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ScaleROPE now works on Lumina models. #10578

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Merged
comfyanonymous merged 1 commit into from
Oct 31, 2025
Merged

ScaleROPE now works on Lumina models. #10578

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20 changes: 16 additions & 4 deletions comfy/ldm/lumina/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -522,7 +522,7 @@ def patchify_and_embed(
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]
Expand All @@ -531,10 +531,22 @@ def patchify_and_embed(
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

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