mirror of
https://github.com/comfyanonymous/ComfyUI.git
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184 lines
7.7 KiB
Python
184 lines
7.7 KiB
Python
import torch
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from torch import Tensor, nn
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from .math import attention
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from comfy.ldm.flux.layers import (
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MLPEmbedder,
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RMSNorm,
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QKNorm,
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SelfAttention,
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ModulationOut,
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)
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class ChromaModulationOut(ModulationOut):
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@classmethod
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def from_offset(cls, tensor: torch.Tensor, offset: int = 0) -> ModulationOut:
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return cls(
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shift=tensor[:, offset : offset + 1, :],
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scale=tensor[:, offset + 1 : offset + 2, :],
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gate=tensor[:, offset + 2 : offset + 3, :],
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)
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class Approximator(nn.Module):
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def __init__(self, in_dim: int, out_dim: int, hidden_dim: int, n_layers = 5, dtype=None, device=None, operations=None):
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super().__init__()
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self.in_proj = operations.Linear(in_dim, hidden_dim, bias=True, dtype=dtype, device=device)
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self.layers = nn.ModuleList([MLPEmbedder(hidden_dim, hidden_dim, dtype=dtype, device=device, operations=operations) for x in range( n_layers)])
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self.norms = nn.ModuleList([RMSNorm(hidden_dim, dtype=dtype, device=device, operations=operations) for x in range( n_layers)])
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self.out_proj = operations.Linear(hidden_dim, out_dim, dtype=dtype, device=device)
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@property
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def device(self):
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# Get the device of the module (assumes all parameters are on the same device)
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return next(self.parameters()).device
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def forward(self, x: Tensor) -> Tensor:
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x = self.in_proj(x)
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for layer, norms in zip(self.layers, self.norms):
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x = x + layer(norms(x))
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x = self.out_proj(x)
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return x
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class DoubleStreamBlock(nn.Module):
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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):
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super().__init__()
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mlp_hidden_dim = int(hidden_size * mlp_ratio)
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self.num_heads = num_heads
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self.hidden_size = hidden_size
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self.img_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
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self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations)
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self.img_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
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self.img_mlp = nn.Sequential(
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operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device),
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nn.GELU(approximate="tanh"),
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operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
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)
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self.txt_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
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self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations)
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self.txt_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
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self.txt_mlp = nn.Sequential(
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operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device),
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nn.GELU(approximate="tanh"),
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operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
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)
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self.flipped_img_txt = flipped_img_txt
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def forward(self, img: Tensor, txt: Tensor, pe: Tensor, vec: Tensor, attn_mask=None):
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(img_mod1, img_mod2), (txt_mod1, txt_mod2) = vec
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# prepare image for attention
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img_modulated = self.img_norm1(img)
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img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift
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img_qkv = self.img_attn.qkv(img_modulated)
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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)
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img_q, img_k = self.img_attn.norm(img_q, img_k, img_v)
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# prepare txt for attention
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txt_modulated = self.txt_norm1(txt)
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txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift
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txt_qkv = self.txt_attn.qkv(txt_modulated)
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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)
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txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v)
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# run actual attention
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attn = attention(torch.cat((txt_q, img_q), dim=2),
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torch.cat((txt_k, img_k), dim=2),
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torch.cat((txt_v, img_v), dim=2),
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pe=pe, mask=attn_mask)
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txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :]
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# calculate the img bloks
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img = img + img_mod1.gate * self.img_attn.proj(img_attn)
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img = img + img_mod2.gate * self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift)
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# calculate the txt bloks
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txt += txt_mod1.gate * self.txt_attn.proj(txt_attn)
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txt += txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift)
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if txt.dtype == torch.float16:
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txt = torch.nan_to_num(txt, nan=0.0, posinf=65504, neginf=-65504)
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return img, txt
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class SingleStreamBlock(nn.Module):
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"""
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A DiT block with parallel linear layers as described in
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https://arxiv.org/abs/2302.05442 and adapted modulation interface.
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"""
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def __init__(
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self,
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hidden_size: int,
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num_heads: int,
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mlp_ratio: float = 4.0,
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qk_scale: float = None,
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dtype=None,
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device=None,
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operations=None
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):
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super().__init__()
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self.hidden_dim = hidden_size
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self.num_heads = num_heads
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head_dim = hidden_size // num_heads
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self.scale = qk_scale or head_dim**-0.5
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self.mlp_hidden_dim = int(hidden_size * mlp_ratio)
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# qkv and mlp_in
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self.linear1 = operations.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim, dtype=dtype, device=device)
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# proj and mlp_out
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self.linear2 = operations.Linear(hidden_size + self.mlp_hidden_dim, hidden_size, dtype=dtype, device=device)
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self.norm = QKNorm(head_dim, dtype=dtype, device=device, operations=operations)
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self.hidden_size = hidden_size
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self.pre_norm = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
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self.mlp_act = nn.GELU(approximate="tanh")
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def forward(self, x: Tensor, pe: Tensor, vec: Tensor, attn_mask=None) -> Tensor:
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mod = vec
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x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift
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qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1)
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q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
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q, k = self.norm(q, k, v)
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# compute attention
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attn = attention(q, k, v, pe=pe, mask=attn_mask)
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# compute activation in mlp stream, cat again and run second linear layer
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output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2))
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x += mod.gate * output
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if x.dtype == torch.float16:
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x = torch.nan_to_num(x, nan=0.0, posinf=65504, neginf=-65504)
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return x
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class LastLayer(nn.Module):
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def __init__(self, hidden_size: int, patch_size: int, out_channels: int, dtype=None, device=None, operations=None):
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super().__init__()
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self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
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self.linear = operations.Linear(hidden_size, out_channels, bias=True, dtype=dtype, device=device)
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def forward(self, x: Tensor, vec: Tensor) -> Tensor:
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shift, scale = vec
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shift = shift.squeeze(1)
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scale = scale.squeeze(1)
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x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :]
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x = self.linear(x)
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return x
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