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# Adapted from
# https://github.com/huggingface/diffusers/blob/73acebb8cfbd1d2954cabe1af4185f9994e61917/src/diffusers/models/unets/unet_2d_condition.py#L1039-L1312
# https://github.com/huggingface/diffusers/blob/73acebb8cfbd1d2954cabe1af4185f9994e61917/src/diffusers/models/unets/unet_2d_blocks.py#L2482-L2564
# https://github.com/huggingface/diffusers/blob/73acebb8cfbd1d2954cabe1af4185f9994e61917/src/diffusers/models/unets/unet_2d_blocks.py#L2617-L2679
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Not a contribution
# Changes made by NVIDIA CORPORATION & AFFILIATES or otherwise documented as
# NVIDIA-proprietary are not a contribution and subject to the following terms and conditions:
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
#
# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
# property and proprietary rights in and to this material, related
# documentation and any modifications thereto. Any use, reproduction,
# disclosure or distribution of this material and related documentation
# without an express license agreement from NVIDIA CORPORATION or
# its affiliates is strictly prohibited.
from typing import Any, Dict, Optional, Tuple, Union
import torch
from diffusers.models.unets.unet_2d_condition import UNet2DConditionOutput
def cachecrossattnupblock2d_forward(
self,
hidden_states: torch.FloatTensor,
res_hidden_states_0: torch.FloatTensor,
res_hidden_states_1: torch.FloatTensor,
res_hidden_states_2: torch.FloatTensor,
temb: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
upsample_size: Optional[int] = None,
attention_mask: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
) -> torch.FloatTensor:
res_hidden_states_tuple = (res_hidden_states_0, res_hidden_states_1, res_hidden_states_2)
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,
encoder_hidden_states=encoder_hidden_states,
cross_attention_kwargs=cross_attention_kwargs,
attention_mask=attention_mask,
encoder_attention_mask=encoder_attention_mask,
return_dict=False,
)[0]
if self.upsamplers is not None:
for upsampler in self.upsamplers:
hidden_states = upsampler(hidden_states, upsample_size)
return hidden_states
def cacheupblock2d_forward(
self,
hidden_states: torch.FloatTensor,
res_hidden_states_0: torch.FloatTensor,
res_hidden_states_1: torch.FloatTensor,
res_hidden_states_2: torch.FloatTensor,
temb: Optional[torch.FloatTensor] = None,
upsample_size: Optional[int] = None,
) -> torch.FloatTensor:
res_hidden_states_tuple = (res_hidden_states_0, res_hidden_states_1, res_hidden_states_2)
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, upsample_size)
return hidden_states
def cacheunet_forward(
self,
sample: torch.FloatTensor,
timestep: Union[torch.Tensor, float, int],
encoder_hidden_states: torch.Tensor,
class_labels: Optional[torch.Tensor] = None,
timestep_cond: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
mid_block_additional_residual: Optional[torch.Tensor] = None,
down_intrablock_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
return_dict: bool = True,
) -> Union[UNet2DConditionOutput, Tuple]:
# 1. time
t_emb = self.get_time_embed(sample=sample, timestep=timestep)
emb = self.time_embedding(t_emb, timestep_cond)
aug_emb = None
aug_emb = self.get_aug_embed(
emb=emb,
encoder_hidden_states=encoder_hidden_states,
added_cond_kwargs=added_cond_kwargs,
)
emb = emb + aug_emb if aug_emb is not None else emb
encoder_hidden_states = self.process_encoder_hidden_states(
encoder_hidden_states=encoder_hidden_states, added_cond_kwargs=added_cond_kwargs
)
# 2. pre-process
sample = self.conv_in(sample)
if hasattr(self, "_export_precess_onnx") and self._export_precess_onnx:
return (
sample,
encoder_hidden_states,
emb,
)
down_block_res_samples = (sample,)
for i, downsample_block in enumerate(self.down_blocks):
if (
hasattr(downsample_block, "has_cross_attention")
and downsample_block.has_cross_attention
):
if hasattr(self, "use_trt_infer") and self.use_trt_infer:
feed_dict = {
"hidden_states": sample,
"temb": emb,
"encoder_hidden_states": encoder_hidden_states,
}
down_results = self.engines[f"down_blocks.{i}"](feed_dict, self.cuda_stream)
sample = down_results["sample"]
res_samples_0 = down_results["res_samples_0"]
res_samples_1 = down_results["res_samples_1"]
if "res_samples_2" in down_results.keys():
res_samples_2 = down_results["res_samples_2"]
else:
# For t2i-adapter CrossAttnDownBlock2D
additional_residuals = {}
sample, res_samples = downsample_block(
hidden_states=sample,
temb=emb,
encoder_hidden_states=encoder_hidden_states,
attention_mask=attention_mask,
cross_attention_kwargs=cross_attention_kwargs,
encoder_attention_mask=encoder_attention_mask,
**additional_residuals,
)
else:
if hasattr(self, "use_trt_infer") and self.use_trt_infer:
feed_dict = {"hidden_states": sample, "temb": emb}
down_results = self.engines[f"down_blocks.{i}"](feed_dict, self.cuda_stream)
sample = down_results["sample"]
res_samples_0 = down_results["res_samples_0"]
res_samples_1 = down_results["res_samples_1"]
if "res_samples_2" in down_results.keys():
res_samples_2 = down_results["res_samples_2"]
else:
sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
if hasattr(self, "use_trt_infer") and self.use_trt_infer:
down_block_res_samples += (
res_samples_0,
res_samples_1,
)
if "res_samples_2" in down_results.keys():
down_block_res_samples += (res_samples_2,)
else:
down_block_res_samples += res_samples
if hasattr(self, "use_trt_infer") and self.use_trt_infer:
feed_dict = {
"hidden_states": sample,
"temb": emb,
"encoder_hidden_states": encoder_hidden_states,
}
mid_results = self.engines["mid_block"](feed_dict, self.cuda_stream)
sample = mid_results["sample"]
else:
sample = self.mid_block(
sample,
emb,
encoder_hidden_states=encoder_hidden_states,
attention_mask=attention_mask,
cross_attention_kwargs=cross_attention_kwargs,
encoder_attention_mask=encoder_attention_mask,
)
# 5. up
for i, upsample_block in enumerate(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, "has_cross_attention") and upsample_block.has_cross_attention:
if hasattr(self, "use_trt_infer") and self.use_trt_infer:
feed_dict = {
"hidden_states": sample,
"res_hidden_states_0": res_samples[0],
"res_hidden_states_1": res_samples[1],
"res_hidden_states_2": res_samples[2],
"temb": emb,
"encoder_hidden_states": encoder_hidden_states,
}
up_results = self.engines[f"up_blocks.{i}"](feed_dict, self.cuda_stream)
sample = up_results["sample"]
else:
sample = upsample_block(
hidden_states=sample,
temb=emb,
res_hidden_states_0=res_samples[0],
res_hidden_states_1=res_samples[1],
res_hidden_states_2=res_samples[2],
encoder_hidden_states=encoder_hidden_states,
cross_attention_kwargs=cross_attention_kwargs,
attention_mask=attention_mask,
encoder_attention_mask=encoder_attention_mask,
)
else:
if hasattr(self, "use_trt_infer") and self.use_trt_infer:
feed_dict = {
"hidden_states": sample,
"res_hidden_states_0": res_samples[0],
"res_hidden_states_1": res_samples[1],
"res_hidden_states_2": res_samples[2],
"temb": emb,
}
up_results = self.engines[f"up_blocks.{i}"](feed_dict, self.cuda_stream)
sample = up_results["sample"]
else:
sample = upsample_block(
hidden_states=sample,
temb=emb,
res_hidden_states_0=res_samples[0],
res_hidden_states_1=res_samples[1],
res_hidden_states_2=res_samples[2],
)
# 6. post-process
if self.conv_norm_out:
sample = self.conv_norm_out(sample)
sample = self.conv_act(sample)
sample = self.conv_out(sample)
if not return_dict:
return (sample,)
return UNet2DConditionOutput(sample=sample)
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