[new-model] Add LingBot-Video Dense and MoE/refiner T2V inference (#1595)

examples/inference/basic/basic_lingbot_video.py

@@ -0,0 +1,52 @@
+"""Generate a five-second Dense LingBot-Video clip with the official defaults."""
+
+import argparse
+from pathlib import Path
+
+from fastvideo import VideoGenerator
+
+
+def parse_args() -> argparse.Namespace:
+    """Parse the converted checkpoint and output paths for the sample."""
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        "--model-path",
+        type=Path,
+        required=True,
+        help="Path to a converted Dense LingBot-Video checkpoint.",
+    )
+    parser.add_argument(
+        "--output-path",
+        type=Path,
+        default=Path("outputs/lingbot-video/dense-t2v"),
+        help="Directory for the generated video.",
+    )
+    return parser.parse_args()
+
+
+def main() -> None:
+    """Load the converted Dense checkpoint and generate the default T2V sample."""
+    args = parse_args()
+    generator = VideoGenerator.from_pretrained(
+        str(args.model_path),
+        num_gpus=1,
+        use_fsdp_inference=False,
+        text_encoder_cpu_offload=True,
+        vae_cpu_offload=False,
+        pin_cpu_memory=True,
+    )
+    try:
+        generator.generate({
+            "prompt": "A red fox runs through fresh snow at sunrise.",
+            "output": {
+                "output_path": str(args.output_path),
+                "save_video": True,
+                "return_frames": False,
+            },
+        })
+    finally:
+        generator.shutdown()
+
+
+if __name__ == "__main__":
+    main()