Timeline-based authoring for MiniMax H3 text/image/video generation and reference-to-video workflows, integrated with ComfyUI's native H3 implementation. Two lanes (Image/Video + Audio), slot-based layout, drag-and-drop / paste / upload, per-clip trims, and structured prompt builders per mode.

MiniMax H3 Director

FL2VA mode: text-to-video (T2VA), first-frame (I2VA), or first+last frame interpolation; up to 2 image slots; automatic alignment-line insertion in the prompt.
REF2VA mode: up to 9 images, 3 videos, 3 audio clips, 12 files total; each video has a compact V / A / V+A switch (Video only / Audio only / Video+embedded-audio) using the same trim range for both streams; standalone audio also supports left/right trim handles.
REFERENCE VIDEO THUMBNAILS: uploaded videos show their actual first frame as a background preview behind each clip tile, making it easy to identify references at a glance.
REFERENCE HANDLING: reorder clips by dragging between slots, attach external soundtracks to videos, crop references visually via draggable markers, preserve incompatible media when toggling FL2VA ↔ REF2VA instead of losing assets.
LANE SELECTION & PASTE: click an Image/Video or Audio lane to select it; Ctrl+V pastes clipboard images/videos/audio into the chosen lane; drag-and-drop from your file manager works too.
PROMPT BUILDERS:
- FL2VA/I2VA/L2VA/T2VA: guided fields for integrated_multimodal_description, overall_soundscape, and non_diegetic_music with automatic alignment headers.
- REF2VA: simplified six-section free-text builder (subject_definitions, summary, retention_analysis, detailed_description, overall_soundscape, non_diegetic_music) with helper buttons: Insert [Shot N] places shot markers at cursor, Prefill Labels & Summary auto-generates Picture/Video/Audio labels from your inserted media, and Preview Prompt shows the exact assembled prompt in a popup with copy-to-clipboard.
VALIDATED LIMITS: 2–15 second reference windows; max 15s combined visual and audio duration each; strict path-safety under ComfyUI's input directory.
NATIVE ROUTING & LAZY LOADING: hands validated data to ComfyUI's built-in MiniMaxH3ImageToVideo / MiniMaxH3ReferenceToVideo nodes; only the selected FL2VA or REF2VA model is requested.
RESOLUTION PANEL: Aspect / Resolution / Input scaling selectors (all default to Auto) drive the output canvas on MiniMax's 16-px grid — Auto aspect follows your first visual reference, Auto resolution sets a 768 px short side, plus fixed aspect, MP and pixel presets with CUSTOM values. The dropdowns are grouped in columns (aspect by orientation, resolution by ###p / MP, ascending) and label the auto options Native (ShortEdge 768px) / Native (ShortEdge 2048px). Input scaling (Off / Auto / Target / Fit / Fill / Fit+pad / Divisible crop) preprocesses visual references via the included Torch Resize before they reach H3.
PROMPT MODE TOGGLE: a Simple / Structured switch in the mode bar changes how builder fields are assembled into the final prompt — Structured keeps the labelled sections, Simple renders one flat block. The choice is persisted in the workflow and honored by Preview Prompt.
FRAME RATE: a frame_rate FLOAT input (0.1–240, default 24) sets the output FPS and is also emitted as a frame_rate output so downstream nodes can read the effective value.
CROP PREVIEW: a ▶ Play crop button previews only the current crop range, and the preview crop range itself is draggable for quick scrubbing.
PASTE-REPLACE: Ctrl+V onto a selected media tile replaces that tile in place, preserving its slot position instead of appending.
EXTERNAL OVERWRITE INPUTS: optional external_prompt_overwrite (STRING) replaces the assembled builder output; connect both external_width_overwrite and external_height_overwrite (INT) to replace the Director canvas and bypass its sizing and input preprocessing entirely.

Full documentation, UI guide, and prompting reference →

⚡ MiniMax H3 Cache

An approximate, model-scoped whole-block-stack residual cache for ComfyUI's native MiniMax H3 model.

MODEL PATCH: clones only the connected MiniMax H3 MODEL; no global model-class monkey patch.
CONTROLLED REUSE: sampled audio/video-token relative-L1 threshold, 15–90% sampling window, and a bounded number of consecutive cache hits.
STORAGE: auto / CUDA / CPU cached-residual storage with CPU fallback if automatic storage runs out of VRAM.
COMPATIBILITY: preserves ComfyUI block replacements and transformer options; can be chained with Patch Comfy Kitchen Attention.
QUALITY: approximate optimization—higher cache thresholds trade fidelity for more skipped block-stack evaluations.

Full documentation, usage, compatibility, and provenance →

🔥 Patch Comfy Kitchen Attention

A one-input model patch that swaps the connected model's attention backend to Comfy Kitchen INT8 attention at runtime.

Model-scoped: clones only the connected MODEL and sets its optimized-attention override; it never monkey-patches ComfyUI globally.
Safe fallback: if Comfy Kitchen INT8 attention is unavailable in your ComfyUI build, it falls back to the ComfyUI default attention and logs the decision.
Chainable: works before or after MiniMax H3 Cache — both are model-clone patches and compose in either order.

MiniMax H3 Model Loader
          │
          ▼
MiniMax H3 Cache ──► Patch Comfy Kitchen Attention ──► Guider / Sampler

💎 RTX Upscaler & Refiner

State-of-the-art image and video enhancement using NVIDIA RTX Video SDK. It executes up to three sequential passes (Denoise, Deblur, and Upscale) in a single node, processing frame-by-frame to keep VRAM usage predictable and low.

Refine: Independent Denoise and Deblur passes (both off by default).
Upscale: AI-powered VSR and High Bitrate upscaling.
Smart Sizing: Multiple resize modes including Constant Megapixel targets.
Efficiency: Frame-by-frame processing for minimal VRAM usage.
Memory Control: The output batch is allocated lazily (like the reference NVIDIA node — the kernel decides, no up-front memory pressure, no temp file). A disk-backed (mmap) fallback (use_mmap, off by default) is opt-in for very long video batches: when enabled it is the last tier of the VRAM -> RAM -> disk chain, taken only when available RAM is still short after automatic model unloading (auto_unload_models, on by default). Warning: enabling use_mmap writes a multi-giB .mmap temp file to your temp drive for the whole run.

Full documentation →

📐 Resolution Scale Calculator

The DaSiWa Scale Calculator provides mathematically precise resolution management for high-performance video models. It uses a Constant-Area Square-Root method to ensure that your GPU VRAM usage remains stable regardless of the aspect ratio.

Unified Resolution Presets: Pick standard p targets from 144p to 2160p/4K or optimized megapixel tiers from one dropdown.
Clear Aspect Modes: IMAGE ASPECT uses the connected image shape; USE ASPECT BELOW uses the always-visible aspect controls.
Video-Safe Snapping: Standard, Div32, Div64, and custom divisor modes keep dimensions aligned for different model families.

Full documentation →

⚡ Torch Resize

A drop-in replacement for ComfyUI's built-in resize nodes that keeps images sharp and video workflows fast without extra dependencies.

Sharper results: Lanczos resampling with optional sRGB-to-linear gamma correction produces cleaner upscaling and downscaling than native bilinear/bicubic.
Video-friendly batching: Automatically splits long frame sequences into memory-safe chunks so you never run out of VRAM, while keeping output order intact.
Zero extra installs: Runs entirely on the PyTorch build ComfyUI already uses — no Pillow, torchlanc, Triton, or vendor SDK required.
Precise sizing control: Divisible-by alignment, five aspect modes (fit, fill/crop, pad, stretch, long-side crop), and configurable crop/pad placement eliminate guesswork for downstream model constraints.
Alpha preserved: Transparency channels are resized independently without gamma conversion artifacts.

Full documentation →

🎛️ Node Status Switch

The DaSiWa Node Status Switch lets you mute or bypass any node in your workflow using a single toggle. Targets are registered by wiring their outputs into the switch's input slots, which grow dynamically as you connect more nodes (up to 99).

Full documentation →

Quick start:

Add a DaSiWa Node Status Switch to your workflow
Drag any output from the node(s) you want to control into the switch's target_01 input — new slots appear as you connect more
Set action to mute or bypass and configure trigger_on to taste
Toggle enabled directly on the switch

🎬 Advanced LoRA Loader

The DaSiWa Advanced LoRA Loader is a 10-slot stacker for ordinary image/video LoRAs and LTX-2.3. In Basic mode, it loads the complete LoRA map, so it is compatible with standard image and video models. Its VIS control means visual strength: it affects the whole LoRA map in Basic mode, including image models. LTX-2.3 additionally supports independent audio separation.

Model Modes: Select Basic for universal image/video compatibility or LTX-2.3 for separate visual/audio branches. MiniMax H3 uses Basic mode because its transformer blocks are shared between video and audio.
Visual Control: STR × VIS is the effective visual strength. In Basic mode, VIS controls the complete LoRA map; it is not video-only.
Dual-Branch Control: LTX-2.3 can adjust visual (VIS×) and audio (A×) multipliers independently per LoRA.
10 LoRA Slots: Stack up to 10 LoRAs with fine-grained strength control (STR: −5.0 to +5.0).
Toggle All: The ALL header button enables every slot; when all slots are enabled, it disables every slot.
Key Count Indicator: Auto-scans each LoRA to show video/audio key counts before generation.
6 Themes: Switch between Jade, Neon, Studio, Chrome, OLED, and Wood color schemes.
Searchable UI: Quick LoRA search with live filtering in the node itself.

DaSiWa Advanced LoRA Loader

Full documentation →

💾 Metadata Image Saver (Civitai Ready)

The DaSiWa Metadata Image Saver ensures your images are fully compatible with Civitai, Hugging Face, and other galleries by embedding A1111-style metadata. It automatically detects LoRAs used in the workflow and supports dynamic filenames.

Civitai Compatibility: Writes the standard parameters block for auto-parsing of prompts and resources.
LoRA Detection: Scans your workflow and appends <lora:name:weight> triggers automatically.
WebP Support: Full "Drag-and-Drop" workflow reconstruction support for both PNG and WebP formats.
Dynamic Filenames: Use placeholders like %seed%, %date%, %model%, %width%, and %height%.
Privacy: Toggle workflow JSON embedding to share images without exposing your full graph.

Full documentation →

🎞️ Enhanced Video Combine

Converts an IMAGE batch into a high-quality video with optional AUDIO muxing and an in-node VHS-style preview.

DaSiWa Enhanced Video Combine

Codecs: Auto (AV1 → VP9 → H.264), or explicit AV1 / VP9 / H.264 / H.265(HEVC). Hardware-first encoder chain (NVENC → QSV → AMF → VAAPI → software); mandatory H.264/MP4 fallback.
Containers: Auto-selects per codec (WebM/MKV/MP4 for AV1/VP9; MP4/MKV for H.264/H.265).
Animated images: Animated AVIF (GPU AV1 or software) and Animated WebP (libwebp_anim). Looping, no audio.
Bit depth & quality: Auto-detects 8-bit vs 10-bit source precision; Auto codec forces 8-bit 4:2:0. CRF/CQ-based quality slider (default 20).
Audio muxing: Opus/AAC/MP3 selectable; Auto uses Opus (WebM) or AAC (MKV/MP4). Bitrates 64–320k. Optional crop-to-audio.
In-node preview: Framed player with native hover-reveal controls and hover-to-unmute audio. Streamed H.264 transcoding for AV1/H.265 where needed.
Frame exports: Save first/last frame as PNG alongside the video; all assets published to ComfyUI Assets.
Ping-pong mode: Forward/reverse frame loop.
Workflow metadata: Embed prompt/workflow JSON where supported.
Logging: Compact CLI output with codec/container/encoder decisions and resolved audio settings. Built-in ? help dialog.

Full documentation →

🎬 Watermark Overlay

A professional-grade watermark tool optimized for image and video batches. It uses a stable CPU compositor with high-quality resampling and precise rotation.

Dynamic Random Positioning: Toggle seeded corner cycling while keeping the selected position as the start position.
Splash Mode: Configure dynamic fade-in and fade-out at the start and end of clips for professional branding.
Optical Padding: Automatically adjusts placement by the watermark's visual center of mass for perfect alignment.
Stable Compositing: Output frames are initialized from the source batch before the watermark region is blended, avoiding flicker and black-frame artifacts.

Full documentation →

🩹 Inpaint Crop Prep & Composite

A two-node crop-inpaint-composite pair for any inpainting model. Inpaint Crop Prep tight-crops to the mask and scales it for a high-res inpainter; Inpaint Composite blends the result back onto the original image.

Crop Prep: Gaussian-blurs the mask, extracts its bounding box (with configurable grow_px padding), crops image + mask, and bicubic-scales both to target_width × target_height. Emits cropped_image, cropped_mask, and the original-space bbox_x/y/w/h so you can composite back. can_shrink (default on) allows downscaling; turn it off to keep the crop at least its native size.
Composite: pastes the inpainted source patch back at (x, y) with the (auto-rescaled) mask, applying optional Match Channels or Histogram color correction against the destination region for a seamless blend.
Pure PyTorch: separable Gaussian blur, bicubic resampling, and channel-statistics color matching with no torchvision or extra dependencies.

Wiring:

IMAGE + MASK ──► Inpaint Crop Prep ──► (cropped_image, cropped_mask)
                                     ──► any inpainter ──► source patch
IMAGE ───────────────────────────────────────────────┐
                                                     ▼
                                  Inpaint Composite (x, y, w, h from Crop Prep)
                                                     │
                                                     ▼
                                                   IMAGE

🖥️ System Monitor

A compact system telemetry bar integrated directly into the ComfyUI top toolbar. The adjacent DaSiWa settings button lets you hide the monitor or choose its display mode.

Multi-GPU Support: Separate metrics per GPU device (NVIDIA, AMD, Intel) labeled as GPU0, GPU1, etc.
Resource Metrics: CPU, RAM, SWAP/Pagefile, DISK, GPU Utilization, GPU VRAM, and GPU Temperature.
Visual Feedback: Color-coded borders and proportional background fills (0–100%) for instant at-a-glance assessment.
Lite / Full Modes: Lite is the default compact toolbar view; Full shows every available metric with detailed values and a live 60-second graph.
Responsive Layout: Lite automatically hides lower-priority metrics when toolbar space is limited; Full is a scrollable panel that adapts to narrow screens. Each Lite chip sizes to its label and value (max-content) so text never clips, at any resolution, font, or DPI.
Cross-Platform: Works on Linux and Windows with automatic fallback detection for GPU tools.
Container-safe: In containers and sandboxes where parts of /proc are missing (e.g. /proc/vmstat), probes degrade to n/a instead of warning every second. Set DASWA_SYSTEM_MONITOR=0 (also false/no/off/disable) to fully stop the backend polling thread.
Independent Placement: Renders as its own toolbar element, not dependent on third-party extensions.

Lite mode

Full mode

Full documentation →

🔀 Random String Picker

Bridge any string/text node through DaSiWa Random String Picker to randomize prompt variants inline.

Text passthrough: Accepts a connected STRING input and returns a STRING output.
Inline variants: Replaces every {A|B|C} segment with one randomly selected option.
Multiple groups: Processes any number of groups independently, such as {red|blue} car in {sun|rain}.
Literal passthrough: Text outside complete {...} groups is left unchanged.

Full documentation →

🎲 Wildcard & Preset Prompt Builder

DaSiWa Wildcard & Preset Prompt Builder builds positive and negative STRING prompts directly from the bundled dual wildcard library—no downstream picker node needed.

DaSiWa Wildcard & Preset Prompt Builder

Dual style: Switch globally between Booru and Natural Language source keys.
Compact selector: Collapsible categories expose subject checkboxes, weights, deterministic live selections, and right-aligned selected-subject counters that remain visible while a category is collapsed.
Fast inspiration: Random Select replaces the current selection with 1–10 secure-random available Preset/Wildcard subjects.
Reproducible rerolls: Seed plus the stored reroll value reproduce every {A|B|C} choice; New Picks only advances the reroll value, while New picks on every queue opts into fresh output for each queue—including Preview as Text selected-output execution.
Weighted, bounded prompts: Non-1.0 enabled subjects use ComfyUI emphasis syntax. Each positive/negative prompt independently removes complete lowest-weight subjects until it meets the token budget.
Optional prompt prefixes: Connect positive_input or negative_input to prepend an existing prompt to that generated side.
Custom library: Edit or replace data/wildcards_and_presets_dual.json with a compatible library; no checksum sidecar or pinned data version is required.

Wildcard & Preset Prompt Builder documentation →

🧠 LLM / VLM Analyze

The DaSiWa LLM / VLM nodes let you run local transformers chat or vision-language models from inside a ComfyUI workflow. They accept native STRING inputs and native IMAGE batches from nodes such as Load Image or VHS frame loaders.

Native ComfyUI Inputs: Analyze connected text, still images, or video/image-sequence frame batches.
Prompt Presets: Custom system instructions, LTX-2.3/Wan2.2 video prompt enhancement, and image/video caption presets for mixed tags, tag-only, or natural language.
Memory Modes: Keep models cached for speed, or use full cleanup to unload DaSiWa and ComfyUI managed models before/after analysis so later image/video models recover VRAM/RAM.
Frame Sampling: Limit video analysis with max frames, stride, frame strategy, resize controls, context limits, and optional KV-cache reduction.
Local, GGUF, Ollama, or HF Models: Load full Transformers folders, local GGUF through llama.cpp, call Ollama, or download a Hugging Face repo id into ComfyUI/models/llm.

Full documentation →

🛠️ Installation

Manual install

Activate your venv inside your ComfyUI folder

Clone this repo into your custom_nodes folder:

git clone https://github.com/darksidewalker/ComfyUI-DaSiWa-Nodes

Install all dependencies:
```
pip install -r requirements.txt
```
Requirement: NVIDIA RTX GPU with drivers 530+. (Windows users may need the NVIDIA Broadcast SDK; Linux usually works out-of-the-box with the pip package).
Restart ComfyUI.

Use ComfyUI-Manager

Search for DaSiWa-Nodes and install.

Credits

The RTX implementation in this collection is based on the excellent work by Deno2026/comfyui-deno-custom-nodes.
Lora-Loader is based on Brojakhoeman/Loradaddyloaderltx.
Ideas for Watermark Overlay are inspired by Artificial-Sweetener/comfyui-WhiteRabbit
MiniMax H3 Director was inspired by the LTX Director concept from whatdreamscost