def forward(
        self,
        sample: torch.Tensor,
        timestep: Union[torch.Tensor, float, int],
        encoder_hidden_states: torch.Tensor,
        controlnet_cond: torch.Tensor,
        conditioning_scale: float = 1.0,
        timestep_cond: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
        conditioning_mask: Optional[torch.Tensor] = None,
        guess_mode: bool = False,
        return_dict: bool = True,
    ) -> Union[SparseControlNetOutput, Tuple[Tuple[torch.Tensor, ...], torch.Tensor]]:
        """
        The [`SparseControlNetModel`] forward method.

        Args:
            sample (`torch.Tensor`):
                The noisy input tensor.
            timestep (`Union[torch.Tensor, float, int]`):
                The number of timesteps to denoise an input.
            encoder_hidden_states (`torch.Tensor`):
                The encoder hidden states.
            controlnet_cond (`torch.Tensor`):
                The conditional input tensor of shape `(batch_size, sequence_length, hidden_size)`.
            conditioning_scale (`float`, defaults to `1.0`):
                The scale factor for ControlNet outputs.
            class_labels (`torch.Tensor`, *optional*, defaults to `None`):
                Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.
            timestep_cond (`torch.Tensor`, *optional*, defaults to `None`):
                Additional conditional embeddings for timestep. If provided, the embeddings will be summed with the
                timestep_embedding passed through the `self.time_embedding` layer to obtain the final timestep
                embeddings.
            attention_mask (`torch.Tensor`, *optional*, defaults to `None`):
                An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
                is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
                negative values to the attention scores corresponding to "discard" tokens.
            added_cond_kwargs (`dict`):
                Additional conditions for the Stable Diffusion XL UNet.
            cross_attention_kwargs (`dict[str]`, *optional*, defaults to `None`):
                A kwargs dictionary that if specified is passed along to the `AttnProcessor`.
            guess_mode (`bool`, defaults to `False`):
                In this mode, the ControlNet encoder tries its best to recognize the input content of the input even if
                you remove all prompts. A `guidance_scale` between 3.0 and 5.0 is recommended.
            return_dict (`bool`, defaults to `True`):
                Whether or not to return a [`~models.controlnet.ControlNetOutput`] instead of a plain tuple.
        Returns:
            [`~models.controlnet.ControlNetOutput`] **or** `tuple`:
                If `return_dict` is `True`, a [`~models.controlnet.ControlNetOutput`] is returned, otherwise a tuple is
                returned where the first element is the sample tensor.
        """
        sample_batch_size, sample_channels, sample_num_frames, sample_height, sample_width = sample.shape
        sample = torch.zeros_like(sample)

        # check channel order
        channel_order = self.config.controlnet_conditioning_channel_order

        if channel_order == "rgb":
            # in rgb order by default
            ...
        elif channel_order == "bgr":
            controlnet_cond = torch.flip(controlnet_cond, dims=[1])
        else:
            raise ValueError(f"unknown `controlnet_conditioning_channel_order`: {channel_order}")

        # prepare attention_mask
        if attention_mask is not None:
            attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
            attention_mask = attention_mask.unsqueeze(1)

        # 1. time
        timesteps = timestep
        if not torch.is_tensor(timesteps):
            # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
            # This would be a good case for the `match` statement (Python 3.10+)
            is_mps = sample.device.type == "mps"
            is_npu = sample.device.type == "npu"
            if isinstance(timestep, float):
                dtype = torch.float32 if (is_mps or is_npu) else torch.float64
            else:
                dtype = torch.int32 if (is_mps or is_npu) else torch.int64
            timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
        elif 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.expand(sample.shape[0])

        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=sample.dtype)

        emb = self.time_embedding(t_emb, timestep_cond)
        emb = emb.repeat_interleave(sample_num_frames, dim=0, output_size=emb.shape[0] * sample_num_frames)

        # 2. pre-process
        batch_size, channels, num_frames, height, width = sample.shape

        sample = sample.permute(0, 2, 1, 3, 4).reshape(batch_size * num_frames, channels, height, width)
        sample = self.conv_in(sample)

        batch_frames, channels, height, width = sample.shape
        sample = sample[:, None].reshape(sample_batch_size, sample_num_frames, channels, height, width)

        if self.concat_conditioning_mask:
            controlnet_cond = torch.cat([controlnet_cond, conditioning_mask], dim=1)

        batch_size, channels, num_frames, height, width = controlnet_cond.shape
        controlnet_cond = controlnet_cond.permute(0, 2, 1, 3, 4).reshape(
            batch_size * num_frames, channels, height, width
        )
        controlnet_cond = self.controlnet_cond_embedding(controlnet_cond)
        batch_frames, channels, height, width = controlnet_cond.shape
        controlnet_cond = controlnet_cond[:, None].reshape(batch_size, num_frames, channels, height, width)

        sample = sample + controlnet_cond

        batch_size, num_frames, channels, height, width = sample.shape
        sample = sample.reshape(sample_batch_size * sample_num_frames, channels, height, width)

        # 3. down
        down_block_res_samples = (sample,)
        for downsample_block in self.down_blocks:
            if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
                sample, res_samples = downsample_block(
                    hidden_states=sample,
                    temb=emb,
                    encoder_hidden_states=encoder_hidden_states,
                    attention_mask=attention_mask,
                    num_frames=num_frames,
                    cross_attention_kwargs=cross_attention_kwargs,
                )
            else:
                sample, res_samples = downsample_block(hidden_states=sample, temb=emb, num_frames=num_frames)

            down_block_res_samples += res_samples

        # 4. mid
        if self.mid_block is not None:
            if hasattr(self.mid_block, "has_cross_attention") and self.mid_block.has_cross_attention:
                sample = self.mid_block(
                    sample,
                    emb,
                    encoder_hidden_states=encoder_hidden_states,
                    attention_mask=attention_mask,
                    cross_attention_kwargs=cross_attention_kwargs,
                )
            else:
                sample = self.mid_block(sample, emb)

        # 5. Control net blocks
        controlnet_down_block_res_samples = ()

        for down_block_res_sample, controlnet_block in zip(down_block_res_samples, self.controlnet_down_blocks):
            down_block_res_sample = controlnet_block(down_block_res_sample)
            controlnet_down_block_res_samples = controlnet_down_block_res_samples + (down_block_res_sample,)

        down_block_res_samples = controlnet_down_block_res_samples
        mid_block_res_sample = self.controlnet_mid_block(sample)

        # 6. scaling
        if guess_mode and not self.config.global_pool_conditions:
            scales = torch.logspace(-1, 0, len(down_block_res_samples) + 1, device=sample.device)  # 0.1 to 1.0
            scales = scales * conditioning_scale
            down_block_res_samples = [sample * scale for sample, scale in zip(down_block_res_samples, scales)]
            mid_block_res_sample = mid_block_res_sample * scales[-1]  # last one
        else:
            down_block_res_samples = [sample * conditioning_scale for sample in down_block_res_samples]
            mid_block_res_sample = mid_block_res_sample * conditioning_scale

        if self.config.global_pool_conditions:
            down_block_res_samples = [
                torch.mean(sample, dim=(2, 3), keepdim=True) for sample in down_block_res_samples
            ]
            mid_block_res_sample = torch.mean(mid_block_res_sample, dim=(2, 3), keepdim=True)

        if not return_dict:
            return (down_block_res_samples, mid_block_res_sample)

        return SparseControlNetOutput(
            down_block_res_samples=down_block_res_samples, mid_block_res_sample=mid_block_res_sample
        )

        The [`SparseControlNetModel`] forward method.

        Args:
            sample (`torch.Tensor`):
                The noisy input tensor.
            timestep (`Union[torch.Tensor, float, int]`):
                The number of timesteps to denoise an input.
            encoder_hidden_states (`torch.Tensor`):
                The encoder hidden states.
            controlnet_cond (`torch.Tensor`):
                The conditional input tensor of shape `(batch_size, sequence_length, hidden_size)`.
            conditioning_scale (`float`, defaults to `1.0`):
                The scale factor for ControlNet outputs.
            class_labels (`torch.Tensor`, *optional*, defaults to `None`):
                Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.
            timestep_cond (`torch.Tensor`, *optional*, defaults to `None`):
                Additional conditional embeddings for timestep. If provided, the embeddings will be summed with the
                timestep_embedding passed through the `self.time_embedding` layer to obtain the final timestep
                embeddings.
            attention_mask (`torch.Tensor`, *optional*, defaults to `None`):
                An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
                is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
                negative values to the attention scores corresponding to "discard" tokens.
            added_cond_kwargs (`dict`):
                Additional conditions for the Stable Diffusion XL UNet.
            cross_attention_kwargs (`dict[str]`, *optional*, defaults to `None`):
                A kwargs dictionary that if specified is passed along to the `AttnProcessor`.
            guess_mode (`bool`, defaults to `False`):
                In this mode, the ControlNet encoder tries its best to recognize the input content of the input even if
                you remove all prompts. A `guidance_scale` between 3.0 and 5.0 is recommended.
            return_dict (`bool`, defaults to `True`):
                Whether or not to return a [`~models.controlnet.ControlNetOutput`] instead of a plain tuple.
        Returns:
            [`~models.controlnet.ControlNetOutput`] **or** `tuple`:
                If `return_dict` is `True`, a [`~models.controlnet.ControlNetOutput`] is returned, otherwise a tuple is
                returned where the first element is the sample tensor.
        
src/diffusers/models/controlnets/controlnet_sparsectrl.py