Production-oriented vision-language, structured prompting, audio, and utility nodes for ComfyUI. Version 3.4 supports ComfyUI's selected NVIDIA CUDA, AMD ROCm, Apple Metal, Intel XPU, and CPU device without replacing its PyTorch build. It removes startup installers and global accelerator cache flushes, adds real image/video batches and live token streaming, and uses ComfyUI model residency and offloading.
The Modern VLM node provides one stable interface with a deliberately small, 12-choice production picker:
- Qwen 3.5 0.8B and 4B
- Qwen 3 VL 2B, 4B, and 8B Instruct
- SmolVLM2 500M and 2.2B Video
- Liquid LFM2.5-VL 450M
- InternVL 3.5 1B
- Granite Vision 4.1 4B
- Gemma 3 4B IT
- a compatible custom Hugging Face image-to-text repository
The separate [Legacy] Modern VLM Compatibility node contains redundant, superseded, experimental, and very large tiers:
- Qwen 3.5 2B, 9B, 27B, and 35B-A3B
- Qwen 3.6 27B
- Qwen 3 VL 30B-A3B Instruct
- Qwen 2.5 VL 3B and 7B for existing workflows
- Gemma 3 12B and 27B IT
- SmolVLM2 256M Video
- Liquid LFM2.5-VL 1.6B
- InternVL 3.5 2B
- Granite Vision 3.3 2B
Previously saved ModernVLM workflows remain valid even when their selected
model moved to Legacy. The server accepts every known catalog value for
backward compatibility; only the visible new-workflow picker is curated.
Dedicated Molmo, PaLI-Gemma, Qwen2-VL, MiniCPM-V, Kosmos-2, MC-LLaVA, UForm,
and script-style MoonDream nodes are also collected under
VLM Nodes/Legacy/Model Loaders. Maintained creator-facing Florence-2,
Moondream2, JoyTag, llama.cpp/GGUF, detection, segmentation, tracking, API,
and video-intelligence nodes stay in their functional categories.
Sixteen curated sub-4B/low-VRAM choices are marked internally as the small-and-fast tier. The default is Qwen 3 VL 2B: it is much quicker to load than larger checkpoints while retaining broad image and video understanding. The catalog intentionally uses official model repositories and maintained Transformers interfaces rather than unverified community quantizations. Curated models use native Transformers implementations; remote repository code is enabled only when the explicit custom-model option requires it. Florence-2 uses the Transformers-native converted checkpoints instead of Microsoft’s legacy repository code.
Modern VLM streams decoded text through ComfyUI's native progress_text
WebSocket channel by default. A connected ViewText node updates while tokens
arrive, shows the final response after execution, and restores the last result
when ComfyUI rehydrates workflow output history. Disable stream_output for
API-only or headless runs that do not need incremental UI updates. Streaming is
best-effort and never changes the final STRING output or makes inference fail.
The original SimpleText, JsonToText, and ViewText node IDs and their
first STRING outputs remain stable for saved workflows. They now live in
organized VLM Nodes/Text subcategories and expose descriptive names, search
aliases, tooltips, appended metrics, and strict error messages:
| Node | Purpose |
|---|---|
Text (SimpleText) |
Multiline/dynamic prompt source with optional edge/newline normalization and character, word, and line outputs |
View Text (Streaming) |
Read-only live output with counts, copy, UTF-8 download, line wrapping, stream following, reroute traversal, and history rehydration |
JSON to Text |
Plain or fenced JSON parsing with readable, values-only, key/value, pretty, and compact render modes |
Text Join |
Join up to eight prompt/context values with empty-value removal and stable deduplication |
Text Template |
Safe named placeholders from a JSON object plus four convenient live text sockets, with explicit missing-key policy |
Text Clean |
Unicode NFC/NFKC, newline/whitespace cleanup, enclosing Markdown-fence removal, line deduplication, and deterministic length caps |
Text Replace |
Literal or regex substitution with case, count, and missing-pattern controls |
JSON Extract |
JSONPath-lite ($.items[0]) and RFC 6901 JSON Pointer extraction from plain or fenced model responses |
Text Split / Batch |
Lines, paragraphs, delimiters, regex, CSV, or JSON arrays converted to a real mapped Comfy STRING list |
Text Inspector |
Pass-through text plus characters, UTF-8 bytes, words, lines, rough token budget, SHA-256, and JSON metadata |
The JSON utilities never evaluate code, follow references, access files, or
make network requests. Template fields are direct names rather than Python
attribute/index expressions. approx_tokens is deliberately labeled as a
rough UTF-8 budget estimate; use the target model tokenizer when exact billing
or context accounting matters.
Specialized nodes remain available where a generic chat node would discard useful model capabilities:
- Moondream 3.1 9B-A2B: official 2B-active Photon runtime with query, caption, and high-throughput image/video detection and pointing.
- Moondream 3 Preview segment: native SVG segmentation through the same
isolated Photon loader. The SVG is preserved and also converted into antialiased
MASK, black/white previews, foreground cutouts, overlays, polygons, canonicalVLM_DETECTIONS, and core bounding boxes. Detection/pointing submit frames concurrently so Photon can dynamically batch them; every run reports measured worker FPS, end-to-end FPS, and real-time factor. - Florence-2: captioning, OCR, detection, region captioning, and referring expression segmentation, with structured JSON, mask, and overlay outputs.
- PaLI-Gemma: caption/VQA plus the official 16-token VQ-VAE segmentation decoder; segmentation tokens are no longer misinterpreted as polygon points.
- Moondream2: pinned query API with explicit decoding controls. The official checkpoint is loaded through its native safetensors state dict, avoiding the silent empty-output regression in Transformers 5 while retaining ComfyUI managed loading and unloading.
- Qwen2-VL: image batches and real video-frame batches.
- Legacy Molmo, Kosmos-2, UForm, MCLLaVA, and MiniCPM-V 2.6 GGUF, plus maintained JoyTag.
- llama.cpp LLaVA/GGUF, structured prompt suggestions, OpenAI-compatible prompting, and AudioLDM2.
The vision nodes use stable, typed sockets instead of passing model-specific lists between nodes:
| Socket | JSON schema | Purpose |
|---|---|---|
VLM_DETECTIONS |
comfyui-vlm/detections, version 1 |
Per-frame boxes, labels, scores, optional polygons/quads, and in-process masks |
VLM_TRACKS |
comfyui-vlm/tracks, version 1 |
Durable object IDs with ordered observations over time |
VLM_POINTS |
comfyui-vlm/points, version 1 |
Pixel-coordinate points, including detection centers |
VLM_EVENTS |
comfyui-vlm/events, version 1 |
Ordered temporal events for downstream video analysis |
VLM_VIDEO_SELECTION |
comfyui-vlm/video-selection, version 1 |
Exact mapping from sampled images to source frame indices and timestamps |
VLM_SCENE_STATE |
comfyui-vlm/scene-state, version 1 |
Compact persistent objects, motion, visibility, and validated events |
All spatial coordinates are source-image pixels. Bounding boxes are
[x1, y1, x2, y2] with an exclusive right/bottom edge; polygons contain at
least three points and quads exactly four. JSON roots contain schema,
version, media dimensions/frame count/FPS, and their ordered records. Dense
mask tensors remain in-process and are deliberately omitted from JSON so API
results do not unexpectedly grow by hundreds of megabytes.
The utility layer converts without model-specific glue:
VLMStructuredSpatialParserstrictly parses pixel, normalized 0–1, or normalized 0–1000 JSON from any VLM intoVLM_DETECTIONSandVLM_POINTS.VLMSpatialPromptBuildercreates the matching constrained prompt.VLMDetectionsToBoundingBoxes,VLMDetectionsToPoints, andVLMDetectionsToMasksemit Comfy core boxes, center points, combined and individual binary masks, inverse masks, ready-to-preview black-and-white images, and stable-color instance maps. Polygon/quad masks are rasterized when present, otherwise the bounding box is used. Existing output indexes remain stable; the creator-facing mask images and instance map are appended.VLMFilterDetections,VLMSelectDetection,VLMCropDetections, andVLMRenderDetectionsprovide label/score/area/frame selection, padded crops, and deterministic overlays.VLMMaskProcessoraccepts any ComfyMASK, including SAM2/SAM3 masks, and returns a feathered matte, strict binary mask, inverse mask, and black-and-white image. Its grow/shrink and Gaussian feathering run in Torch without OpenCV or SciPy.VLMMaskCompositeapplies still-image or video mask batches to a source and returns the replacement composite, isolated foreground, original background-only plate, and black-and-white mask image. A single mask or background broadcasts safely across a video batch.VLMDetectionsFromJSONandVLMDetectionsToJSONare the explicit API and persistence boundary for the versioned detection schema.
The performance nodes sit before any local or hosted VLM, so their savings do not depend on CUDA, ROCm, MPS, XPU, CPU, Transformers, llama.cpp, or Photon:
VLM Performance Profileemits coherentmax_frames, pixel budget, longest-edge, batch-size, andunload_aftervalues.Live / robotics,Fast video,Balanced,High detail, andLow VRAM handoffare explicit starting points rather than hidden global flags.VLM Adaptive Frame Sampleris the existing track-aware temporal gate. It combines uniform coverage, scene changes, motion, and optional track changes while preserving source frame indices and timestamps.VLM Image Pixel Budgetdownsizes the selected analysis copy once, preserves aspect ratio, never upscales, and can align dimensions to 14/28-pixel VLM patches or 32-pixel detector backbones. Fast area and antialiased bicubic modes are available.
The recommended order is Video Slice → VLM Adaptive Frame Sampler →
VLM Image Pixel Budget → any VLM. A model's own official processor still
performs its required normalization/crop; the pixel-budget node simply prevents
every downstream model from repeatedly receiving unnecessary source pixels.
Local torch models remain registered with ComfyUI's smart model manager, while
external allocators reserve space before loading and close only the handle they
own.
On the real vlm_api_people_birds.mp4 input in this repository's D-drive test
environment, the utilities selected 10 of 60 1280×720 frames and resized them
to 938×518 in about 0.44 seconds on a cold WSL run. That reduced the
frame×pixel analysis workload by 11.38× before model inference. This is an
input-work reduction measurement, not a claim that every model runs 11.38×
faster; token generation and model-specific vision encoders still determine
end-to-end speed.
The video-intelligence layer keeps generative VLM inference out of the per-frame loop:
VLMAdaptiveFrameSamplercombines scene-change, motion, track-change, and uniform-coverage signals. It always preserves the real source frame index and timestamp, enforces a frame budget, and returns selection/diagnostic JSON.Uniform coverage, motion, scene, and track-priority modes remain available for deterministic experiments.VLMVideoTemporalReasoneris the one-node path. It adaptively samples the input, downsizes only the VLM analysis copy (448-pixel longest side by default), runs a recommended video-capable model, parses the result into validatedVLM_EVENTS, and returns summary, events, selection, sampled previews, raw response, diagnostics, event JSON, and selection JSON.VLMVideoReasoningPromptandVLMEventsFromVideoJSONexpose the same strict timestamp/evidence contract for custom local or hosted VLM workflows.VLMTrackAwareCropschooses representative observations for each durable track, adds configurable context, and letterboxes crops to one batch size. This lets a VLM label identities without rereading every full frame.VLMBuildSceneStateconverts tracks plus optional events into a compact persistent world-state summary with first/last observation, current box, confidence, state, and pixel velocity.
Small VLMs commonly return evidence as positions in the supplied image batch even when asked for source indices. The parser accepts that form only when every value is an unambiguous valid supplied-image position, maps it back to the immutable source selection, and records the normalization mode. Arbitrary or unsupplied evidence frames, out-of-range timestamps, invalid confidence, duplicate evidence, malformed JSON, and non-finite values fail validation.
On the repository's real-data smoke test (RTX 3090, Qwen3-VL 2B, 157-frame
896x448 H.264 clip), hybrid sampling selected 12 frames in 0.30 seconds,
reduced temporal inputs by 92.36%, reduced analysis pixels by 75%, used
4.24 GiB peak allocated VRAM in the standalone runner, and produced a valid
timestamped result in 35.17 seconds. The equivalent live ComfyUI /prompt
graph completed in 37.45 seconds. These are one-machine measurements, not
portable performance guarantees.
VLMOpenVocabularyDetection exposes one interface for:
- Grounding DINO Tiny and Base
- OWLv2 Base Ensemble
- OmDet Turbo Swin Tiny
It accepts a still image or an IMAGE batch of video frames and processes the
batch frame by frame. Outputs, in socket order, are detections, json,
preview, box_mask, and Comfy core bounding_boxes. Connect the FPS output
of GetVideoComponents when the input is video so every timestamp is correct.
For tracking-by-detection, run detection over the complete bounded batch and
connect it to VLMTrackDetections.
VLMTrackDetections uses a ByteTrack-style two-stage high/low-confidence
association, motion prediction, label-aware matching, and time-based expiry.
IDs are durable within the supplied sequence and survive short missed
detections when emit_predictions is enabled. Independent Comfy queue runs or
independently sliced chunks are separate tracking sessions; they do not
silently reuse IDs.
VLMSAM2VideoSegmentation propagates first-frame detections, one core
BOUNDING_BOX, or seed masks through an IMAGE batch using SAM2.1 Hiera Tiny,
Small, Base+, or Large. It returns VLM_TRACKS, report JSON, per-frame union
masks, frame-major individual object masks, and an overlay batch. The object
IDs assigned at the seed frame remain stable for that video session.
VLMSAM3TrackAdapter is intentionally an adapter, not a second SAM3 loader. It
validates ComfyUI core SAM3_TRACK_DATA, preserves the core bit-packed mask
payload unchanged, and exposes lightweight VLM_TRACKS metadata with mask
references. Connect its passthrough output to core SAM3_TrackPreview or
SAM3_TrackToMask, and connect tracks to VLMTrackReport. This avoids
duplicating dense masks in memory or JSON.
SAM3 weights use Meta's SAM License. The upstream facebook/sam3 repository
requires accepting access terms and sharing the requested account information;
the ComfyUI checkpoint is also marked sam-license. Review and accept the
license before downloading. The example names ComfyUI's
sam3.1_multiplex_fp16.safetensors; if it is unavailable, use the SAM2.1
workflow rather than substituting an unrelated checkpoint.
Florence2 exposes all 15 supported task contracts:
| Task | Extra input | Structured result |
|---|---|---|
| Caption | none | text |
| Detailed caption | none | text |
| More detailed caption | none | text |
| OCR | none | text |
| OCR with regions | none | text plus quadrilateral regions |
| Object detection | none | labeled boxes |
| Dense region caption | none | captions with boxes |
| Caption to phrase grounding | text_input |
phrase boxes |
| Referring expression segmentation | text_input |
polygons and mask |
| Region to segmentation | one BOUNDING_BOX per image |
polygons and mask |
| Open vocabulary detection | text_input |
model-provided spatial records |
| Region to category | one BOUNDING_BOX per image |
text |
| Region to description | one BOUNDING_BOX per image |
text |
| Region to OCR | one BOUNDING_BOX per image |
text |
| Region proposals | none | boxes |
Every task returns text, structured_json, mask, and visualization.
Tasks that do not produce a spatial result return an empty mask and the source
image visualization. Region tasks reject ambiguous multi-box input; use
VLMSelectDetection to isolate the record, then supply exactly one core
BOUNDING_BOX with the same pixel coordinates.
- Trim long media with core
Video Slice, then useGetVideoComponents. Downscale the complete frame batch before detection or segmentation and keep every frame at identical dimensions. - Grounding detection supports configurable micro-batches; keep
batch_size=1for minimum VRAM or increase it when memory allows. It returns both nested per-frame coreBOUNDING_BOXvalues and flat metadata-richBOUNDING_BOXES. - SAM2.1 stores source video frames on CPU, keeps its inference state on CPU by
default, and limits the vision-feature cache to one frame. Union masks and
previews return on CPU. Full per-object mask volumes are opt-in with
mask_output=union_and_objects; disablerender_previewto avoid another full-resolution overlay copy on long clips. - Start with Grounding DINO Tiny plus SAM2.1 Hiera Tiny. Increase detector or
segmenter size only after the pipeline is correct.
unload_after=falsecaches one model per node instance; usetruewhen another large model must run immediately afterward. - A
Video Sliceis an independent propagation session. For very long media, use bounded slices, reseed each slice, and keep the overlap/output mapping in the caller. The pack does not pretend IDs are globally stable across separate queues. - The SAM3 adapter never unpacks the complete mask volume for its report. Use
core
SAM3_TrackToMaskonly when a dense selected mask is actually needed.
API-format examples are in examples/vision:
grounding_dino_image_api.jsonmoondream3_preview_svg_segment_api.jsonmoondream31_video_detect_api.jsonsam2_video_tracking_api.jsonsam3_core_adapter_blueprint_api.jsonvideo_temporal_reasoning_api.jsonvlm_performance_preflight_api.json
The dependency-free text-toolkit example is
examples/text_toolkit_api.json.
Robotics policy, safety, and sidecar examples are in
examples/robotics, including a complete universal
HTTP policy graph.
Upload the named media to ComfyUI's input directory, adjust the filenames and
labels, then submit the JSON object as the prompt value to /prompt. These
are API graphs, not frontend workflow-export JSON.
All 89 registered nodes, grouped by their menu category. The Node ID is the
class_type written into workflow and API JSON — search for that string when
you need to find a node you saw on a canvas.
The main entry point for current vision-language models.
| Node | Node ID | Outputs |
|---|---|---|
| Modern VLM (Qwen / SmolVLM2 / LFM / InternVL / Granite / Gemma) | ModernVLM |
STRING |
| Moondream 2 | Moondream2model |
STRING |
Moondream 3 / 3.1 in an isolated Photon runtime. Load once, then reuse the
MOONDREAM31_MODEL output across the task nodes.
| Node | Node ID | Outputs |
|---|---|---|
| Moondream 3 / 3.1 Loader (Isolated Photon) | Moondream31Loader |
MOONDREAM31_MODEL, STRING |
| Moondream 3 / 3.1 Caption | Moondream31Caption |
STRING, STRING |
| Moondream 3 / 3.1 Query | Moondream31Query |
STRING, STRING, STRING |
| Moondream 3 / 3.1 Detect (Image / Video) | Moondream31Detect |
VLM_DETECTIONS, STRING, IMAGE, MASK, BOUNDING_BOX, BOUNDING_BOXES, STRING |
| Moondream 3 / 3.1 Point (Image / Video) | Moondream31Point |
VLM_POINTS, STRING, IMAGE, STRING |
| Moondream 3 Preview SVG Segment (Image / Video) | Moondream31Segment |
VLM_DETECTIONS, STRING, STRING, MASK, IMAGE, IMAGE, IMAGE, BOUNDING_BOX, BOUNDING_BOXES, STRING |
| Node | Node ID | Outputs |
|---|---|---|
| Florence-2 Multitask Vision | Florence2 |
STRING, STRING, MASK, IMAGE |
Open-vocabulary detection and video segmentation. These emit the structured
VLM_DETECTIONS / VLM_POINTS / VLM_TRACKS types rather than loose strings.
| Node | Node ID | Outputs |
|---|---|---|
| VLM Open-Vocabulary Detection | VLMOpenVocabularyDetection |
VLM_DETECTIONS, STRING, IMAGE, MASK, BOUNDING_BOX, BOUNDING_BOXES |
| VLM SAM2.1 Video Segmentation | VLMSAM2VideoSegmentation |
VLM_TRACKS, STRING, MASK, MASK, IMAGE |
| VLM SAM3 Track Adapter | VLMSAM3TrackAdapter |
VLM_TRACKS, SAM3_TRACK_DATA |
| VLM Track Detections | VLMTrackDetections |
VLM_TRACKS |
| VLM Track Report | VLMTrackReport |
STRING, STRING |
| JoyTag | Joytag |
STRING |
| Node | Node ID | Outputs |
|---|---|---|
| VLM Spatial Prompt Builder | VLMSpatialPromptBuilder |
STRING |
| VLM Structured Spatial Parser | VLMStructuredSpatialParser |
VLM_DETECTIONS, VLM_POINTS, STRING |
Converters and filters between structured detections and ordinary Comfy types.
| Node | Node ID | Outputs |
|---|---|---|
| Filter VLM Detections | VLMFilterDetections |
VLM_DETECTIONS |
| Select VLM Detection | VLMSelectDetection |
VLM_DETECTIONS |
| Crop VLM Detections | VLMCropDetections |
IMAGE, STRING |
| Render VLM Detections | VLMRenderDetections |
IMAGE |
| VLM Detection Centers | VLMDetectionsToPoints |
VLM_POINTS, STRING |
| VLM Detections from JSON | VLMDetectionsFromJSON |
VLM_DETECTIONS |
| VLM Detections to JSON | VLMDetectionsToJSON |
STRING |
| VLM Detections to Bounding Boxes | VLMDetectionsToBoundingBoxes |
BOUNDING_BOXES, STRING |
| VLM Detections to Masks | VLMDetectionsToMasks |
MASK, MASK, STRING, MASK, IMAGE, IMAGE, IMAGE |
| Node | Node ID | Outputs |
|---|---|---|
| VLM Mask Processor | VLMMaskProcessor |
MASK, MASK, MASK, IMAGE |
| VLM Mask Composite | VLMMaskComposite |
IMAGE, IMAGE, IMAGE, IMAGE |
Adaptive frame selection and temporal reasoning for long videos.
| Node | Node ID | Outputs |
|---|---|---|
| VLM Adaptive Frame Sampler | VLMAdaptiveFrameSampler |
IMAGE, VLM_VIDEO_SELECTION, STRING, STRING |
| VLM Video Reasoning Prompt | VLMVideoReasoningPrompt |
STRING, STRING |
| VLM Video Temporal Reasoner | VLMVideoTemporalReasoner |
STRING, VLM_EVENTS, VLM_VIDEO_SELECTION, IMAGE, STRING, STRING, STRING, STRING |
| VLM Temporal Events From JSON | VLMEventsFromVideoJSON |
VLM_EVENTS, STRING, STRING |
| VLM Persistent Scene State | VLMBuildSceneState |
VLM_SCENE_STATE, STRING, STRING |
| VLM Track-Aware Semantic Crops | VLMTrackAwareCrops |
IMAGE, STRING |
llama.cpp text models. LLM Loader (GGUF) produces the CUSTOM model handle
the samplers consume; the Managed Cache variants own their own handle and can
release it after each run.
| Node | Node ID | Outputs |
|---|---|---|
| LLM Loader (GGUF) | LLMLoader |
CUSTOM |
| LLM Sampler | LLMSampler |
STRING |
| LLM Prompt Generator | LLMPromptGenerator |
STRING |
| LLM (Managed Cache) | LLMOptionalMemoryFreeSimple |
STRING |
| LLM (Managed Cache, Advanced) | LLMOptionalMemoryFreeAdvanced |
STRING |
| Structured Output | StructuredOutput |
STRING |
| Structured Keyword Extraction | KeywordExtraction |
STRING |
| Structured Prompt Generator | LLavaPromptGenerator |
STRING |
| Creative Art Prompt Generator | CreativeArtPromptGenerator |
STRING |
| Prompt Suggester | Suggester |
STRING |
Vision models through llama.cpp. These need both a GGUF and its vision projector (mmproj).
| Node | Node ID | Outputs |
|---|---|---|
| LLaVA Loader | LLava Loader Simple |
CUSTOM |
| LLaVA Vision Projector Loader | LlavaClipLoader |
CUSTOM |
| LLaVA Sampler | LLavaSamplerSimple |
STRING |
| LLaVA Sampler (Advanced) | LLavaSamplerAdvanced |
STRING |
| LLaVA (Managed Cache) | LLavaOptionalMemoryFreeSimple |
STRING |
| LLaVA (Managed Cache, Advanced) | LLavaOptionalMemoryFreeAdvanced |
STRING |
| Node | Node ID | Outputs |
|---|---|---|
| Hosted VLM API (Secure) | HostedVLMAPI |
STRING, STRING, INT |
| Hosted LLM API (Secure) | PromptGenerateAPI |
STRING |
These nodes build and inspect policy observations/actions. They never send commands to robot hardware. Heavy policy runtimes stay in isolated LeRobot, openpi, GR00T, OpenVLA/OFT, or JAX environments.
| Node | Node ID | Outputs |
|---|---|---|
| VLA Embodiment Profile | VLAEmbodimentProfile |
VLA_EMBODIMENT, STRING, INT, INT |
| VLA Observation Builder | VLAObservationBuilder |
VLA_OBSERVATION, STRING, INT |
| VLA Policy — Universal HTTP | VLAHTTPPolicy |
VLA_ACTIONS, STRING |
| VLA Policy — OpenPI WebSocket | VLAOpenPIWebSocketPolicy |
VLA_ACTIONS, STRING |
| VLA Policy — GR00T N1.7 ZMQ | VLAGr00tZMQPolicy |
VLA_ACTIONS, STRING |
| VLA Action Safety Gate | VLAActionSafety |
VLA_ACTIONS, STRING, BOOLEAN |
| VLA Actions From JSON | VLAActionsFromJSON |
VLA_ACTIONS, STRING |
| VLA Action Chunk Replan | VLAActionChunkReplan |
VLA_ACTIONS, STRING |
| VLA Action Inspect | VLAActionInspect |
STRING, STRING, INT, INT |
| VLA Trajectory Preview | VLATrajectoryPreview |
IMAGE |
| VLA Model Catalog | VLAModelCatalog |
STRING, STRING, STRING, STRING |
Dependency-free string handling, so a VLM response can be shaped without an extra node pack.
| Node | Node ID | Outputs |
|---|---|---|
| Text | SimpleText |
STRING, INT, INT, INT |
| Text Join | VLMTextJoin |
STRING, STRING, INT |
| Text Template | VLMTextTemplate |
STRING, STRING, STRING |
| Text Clean | VLMTextClean |
STRING, STRING |
| Text Replace | VLMTextReplace |
STRING, INT, STRING |
| Text Split / Batch | VLMTextSplit |
STRING, STRING, INT |
| Text Inspector | VLMTextInspect |
STRING, INT, INT, INT, INT, INT, STRING, STRING |
| View Text (Streaming) | ViewText |
STRING, INT, INT, INT, STRING |
| JSON Extract | VLMJSONExtract |
STRING, BOOLEAN, STRING, STRING |
| JSON to Text | JsonToText |
STRING, STRING, INT |
Run VLM Runtime Diagnostics before reporting a bug — it reports your device, backend, and which optional packages are installed.
| Node | Node ID | Outputs |
|---|---|---|
| VLM Runtime Diagnostics | VLMRuntimeDiagnostics |
STRING |
| VLM Performance Profile | VLMPerformanceProfile |
INT, FLOAT, INT, INT, BOOLEAN, STRING |
| VLM Image Pixel Budget | VLMImagePixelBudget |
IMAGE, INT, INT, STRING |
| Node | Node ID | Outputs |
|---|---|---|
| AudioLDM2 | AudioLDM2Node |
*, INT, AUDIO |
| Chat Musician | ChatMusician |
STRING, *, INT, AUDIO |
| MiniMax Music | MiniMaxMusicNode |
*, INT, AUDIO |
| PlayMusic Node | PlayMusic |
* |
| Save Audio | SaveAudioNode |
— |
MiniMax Music reads MINIMAX_API_KEY only from the ComfyUI server
environment. It uses fixed global_en and cn_zh endpoints, supports music
generation and cover models, decodes URL or hexadecimal responses, and emits
MP3, WAV, or PCM results through the existing waveform and AUDIO sockets.
The aigc_watermark field is sent only for cn_zh requests. See the official
global or
China
music API reference for account and content requirements.
Kept for existing workflows. New graphs should prefer Modern VLM, which covers most of these architectures through one interface.
| Node | Node ID | Outputs |
|---|---|---|
| Qwen2-VL | Qwen2VLNode |
STRING |
| MiniCPM-V 2.6 (GGUF) | MiniCPMNode |
STRING |
| Molmo Vision-Language Model | MolmoNode |
STRING |
| PaLI-Gemma (Official Segmentation) | Paligemma |
STRING, MASK, IMAGE |
| Kosmos-2 | Kosmos2model |
STRING |
| MC-LLaVA | MCLLaVAModel |
STRING |
| UForm Gen2 Qwen | UformGen2QwenNode |
STRING |
| MoonDream (Moondream 2) | MoonDream |
STRING |
| [Legacy] Modern VLM Compatibility | LegacyModernVLM |
STRING |
Install through ComfyUI Manager, or clone into ComfyUI/custom_nodes and run:
python -m pip install -r ComfyUI/custom_nodes/ComfyUI_VLM_nodes/requirements.txtRun that command with ComfyUI's Python. Do not install or replace torch from
this repository: ComfyUI's own installer selects CUDA, ROCm, XPU, Metal, or CPU.
Current official bitsandbytes wheels are installed automatically only on their
supported OS/architecture combinations. Unsupported machines retain all
non-quantized nodes.
The robotics nodes keep policy dependencies outside ComfyUI. The universal HTTP client works without another package. Native openpi WebSocket and GR00T ZeroMQ clients use the lightweight optional extra:
python -m pip install \
-r ComfyUI/custom_nodes/ComfyUI_VLM_nodes/requirements-robotics-client.txtVLA Model Catalog covers current SmolVLA, X-VLA, π0/π0-FAST/π0.5,
GR00T N1.7, WALL-OSS, MolmoAct2, VLA-JEPA, LingBot-VA, FastWAM, EO-1,
EVO-1, OpenVLA-OFT, and Octo routes. “Available” means a supported isolated
runtime/checkpoint path; base and architecture-only entries still require
embodiment-specific training and transforms.
Start with SmolVLA for small consumer hardware. The included authenticated
LeRobot sidecar loads one chosen policy, uses its serialized processors,
returns action chunks over bounded JSON/JPEG, keeps it resident for speed,
and can offload it to CPU after an idle timeout. Remote policy URLs require
encrypted transport and explicit opt-in. Tokens are fixed environment
variables (VLA_POLICY_TOKEN, OPENPI_API_KEY, or GROOT_API_TOKEN) and are
never workflow inputs.
See examples/robotics/README.md for D-drive
WSL setup, platform boundaries, current model readiness, observation schemas,
action safety semantics, and the runnable API example.
Moondream's official Photon package pins Pillow below version 11 while
current ComfyUI uses a newer Pillow. It therefore runs in a dedicated sidecar
environment and never changes ComfyUI's Python packages. Read and accept the
Moondream Model License 1.0, then
create the environment under the registered LLavacheckpoints model folder.
Linux/WSL/macOS:
runtime="ComfyUI/models/LLavacheckpoints/moondream31-runtime"
uv venv "$runtime/.venv" --python 3.12
uv pip install --python "$runtime/.venv/bin/python" \
-r ComfyUI/custom_nodes/ComfyUI_VLM_nodes/requirements-moondream31.txtWindows PowerShell:
$runtime = "ComfyUI\models\LLavacheckpoints\moondream31-runtime"
uv venv "$runtime\.venv" --python 3.12
uv pip install --python "$runtime\.venv\Scripts\python.exe" `
-r "ComfyUI\custom_nodes\ComfyUI_VLM_nodes\requirements-moondream31.txt"The first Loader execution downloads the selected official model below that
runtime's cache directory. Use moondream3.1-9B-A2B for query, caption,
detection, and pointing. Use moondream3-preview only for the SVG segment
skill; the final 3.1 model card does not list segment. Set the server-side
MOONDREAM_PYTHON environment variable
when using a different isolated environment. Do not put this path or any
credential in a workflow.
Official Photon local inference currently supports NVIDIA Ampere-or-newer on Linux/Windows and Apple Silicon on macOS 13 or newer. It does not currently provide local ROCm, Intel GPU, or CPU execution. Those platforms retain every portable Transformers, GGUF, API, and vision utility node in this pack.
On CUDA 12 x86-64 systems the isolated requirements deliberately install
nvidia-cuda-runtime-cu12==12.9.79. Kestrel 0.4.6's AOT kernels require the
cudaLibraryLoadData entry point, which is absent from the CUDA 12.6 runtime
bundled by cu126 PyTorch. This pin updates only Photon's private runtime; it
does not replace ComfyUI's PyTorch build or the host NVIDIA driver.
GGUF nodes use optional llama-cpp-python. Install a wheel built for the
desired CUDA, ROCm/HIP, Metal, Vulkan, SYCL, or CPU backend:
python -m pip install -r ComfyUI/custom_nodes/ComfyUI_VLM_nodes/requirements-llama-cpp.txtSee COMPATIBILITY.md for the tested matrix and official backend-specific GGUF commands.
The GGUF loaders now query the installed llama.cpp build instead of inferring
its capabilities from PyTorch. Accelerator offload automatically falls back to
CPU when a CPU-only wheel is installed. Advanced optional inputs expose logical
and physical prompt batching (n_batch/n_ubatch), flash-attention policy,
mmap, and CUDA/ROCm multi-GPU layer/row splitting without changing legacy
workflow sockets. Auto flash attention retries the portable path if a
backend/model pair rejects it.
The LLaVA Vision Projector Loader supports metadata-driven MTMD plus explicit handlers for LLaVA 1.5/1.6, MiniCPM-V 2.6, Moondream2, NanoLLaVA, Qwen2.5-VL, Gemma 4, Llama 3 Vision Alpha, and Obsidian. Use the default metadata-driven handler for current GGUF + mmproj pairs; select the named legacy handler when a model card requires it.
Models are downloaded only when their node first executes and are stored below
ComfyUI/models/LLavacheckpoints. Hugging Face downloads respect HF_TOKEN.
Gemma 3 and PaLI-Gemma require accepting their model licenses on Hugging Face.
- ComfyUI managed (BF16) is the default and preferred path. BF16 is used only when the active device reports support; otherwise the node safely falls back to FP16 on CUDA/ROCm/Metal/XPU or FP32 on CPU.
- 4-bit/8-bit models and llama.cpp own external allocators. Before loading, the nodes ask ComfyUI to free the required space; unloading closes the exact owned model and then requests a soft cache cleanup. Small quantized models stay on ComfyUI's active device instead of assuming GPU zero. Large-model Accelerate placement is enabled on CUDA/ROCm/XPU; any disk offload remains inside the model's ComfyUI directory.
- llama.cpp model and projector bytes are included in the pre-load reservation. The runtime reports llama.cpp's own compiled backend, GPU-offload, mmap, and mlock capabilities in VLM Runtime Diagnostics.
unload_after=falsecaches one model per node instance for fast repeated queues. Cache creation is serialized, so concurrent API work cannot make the same node allocate duplicate model handles. Turn it on for maximum reclamation between prompts.- Moondream Photon asks ComfyUI to make room before it starts, then owns one
exact isolated process.
unload_after=truegracefully shuts it down and terminates that process if necessary, which releases Photon model, KV-cache, and CUDA-graph allocations without flushing unrelated ComfyUI models. The sidecar intentionally does not inherit ComfyUI's PyTorch allocator override; Photon's CUDA-graph capture uses the native allocator in its own process. The worker does not inherit unrelated provider keys or proxy credentials; onlyHF_TOKEN, andMOONDREAM_API_KEYfor an explicitly selected adapter, may cross into its server-side environment. Base-model sidecars honorDO_NOT_TRACKlocally and do not start Kestrel's anonymous telemetry task. Its random IPC secret is not placed on the process command line. - A connected
video_framesbatch becomes the primary visual input. The optional still-image socket is ignored for video inference so smaller models cannot silently answer from the wrong media. - Qwen 3.5/3.6 thinking is off by default for lower latency and predictable output length; enable it explicitly for tasks that benefit from visual reasoning.
- Auto (SDPA) is portable and preferred. Flash Attention 2 is accepted only on supported CUDA/ROCm builds and otherwise fails before model loading.
- VLM Runtime Diagnostics produces a zero-download JSON report containing OS, Python, PyTorch, backend, dtype capability, and optional package versions.
- Visualization-only companion repositories do not allocate accelerator memory.
Avoid placing several independently quantized VLMs in one workflow unless the GPU can hold them. On a 24 GB card, Qwen 3 VL 2B is the fast default, Qwen 3 VL 8B fits in BF16, and larger models should use NF4. Qwen 3.5/3.6 can be substantially slower when their optional optimized linear-attention kernels are not available for the installed PyTorch/backend combination.
Hosted LLM API (Secure) and Hosted VLM API (Secure) share a provider
layer built around the current OpenAI Responses and Chat Completions request
shapes, with Anthropic using its native Messages/vision contract and Gemini
switching to its native multimodal contract for grounded or structured calls.
The VLM node
accepts a still image or a video-frame batch, samples
frames uniformly, resizes and JPEG-compresses them, and enforces per-image and
total request limits before upload. Both nodes can stream text into a connected
ViewText node.
Both API nodes also expose:
- Native web search for OpenAI, Gemini, Anthropic, xAI, and any compatible model routed through OpenRouter. Unsupported presets fail clearly before a model request instead of silently pretending to search. Search can add provider cost and has provider-specific data terms, so it is off by default.
- JSON object and JSON Schema output. Completed JSON is always parsed locally, JSON Schema results are validated locally, and invalid results fail the node instead of flowing into downstream automation.
- Open-source structured VLM output through Custom / Local endpoints.
OpenAI-standard mode supports vLLM, Ollama, and compatible servers;
llama.cpp JSON Schemaemits llama.cpp's direct schema dialect; andJSON object + local validationis a portable fallback for servers that implement only JSON mode.
User-provided schemas are capped at 64,000 characters, bounded by depth/node
count, checked against their declared JSON Schema draft, and may use only local
fragment $ref values. Remote/file references are rejected so validation can
never turn into an unexpected network or filesystem lookup.
Curated production profiles include:
| Provider | Presets | Server environment variable |
|---|---|---|
| OpenAI | GPT-5.6 Terra, Sol, Luna | OPENAI_API_KEY |
| Gemini 3.6 Flash, 3.5 Flash, 3.5 Flash-Lite | GEMINI_API_KEY |
|
| Anthropic | Claude Fable 5, Opus 5, Sonnet 5, Haiku 4.5 | ANTHROPIC_API_KEY |
| xAI | Grok 4.5 | XAI_API_KEY |
| DeepSeek | V4 Flash, V4 Pro | DEEPSEEK_API_KEY |
| Groq | Qwen 3.6 27B Vision, GPT-OSS 20B | GROQ_API_KEY |
| Mistral | Mistral Large, Mistral Small, Ministral 14B | MISTRAL_API_KEY |
| Together AI | Kimi K2.5, Qwen 3.5 9B | TOGETHER_API_KEY |
| OpenRouter | Any compatible model ID | OPENROUTER_API_KEY |
| Custom/local | OpenAI-compatible endpoint | CUSTOM_API_KEY |
Preset IDs were reviewed on 2026-07-29 against the official
OpenAI,
Gemini,
Claude,
xAI,
DeepSeek,
Groq,
Mistral, and
Together, plus
OpenRouter's multimodal compatibility
catalogs. Use model_override when a provider exposes a newer compatible model
before the next node-pack release.
The capability routing follows the current official OpenAI web-search and structured-output contracts, Gemini grounding and structured output, Claude web-search and structured-output contracts, xAI web search and structured outputs, and OpenRouter server-side search. The local dialect is based on the llama.cpp server API.
API keys are not node inputs. A workflow contains only the provider selection,
and the server resolves that provider's fixed environment variable at execution
time. Built-in credentials are pinned to the provider's official HTTPS host;
only the custom profile accepts a URL, and it can read only CUSTOM_API_KEY.
Remote custom URLs require HTTPS, while keyless HTTP is restricted to
localhost/loopback. Redirect following and environment proxies are disabled
by default, API calls are stateless, OpenAI Responses explicitly use
store=false, and provider exceptions are redacted before ComfyUI receives
them.
Web search sends the prompt (and, where supported, the same multimodal request) to the selected provider's server-side search system. Do not enable it for content that must not be processed under that provider's search terms.
Opening an older PromptGenerateAPI workflow automatically clears its former
plaintext key widget before the graph is configured. Save the migrated workflow
to overwrite the old file, and rotate any key that was previously saved or
shared. See SECURITY.md for setup and the exact threat model.
- Importing the pack performs no network access, compilation, or package install.
- Missing optional backends fail only the node that needs them, with an actionable error.
- Image inputs use ComfyUI
BHWCbatches; text responses preserve every batch item. Florence/PaLI masks useBHW. forceInputstring hacks were removed, preventing frontend widget-index drift.- Downloads stay inside the configured ComfyUI model directory.
- CI installs and imports the full pack on Linux Python 3.10/3.13, Windows Python 3.12, and macOS Python 3.12. Backend contracts for CUDA, ROCm, Metal, XPU, and CPU are exercised without pretending hosted CPU runners are GPUs.
Run local checks with:
PYTHONPATH=/path/to:/path/to/ComfyUI python -m pytest -qReal-weight checks are opt-in because they download multi-gigabyte checkpoints:
python tests/manual_model_smoke.py --model "Qwen 3 VL 4B Instruct"
python tests/manual_specialized_smoke.py --backend florence-large
python tests/manual_llama_cpp_smoke.py --downloadSee MODEL_VALIDATION.md for the exact real-weight and catalog-only evidence matrix.
Please report reproducible bugs at the issue tracker.
Cite this project
If ComfyUI VLM Nodes supports your work, please cite the software. GitHub also provides ready-to-copy APA and BibTeX entries via Cite this repository.
@software{Aydogan_ComfyUI_VLM_Nodes_2026,
author = {Aydoğan, Gökay},
title = {ComfyUI VLM Nodes},
version = {3.5.0},
year = {2026},
url = {https://github.com/gokayfem/ComfyUI_VLM_nodes}
}
