Model validation

Validated on 2026-07-29 with ComfyUI 0.28.0, Python 3.12, Transformers 5.14.1, PyTorch 2.13.0+cu126, and an RTX 3090 24 GB. All models and caches were stored on the D drive and executed through WSL.

Real-weight passes

Family	Representative result	Peak CUDA
Qwen 3.5	0.8B BF16 image/video; 0.8B NF4; 2B, 4B, and 9B images	0.82–17.62 GiB
Qwen 3 VL	2B, 4B, and 8B images returned the correct red object	3.99–16.37 GiB
SmolVLM2	500M image/video and 2.2B video returned the correct object	2.29–5.41 GiB
LFM2.5 VL	450M returned “red … rectangle”	0.88 GiB
InternVL 3.5	1B video returned “green rectangle” after the 448px patch-grid fix	2.14 GiB
Granite Vision 4.1	4B returned “solid red square” through native Transformers code	7.61 GiB
Florence-2	Native converted base-FT returned and parsed a bright-red-square caption	0.59 GiB
llama.cpp GGUF	Official Qwen3.5-0.8B Q4_0 with llama-cpp-python 0.3.34 CUDA loaded in 20.015s and generated the exact requested response in 0.654s	< 1 GiB model weights

One checkpoint covers sibling sizes that use the same architecture and loader. The node does not download every size simply to repeat the same integration test.

Robotics VLA pass

Validated on 2026-07-31 through the included isolated LeRobot HTTP policy server, entirely from WSL and D-drive storage:

Runtime: Python 3.12.12, LeRobot 0.6.1 from current upstream source, PyTorch 2.11.0+cu128, and an NVIDIA RTX 3090.
Checkpoint: lerobot/smolvla_base (about 2.5 GiB of D-drive cache), backed by HuggingFaceTB/SmolVLM2-500M-Video-Instruct.
Real input: local image (23).png, a 256x256 outdoor photograph, repeated across the checkpoint's three declared camera keys with a six-value state vector and the task “Move the end effector toward the backpack and prepare to grasp it.”
Contract: three camera tensors, observation.state, the LeRobot preprocessor, predict_action_chunk, the checkpoint postprocessor, bounded JSON/JPEG transport, action parsing, and the ComfyUI safety layer all ran. The native checkpoint advertises a 50-step chunk; the server returned four steps of six actions for this test.
Five warm requests after one discarded warm-up measured 241.374 ms mean server inference (242.957 ms median, 234.726–249.980 ms range) and 270.404 ms mean HTTP client time (271.483 ms median, 261.477–282.218 ms range).

The final raw action chunk was:

[
  [0.06258623, -0.11250310, -0.13713294, -0.06168950, -0.00926633, -0.08506130],
  [0.15420279, -0.05678255, -0.20159233, 0.06734322, -0.00563951, -0.09575561],
  [0.16482556, -0.07453565, -0.17410603, 0.02461835, -0.00256573, 0.15842065],
  [0.27048433, -0.09272483, -0.19934477, 0.05491992, 0.05286619, 0.07594281]
]

Applying the SO-100/SO-101 template limits from an all-zero previous action found five per-step delta violations, no bounds violations, and no non-finite values. Clamp safely produced:

[
  [0.06258623, -0.1, -0.1, -0.06168950, -0.00926633, -0.08506130],
  [0.15420279, -0.05678255, -0.2, 0.03831051, -0.00563951, -0.09575561],
  [0.16482556, -0.07453565, -0.17410603, 0.02461835, -0.00256573, 0.05424440],
  [0.26482555, -0.09272483, -0.19934477, 0.05491992, 0.05286619, 0.07594281]
]

This is an end-to-end loading, preprocessing, inference, transport, parsing, and safety-contract pass. It is not evidence that a base SmolVLA checkpoint can control an SO-100 from an arbitrary Internet-style photograph. Actual robot deployment still requires embodiment-matched fine-tuning, calibrated state and camera inputs, hardware-certified limits, a deadman/watchdog, collision handling, and an external emergency stop.

The same real checkpoint was then exercised through ComfyUI's actual local POST /prompt API, not by calling the Python node directly. The graph loaded and center-cropped the real image to 256x256, constructed the three-camera checkpoint contract, called the isolated GPU policy, applied the SO-100/SO-101 template safety gate, rendered a 960x480 trajectory preview, and emitted all three text reports. Final prompt 86b31f5a-5a8c-4abc-abdb-634e46da5c93 completed successfully: the policy returned [4, 6] actions, the safety gate found three rate violations and clamped them, safe_for_handoff was true under the declared template, and ComfyUI wrote preview ComfyUI_temp_icynu_00001_.png. The reusable acceptance harness is tests/manual_robotics_smoke.py.

ComfyUI API pass

ComfyUI started from the D-drive WSL installation with all four repaired custom node repositories enabled and no custom-node import failures. A real local API workflow (EmptyImage -> ModernVLM -> ViewText) ran the cached LFM2.5-VL 450M checkpoint on a solid red input, returned Red., and completed with unload_after=true. Prompt ID: 919f92cd-ecb2-487b-abf0-19f5e4d88229.

A second real local API workflow (LLMLoader -> LLMSampler -> ViewText) used the official 563 MB ggml-org/Qwen3.5-0.8B-GGUF Q4_0 checkpoint with full GPU offload, n_batch=256, n_ubatch=128, mmap, and Auto flash attention. It returned exactly ComfyUI llama API ready and completed successfully. Prompt ID: eed8458d-de7f-47ac-8ebf-e48e4dacc2d6.

The installed llama.cpp CUDA 12.4 wheel reported GPU offload, mmap, and mlock support directly. CPU-only fallback, Metal/Vulkan/SYCL/ROCm-independent capability detection, multi-GPU options, and flash-attention retry are covered by simulated backend contract tests; those vendor kernels were not claimed as real hardware passes on the NVIDIA test machine.

Catalog validation

Configuration and processor resolution passed for all 15 ungated entries in the small/fast catalog: Qwen 3.5 0.8B/2B/4B, Qwen 3 VL 2B/4B, Qwen 2.5 VL 3B, SmolVLM2 256M/500M/2.2B, LFM2.5 VL 450M/1.6B, InternVL 3.5 1B/2B, and Granite Vision 3.3 2B/4.1 4B. Gemma 3 4B is the sixteenth entry and correctly requires license acceptance plus HF_TOKEN.

Structured vision validation

The versioned detection/track/point/event payloads, geometry and mask conversion, strict spatial parser, Grounding-family adapters, SAM2.1 session plumbing, SAM3 bit-packed payload adapter, and ByteTrack-style association pass the local WSL contract suite. Those tests validate schemas, shapes, output ordering, bounds, timestamps, deterministic IDs, and error handling.

Representative real-weight checks were then submitted through ComfyUI's local POST /prompt API and verified from /history/{prompt_id}. The test machine used ComfyUI 0.28.0, Python 3.12.12, PyTorch 2.13.0+cu126, Transformers 5.14.1, and an NVIDIA RTX 3090. Input media, checkpoints, model caches, ComfyUI, and this checkout all remained on the D drive under WSL.

Family	Representative checkpoint policy	Real-weight status
Grounding DINO	Tiny; Base uses the same loader/processor contract	Passed: FP16, four real 640x360 video frames in two-frame micro-batches; person and bird boxes/labels were visually checked, serialized, timestamped, and in bounds
OWLv2	Base Ensemble	Pending
OmDet Turbo	Swin Tiny	Pending
SAM2.1 video	Hiera Tiny; sibling sizes use the same session adapter	Passed: FP16, real 12-frame 640x360 clip at 24 FPS with CPU preprocessing/state. Grounding's core `BOUNDING_BOX` output connected directly: the forward union-only run kept one person ID on frames 0-11; a last-frame reverse run kept two IDs for 24 observations and emitted 24 frame-major object masks. All geometry was in bounds and first/last overlays and masks were visually checked
Comfy core SAM3.1	`sam3.1_multiplex_fp16.safetensors`, only after license/access is available	Pending
SAM3 adapter/report	Synthetic core payload contract	Passed without weights; real core handoff pending
ByteTrack-style tracker	Deterministic synthetic crossing, missed-frame, and expiry cases	Passed; no model weights exist
Florence-2 multitask	Base FT; Large uses the same native Transformers contract	Passed: real object-detection API run produced bounded woman, face, and clothing boxes plus a visually checked overlay

The SAM2 API check initially exposed a real session-lifecycle defect that unit fixtures did not: prompt insertion must be followed by inference on the seeded frame before propagation. The implementation now performs that seed pass and also propagates in reverse when seed_frame is greater than zero. Later live checks exercised nested multi-object core boxes, CPU preprocessing/state, union-only low-memory output, optional object-mask output, disabled preview rendering, reverse propagation, and unload_after=true for both models. The final unload run returned total reported GPU memory use to within 4 MiB of the pre-run nvidia-smi baseline.

Grounding DINO and SAM2 sibling sizes are catalog-available but were not downloaded or executed. OWLv2, OmDet Turbo, and gated SAM3 remain explicitly unverified; the UI never presents them as locally tested simply because their schemas import.

The acceptance run for each model family must record:

Exact checkpoint revision, ComfyUI/Python/PyTorch/Transformers versions, device, dtype, peak accelerator allocation, and wall time.
A real image or short bounded video with manually verified boxes, labels, masks, timestamps, and stable IDs.
The canonical JSON schema/version and every advertised output socket, including preview/report output through ComfyUI's local /prompt API.
A second queue using the cached model, followed by an unload_after=true run where that option exists.
Failure behavior for an absent checkpoint or gated access without exposing a token.

One checkpoint per distinct implementation family is enough for sibling model sizes that share the same code path. Validation prioritizes the smallest useful checkpoint and will not download or execute a 30B model. A larger variant is tested only when it has a different loader, processor, postprocessor, or quantization path.

Not marked passed

Qwen 3 VL 30B-A3B: weights are available locally, but inference validation was stopped at the user's request and will not be repeated.
Moondream2 2025-06-21: its pinned remote wrapper needed Transformers 5 loading metadata, but this Torch/CUDA stack produced NaN probabilities when sampling and immediate EOS with greedy decoding. The node defaults to the non-destructive greedy path and raises an actionable error on an empty result.
PaLI-Gemma and Gemma 3: gated checkpoints were not accessible without an accepted license and token.

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