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[MPS] CrossAttention: (partial) fix for NaN attention_scores on PyTorch nightly #2643

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[MPS] CrossAttention: (partial) fix for NaN attention_scores on PyTorch nightly #2643

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12 changes: 8 additions & 4 deletions src/diffusers/models/attention_processor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -298,16 +298,20 @@ def get_attention_scores(self, query, key, attention_mask=None):
key = key.float()

if attention_mask is None:
baddbmm_input = torch.empty(
query.shape[0], query.shape[1], key.shape[1], dtype=query.dtype, device=query.device
batch_x_heads, query_tokens, _ = query.shape
_, key_tokens, _ = key.shape
# expanding dims isn't strictly necessary (baddbmm supports broadcasting bias),
# but documents the expected shape without allocating any additional memory
attention_bias = torch.zeros(1, 1, 1, dtype=query.dtype, device=query.device).expand(
batch_x_heads, query_tokens, key_tokens
)
beta = 0
else:
baddbmm_input = attention_mask
attention_bias = attention_mask
beta = 1

attention_scores = torch.baddbmm(
baddbmm_input,
attention_bias,
query,
key.transpose(-1, -2),
beta=beta,
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