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Make the ScaleRope node work on Z Image and Lumina. (#10994)
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comfyanonymous 2025-11-29 15:00:55 -08:00 committed by GitHub
parent 5151cff293
commit 0a6746898d
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1 changed files with 14 additions and 2 deletions
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comfy/ldm/lumina/model.py
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@ -517,11 +517,23 @@ class NextDiT(nn.Module):
B, C, H, W = x.shape
x = self.x_embedder(x.view(B, C, H // pH, pH, W // pW, pW).permute(0, 2, 4, 3, 5, 1).flatten(3).flatten(1, 2))
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)
H_tokens, W_tokens = H // pH, W // pW
x_pos_ids = torch.zeros((bsz, x.shape[1], 3), dtype=torch.float32, device=device)
x_pos_ids[:, :, 0] = cap_feats.shape[1] + 1
x_pos_ids[:, :, 1] = torch.arange(H_tokens, dtype=torch.float32, device=device).view(-1, 1).repeat(1, W_tokens).flatten()
x_pos_ids[:, :, 2] = torch.arange(W_tokens, dtype=torch.float32, device=device).view(1, -1).repeat(H_tokens, 1).flatten()
x_pos_ids[:, :, 1] = (torch.arange(H_tokens, dtype=torch.float32, device=device) * h_scale + h_start).view(-1, 1).repeat(1, W_tokens).flatten()
x_pos_ids[:, :, 2] = (torch.arange(W_tokens, dtype=torch.float32, device=device) * w_scale + w_start).view(1, -1).repeat(H_tokens, 1).flatten()
if self.pad_tokens_multiple is not None:
pad_extra = (-x.shape[1]) % self.pad_tokens_multiple