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import os
import torch
import torch.nn as nn
import torch.nn.init as init
from einops import rearrange
import numpy as np
from diffusers.models.modeling_utils import ModelMixin
from typing import Any, Dict, Optional
from src.models.attention import BasicTransformerBlock
class PoseGuider(ModelMixin):
def __init__(self, noise_latent_channels=320, use_ca=True):
super(PoseGuider, self).__init__()
self.use_ca = use_ca
self.conv_layers = nn.Sequential(
nn.Conv2d(in_channels=3, out_channels=3, kernel_size=3, padding=1),
nn.BatchNorm2d(3),
nn.ReLU(),
nn.Conv2d(in_channels=3, out_channels=16, kernel_size=4, stride=2, padding=1),
nn.BatchNorm2d(16),
nn.ReLU(),
nn.Conv2d(in_channels=16, out_channels=16, kernel_size=3, padding=1),
nn.BatchNorm2d(16),
nn.ReLU(),
nn.Conv2d(in_channels=16, out_channels=32, kernel_size=4, stride=2, padding=1),
nn.BatchNorm2d(32),
nn.ReLU(),
nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, padding=1),
nn.BatchNorm2d(32),
nn.ReLU(),
nn.Conv2d(in_channels=32, out_channels=64, kernel_size=4, stride=2, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(128),
nn.ReLU()
)
# Final projection layer
self.final_proj = nn.Conv2d(in_channels=128, out_channels=noise_latent_channels, kernel_size=1)
self.conv_layers_1 = nn.Sequential(
nn.Conv2d(in_channels=noise_latent_channels, out_channels=noise_latent_channels, kernel_size=3, padding=1),
nn.BatchNorm2d(noise_latent_channels),
nn.ReLU(),
nn.Conv2d(in_channels=noise_latent_channels, out_channels=noise_latent_channels, kernel_size=3, stride=2, padding=1),
nn.BatchNorm2d(noise_latent_channels),
nn.ReLU(),
)
self.conv_layers_2 = nn.Sequential(
nn.Conv2d(in_channels=noise_latent_channels, out_channels=noise_latent_channels, kernel_size=3, padding=1),
nn.BatchNorm2d(noise_latent_channels),
nn.ReLU(),
nn.Conv2d(in_channels=noise_latent_channels, out_channels=noise_latent_channels*2, kernel_size=3, stride=2, padding=1),
nn.BatchNorm2d(noise_latent_channels*2),
nn.ReLU(),
)
self.conv_layers_3 = nn.Sequential(
nn.Conv2d(in_channels=noise_latent_channels*2, out_channels=noise_latent_channels*2, kernel_size=3, padding=1),
nn.BatchNorm2d(noise_latent_channels*2),
nn.ReLU(),
nn.Conv2d(in_channels=noise_latent_channels*2, out_channels=noise_latent_channels*4, kernel_size=3, stride=2, padding=1),
nn.BatchNorm2d(noise_latent_channels*4),
nn.ReLU(),
)
self.conv_layers_4 = nn.Sequential(
nn.Conv2d(in_channels=noise_latent_channels*4, out_channels=noise_latent_channels*4, kernel_size=3, padding=1),
nn.BatchNorm2d(noise_latent_channels*4),
nn.ReLU(),
)
if self.use_ca:
self.cross_attn1 = Transformer2DModel(in_channels=noise_latent_channels)
self.cross_attn2 = Transformer2DModel(in_channels=noise_latent_channels*2)
self.cross_attn3 = Transformer2DModel(in_channels=noise_latent_channels*4)
self.cross_attn4 = Transformer2DModel(in_channels=noise_latent_channels*4)
# Initialize layers
self._initialize_weights()
self.scale = nn.Parameter(torch.ones(1) * 2)
# def _initialize_weights(self):
# # Initialize weights with Gaussian distribution and zero out the final layer
# for m in self.conv_layers:
# if isinstance(m, nn.Conv2d):
# init.normal_(m.weight, mean=0.0, std=0.02)
# if m.bias is not None:
# init.zeros_(m.bias)
# init.zeros_(self.final_proj.weight)
# if self.final_proj.bias is not None:
# init.zeros_(self.final_proj.bias)
def _initialize_weights(self):
# Initialize weights with He initialization and zero out the biases
conv_blocks = [self.conv_layers, self.conv_layers_1, self.conv_layers_2, self.conv_layers_3, self.conv_layers_4]
for block_item in conv_blocks:
for m in block_item:
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.in_channels
init.normal_(m.weight, mean=0.0, std=np.sqrt(2. / n))
if m.bias is not None:
init.zeros_(m.bias)
# For the final projection layer, initialize weights to zero (or you may choose to use He initialization here as well)
init.zeros_(self.final_proj.weight)
if self.final_proj.bias is not None:
init.zeros_(self.final_proj.bias)
def forward(self, x, ref_x):
fea = []
b = x.shape[0]
x = rearrange(x, "b c f h w -> (b f) c h w")
x = self.conv_layers(x)
x = self.final_proj(x)
x = x * self.scale
# x = rearrange(x, "(b f) c h w -> b c f h w", b=b)
fea.append(rearrange(x, "(b f) c h w -> b c f h w", b=b))
x = self.conv_layers_1(x)
if self.use_ca:
ref_x = self.conv_layers(ref_x)
ref_x = self.final_proj(ref_x)
ref_x = ref_x * self.scale
ref_x = self.conv_layers_1(ref_x)
x = self.cross_attn1(x, ref_x)
fea.append(rearrange(x, "(b f) c h w -> b c f h w", b=b))
x = self.conv_layers_2(x)
if self.use_ca:
ref_x = self.conv_layers_2(ref_x)
x = self.cross_attn2(x, ref_x)
fea.append(rearrange(x, "(b f) c h w -> b c f h w", b=b))
x = self.conv_layers_3(x)
if self.use_ca:
ref_x = self.conv_layers_3(ref_x)
x = self.cross_attn3(x, ref_x)
fea.append(rearrange(x, "(b f) c h w -> b c f h w", b=b))
x = self.conv_layers_4(x)
if self.use_ca:
ref_x = self.conv_layers_4(ref_x)
x = self.cross_attn4(x, ref_x)
fea.append(rearrange(x, "(b f) c h w -> b c f h w", b=b))
return fea
# @classmethod
# def from_pretrained(cls,pretrained_model_path):
# if not os.path.exists(pretrained_model_path):
# print(f"There is no model file in {pretrained_model_path}")
# print(f"loaded PoseGuider's pretrained weights from {pretrained_model_path} ...")
# state_dict = torch.load(pretrained_model_path, map_location="cpu")
# model = Hack_PoseGuider(noise_latent_channels=320)
# m, u = model.load_state_dict(state_dict, strict=True)
# # print(f"### missing keys: {len(m)}; \n### unexpected keys: {len(u)};")
# params = [p.numel() for n, p in model.named_parameters()]
# print(f"### PoseGuider's Parameters: {sum(params) / 1e6} M")
# return model
class Transformer2DModel(ModelMixin):
_supports_gradient_checkpointing = True
def __init__(
self,
num_attention_heads: int = 16,
attention_head_dim: int = 88,
in_channels: Optional[int] = None,
num_layers: int = 1,
dropout: float = 0.0,
norm_num_groups: int = 32,
cross_attention_dim: Optional[int] = None,
attention_bias: bool = False,
activation_fn: str = "geglu",
num_embeds_ada_norm: Optional[int] = None,
use_linear_projection: bool = False,
only_cross_attention: bool = False,
double_self_attention: bool = False,
upcast_attention: bool = False,
norm_type: str = "layer_norm",
norm_elementwise_affine: bool = True,
norm_eps: float = 1e-5,
attention_type: str = "default",
):
super().__init__()
self.use_linear_projection = use_linear_projection
self.num_attention_heads = num_attention_heads
self.attention_head_dim = attention_head_dim
inner_dim = num_attention_heads * attention_head_dim
self.in_channels = in_channels
self.norm = torch.nn.GroupNorm(
num_groups=norm_num_groups,
num_channels=in_channels,
eps=1e-6,
affine=True,
)
if use_linear_projection:
self.proj_in = nn.Linear(in_channels, inner_dim)
else:
self.proj_in = nn.Conv2d(
in_channels, inner_dim, kernel_size=1, stride=1, padding=0
)
# 3. Define transformers blocks
self.transformer_blocks = nn.ModuleList(
[
BasicTransformerBlock(
inner_dim,
num_attention_heads,
attention_head_dim,
dropout=dropout,
cross_attention_dim=cross_attention_dim,
activation_fn=activation_fn,
num_embeds_ada_norm=num_embeds_ada_norm,
attention_bias=attention_bias,
only_cross_attention=only_cross_attention,
double_self_attention=double_self_attention,
upcast_attention=upcast_attention,
norm_type=norm_type,
norm_elementwise_affine=norm_elementwise_affine,
norm_eps=norm_eps,
attention_type=attention_type,
)
for d in range(num_layers)
]
)
if use_linear_projection:
self.proj_out = nn.Linear(inner_dim, in_channels)
else:
self.proj_out = nn.Conv2d(
inner_dim, in_channels, kernel_size=1, stride=1, padding=0
)
self.gradient_checkpointing = False
def _set_gradient_checkpointing(self, module, value=False):
if hasattr(module, "gradient_checkpointing"):
module.gradient_checkpointing = value
def forward(
self,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
timestep: Optional[torch.LongTensor] = None,
):
batch, _, height, width = hidden_states.shape
residual = hidden_states
hidden_states = self.norm(hidden_states)
if not self.use_linear_projection:
hidden_states = self.proj_in(hidden_states)
inner_dim = hidden_states.shape[1]
hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(
batch, height * width, inner_dim
)
else:
inner_dim = hidden_states.shape[1]
hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(
batch, height * width, inner_dim
)
hidden_states = self.proj_in(hidden_states)
for block in self.transformer_blocks:
hidden_states = block(
hidden_states,
encoder_hidden_states=encoder_hidden_states,
timestep=timestep,
)
if not self.use_linear_projection:
hidden_states = (
hidden_states.reshape(batch, height, width, inner_dim)
.permute(0, 3, 1, 2)
.contiguous()
)
hidden_states = self.proj_out(hidden_states)
else:
hidden_states = self.proj_out(hidden_states)
hidden_states = (
hidden_states.reshape(batch, height, width, inner_dim)
.permute(0, 3, 1, 2)
.contiguous()
)
output = hidden_states + residual
return output
if __name__ == '__main__':
model = PoseGuider(noise_latent_channels=320).to(device="cuda")
input_data = torch.randn(1,3,1,512,512).to(device="cuda")
input_data1 = torch.randn(1,3,512,512).to(device="cuda")
output = model(input_data, input_data1)
for item in output:
print(item.shape)
# tf_model = Transformer2DModel(
# in_channels=320
# ).to('cuda')
# input_data = torch.randn(4,320,32,32).to(device="cuda")
# # input_emb = torch.randn(4,1,768).to(device="cuda")
# input_emb = torch.randn(4,320,32,32).to(device="cuda")
# o1 = tf_model(input_data, input_emb)
# print(o1.shape)
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