Unpack -> any processing (e.g. remove the reference background) -> Repack. Unconnected
inputs keep the shot's values; replacement guide frames are fitted to the shot's
generation size and length, and timing, cuts and audio are unchanged so the join still
works. Runs once per shot like the rest of the loop.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
- Filters per shot from a few sampled frames: needs a person, minimum person size,
maximum people, needs a face, skip dark/fades, skip static shots, minimum frames.
People and faces use YOLO from models/ultralytics (Impact Pack person/face models)
with OpenCV fallbacks; stats are cached per shot.
- Per-shot override (filters decide / always run / always skip) and content badges
on every shot card; "Analyse people & faces" previews the numbers.
- Planner outputs a timeline with every shot, including the ones that do not run.
BFS Shot Join fills skipped shots with the original video (full duration and
soundtrack) or removes them together with their audio.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
A video model only follows a guide inside the clip length it was trained on, so long
sources are split into shots that fit and run once per shot.
- BFS Shot Planner: interactive Vue timeline (thumbnails, cut-detector curve, draggable
boundaries, split/merge, per-shot reference gallery with upload, per-shot prompt).
Splits at camera cuts via PySceneDetect (adaptive/content, built-in fallback), fixed
length, or by hand; lengths snap to the model grid (H3 17n+5, LTX/Wan 8n+1, 4n+1).
Outputs the shots as a ComfyUI list so everything downstream runs once per shot.
- BFS Shot Unpack: guide frames, references, prompt, length, audio of one shot.
- BFS Shot H3 Conditioning: native MiniMax H3 conditioning per shot (aligned guide,
native reference video, or both).
- BFS Shot Join: concatenates in order, trims each shot to its true length, cross-fades
soft joins with the real overlap frames, returns the soundtrack.
- Queue loop mode: one shot per run stored on disk, auto re-queue, nodes after the join
blocked until the last shot.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
1.41.0 fixed the wrong half of this. The deformation came from the crop and the
latent having different ASPECTS -- a mask around a face gives a portrait box,
sampled in a landscape latent it is stretched on the way in and squeezed back on
the way out -- and rebuilding the latent at the crop's size did remove the
mismatch, but by throwing away the resolution that cropping exists to buy. A
face 200 px tall in the source was then sampled at 200 px, which is the problem
the crop was there to solve.
The planner already takes an aspect_ratio and we were passing 0.0, letting the
box follow the subject. It now gets the connected latent's aspect, and
_static_box grows its short side to match when the planner is not in play. The
box still contains the subject and stays divisible by the grid.
So the latent's size is yours again -- it is the sampling resolution, choose it
for the quality you want -- and its shape no longer has to be guessed.
68 tests; the new ones pin that a landscape latent gives a landscape box, that
the box still contains the subject, and that a free aspect still follows it.
The crop's size is derived from the mask, so an EmptyLTXVLatentVideo built
upstream cannot know it -- and a latent that disagreed was not an error
anywhere. The model sampled at the latent's shape and the paste-back squeezed
the result into the box: a face crop is portrait, sampled into a landscape
latent it came back visibly deformed, and under crop_mode zoomed the deformation
changed frame to frame. The only cure was to run once, read the size off the
debug string, rebuild the latent and run again, after every change to the mask.
The sampler now rebuilds the latent's video stream at the crop's shape when they
disagree, keeps the audio stream as it is, and reports it in debug. Nothing is
lost: at denoise 1.0 the initial latent contributes nothing, and the inpainting
path overwrites the video stream with the encoded guide regardless.
BFS Crop Size stays useful for building the latent at the right size up front
(skipping the rebuild) and for taking the per-frame boxes straight to BFS Paste
Back, but it is no longer the price of a correct result.
68 tests.
The sampler derives its box from the mask, so the size to build the empty latent
at was only knowable after a run: sample a whole clip, read one number off the
debug string, rebuild the latent, sample again -- and repeat after every change
to the mask or the crop widgets.
Getting it wrong does not look like a sizing problem. A mask around a face in a
1920x1080 frame gives a portrait box, 512x672 say; sampled into a 768x512
landscape latent the crop is stretched horizontally, and the paste-back then
squeezes the result back into the portrait box. The face comes out deformed, and
under crop_mode: zoomed the deformation changes frame to frame.
The new node runs the same planner on the same mask and returns width, height,
the per-frame boxes and a note, so the latent can be built up front. The planner
is deterministic, so identical crop widgets give the sampler the identical box.
Also factored the per-frame box normalisation the sampler did inline into
_boxes_per_frame, which both nodes now share.
63 tests.
Chunked sampling built its chunks with an overlap for continuity and then
concatenated them whole, so the overlap was emitted twice. 97 frames at tile 72
overlap 16 gave 15 latent frames where the clip has 13: the output ran longer
than the input, and from the first seam on, frame i was no longer moment i.
Pasting back made it obvious rather than subtle -- crop_bboxes has one box per
source frame, so every frame after the seam went into a box belonging to a
different moment, and with crop_mode: zoomed into a box of a different size too.
That is the "crooked and out of sync" this was reported as.
Each chunk after the first now contributes only its tail, the part the previous
chunk did not already cover, so the concatenation is exactly the clip's latent
length. Single-chunk runs are untouched.
`import comfy.nested_tensor` inside execute() binds `comfy` as a LOCAL for the
entire function, so every other comfy.* use in it raises UnboundLocalError -- at
compile time Python decides the name is local, and the AV branch that would have
bound it does not run on a video-only latent.
It shipped in 1.37.0 with the chunk-offload line:
got = got.to(comfy.model_management.intermediate_device())
which only runs when a clip is long enough to be sampled in more than one chunk.
That is why it survived the tests and the short runs and failed on the first
real long video:
UnboundLocalError: cannot access local variable 'comfy'
Imported as `from comfy import nested_tensor as _nested` now, which binds
`_nested` and leaves the module-level `comfy` visible. A test asserts neither
node's execute() has `comfy` in co_varnames, which catches the whole class of it.
_paste_back gave up when there was no crop box and returned the sampler's frame
whole. Nothing outside the mask *changed*, but the entire frame had been through
the VAE's encode/decode, so every pixel the edit never touched came back softer.
For a pointwise edit, or anything sharpness-adjacent, that is the opposite of
what the mask was for.
With no crop and a mask, the full frame is now composited into the ORIGINAL
frames through that mask, the way the crop path already did: outside the mask
the output is the source verbatim, never a round trip of it. The mask's own blur
supplies the soft edge -- there is no box border to feather here.
This makes crop_mode a resolution decision again. Before it, preserving the
untouched pixels required turning the crop on even when the region already had
all the resolution it needed.
52 tests; the new ones pin that outside is the source byte for byte, inside is
the result, no mask still composites nothing, and a result of another size is
brought back to the frame first.
Three things.
`identity_image` moves from required to optional. The node was never head-swap
specific below the surface -- the reference slot is simply not packed when it is
empty, and everything else (crop, mask, inpainting, chunking, paste-back) is
indifferent to which LoRA is running. An instruction-edit, restyle or sharpening
IC-LoRA now runs on the guide alone. IMAGE is a link, not a widget, so this
costs no widget position.
Renamed to "BFS Sampler (crop · mask · loop)" and "BFS Paste Back". The
registration keys are deliberately unchanged: saved workflows reference those,
and only the display name is free to move.
And the fix for the reason this was noticed: 1.37.0 inserted auto_config and
identity_headroom BEFORE crop_mode. ComfyUI stores widgets_values positionally,
so every later value in every saved workflow shifted two slots -- crop_mode's
string landed in auto_config, crop_scale's float in identity_headroom, and the
graph failed validation with "input out of range" on crop_scale and
crop_divisible_by. Both widgets are appended at the end now, with a comment
saying why, and a test pins the historical order so the next addition cannot
repeat it. ltx_multiple_controls.py already carried that rule in a comment;
this file did not follow it.
48 tests.
