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Fix TPU (torch_xla) compatibility Error about tensor repeat func along with empty dim. #12770

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Merged
merged 6 commits into from
Dec 2, 2025
Merged

Fix TPU (torch_xla) compatibility Error about tensor repeat func along with empty dim. #12770

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8082e85
Refactor image padding logic to pervent zero tensor in transformer_z_…
lime-j Nov 28, 2025
489e78f
Merge branch 'main' into main
lime-j Nov 30, 2025
e113de6
Apply style fixes
github-actions[bot] Dec 1, 2025
faa7389
Add more support to fix repeat bug on tpu devices.
JerryWu-code Dec 2, 2025
c41e9f5
Merge branch 'main' into lime-j/main
JerryWu-code Dec 2, 2025
b5bb725
Fix for dynamo compile error for multi if-branches.
JerryWu-code Dec 2, 2025
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74 changes: 44 additions & 30 deletions src/diffusers/models/transformers/transformer_z_image.py
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Original file line number Diff line number Diff line change
Expand Up @@ -482,21 +482,23 @@ def patchify_and_embed(
).flatten(0, 2)
all_cap_pos_ids.append(cap_padded_pos_ids)
# pad mask
cap_pad_mask = torch.cat(
[
torch.zeros((cap_ori_len,), dtype=torch.bool, device=device),
torch.ones((cap_padding_len,), dtype=torch.bool, device=device),
],
dim=0,
)
all_cap_pad_mask.append(
torch.cat(
[
torch.zeros((cap_ori_len,), dtype=torch.bool, device=device),
torch.ones((cap_padding_len,), dtype=torch.bool, device=device),
],
dim=0,
)
cap_pad_mask if cap_padding_len > 0 else torch.zeros((cap_ori_len,), dtype=torch.bool, device=device)
)

# padded feature
cap_padded_feat = torch.cat(
[cap_feat, cap_feat[-1:].repeat(cap_padding_len, 1)],
dim=0,
)
all_cap_feats_out.append(cap_padded_feat)
all_cap_feats_out.append(cap_padded_feat if cap_padding_len > 0 else cap_feat)

### Process Image
C, F, H, W = image.size()
Expand All @@ -515,30 +517,35 @@ def patchify_and_embed(
start=(cap_ori_len + cap_padding_len + 1, 0, 0),
device=device,
).flatten(0, 2)
image_padding_pos_ids = (
self.create_coordinate_grid(
size=(1, 1, 1),
start=(0, 0, 0),
device=device,
)
.flatten(0, 2)
.repeat(image_padding_len, 1)
image_padded_pos_ids = torch.cat(
[
image_ori_pos_ids,
self.create_coordinate_grid(size=(1, 1, 1), start=(0, 0, 0), device=device)
.flatten(0, 2)
.repeat(image_padding_len, 1),
],
dim=0,
)
image_padded_pos_ids = torch.cat([image_ori_pos_ids, image_padding_pos_ids], dim=0)
all_image_pos_ids.append(image_padded_pos_ids)
all_image_pos_ids.append(image_padded_pos_ids if image_padding_len > 0 else image_ori_pos_ids)
# pad mask
image_pad_mask = torch.cat(
[
torch.zeros((image_ori_len,), dtype=torch.bool, device=device),
torch.ones((image_padding_len,), dtype=torch.bool, device=device),
],
dim=0,
)
all_image_pad_mask.append(
torch.cat(
[
torch.zeros((image_ori_len,), dtype=torch.bool, device=device),
torch.ones((image_padding_len,), dtype=torch.bool, device=device),
],
dim=0,
)
image_pad_mask
if image_padding_len > 0
else torch.zeros((image_ori_len,), dtype=torch.bool, device=device)
)
# padded feature
image_padded_feat = torch.cat([image, image[-1:].repeat(image_padding_len, 1)], dim=0)
all_image_out.append(image_padded_feat)
image_padded_feat = torch.cat(
[image, image[-1:].repeat(image_padding_len, 1)],
dim=0,
)
all_image_out.append(image_padded_feat if image_padding_len > 0 else image)

return (
all_image_out,
Expand Down Expand Up @@ -588,10 +595,13 @@ def forward(
adaln_input = t.type_as(x)
x[torch.cat(x_inner_pad_mask)] = self.x_pad_token
x = list(x.split(x_item_seqlens, dim=0))
x_freqs_cis = list(self.rope_embedder(torch.cat(x_pos_ids, dim=0)).split(x_item_seqlens, dim=0))
x_freqs_cis = list(self.rope_embedder(torch.cat(x_pos_ids, dim=0)).split([len(_) for _ in x_pos_ids], dim=0))

x = pad_sequence(x, batch_first=True, padding_value=0.0)
x_freqs_cis = pad_sequence(x_freqs_cis, batch_first=True, padding_value=0.0)
# Clarify the length matches to satisfy Dynamo due to "Symbolic Shape Inference" to avoid compilation errors
x_freqs_cis = x_freqs_cis[:, : x.shape[1]]

x_attn_mask = torch.zeros((bsz, x_max_item_seqlen), dtype=torch.bool, device=device)
for i, seq_len in enumerate(x_item_seqlens):
x_attn_mask[i, :seq_len] = 1
Expand All @@ -605,17 +615,21 @@ def forward(

# cap embed & refine
cap_item_seqlens = [len(_) for _ in cap_feats]
assert all(_ % SEQ_MULTI_OF == 0 for _ in cap_item_seqlens)
cap_max_item_seqlen = max(cap_item_seqlens)

cap_feats = torch.cat(cap_feats, dim=0)
cap_feats = self.cap_embedder(cap_feats)
cap_feats[torch.cat(cap_inner_pad_mask)] = self.cap_pad_token
cap_feats = list(cap_feats.split(cap_item_seqlens, dim=0))
cap_freqs_cis = list(self.rope_embedder(torch.cat(cap_pos_ids, dim=0)).split(cap_item_seqlens, dim=0))
cap_freqs_cis = list(
self.rope_embedder(torch.cat(cap_pos_ids, dim=0)).split([len(_) for _ in cap_pos_ids], dim=0)
)

cap_feats = pad_sequence(cap_feats, batch_first=True, padding_value=0.0)
cap_freqs_cis = pad_sequence(cap_freqs_cis, batch_first=True, padding_value=0.0)
# Clarify the length matches to satisfy Dynamo due to "Symbolic Shape Inference" to avoid compilation errors
cap_freqs_cis = cap_freqs_cis[:, : cap_feats.shape[1]]

cap_attn_mask = torch.zeros((bsz, cap_max_item_seqlen), dtype=torch.bool, device=device)
for i, seq_len in enumerate(cap_item_seqlens):
cap_attn_mask[i, :seq_len] = 1
Expand Down
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