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ComfyUI_Rebels_LingBot

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a custom node set to run LingBot 1.3B and 30B-3b in comfyui

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- - config.json
  - configuration.json
  - generation_config.json
  - preprocessor_config.json
  - tokenizer_config.json
  - video_preprocessor_config.json
  - vocab.json
- - __init__.py
  - model_paths.py
  - moe_pack_kernels.py
  - moe_restore_kernels.py
  - pipeline_lingbot_video.py
  - pipeline_lingbot_video_i2v.py
  - scheduling_flow_unipc.py
  - sglang_moe_shim.py
  - transformer_lingbot_video.py
  - utils.py
- - transformer_config_30b.json
- __init__.py
- lingbot_nodes.py
- merge_qwen_encoder.py
- model_index.json
- model_index.json.bak_lingbot_video_diffusers
- requirements.txt
- scheduling_flow_unipc.py
LingBot 1.3B T2V Workflow.json
LingBot 30B-3b T2V Workflow.json
README.md

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ComfyUI custom nodes for **LingBot-Video** (Robbyant) — text-to-video and text+image-to-video
on consumer GPUs. Built and tested on an **RTX 3070 8GB / 16GB RAM**.

Two model families are supported:
- **LingBot-Video-Dense-1.3B** — fits entirely in 8GB VRAM.
- **LingBot-Video-MoE-30B-A3B** — a 128-expert MoE, run from GGUF with per-expert
  streaming dequant so only routed experts ever touch VRAM. Runs on the same 8GB card.

The heavy shared piece is the Qwen3-VL text encoder (~8GB): these nodes load it **on CPU,
encode, and free it** before any DiT touches the GPU, so the two never coexist.

## The structured caption is not optional

**LingBot-Video consumes structured JSON captions, not prose.** The DiT was trained on
captions from Robbyant's prompt rewriter, where lighting, color, framing, and camera
movement are explicit fields. Feed it plain text (or ad-hoc JSON) and exposure is
unconditioned — output drifts dark and loses motion. The **LingBot Structured Prompt**
node builds the exact schema the model expects, so this is the node you start from.

## example prompt:

"{
  "comprehensive_description": {
    "scene_content_description": "A tuxedo cat sits on a wooden ledge in a dim alley, with a red lantern glowing warmly behind it.",
    "camera_movement_description": "The camera slowly dollies past the cat and down the alley."
  },
  "prominent_elements": [
    {
      "name": "tuxedo cat",
      "description": "A feline with a black and white tuxedo coat resting on a wooden ledge.",
      "actions": [
        {
          "timestamp": "[0.0s - 5.0s]",
          "action": "Sits still on the ledge."
        }
      ],
      "location": "Center",
      "relative_size": "medium",
      "shape_and_color": "Feline shape with black fur and white patches on the chest and paws.",
      "texture": "Soft, sleek fur.",
      "appearance_details": "Contrasting black and white markings.",
      "relationship": "The primary subject sitting in front of the lantern.",
      "orientation": "Facing forward.",
      "pose": "Sitting.",
      "expression": "Neutral.",
      "clothing": "",
      "gender": "",
      "skin_tone_and_texture": "",
      "number_of_objects": "1"
    },
    {
      "name": "dim alley",
      "description": "A dark, atmospheric alleyway featuring a wooden ledge and a brightly glowing red lantern.",
      "actions": [
        {
          "timestamp": "[0.0s - 5.0s]",
          "action": ""
        }
      ],
      "location": "Background and foreground",
      "relative_size": "dominant",
      "shape_and_color": "Dark, shadowed surroundings with a bright red light source.",
      "texture": "Weathered wood on the ledge and rough alley walls.",
      "appearance_details": "Dimly lit with strong contrast and a red ambient glow.",
      "relationship": "Surrounds the cat and provides atmospheric backlighting.",
      "orientation": "Deep linear perspective down the alley.",
      "pose": "",
      "expression": "",
      "clothing": "",
      "gender": "",
      "skin_tone_and_texture": "",
      "number_of_objects": "1"
    }
  ],
  "camera_info": {
    "color": "Natural",
    "frame_size": "Medium",
    "shot_type_angle": "Eye level",
    "lens_size": "Medium",
    "composition": "Balanced",
    "lighting": "Soft light",
    "lighting_type": "Daylight"
  }
}"

## Nodes

| Node | What it does |
|---|---|
| **LingBot Structured Prompt (JSON caption)** | Builds the exact caption schema the model was trained on. Fields for scene description, **camera movement** (a first-class field — this is how you direct the camera), subject + environment elements, and a camera_info block with lighting/color/framing dropdowns using the model's own controlled vocabulary. Wire `prompt_json` into the encoder. |
| **LingBot Text Encode (lazy Qwen3-VL)** | Encodes prompt + negative on CPU with the exact template/skip-layer the checkpoint was trained with, then frees the encoder. Detects a structured caption and passes it through in the trained format. Optional `image` input for TI2V (run through the pipeline's own `preprocess_image` → `_vlm_image` chain). Embeds cached (clone-safe). |
| **LingBot Loader (1.3B dense)** | Builds the 1.3B DiT from bundled config + your safetensors, honors the model's fp32-module list (norms/modulation), loads the Wan VAE. |
| **LingBot 30B MoE Loader (GGUF)** | Loads the 30B-A3B MoE from a GGUF. Regular linears and the 128-expert fused stacks stay as quantized bytes in RAM; each is dequantized on demand per forward, one expert matrix at a time. Outputs the same model type as the 1.3B loader, so the same sampler drives it. |
| **LingBot Sampler** | Sequential CFG (the pipeline default), denoises to latents, **evicts the DiT, then decodes** with the full card. Optional `image` input switches to TI2V. `vae_tiling` off by default (see Known Issues). Drives both the 1.3B and 30B loaders unchanged. |

## Prompting: how to direct the scene and the camera

- **Scene content** goes in `scene_description` — what's in the frame, the setting, the light.
- **Camera movement** goes in `camera_movement` — e.g. "a smooth, slow push-in" or "an
  eye-level tracking shot alongside the subject." This is a trained field, so the model
  actually follows it (within the limits of the model size).
- **Lighting matters most for exposure.** Always set `lighting_type` and `lighting`. A
  night scene wants `Artificial light`; daylight wants `Daylight` or `Sunny`. Leaving
  exposure unspecified is what makes output go dark.
- Fill the dropdowns from the provided vocabulary — they map to how the model was trained,
  so arbitrary values (e.g. "50mm prime" for lens) fall out of distribution.

## Install

1. Clone into `ComfyUI/custom_nodes/`.
2. `python_embeded\python.exe -m pip install -U diffusers transformers accelerate safetensors einops gguf`
   (needs `diffusers >= 0.37` and a `transformers` recent enough for Qwen3-VL). For the
   30B loader, install **city96's ComfyUI-GGUF** next to this pack (its dequant kernels
   are reused).
3. Download the weights (see the companion HF repos) into:
   - 1.3B: `models/diffusion_models/LingBot_1.3b_DiT.safetensors`
   - 30B: `models/diffusion_models/LingBot-Video-30B-A3B-Q3_K_M.gguf` (or another tier)
   - `models/vae/LingBot_vae.safetensors`
   - `models/text_encoders/LingBot_text-encoder.safetensors`
4. **For the 30B loader only:** copy the 30B repo's `transformer/config.json` into
   `custom_nodes/ComfyUI_Rebels_LingBot/model_assets/transformer_config_30b.json`.
5. Restart ComfyUI.

If packaging from the original HF repo yourself: `merge_qwen_encoder.py` merges the encoder
shards into one file (streaming), and `prepare_lingbot_assets.py` copies configs + processor
into `model_assets/`.

## Settings that matter

- **Resolution: 832×480** (1.3B trained resolution; defaults set to it). Square or other
  aspects go out-of-distribution: letterbox bars, broken anatomy.
- **Frames: 81 max** (widget-capped). Beyond the trained window the video collapses to noise.
- **Guidance: 6.0 for t2v, ~3.0 for TI2V** (higher burns saturation on image runs).
- **Export at 24 fps** in Video Combine.
- steps 40, shift 3.0 are the upstream defaults.

**30B notes:** it's a big model streamed from disk through per-expert dequant, so the first
chunk is slow (page cache cold) — minutes, not seconds. Q3_K_M (~13GB) fits a 16GB machine's
page cache and runs at RAM speed; Q4_K_M (~17GB) suits 32GB-RAM machines. VRAM use is the
same across tiers since experts stream.

## Known issues

- **VAE tiling can drift color/exposure.** The Wan VAE is temporally causal; tiled decode
  keeps per-tile causal caches and long clips shift hue/brightness. Off by default (decode
  fits ~6GB once the DiT is evicted). Turn on only if decode OOMs.
- **Dark / low-motion output is almost always the prompt format** — use the Structured
  Prompt node and set lighting, don't feed prose.
- **TI2V / small-model limits.** Don't expect large prompted actions or legible rendered
  text from the 1.3B. The 30B is stronger but far slower on 8GB.
- The encoder logs `missing keys: ['lm_head.weight']` — expected; tied to `embed_tokens`
  and re-tied at load.

## Credits

- Model & pipeline: [Robbyant / lingbot-video](https://github.com/Robbyant/lingbot-video)
- Wan VAE: diffusers `AutoencoderKLWan`
- GGUF dequant kernels: city96 (ComfyUI-GGUF)
- Nodes & quantizations: [RealRebelAI](https://github.com/RealRebelAI)

Node code: MIT. Model weights are governed by the upstream LingBot-Video license — see the
source repo before redistribution or commercial use.