Spaces:
Running
on
Zero
Running
on
Zero
| import functools | |
| from typing import Tuple | |
| import torch | |
| import torch.nn as nn | |
| import math | |
| from functools import lru_cache | |
| from torch.nn.functional import scaled_dot_product_attention | |
| def modulate(x, shift, scale): | |
| return x * (1 + scale) + shift | |
| class Embed(nn.Module): | |
| def __init__( | |
| self, | |
| in_chans: int = 3, | |
| embed_dim: int = 768, | |
| norm_layer = None, | |
| bias: bool = True, | |
| ): | |
| super().__init__() | |
| self.in_chans = in_chans | |
| self.embed_dim = embed_dim | |
| self.proj = nn.Linear(in_chans, embed_dim, bias=bias) | |
| self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() | |
| def forward(self, x): | |
| x = self.proj(x) | |
| x = self.norm(x) | |
| return x | |
| class TimestepEmbedder(nn.Module): | |
| def __init__(self, hidden_size, frequency_embedding_size=256): | |
| super().__init__() | |
| self.mlp = nn.Sequential( | |
| nn.Linear(frequency_embedding_size, hidden_size, bias=True), | |
| nn.SiLU(), | |
| nn.Linear(hidden_size, hidden_size, bias=True), | |
| ) | |
| self.frequency_embedding_size = frequency_embedding_size | |
| def timestep_embedding(t, dim, max_period=10): | |
| half = dim // 2 | |
| freqs = torch.exp( | |
| -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32, device=t.device) / half | |
| ) | |
| args = t[..., None].float() * freqs[None, ...] | |
| embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) | |
| if dim % 2: | |
| embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) | |
| return embedding | |
| def forward(self, t): | |
| t_freq = self.timestep_embedding(t, self.frequency_embedding_size) | |
| t_emb = self.mlp(t_freq) | |
| return t_emb | |
| class LabelEmbedder(nn.Module): | |
| def __init__(self, num_classes, hidden_size): | |
| super().__init__() | |
| self.embedding_table = nn.Embedding(num_classes, hidden_size) | |
| self.num_classes = num_classes | |
| def forward(self, labels,): | |
| embeddings = self.embedding_table(labels) | |
| return embeddings | |
| class FinalLayer(nn.Module): | |
| def __init__(self, hidden_size, out_channels): | |
| super().__init__() | |
| self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) | |
| self.linear = nn.Linear(hidden_size, out_channels, bias=True) | |
| self.adaLN_modulation = nn.Sequential( | |
| nn.Linear(hidden_size, 2*hidden_size, bias=True) | |
| ) | |
| def forward(self, x, c): | |
| shift, scale = self.adaLN_modulation(c).chunk(2, dim=-1) | |
| x = modulate(self.norm_final(x), shift, scale) | |
| x = self.linear(x) | |
| return x | |
| class RMSNorm(nn.Module): | |
| def __init__(self, hidden_size, eps=1e-6): | |
| """ | |
| LlamaRMSNorm is equivalent to T5LayerNorm | |
| """ | |
| super().__init__() | |
| self.weight = nn.Parameter(torch.ones(hidden_size)) | |
| self.variance_epsilon = eps | |
| def forward(self, hidden_states): | |
| input_dtype = hidden_states.dtype | |
| hidden_states = hidden_states.to(torch.float32) | |
| variance = hidden_states.pow(2).mean(-1, keepdim=True) | |
| hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) | |
| return self.weight * hidden_states.to(input_dtype) | |
| class FeedForward(nn.Module): | |
| def __init__( | |
| self, | |
| dim: int, | |
| hidden_dim: int, | |
| ): | |
| super().__init__() | |
| hidden_dim = int(2 * hidden_dim / 3) | |
| self.w1 = nn.Linear(dim, hidden_dim, bias=False) | |
| self.w3 = nn.Linear(dim, hidden_dim, bias=False) | |
| self.w2 = nn.Linear(hidden_dim, dim, bias=False) | |
| def forward(self, x): | |
| x = self.w2(torch.nn.functional.silu(self.w1(x)) * self.w3(x)) | |
| return x | |
| def precompute_freqs_cis_2d(dim: int, height: int, width:int, theta: float = 10000.0, scale=16.0): | |
| # assert H * H == end | |
| # flat_patch_pos = torch.linspace(-1, 1, end) # N = end | |
| x_pos = torch.linspace(0, scale, width) | |
| y_pos = torch.linspace(0, scale, height) | |
| y_pos, x_pos = torch.meshgrid(y_pos, x_pos, indexing="ij") | |
| y_pos = y_pos.reshape(-1) | |
| x_pos = x_pos.reshape(-1) | |
| freqs = 1.0 / (theta ** (torch.arange(0, dim, 4)[: (dim // 4)].float() / dim)) # Hc/4 | |
| x_freqs = torch.outer(x_pos, freqs).float() # N Hc/4 | |
| y_freqs = torch.outer(y_pos, freqs).float() # N Hc/4 | |
| x_cis = torch.polar(torch.ones_like(x_freqs), x_freqs) | |
| y_cis = torch.polar(torch.ones_like(y_freqs), y_freqs) | |
| freqs_cis = torch.cat([x_cis.unsqueeze(dim=-1), y_cis.unsqueeze(dim=-1)], dim=-1) # N,Hc/4,2 | |
| freqs_cis = freqs_cis.reshape(height*width, -1) | |
| return freqs_cis | |
| def apply_rotary_emb( | |
| xq: torch.Tensor, | |
| xk: torch.Tensor, | |
| freqs_cis: torch.Tensor, | |
| ) -> Tuple[torch.Tensor, torch.Tensor]: | |
| freqs_cis = freqs_cis[None, :, None, :] | |
| # xq : B N H Hc | |
| xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2)) # B N H Hc/2 | |
| xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2)) | |
| xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3) # B, N, H, Hc | |
| xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3) | |
| return xq_out.type_as(xq), xk_out.type_as(xk) | |
| class RAttention(nn.Module): | |
| def __init__( | |
| self, | |
| dim: int, | |
| num_heads: int = 8, | |
| qkv_bias: bool = False, | |
| qk_norm: bool = True, | |
| attn_drop: float = 0., | |
| proj_drop: float = 0., | |
| norm_layer: nn.Module = RMSNorm, | |
| ) -> None: | |
| super().__init__() | |
| assert dim % num_heads == 0, 'dim should be divisible by num_heads' | |
| self.dim = dim | |
| self.num_heads = num_heads | |
| self.head_dim = dim // num_heads | |
| self.scale = self.head_dim ** -0.5 | |
| 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, pos, mask) -> torch.Tensor: | |
| B, N, C = x.shape | |
| qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 1, 3, 4) | |
| q, k, v = qkv[0], qkv[1], qkv[2] # B N H Hc | |
| q = self.q_norm(q) | |
| k = self.k_norm(k) | |
| q, k = apply_rotary_emb(q, k, freqs_cis=pos) | |
| q = q.view(B, -1, self.num_heads, C // self.num_heads).transpose(1, 2) # B, H, N, Hc | |
| k = k.view(B, -1, self.num_heads, C // self.num_heads).transpose(1, 2).contiguous() # B, H, N, Hc | |
| v = v.view(B, -1, self.num_heads, C // self.num_heads).transpose(1, 2).contiguous() | |
| x = scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0) | |
| x = x.transpose(1, 2).reshape(B, N, C) | |
| x = self.proj(x) | |
| x = self.proj_drop(x) | |
| return x | |
| class FlattenDiTBlock(nn.Module): | |
| def __init__(self, hidden_size, groups, mlp_ratio=4.0, ): | |
| super().__init__() | |
| self.norm1 = RMSNorm(hidden_size, eps=1e-6) | |
| self.attn = RAttention(hidden_size, num_heads=groups, qkv_bias=False) | |
| self.norm2 = RMSNorm(hidden_size, eps=1e-6) | |
| mlp_hidden_dim = int(hidden_size * mlp_ratio) | |
| self.mlp = FeedForward(hidden_size, mlp_hidden_dim) | |
| self.adaLN_modulation = nn.Sequential( | |
| nn.Linear(hidden_size, 6 * hidden_size, bias=True) | |
| ) | |
| def forward(self, x, c, pos, mask=None): | |
| shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(6, dim=-1) | |
| x = x + gate_msa * self.attn(modulate(self.norm1(x), shift_msa, scale_msa), pos, mask=mask) | |
| x = x + gate_mlp * self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp)) | |
| return x | |
| class NerfEmbedder(nn.Module): | |
| def __init__(self, in_channels, hidden_size_input, max_freqs): | |
| super().__init__() | |
| self.max_freqs = max_freqs | |
| self.hidden_size_input = hidden_size_input | |
| self.embedder = nn.Sequential( | |
| nn.Linear(in_channels+max_freqs**2, hidden_size_input, bias=True), | |
| ) | |
| def fetch_pos(self, patch_size, device, dtype): | |
| pos_x = torch.linspace(0, 1, patch_size, device=device, dtype=dtype) | |
| pos_y = torch.linspace(0, 1, patch_size, device=device, dtype=dtype) | |
| pos_y, pos_x = torch.meshgrid(pos_y, pos_x, indexing="ij") | |
| pos_x = pos_x.reshape(-1, 1, 1) | |
| pos_y = pos_y.reshape(-1, 1, 1) | |
| freqs = torch.linspace(0, self.max_freqs, self.max_freqs, dtype=dtype, device=device) | |
| freqs_x = freqs[None, :, None] | |
| freqs_y = freqs[None, None, :] | |
| coeffs = (1 + freqs_x * freqs_y) ** -1 | |
| dct_x = torch.cos(pos_x * freqs_x * torch.pi) | |
| dct_y = torch.cos(pos_y * freqs_y * torch.pi) | |
| dct = (dct_x * dct_y * coeffs).view(1, -1, self.max_freqs ** 2) | |
| return dct | |
| def forward(self, inputs): | |
| B, P2, C = inputs.shape | |
| patch_size = int(P2 ** 0.5) | |
| device = inputs.device | |
| dtype = inputs.dtype | |
| dct = self.fetch_pos(patch_size, device, dtype) | |
| dct = dct.repeat(B, 1, 1) | |
| inputs = torch.cat([inputs, dct], dim=-1) | |
| inputs = self.embedder(inputs) | |
| return inputs | |
| class NerfBlock(nn.Module): | |
| def __init__(self, hidden_size_s, hidden_size_x, mlp_ratio=4): | |
| super().__init__() | |
| self.param_generator1 = nn.Sequential( | |
| nn.Linear(hidden_size_s, 2*hidden_size_x**2*mlp_ratio, bias=True), | |
| ) | |
| self.norm = RMSNorm(hidden_size_x, eps=1e-6) | |
| self.mlp_ratio = mlp_ratio | |
| def forward(self, x, s): | |
| batch_size, num_x, hidden_size_x = x.shape | |
| mlp_params1 = self.param_generator1(s) | |
| fc1_param1, fc2_param1 = mlp_params1.chunk(2, dim=-1) | |
| fc1_param1 = fc1_param1.view(batch_size, hidden_size_x, hidden_size_x*self.mlp_ratio) | |
| fc2_param1 = fc2_param1.view(batch_size, hidden_size_x*self.mlp_ratio, hidden_size_x) | |
| # normalize fc1 | |
| normalized_fc1_param1 = torch.nn.functional.normalize(fc1_param1, dim=-2) | |
| # normalize fc2 | |
| normalized_fc2_param1 = torch.nn.functional.normalize(fc2_param1, dim=-2) | |
| # mlp 1 | |
| res_x = x | |
| x = self.norm(x) | |
| x = torch.bmm(x, normalized_fc1_param1) | |
| x = torch.nn.functional.silu(x) | |
| x = torch.bmm(x, normalized_fc2_param1) | |
| x = x + res_x | |
| return x | |
| class NerfFinalLayer(nn.Module): | |
| def __init__(self, hidden_size, out_channels): | |
| super().__init__() | |
| self.norm = RMSNorm(hidden_size, eps=1e-6) | |
| self.linear = nn.Linear(hidden_size, out_channels, bias=True) | |
| def forward(self, x): | |
| x = self.norm(x) | |
| x = self.linear(x) | |
| return x | |
| class PixNerDiT(nn.Module): | |
| def __init__( | |
| self, | |
| in_channels=4, | |
| num_groups=12, | |
| hidden_size=1152, | |
| hidden_size_x=64, | |
| nerf_mlpratio=4, | |
| num_blocks=18, | |
| num_cond_blocks=4, | |
| patch_size=2, | |
| num_classes=1000, | |
| learn_sigma=True, | |
| deep_supervision=0, | |
| weight_path=None, | |
| load_ema=False, | |
| ): | |
| super().__init__() | |
| self.deep_supervision = deep_supervision | |
| self.learn_sigma = learn_sigma | |
| self.in_channels = in_channels | |
| self.out_channels = in_channels | |
| self.hidden_size = hidden_size | |
| self.num_groups = num_groups | |
| self.num_blocks = num_blocks | |
| self.num_cond_blocks = num_cond_blocks | |
| self.patch_size = patch_size | |
| self.x_embedder = NerfEmbedder(in_channels, hidden_size_x, max_freqs=8) | |
| self.s_embedder = Embed(in_channels*patch_size**2, hidden_size, bias=True) | |
| self.t_embedder = TimestepEmbedder(hidden_size) | |
| self.y_embedder = LabelEmbedder(num_classes+1, hidden_size) | |
| self.final_layer = NerfFinalLayer(hidden_size_x, self.out_channels) | |
| self.weight_path = weight_path | |
| self.load_ema = load_ema | |
| self.blocks = nn.ModuleList([ | |
| FlattenDiTBlock(self.hidden_size, self.num_groups) for _ in range(self.num_cond_blocks) | |
| ]) | |
| self.blocks.extend([ | |
| NerfBlock(self.hidden_size, hidden_size_x, nerf_mlpratio) for _ in range(self.num_cond_blocks, self.num_blocks) | |
| ]) | |
| self.initialize_weights() | |
| self.precompute_pos = dict() | |
| def fetch_pos(self, height, width, device): | |
| if (height, width) in self.precompute_pos: | |
| return self.precompute_pos[(height, width)].to(device) | |
| else: | |
| pos = precompute_freqs_cis_2d(self.hidden_size // self.num_groups, height, width).to(device) | |
| self.precompute_pos[(height, width)] = pos | |
| return pos | |
| def initialize_weights(self): | |
| # Initialize patch_embed like nn.Linear (instead of nn.Conv2d): | |
| w = self.s_embedder.proj.weight.data | |
| nn.init.xavier_uniform_(w.view([w.shape[0], -1])) | |
| nn.init.constant_(self.s_embedder.proj.bias, 0) | |
| # Initialize label embedding table: | |
| nn.init.normal_(self.y_embedder.embedding_table.weight, std=0.02) | |
| # Initialize timestep embedding MLP: | |
| nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02) | |
| nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02) | |
| # zero init final layer | |
| nn.init.zeros_(self.final_layer.linear.weight) | |
| nn.init.zeros_(self.final_layer.linear.bias) | |
| def forward(self, x, t, y, s=None, mask=None): | |
| B, _, H, W = x.shape | |
| pos = self.fetch_pos(H//self.patch_size, W//self.patch_size, x.device) | |
| x = torch.nn.functional.unfold(x, kernel_size=self.patch_size, stride=self.patch_size).transpose(1, 2) | |
| t = self.t_embedder(t.view(-1)).view(B, -1, self.hidden_size) | |
| y = self.y_embedder(y).view(B, 1, self.hidden_size) | |
| c = nn.functional.silu(t + y) | |
| if s is None: | |
| s = self.s_embedder(x) | |
| for i in range(self.num_cond_blocks): | |
| s = self.blocks[i](s, c, pos, mask) | |
| s = nn.functional.silu(t + s) | |
| batch_size, length, _ = s.shape | |
| x = x.reshape(batch_size*length, self.in_channels, self.patch_size**2) | |
| x = x.transpose(1, 2) | |
| s = s.view(batch_size*length, self.hidden_size) | |
| x = self.x_embedder(x) | |
| for i in range(self.num_cond_blocks, self.num_blocks): | |
| x = self.blocks[i](x, s) | |
| x = self.final_layer(x) | |
| x = x.transpose(1, 2) | |
| x = x.reshape(batch_size, length, -1) | |
| x = torch.nn.functional.fold(x.transpose(1, 2).contiguous(), (H, W), kernel_size=self.patch_size, stride=self.patch_size) | |
| return x |