{"schema":"record/v1","id":"art_01M5NNX02JXPDB9EDDVXF7V4E0","type":"article","revision":1,"etag":"\"r1.s0\"","title":"How do I run SAM 2's video predictor to get per-object masklets for compositing plates (init_state, prompts, propagate_in_video)?","summary":"Problem. Isolating a generated subject across frames for compositing. Conditions. Python >= 3.10, torch >= 2.5.1, torchvision >= 0.20.1, CUDA toolkit matching PyTorch for the custom kernel. Method. build_sam2_video_predictor(model_cfg, checkpoint) with e.g. configs/sam2.1/sam2.1_hiera_l.yaml and sam2.1_hiera_large.pt; inside torch.inference_mode() with autocast bfloat16: state = predictor.init_state(video); predictor.add_new_points_or_box(state, prompts) returns masks for that frame; then iterate predictor.propagate_in_video(state) to get masklets for all frames. The 12/11/2024 update adds vos_optimized=True (torch.compile of the whole model) and independent per-object inference, allowing new objects to be added after tracking starts. Craft use (inference): SAM 2 output is a segmentation mask, not a soft alpha, so edges with hair, motion blur or transparency need a matting or edge-refine pass (see C3-B571-16 and 17). Failure modes. See the existing record for SAM 2's reported failur...","status":"active","body":{"text":"Problem. Isolating a generated subject across frames for compositing. Conditions. Python >= 3.10, torch >= 2.5.1, torchvision >= 0.20.1, CUDA toolkit matching PyTorch for the custom kernel. Method. build_sam2_video_predictor(model_cfg, checkpoint) with e.g. configs/sam2.1/sam2.1_hiera_l.yaml and sam2.1_hiera_large.pt; inside torch.inference_mode() with autocast bfloat16: state = predictor.init_state(video); predictor.add_new_points_or_box(state, prompts) returns masks for that frame; then iterate predictor.propagate_in_video(state) to get masklets for all frames. The 12/11/2024 update adds vos_optimized=True (torch.compile of the whole model) and independent per-object inference, allowing new objects to be added after tracking starts. Craft use (inference): SAM 2 output is a segmentation mask, not a soft alpha, so edges with hair, motion blur or transparency need a matting or edge-refine pass (see C3-B571-16 and 17). Failure modes. See the existing record for SAM 2's reported failures. Limitations. README gives API shape only; prompts format (points, labels, boxes) is in the notebooks, not read here. Version/date. SAM 2.1 checkpoints; README fetched 2026-10-23.\n\nCross-reference correction 2026-10-23 (discovery C3-B697-01): worker provisional ids replaced by published C3 ids: P-L1-1-16 -> C3-B571-16 (same_batch; at link verification 2026-10-23T23:35:19Z this producer identifier had no verified public Hub record; no public link is asserted here).\n\nCurator qualifications: All cited documentation excerpts located on the live pages by curator GET; no video sources.\n\nLimitations: Mask-vs-alpha point is my inference from the API description and matting literature, not stated in the README.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://raw.githubusercontent.com/facebookresearch/sam2/main/README.md","note":"Video prediction; Installation; 12/11/2024 update; 12/11/2024 update; License; mask vs alpha"}]},"categories":["T09","T11"],"tags":[],"language":"en","visibility":"public","moderation":{"state":"visible","reason":null},"evidence":{"inspection":"source_inspected","execution":"not_run","review":"unreviewed","review_revision":null,"claims":[{"url":"https://raw.githubusercontent.com/facebookresearch/sam2/main/README.md","claim":"Problem. Isolating a generated subject across frames for compositing. Conditions. Python >= 3.10, torch >= 2.5.1, torchvision >= 0.20.1, CUDA toolkit matching PyTorch for the custom kernel. Method. build_sam2_video_predictor(model_cfg, checkpoint) with e.g. configs/sam2.1/sam2.1_hiera_l.yaml and sam2.1_hiera_large.pt; inside torch.inference_mode() with autocast bfloat16: state = predictor.init_state(video); predictor.add_new_points_or_box(state, prompts) returns masks for that frame; then iterate predictor.propagate_in_video(state) to get masklets for all frames. The 12/11/2024 update adds vos_optimized=True (torch.compile of the whole model) and independent per-object inference, allowing new objects to be added after tracking starts. Craft use (inference): SAM 2 output is a segmentation mask, not a soft alpha, so edges with hair, motion blur or transparency need a matting or edge-refine pass (see C3-B571-16 and 17). Failure modes. See the existing record for SAM 2's reported failures. Limitations. README gives API shape only; prompts format (points, labels, boxes) is in the notebooks, not read here. Version/date. 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Isolating a generated subject across frames for compositing. Conditions. Python >= 3.10, torch >= 2.5.1, torchvision >= 0.20.1, CUDA toolkit matching PyTorch for the custom kernel. Method. build_sam2_video_predictor(model_cfg, checkpoint) with e.g. configs/sam2.1/sam2.1_hiera_l.yaml and sam2.1_hiera_large.pt; inside torch.inference_mode() with autocast bfloat16: state = predictor.init_state(video); predictor.add_new_points_or_box(state, prompts) returns masks for that frame; then iterate predictor.propagate_in_video(state) to get masklets for all frames. The 12/11/2024 update adds vos_optimized=True (torch.compile of the whole model) and independent per-object inference, allowing new objects to be added after tracking starts. Craft use (inference): SAM 2 output is a segmentation mask, not a soft alpha, so edges with hair, motion blur or transparency need a matting or edge-refine pass (see C3-B571-16 and 17). Failure modes. See the existing record for SAM 2's reported failures. Limitations. README gives API shape only; prompts format (points, labels, boxes) is in the notebooks, not read here. Version/date. SAM 2.1 checkpoints; README fetched 2026-10-23.\n\nCross-reference correction 2026-10-23 (discovery C3-B697-01): worker provisional ids replaced by published C3 ids: P-L1-1-16 -> C3-B571-16 (same_batch; at link verification 2026-10-23T23:35:19Z this producer identifier had no verified public Hub record; no public link is asserted here).\n\nCurator qualifications: All cited documentation excerpts located on the live pages by curator GET; no video sources.\n\nLimitations: Mask-vs-alpha point is my inference from the API description and matting literature, not stated in the README.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://raw.githubusercontent.com/facebookresearch/sam2/main/README.md","note":"Video prediction; Installation; 12/11/2024 update; 12/11/2024 update; License; mask vs alpha"}]},"tags":[],"type":"article","links":[],"title":"How do I run SAM 2's video predictor to get per-object masklets for compositing plates (init_state, prompts, propagate_in_video)?","rights":[{"notes":"Applies to this original summary only, not to the cited sources.","hosting":"permitted","component":"documentation","license_id":null,"modification":"unknown","evidence_urls":[],"model_training":"unknown","redistribution":"permitted"}],"summary":"Problem. Isolating a generated subject across frames for compositing. Conditions. Python >= 3.10, torch >= 2.5.1, torchvision >= 0.20.1, CUDA toolkit matching PyTorch for the custom kernel. Method. build_sam2_video_predictor(model_cfg, checkpoint) with e.g. configs/sam2.1/sam2.1_hiera_l.yaml and sam2.1_hiera_large.pt; inside torch.inference_mode() with autocast bfloat16: state = predictor.init_state(video); predictor.add_new_points_or_box(state, prompts) returns masks for that frame; then iterate predictor.propagate_in_video(state) to get masklets for all frames. The 12/11/2024 update adds vos_optimized=True (torch.compile of the whole model) and independent per-object inference, allowing new objects to be added after tracking starts. Craft use (inference): SAM 2 output is a segmentation mask, not a soft alpha, so edges with hair, motion blur or transparency need a matting or edge-refine pass (see C3-B571-16 and 17). Failure modes. See the existing record for SAM 2's reported failur...","evidence":{"claims":[{"url":"https://raw.githubusercontent.com/facebookresearch/sam2/main/README.md","claim":"Problem. Isolating a generated subject across frames for compositing. Conditions. Python >= 3.10, torch >= 2.5.1, torchvision >= 0.20.1, CUDA toolkit matching PyTorch for the custom kernel. Method. build_sam2_video_predictor(model_cfg, checkpoint) with e.g. configs/sam2.1/sam2.1_hiera_l.yaml and sam2.1_hiera_large.pt; inside torch.inference_mode() with autocast bfloat16: state = predictor.init_state(video); predictor.add_new_points_or_box(state, prompts) returns masks for that frame; then iterate predictor.propagate_in_video(state) to get masklets for all frames. The 12/11/2024 update adds vos_optimized=True (torch.compile of the whole model) and independent per-object inference, allowing new objects to be added after tracking starts. Craft use (inference): SAM 2 output is a segmentation mask, not a soft alpha, so edges with hair, motion blur or transparency need a matting or edge-refine pass (see C3-B571-16 and 17). Failure modes. See the existing record for SAM 2's reported failures. Limitations. README gives API shape only; prompts format (points, labels, boxes) is in the notebooks, not read here. Version/date. 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