389 lines
14 KiB
Python
389 lines
14 KiB
Python
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from typing import Dict, Any, Optional, Tuple
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import torch
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from diffusers.models.resnet import ResnetBlock2D
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from diffusers.utils import is_torch_version
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from diffusers.utils.torch_utils import apply_freeu
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from torch import nn
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class MidBlock2D(nn.Module):
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def __init__(
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self,
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in_channels: int,
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temb_channels: int,
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dropout: float = 0.0,
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num_layers: int = 1,
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resnet_eps: float = 1e-6,
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resnet_time_scale_shift: str = "default",
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resnet_act_fn: str = "swish",
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resnet_groups: int = 32,
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resnet_pre_norm: bool = True,
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output_scale_factor: float = 1.0,
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use_linear_projection: bool = False,
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):
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super().__init__()
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self.has_cross_attention = False
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resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
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# there is always at least one resnet
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resnets = [
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ResnetBlock2D(
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in_channels=in_channels,
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out_channels=in_channels,
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temb_channels=temb_channels,
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eps=resnet_eps,
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groups=resnet_groups,
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dropout=dropout,
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time_embedding_norm=resnet_time_scale_shift,
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non_linearity=resnet_act_fn,
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output_scale_factor=output_scale_factor,
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pre_norm=resnet_pre_norm,
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)
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]
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for i in range(num_layers):
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resnets.append(
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ResnetBlock2D(
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in_channels=in_channels,
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out_channels=in_channels,
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temb_channels=temb_channels,
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eps=resnet_eps,
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groups=resnet_groups,
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dropout=dropout,
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time_embedding_norm=resnet_time_scale_shift,
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non_linearity=resnet_act_fn,
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output_scale_factor=output_scale_factor,
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pre_norm=resnet_pre_norm,
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)
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)
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self.resnets = nn.ModuleList(resnets)
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self.gradient_checkpointing = False
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def forward(
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self,
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hidden_states: torch.FloatTensor,
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temb: Optional[torch.FloatTensor] = None,
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) -> torch.FloatTensor:
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lora_scale = 1.0
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hidden_states = self.resnets[0](hidden_states, temb, scale=lora_scale)
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for resnet in self.resnets[1:]:
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if self.training and self.gradient_checkpointing:
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def create_custom_forward(module, return_dict=None):
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def custom_forward(*inputs):
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if return_dict is not None:
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return module(*inputs, return_dict=return_dict)
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else:
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return module(*inputs)
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return custom_forward
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ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
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hidden_states = torch.utils.checkpoint.checkpoint(
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create_custom_forward(resnet),
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hidden_states,
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temb,
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**ckpt_kwargs,
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)
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else:
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hidden_states = resnet(hidden_states, temb, scale=lora_scale)
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return hidden_states
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def DownBlock2D_forward(
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self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None, scale: float = 1.0,
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down_block_add_samples: Optional[torch.FloatTensor] = None,
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) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]:
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output_states = ()
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for resnet in self.resnets:
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if self.training and self.gradient_checkpointing:
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def create_custom_forward(module):
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def custom_forward(*inputs):
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return module(*inputs)
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return custom_forward
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if is_torch_version(">=", "1.11.0"):
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hidden_states = torch.utils.checkpoint.checkpoint(
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create_custom_forward(resnet), hidden_states, temb, use_reentrant=False
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)
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else:
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hidden_states = torch.utils.checkpoint.checkpoint(
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create_custom_forward(resnet), hidden_states, temb
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)
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else:
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hidden_states = resnet(hidden_states, temb, scale=scale)
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if down_block_add_samples is not None:
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hidden_states = hidden_states + down_block_add_samples.pop(0)
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output_states = output_states + (hidden_states,)
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if self.downsamplers is not None:
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for downsampler in self.downsamplers:
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hidden_states = downsampler(hidden_states, scale=scale)
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if down_block_add_samples is not None:
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hidden_states = hidden_states + down_block_add_samples.pop(0) # todo: add before or after
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output_states = output_states + (hidden_states,)
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return hidden_states, output_states
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def CrossAttnDownBlock2D_forward(
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self,
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hidden_states: torch.FloatTensor,
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temb: Optional[torch.FloatTensor] = None,
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encoder_hidden_states: Optional[torch.FloatTensor] = None,
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attention_mask: Optional[torch.FloatTensor] = None,
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cross_attention_kwargs: Optional[Dict[str, Any]] = None,
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encoder_attention_mask: Optional[torch.FloatTensor] = None,
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additional_residuals: Optional[torch.FloatTensor] = None,
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down_block_add_samples: Optional[torch.FloatTensor] = None,
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) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]:
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output_states = ()
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lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0
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blocks = list(zip(self.resnets, self.attentions))
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for i, (resnet, attn) in enumerate(blocks):
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if self.training and self.gradient_checkpointing:
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def create_custom_forward(module, return_dict=None):
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def custom_forward(*inputs):
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if return_dict is not None:
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return module(*inputs, return_dict=return_dict)
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else:
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return module(*inputs)
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return custom_forward
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ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
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hidden_states = torch.utils.checkpoint.checkpoint(
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create_custom_forward(resnet),
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hidden_states,
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temb,
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**ckpt_kwargs,
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)
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hidden_states = attn(
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hidden_states,
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encoder_hidden_states=encoder_hidden_states,
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cross_attention_kwargs=cross_attention_kwargs,
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attention_mask=attention_mask,
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encoder_attention_mask=encoder_attention_mask,
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return_dict=False,
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)[0]
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else:
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hidden_states = resnet(hidden_states, temb, scale=lora_scale)
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hidden_states = attn(
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hidden_states,
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encoder_hidden_states=encoder_hidden_states,
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cross_attention_kwargs=cross_attention_kwargs,
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attention_mask=attention_mask,
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encoder_attention_mask=encoder_attention_mask,
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return_dict=False,
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)[0]
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# apply additional residuals to the output of the last pair of resnet and attention blocks
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if i == len(blocks) - 1 and additional_residuals is not None:
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hidden_states = hidden_states + additional_residuals
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if down_block_add_samples is not None:
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hidden_states = hidden_states + down_block_add_samples.pop(0)
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output_states = output_states + (hidden_states,)
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if self.downsamplers is not None:
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for downsampler in self.downsamplers:
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hidden_states = downsampler(hidden_states, scale=lora_scale)
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if down_block_add_samples is not None:
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hidden_states = hidden_states + down_block_add_samples.pop(0) # todo: add before or after
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output_states = output_states + (hidden_states,)
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return hidden_states, output_states
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def CrossAttnUpBlock2D_forward(
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self,
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hidden_states: torch.FloatTensor,
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res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
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temb: Optional[torch.FloatTensor] = None,
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encoder_hidden_states: Optional[torch.FloatTensor] = None,
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cross_attention_kwargs: Optional[Dict[str, Any]] = None,
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upsample_size: Optional[int] = None,
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attention_mask: Optional[torch.FloatTensor] = None,
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encoder_attention_mask: Optional[torch.FloatTensor] = None,
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return_res_samples: Optional[bool] = False,
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up_block_add_samples: Optional[torch.FloatTensor] = None,
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) -> torch.FloatTensor:
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lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0
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is_freeu_enabled = (
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getattr(self, "s1", None)
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and getattr(self, "s2", None)
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and getattr(self, "b1", None)
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and getattr(self, "b2", None)
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)
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if return_res_samples:
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output_states = ()
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for resnet, attn in zip(self.resnets, self.attentions):
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# pop res hidden states
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res_hidden_states = res_hidden_states_tuple[-1]
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res_hidden_states_tuple = res_hidden_states_tuple[:-1]
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# FreeU: Only operate on the first two stages
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if is_freeu_enabled:
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hidden_states, res_hidden_states = apply_freeu(
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self.resolution_idx,
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hidden_states,
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res_hidden_states,
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s1=self.s1,
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s2=self.s2,
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b1=self.b1,
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b2=self.b2,
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)
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hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
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if self.training and self.gradient_checkpointing:
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def create_custom_forward(module, return_dict=None):
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def custom_forward(*inputs):
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if return_dict is not None:
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return module(*inputs, return_dict=return_dict)
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else:
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return module(*inputs)
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return custom_forward
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ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
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hidden_states = torch.utils.checkpoint.checkpoint(
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create_custom_forward(resnet),
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hidden_states,
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temb,
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**ckpt_kwargs,
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)
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hidden_states = attn(
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hidden_states,
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encoder_hidden_states=encoder_hidden_states,
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cross_attention_kwargs=cross_attention_kwargs,
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attention_mask=attention_mask,
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encoder_attention_mask=encoder_attention_mask,
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return_dict=False,
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)[0]
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else:
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hidden_states = resnet(hidden_states, temb, scale=lora_scale)
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hidden_states = attn(
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hidden_states,
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encoder_hidden_states=encoder_hidden_states,
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cross_attention_kwargs=cross_attention_kwargs,
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attention_mask=attention_mask,
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encoder_attention_mask=encoder_attention_mask,
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return_dict=False,
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)[0]
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if return_res_samples:
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output_states = output_states + (hidden_states,)
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if up_block_add_samples is not None:
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hidden_states = hidden_states + up_block_add_samples.pop(0)
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if self.upsamplers is not None:
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for upsampler in self.upsamplers:
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hidden_states = upsampler(hidden_states, upsample_size, scale=lora_scale)
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if return_res_samples:
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output_states = output_states + (hidden_states,)
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if up_block_add_samples is not None:
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hidden_states = hidden_states + up_block_add_samples.pop(0)
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if return_res_samples:
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return hidden_states, output_states
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else:
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return hidden_states
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def UpBlock2D_forward(
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self,
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hidden_states: torch.FloatTensor,
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res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
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temb: Optional[torch.FloatTensor] = None,
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upsample_size: Optional[int] = None,
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scale: float = 1.0,
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return_res_samples: Optional[bool] = False,
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up_block_add_samples: Optional[torch.FloatTensor] = None,
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) -> torch.FloatTensor:
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is_freeu_enabled = (
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getattr(self, "s1", None)
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and getattr(self, "s2", None)
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and getattr(self, "b1", None)
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and getattr(self, "b2", None)
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)
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if return_res_samples:
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output_states = ()
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for resnet in self.resnets:
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# pop res hidden states
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res_hidden_states = res_hidden_states_tuple[-1]
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res_hidden_states_tuple = res_hidden_states_tuple[:-1]
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# FreeU: Only operate on the first two stages
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if is_freeu_enabled:
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hidden_states, res_hidden_states = apply_freeu(
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self.resolution_idx,
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hidden_states,
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res_hidden_states,
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s1=self.s1,
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s2=self.s2,
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b1=self.b1,
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b2=self.b2,
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)
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hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
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if self.training and self.gradient_checkpointing:
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def create_custom_forward(module):
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def custom_forward(*inputs):
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return module(*inputs)
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return custom_forward
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if is_torch_version(">=", "1.11.0"):
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hidden_states = torch.utils.checkpoint.checkpoint(
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create_custom_forward(resnet), hidden_states, temb, use_reentrant=False
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)
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else:
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|
hidden_states = torch.utils.checkpoint.checkpoint(
|
||
|
|
create_custom_forward(resnet), hidden_states, temb
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
hidden_states = resnet(hidden_states, temb, scale=scale)
|
||
|
|
|
||
|
|
if return_res_samples:
|
||
|
|
output_states = output_states + (hidden_states,)
|
||
|
|
if up_block_add_samples is not None:
|
||
|
|
hidden_states = hidden_states + up_block_add_samples.pop(0) # todo: add before or after
|
||
|
|
|
||
|
|
if self.upsamplers is not None:
|
||
|
|
for upsampler in self.upsamplers:
|
||
|
|
hidden_states = upsampler(hidden_states, upsample_size, scale=scale)
|
||
|
|
|
||
|
|
if return_res_samples:
|
||
|
|
output_states = output_states + (hidden_states,)
|
||
|
|
if up_block_add_samples is not None:
|
||
|
|
hidden_states = hidden_states + up_block_add_samples.pop(0) # todo: add before or after
|
||
|
|
|
||
|
|
if return_res_samples:
|
||
|
|
return hidden_states, output_states
|
||
|
|
else:
|
||
|
|
return hidden_states
|