import math from dataclasses import dataclass import torch from einops import rearrange from torch import Tensor, nn import torch.nn.functional as F from .math import attention, rope class EmbedND(nn.Module): def __init__(self, dim: int, theta: int, axes_dim: list[int]): super().__init__() self.dim = dim self.theta = theta self.axes_dim = axes_dim def forward(self, ids: Tensor) -> Tensor: n_axes = ids.shape[-1] emb = torch.cat( [rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)], dim=-3, ) return emb.unsqueeze(1) def timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: float = 1000.0): """ Create sinusoidal timestep embeddings. :param t: a 1-D Tensor of N indices, one per batch element. These may be fractional. :param dim: the dimension of the output. :param max_period: controls the minimum frequency of the embeddings. :return: an (N, D) Tensor of positional embeddings. """ t = time_factor * t half = dim // 2 freqs = torch.exp( -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half ).to(t.device) 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) if torch.is_floating_point(t): embedding = embedding.to(t) return embedding class MLPEmbedder(nn.Module): def __init__(self, in_dim: int, hidden_dim: int): super().__init__() self.in_layer = nn.Linear(in_dim, hidden_dim, bias=True) self.silu = nn.SiLU() self.out_layer = nn.Linear(hidden_dim, hidden_dim, bias=True) @property def device(self): # Get the device of the module (assumes all parameters are on the same device) return next(self.parameters()).device def forward(self, x: Tensor) -> Tensor: return self.out_layer(self.silu(self.in_layer(x))) class RMSNorm(torch.nn.Module): def __init__(self, dim: int, use_compiled: bool = False): super().__init__() self.scale = nn.Parameter(torch.ones(dim)) self.use_compiled = use_compiled def _forward(self, x: Tensor): x_dtype = x.dtype x = x.float() rrms = torch.rsqrt(torch.mean(x**2, dim=-1, keepdim=True) + 1e-6) return (x * rrms).to(dtype=x_dtype) * self.scale def forward(self, x: Tensor): return F.rms_norm(x, self.scale.shape, weight=self.scale, eps=1e-6) # if self.use_compiled: # return torch.compile(self._forward)(x) # else: # return self._forward(x) def distribute_modulations(tensor: torch.Tensor): """ Distributes slices of the tensor into the block_dict as ModulationOut objects. Args: tensor (torch.Tensor): Input tensor with shape [batch_size, vectors, dim]. """ batch_size, vectors, dim = tensor.shape block_dict = {} # HARD CODED VALUES! lookup table for the generated vectors # TODO: move this into chroma config! # Add 38 single mod blocks for i in range(38): key = f"single_blocks.{i}.modulation.lin" block_dict[key] = None # Add 19 image double blocks for i in range(19): key = f"double_blocks.{i}.img_mod.lin" block_dict[key] = None # Add 19 text double blocks for i in range(19): key = f"double_blocks.{i}.txt_mod.lin" block_dict[key] = None # Add the final layer block_dict["final_layer.adaLN_modulation.1"] = None # 6.2b version block_dict["lite_double_blocks.4.img_mod.lin"] = None block_dict["lite_double_blocks.4.txt_mod.lin"] = None idx = 0 # Index to keep track of the vector slices for key in block_dict.keys(): if "single_blocks" in key: # Single block: 1 ModulationOut block_dict[key] = ModulationOut( shift=tensor[:, idx : idx + 1, :], scale=tensor[:, idx + 1 : idx + 2, :], gate=tensor[:, idx + 2 : idx + 3, :], ) idx += 3 # Advance by 3 vectors elif "img_mod" in key: # Double block: List of 2 ModulationOut double_block = [] for _ in range(2): # Create 2 ModulationOut objects double_block.append( ModulationOut( shift=tensor[:, idx : idx + 1, :], scale=tensor[:, idx + 1 : idx + 2, :], gate=tensor[:, idx + 2 : idx + 3, :], ) ) idx += 3 # Advance by 3 vectors per ModulationOut block_dict[key] = double_block elif "txt_mod" in key: # Double block: List of 2 ModulationOut double_block = [] for _ in range(2): # Create 2 ModulationOut objects double_block.append( ModulationOut( shift=tensor[:, idx : idx + 1, :], scale=tensor[:, idx + 1 : idx + 2, :], gate=tensor[:, idx + 2 : idx + 3, :], ) ) idx += 3 # Advance by 3 vectors per ModulationOut block_dict[key] = double_block elif "final_layer" in key: # Final layer: 1 ModulationOut block_dict[key] = [ tensor[:, idx : idx + 1, :], tensor[:, idx + 1 : idx + 2, :], ] idx += 2 # Advance by 3 vectors return block_dict class Approximator(nn.Module): def __init__(self, in_dim: int, out_dim: int, hidden_dim: int, n_layers=4): super().__init__() self.in_proj = nn.Linear(in_dim, hidden_dim, bias=True) self.layers = nn.ModuleList( [MLPEmbedder(hidden_dim, hidden_dim) for x in range(n_layers)] ) self.norms = nn.ModuleList([RMSNorm(hidden_dim) for x in range(n_layers)]) self.out_proj = nn.Linear(hidden_dim, out_dim) @property def device(self): # Get the device of the module (assumes all parameters are on the same device) return next(self.parameters()).device def forward(self, x: Tensor) -> Tensor: x = self.in_proj(x) for layer, norms in zip(self.layers, self.norms): x = x + layer(norms(x)) x = self.out_proj(x) return x class QKNorm(torch.nn.Module): def __init__(self, dim: int, use_compiled: bool = False): super().__init__() self.query_norm = RMSNorm(dim, use_compiled=use_compiled) self.key_norm = RMSNorm(dim, use_compiled=use_compiled) self.use_compiled = use_compiled def forward(self, q: Tensor, k: Tensor, v: Tensor) -> tuple[Tensor, Tensor]: q = self.query_norm(q) k = self.key_norm(k) return q.to(v), k.to(v) class SelfAttention(nn.Module): def __init__( self, dim: int, num_heads: int = 8, qkv_bias: bool = False, use_compiled: bool = False, ): super().__init__() self.num_heads = num_heads head_dim = dim // num_heads self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) self.norm = QKNorm(head_dim, use_compiled=use_compiled) self.proj = nn.Linear(dim, dim) self.use_compiled = use_compiled def forward(self, x: Tensor, pe: Tensor) -> Tensor: qkv = self.qkv(x) q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads) q, k = self.norm(q, k, v) x = attention(q, k, v, pe=pe) x = self.proj(x) return x @dataclass class ModulationOut: shift: Tensor scale: Tensor gate: Tensor def _modulation_shift_scale_fn(x, scale, shift): return (1 + scale) * x + shift def _modulation_gate_fn(x, gate, gate_params): return x + gate * gate_params class DoubleStreamBlock(nn.Module): def __init__( self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, use_compiled: bool = False, ): super().__init__() mlp_hidden_dim = int(hidden_size * mlp_ratio) self.num_heads = num_heads self.hidden_size = hidden_size self.img_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) self.img_attn = SelfAttention( dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, use_compiled=use_compiled, ) self.img_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) self.img_mlp = nn.Sequential( nn.Linear(hidden_size, mlp_hidden_dim, bias=True), nn.GELU(approximate="tanh"), nn.Linear(mlp_hidden_dim, hidden_size, bias=True), ) self.txt_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) self.txt_attn = SelfAttention( dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, use_compiled=use_compiled, ) self.txt_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) self.txt_mlp = nn.Sequential( nn.Linear(hidden_size, mlp_hidden_dim, bias=True), nn.GELU(approximate="tanh"), nn.Linear(mlp_hidden_dim, hidden_size, bias=True), ) self.use_compiled = use_compiled @property def device(self): # Get the device of the module (assumes all parameters are on the same device) return next(self.parameters()).device def modulation_shift_scale_fn(self, x, scale, shift): if self.use_compiled: return torch.compile(_modulation_shift_scale_fn)(x, scale, shift) else: return _modulation_shift_scale_fn(x, scale, shift) def modulation_gate_fn(self, x, gate, gate_params): if self.use_compiled: return torch.compile(_modulation_gate_fn)(x, gate, gate_params) else: return _modulation_gate_fn(x, gate, gate_params) def forward( self, img: Tensor, txt: Tensor, pe: Tensor, distill_vec: list[ModulationOut], mask: Tensor, ) -> tuple[Tensor, Tensor]: (img_mod1, img_mod2), (txt_mod1, txt_mod2) = distill_vec # prepare image for attention img_modulated = self.img_norm1(img) # replaced with compiled fn # img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift img_modulated = self.modulation_shift_scale_fn( img_modulated, img_mod1.scale, img_mod1.shift ) img_qkv = self.img_attn.qkv(img_modulated) img_q, img_k, img_v = rearrange( img_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads ) img_q, img_k = self.img_attn.norm(img_q, img_k, img_v) # prepare txt for attention txt_modulated = self.txt_norm1(txt) # replaced with compiled fn # txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift txt_modulated = self.modulation_shift_scale_fn( txt_modulated, txt_mod1.scale, txt_mod1.shift ) txt_qkv = self.txt_attn.qkv(txt_modulated) txt_q, txt_k, txt_v = rearrange( txt_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads ) txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v) # run actual attention q = torch.cat((txt_q, img_q), dim=2) k = torch.cat((txt_k, img_k), dim=2) v = torch.cat((txt_v, img_v), dim=2) attn = attention(q, k, v, pe=pe, mask=mask) txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :] # calculate the img bloks # replaced with compiled fn # img = img + img_mod1.gate * self.img_attn.proj(img_attn) # img = img + img_mod2.gate * self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift) img = self.modulation_gate_fn(img, img_mod1.gate, self.img_attn.proj(img_attn)) img = self.modulation_gate_fn( img, img_mod2.gate, self.img_mlp( self.modulation_shift_scale_fn( self.img_norm2(img), img_mod2.scale, img_mod2.shift ) ), ) # calculate the txt bloks # replaced with compiled fn # txt = txt + txt_mod1.gate * self.txt_attn.proj(txt_attn) # txt = txt + txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift) txt = self.modulation_gate_fn(txt, txt_mod1.gate, self.txt_attn.proj(txt_attn)) txt = self.modulation_gate_fn( txt, txt_mod2.gate, self.txt_mlp( self.modulation_shift_scale_fn( self.txt_norm2(txt), txt_mod2.scale, txt_mod2.shift ) ), ) return img, txt class SingleStreamBlock(nn.Module): """ A DiT block with parallel linear layers as described in https://arxiv.org/abs/2302.05442 and adapted modulation interface. """ def __init__( self, hidden_size: int, num_heads: int, mlp_ratio: float = 4.0, qk_scale: float | None = None, use_compiled: bool = False, ): super().__init__() self.hidden_dim = hidden_size self.num_heads = num_heads head_dim = hidden_size // num_heads self.scale = qk_scale or head_dim**-0.5 self.mlp_hidden_dim = int(hidden_size * mlp_ratio) # qkv and mlp_in self.linear1 = nn.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim) # proj and mlp_out self.linear2 = nn.Linear(hidden_size + self.mlp_hidden_dim, hidden_size) self.norm = QKNorm(head_dim, use_compiled=use_compiled) self.hidden_size = hidden_size self.pre_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) self.mlp_act = nn.GELU(approximate="tanh") self.use_compiled = use_compiled @property def device(self): # Get the device of the module (assumes all parameters are on the same device) return next(self.parameters()).device def modulation_shift_scale_fn(self, x, scale, shift): if self.use_compiled: return torch.compile(_modulation_shift_scale_fn)(x, scale, shift) else: return _modulation_shift_scale_fn(x, scale, shift) def modulation_gate_fn(self, x, gate, gate_params): if self.use_compiled: return torch.compile(_modulation_gate_fn)(x, gate, gate_params) else: return _modulation_gate_fn(x, gate, gate_params) def forward( self, x: Tensor, pe: Tensor, distill_vec: list[ModulationOut], mask: Tensor ) -> Tensor: mod = distill_vec # replaced with compiled fn # x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift x_mod = self.modulation_shift_scale_fn(self.pre_norm(x), mod.scale, mod.shift) qkv, mlp = torch.split( self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1 ) q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads) q, k = self.norm(q, k, v) # compute attention attn = attention(q, k, v, pe=pe, mask=mask) # compute activation in mlp stream, cat again and run second linear layer output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2)) # replaced with compiled fn # return x + mod.gate * output return self.modulation_gate_fn(x, mod.gate, output) class LastLayer(nn.Module): def __init__( self, hidden_size: int, patch_size: int, out_channels: int, use_compiled: bool = False, ): super().__init__() self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) self.linear = nn.Linear( hidden_size, patch_size * patch_size * out_channels, bias=True ) self.use_compiled = use_compiled @property def device(self): # Get the device of the module (assumes all parameters are on the same device) return next(self.parameters()).device def modulation_shift_scale_fn(self, x, scale, shift): if self.use_compiled: return torch.compile(_modulation_shift_scale_fn)(x, scale, shift) else: return _modulation_shift_scale_fn(x, scale, shift) def forward(self, x: Tensor, distill_vec: list[Tensor]) -> Tensor: shift, scale = distill_vec shift = shift.squeeze(1) scale = scale.squeeze(1) # replaced with compiled fn # x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :] x = self.modulation_shift_scale_fn( self.norm_final(x), scale[:, None, :], shift[:, None, :] ) x = self.linear(x) return x