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fix: make attention_mask optional in LTXBaseModel.forward (fixes #13299)
The LTXBaseModel.forward and _forward methods required attention_mask as a positional argument with no default value. However, LTXV.extra_conds only conditionally adds attention_mask to model_conds when it is present in kwargs. If attention_mask is not provided by the text encoder, the diffusion_model forward call fails with: TypeError: LTXBaseModel.forward() missing 1 required positional argument: 'attention_mask' The model already handles attention_mask=None correctly in both _prepare_attention_mask and _prepare_context, so making the parameter optional is the minimal safe fix. This also aligns with how LTXAVDoubleStreamBlock.forward handles the parameter.
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@ -858,7 +858,7 @@ class LTXBaseModel(torch.nn.Module, ABC):
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return attention_mask
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def forward(
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self, x, timestep, context, attention_mask, frame_rate=25, transformer_options={}, keyframe_idxs=None, denoise_mask=None, **kwargs
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self, x, timestep, context, attention_mask=None, frame_rate=25, transformer_options={}, keyframe_idxs=None, denoise_mask=None, **kwargs
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):
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"""
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Forward pass for LTX models.
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@ -867,7 +867,7 @@ class LTXBaseModel(torch.nn.Module, ABC):
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x: Input tensor
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timestep: Timestep tensor
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context: Context tensor (e.g., text embeddings)
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attention_mask: Attention mask tensor
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attention_mask: Attention mask tensor (optional)
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frame_rate: Frame rate for temporal processing
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transformer_options: Additional options for transformer blocks
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keyframe_idxs: Keyframe indices for temporal processing
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@ -885,7 +885,7 @@ class LTXBaseModel(torch.nn.Module, ABC):
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).execute(x, timestep, context, attention_mask, frame_rate, transformer_options, keyframe_idxs, denoise_mask=denoise_mask, **kwargs)
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def _forward(
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self, x, timestep, context, attention_mask, frame_rate=25, transformer_options={}, keyframe_idxs=None, denoise_mask=None, **kwargs
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self, x, timestep, context, attention_mask=None, frame_rate=25, transformer_options={}, keyframe_idxs=None, denoise_mask=None, **kwargs
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):
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"""
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Internal forward pass for LTX models.
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