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import torch
import torch.nn as nn
import einops
from torch.nn import functional as F
from torch.jit import Final
from timm.layers import use_fused_attn
from timm.models.layers import PatchEmbed, Mlp, DropPath, trunc_normal_, lecun_normal_, get_act_layer
from abc import abstractmethod
from NoiseTransformer import NoiseTransformer
from einops import rearrange
__all__ = ['SVDNoiseUnet', 'SVDNoiseUnet_Concise']
class Attention(nn.Module):
fused_attn: Final[bool]
def __init__(
self,
dim: int,
num_heads: int = 8,
qkv_bias: bool = False,
qk_norm: bool = False,
attn_drop: float = 0.,
proj_drop: float = 0.,
norm_layer: nn.Module = nn.LayerNorm,
) -> None:
super().__init__()
assert dim % num_heads == 0, 'dim should be divisible by num_heads'
self.num_heads = num_heads
self.head_dim = dim // num_heads
self.scale = self.head_dim ** -0.5
self.fused_attn = use_fused_attn()
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x: torch.Tensor) -> torch.Tensor:
B, N, C = x.shape
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
q, k, v = qkv.unbind(0)
q, k = self.q_norm(q), self.k_norm(k)
if self.fused_attn:
x = F.scaled_dot_product_attention(
q, k, v,
dropout_p=self.attn_drop.p if self.training else 0.,
)
else:
q = q * self.scale
attn = q @ k.transpose(-2, -1)
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = attn @ v
x = x.transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class SVDNoiseUnet(nn.Module):
def __init__(self, in_channels=4, out_channels=4, resolution=(128,96)): # resolution = size // 8
super(SVDNoiseUnet, self).__init__()
_in_1 = int(resolution[0] * in_channels // 2)
_out_1 = int(resolution[0] * out_channels // 2)
_in_2 = int(resolution[1] * in_channels // 2)
_out_2 = int(resolution[1] * out_channels // 2)
self.mlp1 = nn.Sequential(
nn.Linear(_in_1, 64),
nn.ReLU(inplace=True),
nn.Linear(64, _out_1),
)
self.mlp2 = nn.Sequential(
nn.Linear(_in_2, 64),
nn.ReLU(inplace=True),
nn.Linear(64, _out_2),
)
self.mlp3 = nn.Sequential(
nn.Linear(_in_2, _out_2),
)
self.attention = Attention(_out_2)
self.bn = nn.BatchNorm1d(256)
self.bn2 = nn.BatchNorm1d(192)
self.mlp4 = nn.Sequential(
nn.Linear(_out_2, 1024),
nn.ReLU(inplace=True),
nn.Linear(1024, _out_2),
)
self.ffn = nn.Sequential(
nn.Linear(256, 384), # Expand
nn.ReLU(inplace=True),
nn.Linear(384, 192) # Reduce to target size
)
self.ffn2 = nn.Sequential(
nn.Linear(256, 384), # Expand
nn.ReLU(inplace=True),
nn.Linear(384, 192) # Reduce to target size
)
# self.adaptive_pool = nn.AdaptiveAvgPool2d((256, 192))
def forward(self, x, residual=False):
b, c, h, w = x.shape
x = einops.rearrange(x, "b (a c)h w ->b (a h)(c w)", a=2,c=2) # x -> [1, 256, 256]
U, s, V = torch.linalg.svd(x) # U->[b 256 256], s-> [b 256], V->[b 256 256]
U_T = U.permute(0, 2, 1)
U_out = self.ffn(self.mlp1(U_T))
U_out = self.bn(U_out)
U_out = U_out.transpose(1, 2)
U_out = self.ffn2(U_out) # [b, 256, 256] -> [b, 256, 192]
U_out = self.bn2(U_out)
U_out = U_out.transpose(1, 2)
# U_out = self.bn(U_out)
V_out = self.mlp2(V)
s_out = self.mlp3(s).unsqueeze(1) # s -> [b, 1, 256] => [b, 256, 256]
out = U_out + V_out + s_out
# print(out.size())
out = out.squeeze(1)
out = self.attention(out).mean(1)
out = self.mlp4(out) + s
diagonal_out = torch.diag_embed(out)
padded_diag = F.pad(diagonal_out, (0, 0, 0, 64), mode='constant', value=0) # Shape: [b, 1, 256, 192]
pred = U @ padded_diag @ V
return einops.rearrange(pred, "b (a h)(c w) -> b (a c) h w", a=2,c=2)
class SVDNoiseUnet64(nn.Module):
def __init__(self, in_channels=4, out_channels=4, resolution=64): # resolution = size // 8
super(SVDNoiseUnet64, self).__init__()
_in = int(resolution * in_channels // 2)
_out = int(resolution * out_channels // 2)
self.mlp1 = nn.Sequential(
nn.Linear(_in, 64),
nn.ReLU(inplace=True),
nn.Linear(64, _out),
)
self.mlp2 = nn.Sequential(
nn.Linear(_in, 64),
nn.ReLU(inplace=True),
nn.Linear(64, _out),
)
self.mlp3 = nn.Sequential(
nn.Linear(_in, _out),
)
self.attention = Attention(_out)
self.bn = nn.BatchNorm2d(_out)
self.mlp4 = nn.Sequential(
nn.Linear(_out, 1024),
nn.ReLU(inplace=True),
nn.Linear(1024, _out),
)
def forward(self, x, residual=False):
b, c, h, w = x.shape
x = einops.rearrange(x, "b (a c)h w ->b (a h)(c w)", a=2,c=2) # x -> [1, 256, 256]
U, s, V = torch.linalg.svd(x) # U->[b 256 256], s-> [b 256], V->[b 256 256]
U_T = U.permute(0, 2, 1)
out = self.mlp1(U_T) + self.mlp2(V) + self.mlp3(s).unsqueeze(1) # s -> [b, 1, 256] => [b, 256, 256]
out = self.attention(out).mean(1)
out = self.mlp4(out) + s
pred = U @ torch.diag_embed(out) @ V
return einops.rearrange(pred, "b (a h)(c w) -> b (a c) h w", a=2,c=2)
class SVDNoiseUnet128(nn.Module):
def __init__(self, in_channels=4, out_channels=4, resolution=128): # resolution = size // 8
super(SVDNoiseUnet128, self).__init__()
_in = int(resolution * in_channels // 2)
_out = int(resolution * out_channels // 2)
self.mlp1 = nn.Sequential(
nn.Linear(_in, 64),
nn.ReLU(inplace=True),
nn.Linear(64, _out),
)
self.mlp2 = nn.Sequential(
nn.Linear(_in, 64),
nn.ReLU(inplace=True),
nn.Linear(64, _out),
)
self.mlp3 = nn.Sequential(
nn.Linear(_in, _out),
)
self.attention = Attention(_out)
self.bn = nn.BatchNorm2d(_out)
self.mlp4 = nn.Sequential(
nn.Linear(_out, 1024),
nn.ReLU(inplace=True),
nn.Linear(1024, _out),
)
def forward(self, x, residual=False):
b, c, h, w = x.shape
x = einops.rearrange(x, "b (a c)h w ->b (a h)(c w)", a=2,c=2) # x -> [1, 256, 256]
U, s, V = torch.linalg.svd(x) # U->[b 256 256], s-> [b 256], V->[b 256 256]
U_T = U.permute(0, 2, 1)
out = self.mlp1(U_T) + self.mlp2(V) + self.mlp3(s).unsqueeze(1) # s -> [b, 1, 256] => [b, 256, 256]
out = self.attention(out).mean(1)
out = self.mlp4(out) + s
pred = U @ torch.diag_embed(out) @ V
return einops.rearrange(pred, "b (a h)(c w) -> b (a c) h w", a=2,c=2)
class SVDNoiseUnet_Concise(nn.Module):
def __init__(self, in_channels=4, out_channels=4, resolution=64):
super(SVDNoiseUnet_Concise, self).__init__()
from diffusers.models.normalization import AdaGroupNorm
class NPNet(nn.Module):
def __init__(self, model_id, pretrained_path=' ', device='cuda') -> None:
super(NPNet, self).__init__()
assert model_id in ['SD1.5', 'DreamShaper', 'DiT']
self.model_id = model_id
self.device = device
self.pretrained_path = pretrained_path
(
self.unet_svd,
self.unet_embedding,
self.text_embedding,
self._alpha,
self._beta
) = self.get_model()
def save_model(self, save_path: str):
"""
Save this NPNet so that get_model() can later reload it.
"""
torch.save({
"unet_svd": self.unet_svd.state_dict(),
"unet_embedding": self.unet_embedding.state_dict(),
"embeeding": self.text_embedding.state_dict(), # matches get_model’s key
"alpha": self._alpha,
"beta": self._beta,
}, save_path)
print(f"NPNet saved to {save_path}")
def get_model(self):
unet_embedding = NoiseTransformer(resolution=(128,96)).to(self.device).to(torch.float32)
unet_svd = SVDNoiseUnet(resolution=(128,96)).to(self.device).to(torch.float32)
if self.model_id == 'DiT':
text_embedding = AdaGroupNorm(768 * 77, 4, 1, eps=1e-6).to(self.device).to(torch.float32)
else:
text_embedding = AdaGroupNorm(768 * 77, 4, 1, eps=1e-6).to(self.device).to(torch.float32)
# initialize random _alpha and _beta when no checkpoint is provided
_alpha = torch.randn(1, device=self.device)
_beta = torch.randn(1, device=self.device)
if '.pth' in self.pretrained_path:
gloden_unet = torch.load(self.pretrained_path)
unet_svd.load_state_dict(gloden_unet["unet_svd"],strict=True)
unet_embedding.load_state_dict(gloden_unet["unet_embedding"],strict=True)
text_embedding.load_state_dict(gloden_unet["embeeding"],strict=True)
_alpha = gloden_unet["alpha"]
_beta = gloden_unet["beta"]
print("Load Successfully!")
return unet_svd, unet_embedding, text_embedding, _alpha, _beta
else:
return unet_svd, unet_embedding, text_embedding, _alpha, _beta
def forward(self, initial_noise, prompt_embeds):
prompt_embeds = prompt_embeds.float().view(prompt_embeds.shape[0], -1)
text_emb = self.text_embedding(initial_noise.float(), prompt_embeds)
encoder_hidden_states_svd = initial_noise
encoder_hidden_states_embedding = initial_noise + text_emb
golden_embedding = self.unet_embedding(encoder_hidden_states_embedding.float())
golden_noise = self.unet_svd(encoder_hidden_states_svd.float()) + (
2 * torch.sigmoid(self._alpha) - 1) * text_emb + self._beta * golden_embedding
return golden_noise
class NPNet64(nn.Module):
def __init__(self, model_id, pretrained_path=' ', device='cuda') -> None:
super(NPNet64, self).__init__()
self.model_id = model_id
self.device = device
self.pretrained_path = pretrained_path
(
self.unet_svd,
self.unet_embedding,
self.text_embedding,
self._alpha,
self._beta
) = self.get_model()
def save_model(self, save_path: str):
"""
Save this NPNet so that get_model() can later reload it.
"""
torch.save({
"unet_svd": self.unet_svd.state_dict(),
"unet_embedding": self.unet_embedding.state_dict(),
"embeeding": self.text_embedding.state_dict(), # matches get_model’s key
"alpha": self._alpha,
"beta": self._beta,
}, save_path)
print(f"NPNet saved to {save_path}")
def get_model(self):
unet_embedding = NoiseTransformer(resolution=(64,64)).to(self.device).to(torch.float32)
unet_svd = SVDNoiseUnet64(resolution=64).to(self.device).to(torch.float32)
_alpha = torch.randn(1, device=self.device)
_beta = torch.randn(1, device=self.device)
text_embedding = AdaGroupNorm(768 * 77, 4, 1, eps=1e-6).to(self.device).to(torch.float32)
if '.pth' in self.pretrained_path:
gloden_unet = torch.load(self.pretrained_path)
unet_svd.load_state_dict(gloden_unet["unet_svd"])
unet_embedding.load_state_dict(gloden_unet["unet_embedding"])
text_embedding.load_state_dict(gloden_unet["embeeding"])
_alpha = gloden_unet["alpha"]
_beta = gloden_unet["beta"]
print("Load Successfully!")
return unet_svd, unet_embedding, text_embedding, _alpha, _beta
def forward(self, initial_noise, prompt_embeds):
prompt_embeds = prompt_embeds.float().view(prompt_embeds.shape[0], -1)
text_emb = self.text_embedding(initial_noise.float(), prompt_embeds)
encoder_hidden_states_svd = initial_noise
encoder_hidden_states_embedding = initial_noise + text_emb
golden_embedding = self.unet_embedding(encoder_hidden_states_embedding.float())
golden_noise = self.unet_svd(encoder_hidden_states_svd.float()) + (
2 * torch.sigmoid(self._alpha) - 1) * text_emb + self._beta * golden_embedding
return golden_noise
class NPNet128(nn.Module):
def __init__(self, model_id, pretrained_path=True, device='cuda') -> None:
super(NPNet128, self).__init__()
assert model_id in ['SDXL', 'DreamShaper', 'DiT']
self.model_id = model_id
self.device = device
self.pretrained_path = pretrained_path
(
self.unet_svd,
self.unet_embedding,
self.text_embedding,
self._alpha,
self._beta
) = self.get_model()
def get_model(self):
unet_embedding = NoiseTransformer(resolution=(128,128)).to(self.device).to(torch.float32)
unet_svd = SVDNoiseUnet128(resolution=128).to(self.device).to(torch.float32)
if self.model_id == 'DiT':
text_embedding = AdaGroupNorm(1024 * 77, 4, 1, eps=1e-6).to(self.device).to(torch.float32)
else:
text_embedding = AdaGroupNorm(2048 * 77, 4, 1, eps=1e-6).to(self.device).to(torch.float32)
if '.pth' in self.pretrained_path:
gloden_unet = torch.load(self.pretrained_path)
unet_svd.load_state_dict(gloden_unet["unet_svd"])
unet_embedding.load_state_dict(gloden_unet["unet_embedding"])
text_embedding.load_state_dict(gloden_unet["embeeding"])
_alpha = gloden_unet["alpha"]
_beta = gloden_unet["beta"]
print("Load Successfully!")
return unet_svd, unet_embedding, text_embedding, _alpha, _beta
else:
assert ("No Pretrained Weights Found!")
def forward(self, initial_noise, prompt_embeds):
prompt_embeds = prompt_embeds.float().view(prompt_embeds.shape[0], -1)
text_emb = self.text_embedding(initial_noise.float(), prompt_embeds)
encoder_hidden_states_svd = initial_noise
encoder_hidden_states_embedding = initial_noise + text_emb
golden_embedding = self.unet_embedding(encoder_hidden_states_embedding.float())
golden_noise = self.unet_svd(encoder_hidden_states_svd.float()) + (
2 * torch.sigmoid(self._alpha) - 1) * text_emb + self._beta * golden_embedding
return golden_noise
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