init

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README.md

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+---
+license: other
+license_name: base-model-terms
+pipeline_tag: image-to-video
+tags:
+  - world-model
+  - video-generation
+  - camera-control
+  - reinforcement-learning
+  - lora
+base_model:
+  - nvidia/Lyra-2.0
+  - robbyant/lingbot-world-v2-14b-causal-fast
+  - Drexubery/UniView
+datasets:
+  - ziqima/TrajectoryBench
+---
+
+# LoGo checkpoints
+
+The LoRA adapter each model was post-trained to with the LoGo reward.
+
+| file | model | base weights |
+|---|---|---|
+| `logo-lyra2-lora.pt` | Lyra-2 (use DMD scheduler) | `nvidia/Lyra-2.0` |
+| `logo-lingbot-world-v2-lora.pt` | LingBot-World 2.0 | `robbyant/lingbot-world-v2-14b-causal-fast` |
+| `logo-uniworld-view-lora.safetensors` | UniWorld-View | `Drexubery/UniView` + `Wan-AI/Wan2.1-VACE-14B-diffusers` + CausVid LoRA |
+
+Usage, with the code at https://github.com/ziqi-ma/logo.
+
+    # Lyra-2
+    torchrun --standalone --nproc_per_node=8 -m lyra_2._src.rl.inference.evaluate \
+      --adapter logo-lyra2-lora.pt --scenes-root <scene dir> --scenes "<ids>"
+
+    # LingBot-World 2.0
+    python -m wan.rl.inference.gen_scenes --input_base <scene root> --videos_base <out> \
+      --lora_path logo-lingbot-world-v2-lora.pt
+
+    # UniWorld-View
+    python -m rl.inference.eval_gen --ws_prefix <out> \
+      --rl-ckpt logo-uniworld-view-lora.safetensors --pose-scale 1.5
+
+Evaluation on TrajectoryBench, including the per-group generation settings, is described in
+`eval/README.md` in that repository.

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