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