def forward(
        self,
        hidden_states: torch.Tensor,
        encoder_hidden_states: torch.Tensor,
        timestep: torch.LongTensor,
        original_size: torch.Tensor,
        target_size: torch.Tensor,
        crop_coords: torch.Tensor,
        return_dict: bool = True,
    ) -> Union[torch.Tensor, Transformer2DModelOutput]:
        """
        The [`CogView3PlusTransformer2DModel`] forward method.

        Args:
            hidden_states (`torch.Tensor`):
                Input `hidden_states` of shape `(batch size, channel, height, width)`.
            encoder_hidden_states (`torch.Tensor`):
                Conditional embeddings (embeddings computed from the input conditions such as prompts) of shape
                `(batch_size, sequence_len, text_embed_dim)`
            timestep (`torch.LongTensor`):
                Used to indicate denoising step.
            original_size (`torch.Tensor`):
                CogView3 uses SDXL-like micro-conditioning for original image size as explained in section 2.2 of
                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
            target_size (`torch.Tensor`):
                CogView3 uses SDXL-like micro-conditioning for target image size as explained in section 2.2 of
                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
            crop_coords (`torch.Tensor`):
                CogView3 uses SDXL-like micro-conditioning for crop coordinates as explained in section 2.2 of
                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain
                tuple.

        Returns:
            `torch.Tensor` or [`~models.transformer_2d.Transformer2DModelOutput`]:
                The denoised latents using provided inputs as conditioning.
        """
        height, width = hidden_states.shape[-2:]
        text_seq_length = encoder_hidden_states.shape[1]

        hidden_states = self.patch_embed(
            hidden_states, encoder_hidden_states
        )  # takes care of adding positional embeddings too.
        emb = self.time_condition_embed(timestep, original_size, target_size, crop_coords, hidden_states.dtype)

        encoder_hidden_states = hidden_states[:, :text_seq_length]
        hidden_states = hidden_states[:, text_seq_length:]

        for index_block, block in enumerate(self.transformer_blocks):
            if torch.is_grad_enabled() and self.gradient_checkpointing:
                hidden_states, encoder_hidden_states = self._gradient_checkpointing_func(
                    block,
                    hidden_states,
                    encoder_hidden_states,
                    emb,
                )
            else:
                hidden_states, encoder_hidden_states = block(
                    hidden_states=hidden_states,
                    encoder_hidden_states=encoder_hidden_states,
                    emb=emb,
                )

        hidden_states = self.norm_out(hidden_states, emb)
        hidden_states = self.proj_out(hidden_states)  # (batch_size, height*width, patch_size*patch_size*out_channels)

        # unpatchify
        patch_size = self.config.patch_size
        height = height // patch_size
        width = width // patch_size

        hidden_states = hidden_states.reshape(
            shape=(hidden_states.shape[0], height, width, self.out_channels, patch_size, patch_size)
        )
        hidden_states = torch.einsum("nhwcpq->nchpwq", hidden_states)
        output = hidden_states.reshape(
            shape=(hidden_states.shape[0], self.out_channels, height * patch_size, width * patch_size)
        )

        if not return_dict:
            return (output,)

        return Transformer2DModelOutput(sample=output)

        The [`CogView3PlusTransformer2DModel`] forward method.

        Args:
            hidden_states (`torch.Tensor`):
                Input `hidden_states` of shape `(batch size, channel, height, width)`.
            encoder_hidden_states (`torch.Tensor`):
                Conditional embeddings (embeddings computed from the input conditions such as prompts) of shape
                `(batch_size, sequence_len, text_embed_dim)`
            timestep (`torch.LongTensor`):
                Used to indicate denoising step.
            original_size (`torch.Tensor`):
                CogView3 uses SDXL-like micro-conditioning for original image size as explained in section 2.2 of
                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
            target_size (`torch.Tensor`):
                CogView3 uses SDXL-like micro-conditioning for target image size as explained in section 2.2 of
                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
            crop_coords (`torch.Tensor`):
                CogView3 uses SDXL-like micro-conditioning for crop coordinates as explained in section 2.2 of
                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain
                tuple.

        Returns:
            `torch.Tensor` or [`~models.transformer_2d.Transformer2DModelOutput`]:
                The denoised latents using provided inputs as conditioning.
        
src/diffusers/models/transformers/transformer_cogview3plus.py