[wip] add example script and dependent material for port.

examples/inference/basic/basic_lingbotworld_causal_fast.py

@@ -0,0 +1,50 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Run LingBot-World v2 14B causal-fast I2V generation with FastVideo."""
+
+from pathlib import Path
+
+from fastvideo import VideoGenerator
+
+
+REPO_ROOT = Path(__file__).resolve().parents[3]
+DATASET_DIR = REPO_ROOT / "examples" / "dataset" / "lingbot_world_v2"
+OUTPUT_PATH = REPO_ROOT / "outputs" / "lingbotworld_causal_fast.mp4"
+
+
+def main() -> None:
+    """Load the native FastVideo LingBot causal-fast pipeline and generate one video."""
+    generator = VideoGenerator.from_pretrained(
+        "/mnt/weka/shrd/wm/junda/fv-hub/lingbot-world-v2/ckpts/lingbot-world-v2-14b-causal-fast-fastvideo",
+        num_gpus=8,
+        sp_size=8,
+        hsdp_shard_dim=8,
+        use_fsdp_inference=True,
+        dit_layerwise_offload=False,
+        dit_cpu_offload=False,
+        vae_cpu_offload=False,
+        text_encoder_cpu_offload=False,
+        pin_cpu_memory=True,
+        override_pipeline_cls_name="LingBotWorldCausalFastPipeline",
+    )
+
+    try:
+        generator.generate_video(
+            "A serene lakeside scene with a lone tree standing in calm water, surrounded by distant snow-capped mountains under a bright blue sky with drifting white clouds; gentle ripples reflect the tree and sky, creating a tranquil, meditative atmosphere.",
+            action_path=str(DATASET_DIR),
+            output_path=str(OUTPUT_PATH),
+            save_video=True,
+            height=480,
+            width=832,
+            num_frames=65,
+            num_inference_steps=4,
+            guidance_scale=1.0,
+            negative_prompt="",
+            fps=16,
+            seed=42,
+        )
+    finally:
+        generator.shutdown()
+
+
+if __name__ == "__main__":
+    main()