alisson-anjosandClaude Opus 5.5
bf473e1dbd
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>
ComfyUI-BFSNodes
This repository includes general video composition helpers and the LTXV Edit Anything nodes required by Edit Anything LoRAs trained for LTX-Video 2.3 style video_to_video_ref_adaln workflows. The first public reference LoRA is expected to be edit_anything_reference_v0.1_r128_12000.safetensors.
Nodes
LTXV Edit Anything (Apply)
A unified conditioning node for LTXV Edit Anything LoRAs. It combines three conditioning paths from one LoRA checkpoint:
- IC-LoRA sequence conditioning: appends a reference image and optional guide video frames as clean conditioning frames.
- Role embedding: injects the learned reference-role token embedding before
patchify_proj, when the LoRA contains role embedding weights. - AdaLN reference conditioning: pools the encoded reference image and injects a global appearance/style condition into the LTXV timestep path, when the LoRA contains the AdaLN projector weights.
Use this node when a LoRA was trained with the Edit Anything / video_to_video_ref_adaln strategy and requires reference-image conditioning beyond a standard LoRA load.
LTXV Apply Neutral Mask
Replaces masked-out regions in an image or video batch with a clean solid background (white, neutral_gray, or black). This is useful for preparing reference images or guide frames before passing them to Edit Anything conditioning.
LTXV Resize Reference By Mask
Resizes a reference object onto a clean canvas using a mask bounding box as the target object size. This helps match the scale expected by the guide/video workflow while keeping the reference image clean.
Video Composition Nodes
The repository also includes the existing BFS video composition utilities:
- Reserved Region Frame Composer
- Frame Ranged Face Loader
- Face Sequence Batch
Installation
Clone this repository into your ComfyUI custom_nodes directory:
cd ComfyUI/custom_nodes
git clone https://github.com/alisson-anjos/ComfyUI-BFSNodes.git
Install the Python dependencies if your ComfyUI environment does not already provide them:
cd ComfyUI-BFSNodes
pip install -r requirements.txt
Restart ComfyUI after installation.
LoRA Placement
Put your Edit Anything LoRA checkpoint in ComfyUI's LoRA folder, for example:
ComfyUI/models/loras/
Subfolders are supported by ComfyUI, so organized paths such as this are fine:
ComfyUI/models/loras/ltx-2/2.3/edit_anything/edit_anything_reference_v0.1_r128_12000.safetensors
Basic Workflow
The expected graph order is:
LTXV model loader
-> standard ComfyUI Load LoRA
-> LTXV Edit Anything (Apply)
-> KSampler / sampler node
Connect the node inputs as follows:
model: the model output after the standard LoRA loader.positive/negative: your conditioning.vae: the LTXV VAE.latent: the target video latent.ref_image: the appearance/reference image.lora_name: the same Edit Anything LoRA checkpoint.guide_framesoptional: frames used for motion or structure guidance.
The node returns the patched model, updated positive/negative conditioning, updated latent, and a preview of the resized reference image.
Recommended Starting Values
Start with:
guide_strength: 1.0
ref_strength: 1.0
role_strength: 1.0
adaln_scale: 1.0
enable_adaln: true
enable_role_embedding: true
resize_mode: pad_to_fit
If the model ignores the reference image, try increasing role_strength. If the output copies color/texture too strongly, reduce adaln_scale.
Compatibility Notes
These nodes target ComfyUI's LTXV/LTX-Video model implementation. The main apply node monkey-patches LTXV internals at runtime so the reference-role embedding and AdaLN conditioning match the training-time conditioning path.
A plain LoRA loader alone is not enough for Edit Anything LoRAs that include role embedding and/or AdaLN reference conditioning. Use the standard LoRA loader first, then pass the result through LTXV Edit Anything (Apply).
Reference temporal offset
Nodes that inject clean appearance references expose a reference-only temporal offset in VAE
latent steps. The default is 0, matching the standard frame-zero placement used by existing
LoRAs and preserving their expected behavior. Negative values are an experimental way to reduce
the overlap frame-0 artifact where the reference composition briefly appears at the start of the
generated video.
0: standard frame-zero placement (default)-1: one latent step before frame zero (-8pixel frames with the standard LTX VAE)-2: two latent steps before frame zero (stronger separation, possibly weaker identity)
The offset is applied only to appearance/reference inputs. Guide videos, masks, identity masks,
and target video positions remain unchanged. In LTX Multiple Controls, the field is named
identity_temporal_offset_latents and affects only identity_image.
Shot Planner / Shot Loop (1.48.0)
Split a long video into shots a video model can follow (at camera cuts via PySceneDetect, fixed length, or by hand on a timeline), set a reference image and prompt per shot, run the rest of the workflow once per shot, and join the results back with the original timing and soundtrack.
- BFS Shot Planner — interactive timeline (thumbnails, cut detector curve, draggable boundaries, per-shot reference gallery and prompt). Outputs the shots as a ComfyUI list, so every node downstream runs once per shot.
- BFS Shot Unpack — guide frames, references, prompt and length of one shot, for any model.
- BFS Shot H3 Conditioning — MiniMax H3 conditioning for one shot with the native nodes (aligned guide and/or native reference video).
- BFS Shot Join — concatenates, trims to the true lengths, cross-fades soft joins, returns audio.
Two run modes: auto loop (all shots in one run) and queue loop (one shot per run, stored on
disk, re-queued automatically; nodes after the join only run on the last shot). See
web/docs/BFSShotPlanner.md.
Requirements
- ComfyUI
- PyTorch
- safetensors
- An LTXV/LTX-Video model and compatible VAE
- An Edit Anything LoRA trained for this conditioning strategy
License
This project follows the repository license. See LICENSE.
MiniMax-H3 downscaled latent guides (1.46.0)
MiniMax-H3 Downscaled Latent Guide (BFS) supports aligned lower-resolution
image/video guides for H3 LoRAs trained with target_grid_stride_v1. It encodes
the smaller guide and patches only the returned model's packed coordinates.
Connect both its MODEL and positive CONDITIONING outputs to sampling.
MiniMax-H3 Guide Target — Image / Video (BFS) creates an exact output canvas: use one frame for images or 73 frames for approximately three seconds at 24 fps. At output 1024 x 768 and factor 4, the guide preview is 256 x 192.
See setup, workflow template, compatibility and validation.
BFSNodes 1.47.0 adds optional MiniMax-H3 overlap/sidecar source-phase RoPE and MiniMax-H3 Identity Reference + RoPE (BFS). See the H3 guide.