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

examples/dataset/lingbot_world_v2/README.md

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+# LingBot World v2 Example Dataset
+
+These files were copied unchanged from the LingBot World v2 repository for the
+FastVideo causal-fast inference example.
+
+- Repository: `https://github.com/Robbyant/lingbot-world-v2.git`
+- Source commit: `94f43115de8d4a4f9f282126528c300a0b232c5f`
+- Source directory: `examples/03`
+
+## Files
+
+- `image.jpg`: source image for image-to-video generation. SHA-256:
+  `6ee3dacfef32cfef504dd698adb8a660cf15f686535c52fed4903fef27c0edd0`
+- `poses.npy`: camera-to-world trajectory matrices. SHA-256:
+  `bd0a23a696e184b0b43e7767eb432bfe644690560fe327fa96961affc941c404`
+- `intrinsics.npy`: camera intrinsic parameters. SHA-256:
+  `821fca6cf957ae8fbb1181307f02479efb1705e04c9e05734cd02fb43462e082`

examples/inference/basic/basic_lingbotworld_causal_fast.py

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+# 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()