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Merge 43b0dab91e into c5ecd231a2

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Cezarijus Kivylius 2026-05-08 23:08:52 +08:00 committed by GitHub
commit 47d8bd5bb0
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1 changed files with 10 additions and 7 deletions
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comfy/ldm/hunyuan3dv2_1/hunyuandit.py
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@ -328,7 +328,7 @@ class CrossAttention(nn.Module):
kv = torch.cat((k, v), dim=-1) kv = torch.cat((k, v), dim=-1)
split_size = kv.shape[-1] // self.num_heads // 2 split_size = kv.shape[-1] // self.num_heads // 2
kv = kv.view(1, -1, self.num_heads, split_size * 2) kv = kv.view(b, -1, self.num_heads, split_size * 2)
k, v = torch.split(kv, split_size, dim=-1) k, v = torch.split(kv, split_size, dim=-1)
q = q.view(b, s1, self.num_heads, self.head_dim) q = q.view(b, s1, self.num_heads, self.head_dim)
@ -398,7 +398,7 @@ class Attention(nn.Module):
qkv_combined = torch.cat((query, key, value), dim=-1) qkv_combined = torch.cat((query, key, value), dim=-1)
split_size = qkv_combined.shape[-1] // self.num_heads // 3 split_size = qkv_combined.shape[-1] // self.num_heads // 3
qkv = qkv_combined.view(1, -1, self.num_heads, split_size * 3) qkv = qkv_combined.view(B, -1, self.num_heads, split_size * 3)
query, key, value = torch.split(qkv, split_size, dim=-1) query, key, value = torch.split(qkv, split_size, dim=-1)
query = query.reshape(B, N, self.num_heads, self.head_dim) query = query.reshape(B, N, self.num_heads, self.head_dim)
@ -607,9 +607,9 @@ class HunYuanDiTPlain(nn.Module):
def forward(self, x, t, context, transformer_options = {}, **kwargs): def forward(self, x, t, context, transformer_options = {}, **kwargs):
x = x.movedim(-1, -2) x = x.movedim(-1, -2)
uncond_emb, cond_emb = context.chunk(2, dim = 0) if context.shape[0] >= 2:
uncond_emb, cond_emb = context.chunk(2, dim = 0)
context = torch.cat([cond_emb, uncond_emb], dim = 0) context = torch.cat([cond_emb, uncond_emb], dim = 0)
main_condition = context main_condition = context
t = 1.0 - t t = 1.0 - t
@ -657,5 +657,8 @@ class HunYuanDiTPlain(nn.Module):
output = self.final_layer(combined) output = self.final_layer(combined)
output = output.movedim(-2, -1) * (-1.0) output = output.movedim(-2, -1) * (-1.0)
cond_emb, uncond_emb = output.chunk(2, dim = 0) if output.shape[0] >= 2:
return torch.cat([uncond_emb, cond_emb]) cond_emb, uncond_emb = output.chunk(2, dim = 0)
return torch.cat([uncond_emb, cond_emb])
else:
return output