fix(ltx2): use actual video sequence length for dynamic timestep shift

calculate_shift() was being called with max_image_seq_len as its first
argument (image_seq_len), which collapses mu to a constant max_shift
regardless of resolution or frame count. With use_dynamic_shifting=True
this had no effect. Pass the sample's packed sequence length
(latent_num_frames * latent_height * latent_width) instead, matching the
Flux/SD3 pipelines and the LTX reference (math.prod(latent.shape[2:])).

Fixes #14243

Signed-off-by: Sergio Perez <sergio@checo.cc>

Author: sergioperezcheco

src/diffusers/pipelines/ltx2/pipeline_ltx2.py

@@ -1276,7 +1276,6 @@ def __call__(
                 raise ValueError(
                     f"Provided `latents` tensor has shape {latents.shape}, but the expected shape is either [batch_size, seq_len, num_features] or [batch_size, latent_dim, latent_frames, latent_height, latent_width]."
                 )
-        # video_sequence_length = latent_num_frames * latent_height * latent_width
 
         num_channels_latents = self.transformer.config.in_channels
         latents = self.prepare_latents(
@@ -1332,8 +1331,9 @@ def __call__(
 
         # 5. Prepare timesteps
         sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps) if sigmas is None else sigmas
+        video_sequence_length = latent_num_frames * latent_height * latent_width
         mu = calculate_shift(
-            latents.shape[1],
+            video_sequence_length,
             self.scheduler.config.get("base_image_seq_len", 1024),
             self.scheduler.config.get("max_image_seq_len", 4096),
             self.scheduler.config.get("base_shift", 0.95),