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AuraFlow model implementation.
This commit is contained in:
comfyanonymous
479
comfy/ldm/aura/mmdit.py
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479
comfy/ldm/aura/mmdit.py
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#AuraFlow MMDiT
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#Originally written by the AuraFlow Authors
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import math
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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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from comfy.ldm.modules.attention import optimized_attention
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def modulate(x, shift, scale):
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return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
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def find_multiple(n: int, k: int) -> int:
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if n % k == 0:
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return n
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return n + k - (n % k)
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class MLP(nn.Module):
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def __init__(self, dim, hidden_dim=None, dtype=None, device=None, operations=None) -> None:
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super().__init__()
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if hidden_dim is None:
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hidden_dim = 4 * dim
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n_hidden = int(2 * hidden_dim / 3)
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n_hidden = find_multiple(n_hidden, 256)
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self.c_fc1 = operations.Linear(dim, n_hidden, bias=False, dtype=dtype, device=device)
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self.c_fc2 = operations.Linear(dim, n_hidden, bias=False, dtype=dtype, device=device)
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self.c_proj = operations.Linear(n_hidden, dim, bias=False, dtype=dtype, device=device)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x = F.silu(self.c_fc1(x)) * self.c_fc2(x)
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x = self.c_proj(x)
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return x
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class MultiHeadLayerNorm(nn.Module):
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def __init__(self, hidden_size=None, eps=1e-5, dtype=None, device=None):
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# Copy pasta from https://github.com/huggingface/transformers/blob/e5f71ecaae50ea476d1e12351003790273c4b2ed/src/transformers/models/cohere/modeling_cohere.py#L78
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super().__init__()
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self.weight = nn.Parameter(torch.empty(hidden_size, dtype=dtype, device=device))
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self.variance_epsilon = eps
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def forward(self, hidden_states):
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input_dtype = hidden_states.dtype
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hidden_states = hidden_states.to(torch.float32)
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mean = hidden_states.mean(-1, keepdim=True)
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variance = (hidden_states - mean).pow(2).mean(-1, keepdim=True)
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hidden_states = (hidden_states - mean) * torch.rsqrt(
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variance + self.variance_epsilon
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)
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hidden_states = self.weight.to(torch.float32) * hidden_states
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return hidden_states.to(input_dtype)
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class SingleAttention(nn.Module):
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def __init__(self, dim, n_heads, mh_qknorm=False, dtype=None, device=None, operations=None):
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super().__init__()
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self.n_heads = n_heads
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self.head_dim = dim // n_heads
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# this is for cond
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self.w1q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
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self.w1k = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
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self.w1v = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
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self.w1o = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
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self.q_norm1 = (
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MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
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if mh_qknorm
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else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
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)
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self.k_norm1 = (
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MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
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if mh_qknorm
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else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
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)
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#@torch.compile()
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def forward(self, c):
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bsz, seqlen1, _ = c.shape
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q, k, v = self.w1q(c), self.w1k(c), self.w1v(c)
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q = q.view(bsz, seqlen1, self.n_heads, self.head_dim)
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k = k.view(bsz, seqlen1, self.n_heads, self.head_dim)
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v = v.view(bsz, seqlen1, self.n_heads, self.head_dim)
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q, k = self.q_norm1(q), self.k_norm1(k)
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output = optimized_attention(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True)
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c = self.w1o(output)
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return c
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class DoubleAttention(nn.Module):
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def __init__(self, dim, n_heads, mh_qknorm=False, dtype=None, device=None, operations=None):
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super().__init__()
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self.n_heads = n_heads
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self.head_dim = dim // n_heads
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# this is for cond
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self.w1q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
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self.w1k = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
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self.w1v = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
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self.w1o = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
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# this is for x
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self.w2q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
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self.w2k = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
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self.w2v = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
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self.w2o = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
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self.q_norm1 = (
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MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
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if mh_qknorm
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else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
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)
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self.k_norm1 = (
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MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
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if mh_qknorm
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else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
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)
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self.q_norm2 = (
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MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
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if mh_qknorm
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else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
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)
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self.k_norm2 = (
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MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
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if mh_qknorm
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else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
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)
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#@torch.compile()
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def forward(self, c, x):
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bsz, seqlen1, _ = c.shape
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bsz, seqlen2, _ = x.shape
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seqlen = seqlen1 + seqlen2
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cq, ck, cv = self.w1q(c), self.w1k(c), self.w1v(c)
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cq = cq.view(bsz, seqlen1, self.n_heads, self.head_dim)
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ck = ck.view(bsz, seqlen1, self.n_heads, self.head_dim)
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cv = cv.view(bsz, seqlen1, self.n_heads, self.head_dim)
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cq, ck = self.q_norm1(cq), self.k_norm1(ck)
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xq, xk, xv = self.w2q(x), self.w2k(x), self.w2v(x)
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xq = xq.view(bsz, seqlen2, self.n_heads, self.head_dim)
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xk = xk.view(bsz, seqlen2, self.n_heads, self.head_dim)
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xv = xv.view(bsz, seqlen2, self.n_heads, self.head_dim)
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xq, xk = self.q_norm2(xq), self.k_norm2(xk)
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# concat all
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q, k, v = (
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torch.cat([cq, xq], dim=1),
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torch.cat([ck, xk], dim=1),
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torch.cat([cv, xv], dim=1),
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)
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output = optimized_attention(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True)
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c, x = output.split([seqlen1, seqlen2], dim=1)
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c = self.w1o(c)
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x = self.w2o(x)
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return c, x
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class MMDiTBlock(nn.Module):
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def __init__(self, dim, heads=8, global_conddim=1024, is_last=False, dtype=None, device=None, operations=None):
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super().__init__()
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self.normC1 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
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self.normC2 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
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if not is_last:
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self.mlpC = MLP(dim, hidden_dim=dim * 4, dtype=dtype, device=device, operations=operations)
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self.modC = nn.Sequential(
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nn.SiLU(),
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operations.Linear(global_conddim, 6 * dim, bias=False, dtype=dtype, device=device),
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)
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else:
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self.modC = nn.Sequential(
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nn.SiLU(),
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operations.Linear(global_conddim, 2 * dim, bias=False, dtype=dtype, device=device),
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)
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self.normX1 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
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self.normX2 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
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self.mlpX = MLP(dim, hidden_dim=dim * 4, dtype=dtype, device=device, operations=operations)
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self.modX = nn.Sequential(
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nn.SiLU(),
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operations.Linear(global_conddim, 6 * dim, bias=False, dtype=dtype, device=device),
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)
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self.attn = DoubleAttention(dim, heads, dtype=dtype, device=device, operations=operations)
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self.is_last = is_last
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#@torch.compile()
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def forward(self, c, x, global_cond, **kwargs):
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cres, xres = c, x
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cshift_msa, cscale_msa, cgate_msa, cshift_mlp, cscale_mlp, cgate_mlp = (
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self.modC(global_cond).chunk(6, dim=1)
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)
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c = modulate(self.normC1(c), cshift_msa, cscale_msa)
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# xpath
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xshift_msa, xscale_msa, xgate_msa, xshift_mlp, xscale_mlp, xgate_mlp = (
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self.modX(global_cond).chunk(6, dim=1)
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)
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x = modulate(self.normX1(x), xshift_msa, xscale_msa)
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# attention
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c, x = self.attn(c, x)
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c = self.normC2(cres + cgate_msa.unsqueeze(1) * c)
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c = cgate_mlp.unsqueeze(1) * self.mlpC(modulate(c, cshift_mlp, cscale_mlp))
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c = cres + c
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x = self.normX2(xres + xgate_msa.unsqueeze(1) * x)
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x = xgate_mlp.unsqueeze(1) * self.mlpX(modulate(x, xshift_mlp, xscale_mlp))
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x = xres + x
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return c, x
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class DiTBlock(nn.Module):
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# like MMDiTBlock, but it only has X
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def __init__(self, dim, heads=8, global_conddim=1024, dtype=None, device=None, operations=None):
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super().__init__()
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self.norm1 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
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self.norm2 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
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self.modCX = nn.Sequential(
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nn.SiLU(),
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operations.Linear(global_conddim, 6 * dim, bias=False, dtype=dtype, device=device),
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)
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self.attn = SingleAttention(dim, heads, dtype=dtype, device=device, operations=operations)
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self.mlp = MLP(dim, hidden_dim=dim * 4, dtype=dtype, device=device, operations=operations)
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#@torch.compile()
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def forward(self, cx, global_cond, **kwargs):
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cxres = cx
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shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.modCX(
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global_cond
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).chunk(6, dim=1)
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cx = modulate(self.norm1(cx), shift_msa, scale_msa)
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cx = self.attn(cx)
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cx = self.norm2(cxres + gate_msa.unsqueeze(1) * cx)
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mlpout = self.mlp(modulate(cx, shift_mlp, scale_mlp))
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cx = gate_mlp.unsqueeze(1) * mlpout
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cx = cxres + cx
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return cx
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class TimestepEmbedder(nn.Module):
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def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None, operations=None):
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super().__init__()
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self.mlp = nn.Sequential(
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operations.Linear(frequency_embedding_size, hidden_size, dtype=dtype, device=device),
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nn.SiLU(),
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operations.Linear(hidden_size, hidden_size, dtype=dtype, device=device),
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)
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self.frequency_embedding_size = frequency_embedding_size
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@staticmethod
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def timestep_embedding(t, dim, max_period=10000):
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half = dim // 2
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freqs = 1000 * torch.exp(
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-math.log(max_period) * torch.arange(start=0, end=half) / half
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).to(t.device)
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args = t[:, None] * freqs[None]
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embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
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if dim % 2:
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embedding = torch.cat(
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[embedding, torch.zeros_like(embedding[:, :1])], dim=-1
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)
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return embedding
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#@torch.compile()
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def forward(self, t, dtype):
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t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(dtype)
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t_emb = self.mlp(t_freq)
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return t_emb
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class MMDiT(nn.Module):
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def __init__(
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self,
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in_channels=4,
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out_channels=4,
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patch_size=2,
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dim=3072,
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n_layers=36,
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n_double_layers=4,
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n_heads=12,
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global_conddim=3072,
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cond_seq_dim=2048,
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max_seq=32 * 32,
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device=None,
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dtype=None,
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operations=None,
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):
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super().__init__()
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self.dtype = dtype
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self.t_embedder = TimestepEmbedder(global_conddim, dtype=dtype, device=device, operations=operations)
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self.cond_seq_linear = operations.Linear(
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cond_seq_dim, dim, bias=False, dtype=dtype, device=device
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) # linear for something like text sequence.
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self.init_x_linear = operations.Linear(
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patch_size * patch_size * in_channels, dim, dtype=dtype, device=device
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) # init linear for patchified image.
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self.positional_encoding = nn.Parameter(torch.empty(1, max_seq, dim, dtype=dtype, device=device))
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self.register_tokens = nn.Parameter(torch.empty(1, 8, dim, dtype=dtype, device=device))
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self.double_layers = nn.ModuleList([])
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self.single_layers = nn.ModuleList([])
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for idx in range(n_double_layers):
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self.double_layers.append(
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MMDiTBlock(dim, n_heads, global_conddim, is_last=(idx == n_layers - 1), dtype=dtype, device=device, operations=operations)
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)
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for idx in range(n_double_layers, n_layers):
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self.single_layers.append(
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DiTBlock(dim, n_heads, global_conddim, dtype=dtype, device=device, operations=operations)
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)
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self.final_linear = operations.Linear(
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dim, patch_size * patch_size * out_channels, bias=False, dtype=dtype, device=device
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)
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self.modF = nn.Sequential(
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nn.SiLU(),
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operations.Linear(global_conddim, 2 * dim, bias=False, dtype=dtype, device=device),
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)
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self.out_channels = out_channels
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self.patch_size = patch_size
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self.n_double_layers = n_double_layers
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self.n_layers = n_layers
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self.h_max = round(max_seq**0.5)
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self.w_max = round(max_seq**0.5)
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@torch.no_grad()
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def extend_pe(self, init_dim=(16, 16), target_dim=(64, 64)):
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# extend pe
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pe_data = self.positional_encoding.data.squeeze(0)[: init_dim[0] * init_dim[1]]
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pe_as_2d = pe_data.view(init_dim[0], init_dim[1], -1).permute(2, 0, 1)
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# now we need to extend this to target_dim. for this we will use interpolation.
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# we will use torch.nn.functional.interpolate
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pe_as_2d = F.interpolate(
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pe_as_2d.unsqueeze(0), size=target_dim, mode="bilinear"
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)
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pe_new = pe_as_2d.squeeze(0).permute(1, 2, 0).flatten(0, 1)
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self.positional_encoding.data = pe_new.unsqueeze(0).contiguous()
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self.h_max, self.w_max = target_dim
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print("PE extended to", target_dim)
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def pe_selection_index_based_on_dim(self, h, w):
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h_p, w_p = h // self.patch_size, w // self.patch_size
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original_pe_indexes = torch.arange(self.positional_encoding.shape[1])
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original_pe_indexes = original_pe_indexes.view(self.h_max, self.w_max)
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starth = self.h_max // 2 - h_p // 2
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endh =starth + h_p
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startw = self.w_max // 2 - w_p // 2
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endw = startw + w_p
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original_pe_indexes = original_pe_indexes[
|
||||
starth:endh, startw:endw
|
||||
]
|
||||
return original_pe_indexes.flatten()
|
||||
|
||||
def unpatchify(self, x, h, w):
|
||||
c = self.out_channels
|
||||
p = self.patch_size
|
||||
|
||||
x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
|
||||
x = torch.einsum("nhwpqc->nchpwq", x)
|
||||
imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p))
|
||||
return imgs
|
||||
|
||||
def patchify(self, x):
|
||||
B, C, H, W = x.size()
|
||||
pad_h = (self.patch_size - H % self.patch_size) % self.patch_size
|
||||
pad_w = (self.patch_size - W % self.patch_size) % self.patch_size
|
||||
|
||||
x = torch.nn.functional.pad(x, (0, pad_w, 0, pad_h), mode='reflect')
|
||||
x = x.view(
|
||||
B,
|
||||
C,
|
||||
(H + 1) // self.patch_size,
|
||||
self.patch_size,
|
||||
(W + 1) // self.patch_size,
|
||||
self.patch_size,
|
||||
)
|
||||
x = x.permute(0, 2, 4, 1, 3, 5).flatten(-3).flatten(1, 2)
|
||||
return x
|
||||
|
||||
def apply_pos_embeds(self, x, h, w):
|
||||
h = (h + 1) // self.patch_size
|
||||
w = (w + 1) // self.patch_size
|
||||
max_dim = max(h, w)
|
||||
|
||||
cur_dim = self.h_max
|
||||
pos_encoding = self.positional_encoding.reshape(1, cur_dim, cur_dim, -1).to(device=x.device, dtype=x.dtype)
|
||||
|
||||
if max_dim > cur_dim:
|
||||
pos_encoding = F.interpolate(pos_encoding.movedim(-1, 1), (max_dim, max_dim), mode="bilinear").movedim(1, -1)
|
||||
cur_dim = max_dim
|
||||
|
||||
from_h = (cur_dim - h) // 2
|
||||
from_w = (cur_dim - w) // 2
|
||||
pos_encoding = pos_encoding[:,from_h:from_h+h,from_w:from_w+w]
|
||||
return x + pos_encoding.reshape(1, -1, self.positional_encoding.shape[-1])
|
||||
|
||||
def forward(self, x, timestep, context, **kwargs):
|
||||
# patchify x, add PE
|
||||
b, c, h, w = x.shape
|
||||
|
||||
# pe_indexes = self.pe_selection_index_based_on_dim(h, w)
|
||||
# print(pe_indexes, pe_indexes.shape)
|
||||
|
||||
x = self.init_x_linear(self.patchify(x)) # B, T_x, D
|
||||
x = self.apply_pos_embeds(x, h, w)
|
||||
# x = x + self.positional_encoding[:, : x.size(1)].to(device=x.device, dtype=x.dtype)
|
||||
# x = x + self.positional_encoding[:, pe_indexes].to(device=x.device, dtype=x.dtype)
|
||||
|
||||
# process conditions for MMDiT Blocks
|
||||
c_seq = context # B, T_c, D_c
|
||||
t = timestep
|
||||
|
||||
c = self.cond_seq_linear(c_seq) # B, T_c, D
|
||||
c = torch.cat([self.register_tokens.to(device=c.device, dtype=c.dtype).repeat(c.size(0), 1, 1), c], dim=1)
|
||||
|
||||
global_cond = self.t_embedder(t, x.dtype) # B, D
|
||||
|
||||
if len(self.double_layers) > 0:
|
||||
for layer in self.double_layers:
|
||||
c, x = layer(c, x, global_cond, **kwargs)
|
||||
|
||||
if len(self.single_layers) > 0:
|
||||
c_len = c.size(1)
|
||||
cx = torch.cat([c, x], dim=1)
|
||||
for layer in self.single_layers:
|
||||
cx = layer(cx, global_cond, **kwargs)
|
||||
|
||||
x = cx[:, c_len:]
|
||||
|
||||
fshift, fscale = self.modF(global_cond).chunk(2, dim=1)
|
||||
|
||||
x = modulate(x, fshift, fscale)
|
||||
x = self.final_linear(x)
|
||||
x = self.unpatchify(x, (h + 1) // self.patch_size, (w + 1) // self.patch_size)[:,:,:h,:w]
|
||||
return x
|
||||
Reference in New Issue
Block a user