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

Optional: pip install insightface with onnxruntime or onnxruntime-gpu, only for LTX Identity. The Shot Planner's Find people / Cast no longer needs it (1.93.0): it uses OpenCV's YuNet + SFace (Apache-2.0, downloaded once), and uses InsightFace only when it is installed. They are not in the requirements because insightface pins packages that conflict with many environments; without them everything else works and those features say what to install.

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_frames optional: 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 (-8 pixel 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, native reference video, or aligned guide + CLIP video context).
MiniMax H3 Reference to Video — Guide 0 CLIP Context (BFS) — lets Qwen see the video guide at 2 fps without native guide video-reference latents; use Add Guide separately for motion. See H3 guide modes.
BFS Shot Join — concatenates, trims to the true lengths, cross-fades soft joins, returns audio. With comparison on, its guide output is the original video frame for frame.
BFS Video Compare — one player for two videos (Join's guide and images): wipe ↔ / ↕, overlay or side by side, any of guide / target / difference on top, plus a per-pixel difference map and the difference per frame (click to jump).

Cast (1.49.0): Find people tracks faces through the video (InsightFace) and groups them into people. Link each person to a reference and every shot uses the reference of the person with the most screen time in it; optionally split where the main person changes and run only the shots where a linked person appears.

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/js/docs/BFSShotPlanner.md.

Shots can also be cropped to a SAM 3 mask (text or points per shot) and pasted back by the join, continue from the previous shot's result, and get VLM suggestions (what to segment, a shot description for the prompt, run/skip) from a Qwen3-VL connected to the planner.

H3 Duet / Side Panel (1.50.0)

Training-free split-screen generation for MiniMax H3 (TSC's latent pin): a panel (the source clip or a reference) is pinned beside the video with a noise mask, the video is generated in sync with it, and the panel is cropped off before decoding. BFS H3 Duet does it all in one node, BFS Shot H3 Duet renders each shot of the Shot Loop that way (or with an aligned guide for body-swap LoRAs); BFS H3 Side Panel + BFS H3 Side Panel Crop are the building blocks. Optional aligned latent guide. 1.84.0: panel_static (TV static over the face / person of the pinned clip, SAM 3) and a setting picture (BFS Setting Picture) for same-world swaps. See web/js/docs/BFSH3Duet.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.

Experimental native temporal motion-transfer inference (standalone node and Shot Conditioning mode): see H3_NATIVE_TEMPORAL.md. Requires the matching experimental LoRA; preserves native image references and applies temporal RoPE alignment plus soft temporal attention.

H3 Flash Partitions

Illustrated explanation: native temporal alignment with compile-free Flash Partitions covers the exact temporal rule, positive partition decomposition, log-sum-exp merging, measured performance, guide scaling and current backend limitations.

Experimental temporal mode automatically recognizes Model Attention Backend (Kitchen/PyTorch) and, from 1.115.14, Model Sparse Attention → sol-attn. Version 1.115.22 automatically selects verified prebuilts for Linux SM80/86/89/90/100/120 and Windows SM86/89/120 (x86_64, CUDA13-capable driver); cross-platform GPU validation remains experimental. Version 1.115.16+ can use the one-pass Kitchen extension for dense INT8 and sol-attn. The earlier compatibility path repeats frame-wise calls and was slower than Flash Partitions; see setup, measurements and limitations. SLA/VSA remain unsupported.

Native H3 auto-prompt workflow

Automatic reference descriptions, six-section prompt writing and source-derived video size/frame count. Includes visual workflow and API graph, with native Generate Text stages and public BFS helpers; no private benchmark nodes.

TARA kernel downloads, including RTX4090 (SM89), H100 (SM90) and RTX5090 (SM120): direct download table and setup checks.

Animated TARA parameter guide: free window, falloff, strength and cap, with an offline interactive version.

Native reference image preview (1.115.25)

BFS H3 Describe References (Generate Text per Picture) adds a third output, reference_images (IMAGE list). Connect it to Preview Image to view every attached reference in Picture 1..N order. Original sizes and pixels are preserved; images of different sizes are not resized into a batch. The existing reference bundle and text outputs retain their indices. This preview connection does not change which images are sent to the encoder.

Native shot loop without Planner (1.115.27, experimental)

Separate Start, Continuity and End/Join nodes now have a native auto-prompt loop example. See wiring, manual cuts, continuity and validation limits.