diff --git a/fastvideo/models/dits/causal_wanvideo.py b/fastvideo/models/dits/causal_wanvideo.py index 88eced7ce..015615668 100644 --- a/fastvideo/models/dits/causal_wanvideo.py +++ b/fastvideo/models/dits/causal_wanvideo.py @@ -588,11 +588,9 @@ class CausalWanTransformer3DModel(BaseDiT): orig_dtype = hidden_states.dtype if not isinstance(encoder_hidden_states, torch.Tensor): encoder_hidden_states = encoder_hidden_states[0] - if isinstance(encoder_hidden_states_image, - list) and len(encoder_hidden_states_image) > 0: - encoder_hidden_states_image = encoder_hidden_states_image[0] - else: - encoder_hidden_states_image = None + if isinstance(encoder_hidden_states_image, list): + encoder_hidden_states_image = (encoder_hidden_states_image[0] + if len(encoder_hidden_states_image) > 0 else None) batch_size, num_channels, num_frames, height, width = hidden_states.shape p_t, p_h, p_w = self.patch_size @@ -702,11 +700,9 @@ class CausalWanTransformer3DModel(BaseDiT): teacher_forcing = clean_x is not None if not isinstance(encoder_hidden_states, torch.Tensor): encoder_hidden_states = encoder_hidden_states[0] - if isinstance(encoder_hidden_states_image, - list) and len(encoder_hidden_states_image) > 0: - encoder_hidden_states_image = encoder_hidden_states_image[0] - else: - encoder_hidden_states_image = None + if isinstance(encoder_hidden_states_image, list): + encoder_hidden_states_image = (encoder_hidden_states_image[0] + if len(encoder_hidden_states_image) > 0 else None) batch_size, num_channels, num_frames, height, width = hidden_states.shape p_t, p_h, p_w = self.patch_size diff --git a/fastvideo/models/dits/dreamx_world.py b/fastvideo/models/dits/dreamx_world.py index 4a24d9550..e80164d6b 100644 --- a/fastvideo/models/dits/dreamx_world.py +++ b/fastvideo/models/dits/dreamx_world.py @@ -416,11 +416,9 @@ class DreamXWorldTransformer3DModel(WanTransformer3DModel): if encoder_hidden_states is not None and not isinstance( encoder_hidden_states, torch.Tensor): encoder_hidden_states = encoder_hidden_states[0] - if isinstance(encoder_hidden_states_image, - list) and len(encoder_hidden_states_image) > 0: - encoder_hidden_states_image = encoder_hidden_states_image[0] - else: - encoder_hidden_states_image = None + if isinstance(encoder_hidden_states_image, list): + encoder_hidden_states_image = (encoder_hidden_states_image[0] + if len(encoder_hidden_states_image) > 0 else None) batch_size, _, num_frames, height, width = hidden_states.shape p_t, p_h, p_w = self.patch_size diff --git a/fastvideo/models/dits/lingbotworld/model.py b/fastvideo/models/dits/lingbotworld/model.py index c15014015..56c51e972 100644 --- a/fastvideo/models/dits/lingbotworld/model.py +++ b/fastvideo/models/dits/lingbotworld/model.py @@ -311,11 +311,9 @@ class LingBotWorldTransformer3DModel(BaseDiT): orig_dtype = hidden_states.dtype if encoder_hidden_states is not None and not isinstance(encoder_hidden_states, torch.Tensor): encoder_hidden_states = encoder_hidden_states[0] - if isinstance(encoder_hidden_states_image, - list) and len(encoder_hidden_states_image) > 0: - encoder_hidden_states_image = encoder_hidden_states_image[0] - else: - encoder_hidden_states_image = None + if isinstance(encoder_hidden_states_image, list): + encoder_hidden_states_image = (encoder_hidden_states_image[0] + if len(encoder_hidden_states_image) > 0 else None) batch_size, num_channels, num_frames, height, width = hidden_states.shape p_t, p_h, p_w = self.patch_size diff --git a/fastvideo/models/dits/wanvideo.py b/fastvideo/models/dits/wanvideo.py index 6b925c5f0..6a153c2ee 100644 --- a/fastvideo/models/dits/wanvideo.py +++ b/fastvideo/models/dits/wanvideo.py @@ -694,11 +694,9 @@ class WanTransformer3DModel(BaseDiT): orig_dtype = hidden_states.dtype if encoder_hidden_states is not None and not isinstance(encoder_hidden_states, torch.Tensor): encoder_hidden_states = encoder_hidden_states[0] - if isinstance(encoder_hidden_states_image, - list) and len(encoder_hidden_states_image) > 0: - encoder_hidden_states_image = encoder_hidden_states_image[0] - else: - encoder_hidden_states_image = None + if isinstance(encoder_hidden_states_image, list): + encoder_hidden_states_image = (encoder_hidden_states_image[0] + if len(encoder_hidden_states_image) > 0 else None) batch_size, num_channels, num_frames, height, width = hidden_states.shape p_t, p_h, p_w = self.patch_size