alisson-anjos-ComfyUI-BFSNodes/web/docs/BFSHeadSwapMaskedSampler.md

BFS Head Swap Sampler (crop · mask · loop)

Runs an LTX head-swap LoRA and adds three things around it, each optional. Connect only what you need — the node degrades to whatever is wired.

what you connect	what you get
guide_video + identity_image	plain head swap, one pass
+ subject_mask	only the masked region is denoised
+ crop_mode	the swap runs inside a box around the subject, then is pasted back
+ temporal_tile_size	long clips sampled in chunks with overlap

The LoRA is never asked to understand a mask or a crop. It keeps doing its one job: the guide rides on source_id 1, the identity on source_id 2. Everything else here is plumbing around it.

The mask is native inpainting, not a LoRA feature

The mask becomes latent["noise_mask"], which the guider consumes as denoise_mask — ComfyUI's own inpainting path. Pixels outside the mask keep the guide's own content, so the original face stays visible to the model. This is deliberately different from a masked-hole recipe, where the region is painted out before the model sees it.

When a mask is connected the node seeds the sampling latent with the VAE-encoded guide, not with the empty latent you wired in — inpainting keeps the initial latent outside the mask, and an empty one leaves grey where the video should be. The connected latent still sets the size (and carries the audio stream); its video content is replaced.

Masks are reduced to the latent grid with max, not with ComfyUI's trilinear resize. Trilinear blurs a mask across frames and lets the original content bleed through the edit. Max keeps a latent cell that any masked pixel touches fully editable.

Does the mask need to be semi-transparent so the LoRA can see the expression?

No — and it would cost you the swap. The expression does not travel under the mask: the whole source clip, original face included, is in the conditioning as an aligned reference on source_id 1, frame by frame. The model reads the performance from there whatever the mask does. That is what the LoRA was trained on: the new head follows the old head's acting.

Partial mask values do something else. In ComfyUI's inpaint path they blend the original latent back in, so you keep the original geometry in pixels and the original identity with it — the result drifts toward an average of both faces. That is what mask_strength below 1.0 buys, and it is a deliberate choice, not a way to get expressions. Soft edges are for hiding the seam; the middle of the mask should stay at 1.0.

Watch the latent_mask output. It has one frame per latent frame, not per video frame, and that is the real resolution of the edit. A tight outline around a face can nearly vanish at that scale — and then nothing changes, no matter how good the LoRA is. If the mask looks thin there, raise mask_grow.

Why crop, and which mode

At 512×288 a person filling a fifth of the frame leaves a face about 25 px tall. No LoRA recovers identity from 25 px — it is missing pixels, not missing training. Cropping the head region and sampling it full-frame gives that same face 200–300 px, and the result is feathered back into untouched frames.

crop_mode	behaviour
off	the whole frame is sampled. Use it first, to confirm the swap works at all.
combined	one static box around the subject's whole travel, held for the clip. No dependency.
tracked	constant-size box that stays still until the subject would leave it, then moves as little as possible.
zoomed	the box also follows the subject's size, planned over the whole clip, every crop resampled to one output size.

tracked and zoomed use a planner vendored from drozbay/MaskVidExperiments (GPL-3.0, the same licence this pack carries) — nothing extra to install. Those boxes hold still through mask noise and occlusion, and a jittering crop reads to a video model as camera motion, which is what a naive per-frame crop around a mask produces.

crop_scale is the box as a multiple of the subject. At 1.5 the subject occupies two thirds and the rest is margin. Keep neck and shoulders inside: a face-tight crop is a framing the LoRA never saw in training, and it will show.

Sizing, which is the one fiddly part

The connected latent sets the sampled size. When you crop, the box is smaller than the frame, so the latent should match the box — the debug output prints the size to use:

sample size 416x768 (connect EmptyLTXVLatentVideo at this size to avoid a resize)

If they disagree the node resamples the crop on the way back, which costs exactly the sharpness the crop was there to buy. Run once, read debug, set EmptyLTXVLatentVideo, run again.

The latent must come from the graph. LTX‑2.5 latents are AV — video and audio nested together — and the node cannot fabricate that structure. Without one it builds a plain video latent, warns, and the guide may not reach the model at all.

Chunking

temporal_tile_size at 0 samples the clip in one pass. Set it to the length the LoRA trained at (73 for the LTX head-swap recipe) for longer clips, and the node slices the guide, the mask and the overlap together per chunk.

Chunked sampling with an AV latent is refused rather than silently corrupted — slicing a nested latent in time is not a thing the node does yet.

Second pass at higher resolution

With cropping on, latent is the crop's latent, not the frame's — which is what you want to refine, because that is where the face has pixels. To chain a second pass:

Head Swap Sampler (paste_back: off)
   ├─ latent      → LTXV upscaler / second sampler → VAE Decode ─┐
   └─ crop_bboxes ───────────────────────────────────────────────┤
guide_video (original frames) ────────────────────────────────────┤
                                                                  ▼
                                              BFS Head Swap Paste Back → frames

With paste_back: off the node stops compositing, so images and latent stay in the crop's own space. Refine there, then bring the result home with BFS Head Swap Paste Back, which takes the crops, the original frames and crop_bboxes. It does the same edge-aware feather and optional mask confinement the sampler would have done.

Refining before compositing is the right order: upscaling the crop puts the pixels where the identity is, and the frame around it never needed a second pass.

Reading the outputs

output	what it answers
images	the result, with the crop pasted back
mask_over_source	is the mask where I think it is? Red over the original frames, green box for the crop
cropped_guide	exactly what the model was fed. If this is wrong, nothing downstream can be right
crop_mask	the mask after grow/blur, in the crop's pixel space
latent_mask	the mask as the sampler sees it — where a thin mask disappears
latent	the sampled latent — the CROP's, when cropping is on
crop_bboxes	the boxes, for Head Swap Paste Back after a second pass
debug	crop mode and box, mask ops, tiling, the size to build the latent at

Suggested order when something looks wrong

1. crop_mode: off, no mask, temporal_tile_size: 0. This is the plain swap; if it fails, the problem is the recipe or the LoRA, not this node.
2. Add the mask. Check latent_mask before judging the result.
3. Add the crop. Check mask_over_source for the box, and set the latent to the size debug reports.
4. Only then raise the frame count and turn on chunking.

Sampling settings follow the LoRA's recipe: CFG 3–5 with a real negative, and no LightX2V (CFG 1.0 means no amplification, and the reference stops being amplified).