IAMCCS Cine Director Guide B
The guide stage that turns shotboard data into real conditioning
- positive
- negative
- vae
- latent
- control_frames
- guide_data
- model
- positive
- negative
- latent
- model
- latent_downscale_factor
- report
The IAMCCS Shotboard V4 pipeline is a full multi-timeline production system - storyboard, motion plans, audio lanes - and none of it generates a frame until something converts that planning data into actual model conditioning. IAMCCS_CineDirectorGuideB is that conversion point: it's the guide stage that takes shotboard GUIDE_DATA, control frames, and a motion render plan, and produces the positive, negative, latent, and patched model that feed a Wan sampler. If you're working with the pack's Shotboard V4 workflows, this is a stage you'll see in the middle of the graph. If you're not, this node has nothing to consume - it's deeply coupled to the pack's own data types.
What it takes
Required: positive/negative conditioning, a vae, a latent, control_frames (the per-shot image sequence), motion_render_plan_json (the motion plan), plus default_strength (0.75) and default_attention_strength (0.65) that scale how hard the guide influences the result. Optional but load-bearing:
guide_data(typeGUIDE_DATA) - the shotboard's shot metadata. This is the pack-specific handoff from the planner side.model- if connected, an IC-LoRA gets applied (viaic_lora_name+ic_lora_strength, andimage_attention_strengthfor how strongly the reference image is attended).latent_downscale_factor,crop(disabled/center),use_tiled_encode+tile_size/tile_overlap- resolution and VRAM management for the latent encode.scale_by+upscale_method- if the latent or guide needs scaling to match.
Outputs: the edited positive, negative, latent, the optionally patched model, a pass-through latent_downscale_factor, and a report. Wire the conditioning and latent into the sampler and the shot renders.
The honest scope
This is not a beginner node, and it's not a general-purpose conditioning tool. It's a specific stage in the Shotboard V4 / cine_linx authoring model - the class docstring says it keeps IAMCCS's shotboard/motionParts contract while executing the guide-stage job. If your workflow is the pack's cinematic pipeline, this is where the shot becomes samplable. If you grabbed it expecting a generic "apply reference image" node, you'll find the GUIDE_DATA input refuses to cooperate, because the whole design assumes shotboard planning upstream.
Install
Part of IAMCCS-nodes:
- ComfyUI Manager → search "IAMCCS" → install → restart.
- Or:
cd ComfyUI/custom_nodes
git clone https://github.com/IAMCCS/IAMCCS-nodes.git
Restart ComfyUI. Requirements: ComfyUI ≥ 0.3.0, Python ≥ 3.12, Torch ≥ 2.8. Model-side you'll need whatever Wan base your Shotboard workflow targets plus the pack's own planner/guide nodes upstream.
Inputs (20)
| Name | Type | Default | Description |
|---|---|---|---|
| positive | CONDITIONING | — | |
| negative | CONDITIONING | — | |
| vae | VAE | — | |
| latent | LATENT | — | |
| control_frames | IMAGE | — | |
| motion_render_plan_json | STRING | {} | — |
| default_strength | FLOAT | 0.750–1 | — |
| default_attention_strength | FLOAT | 0.650–1 | — |
| latent_downscale_factor | FLOAT | 11–10 | — |
| crop | COMBO | center | 2 options: disabled, center |
| use_tiled_encode | BOOLEAN | false | — |
| tile_size | INT | 25664–512 | — |
| tile_overlap | INT | 6416–256 | — |
| guide_dataopt | GUIDE_DATA | — | |
| modelopt | MODEL | — | |
| ic_lora_nameopt | COMBO | None | 1 options: None |
| ic_lora_strengthopt | FLOAT | 1.00-100–100 | — |
| image_attention_strengthopt | FLOAT | 1.000–1 | — |
| scale_byopt | FLOAT | 1.000.01–8 | — |
| upscale_methodopt | COMBO | bicubic | 5 options: nearest-exact, bilinear, area, bicubic, bislerp |
Outputs (6)
| Name | Type | Description |
|---|---|---|
| positive | CONDITIONING | — |
| negative | CONDITIONING | — |
| latent | LATENT | — |
| model | MODEL | — |
| latent_downscale_factor | FLOAT | — |
| report | STRING | — |