Model-Optimizer/examples/diffusers/cache_diffusion/pipeline/models/sd3.py

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# SPDX-License-Identifier: Apache-2.0
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# Licensed under the Apache License, Version 2.0 (the "License");
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# http://www.apache.org/licenses/LICENSE-2.0
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from typing import Any

import torch
from diffusers.models.modeling_outputs import Transformer2DModelOutput
from diffusers.utils import (
    USE_PEFT_BACKEND,
    is_torch_version,
    scale_lora_layers,
    unscale_lora_layers,
)


def sd3_forward(
    self,
    hidden_states: torch.FloatTensor,
    encoder_hidden_states: torch.FloatTensor = None,
    pooled_projections: torch.FloatTensor = None,
    timestep: torch.LongTensor = None,
    block_controlnet_hidden_states: list | None = None,
    joint_attention_kwargs: dict | None = None,
    return_dict: bool = True,
) -> torch.FloatTensor | Transformer2DModelOutput:
    """
    The [`SD3Transformer2DModel`] forward method.

    Args:
        hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`):
            Input `hidden_states`.
        encoder_hidden_states (`torch.FloatTensor` of shape `(batch size, sequence_len, embed_dims)`):
            Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
        pooled_projections (`torch.FloatTensor` of shape `(batch_size, projection_dim)`): Embeddings projected
            from the embeddings of input conditions.
        timestep ( `torch.LongTensor`):
            Used to indicate denoising step.
        block_controlnet_hidden_states: (`list` of `torch.Tensor`):
            A list of tensors that if specified are added to the residuals of transformer blocks.
        joint_attention_kwargs (`dict`, *optional*):
            A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
            `self.processor` in
            [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
        return_dict (`bool`, *optional*, defaults to `True`):
            Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain
            tuple.

    Returns:
        If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
        `tuple` where the first element is the sample tensor.
    """
    if joint_attention_kwargs is not None:
        joint_attention_kwargs = joint_attention_kwargs.copy()
        lora_scale = joint_attention_kwargs.pop("scale", 1.0)
    else:
        lora_scale = 1.0

    if USE_PEFT_BACKEND:
        # weight the lora layers by setting `lora_scale` for each PEFT layer
        scale_lora_layers(self, lora_scale)

    height, width = hidden_states.shape[-2:]

    hidden_states = self.pos_embed(hidden_states)  # takes care of adding positional embeddings too.
    temb = self.time_text_embed(timestep, pooled_projections)
    encoder_hidden_states = self.context_embedder(encoder_hidden_states)

    for index_block, block in enumerate(self.transformer_blocks):
        if self.training and self.gradient_checkpointing:

            def create_custom_forward(module, return_dict=None):
                def custom_forward(*inputs):
                    if return_dict is not None:
                        return module(*inputs, return_dict=return_dict)
                    else:
                        return module(*inputs)

                return custom_forward

            ckpt_kwargs: dict[str, Any] = (
                {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
            )
            encoder_hidden_states, hidden_states = torch.utils.checkpoint.checkpoint(
                create_custom_forward(block),
                hidden_states,
                encoder_hidden_states,
                temb,
                **ckpt_kwargs,
            )

        elif hasattr(self, "use_trt_infer") and self.use_trt_infer:
            feed_dict = {
                "hidden_states": hidden_states,
                "encoder_hidden_states": encoder_hidden_states,
                "temb": temb,
            }
            _results = self.engines[f"transformer_blocks.{index_block}"](
                feed_dict, self.cuda_stream
            )
            if index_block != 23:
                encoder_hidden_states = _results["encoder_hidden_states_out"]
            hidden_states = _results["hidden_states_out"]
        else:
            encoder_hidden_states, hidden_states = block(
                hidden_states=hidden_states,
                encoder_hidden_states=encoder_hidden_states,
                temb=temb,
            )

        # controlnet residual
        if block_controlnet_hidden_states is not None and block.context_pre_only is False:
            interval_control = len(self.transformer_blocks) // len(block_controlnet_hidden_states)
            hidden_states = (
                hidden_states + block_controlnet_hidden_states[index_block // interval_control]
            )

    hidden_states = self.norm_out(hidden_states, temb)
    hidden_states = self.proj_out(hidden_states)

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

    if USE_PEFT_BACKEND:
        # remove `lora_scale` from each PEFT layer
        unscale_lora_layers(self, lora_scale)

    if not return_dict:
        return (output,)

    return Transformer2DModelOutput(sample=output)