VincentNi/wan22-rollout-put-object-cabinet-step0

Wan2.2 TI2V Step-0 Rollout — RoboTwin put_object_cabinet

160 video rollouts (10 scenes × 16 samples) generated by Wan2.2-TI2V-5B + merged Vidar LoRA on the RoboTwin put_object_cabinet task. These are the pre-NFT-training (step-0) baseline samples used to evaluate reward-model behaviour and seed RL fine-tuning.

Generation config

• Base model : Wan2.2-TI2V-5B
• LoRA : vidar/merged_vidar_lora.pt (vidar baseline merged into DiT)
• Sampler : deterministic ODE (Euler flow-matching), eta=0
• Steps : 50
• Shift : 5.0
• CFG scale : 5.0
• Frames : 121 @ 640×736
• Seeds : 42–57 per scene (one per rollout index 0–15)

The sampler is byte-aligned with fastvideo/train_nft_wan_2_2_ti2v.py::ode_rollout_batch (same flow_ode_step, same batched-CFG forward, same per-step first-frame mask re-application), so each video reproduces the corresponding step-0 video the NFT trainer would emit for the same (scene, rollout_index).

Layout

robotwin_put_object_cabinet_<scene_id>/
  robotwin_put_object_cabinet_<scene_id>_g000_s42.mp4
  robotwin_put_object_cabinet_<scene_id>_g001_s43.mp4
  ...
  robotwin_put_object_cabinet_<scene_id>_g015_s57.mp4

• 10 scenes × 16 rollouts = 160 mp4 files (~441 MB total)

Reproducing

Run scripts/inference/rollout_first_round_put_object_cabinet.sh from the EmbodiedVideoRL repo with the default config above.