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Use regular numbers for rope in lumina model.

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comfyanonymous 2025-02-05 04:16:59 -05:00
parent a57d635c5f
commit 60653004e5
1 changed files with 8 additions and 6 deletions
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14
comfy/ldm/lumina/model.py
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@ -9,6 +9,7 @@ import torch.nn.functional as F
from comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder, RMSNorm from comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder, RMSNorm
from comfy.ldm.modules.attention import optimized_attention_masked from comfy.ldm.modules.attention import optimized_attention_masked
from comfy.ldm.flux.layers import EmbedND
def modulate(x, scale): def modulate(x, scale):
@ -92,10 +93,9 @@ class JointAttention(nn.Module):
and key tensor with rotary embeddings. and key tensor with rotary embeddings.
""" """
x = torch.view_as_complex(x_in.float().reshape(*x_in.shape[:-1], -1, 2)) t_ = x_in.reshape(*x_in.shape[:-1], -1, 1, 2).float()
freqs_cis = freqs_cis.unsqueeze(2) t_out = freqs_cis[..., 0] * t_[..., 0] + freqs_cis[..., 1] * t_[..., 1]
x_out = torch.view_as_real(x * freqs_cis).flatten(3) return t_out.reshape(*x_in.shape).type_as(x_in)
return x_out.type_as(x_in)
def forward( def forward(
self, self,
@ -130,6 +130,7 @@ class JointAttention(nn.Module):
xq = self.q_norm(xq) xq = self.q_norm(xq)
xk = self.k_norm(xk) xk = self.k_norm(xk)
xq = JointAttention.apply_rotary_emb(xq, freqs_cis=freqs_cis) xq = JointAttention.apply_rotary_emb(xq, freqs_cis=freqs_cis)
xk = JointAttention.apply_rotary_emb(xk, freqs_cis=freqs_cis) xk = JointAttention.apply_rotary_emb(xk, freqs_cis=freqs_cis)
@ -480,7 +481,8 @@ class NextDiT(nn.Module):
assert (dim // n_heads) == sum(axes_dims) assert (dim // n_heads) == sum(axes_dims)
self.axes_dims = axes_dims self.axes_dims = axes_dims
self.axes_lens = axes_lens self.axes_lens = axes_lens
self.rope_embedder = RopeEmbedder(axes_dims=axes_dims, axes_lens=axes_lens) # self.rope_embedder = RopeEmbedder(axes_dims=axes_dims, axes_lens=axes_lens)
self.rope_embedder = EmbedND(dim=dim // n_heads, theta=10000.0, axes_dim=axes_dims)
self.dim = dim self.dim = dim
self.n_heads = n_heads self.n_heads = n_heads
@ -550,7 +552,7 @@ class NextDiT(nn.Module):
position_ids[i, cap_len:cap_len+img_len, 1] = row_ids position_ids[i, cap_len:cap_len+img_len, 1] = row_ids
position_ids[i, cap_len:cap_len+img_len, 2] = col_ids position_ids[i, cap_len:cap_len+img_len, 2] = col_ids
freqs_cis = self.rope_embedder(position_ids) freqs_cis = self.rope_embedder(position_ids).movedim(1, 2)
# build freqs_cis for cap and image individually # build freqs_cis for cap and image individually
cap_freqs_cis_shape = list(freqs_cis.shape) cap_freqs_cis_shape = list(freqs_cis.shape)