import torch from torch import Tensor, nn from .math import attention from comfy.ldm.flux.layers import ( MLPEmbedder, RMSNorm, QKNorm, SelfAttention, ModulationOut, ) class ChromaModulationOut(ModulationOut): @classmethod def from_offset(cls, tensor: torch.Tensor, offset: int = 0) -> ModulationOut: return cls( shift=tensor[:, offset : offset + 1, :], scale=tensor[:, offset + 1 : offset + 2, :], gate=tensor[:, offset + 2 : offset + 3, :], ) class Approximator(nn.Module): def __init__(self, in_dim: int, out_dim: int, hidden_dim: int, n_layers = 5, dtype=None, device=None, operations=None): super().__init__() self.in_proj = operations.Linear(in_dim, hidden_dim, bias=True, dtype=dtype, device=device) self.layers = nn.ModuleList([MLPEmbedder(hidden_dim, hidden_dim, dtype=dtype, device=device, operations=operations) for x in range( n_layers)]) self.norms = nn.ModuleList([RMSNorm(hidden_dim, dtype=dtype, device=device, operations=operations) for x in range( n_layers)]) self.out_proj = operations.Linear(hidden_dim, out_dim, dtype=dtype, device=device) @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 DoubleStreamBlock(nn.Module): def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, flipped_img_txt=False, dtype=None, device=None, operations=None): super().__init__() mlp_hidden_dim = int(hidden_size * mlp_ratio) self.num_heads = num_heads self.hidden_size = hidden_size self.img_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations) self.img_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) self.img_mlp = nn.Sequential( operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device), nn.GELU(approximate="tanh"), operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device), ) self.txt_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations) self.txt_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) self.txt_mlp = nn.Sequential( operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device), nn.GELU(approximate="tanh"), operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device), ) self.flipped_img_txt = flipped_img_txt def forward(self, img: Tensor, txt: Tensor, pe: Tensor, vec: Tensor, attn_mask=None): (img_mod1, img_mod2), (txt_mod1, txt_mod2) = vec # prepare image for attention img_modulated = self.img_norm1(img) img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift img_qkv = self.img_attn.qkv(img_modulated) img_q, img_k, img_v = img_qkv.view(img_qkv.shape[0], img_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) img_q, img_k = self.img_attn.norm(img_q, img_k, img_v) # prepare txt for attention txt_modulated = self.txt_norm1(txt) txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift txt_qkv = self.txt_attn.qkv(txt_modulated) txt_q, txt_k, txt_v = txt_qkv.view(txt_qkv.shape[0], txt_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v) # run actual attention attn = attention(torch.cat((txt_q, img_q), dim=2), torch.cat((txt_k, img_k), dim=2), torch.cat((txt_v, img_v), dim=2), pe=pe, mask=attn_mask) txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :] # calculate the img bloks 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) # calculate the txt bloks txt += txt_mod1.gate * self.txt_attn.proj(txt_attn) txt += txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift) if txt.dtype == torch.float16: txt = torch.nan_to_num(txt, nan=0.0, posinf=65504, neginf=-65504) 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, dtype=None, device=None, operations=None ): 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 = operations.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim, dtype=dtype, device=device) # proj and mlp_out self.linear2 = operations.Linear(hidden_size + self.mlp_hidden_dim, hidden_size, dtype=dtype, device=device) self.norm = QKNorm(head_dim, dtype=dtype, device=device, operations=operations) self.hidden_size = hidden_size self.pre_norm = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) self.mlp_act = nn.GELU(approximate="tanh") def forward(self, x: Tensor, pe: Tensor, vec: Tensor, attn_mask=None) -> Tensor: mod = vec x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1) q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) q, k = self.norm(q, k, v) # compute attention attn = attention(q, k, v, pe=pe, mask=attn_mask) # compute activation in mlp stream, cat again and run second linear layer output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2)) x += mod.gate * output if x.dtype == torch.float16: x = torch.nan_to_num(x, nan=0.0, posinf=65504, neginf=-65504) return x class LastLayer(nn.Module): def __init__(self, hidden_size: int, patch_size: int, out_channels: int, dtype=None, device=None, operations=None): super().__init__() self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) self.linear = operations.Linear(hidden_size, out_channels, bias=True, dtype=dtype, device=device) def forward(self, x: Tensor, vec: Tensor) -> Tensor: shift, scale = vec shift = shift.squeeze(1) scale = scale.squeeze(1) x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :] x = self.linear(x) return x