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Fix HunyuanVideo produces NaN on PyTorch<2.5 #10482

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merged 2 commits into from
Jan 7, 2025
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Fix HunyuanVideo produces NaN on PyTorch<2.5 #10482

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6cbb05f
Fix HunyuanVideo produces NaN on PyTorch<2.5
hlky Jan 7, 2025
ab27fd8
Merge branch 'main' into hunyuanvideo-torch_2.5
sayakpaul Jan 7, 2025
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8 changes: 4 additions & 4 deletions src/diffusers/models/transformers/transformer_hunyuan_video.py
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Original file line number Diff line number Diff line change
Expand Up @@ -713,15 +713,15 @@ def forward(
condition_sequence_length = encoder_hidden_states.shape[1]
sequence_length = latent_sequence_length + condition_sequence_length
attention_mask = torch.zeros(
batch_size, sequence_length, sequence_length, device=hidden_states.device, dtype=torch.bool
) # [B, N, N]
batch_size, sequence_length, device=hidden_states.device, dtype=torch.bool
) # [B, N]

effective_condition_sequence_length = encoder_attention_mask.sum(dim=1, dtype=torch.int) # [B,]
effective_sequence_length = latent_sequence_length + effective_condition_sequence_length

for i in range(batch_size):
attention_mask[i, : effective_sequence_length[i], : effective_sequence_length[i]] = True
attention_mask = attention_mask.unsqueeze(1) # [B, 1, N, N], for broadcasting across attention heads
attention_mask[i, : effective_sequence_length[i]] = True
attention_mask = attention_mask.unsqueeze(1) # [B, 1, N], for broadcasting across attention heads

# 4. Transformer blocks
if torch.is_grad_enabled() and self.gradient_checkpointing:
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