Skip to content

ZImageTransformer2D: Only build attention mask if seqlens are not equal #12955

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
Beinsezii wants to merge 1 commit into
base: main
Choose a base branch
from
+12 −6
Open

ZImageTransformer2D: Only build attention mask if seqlens are not equal #12955

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
18 changes: 12 additions & 6 deletions src/diffusers/models/transformers/transformer_z_image.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -789,9 +789,12 @@ def _prepare_sequence(
freqs_cis = pad_sequence(freqs_cis, batch_first=True, padding_value=0.0)[:, : feats.shape[1]]

# Attention mask
attn_mask = torch.zeros((bsz, max_seqlen), dtype=torch.bool, device=device)
for i, seq_len in enumerate(item_seqlens):
attn_mask[i, :seq_len] = 1
if all(seq == max_seqlen for seq in item_seqlens):
attn_mask = None
else:
attn_mask = torch.zeros((bsz, max_seqlen), dtype=torch.bool, device=device)
for i, seq_len in enumerate(item_seqlens):
attn_mask[i, :seq_len] = 1

# Noise mask
noise_mask_tensor = None
Expand Down Expand Up @@ -872,9 +875,12 @@ def _build_unified_sequence(
unified_freqs = pad_sequence(unified_freqs, batch_first=True, padding_value=0.0)

# Attention mask
attn_mask = torch.zeros((bsz, max_seqlen), dtype=torch.bool, device=device)
for i, seq_len in enumerate(unified_seqlens):
attn_mask[i, :seq_len] = 1
if all(seq == max_seqlen for seq in unified_seqlens):
attn_mask = None
else:
attn_mask = torch.zeros((bsz, max_seqlen), dtype=torch.bool, device=device)
for i, seq_len in enumerate(unified_seqlens):
attn_mask[i, :seq_len] = 1

# Noise mask
noise_mask_tensor = None
Expand Down