effectcraft/docs/roto-brush-sam2.md

Roto Brush with SAM 2

Roto Brush can follow an object through a clip with Meta's SAM 2.1 Tiny (Hiera-Tiny, 39M parameters, Apache-2.0). It is optional: nothing changes until you install the model and choose it.

Using it

1. Settings ▸ Roto Brush ▸ SAM 2.1 Tiny ▸ Install (downloads model.safetensors, 155.9 MB, from the pinned Hugging Face commit; the SHA-256 is checked and the licence notice is kept next to the file). Or install from a file you downloaded. Agents: roto.model.install {id: "sam2.1-hiera-tiny"}.
2. Choose it (roto.model.select {id: "sam2.1-hiera-tiny"}).
3. Paint strokes on the base frame as usual, then Propagate. One tracking pass follows the object through the whole span in both directions, using SAM 2's streaming memory, so it keeps tracking through occlusion and when the object leaves the frame and returns. Each frame's probability map is then refined by the same graph cut as every other Roto Brush frame: your strokes stay hard constraints and edges snap to colour.
4. roto.status {frame} reports source (tracker, model for a frame the model segmented on its own, or classical) and, for tracked frames, tracker.frames[f]: confidence (0–1, the model's object score times its predicted mask quality) and occluded (the object is not in the frame: an empty matte). model says what Settings chose and whether it is installed, loaded and loading; notice says when it is not the one in use for the layer. tracker.saved names the model the saved frames came from.

Propagate never falls back to the classic engine without saying so. With SAM 2 chosen but not installed, or failing to load, it refuses with the reason. While it is still loading, wait: true (agents) loads it first; without wait it refuses, and the load goes on in the background. Deselecting SAM 2 (choosing Classic) runs Propagate exactly as before, and Roto Brush 1.0 never uses a model.

Tracked frames are saved in the project with the instance, as Freeze's are, so a saved project renders the same when it reopens, in the app or with effectcraft-cli, without the model being loaded or chosen there. They apply while the strokes and span they came from are unchanged. Changing a stroke or the span makes them stale until the next Propagate. Choosing another model, or the classic engine, leaves them in place and roto.status says so; a classic Propagate replaces them with classic mattes. Object Matte saves nothing.

Corrections re-track only what they change

A correction stroke changes the keys of the frames from it on (in both directions from the base), so Propagate tracks only those again: the window starts at the nearest prompted frame at or before the first frame it needs (the corrected frame, which restarts the object with its own prompt) and runs to the last frame it needs. Frames before the correction keep their tracked results, and a Propagate with nothing changed does no tracking. The older non-conditioning memory frames before the corrected frame are not rebuilt, so frames just after it can differ slightly from a from-scratch pass. Tracked frames of a session are in memory; a project reopened in a new process re-tracks the whole span after a stroke change (its saved tracking is stale as a whole).

Memory and limits

The weights take about 156 MB. A model idle for 10 minutes is unloaded (a toast says so; the choice stays) and loads again when a Propagate or a stroke needs it. The tracker holds the memory banks of the objects it follows (about 2 MB per stored frame, pruned to the frames later ones can still look at) and refuses to grow past 2 GiB, or past a time limit when one is set, with a clear error instead of running out of memory. Two runs of the same prompts give bit-identical masks on the CPU.

How it works

crates/sam2 is a Rust port (on candle, native only) of the Hugging Face transformers SAM 2 implementation, Apache-2.0 (see its NOTICE). Every stage is checked against PyTorch by crates/sam2/tests/reference.rs (they skip without the weights; reference tensors come from tools/sam2_reference.py and tools/sam2_video_reference.py):

Stage	Max abs error vs PyTorch
Hiera backbone, 12 blocks	2.6e-5
Neck features / position encodings	9.7e-6 / 6e-8
Prompt embeddings	1.2e-7
Mask decoder (logits up to 11.6)	1.1e-4
Memory encoder	3.5e-6
Memory attention (rotary)	2.6e-5
7-frame clip, forwards and backwards (9 frame results)	logits 0.0038, IoU 1.0000

SAM 2 is prompted with at most three foreground and three background points per frame: it was trained on a handful of clicks, and measured confidence collapses with many (0.99 with 1–7 points, 0.25 with 16, 0.00 with 32).

Speed (this PC: 16 threads, CPU only, release build)

Step	Time
Encode one 1024 × 1024 frame	about 2.1 s (3.4 s before the speed work: a parallel GELU, cached position encodings, pooling without permutes, a parallel bias add)
A click on an encoded frame	about 100 ms
Memory attention on a tracked frame (full bank of 7 memory frames)	about 2.3 s
A tracked frame in all	about 4.6 s (the first six have fewer memories)

A 300-frame clip therefore takes on the order of 23 minutes on this CPU. (An earlier version of this page said one encode per frame; the memory attention is as large as the encode.) What was tried and did not help: chunking the queries of the global and memory attention to keep scores in cache. Candle has no AMD/Vulkan backend, so a GPU path is future work. A spike (--example gpu_matmul) measured a plain tiled WGSL matmul on the RX 7900 GRE at 1,900–3,600 GFLOPS against 160–470 for candle's CPU matmul on the same shapes (6–13×), so hand-written wgpu kernels could bring a tracked frame to a second or less; cargo run --release -p effectcraft-sam2 --example sam2_bench and --example op_bench time the pieces.

Benchmark

effectcraft-cli bench --roto [--frames N] [--sam2 model.safetensors] [--mobilesam mobile_sam.pt] runs synthetic clips with known mattes through all three. 12 frames, debug build, this PC; mean IoU (mean IoU after the object returns), seconds per frame:

Clip	Classical	MobileSAM	SAM 2.1 Tiny
move	0.982	0.992	0.993
light	0.981	0.992	0.992
occlusion	0.978	0.881	0.988
reenter	0.632 (0.000)	0.499 (0.725)	0.975 (0.967)
s/frame	0.01	1.1	8.2

MobileSAM prompts every frame independently from the flow-warped matte, so it follows the object while the flow does and loses it when it is hidden; SAM 2's streaming memory is what lets it re-acquire the object. The synthetic clips are easy (one textured disc), so the "move" and "light" rows mostly show all three working; the re-entry row is the difference.