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Running
on
Zero
| import torch | |
| from torch import Tensor, nn | |
| from comfy.ldm.flux.math import attention | |
| from comfy.ldm.flux.layers import ( | |
| MLPEmbedder, | |
| RMSNorm, | |
| QKNorm, | |
| SelfAttention, | |
| ModulationOut, | |
| ) | |
| class ChromaModulationOut(ModulationOut): | |
| 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) | |
| 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 | |