )
+            self.emb_layers = nn.Sequential(
+                nn.SiLU(),
+                linear(
+                    emb_channels,
+                    2 * self.out_channels if self.time_embedding_norm == "scale_shift" 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 == in_channels:
+                self.skip_connection = nn.Identity()
+            else:
+                self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
+
+    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 set_weights_ldm(self):
+        self.norm1.weight.data = self.in_layers[0].weight.data
+        self.norm1.bias.data = self.in_layers[0].bias.data
+
+        self.conv1.weight.data = self.in_layers[-1].weight.data
+        self.conv1.bias.data = self.in_layers[-1].bias.data
+
+        self.temb_proj.weight.data = self.emb_layers[-1].weight.data
+        self.temb_proj.bias.data = self.emb_layers[-1].bias.data
+
+        self.norm2.weight.data = self.out_layers[0].weight.data
+        self.norm2.bias.data = self.out_layers[0].bias.data
+
+        self.conv2.weight.data = self.out_layers[-1].weight.data
+        self.conv2.bias.data = self.out_layers[-1].bias.data
+
+        if self.in_channels != self.out_channels:
+            self.nin_shortcut.weight.data = self.skip_connection.weight.data
+            self.nin_shortcut.bias.data = self.skip_connection.bias.data
+
+    def forward(self, x, temb, mask=1.0):
+        # TODO(Patrick) eventually this class should be split into multiple classes
+        # too many if else statements
+        if self.overwrite_for_grad_tts and not self.is_overwritten:
+            self.set_weights_grad_tts()
+            self.is_overwritten = True
+        elif self.overwrite_for_ldm and not self.is_overwritten:
+            self.set_weights_ldm()
+            self.is_overwritten = True
+
+        h = x
+        h = h * mask
+        if self.pre_norm:
+            h = self.norm1(h)
+            h = self.nonlinearity(h)
+
+        if self.up or self.down:
+            x = self.x_upd(x)
+            h = self.h_upd(h)
+
+        h = self.conv1(h)
+
+        if not self.pre_norm:
+            h = self.norm1(h)
+            h = self.nonlinearity(h)
+        h = h * mask
+
+        temb = self.temb_proj(self.nonlinearity(temb))[:, :, None, None]
+
+        if self.time_embedding_norm == "scale_shift":
+            scale, shift = torch.chunk(temb, 2, dim=1)
+
+            h = self.norm2(h)
+            h = h + h * scale + shift
+            h = self.nonlinearity(h)
+        elif self.time_embedding_norm == "default":
+            h = h + temb
+            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:
+            x = self.nin_shortcut(x)
+
+        return x + h
+
+
+# TODO(Patrick) - just there to convert the weights; can delete afterward
+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()
+        )
+
+
+# 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_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_kernel = fir_kernel
+
+        self.Conv_0 = conv2d(in_ch, out_ch, kernel_size=3, padding=1)
+        if temb_dim is not None:
+            self.Dense_0 = nn.Linear(temb_dim, out_ch)
+            self.Dense_0.weight.data = variance_scaling()(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 = conv2d(out_ch, out_ch, init_scale=init_scale, kernel_size=3, padding=1)
+        if in_ch != out_ch or up or down:
+            # 1x1 convolution with DDPM initialization.
+            self.Conv_2 = conv2d(in_ch, out_ch, kernel_size=1, padding=0)
+
+        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:
+            h = upsample_2d(h, self.fir_kernel, factor=2)
+            x = upsample_2d(x, self.fir_kernel, factor=2)
+        elif self.down:
+            h = downsample_2d(h, self.fir_kernel, factor=2)
+            x = downsample_2d(x, self.fir_kernel, 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_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 conv2d(in_planes, out_planes, kernel_size=3, stride=1, bias=True, init_scale=1.0, padding=1):
+    """nXn convolution with DDPM initialization."""
+    conv = nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, bias=bias)
+    conv.weight.data = variance_scaling(init_scale)(conv.weight.data.shape)
+    nn.init.zeros_(conv.bias)
+    return conv
+
+
+def variance_scaling(scale=1.0, in_axis=1, out_axis=0, dtype=torch.float32, device="cpu"):
+    """Ported from JAX."""
+    scale = 1e-10 if scale == 0 else scale
+
+    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)
+        denominator = (fan_in + fan_out) / 2
+        variance = scale / denominator
+        return (torch.rand(*shape, dtype=dtype, device=device) * 2.0 - 1.0) * np.sqrt(3 * variance)
+
+    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 _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

src/diffusers/models/resnet.py

@@ -160,6 +160,208 @@ class Downsample(nn.Module):
 
 
 # RESNETS
+# unet_score_estimation.py
+class ResnetBlockBigGANppNew(nn.Module):
+    def __init__(
+        self,
+        act,
+        in_ch,
+        out_ch=None,
+        temb_dim=None,
+        up=False,
+        down=False,
+        dropout=0.1,
+        fir_kernel=(1, 3, 3, 1),
+        skip_rescale=True,
+        init_scale=0.0,
+        overwrite=True,
+    ):
+        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_kernel = fir_kernel
+
+        self.Conv_0 = conv2d(in_ch, out_ch, kernel_size=3, padding=1)
+        if temb_dim is not None:
+            self.Dense_0 = nn.Linear(temb_dim, out_ch)
+            self.Dense_0.weight.data = variance_scaling()(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 = conv2d(out_ch, out_ch, init_scale=init_scale, kernel_size=3, padding=1)
+        if in_ch != out_ch or up or down:
+            # 1x1 convolution with DDPM initialization.
+            self.Conv_2 = conv2d(in_ch, out_ch, kernel_size=1, padding=0)
+
+        self.skip_rescale = skip_rescale
+        self.act = act
+        self.in_ch = in_ch
+        self.out_ch = out_ch
+
+        self.is_overwritten = False
+        self.overwrite = overwrite
+        if overwrite:
+            self.output_scale_factor = np.sqrt(2.0)
+            self.in_channels = in_channels = in_ch
+            self.out_channels = out_channels = out_ch
+            groups = min(in_ch // 4, 32)
+            out_groups = min(out_ch // 4, 32)
+            eps = 1e-6
+            self.pre_norm = True
+            temb_channels = temb_dim
+            non_linearity = "silu"
+            self.time_embedding_norm = time_embedding_norm = "default"
+
+            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)
+
+            if time_embedding_norm == "default":
+                self.temb_proj = torch.nn.Linear(temb_channels, out_channels)
+            elif time_embedding_norm == "scale_shift":
+                self.temb_proj = torch.nn.Linear(temb_channels, 2 * out_channels)
+
+            self.norm2 = Normalize(out_channels, num_groups=out_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()
+            elif non_linearity == "silu":
+                self.nonlinearity = nn.SiLU()
+
+            if up:
+                self.h_upd = Upsample(in_channels, use_conv=False, dims=2)
+                self.x_upd = Upsample(in_channels, use_conv=False, dims=2)
+            elif down:
+                self.h_upd = Downsample(in_channels, use_conv=False, dims=2, padding=1, name="op")
+                self.x_upd = Downsample(in_channels, use_conv=False, dims=2, padding=1, name="op")
+
+            if self.in_channels != self.out_channels or self.up or self.down:
+                self.nin_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
+
+    def set_weights(self):
+        self.conv1.weight.data = self.Conv_0.weight.data
+        self.conv1.bias.data = self.Conv_0.bias.data
+        self.norm1.weight.data = self.GroupNorm_0.weight.data
+        self.norm1.bias.data = self.GroupNorm_0.bias.data
+
+        self.conv2.weight.data = self.Conv_1.weight.data
+        self.conv2.bias.data = self.Conv_1.bias.data
+        self.norm2.weight.data = self.GroupNorm_1.weight.data
+        self.norm2.bias.data = self.GroupNorm_1.bias.data
+
+        self.temb_proj.weight.data = self.Dense_0.weight.data
+        self.temb_proj.bias.data = self.Dense_0.bias.data
+
+        if self.in_channels != self.out_channels or self.up or self.down:
+            self.nin_shortcut.weight.data = self.Conv_2.weight.data
+            self.nin_shortcut.bias.data = self.Conv_2.bias.data
+
+    def forward(self, x, temb=None):
+        if self.overwrite and not self.is_overwritten:
+            self.set_weights()
+            self.is_overwritten = True
+
+        orig_x = x
+        h = self.act(self.GroupNorm_0(x))
+
+        if self.up:
+            h = upsample_2d(h, self.fir_kernel, factor=2)
+            x = upsample_2d(x, self.fir_kernel, factor=2)
+        elif self.down:
+            h = downsample_2d(h, self.fir_kernel, factor=2)
+            x = downsample_2d(x, self.fir_kernel, 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:
+            raise ValueError("Is this branch run?!")
+#            import ipdb; ipdb.set_trace()
+            result = x + h
+        else:
+            result = (x + h) / np.sqrt(2.0)
+
+        result_2 = self.forward_2(orig_x, temb)
+
+        return result_2
+
+    def forward_2(self, x, temb, mask=1.0):
+        h = x
+        h = h * mask
+        if self.pre_norm:
+            h = self.norm1(h)
+            h = self.nonlinearity(h)
+
+#        if self.up or self.down:
+#            x = self.x_upd(x)
+#            h = self.h_upd(h)
+        if self.up:
+            h = upsample_2d(h, self.fir_kernel, factor=2)
+            x = upsample_2d(x, self.fir_kernel, factor=2)
+        elif self.down:
+            h = downsample_2d(h, self.fir_kernel, factor=2)
+            x = downsample_2d(x, self.fir_kernel, factor=2)
+
+        h = self.conv1(h)
+
+        if not self.pre_norm:
+            h = self.norm1(h)
+            h = self.nonlinearity(h)
+        h = h * mask
+
+        temb = self.temb_proj(self.nonlinearity(temb))[:, :, None, None]
+
+        if self.time_embedding_norm == "scale_shift":
+            scale, shift = torch.chunk(temb, 2, dim=1)
+
+            h = self.norm2(h)
+            h = h + h * scale + shift
+            h = self.nonlinearity(h)
+        elif self.time_embedding_norm == "default":
+            h = h + temb
+            h = h * mask
+            if self.pre_norm:
+                h = self.norm2(h)
+                h = self.nonlinearity(h)
+        else:
+            raise ValueError("Nananan nanana - don't go here!")
+
+        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.in_channels != self.out_channels or self.up or self.down:
+            x = self.nin_shortcut(x)
+
+        result = x + h
+
+        return result / self.output_scale_factor
+
 
 # unet.py, unet_grad_tts.py, unet_ldm.py, unet_glide.py
 class ResnetBlock(nn.Module):
@@ -245,9 +447,6 @@ class ResnetBlock(nn.Module):
                 self.res_conv = torch.nn.Identity()
         elif self.overwrite_for_ldm:
             dims = 2
-            #            eps = 1e-5
-            #            non_linearity = "silu"
-            #            overwrite_for_ldm
             channels = in_channels
             emb_channels = temb_channels
             use_scale_shift_norm = False

src/diffusers/models/unet_sde_score_estimation.py

@@ -27,7 +27,8 @@ from ..configuration_utils import ConfigMixin
 from ..modeling_utils import ModelMixin
 from .attention import AttentionBlock
 from .embeddings import GaussianFourierProjection, get_timestep_embedding
-from .resnet import ResnetBlockBigGANpp, downsample_2d, upfirdn2d, upsample_2d
+from .resnet import downsample_2d, upfirdn2d, upsample_2d
+from .resnet import ResnetBlockBigGANppNew as ResnetBlockBigGANpp
 
 
 def _setup_kernel(k):