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ScaleROPE now works on Lumina models. (#10578)

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comfyanonymous 2025-10-31 12:41:40 -07:00 committed by GitHub
parent 27d1bd8829
commit 7f374e42c8
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1 changed files with 16 additions and 4 deletions
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20
comfy/ldm/lumina/model.py
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@ -522,7 +522,7 @@ class NextDiT(nn.Module):
max_cap_len = max(l_effective_cap_len)
max_img_len = max(l_effective_img_len)
position_ids = torch.zeros(bsz, max_seq_len, 3, dtype=torch.int32, device=device)
position_ids = torch.zeros(bsz, max_seq_len, 3, dtype=torch.float32, device=device)
for i in range(bsz):
cap_len = l_effective_cap_len[i]
@ -531,10 +531,22 @@ class NextDiT(nn.Module):
H_tokens, W_tokens = H // pH, W // pW
assert H_tokens * W_tokens == img_len
position_ids[i, :cap_len, 0] = torch.arange(cap_len, dtype=torch.int32, device=device)
rope_options = transformer_options.get("rope_options", None)
h_scale = 1.0
w_scale = 1.0
h_start = 0
w_start = 0
if rope_options is not None:
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)
position_ids[i, :cap_len, 0] = torch.arange(cap_len, dtype=torch.float32, device=device)
position_ids[i, cap_len:cap_len+img_len, 0] = cap_len
row_ids = torch.arange(H_tokens, dtype=torch.int32, device=device).view(-1, 1).repeat(1, W_tokens).flatten()
col_ids = torch.arange(W_tokens, dtype=torch.int32, device=device).view(1, -1).repeat(H_tokens, 1).flatten()
row_ids = (torch.arange(H_tokens, dtype=torch.float32, device=device) * h_scale + h_start).view(-1, 1).repeat(1, W_tokens).flatten()
col_ids = (torch.arange(W_tokens, dtype=torch.float32, device=device) * w_scale + w_start).view(1, -1).repeat(H_tokens, 1).flatten()
position_ids[i, cap_len:cap_len+img_len, 1] = row_ids
position_ids[i, cap_len:cap_len+img_len, 2] = col_ids