Add unconditional image generation (#79)

* uP

* finish downsampling layers

* finish major refactor

* remove bugus file

src/diffusers/__init__.py

@@ -7,7 +7,7 @@ from .utils import is_inflect_available, is_transformers_available, is_unidecode
 __version__ = "0.0.4"
 
 from .modeling_utils import ModelMixin
-from .models import AutoencoderKL, NCSNpp, TemporalUNet, UNetLDMModel, UNetModel, VQModel
+from .models import AutoencoderKL, NCSNpp, TemporalUNet, UNetLDMModel, UNetModel, UNetUnconditionalModel, VQModel
 from .pipeline_utils import DiffusionPipeline
 from .pipelines import (
     BDDMPipeline,

src/diffusers/models/__init__.py

@@ -22,4 +22,5 @@ from .unet_grad_tts import UNetGradTTSModel
 from .unet_ldm import UNetLDMModel
 from .unet_rl import TemporalUNet
 from .unet_sde_score_estimation import NCSNpp
+from .unet_unconditional import UNetUnconditionalModel
 from .vae import AutoencoderKL, VQModel

src/diffusers/models/attention.py

@@ -93,6 +93,7 @@ class AttentionBlock(nn.Module):
             self.GroupNorm_0 = nn.GroupNorm(num_groups=num_groups, num_channels=channels, eps=1e-6)
         else:
             self.proj_out = zero_module(nn.Conv1d(channels, channels, 1))
+            self.set_weights(self)
 
         self.is_overwritten = False
 
@@ -123,11 +124,11 @@ class AttentionBlock(nn.Module):
             self.norm.weight.data = self.GroupNorm_0.weight.data
             self.norm.bias.data = self.GroupNorm_0.bias.data
         else:
-            self.proj.weight.data = module.proj_out.weight.data
-            self.proj.bias.data = module.proj_out.bias.data
+            self.proj.weight.data = self.proj_out.weight.data
+            self.proj.bias.data = self.proj_out.bias.data
 
     def forward(self, x, encoder_out=None):
-        if not self.is_overwritten:
+        if not self.is_overwritten and (self.overwrite_qkv or self.overwrite_linear):
             self.set_weights(self)
             self.is_overwritten = True
 
@@ -164,6 +165,77 @@ class AttentionBlock(nn.Module):
         return result
 
 
+class AttentionBlockNew(nn.Module):
+    """
+    An attention block that allows spatial positions to attend to each other.
+
+    Originally ported from here, but adapted to the N-d case.
+    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
+    """
+
+    def __init__(
+        self,
+        channels,
+        num_head_channels=1,
+        num_groups=32,
+        encoder_channels=None,
+        rescale_output_factor=1.0,
+    ):
+        super().__init__()
+        self.norm = nn.GroupNorm(num_channels=channels, num_groups=num_groups, eps=1e-5, affine=True)
+        self.qkv = nn.Conv1d(channels, channels * 3, 1)
+        self.n_heads = channels // num_head_channels
+        self.rescale_output_factor = rescale_output_factor
+
+        if encoder_channels is not None:
+            self.encoder_kv = nn.Conv1d(encoder_channels, channels * 2, 1)
+
+        self.proj = zero_module(nn.Conv1d(channels, channels, 1))
+
+    def set_weight(self, attn_layer):
+        self.norm.weight.data = attn_layer.norm.weight.data
+        self.norm.bias.data = attn_layer.norm.bias.data
+
+        self.qkv.weight.data = attn_layer.qkv.weight.data
+        self.qkv.bias.data = attn_layer.qkv.bias.data
+
+        self.proj.weight.data = attn_layer.proj.weight.data
+        self.proj.bias.data = attn_layer.proj.bias.data
+
+    def forward(self, x, encoder_out=None):
+        b, c, *spatial = x.shape
+        hid_states = self.norm(x).view(b, c, -1)
+
+        qkv = self.qkv(hid_states)
+        bs, width, length = qkv.shape
+        assert width % (3 * self.n_heads) == 0
+        ch = width // (3 * self.n_heads)
+        q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
+
+        if encoder_out is not None:
+            encoder_kv = self.encoder_kv(encoder_out)
+            assert encoder_kv.shape[1] == self.n_heads * ch * 2
+            ek, ev = encoder_kv.reshape(bs * self.n_heads, ch * 2, -1).split(ch, dim=1)
+            k = torch.cat([ek, k], dim=-1)
+            v = torch.cat([ev, v], dim=-1)
+
+        scale = 1 / math.sqrt(math.sqrt(ch))
+        weight = torch.einsum("bct,bcs->bts", q * scale, k * scale)  # More stable with f16 than dividing afterwards
+        weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
+
+        a = torch.einsum("bts,bcs->bct", weight, v)
+        h = a.reshape(bs, -1, length)
+
+        h = self.proj(h)
+        h = h.reshape(b, c, *spatial)
+
+        result = x + h
+
+        result = result / self.rescale_output_factor
+
+        return result
+
+
 class SpatialTransformer(nn.Module):
     """
     Transformer block for image-like data. First, project the input (aka embedding) and reshape to b, t, d. Then apply

src/diffusers/models/resnet.py

@@ -461,6 +461,7 @@ class ResnetBlock2D(nn.Module):
                 self.skip_connection = nn.Identity()
             else:
                 self.skip_connection = nn.Conv2d(channels, self.out_channels, 1)
+            self.set_weights_ldm()
         elif self.overwrite_for_score_vde:
             in_ch = in_channels
             out_ch = out_channels
@@ -550,9 +551,6 @@ class ResnetBlock2D(nn.Module):
         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
         elif self.overwrite_for_score_vde and not self.is_overwritten:
             self.set_weights_score_vde()
             self.is_overwritten = True
@@ -610,6 +608,162 @@ class ResnetBlock2D(nn.Module):
         return (x + h) / self.output_scale_factor
 
 
+class ResnetBlock(nn.Module):
+    def __init__(
+        self,
+        *,
+        in_channels,
+        out_channels=None,
+        conv_shortcut=False,
+        dropout=0.0,
+        temb_channels=512,
+        groups=32,
+        groups_out=None,
+        pre_norm=True,
+        eps=1e-6,
+        non_linearity="swish",
+        time_embedding_norm="default",
+        kernel=None,
+        output_scale_factor=1.0,
+        use_nin_shortcut=None,
+        up=False,
+        down=False,
+        overwrite_for_grad_tts=False,
+        overwrite_for_ldm=False,
+        overwrite_for_glide=False,
+        overwrite_for_score_vde=False,
+    ):
+        super().__init__()
+        self.pre_norm = pre_norm
+        self.pre_norm = True
+        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
+        self.output_scale_factor = output_scale_factor
+
+        if groups_out is None:
+            groups_out = groups
+
+        if self.pre_norm:
+            self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True)
+        else:
+            self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=out_channels, eps=eps, affine=True)
+
+        self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
+
+        if time_embedding_norm == "default" and temb_channels > 0:
+            self.temb_proj = torch.nn.Linear(temb_channels, out_channels)
+        elif time_embedding_norm == "scale_shift" and temb_channels > 0:
+            self.temb_proj = torch.nn.Linear(temb_channels, 2 * out_channels)
+
+        self.norm2 = torch.nn.GroupNorm(num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True)
+        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 = lambda x: F.silu(x)
+        elif non_linearity == "mish":
+            self.nonlinearity = Mish()
+        elif non_linearity == "silu":
+            self.nonlinearity = nn.SiLU()
+
+        self.upsample = self.downsample = None
+        if self.up:
+            if kernel == "fir":
+                fir_kernel = (1, 3, 3, 1)
+                self.upsample = lambda x: upsample_2d(x, k=fir_kernel)
+            elif kernel == "sde_vp":
+                self.upsample = partial(F.interpolate, scale_factor=2.0, mode="nearest")
+            else:
+                self.upsample = Upsample2D(in_channels, use_conv=False)
+        elif self.down:
+            if kernel == "fir":
+                fir_kernel = (1, 3, 3, 1)
+                self.downsample = lambda x: downsample_2d(x, k=fir_kernel)
+            elif kernel == "sde_vp":
+                self.downsample = partial(F.avg_pool2d, kernel_size=2, stride=2)
+            else:
+                self.downsample = Downsample2D(in_channels, use_conv=False, padding=1, name="op")
+
+        self.use_nin_shortcut = self.in_channels != self.out_channels if use_nin_shortcut is None else use_nin_shortcut
+
+        self.nin_shortcut = None
+        if self.use_nin_shortcut:
+            self.nin_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
+
+    def forward(self, x, temb):
+        h = x
+        if self.pre_norm:
+            h = self.norm1(h)
+            h = self.nonlinearity(h)
+
+        if self.upsample is not None:
+            x = self.upsample(x)
+            h = self.upsample(h)
+        elif self.downsample is not None:
+            x = self.downsample(x)
+            h = self.downsample(h)
+
+        h = self.conv1(h)
+
+        if not self.pre_norm:
+            h = self.norm1(h)
+            h = self.nonlinearity(h)
+
+        if temb is not None:
+            temb = self.temb_proj(self.nonlinearity(temb))[:, :, None, None]
+        else:
+            temb = 0
+
+        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
+            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)
+
+        if self.nin_shortcut is not None:
+            x = self.nin_shortcut(x)
+
+        return (x + h) / self.output_scale_factor
+
+    def set_weight(self, resnet):
+        self.norm1.weight.data = resnet.norm1.weight.data
+        self.norm1.bias.data = resnet.norm1.bias.data
+
+        self.conv1.weight.data = resnet.conv1.weight.data
+        self.conv1.bias.data = resnet.conv1.bias.data
+
+        self.temb_proj.weight.data = resnet.temb_proj.weight.data
+        self.temb_proj.bias.data = resnet.temb_proj.bias.data
+
+        self.norm2.weight.data = resnet.norm2.weight.data
+        self.norm2.bias.data = resnet.norm2.bias.data
+
+        self.conv2.weight.data = resnet.conv2.weight.data
+        self.conv2.bias.data = resnet.conv2.bias.data
+
+        if self.use_nin_shortcut:
+            self.nin_shortcut.weight.data = resnet.nin_shortcut.weight.data
+            self.nin_shortcut.bias.data = resnet.nin_shortcut.bias.data
+
+
 # TODO(Patrick) - just there to convert the weights; can delete afterward
 class Block(torch.nn.Module):
     def __init__(self, dim, dim_out, groups=8):

src/diffusers/models/unet.py

@@ -114,9 +114,7 @@ class UNetModel(ModelMixin, ConfigMixin):
         self.mid.block_2 = ResnetBlock2D(
             in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout
         )
-        self.mid_new = UNetMidBlock2D(
-            in_channels=block_in, temb_channels=self.temb_ch, dropout=dropout, overwrite_qkv=True, overwrite_unet=True
-        )
+        self.mid_new = UNetMidBlock2D(in_channels=block_in, temb_channels=self.temb_ch, dropout=dropout)
         self.mid_new.resnets[0] = self.mid.block_1
         self.mid_new.attentions[0] = self.mid.attn_1
         self.mid_new.resnets[1] = self.mid.block_2
@@ -154,7 +152,8 @@ class UNetModel(ModelMixin, ConfigMixin):
         self.norm_out = Normalize(block_in)
         self.conv_out = torch.nn.Conv2d(block_in, out_ch, kernel_size=3, stride=1, padding=1)
 
-    def forward(self, x, timesteps):
+    def forward(self, sample, timesteps):
+        x = sample
         assert x.shape[2] == x.shape[3] == self.resolution
 
         if not torch.is_tensor(timesteps):

src/diffusers/models/unet_glide.py

@@ -438,7 +438,8 @@ class GlideTextToImageUNetModel(GlideUNetModel):
 
         self.transformer_proj = nn.Linear(transformer_dim, self.model_channels * 4)
 
-    def forward(self, x, timesteps, transformer_out=None):
+    def forward(self, sample, timesteps, transformer_out=None):
+        x = sample
         hs = []
         emb = self.time_embed(
             get_timestep_embedding(timesteps, self.model_channels, flip_sin_to_cos=True, downscale_freq_shift=0)
@@ -528,7 +529,8 @@ class GlideSuperResUNetModel(GlideUNetModel):
             resblock_updown=resblock_updown,
         )
 
-    def forward(self, x, timesteps, low_res=None):
+    def forward(self, sample, timesteps, low_res=None):
+        x = sample
         _, _, new_height, new_width = x.shape
         upsampled = F.interpolate(low_res, (new_height, new_width), mode="bilinear")
         x = torch.cat([x, upsampled], dim=1)

src/diffusers/models/unet_grad_tts.py

@@ -180,7 +180,8 @@ class UNetGradTTSModel(ModelMixin, ConfigMixin):
         self.final_block = Block(dim, dim)
         self.final_conv = torch.nn.Conv2d(dim, 1, 1)
 
-    def forward(self, x, timesteps, mu, mask, spk=None):
+    def forward(self, sample, timesteps, mu, mask, spk=None):
+        x = sample
         if self.n_spks > 1:
             # Get speaker embedding
             spk = self.spk_emb(spk)

src/diffusers/models/unet_ldm.py

@@ -301,6 +301,12 @@ class UNetLDMModel(ModelMixin, ConfigMixin):
         self.input_blocks = nn.ModuleList(
             [TimestepEmbedSequential(conv_nd(dims, in_channels, model_channels, 3, padding=1))]
         )
+
+        self.down_in_conv = self.input_blocks[0][0]
+        self.downsample_blocks = nn.ModuleList([])
+        self.upsample_blocks = nn.ModuleList([])
+
+        # ========================= Down (OLD) =================== #
         self._feature_size = model_channels
         input_block_chans = [model_channels]
         ch = model_channels
@@ -354,6 +360,9 @@ class UNetLDMModel(ModelMixin, ConfigMixin):
                 ds *= 2
                 self._feature_size += ch
 
+        input_channels = [model_channels * mult for mult in [1] + list(channel_mult[:-1])]
+        output_channels = [model_channels * mult for mult in channel_mult]
+
         if num_head_channels == -1:
             dim_head = ch // num_heads
         else:
@@ -365,6 +374,8 @@ class UNetLDMModel(ModelMixin, ConfigMixin):
 
         if dim_head < 0:
             dim_head = None
+
+        # ========================= MID (New) =================== #
         self.mid = UNetMidBlock2D(
             in_channels=ch,
             dropout=dropout,
@@ -414,6 +425,7 @@ class UNetLDMModel(ModelMixin, ConfigMixin):
 
         self._feature_size += ch
 
+        # ========================= Up (Old) =================== #
         self.output_blocks = nn.ModuleList([])
         for level, mult in list(enumerate(channel_mult))[::-1]:
             for i in range(num_res_blocks + 1):
@@ -462,28 +474,6 @@ class UNetLDMModel(ModelMixin, ConfigMixin):
             nn.SiLU(),
             zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
         )
-        if self.predict_codebook_ids:
-            self.id_predictor = nn.Sequential(
-                normalization(ch),
-                conv_nd(dims, model_channels, n_embed, 1),
-                # nn.LogSoftmax(dim=1)  # change to cross_entropy and produce non-normalized logits
-            )
-
-    def convert_to_fp16(self):
-        """
-        Convert the torso of the model to float16.
-        """
-        self.input_blocks.apply(convert_module_to_f16)
-        self.middle_block.apply(convert_module_to_f16)
-        self.output_blocks.apply(convert_module_to_f16)
-
-    def convert_to_fp32(self):
-        """
-        Convert the torso of the model to float32.
-        """
-        self.input_blocks.apply(convert_module_to_f32)
-        self.middle_block.apply(convert_module_to_f32)
-        self.output_blocks.apply(convert_module_to_f32)
 
     def forward(self, x, timesteps=None, context=None, y=None, **kwargs):
         """
@@ -505,18 +495,18 @@ class UNetLDMModel(ModelMixin, ConfigMixin):
             emb = emb + self.label_emb(y)
 
         h = x.type(self.dtype_)
+
         for module in self.input_blocks:
             h = module(h, emb, context)
             hs.append(h)
+
         h = self.mid(h, emb, context)
+
         for module in self.output_blocks:
             h = torch.cat([h, hs.pop()], dim=1)
             h = module(h, emb, context)
-        h = h.type(x.dtype)
-        if self.predict_codebook_ids:
-            return self.id_predictor(h)
-        else:
-            return self.out(h)
+
+        return self.out(h)
 
 
 class SpatialTransformer(nn.Module):

src/diffusers/models/unet_new.py

@@ -12,10 +12,11 @@
 # See the License for the specific language governing permissions and
 
 # limitations under the License.
+import torch
 from torch import nn
 
-from .attention import AttentionBlock, LinearAttention, SpatialTransformer
-from .resnet import ResnetBlock2D
+from .attention import AttentionBlockNew
+from .resnet import Downsample2D, ResnetBlock, Upsample2D
 
 
 class UNetMidBlock2D(nn.Module):
@@ -24,30 +25,25 @@ class UNetMidBlock2D(nn.Module):
         in_channels: int,
         temb_channels: int,
         dropout: float = 0.0,
-        num_blocks: int = 1,
+        num_layers: int = 1,
         resnet_eps: float = 1e-6,
         resnet_time_scale_shift: str = "default",
         resnet_act_fn: str = "swish",
         resnet_groups: int = 32,
         resnet_pre_norm: bool = True,
-        attention_layer_type: str = "self",
-        attn_num_heads=1,
-        attn_num_head_channels=None,
-        attn_encoder_channels=None,
-        attn_dim_head=None,
-        attn_depth=None,
+        attn_num_head_channels=1,
         output_scale_factor=1.0,
-        overwrite_qkv=False,
-        overwrite_unet=False,
+        **kwargs,
     ):
         super().__init__()
 
         # there is always at least one resnet
         resnets = [
-            ResnetBlock2D(
+            ResnetBlock(
                 in_channels=in_channels,
                 out_channels=in_channels,
                 temb_channels=temb_channels,
+                eps=resnet_eps,
                 groups=resnet_groups,
                 dropout=dropout,
                 time_embedding_norm=resnet_time_scale_shift,
@@ -58,36 +54,20 @@ class UNetMidBlock2D(nn.Module):
         ]
         attentions = []
 
-        for _ in range(num_blocks):
-            if attention_layer_type == "self":
-                attentions.append(
-                    AttentionBlock(
-                        in_channels,
-                        num_heads=attn_num_heads,
-                        num_head_channels=attn_num_head_channels,
-                        encoder_channels=attn_encoder_channels,
-                        overwrite_qkv=overwrite_qkv,
-                        rescale_output_factor=output_scale_factor,
-                    )
-                )
-            elif attention_layer_type == "spatial":
-                attentions.append(
-                    SpatialTransformer(
-                        in_channels,
-                        attn_num_heads,
-                        attn_num_head_channels,
-                        depth=attn_depth,
-                        context_dim=attn_encoder_channels,
-                    )
+        for _ in range(num_layers):
+            attentions.append(
+                AttentionBlockNew(
+                    in_channels,
+                    num_head_channels=attn_num_head_channels,
+                    rescale_output_factor=output_scale_factor,
                 )
-            elif attention_layer_type == "linear":
-                attentions.append(LinearAttention(in_channels))
-
+            )
             resnets.append(
-                ResnetBlock2D(
+                ResnetBlock(
                     in_channels=in_channels,
                     out_channels=in_channels,
                     temb_channels=temb_channels,
+                    eps=resnet_eps,
                     groups=resnet_groups,
                     dropout=dropout,
                     time_embedding_norm=resnet_time_scale_shift,
@@ -100,37 +80,283 @@ class UNetMidBlock2D(nn.Module):
         self.attentions = nn.ModuleList(attentions)
         self.resnets = nn.ModuleList(resnets)
 
-    def forward(self, hidden_states, temb=None, encoder_states=None, mask=1.0):
-        hidden_states = self.resnets[0](hidden_states, temb, mask=mask)
+    def forward(self, hidden_states, temb=None, encoder_states=None, mask=None):
+        if mask is not None:
+            hidden_states = self.resnets[0](hidden_states, temb, mask=mask)
+        else:
+            hidden_states = self.resnets[0](hidden_states, temb)
 
         for attn, resnet in zip(self.attentions, self.resnets[1:]):
             hidden_states = attn(hidden_states, encoder_states)
-            hidden_states = resnet(hidden_states, temb, mask=mask)
+            if mask is not None:
+                hidden_states = resnet(hidden_states, temb, mask=mask)
+            else:
+                hidden_states = resnet(hidden_states, temb)
 
         return hidden_states
 
 
-# class UNetResAttnDownBlock(nn.Module):
-#    def __init__(
-#        self,
-#        in_channels: int,
-#        out_channels: int,
-#        temb_channels: int,
-#        dropout: float = 0.0,
-#        resnet_eps: float = 1e-6,
-#        resnet_time_scale_shift: str = "default",
-#        resnet_act_fn: str = "swish",
-#        resnet_groups: int = 32,
-#        resnet_pre_norm: bool = True,
-#        attention_layer_type: str = "self",
-#        attn_num_heads=1,
-#        attn_num_head_channels=None,
-#        attn_encoder_channels=None,
-#        attn_dim_head=None,
-#        attn_depth=None,
-#        output_scale_factor=1.0,
-#        overwrite_qkv=False,
-#        overwrite_unet=False,
-#    ):
-#
-#        self.resents =
+class UNetResAttnDownBlock2D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        temb_channels: int,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        resnet_eps: float = 1e-6,
+        resnet_time_scale_shift: str = "default",
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        resnet_pre_norm: bool = True,
+        attn_num_head_channels=1,
+        output_scale_factor=1.0,
+        add_downsample=True,
+    ):
+        super().__init__()
+        resnets = []
+        attentions = []
+
+        for i in range(num_layers):
+            in_channels = in_channels if i == 0 else out_channels
+            resnets.append(
+                ResnetBlock(
+                    in_channels=in_channels,
+                    out_channels=out_channels,
+                    temb_channels=temb_channels,
+                    eps=resnet_eps,
+                    groups=resnet_groups,
+                    dropout=dropout,
+                    time_embedding_norm=resnet_time_scale_shift,
+                    non_linearity=resnet_act_fn,
+                    output_scale_factor=output_scale_factor,
+                    pre_norm=resnet_pre_norm,
+                )
+            )
+            attentions.append(
+                AttentionBlockNew(
+                    out_channels,
+                    num_head_channels=attn_num_head_channels,
+                    rescale_output_factor=output_scale_factor,
+                )
+            )
+
+        self.attentions = nn.ModuleList(attentions)
+        self.resnets = nn.ModuleList(resnets)
+
+        if add_downsample:
+            self.downsamplers = nn.ModuleList(
+                [Downsample2D(in_channels, use_conv=True, out_channels=out_channels, padding=1, name="op")]
+            )
+        else:
+            self.downsamplers = None
+
+    def forward(self, hidden_states, temb=None):
+        output_states = ()
+
+        for resnet, attn in zip(self.resnets, self.attentions):
+            hidden_states = resnet(hidden_states, temb)
+            hidden_states = attn(hidden_states)
+            output_states += (hidden_states,)
+
+        if self.downsamplers is not None:
+            for downsampler in self.downsamplers:
+                hidden_states = downsampler(hidden_states)
+
+            output_states += (hidden_states,)
+
+        return hidden_states, output_states
+
+
+class UNetResDownBlock2D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        temb_channels: int,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        resnet_eps: float = 1e-6,
+        resnet_time_scale_shift: str = "default",
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        resnet_pre_norm: bool = True,
+        output_scale_factor=1.0,
+        add_downsample=True,
+    ):
+        super().__init__()
+        resnets = []
+
+        for i in range(num_layers):
+            in_channels = in_channels if i == 0 else out_channels
+            resnets.append(
+                ResnetBlock(
+                    in_channels=in_channels,
+                    out_channels=out_channels,
+                    temb_channels=temb_channels,
+                    eps=resnet_eps,
+                    groups=resnet_groups,
+                    dropout=dropout,
+                    time_embedding_norm=resnet_time_scale_shift,
+                    non_linearity=resnet_act_fn,
+                    output_scale_factor=output_scale_factor,
+                    pre_norm=resnet_pre_norm,
+                )
+            )
+
+        self.resnets = nn.ModuleList(resnets)
+
+        if add_downsample:
+            self.downsamplers = nn.ModuleList(
+                [Downsample2D(in_channels, use_conv=True, out_channels=out_channels, padding=1, name="op")]
+            )
+        else:
+            self.downsamplers = None
+
+    def forward(self, hidden_states, temb=None):
+        output_states = ()
+
+        for resnet in self.resnets:
+            hidden_states = resnet(hidden_states, temb)
+            output_states += (hidden_states,)
+
+        if self.downsamplers is not None:
+            for downsampler in self.downsamplers:
+                hidden_states = downsampler(hidden_states)
+
+            output_states += (hidden_states,)
+
+        return hidden_states, output_states
+
+
+class UNetResAttnUpBlock2D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        next_channels: int,
+        temb_channels: int,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        resnet_eps: float = 1e-6,
+        resnet_time_scale_shift: str = "default",
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        resnet_pre_norm: bool = True,
+        attention_layer_type: str = "self",
+        attn_num_head_channels=1,
+        output_scale_factor=1.0,
+        add_upsample=True,
+    ):
+        super().__init__()
+        resnets = []
+        attentions = []
+
+        for i in range(num_layers):
+            resnet_channels = in_channels if i < num_layers - 1 else next_channels
+            resnets.append(
+                ResnetBlock(
+                    in_channels=in_channels + resnet_channels,
+                    out_channels=in_channels,
+                    temb_channels=temb_channels,
+                    eps=resnet_eps,
+                    groups=resnet_groups,
+                    dropout=dropout,
+                    time_embedding_norm=resnet_time_scale_shift,
+                    non_linearity=resnet_act_fn,
+                    output_scale_factor=output_scale_factor,
+                    pre_norm=resnet_pre_norm,
+                )
+            )
+            attentions.append(
+                AttentionBlockNew(
+                    in_channels,
+                    num_head_channels=attn_num_head_channels,
+                    rescale_output_factor=output_scale_factor,
+                )
+            )
+
+        self.attentions = nn.ModuleList(attentions)
+        self.resnets = nn.ModuleList(resnets)
+
+        if add_upsample:
+            self.upsamplers = nn.ModuleList([Upsample2D(in_channels, use_conv=True, out_channels=in_channels)])
+        else:
+            self.upsamplers = None
+
+    def forward(self, hidden_states, res_hidden_states_tuple, temb=None):
+        for resnet, attn in zip(self.resnets, self.attentions):
+
+            # pop res hidden states
+            res_hidden_states = res_hidden_states_tuple[-1]
+            res_hidden_states_tuple = res_hidden_states_tuple[:-1]
+            hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
+
+            hidden_states = resnet(hidden_states, temb)
+            hidden_states = attn(hidden_states)
+
+        if self.upsamplers is not None:
+            for upsampler in self.upsamplers:
+                hidden_states = upsampler(hidden_states)
+
+        return hidden_states
+
+
+class UNetResUpBlock2D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        next_channels: int,
+        temb_channels: int,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        resnet_eps: float = 1e-6,
+        resnet_time_scale_shift: str = "default",
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        resnet_pre_norm: bool = True,
+        attention_layer_type: str = "self",
+        output_scale_factor=1.0,
+        add_upsample=True,
+    ):
+        super().__init__()
+        resnets = []
+
+        for i in range(num_layers):
+            resnet_channels = in_channels if i < num_layers - 1 else next_channels
+            resnets.append(
+                ResnetBlock(
+                    in_channels=in_channels + resnet_channels,
+                    out_channels=in_channels,
+                    temb_channels=temb_channels,
+                    eps=resnet_eps,
+                    groups=resnet_groups,
+                    dropout=dropout,
+                    time_embedding_norm=resnet_time_scale_shift,
+                    non_linearity=resnet_act_fn,
+                    output_scale_factor=output_scale_factor,
+                    pre_norm=resnet_pre_norm,
+                )
+            )
+
+        self.resnets = nn.ModuleList(resnets)
+
+        if add_upsample:
+            self.upsamplers = nn.ModuleList([Upsample2D(in_channels, use_conv=True, out_channels=in_channels)])
+        else:
+            self.upsamplers = None
+
+    def forward(self, hidden_states, res_hidden_states_tuple, temb=None):
+        for resnet in self.resnets:
+
+            # pop res hidden states
+            res_hidden_states = res_hidden_states_tuple[-1]
+            res_hidden_states_tuple = res_hidden_states_tuple[:-1]
+            hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
+
+            hidden_states = resnet(hidden_states, temb)
+
+        if self.upsamplers is not None:
+            for upsampler in self.upsamplers:
+                hidden_states = upsampler(hidden_states)
+
+        return hidden_states

src/diffusers/models/unet_rl.py

@@ -129,10 +129,11 @@ class TemporalUNet(ModelMixin, ConfigMixin):  # (nn.Module):
             nn.Conv1d(dim, transition_dim, 1),
         )
 
-    def forward(self, x, timesteps):
+    def forward(self, sample, timesteps):
         """
         x : [ batch x horizon x transition ]
         """
+        x = sample
 
         x = x.permute(0, 2, 1)
 
@@ -212,7 +213,6 @@ class TemporalValue(nn.Module):
         """
         x : [ batch x horizon x transition ]
         """
-
         x = x.permute(0, 2, 1)
 
         t = self.time_mlp(time)

src/diffusers/models/unet_sde_score_estimation.py

@@ -323,7 +323,8 @@ class NCSNpp(ModelMixin, ConfigMixin):
 
         self.all_modules = nn.ModuleList(modules)
 
-    def forward(self, x, timesteps, sigmas=None):
+    def forward(self, sample, timesteps, sigmas=None):
+        x = sample
         # timestep/noise_level embedding; only for continuous training
         modules = self.all_modules
         m_idx = 0

src/diffusers/models/unet_unconditional.py

+import torch
+import torch.nn as nn
+
+from ..configuration_utils import ConfigMixin
+from ..modeling_utils import ModelMixin
+from .attention import AttentionBlock
+from .embeddings import get_timestep_embedding
+from .resnet import Downsample2D, ResnetBlock2D, Upsample2D
+from .unet_new import (
+    UNetMidBlock2D,
+    UNetResAttnDownBlock2D,
+    UNetResAttnUpBlock2D,
+    UNetResDownBlock2D,
+    UNetResUpBlock2D,
+)
+
+
+class UNetUnconditionalModel(ModelMixin, ConfigMixin):
+    """
+    The full UNet model with attention and timestep embedding. :param in_channels: channels in the input Tensor. :param
+    model_channels: base channel count for the model. :param out_channels: channels in the output Tensor. :param
+    num_res_blocks: number of residual blocks per downsample. :param attention_resolutions: a collection of downsample
+    rates at which
+        attention will take place. May be a set, list, or tuple. For example, if this contains 4, then at 4x
+        downsampling, attention will be used.
+    :param dropout: the dropout probability. :param channel_mult: channel multiplier for each level of the UNet. :param
+    conv_resample: if True, use learned convolutions for upsampling and
+        downsampling.
+    :param dims: determines if the signal is 1D, 2D, or 3D. :param num_classes: if specified (as an int), then this
+    model will be
+        class-conditional with `num_classes` classes.
+    :param use_checkpoint: use gradient checkpointing to reduce memory usage. :param num_heads: the number of attention
+    heads in each attention layer. :param num_heads_channels: if specified, ignore num_heads and instead use
+                               a fixed channel width per attention head.
+    :param num_heads_upsample: works with num_heads to set a different number
+                               of heads for upsampling. Deprecated.
+    :param use_scale_shift_norm: use a FiLM-like conditioning mechanism. :param resblock_updown: use residual blocks
+    for up/downsampling. :param use_new_attention_order: use a different attention pattern for potentially
+                                    increased efficiency.
+    """
+
+    def init_for_ldm(
+        self,
+        dims,
+        in_channels,
+        model_channels,
+        channel_mult,
+        num_res_blocks,
+        dropout,
+        time_embed_dim,
+        attention_resolutions,
+        num_head_channels,
+        num_heads,
+        legacy,
+        use_spatial_transformer,
+        transformer_depth,
+        context_dim,
+        conv_resample,
+        out_channels,
+    ):
+        # TODO(PVP) - delete after weight conversion
+
+        class TimestepEmbedSequential(nn.Sequential):
+            """
+            A sequential module that passes timestep embeddings to the children that support it as an extra input.
+            """
+
+            pass
+
+        # TODO(PVP) - delete after weight conversion
+        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}")
+
+        self.input_blocks = nn.ModuleList(
+            [TimestepEmbedSequential(conv_nd(dims, in_channels, model_channels, 3, padding=1))]
+        )
+
+        self._feature_size = model_channels
+        input_block_chans = [model_channels]
+        ch = model_channels
+        ds = 1
+        for level, mult in enumerate(channel_mult):
+            for _ in range(num_res_blocks):
+                layers = [
+                    ResnetBlock2D(
+                        in_channels=ch,
+                        out_channels=mult * model_channels,
+                        dropout=dropout,
+                        temb_channels=time_embed_dim,
+                        eps=1e-5,
+                        non_linearity="silu",
+                        overwrite_for_ldm=True,
+                    )
+                ]
+                ch = mult * model_channels
+                if ds in attention_resolutions:
+                    if num_head_channels == -1:
+                        dim_head = ch // num_heads
+                    else:
+                        num_heads = ch // num_head_channels
+                        dim_head = num_head_channels
+                    if legacy:
+                        # num_heads = 1
+                        dim_head = num_head_channels
+                    layers.append(
+                        AttentionBlock(
+                            ch,
+                            num_heads=num_heads,
+                            num_head_channels=dim_head,
+                        ),
+                    )
+                self.input_blocks.append(TimestepEmbedSequential(*layers))
+                self._feature_size += ch
+                input_block_chans.append(ch)
+            if level != len(channel_mult) - 1:
+                out_ch = ch
+                self.input_blocks.append(
+                    TimestepEmbedSequential(
+                        Downsample2D(ch, use_conv=conv_resample, out_channels=out_ch, padding=1, name="op")
+                    )
+                )
+                ch = out_ch
+                input_block_chans.append(ch)
+                ds *= 2
+                self._feature_size += ch
+
+        if num_head_channels == -1:
+            dim_head = ch // num_heads
+        else:
+            num_heads = ch // num_head_channels
+            dim_head = num_head_channels
+        if legacy:
+            # num_heads = 1
+            dim_head = num_head_channels
+
+        if dim_head < 0:
+            dim_head = None
+
+        # TODO(Patrick) - delete after weight conversion
+        # init to be able to overwrite `self.mid`
+        self.middle_block = TimestepEmbedSequential(
+            ResnetBlock2D(
+                in_channels=ch,
+                out_channels=None,
+                dropout=dropout,
+                temb_channels=time_embed_dim,
+                eps=1e-5,
+                non_linearity="silu",
+                overwrite_for_ldm=True,
+            ),
+            AttentionBlock(
+                ch,
+                num_heads=num_heads,
+                num_head_channels=dim_head,
+            ),
+            ResnetBlock2D(
+                in_channels=ch,
+                out_channels=None,
+                dropout=dropout,
+                temb_channels=time_embed_dim,
+                eps=1e-5,
+                non_linearity="silu",
+                overwrite_for_ldm=True,
+            ),
+        )
+        self._feature_size += ch
+
+        self.output_blocks = nn.ModuleList([])
+        for level, mult in list(enumerate(channel_mult))[::-1]:
+            for i in range(num_res_blocks + 1):
+                ich = input_block_chans.pop()
+                layers = [
+                    ResnetBlock2D(
+                        in_channels=ch + ich,
+                        out_channels=model_channels * mult,
+                        dropout=dropout,
+                        temb_channels=time_embed_dim,
+                        eps=1e-5,
+                        non_linearity="silu",
+                        overwrite_for_ldm=True,
+                    ),
+                ]
+                ch = model_channels * mult
+                if ds in attention_resolutions:
+                    if num_head_channels == -1:
+                        dim_head = ch // num_heads
+                    else:
+                        num_heads = ch // num_head_channels
+                        dim_head = num_head_channels
+                    if legacy:
+                        # num_heads = 1
+                        dim_head = num_head_channels
+                    layers.append(
+                        AttentionBlock(
+                            ch,
+                            num_heads=-1,
+                            num_head_channels=dim_head,
+                        ),
+                    )
+                if level and i == num_res_blocks:
+                    out_ch = ch
+                    layers.append(Upsample2D(ch, use_conv=conv_resample, out_channels=out_ch))
+                    ds //= 2
+                self.output_blocks.append(TimestepEmbedSequential(*layers))
+                self._feature_size += ch
+
+        # ================ SET WEIGHTS OF ALL WEIGHTS ==================
+        for i, input_layer in enumerate(self.input_blocks[1:]):
+            block_id = i // (num_res_blocks + 1)
+            layer_in_block_id = i % (num_res_blocks + 1)
+
+            if layer_in_block_id == 2:
+                self.downsample_blocks[block_id].downsamplers[0].op.weight.data = input_layer[0].op.weight.data
+                self.downsample_blocks[block_id].downsamplers[0].op.bias.data = input_layer[0].op.bias.data
+            elif len(input_layer) > 1:
+                self.downsample_blocks[block_id].resnets[layer_in_block_id].set_weight(input_layer[0])
+                self.downsample_blocks[block_id].attentions[layer_in_block_id].set_weight(input_layer[1])
+            else:
+                self.downsample_blocks[block_id].resnets[layer_in_block_id].set_weight(input_layer[0])
+
+        self.mid.resnets[0].set_weight(self.middle_block[0])
+        self.mid.resnets[1].set_weight(self.middle_block[2])
+        self.mid.attentions[0].set_weight(self.middle_block[1])
+
+        for i, input_layer in enumerate(self.output_blocks):
+            block_id = i // (num_res_blocks + 1)
+            layer_in_block_id = i % (num_res_blocks + 1)
+
+            if len(input_layer) > 2:
+                self.upsample_blocks[block_id].resnets[layer_in_block_id].set_weight(input_layer[0])
+                self.upsample_blocks[block_id].attentions[layer_in_block_id].set_weight(input_layer[1])
+                self.upsample_blocks[block_id].upsamplers[0].conv.weight.data = input_layer[2].conv.weight.data
+                self.upsample_blocks[block_id].upsamplers[0].conv.bias.data = input_layer[2].conv.bias.data
+            elif len(input_layer) > 1 and "Upsample2D" in input_layer[1].__class__.__name__:
+                self.upsample_blocks[block_id].resnets[layer_in_block_id].set_weight(input_layer[0])
+                self.upsample_blocks[block_id].upsamplers[0].conv.weight.data = input_layer[1].conv.weight.data
+                self.upsample_blocks[block_id].upsamplers[0].conv.bias.data = input_layer[1].conv.bias.data
+            elif len(input_layer) > 1:
+                self.upsample_blocks[block_id].resnets[layer_in_block_id].set_weight(input_layer[0])
+                self.upsample_blocks[block_id].attentions[layer_in_block_id].set_weight(input_layer[1])
+            else:
+                self.upsample_blocks[block_id].resnets[layer_in_block_id].set_weight(input_layer[0])
+
+        self.conv_in.weight.data = self.input_blocks[0][0].weight.data
+        self.conv_in.bias.data = self.input_blocks[0][0].bias.data
+
+    def __init__(
+        self,
+        image_size,
+        in_channels,
+        model_channels,
+        out_channels,
+        num_res_blocks,
+        attention_resolutions,
+        dropout=0,
+        channel_mult=(1, 2, 4, 8),
+        conv_resample=True,
+        dims=2,
+        num_classes=None,
+        use_checkpoint=False,
+        use_fp16=False,
+        num_heads=-1,
+        num_head_channels=-1,
+        num_heads_upsample=-1,
+        use_scale_shift_norm=False,
+        resblock_updown=False,
+        use_new_attention_order=False,
+        transformer_depth=1,  # custom transformer support
+        context_dim=None,  # custom transformer support
+        n_embed=None,  # custom support for prediction of discrete ids into codebook of first stage vq model
+        legacy=True,
+    ):
+        super().__init__()
+
+        # register all __init__ params with self.register
+        self.register_to_config(
+            image_size=image_size,
+            in_channels=in_channels,
+            model_channels=model_channels,
+            out_channels=out_channels,
+            num_res_blocks=num_res_blocks,
+            attention_resolutions=attention_resolutions,
+            dropout=dropout,
+            channel_mult=channel_mult,
+            conv_resample=conv_resample,
+            dims=dims,
+            num_classes=num_classes,
+            use_fp16=use_fp16,
+            num_heads=num_heads,
+            num_heads_upsample=num_heads_upsample,
+            num_head_channels=num_head_channels,
+            use_scale_shift_norm=use_scale_shift_norm,
+            resblock_updown=resblock_updown,
+            transformer_depth=transformer_depth,
+            context_dim=context_dim,
+            n_embed=n_embed,
+            legacy=legacy,
+        )
+
+        self.image_size = image_size
+        self.in_channels = in_channels
+        self.model_channels = model_channels
+        self.out_channels = out_channels
+        self.num_res_blocks = num_res_blocks
+        self.attention_resolutions = attention_resolutions
+        self.dropout = dropout
+        self.channel_mult = channel_mult
+        self.conv_resample = conv_resample
+        self.num_classes = num_classes
+        self.dtype_ = torch.float16 if use_fp16 else torch.float32
+        self.num_heads = num_heads
+        self.num_heads_upsample = num_heads_upsample
+        self.predict_codebook_ids = n_embed is not None
+
+        time_embed_dim = model_channels * 4
+
+        # ======================== Input ===================
+        self.conv_in = nn.Conv2d(in_channels, model_channels, kernel_size=3, padding=(1, 1))
+
+        # ======================== Time ====================
+        self.time_embed = nn.Sequential(
+            nn.Linear(model_channels, time_embed_dim),
+            nn.SiLU(),
+            nn.Linear(time_embed_dim, time_embed_dim),
+        )
+
+        # ======================== Down ====================
+        input_channels = [model_channels * mult for mult in [1] + list(channel_mult[:-1])]
+        output_channels = [model_channels * mult for mult in channel_mult]
+
+        ds_new = 1
+        self.downsample_blocks = nn.ModuleList([])
+        for i, (input_channel, output_channel) in enumerate(zip(input_channels, output_channels)):
+            is_final_block = i == len(input_channels) - 1
+
+            if ds_new in attention_resolutions:
+                down_block = UNetResAttnDownBlock2D(
+                    num_layers=num_res_blocks,
+                    in_channels=input_channel,
+                    out_channels=output_channel,
+                    temb_channels=time_embed_dim,
+                    add_downsample=not is_final_block,
+                    resnet_eps=1e-5,
+                    resnet_act_fn="silu",
+                    attn_num_head_channels=num_head_channels,
+                )
+            else:
+                down_block = UNetResDownBlock2D(
+                    num_layers=num_res_blocks,
+                    in_channels=input_channel,
+                    out_channels=output_channel,
+                    temb_channels=time_embed_dim,
+                    add_downsample=not is_final_block,
+                    resnet_eps=1e-5,
+                    resnet_act_fn="silu",
+                )
+
+            self.downsample_blocks.append(down_block)
+
+            ds_new *= 2
+
+        ds_new = ds_new / 2
+
+        # ======================== Mid ====================
+        self.mid = UNetMidBlock2D(
+            in_channels=output_channels[-1],
+            dropout=dropout,
+            temb_channels=time_embed_dim,
+            resnet_eps=1e-5,
+            resnet_act_fn="silu",
+            resnet_time_scale_shift="scale_shift" if use_scale_shift_norm else "default",
+            attn_num_head_channels=num_head_channels,
+        )
+
+        # ======================== Up =====================
+        #        input_channels = [model_channels * mult for mult in channel_mult]
+        #        output_channels = [model_channels * mult for mult in channel_mult]
+
+        self.upsample_blocks = nn.ModuleList([])
+        for i, (input_channel, output_channel) in enumerate(zip(reversed(input_channels), reversed(output_channels))):
+            is_final_block = i == len(input_channels) - 1
+
+            if ds_new in attention_resolutions:
+                up_block = UNetResAttnUpBlock2D(
+                    num_layers=num_res_blocks + 1,
+                    in_channels=output_channel,
+                    next_channels=input_channel,
+                    temb_channels=time_embed_dim,
+                    add_upsample=not is_final_block,
+                    resnet_eps=1e-5,
+                    resnet_act_fn="silu",
+                    attn_num_head_channels=num_head_channels,
+                )
+            else:
+                up_block = UNetResUpBlock2D(
+                    num_layers=num_res_blocks + 1,
+                    in_channels=output_channel,
+                    next_channels=input_channel,
+                    temb_channels=time_embed_dim,
+                    add_upsample=not is_final_block,
+                    resnet_eps=1e-5,
+                    resnet_act_fn="silu",
+                )
+
+            self.upsample_blocks.append(up_block)
+
+            ds_new /= 2
+
+        # ======================== Out ====================
+        self.out = nn.Sequential(
+            nn.GroupNorm(num_channels=output_channels[0], num_groups=32, eps=1e-5),
+            nn.SiLU(),
+            nn.Conv2d(model_channels, out_channels, 3, padding=1),
+        )
+
+        # =========== TO DELETE AFTER CONVERSION ==========
+        self.init_for_ldm(
+            dims,
+            in_channels,
+            model_channels,
+            channel_mult,
+            num_res_blocks,
+            dropout,
+            time_embed_dim,
+            attention_resolutions,
+            num_head_channels,
+            num_heads,
+            legacy,
+            False,
+            transformer_depth,
+            context_dim,
+            conv_resample,
+            out_channels,
+        )
+
+    def forward(self, sample, timesteps=None):
+        # 1. time step embeddings
+        if not torch.is_tensor(timesteps):
+            timesteps = torch.tensor([timesteps], dtype=torch.long, device=sample.device)
+        t_emb = get_timestep_embedding(timesteps, self.model_channels, flip_sin_to_cos=True, downscale_freq_shift=0)
+        emb = self.time_embed(t_emb)
+
+        # 2. pre-process sample
+        sample = sample.type(self.dtype_)
+        sample = self.conv_in(sample)
+
+        # 3. down blocks
+        down_block_res_samples = (sample,)
+        for downsample_block in self.downsample_blocks:
+            sample, res_samples = downsample_block(sample, emb)
+
+            # append to tuple
+            down_block_res_samples += res_samples
+
+        # 4. mid block
+        sample = self.mid(sample, emb)
+
+        # 5. up blocks
+        for upsample_block in self.upsample_blocks:
+
+            # pop from tuple
+            res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
+            down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
+
+            sample = upsample_block(sample, res_samples, emb)
+
+        # 6. post-process sample
+        sample = self.out(sample)
+
+        return sample

src/diffusers/models/vae.py

@@ -470,7 +470,8 @@ class VQModel(ModelMixin, ConfigMixin):
         dec = self.decoder(quant)
         return dec
 
-    def forward(self, x):
+    def forward(self, sample):
+        x = sample
         h = self.encode(x)
         dec = self.decode(h)
         return dec
@@ -561,7 +562,8 @@ class AutoencoderKL(ModelMixin, ConfigMixin):
         dec = self.decoder(z)
         return dec
 
-    def forward(self, x, sample_posterior=False):
+    def forward(self, sample, sample_posterior=False):
+        x = sample
         posterior = self.encode(x)
         if sample_posterior:
             z = posterior.sample()

tests/test_modeling_utils.py

@@ -46,6 +46,7 @@ from diffusers import (
     UNetGradTTSModel,
     UNetLDMModel,
     UNetModel,
+    UNetUnconditionalModel,
     VQModel,
 )
 from diffusers.configuration_utils import ConfigMixin
@@ -146,7 +147,7 @@ class ModelTesterMixin:
             output = model(**inputs_dict)
 
         self.assertIsNotNone(output)
-        expected_shape = inputs_dict["x"].shape
+        expected_shape = inputs_dict["sample"].shape
         self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")
 
     def test_forward_signature(self):
@@ -157,7 +158,7 @@ class ModelTesterMixin:
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         arg_names = [*signature.parameters.keys()]
 
-        expected_arg_names = ["x", "timesteps"]
+        expected_arg_names = ["sample", "timesteps"]
         self.assertListEqual(arg_names[:2], expected_arg_names)
 
     def test_model_from_config(self):
@@ -194,7 +195,7 @@ class ModelTesterMixin:
         model.to(torch_device)
         model.train()
         output = model(**inputs_dict)
-        noise = torch.randn((inputs_dict["x"].shape[0],) + self.output_shape).to(torch_device)
+        noise = torch.randn((inputs_dict["sample"].shape[0],) + self.output_shape).to(torch_device)
         loss = torch.nn.functional.mse_loss(output, noise)
         loss.backward()
 
@@ -207,7 +208,7 @@ class ModelTesterMixin:
         ema_model = EMAModel(model, device=torch_device)
 
         output = model(**inputs_dict)
-        noise = torch.randn((inputs_dict["x"].shape[0],) + self.output_shape).to(torch_device)
+        noise = torch.randn((inputs_dict["sample"].shape[0],) + self.output_shape).to(torch_device)
         loss = torch.nn.functional.mse_loss(output, noise)
         loss.backward()
         ema_model.step(model)
@@ -225,7 +226,7 @@ class UnetModelTests(ModelTesterMixin, unittest.TestCase):
         noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
         time_step = torch.tensor([10]).to(torch_device)
 
-        return {"x": noise, "timesteps": time_step}
+        return {"sample": noise, "timesteps": time_step}
 
     @property
     def input_shape(self):
@@ -291,7 +292,7 @@ class GlideSuperResUNetTests(ModelTesterMixin, unittest.TestCase):
         low_res = torch.randn((batch_size, 3) + low_res_size).to(torch_device)
         time_step = torch.tensor([10] * noise.shape[0], device=torch_device)
 
-        return {"x": noise, "timesteps": time_step, "low_res": low_res}
+        return {"sample": noise, "timesteps": time_step, "low_res": low_res}
 
     @property
     def input_shape(self):
@@ -330,7 +331,7 @@ class GlideSuperResUNetTests(ModelTesterMixin, unittest.TestCase):
         output, _ = torch.split(output, 3, dim=1)
 
         self.assertIsNotNone(output)
-        expected_shape = inputs_dict["x"].shape
+        expected_shape = inputs_dict["sample"].shape
         self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")
 
     def test_from_pretrained_hub(self):
@@ -382,7 +383,7 @@ class GlideTextToImageUNetModelTests(ModelTesterMixin, unittest.TestCase):
         emb = torch.randn((batch_size, seq_len, transformer_dim)).to(torch_device)
         time_step = torch.tensor([10] * noise.shape[0], device=torch_device)
 
-        return {"x": noise, "timesteps": time_step, "transformer_out": emb}
+        return {"sample": noise, "timesteps": time_step, "transformer_out": emb}
 
     @property
     def input_shape(self):
@@ -422,7 +423,7 @@ class GlideTextToImageUNetModelTests(ModelTesterMixin, unittest.TestCase):
         output, _ = torch.split(output, 3, dim=1)
 
         self.assertIsNotNone(output)
-        expected_shape = inputs_dict["x"].shape
+        expected_shape = inputs_dict["sample"].shape
         self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")
 
     def test_from_pretrained_hub(self):
@@ -463,7 +464,7 @@ class GlideTextToImageUNetModelTests(ModelTesterMixin, unittest.TestCase):
 
 
 class UNetLDMModelTests(ModelTesterMixin, unittest.TestCase):
-    model_class = UNetLDMModel
+    model_class = UNetUnconditionalModel
 
     @property
     def dummy_input(self):
@@ -474,7 +475,7 @@ class UNetLDMModelTests(ModelTesterMixin, unittest.TestCase):
         noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
         time_step = torch.tensor([10]).to(torch_device)
 
-        return {"x": noise, "timesteps": time_step}
+        return {"sample": noise, "timesteps": time_step}
 
     @property
     def input_shape(self):
@@ -493,14 +494,14 @@ class UNetLDMModelTests(ModelTesterMixin, unittest.TestCase):
             "num_res_blocks": 2,
             "attention_resolutions": (16,),
             "channel_mult": (1, 2),
-            "num_heads": 2,
+            "num_head_channels": 32,
             "conv_resample": True,
         }
         inputs_dict = self.dummy_input
         return init_dict, inputs_dict
 
     def test_from_pretrained_hub(self):
-        model, loading_info = UNetLDMModel.from_pretrained("fusing/unet-ldm-dummy", output_loading_info=True)
+        model, loading_info = UNetUnconditionalModel.from_pretrained("fusing/unet-ldm-dummy", output_loading_info=True)
         self.assertIsNotNone(model)
         self.assertEqual(len(loading_info["missing_keys"]), 0)
 
@@ -510,7 +511,7 @@ class UNetLDMModelTests(ModelTesterMixin, unittest.TestCase):
         assert image is not None, "Make sure output is not None"
 
     def test_output_pretrained(self):
-        model = UNetLDMModel.from_pretrained("fusing/unet-ldm-dummy")
+        model = UNetUnconditionalModel.from_pretrained("fusing/unet-ldm-dummy")
         model.eval()
 
         torch.manual_seed(0)
@@ -567,7 +568,7 @@ class UNetGradTTSModelTests(ModelTesterMixin, unittest.TestCase):
         mask = floats_tensor((batch_size, 1, seq_len)).to(torch_device)
         time_step = torch.tensor([10] * batch_size).to(torch_device)
 
-        return {"x": noise, "timesteps": time_step, "mu": condition, "mask": mask}
+        return {"sample": noise, "timesteps": time_step, "mu": condition, "mask": mask}
 
     @property
     def input_shape(self):
@@ -637,7 +638,7 @@ class TemporalUNetModelTests(ModelTesterMixin, unittest.TestCase):
         noise = floats_tensor((batch_size, seq_len, num_features)).to(torch_device)
         time_step = torch.tensor([10] * batch_size).to(torch_device)
 
-        return {"x": noise, "timesteps": time_step}
+        return {"sample": noise, "timesteps": time_step}
 
     @property
     def input_shape(self):
@@ -708,7 +709,7 @@ class NCSNppModelTests(ModelTesterMixin, unittest.TestCase):
         noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
         time_step = torch.tensor(batch_size * [10]).to(torch_device)
 
-        return {"x": noise, "timesteps": time_step}
+        return {"sample": noise, "timesteps": time_step}
 
     @property
     def input_shape(self):
@@ -834,7 +835,7 @@ class VQModelTests(ModelTesterMixin, unittest.TestCase):
 
         image = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
 
-        return {"x": image}
+        return {"sample": image}
 
     @property
     def input_shape(self):
@@ -909,7 +910,7 @@ class AutoEncoderKLTests(ModelTesterMixin, unittest.TestCase):
 
         image = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
 
-        return {"x": image}
+        return {"sample": image}
 
     @property
     def input_shape(self):