DDPM [diffusers] 保姆级代码解释 (1)

class UNet2DModel(ModelMixin, ConfigMixin):
    r"""
    A 2D UNet model that takes a noisy sample and a timestep and returns a sample shaped output.

    This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
    for all models (such as downloading or saving).

    Parameters:
        sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):
            Height and width of input/output sample. Dimensions must be a multiple of `2 ** (len(block_out_channels) -
            1)`.
        in_channels (`int`, *optional*, defaults to 3): Number of channels in the input sample.
        out_channels (`int`, *optional*, defaults to 3): Number of channels in the output.
        center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample.
        time_embedding_type (`str`, *optional*, defaults to `"positional"`): Type of time embedding to use.
        freq_shift (`int`, *optional*, defaults to 0): Frequency shift for Fourier time embedding.
        flip_sin_to_cos (`bool`, *optional*, defaults to `True`):
            Whether to flip sin to cos for Fourier time embedding.
        down_block_types (`Tuple[str]`, *optional*, defaults to `("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")`):
            Tuple of downsample block types.
        mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2D"`):
            Block type for middle of UNet, it can be either `UNetMidBlock2D` or `UnCLIPUNetMidBlock2D`.
        up_block_types (`Tuple[str]`, *optional*, defaults to `("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")`):
            Tuple of upsample block types.
        block_out_channels (`Tuple[int]`, *optional*, defaults to `(224, 448, 672, 896)`):
            Tuple of block output channels.
        layers_per_block (`int`, *optional*, defaults to `2`): The number of layers per block.
        mid_block_scale_factor (`float`, *optional*, defaults to `1`): The scale factor for the mid block.
        downsample_padding (`int`, *optional*, defaults to `1`): The padding for the downsample convolution.
        downsample_type (`str`, *optional*, defaults to `conv`):
            The downsample type for downsampling layers. Choose between "conv" and "resnet"
        upsample_type (`str`, *optional*, defaults to `conv`):
            The upsample type for upsampling layers. Choose between "conv" and "resnet"
        act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
        attention_head_dim (`int`, *optional*, defaults to `8`): The attention head dimension.
        norm_num_groups (`int`, *optional*, defaults to `32`): The number of groups for normalization.
        norm_eps (`float`, *optional*, defaults to `1e-5`): The epsilon for normalization.
        resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config
            for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`.
        class_embed_type (`str`, *optional*, defaults to `None`):
            The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`,
            `"timestep"`, or `"identity"`.
        num_class_embeds (`int`, *optional*, defaults to `None`):
            Input dimension of the learnable embedding matrix to be projected to `time_embed_dim` when performing class
            conditioning with `class_embed_type` equal to `None`.
    """

@register_to_config
    def __init__(
        self,
        sample_size: Optional[Union[int, Tuple[int, int]]] = None,
        in_channels: int = 3,
        out_channels: int = 3,
        center_input_sample: bool = False,
        time_embedding_type: str = "positional",
        freq_shift: int = 0,
        flip_sin_to_cos: bool = True,
        down_block_types: Tuple[str] = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D"),
        up_block_types: Tuple[str] = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D"),
        block_out_channels: Tuple[int] = (224, 448, 672, 896),
        layers_per_block: int = 2,
        mid_block_scale_factor: float = 1,
        downsample_padding: int = 1,
        downsample_type: str = "conv",
        upsample_type: str = "conv",
        act_fn: str = "silu",
        attention_head_dim: Optional[int] = 8,
        norm_num_groups: int = 32,
        norm_eps: float = 1e-5,
        resnet_time_scale_shift: str = "default",
        add_attention: bool = True,
        class_embed_type: Optional[str] = None,
        num_class_embeds: Optional[int] = None,
    ):
        super().__init__()

        self.sample_size = sample_size
        time_embed_dim = block_out_channels[0] * 4

        # Check inputs
        if len(down_block_types) != len(up_block_types):
            raise ValueError(
                f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."
            )

        if len(block_out_channels) != len(down_block_types):
            raise ValueError(
                f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
            )

        # input
        self.conv_in = nn.Conv2d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1))

        # time
        if time_embedding_type == "fourier":
            self.time_proj = GaussianFourierProjection(embedding_size=block_out_channels[0], scale=16)
            timestep_input_dim = 2 * block_out_channels[0]
        elif time_embedding_type == "positional":
            self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
            timestep_input_dim = block_out_channels[0]

        self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)

        # class embedding
        if class_embed_type is None and num_class_embeds is not None:
            self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
        elif class_embed_type == "timestep":
            self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
        elif class_embed_type == "identity":
            self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
        else:
            self.class_embedding = None

        self.down_blocks = nn.ModuleList([])
        self.mid_block = None
        self.up_blocks = nn.ModuleList([])

        # down
        output_channel = block_out_channels[0]
        for i, down_block_type in enumerate(down_block_types):
            input_channel = output_channel
            output_channel = block_out_channels[i]
            is_final_block = i == len(block_out_channels) - 1

            down_block = get_down_block(
                down_block_type,
                num_layers=layers_per_block,
                in_channels=input_channel,
                out_channels=output_channel,
                temb_channels=time_embed_dim,
                add_downsample=not is_final_block,
                resnet_eps=norm_eps,
                resnet_act_fn=act_fn,
                resnet_groups=norm_num_groups,
                attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel,
                downsample_padding=downsample_padding,
                resnet_time_scale_shift=resnet_time_scale_shift,
                downsample_type=downsample_type,
            )
            self.down_blocks.append(down_block)

        # mid
        self.mid_block = UNetMidBlock2D(
            in_channels=block_out_channels[-1],
            temb_channels=time_embed_dim,
            resnet_eps=norm_eps,
            resnet_act_fn=act_fn,
            output_scale_factor=mid_block_scale_factor,
            resnet_time_scale_shift=resnet_time_scale_shift,
            attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1],
            resnet_groups=norm_num_groups,
            add_attention=add_attention,
        )

        # up
        reversed_block_out_channels = list(reversed(block_out_channels))
        output_channel = reversed_block_out_channels[0]
        for i, up_block_type in enumerate(up_block_types):
            prev_output_channel = output_channel
            output_channel = reversed_block_out_channels[i]
            input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]

            is_final_block = i == len(block_out_channels) - 1

            up_block = get_up_block(
                up_block_type,
                num_layers=layers_per_block + 1,
                in_channels=input_channel,
                out_channels=output_channel,
                prev_output_channel=prev_output_channel,
                temb_channels=time_embed_dim,
                add_upsample=not is_final_block,
                resnet_eps=norm_eps,
                resnet_act_fn=act_fn,
                resnet_groups=norm_num_groups,
                attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel,
                resnet_time_scale_shift=resnet_time_scale_shift,
                upsample_type=upsample_type,
            )
            self.up_blocks.append(up_block)
            prev_output_channel = output_channel

        # out
        num_groups_out = norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4, 32)
        self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=num_groups_out, eps=norm_eps)
        self.conv_act = nn.SiLU()
        self.conv_out = nn.Conv2d(block_out_channels[0], out_channels, kernel_size=3, padding=1)

class Timesteps(nn.Module):
    def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float):
        super().__init__()
        self.num_channels = num_channels
        self.flip_sin_to_cos = flip_sin_to_cos
        self.downscale_freq_shift = downscale_freq_shift

    def forward(self, timesteps):
        t_emb = get_timestep_embedding(
            timesteps,
            self.num_channels,
            flip_sin_to_cos=self.flip_sin_to_cos,
            downscale_freq_shift=self.downscale_freq_shift,
        )
        return t_emb


def get_timestep_embedding(
    timesteps: torch.Tensor,
    embedding_dim: int,
    flip_sin_to_cos: bool = False,
    downscale_freq_shift: float = 1,
    scale: float = 1,
    max_period: int = 10000,
):
    """
    This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings.

    :param timesteps: a 1-D Tensor of N indices, one per batch element.
                      These may be fractional.
    :param embedding_dim: the dimension of the output. :param max_period: controls the minimum frequency of the
    embeddings. :return: an [N x dim] Tensor of positional embeddings.
    """
    assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array"

    half_dim = embedding_dim // 2
    exponent = -math.log(max_period) * torch.arange(
        start=0, end=half_dim, dtype=torch.float32, device=timesteps.device
    )
    exponent = exponent / (half_dim - downscale_freq_shift)

    emb = torch.exp(exponent)
    emb = timesteps[:, None].float() * emb[None, :]

    # scale embeddings
    emb = scale * emb

    # concat sine and cosine embeddings
    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1)

    # flip sine and cosine embeddings
    if flip_sin_to_cos:
        emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1)

    # zero pad
    if embedding_dim % 2 == 1:
        emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))
    return emb

    def forward(
        self,
        sample: torch.FloatTensor,
        timestep: Union[torch.Tensor, float, int],
        class_labels: Optional[torch.Tensor] = None,
        return_dict: bool = True,
    ) -> Union[UNet2DOutput, Tuple]:
        r"""
        The [`UNet2DModel`] forward method.

        Args:
            sample (`torch.FloatTensor`):
                The noisy input tensor with the following shape `(batch, channel, height, width)`.
            timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input.
            class_labels (`torch.FloatTensor`, *optional*, defaults to `None`):
                Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~models.unet_2d.UNet2DOutput`] instead of a plain tuple.

        Returns:
            [`~models.unet_2d.UNet2DOutput`] or `tuple`:
                If `return_dict` is True, an [`~models.unet_2d.UNet2DOutput`] is returned, otherwise a `tuple` is
                returned where the first element is the sample tensor.
        """
        # 0. center input if necessary
        if self.config.center_input_sample:
            sample = 2 * sample - 1.0

        # 1. time
        timesteps = timestep
        if not torch.is_tensor(timesteps):
            timesteps = torch.tensor([timesteps], dtype=torch.long, device=sample.device)
        elif torch.is_tensor(timesteps) and len(timesteps.shape) == 0:
            timesteps = timesteps[None].to(sample.device)

        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
        timesteps = timesteps * torch.ones(sample.shape[0], dtype=timesteps.dtype, device=timesteps.device)

        t_emb = self.time_proj(timesteps)

        # timesteps does not contain any weights and will always return f32 tensors
        # but time_embedding might actually be running in fp16. so we need to cast here.
        # there might be better ways to encapsulate this.
        t_emb = t_emb.to(dtype=self.dtype)
        emb = self.time_embedding(t_emb)

        if self.class_embedding is not None:
            if class_labels is None:
                raise ValueError("class_labels should be provided when doing class conditioning")

            if self.config.class_embed_type == "timestep":
                class_labels = self.time_proj(class_labels)

            class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)
            emb = emb + class_emb

        # 2. pre-process
        skip_sample = sample
        sample = self.conv_in(sample)

        # 3. down
        down_block_res_samples = (sample,)
        for downsample_block in self.down_blocks:
            if hasattr(downsample_block, "skip_conv"):
                sample, res_samples, skip_sample = downsample_block(
                    hidden_states=sample, temb=emb, skip_sample=skip_sample
                )
            else:
                sample, res_samples = downsample_block(hidden_states=sample, temb=emb)

            down_block_res_samples += res_samples

        # 4. mid
        sample = self.mid_block(sample, emb)

        # 5. up
        skip_sample = None
        for upsample_block in self.up_blocks:
            res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
            down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]

            if hasattr(upsample_block, "skip_conv"):
                sample, skip_sample = upsample_block(sample, res_samples, emb, skip_sample)
            else:
                sample = upsample_block(sample, res_samples, emb)

        # 6. post-process
        sample = self.conv_norm_out(sample)
        sample = self.conv_act(sample)
        sample = self.conv_out(sample)

        if skip_sample is not None:
            sample += skip_sample

        if self.config.time_embedding_type == "fourier":
            timesteps = timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]))))
            sample = sample / timesteps

        if not return_dict:
            return (sample,)

        return UNet2DOutput(sample=sample)

model = UNet2DModel(
    sample_size=config.image_size,  # the target image resolution
    in_channels=3,  # the number of input channels, 3 for RGB images
    out_channels=3,  # the number of output channels
    layers_per_block=2,  # how many ResNet layers to use per UNet block
    block_out_channels=(128, 128, 256, 256, 512, 512),  # the number of output channels for each UNet block
    down_block_types=(
        "DownBlock2D",  # a regular ResNet downsampling block
        "DownBlock2D",
        "DownBlock2D",
        "DownBlock2D",
        "AttnDownBlock2D",  # a ResNet downsampling block with spatial self-attention
        "DownBlock2D",
    ),
    up_block_types=(
        "UpBlock2D",  # a regular ResNet upsampling block
        "AttnUpBlock2D",  # a ResNet upsampling block with spatial self-attention
        "UpBlock2D",
        "UpBlock2D",
        "UpBlock2D",
        "UpBlock2D",
    ),
)

Input shape: torch.Size([1, 3, 128, 128])
[intput] sample shape: torch.Size([1, 128, 128, 128])
DownBlock2D [resnet]--> input torch.Size([1, 128, 128, 128]), output torch.Size([1, 128, 128, 128])
DownBlock2D [resnet]--> input torch.Size([1, 128, 128, 128]), output torch.Size([1, 128, 128, 128])
DownBlock2D [downsampler]--> input torch.Size([1, 128, 128, 128]), output torch.Size([1, 128, 64, 64])
[output] sample shape: torch.Size([1, 128, 64, 64]) [output] res_sample len 3 [output] down_block_res_samples len 4



[intput] sample shape: torch.Size([1, 128, 64, 64])
DownBlock2D [resnet]--> input torch.Size([1, 128, 64, 64]), output torch.Size([1, 128, 64, 64])
DownBlock2D [resnet]--> input torch.Size([1, 128, 64, 64]), output torch.Size([1, 128, 64, 64])
DownBlock2D [downsampler]--> input torch.Size([1, 128, 64, 64]), output torch.Size([1, 128, 32, 32])
[output] sample shape: torch.Size([1, 128, 32, 32]) [output] res_sample len 3 [output] down_block_res_samples len 7



[intput] sample shape: torch.Size([1, 128, 32, 32])
DownBlock2D [resnet]--> input torch.Size([1, 128, 32, 32]), output torch.Size([1, 256, 32, 32])
DownBlock2D [resnet]--> input torch.Size([1, 256, 32, 32]), output torch.Size([1, 256, 32, 32])
DownBlock2D [downsampler]--> input torch.Size([1, 256, 32, 32]), output torch.Size([1, 256, 16, 16])
[output] sample shape: torch.Size([1, 256, 16, 16]) [output] res_sample len 3 [output] down_block_res_samples len 10



[intput] sample shape: torch.Size([1, 256, 16, 16])
DownBlock2D [resnet]--> input torch.Size([1, 256, 16, 16]), output torch.Size([1, 256, 16, 16])
DownBlock2D [resnet]--> input torch.Size([1, 256, 16, 16]), output torch.Size([1, 256, 16, 16])
DownBlock2D [downsampler]--> input torch.Size([1, 256, 16, 16]), output torch.Size([1, 256, 8, 8])
[output] sample shape: torch.Size([1, 256, 8, 8]) [output] res_sample len 3 [output] down_block_res_samples len 13



[intput] sample shape: torch.Size([1, 256, 8, 8])
DownBlock2D [resnet+attn] --> input torch.Size([1, 256, 8, 8]), output torch.Size([1, 512, 8, 8]).
DownBlock2D [resnet+attn] --> input torch.Size([1, 512, 8, 8]), output torch.Size([1, 512, 8, 8]).
[output] sample shape: torch.Size([1, 512, 4, 4]) [output] res_sample len 3 [output] down_block_res_samples len 16



[intput] sample shape: torch.Size([1, 512, 4, 4])
DownBlock2D [resnet]--> input torch.Size([1, 512, 4, 4]), output torch.Size([1, 512, 4, 4])
DownBlock2D [resnet]--> input torch.Size([1, 512, 4, 4]), output torch.Size([1, 512, 4, 4])
[output] sample shape: torch.Size([1, 512, 4, 4]) [output] res_sample len 2 [output] down_block_res_samples len 18



------------------ mid --------------------------
[intput] sample shape: torch.Size([1, 512, 4, 4])
[output] sample shape: torch.Size([1, 512, 4, 4])
------------------ up ------------------------

[intput] sample shape: torch.Size([1, 512, 4, 4])
UpBlock2D [resnet]--> input1 torch.Size([1, 512, 4, 4]), input2 torch.Size([1, 512, 4, 4]), input1+input2 torch.Size([1, 1024, 4, 4])output torch.Size([1, 512, 4, 4])
UpBlock2D [resnet]--> input1 torch.Size([1, 512, 4, 4]), input2 torch.Size([1, 512, 4, 4]), input1+input2 torch.Size([1, 1024, 4, 4])output torch.Size([1, 512, 4, 4])
UpBlock2D [resnet]--> input1 torch.Size([1, 512, 4, 4]), input2 torch.Size([1, 512, 4, 4]), input1+input2 torch.Size([1, 1024, 4, 4])output torch.Size([1, 512, 4, 4])
UpBlock2D [upsampler]--> input torch.Size([1, 512, 4, 4]), output torch.Size([1, 512, 8, 8])
[output] sample shape: torch.Size([1, 512, 8, 8])



[intput] sample shape: torch.Size([1, 512, 8, 8])
AttnUpBlock2D [resnet+atten]--> input1 torch.Size([1, 512, 8, 8]), input2 torch.Size([1, 512, 8, 8]), input1+input2 torch.Size([1, 1024, 8, 8]), output torch.Size([1, 512, 8, 8])
AttnUpBlock2D [resnet+atten]--> input1 torch.Size([1, 512, 8, 8]), input2 torch.Size([1, 512, 8, 8]), input1+input2 torch.Size([1, 1024, 8, 8]), output torch.Size([1, 512, 8, 8])
AttnUpBlock2D [resnet+atten]--> input1 torch.Size([1, 512, 8, 8]), input2 torch.Size([1, 256, 8, 8]), input1+input2 torch.Size([1, 768, 8, 8]), output torch.Size([1, 512, 8, 8])
AttnUpBlock2D [upsampler]--> input torch.Size([1, 512, 8, 8]), output torch.Size([1, 512, 16, 16])
[output] sample shape: torch.Size([1, 512, 16, 16])



[intput] sample shape: torch.Size([1, 512, 16, 16])
UpBlock2D [resnet]--> input1 torch.Size([1, 512, 16, 16]), input2 torch.Size([1, 256, 16, 16]), input1+input2 torch.Size([1, 768, 16, 16])output torch.Size([1, 256, 16, 16])
UpBlock2D [resnet]--> input1 torch.Size([1, 256, 16, 16]), input2 torch.Size([1, 256, 16, 16]), input1+input2 torch.Size([1, 512, 16, 16])output torch.Size([1, 256, 16, 16])
UpBlock2D [resnet]--> input1 torch.Size([1, 256, 16, 16]), input2 torch.Size([1, 256, 16, 16]), input1+input2 torch.Size([1, 512, 16, 16])output torch.Size([1, 256, 16, 16])
UpBlock2D [upsampler]--> input torch.Size([1, 256, 16, 16]), output torch.Size([1, 256, 32, 32])
[output] sample shape: torch.Size([1, 256, 32, 32])



[intput] sample shape: torch.Size([1, 256, 32, 32])
UpBlock2D [resnet]--> input1 torch.Size([1, 256, 32, 32]), input2 torch.Size([1, 256, 32, 32]), input1+input2 torch.Size([1, 512, 32, 32])output torch.Size([1, 256, 32, 32])
UpBlock2D [resnet]--> input1 torch.Size([1, 256, 32, 32]), input2 torch.Size([1, 256, 32, 32]), input1+input2 torch.Size([1, 512, 32, 32])output torch.Size([1, 256, 32, 32])
UpBlock2D [resnet]--> input1 torch.Size([1, 256, 32, 32]), input2 torch.Size([1, 128, 32, 32]), input1+input2 torch.Size([1, 384, 32, 32])output torch.Size([1, 256, 32, 32])
UpBlock2D [upsampler]--> input torch.Size([1, 256, 32, 32]), output torch.Size([1, 256, 64, 64])
[output] sample shape: torch.Size([1, 256, 64, 64])



[intput] sample shape: torch.Size([1, 256, 64, 64])
UpBlock2D [resnet]--> input1 torch.Size([1, 256, 64, 64]), input2 torch.Size([1, 128, 64, 64]), input1+input2 torch.Size([1, 384, 64, 64])output torch.Size([1, 128, 64, 64])
UpBlock2D [resnet]--> input1 torch.Size([1, 128, 64, 64]), input2 torch.Size([1, 128, 64, 64]), input1+input2 torch.Size([1, 256, 64, 64])output torch.Size([1, 128, 64, 64])
UpBlock2D [resnet]--> input1 torch.Size([1, 128, 64, 64]), input2 torch.Size([1, 128, 64, 64]), input1+input2 torch.Size([1, 256, 64, 64])output torch.Size([1, 128, 64, 64])
UpBlock2D [upsampler]--> input torch.Size([1, 128, 64, 64]), output torch.Size([1, 128, 128, 128])
[output] sample shape: torch.Size([1, 128, 128, 128])



[intput] sample shape: torch.Size([1, 128, 128, 128])
UpBlock2D [resnet]--> input1 torch.Size([1, 128, 128, 128]), input2 torch.Size([1, 128, 128, 128]), input1+input2 torch.Size([1, 256, 128, 128])output torch.Size([1, 128, 128, 128])
UpBlock2D [resnet]--> input1 torch.Size([1, 128, 128, 128]), input2 torch.Size([1, 128, 128, 128]), input1+input2 torch.Size([1, 256, 128, 128])output torch.Size([1, 128, 128, 128])
UpBlock2D [resnet]--> input1 torch.Size([1, 128, 128, 128]), input2 torch.Size([1, 128, 128, 128]), input1+input2 torch.Size([1, 256, 128, 128])output torch.Size([1, 128, 128, 128])
[output] sample shape: torch.Size([1, 128, 128, 128])

• 图像
  • [Im1] input/output: 1x3x128x128
• 输入预处理 Sp
  • [Im1]input: 1x3x128x128
  • [Sp1]output: 1x128x128x128

• 收缩阶段 Sd

  • DownBlock2D

    • resnet
      • [Sp1] input: 1x128x128x128
      • [Sd1] output: 1x128x128x128
    • resnet
      • [Sd1] input: 1x128x128x128
      • [Sd2] output: 1x128x128x128
    • downsampler
      • [Sd2] input: 1x128x128x128
      • [Sd3] output: 1x128x64x64

  • DownBlock2D

    • resnet
      • [Sd3] input: 1x128x64x64
      • [Sd4] output: 1x128x64x64
    • resnet
      • [Sd4] input: 1x128x64x64
      • [Sd5] output: 1x128x64x64
    • downsampler
      • [Sd5] input: 1x128x64x64
      • [Sd6] output: 1x128x32x32

  • DownBlock2D

    • resnet
      • [Sd6] input: 1x128x32x32
      • [Sd7] output: 1x256x32x32
    • resnet
      • [Sd7] input: 1x256x32x32
      • [Sd8] output: 1x256x32x32
    • downsampler
      • [Sd8] input: 1x256x32x32
      • [Sd9] output: 1x256x16x16

  • DownBlock2D

    • resnet
      • [Sd9] input: 1x256x16x16
      • [Sd10] output: 1x256x16x16
    • resnet
      • [Sd10] input: 1x256x16x16
      • [Sd11] output: 1x256x16x16
    • downsampler
      • [Sd11] input: 1x256x16x16
      • [Sd12] output: 1x256x8x8

  • AttnDownBlock2D

    • resnet+atten
      • [Sd12] input: 1x256x8x8
      • [Sd13] output: 1x512x8x8
    • resnet+atten
      • [Sd13] input: 1x512x8x8
      • [Sd14] output: 1x512x8x8
    • downsampler
      • [Sd14] input: 1x512x8x8
      • [Sd15] output: 1x512x4x4

  • DownBlock2D

    • resnet
      • [Sd15] input: 1x512x4x4
      • [Sd16] output: 1x512x4x4
    • resnet
      • [Sd16] input: 1x512x4x4
      • [Sd17] output: 1x512x4x4

• UNetMiddle2D
  • [Sd17] input: 1x512x4x4
  • [Sm1] output: 1x512x4x4

• 扩张阶段 Se

  • UpBlock2D

    • resnet
      • [Sm1] input: 1x512x4x4
      • [Sd17] input: 1x512x4x4
      • [Se1] output: 1x512x4x4
    • resnet
      • [Se1] input: 1x512x4x4
      • [Sd16] input: 1x512x4x4
      • [Se2] input: 1x512x4x4
    • resnet
      • [Se2] input: 1x512x4x4
      • [Sd15] input: 1x512x4x4
      • [Se3] output: 1x512x4x4
    • upsample
      • [Se3] input: 1x512x4x4
      • [Se4] output: 1x512x8x8

  • AttnUpBlock2D

    • resnet+atten
      • [Se4] input: 1x512x8x8
      • [Sd14] input: 1x512x8x8
      • [Se5] output: 1x512x8x8
    • resnet+atten
      • [Se5] input: 1x512x8x8
      • [Sd13] input: 1x512x8x8
      • [Se6] input: 1x512x8x8
    • resnet+atten
      • [Se6] input: 1x512x8x8
      • [Sd12] input: 1x256x8x8
      • [Se7] output: 1x512x8x8
    • upsample+atten
      • [Se7] input: 1x512x8x8
      • [Se8] output: 1x512x16x16

  • UpBlock2D

    • resnet
      • [Se8] input: 1x512x16x16
      • [Sd11] input: 1x256x16x16
      • [Se9] output: 1x256x16x16
    • resnet
      • [Se9] input: 1x256x16x16
      • [Sd10] input: 1x256x16x16
      • [Se10] input: 1x256x16x16
    • resnet
      • [Se10] input: 1x256x16x16
      • [Sd9] input: 1x256x16x16
      • [Se11] output: 1x256x16x16
    • upsample
      • [Se11] input: 1x256x16x16
      • [Se12] output: 1x256x32x32

  • UpBlock2D

    • resnet
      • [Se12] input: 1x256x32x32
      • [Sd8] input: 1x256x32x32
      • [Se13] output: 1x256x32x32
    • resnet
      • [Se13] input: 1x256x32x32
      • [Sd7] input: 1x256x32x32
      • [Se14] input: 1x256x32x32
    • resnet
      • [Se14] input: 1x256x32x32
      • [Sd6] input: 1x128x32x32
      • [Se15] output: 1x256x32x32
    • upsample
      • [Se15] input: 1x256x32x32
      • [Se16] output: 1x256x64x64

  • UpBlock2D

    • resnet
      • [Se16] input: 1x256x64x64
      • [Sd5] input: 1x128x64x64
      • [Se17] output: 1x128x64x64
    • resnet
      • [Se17] input: 1x128x64x64
      • [Sd4] input: 1x128x64x64
      • [Se18] input: 1x128x64x64
    • resnet
      • [Se18] input: 1x128x64x64
      • [Sd3] input: 1x128x64x64
      • [Se19] output: 1x128x64x64
    • upsample
      • [Se19] input: 1x128x64x64
      • [Se20] output: 1x128x128x128

  • UpBlock2D

    • resnet
      • [Se20] input: 1x128x128x128
      • [Sd2] input: 1x128x128x128
      • [Se21] output: 1x128x128x128
    • resnet
      • [Se21] input: 1x128x128x128
      • [Sd1] input: 1x128x128x128
      • [Se22] input: 1x128x128x128
    • resnet
      • [Se22] input: 1x128x128x128
      • [Sp1] input: 1x128x128x128
      • [Se23] output: 1x128x128x128

• 输出后处理 So
  • [Se23]input: 1x128x128x128
  • [So1]output: 1x3x128x128

class DownBlock2D(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,
        downsample_padding=1,
    ):
        super().__init__()
        resnets = []

        for i in range(num_layers):
            in_channels = in_channels if i == 0 else out_channels
            resnets.append(
                ResnetBlock2D(
                    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(
                        out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
                    )
                ]
            )
        else:
            self.downsamplers = None

        self.gradient_checkpointing = False

    def forward(self, hidden_states, temb=None):
        output_states = ()

        for resnet in self.resnets:
            if self.training and self.gradient_checkpointing:

                def create_custom_forward(module):
                    def custom_forward(*inputs):
                        return module(*inputs)

                    return custom_forward

                if is_torch_version(">=", "1.11.0"):
                    hidden_states = torch.utils.checkpoint.checkpoint(
                        create_custom_forward(resnet), hidden_states, temb, use_reentrant=False
                    )
                else:
                    hidden_states = torch.utils.checkpoint.checkpoint(
                        create_custom_forward(resnet), hidden_states, temb
                    )
            else:
                hidden_states = resnet(hidden_states, temb)

            output_states = output_states + (hidden_states,)

        if self.downsamplers is not None:
            for downsampler in self.downsamplers:
                hidden_states = downsampler(hidden_states)

            output_states = output_states + (hidden_states,)

        return hidden_states, output_states

class AttnDownBlock2D(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,
        attention_head_dim=1,
        output_scale_factor=1.0,
        downsample_padding=1,
        downsample_type="conv",
    ):
        super().__init__()
        resnets = []
        attentions = []
        self.downsample_type = downsample_type

        if attention_head_dim is None:
            logger.warn(
                f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `in_channels`: {out_channels}."
            )
            attention_head_dim = out_channels

        for i in range(num_layers):
            in_channels = in_channels if i == 0 else out_channels
            resnets.append(
                ResnetBlock2D(
                    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(
                Attention(
                    out_channels,
                    heads=out_channels // attention_head_dim,
                    dim_head=attention_head_dim,
                    rescale_output_factor=output_scale_factor,
                    eps=resnet_eps,
                    norm_num_groups=resnet_groups,
                    residual_connection=True,
                    bias=True,
                    upcast_softmax=True,
                    _from_deprecated_attn_block=True,
                )
            )

        self.attentions = nn.ModuleList(attentions)
        self.resnets = nn.ModuleList(resnets)

        if downsample_type == "conv":
            self.downsamplers = nn.ModuleList(
                [
                    Downsample2D(
                        out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
                    )
                ]
            )
        elif downsample_type == "resnet":
            self.downsamplers = nn.ModuleList(
                [
                    ResnetBlock2D(
                        in_channels=out_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,
                        down=True,
                    )
                ]
            )
        else:
            self.downsamplers = None

    def forward(self, hidden_states, temb=None, upsample_size=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 = output_states + (hidden_states,)

        if self.downsamplers is not None:
            for downsampler in self.downsamplers:
                if self.downsample_type == "resnet":
                    hidden_states = downsampler(hidden_states, temb=temb)
                else:
                    hidden_states = downsampler(hidden_states)

            output_states += (hidden_states,)

        return hidden_states, output_states

class UNetMidBlock2D(nn.Module):
    def __init__(
        self,
        in_channels: int,
        temb_channels: int,
        dropout: float = 0.0,
        num_layers: int = 1,
        resnet_eps: float = 1e-6,
        resnet_time_scale_shift: str = "default",  # default, spatial
        resnet_act_fn: str = "swish",
        resnet_groups: int = 32,
        resnet_pre_norm: bool = True,
        add_attention: bool = True,
        attention_head_dim=1,
        output_scale_factor=1.0,
    ):
        super().__init__()
        resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
        self.add_attention = add_attention

        # there is always at least one resnet
        resnets = [
            ResnetBlock2D(
                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,
                non_linearity=resnet_act_fn,
                output_scale_factor=output_scale_factor,
                pre_norm=resnet_pre_norm,
            )
        ]
        attentions = []

        if attention_head_dim is None:
            logger.warn(
                f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `in_channels`: {in_channels}."
            )
            attention_head_dim = in_channels

        for _ in range(num_layers):
            if self.add_attention:
                attentions.append(
                    Attention(
                        in_channels,
                        heads=in_channels // attention_head_dim,
                        dim_head=attention_head_dim,
                        rescale_output_factor=output_scale_factor,
                        eps=resnet_eps,
                        norm_num_groups=resnet_groups if resnet_time_scale_shift == "default" else None,
                        spatial_norm_dim=temb_channels if resnet_time_scale_shift == "spatial" else None,
                        residual_connection=True,
                        bias=True,
                        upcast_softmax=True,
                        _from_deprecated_attn_block=True,
                    )
                )
            else:
                attentions.append(None)

            resnets.append(
                ResnetBlock2D(
                    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,
                    non_linearity=resnet_act_fn,
                    output_scale_factor=output_scale_factor,
                    pre_norm=resnet_pre_norm,
                )
            )

        self.attentions = nn.ModuleList(attentions)
        self.resnets = nn.ModuleList(resnets)

    def forward(self, hidden_states, temb=None):
        hidden_states = self.resnets[0](hidden_states, temb)
        for attn, resnet in zip(self.attentions, self.resnets[1:]):
            if attn is not None:
                hidden_states = attn(hidden_states, temb=temb)
            hidden_states = resnet(hidden_states, temb)

        return hidden_states

class UpBlock2D(nn.Module):
    def __init__(
        self,
        in_channels: int,
        prev_output_channel: 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_upsample=True,
    ):
        super().__init__()
        resnets = []

        for i in range(num_layers):
            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
            resnet_in_channels = prev_output_channel if i == 0 else out_channels

            resnets.append(
                ResnetBlock2D(
                    in_channels=resnet_in_channels + res_skip_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_upsample:
            self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
        else:
            self.upsamplers = None

        self.gradient_checkpointing = False

    def forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=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)

            if self.training and self.gradient_checkpointing:

                def create_custom_forward(module):
                    def custom_forward(*inputs):
                        return module(*inputs)

                    return custom_forward

                if is_torch_version(">=", "1.11.0"):
                    hidden_states = torch.utils.checkpoint.checkpoint(
                        create_custom_forward(resnet), hidden_states, temb, use_reentrant=False
                    )
                else:
                    hidden_states = torch.utils.checkpoint.checkpoint(
                        create_custom_forward(resnet), hidden_states, temb
                    )
            else:
                hidden_states = resnet(hidden_states, temb)

        if self.upsamplers is not None:
            for upsampler in self.upsamplers:
                hidden_states = upsampler(hidden_states, upsample_size)

        return hidden_states

class AttnUpBlock2D(nn.Module):
    def __init__(
        self,
        in_channels: int,
        prev_output_channel: 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,
        attention_head_dim=1,
        output_scale_factor=1.0,
        upsample_type="conv",
    ):
        super().__init__()
        resnets = []
        attentions = []

        self.upsample_type = upsample_type

        if attention_head_dim is None:
            logger.warn(
                f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `in_channels`: {out_channels}."
            )
            attention_head_dim = out_channels

        for i in range(num_layers):
            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
            resnet_in_channels = prev_output_channel if i == 0 else out_channels

            resnets.append(
                ResnetBlock2D(
                    in_channels=resnet_in_channels + res_skip_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(
                Attention(
                    out_channels,
                    heads=out_channels // attention_head_dim,
                    dim_head=attention_head_dim,
                    rescale_output_factor=output_scale_factor,
                    eps=resnet_eps,
                    norm_num_groups=resnet_groups,
                    residual_connection=True,
                    bias=True,
                    upcast_softmax=True,
                    _from_deprecated_attn_block=True,
                )
            )

        self.attentions = nn.ModuleList(attentions)
        self.resnets = nn.ModuleList(resnets)

        if upsample_type == "conv":
            self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
        elif upsample_type == "resnet":
            self.upsamplers = nn.ModuleList(
                [
                    ResnetBlock2D(
                        in_channels=out_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,
                        up=True,
                    )
                ]
            )
        else:
            self.upsamplers = None

    def forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=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:
                if self.upsample_type == "resnet":
                    hidden_states = upsampler(hidden_states, temb=temb)
                else:
                    hidden_states = upsampler(hidden_states)

        return hidden_states

class ResnetBlock2D(nn.Module):
    r"""
    A Resnet block.

    Parameters:
        in_channels (`int`): The number of channels in the input.
        out_channels (`int`, *optional*, default to be `None`):
            The number of output channels for the first conv2d layer. If None, same as `in_channels`.
        dropout (`float`, *optional*, defaults to `0.0`): The dropout probability to use.
        temb_channels (`int`, *optional*, default to `512`): the number of channels in timestep embedding.
        groups (`int`, *optional*, default to `32`): The number of groups to use for the first normalization layer.
        groups_out (`int`, *optional*, default to None):
            The number of groups to use for the second normalization layer. if set to None, same as `groups`.
        eps (`float`, *optional*, defaults to `1e-6`): The epsilon to use for the normalization.
        non_linearity (`str`, *optional*, default to `"swish"`): the activation function to use.
        time_embedding_norm (`str`, *optional*, default to `"default"` ): Time scale shift config.
            By default, apply timestep embedding conditioning with a simple shift mechanism. Choose "scale_shift" or
            "ada_group" for a stronger conditioning with scale and shift.
        kernel (`torch.FloatTensor`, optional, default to None): FIR filter, see
            [`~models.resnet.FirUpsample2D`] and [`~models.resnet.FirDownsample2D`].
        output_scale_factor (`float`, *optional*, default to be `1.0`): the scale factor to use for the output.
        use_in_shortcut (`bool`, *optional*, default to `True`):
            If `True`, add a 1x1 nn.conv2d layer for skip-connection.
        up (`bool`, *optional*, default to `False`): If `True`, add an upsample layer.
        down (`bool`, *optional*, default to `False`): If `True`, add a downsample layer.
        conv_shortcut_bias (`bool`, *optional*, default to `True`):  If `True`, adds a learnable bias to the
            `conv_shortcut` output.
        conv_2d_out_channels (`int`, *optional*, default to `None`): the number of channels in the output.
            If None, same as `out_channels`.
    """

    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",
        skip_time_act=False,
        time_embedding_norm="default",  # default, scale_shift, ada_group, spatial
        kernel=None,
        output_scale_factor=1.0,
        use_in_shortcut=None,
        up=False,
        down=False,
        conv_shortcut_bias: bool = True,
        conv_2d_out_channels: Optional[int] = None,
    ):
        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.up = up
        self.down = down
        self.output_scale_factor = output_scale_factor
        self.time_embedding_norm = time_embedding_norm
        self.skip_time_act = skip_time_act

        if groups_out is None:
            groups_out = groups

        if self.time_embedding_norm == "ada_group":
            self.norm1 = AdaGroupNorm(temb_channels, in_channels, groups, eps=eps)
        elif self.time_embedding_norm == "spatial":
            self.norm1 = SpatialNorm(in_channels, temb_channels)
        else:
            self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True)

        self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)

        if temb_channels is not None:
            if self.time_embedding_norm == "default":
                self.time_emb_proj = torch.nn.Linear(temb_channels, out_channels)
            elif self.time_embedding_norm == "scale_shift":
                self.time_emb_proj = torch.nn.Linear(temb_channels, 2 * out_channels)
            elif self.time_embedding_norm == "ada_group" or self.time_embedding_norm == "spatial":
                self.time_emb_proj = None
            else:
                raise ValueError(f"unknown time_embedding_norm : {self.time_embedding_norm} ")
        else:
            self.time_emb_proj = None

        if self.time_embedding_norm == "ada_group":
            self.norm2 = AdaGroupNorm(temb_channels, out_channels, groups_out, eps=eps)
        elif self.time_embedding_norm == "spatial":
            self.norm2 = SpatialNorm(out_channels, temb_channels)
        else:
            self.norm2 = torch.nn.GroupNorm(num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True)

        self.dropout = torch.nn.Dropout(dropout)
        conv_2d_out_channels = conv_2d_out_channels or out_channels
        self.conv2 = torch.nn.Conv2d(out_channels, conv_2d_out_channels, kernel_size=3, stride=1, padding=1)

        self.nonlinearity = get_activation(non_linearity)

        self.upsample = self.downsample = None
        if self.up:
            if kernel == "fir":
                fir_kernel = (1, 3, 3, 1)
                self.upsample = lambda x: upsample_2d(x, kernel=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, kernel=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_in_shortcut = self.in_channels != conv_2d_out_channels if use_in_shortcut is None else use_in_shortcut

        self.conv_shortcut = None
        if self.use_in_shortcut:
            self.conv_shortcut = torch.nn.Conv2d(
                in_channels, conv_2d_out_channels, kernel_size=1, stride=1, padding=0, bias=conv_shortcut_bias
            )

    def forward(self, input_tensor, temb):
        hidden_states = input_tensor

        if self.time_embedding_norm == "ada_group" or self.time_embedding_norm == "spatial":
            hidden_states = self.norm1(hidden_states, temb)
        else:
            hidden_states = self.norm1(hidden_states)

        hidden_states = self.nonlinearity(hidden_states)

        if self.upsample is not None:
            # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984
            if hidden_states.shape[0] >= 64:
                input_tensor = input_tensor.contiguous()
                hidden_states = hidden_states.contiguous()
            input_tensor = self.upsample(input_tensor)
            hidden_states = self.upsample(hidden_states)
        elif self.downsample is not None:
            input_tensor = self.downsample(input_tensor)
            hidden_states = self.downsample(hidden_states)

        hidden_states = self.conv1(hidden_states)

        if self.time_emb_proj is not None:
            if not self.skip_time_act:
                temb = self.nonlinearity(temb)
            temb = self.time_emb_proj(temb)[:, :, None, None]

        if temb is not None and self.time_embedding_norm == "default":
            hidden_states = hidden_states + temb

        if self.time_embedding_norm == "ada_group" or self.time_embedding_norm == "spatial":
            hidden_states = self.norm2(hidden_states, temb)
        else:
            hidden_states = self.norm2(hidden_states)

        if temb is not None and self.time_embedding_norm == "scale_shift":
            scale, shift = torch.chunk(temb, 2, dim=1)
            hidden_states = hidden_states * (1 + scale) + shift

        hidden_states = self.nonlinearity(hidden_states)

        hidden_states = self.dropout(hidden_states)
        hidden_states = self.conv2(hidden_states)

        if self.conv_shortcut is not None:
            input_tensor = self.conv_shortcut(input_tensor)

        output_tensor = (input_tensor + hidden_states) / self.output_scale_factor

        return output_tensor