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Use same code for chroma and flux blocks so that optimizations are shared. (#10746)

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comfyanonymous
2025-11-13 22:28:05 -08:00
committed by GitHub
parent 1ef328c007
commit f60923590c
4 changed files with 31 additions and 135 deletions
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121
comfy/ldm/chroma/layers.py
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@@ -1,12 +1,9 @@
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,
)
@@ -48,124 +45,6 @@ class Approximator(nn.Module):
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, transformer_options={}):
(img_mod1, img_mod2), (txt_mod1, txt_mod2) = vec
# prepare image for attention
img_modulated = torch.addcmul(img_mod1.shift, 1 + img_mod1.scale, self.img_norm1(img))
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 = torch.addcmul(txt_mod1.shift, 1 + txt_mod1.scale, self.txt_norm1(txt))
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, transformer_options=transformer_options)
txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :]
# calculate the img bloks
img.addcmul_(img_mod1.gate, self.img_attn.proj(img_attn))
img.addcmul_(img_mod2.gate, self.img_mlp(torch.addcmul(img_mod2.shift, 1 + img_mod2.scale, self.img_norm2(img))))
# calculate the txt bloks
txt.addcmul_(txt_mod1.gate, self.txt_attn.proj(txt_attn))
txt.addcmul_(txt_mod2.gate, self.txt_mlp(torch.addcmul(txt_mod2.shift, 1 + txt_mod2.scale, self.txt_norm2(txt))))
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, transformer_options={}) -> Tensor:
mod = vec
x_mod = torch.addcmul(mod.shift, 1 + mod.scale, self.pre_norm(x))
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, transformer_options=transformer_options)
# compute activation in mlp stream, cat again and run second linear layer
output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2))
x.addcmul_(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__()