mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2026-04-15 21:12:30 +08:00
734 lines
24 KiB
Python
734 lines
24 KiB
Python
"""
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TwinFlow-Z-Image custom model architecture for ComfyUI.
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Based on the Lumina-Image 2.0 / Z-Image architecture.
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Supports the unique dual timestep embedding architecture of TwinFlow.
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"""
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from __future__ import annotations
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from typing import List, Optional, Tuple
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import comfy.ldm.common_dit
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from comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder
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from comfy.ldm.modules.attention import optimized_attention_masked
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from comfy.ldm.flux.layers import EmbedND
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from comfy.ldm.flux.math import apply_rope
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import comfy.patcher_extension
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def clamp_fp16(x):
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if x.dtype == torch.float16:
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return torch.nan_to_num(x, nan=0.0, posinf=65504, neginf=-65504)
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return x
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def modulate(x, scale):
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return x * (1 + scale.unsqueeze(1))
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class JointAttention(nn.Module):
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"""Multi-head attention module with combined QKV weights."""
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def __init__(
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self,
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dim: int,
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n_heads: int,
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n_kv_heads: Optional[int],
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qk_norm: bool,
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out_bias: bool = False,
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operation_settings={},
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):
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super().__init__()
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self.n_kv_heads = n_heads if n_kv_heads is None else n_kv_heads
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self.n_local_heads = n_heads
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self.n_local_kv_heads = self.n_kv_heads
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self.n_rep = self.n_local_heads // self.n_local_kv_heads
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self.head_dim = dim // n_heads
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self.qkv = operation_settings.get("operations").Linear(
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dim,
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(n_heads + self.n_kv_heads + self.n_kv_heads) * self.head_dim,
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bias=False,
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device=operation_settings.get("device"),
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dtype=operation_settings.get("dtype"),
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)
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self.out = operation_settings.get("operations").Linear(
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n_heads * self.head_dim,
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dim,
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bias=out_bias,
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device=operation_settings.get("device"),
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dtype=operation_settings.get("dtype"),
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)
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if qk_norm:
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self.q_norm = operation_settings.get("operations").RMSNorm(
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self.head_dim,
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elementwise_affine=True,
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device=operation_settings.get("device"),
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dtype=operation_settings.get("dtype"),
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)
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self.k_norm = operation_settings.get("operations").RMSNorm(
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self.head_dim,
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elementwise_affine=True,
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device=operation_settings.get("device"),
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dtype=operation_settings.get("dtype"),
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)
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else:
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self.q_norm = self.k_norm = nn.Identity()
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def forward(
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self,
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x: torch.Tensor,
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x_mask: torch.Tensor,
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freqs_cis: torch.Tensor,
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transformer_options={},
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) -> torch.Tensor:
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bsz, seqlen, _ = x.shape
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xq, xk, xv = torch.split(
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self.qkv(x),
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[
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self.n_local_heads * self.head_dim,
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self.n_local_kv_heads * self.head_dim,
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self.n_local_kv_heads * self.head_dim,
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],
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dim=-1,
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)
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xq = xq.view(bsz, seqlen, self.n_local_heads, self.head_dim)
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xk = xk.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)
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xv = xv.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)
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xq = self.q_norm(xq)
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xk = self.k_norm(xk)
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xq, xk = apply_rope(xq, xk, freqs_cis)
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n_rep = self.n_local_heads // self.n_local_kv_heads
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if n_rep >= 1:
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xk = xk.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3)
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xv = xv.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3)
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output = optimized_attention_masked(
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xq.movedim(1, 2),
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xk.movedim(1, 2),
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xv.movedim(1, 2),
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self.n_local_heads,
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x_mask,
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skip_reshape=True,
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transformer_options=transformer_options,
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)
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return self.out(output)
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class FeedForward(nn.Module):
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"""Feed-forward module with SiLU gating."""
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def __init__(
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self,
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dim: int,
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hidden_dim: int,
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multiple_of: int,
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ffn_dim_multiplier: Optional[float],
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operation_settings={},
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):
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super().__init__()
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if ffn_dim_multiplier is not None:
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hidden_dim = int(ffn_dim_multiplier * hidden_dim)
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hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
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self.w1 = operation_settings.get("operations").Linear(
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dim,
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hidden_dim,
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bias=False,
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device=operation_settings.get("device"),
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dtype=operation_settings.get("dtype"),
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)
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self.w2 = operation_settings.get("operations").Linear(
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hidden_dim,
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dim,
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bias=False,
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device=operation_settings.get("device"),
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dtype=operation_settings.get("dtype"),
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)
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self.w3 = operation_settings.get("operations").Linear(
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dim,
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hidden_dim,
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bias=False,
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device=operation_settings.get("device"),
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dtype=operation_settings.get("dtype"),
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)
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def _forward_silu_gating(self, x1, x3):
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return clamp_fp16(F.silu(x1) * x3)
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def forward(self, x):
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return self.w2(self._forward_silu_gating(self.w1(x), self.w3(x)))
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class TwinFlowTransformerBlock(nn.Module):
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"""Transformer block with adaLN modulation for TwinFlow."""
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def __init__(
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self,
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layer_id: int,
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dim: int,
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n_heads: int,
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n_kv_heads: int,
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multiple_of: int,
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ffn_dim_multiplier: float,
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norm_eps: float,
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qk_norm: bool,
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modulation=False,
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z_image_modulation=False,
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attn_out_bias=False,
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operation_settings={},
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) -> None:
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super().__init__()
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self.dim = dim
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self.head_dim = dim // n_heads
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self.attention = JointAttention(
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dim,
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n_heads,
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n_kv_heads,
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qk_norm,
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out_bias=attn_out_bias,
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operation_settings=operation_settings,
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)
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self.feed_forward = FeedForward(
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dim=dim,
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hidden_dim=dim,
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multiple_of=multiple_of,
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ffn_dim_multiplier=ffn_dim_multiplier,
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operation_settings=operation_settings,
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)
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self.layer_id = layer_id
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self.attention_norm1 = operation_settings.get("operations").RMSNorm(
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dim,
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eps=norm_eps,
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elementwise_affine=True,
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device=operation_settings.get("device"),
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dtype=operation_settings.get("dtype"),
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)
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self.ffn_norm1 = operation_settings.get("operations").RMSNorm(
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dim,
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eps=norm_eps,
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elementwise_affine=True,
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device=operation_settings.get("device"),
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dtype=operation_settings.get("dtype"),
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)
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self.attention_norm2 = operation_settings.get("operations").RMSNorm(
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dim,
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eps=norm_eps,
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elementwise_affine=True,
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device=operation_settings.get("device"),
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dtype=operation_settings.get("dtype"),
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)
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self.ffn_norm2 = operation_settings.get("operations").RMSNorm(
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dim,
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eps=norm_eps,
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elementwise_affine=True,
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device=operation_settings.get("device"),
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dtype=operation_settings.get("dtype"),
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)
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if z_image_modulation:
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self.adaLN_modulation = nn.Sequential(
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operation_settings.get("operations").Linear(
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min(dim, 256),
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4 * dim,
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bias=True,
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device=operation_settings.get("device"),
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dtype=operation_settings.get("dtype"),
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),
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)
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def forward(
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self,
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x: torch.Tensor,
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x_mask: torch.Tensor,
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freqs_cis: torch.Tensor,
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adaln_input: Optional[torch.Tensor] = None,
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transformer_options={},
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):
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if adaln_input is not None:
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scale_msa, gate_msa, scale_mlp, gate_mlp = self.adaLN_modulation(adaln_input).chunk(4, dim=1)
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x = x + gate_msa.unsqueeze(1).tanh() * self.attention_norm2(
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clamp_fp16(
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self.attention(
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modulate(self.attention_norm1(x), scale_msa),
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x_mask,
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freqs_cis,
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transformer_options=transformer_options,
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)
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)
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)
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x = x + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2(
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clamp_fp16(
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self.feed_forward(
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modulate(self.ffn_norm1(x), scale_mlp),
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)
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)
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)
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else:
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x = x + self.attention_norm2(
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clamp_fp16(
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self.attention(
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self.attention_norm1(x),
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x_mask,
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freqs_cis,
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transformer_options=transformer_options,
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)
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)
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)
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x = x + self.ffn_norm2(self.feed_forward(self.ffn_norm1(x)))
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return x
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class FinalLayer(nn.Module):
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"""Final layer with LayerNorm and output projection."""
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def __init__(
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self,
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hidden_size: int,
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patch_size: int,
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out_channels: int,
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z_image_modulation=False,
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operation_settings={},
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):
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super().__init__()
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self.norm_final = operation_settings.get("operations").LayerNorm(
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hidden_size,
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elementwise_affine=False,
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eps=1e-6,
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device=operation_settings.get("device"),
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dtype=operation_settings.get("dtype"),
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)
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self.linear = operation_settings.get("operations").Linear(
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hidden_size,
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patch_size * patch_size * out_channels,
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bias=True,
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device=operation_settings.get("device"),
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dtype=operation_settings.get("dtype"),
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)
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min_mod = 256 if z_image_modulation else 1024
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self.adaLN_modulation = nn.Sequential(
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nn.SiLU(),
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operation_settings.get("operations").Linear(
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min(hidden_size, min_mod),
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hidden_size,
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bias=True,
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device=operation_settings.get("device"),
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dtype=operation_settings.get("dtype"),
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),
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)
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def forward(self, x: torch.Tensor, c: torch.Tensor):
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scale = self.adaLN_modulation(c)
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x = modulate(self.norm_final(x), scale)
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x = self.linear(x)
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return x
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class TwinFlowZImageTransformer(nn.Module):
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"""
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TwinFlow-Z-Image transformer model.
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This custom architecture handles dual timestep embeddings
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(t_embedder and t_embedder_2), the primary TwinFlow distinction.
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"""
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def __init__(
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self,
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patch_size: int = 2,
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in_channels: int = 16,
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dim: int = 3840,
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n_layers: int = 30,
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n_refiner_layers: int = 2,
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n_heads: int = 30,
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n_kv_heads: Optional[int] = None,
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multiple_of: int = 256,
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ffn_dim_multiplier: float = 2.6666666666666665,
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norm_eps: float = 1e-5,
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qk_norm: bool = True,
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cap_feat_dim: int = 2560,
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axes_dims: List[int] = (32, 48, 48),
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axes_lens: List[int] = (1, 1536, 512, 512),
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rope_theta: float = 256.0,
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z_image_modulation: bool = True,
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time_scale: float = 1000.0,
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pad_tokens_multiple=None,
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clip_text_dim=None,
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image_model=None,
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device=None,
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dtype=None,
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operations=None,
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**kwargs,
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) -> None:
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super().__init__()
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self.dtype = dtype
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operation_settings = {
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"operations": operations,
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"device": device,
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"dtype": dtype,
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}
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self.in_channels = in_channels
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self.out_channels = in_channels
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self.patch_size = patch_size
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self.time_scale = time_scale
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self.pad_tokens_multiple = pad_tokens_multiple
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self.x_embedder = operation_settings.get("operations").Linear(
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in_features=patch_size * patch_size * in_channels,
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out_features=dim,
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bias=True,
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device=operation_settings.get("device"),
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dtype=operation_settings.get("dtype"),
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)
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self.t_embedder = TimestepEmbedder(
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min(dim, 1024),
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output_size=256 if z_image_modulation else None,
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**operation_settings,
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)
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self.t_embedder_2 = TimestepEmbedder(
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min(dim, 1024),
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output_size=256 if z_image_modulation else None,
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**operation_settings,
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)
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self.noise_refiner = nn.ModuleList(
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[
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TwinFlowTransformerBlock(
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layer_id,
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dim,
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n_heads,
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n_kv_heads,
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multiple_of,
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ffn_dim_multiplier,
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norm_eps,
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qk_norm,
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modulation=True,
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z_image_modulation=z_image_modulation,
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operation_settings=operation_settings,
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)
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for layer_id in range(n_refiner_layers)
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]
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)
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self.context_refiner = nn.ModuleList(
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[
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TwinFlowTransformerBlock(
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layer_id,
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dim,
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n_heads,
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n_kv_heads,
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multiple_of,
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ffn_dim_multiplier,
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norm_eps,
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qk_norm,
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modulation=False,
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operation_settings=operation_settings,
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)
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for layer_id in range(n_refiner_layers)
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]
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)
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self.cap_embedder = nn.Sequential(
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operation_settings.get("operations").RMSNorm(
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cap_feat_dim,
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eps=norm_eps,
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elementwise_affine=True,
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device=operation_settings.get("device"),
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dtype=operation_settings.get("dtype"),
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),
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operation_settings.get("operations").Linear(
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cap_feat_dim,
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dim,
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bias=True,
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device=operation_settings.get("device"),
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dtype=operation_settings.get("dtype"),
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),
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)
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self.clip_text_pooled_proj = None
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if clip_text_dim is not None:
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self.clip_text_dim = clip_text_dim
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self.clip_text_pooled_proj = nn.Sequential(
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operation_settings.get("operations").RMSNorm(
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clip_text_dim,
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eps=norm_eps,
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elementwise_affine=True,
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|
device=operation_settings.get("device"),
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|
dtype=operation_settings.get("dtype"),
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),
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operation_settings.get("operations").Linear(
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clip_text_dim,
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|
clip_text_dim,
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|
bias=True,
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|
device=operation_settings.get("device"),
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dtype=operation_settings.get("dtype"),
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),
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)
|
|
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self.layers = nn.ModuleList(
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[
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TwinFlowTransformerBlock(
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layer_id,
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dim,
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n_heads,
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|
n_kv_heads,
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multiple_of,
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ffn_dim_multiplier,
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norm_eps,
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qk_norm,
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|
z_image_modulation=z_image_modulation,
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attn_out_bias=False,
|
|
operation_settings=operation_settings,
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)
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|
for layer_id in range(n_layers)
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]
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)
|
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self.final_layer = FinalLayer(
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dim,
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patch_size,
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self.out_channels,
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z_image_modulation=z_image_modulation,
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operation_settings=operation_settings,
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)
|
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if self.pad_tokens_multiple is not None:
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self.x_pad_token = nn.Parameter(torch.empty((1, dim), device=device, dtype=dtype))
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self.cap_pad_token = nn.Parameter(torch.empty((1, dim), device=device, dtype=dtype))
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assert (dim // n_heads) == sum(axes_dims)
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self.axes_dims = axes_dims
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self.axes_lens = axes_lens
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self.rope_embedder = EmbedND(dim=dim // n_heads, theta=rope_theta, axes_dim=axes_dims)
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self.dim = dim
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self.n_heads = n_heads
|
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|
|
def _compute_twinflow_adaln(self, t: torch.Tensor, x_dtype: torch.dtype, transformer_options={}):
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"""
|
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Compute TwinFlow adaLN input.
|
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|
If `target_timestep` is provided in transformer options, apply the
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TwinFlow delta-time conditioning:
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t_emb + t_embedder_2((target - t) * time_scale) * abs(target - t)
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otherwise fallback to the baseline additive embedding.
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"""
|
|
t_emb = self.t_embedder(t * self.time_scale, dtype=x_dtype)
|
|
target_timestep = transformer_options.get("target_timestep", None)
|
|
if target_timestep is None:
|
|
t_emb_2 = self.t_embedder_2(t * self.time_scale, dtype=x_dtype)
|
|
return t_emb + t_emb_2
|
|
|
|
target_t = target_timestep.to(device=t.device, dtype=t.dtype)
|
|
if target_t.ndim == 0:
|
|
target_t = target_t.expand_as(t)
|
|
|
|
# If values look scaled (roughly sigma/timestep in [0..1000]), normalize.
|
|
t_abs_max = float(t.detach().abs().max().item()) if t.numel() else 0.0
|
|
tt_abs_max = float(target_t.detach().abs().max().item()) if target_t.numel() else 0.0
|
|
scaled_domain = (max(t_abs_max, tt_abs_max) > 2.0) and (self.time_scale > 2.0)
|
|
if scaled_domain:
|
|
t_norm = t / self.time_scale
|
|
tt_norm = target_t / self.time_scale
|
|
else:
|
|
t_norm = t
|
|
tt_norm = target_t
|
|
|
|
delta_abs = (t_norm - tt_norm).abs().unsqueeze(1).to(t_emb.dtype)
|
|
diff_in = (tt_norm - t_norm) * self.time_scale
|
|
t_emb_2 = self.t_embedder_2(diff_in, dtype=x_dtype)
|
|
return t_emb + t_emb_2 * delta_abs
|
|
|
|
def unpatchify(
|
|
self,
|
|
x: torch.Tensor,
|
|
img_size: List[Tuple[int, int]],
|
|
cap_size: List[int],
|
|
return_tensor=False,
|
|
) -> List[torch.Tensor]:
|
|
pH = pW = self.patch_size
|
|
imgs = []
|
|
for i in range(x.size(0)):
|
|
H, W = img_size[i]
|
|
begin = cap_size[i]
|
|
end = begin + (H // pH) * (W // pW)
|
|
imgs.append(
|
|
x[i][begin:end]
|
|
.view(H // pH, W // pW, pH, pW, self.out_channels)
|
|
.permute(4, 0, 2, 1, 3)
|
|
.flatten(3, 4)
|
|
.flatten(1, 2)
|
|
)
|
|
|
|
if return_tensor:
|
|
imgs = torch.stack(imgs, dim=0)
|
|
return imgs
|
|
|
|
def patchify_and_embed(
|
|
self,
|
|
x: List[torch.Tensor] | torch.Tensor,
|
|
cap_feats: torch.Tensor,
|
|
cap_mask: torch.Tensor,
|
|
t: torch.Tensor,
|
|
num_tokens,
|
|
transformer_options={},
|
|
) -> Tuple[torch.Tensor, torch.Tensor, List[Tuple[int, int]], List[int], torch.Tensor]:
|
|
bsz = len(x)
|
|
pH = pW = self.patch_size
|
|
device = x[0].device
|
|
|
|
cap_pos_ids = torch.zeros(bsz, cap_feats.shape[1], 3, dtype=torch.float32, device=device)
|
|
cap_pos_ids[:, :, 0] = torch.arange(cap_feats.shape[1], dtype=torch.float32, device=device) + 1.0
|
|
|
|
B, C, H, W = x.shape
|
|
x = self.x_embedder(
|
|
x.view(B, C, H // pH, pH, W // pW, pW).permute(0, 2, 4, 3, 5, 1).flatten(3).flatten(1, 2)
|
|
)
|
|
|
|
rope_options = transformer_options.get("rope_options", {})
|
|
h_scale = rope_options.get("scale_y", 1.0)
|
|
w_scale = rope_options.get("scale_x", 1.0)
|
|
h_start = rope_options.get("shift_y", 0.0)
|
|
w_start = rope_options.get("shift_x", 0.0)
|
|
|
|
H_tokens, W_tokens = H // pH, W // pW
|
|
x_pos_ids = torch.zeros((bsz, x.shape[1], 3), dtype=torch.float32, device=device)
|
|
x_pos_ids[:, :, 0] = cap_feats.shape[1] + 1
|
|
x_pos_ids[:, :, 1] = (
|
|
torch.arange(H_tokens, dtype=torch.float32, device=device) * h_scale + h_start
|
|
).view(-1, 1).repeat(1, W_tokens).flatten()
|
|
x_pos_ids[:, :, 2] = (
|
|
torch.arange(W_tokens, dtype=torch.float32, device=device) * w_scale + w_start
|
|
).view(1, -1).repeat(H_tokens, 1).flatten()
|
|
|
|
if self.pad_tokens_multiple is not None:
|
|
pad_extra = (-x.shape[1]) % self.pad_tokens_multiple
|
|
x = torch.cat(
|
|
(
|
|
x,
|
|
self.x_pad_token.to(device=x.device, dtype=x.dtype, copy=True).unsqueeze(0).repeat(x.shape[0], pad_extra, 1),
|
|
),
|
|
dim=1,
|
|
)
|
|
x_pos_ids = torch.nn.functional.pad(x_pos_ids, (0, 0, 0, pad_extra))
|
|
|
|
if self.pad_tokens_multiple is not None:
|
|
pad_extra = (-cap_feats.shape[1]) % self.pad_tokens_multiple
|
|
cap_feats = torch.cat(
|
|
(
|
|
cap_feats,
|
|
self.cap_pad_token.to(device=cap_feats.device, dtype=cap_feats.dtype, copy=True)
|
|
.unsqueeze(0)
|
|
.repeat(cap_feats.shape[0], pad_extra, 1),
|
|
),
|
|
dim=1,
|
|
)
|
|
cap_pos_ids = torch.nn.functional.pad(cap_pos_ids, (0, 0, 0, pad_extra), value=0)
|
|
|
|
freqs_cis = self.rope_embedder(torch.cat((cap_pos_ids, x_pos_ids), dim=1)).movedim(1, 2)
|
|
|
|
for layer in self.context_refiner:
|
|
cap_feats = layer(
|
|
cap_feats,
|
|
cap_mask,
|
|
freqs_cis[:, : cap_pos_ids.shape[1]],
|
|
transformer_options=transformer_options,
|
|
)
|
|
|
|
padded_img_mask = None
|
|
for _, layer in enumerate(self.noise_refiner):
|
|
x = layer(
|
|
x,
|
|
padded_img_mask,
|
|
freqs_cis[:, cap_pos_ids.shape[1] :],
|
|
t,
|
|
transformer_options=transformer_options,
|
|
)
|
|
|
|
padded_full_embed = torch.cat((cap_feats, x), dim=1)
|
|
mask = None
|
|
img_sizes = [(H, W)] * bsz
|
|
l_effective_cap_len = [cap_feats.shape[1]] * bsz
|
|
|
|
return padded_full_embed, mask, img_sizes, l_effective_cap_len, freqs_cis
|
|
|
|
def forward(self, x, timesteps, context, num_tokens, attention_mask=None, **kwargs):
|
|
return comfy.patcher_extension.WrapperExecutor.new_class_executor(
|
|
self._forward,
|
|
self,
|
|
comfy.patcher_extension.get_all_wrappers(
|
|
comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL,
|
|
kwargs.get("transformer_options", {}),
|
|
),
|
|
).execute(x, timesteps, context, num_tokens, attention_mask, **kwargs)
|
|
|
|
def _forward(
|
|
self,
|
|
x,
|
|
timesteps,
|
|
context,
|
|
num_tokens,
|
|
attention_mask=None,
|
|
transformer_options={},
|
|
**kwargs,
|
|
):
|
|
t = 1.0 - timesteps
|
|
|
|
adaln_input = self._compute_twinflow_adaln(t, x.dtype, transformer_options=transformer_options)
|
|
t_emb = self.t_embedder(t * self.time_scale, dtype=x.dtype)
|
|
|
|
cap_feats = context
|
|
cap_mask = attention_mask
|
|
bs, c, h, w = x.shape
|
|
x = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size))
|
|
|
|
cap_feats = self.cap_embedder(cap_feats)
|
|
|
|
if self.clip_text_pooled_proj is not None:
|
|
pooled = kwargs.get("clip_text_pooled", None)
|
|
if pooled is not None:
|
|
pooled = self.clip_text_pooled_proj(pooled)
|
|
else:
|
|
pooled = torch.zeros((x.shape[0], self.clip_text_dim), device=x.device, dtype=x.dtype)
|
|
adaln_input = torch.cat((t_emb, pooled), dim=-1)
|
|
adaln_input = self.clip_text_pooled_proj(adaln_input)
|
|
|
|
img, mask, img_size, cap_size, freqs_cis = self.patchify_and_embed(
|
|
x,
|
|
cap_feats,
|
|
cap_mask,
|
|
adaln_input,
|
|
num_tokens,
|
|
transformer_options=transformer_options,
|
|
)
|
|
freqs_cis = freqs_cis.to(img.device)
|
|
|
|
transformer_options["total_blocks"] = len(self.layers)
|
|
transformer_options["block_type"] = "double"
|
|
|
|
for i, layer in enumerate(self.layers):
|
|
transformer_options["block_index"] = i
|
|
img = layer(img, mask, freqs_cis, adaln_input, transformer_options=transformer_options)
|
|
|
|
img = self.final_layer(img, adaln_input)
|
|
img = self.unpatchify(
|
|
img,
|
|
img_size,
|
|
cap_size,
|
|
return_tensor=isinstance(x, torch.Tensor),
|
|
)[:, :, :h, :w]
|
|
|
|
return -img
|