foramt review

Author: wangshankun

lightx2v/models/runners/wan/wan_audio_runner.py

@@ -391,7 +391,7 @@ def read_image_input(self, img_path):
         latent_h = patched_h * self.config["patch_size"][1]
         latent_w = patched_w * self.config["patch_size"][2]
 
-        if hasattr(self.input_info, "target_video_length"):
+        if hasattr(self.input_info, "target_video_length") and self.input_info.target_video_length is not None:
             target_video_length = self.input_info.target_video_length
             latent_shape = self.get_latent_shape_with_lat_hw(latent_h, latent_w, target_video_length)
         else:
@@ -510,7 +510,7 @@ def prepare_prev_latents(self, prev_video: Optional[torch.Tensor], prev_frame_le
         """Prepare previous latents for conditioning"""
         dtype = GET_DTYPE()
         tgt_h, tgt_w = self.input_info.target_shape[0], self.input_info.target_shape[1]
-        if hasattr(self.input_info, "target_video_length"):
+        if hasattr(self.input_info, "target_video_length") and self.input_info.target_video_length is not None:
             target_video_length = self.input_info.target_video_length
         else:
             target_video_length = self.config["target_video_length"]
@@ -836,7 +836,6 @@ def load_model(self):
 
     def get_latent_shape_with_lat_hw(self, latent_h, latent_w, target_video_length=None):
         target_video_length = target_video_length if target_video_length is not None else self.config["target_video_length"]
-        self.input_info.latent_freams = latent_h
         latent_shape = [
             self.config.get("num_channels_latents", 16),
             (target_video_length - 1) // self.config["vae_stride"][0] + 1,