DOGMA Nodes 1.0.7 — one VLM load per image list

Use workflow V56.19. DOGMAVLMListV568 owns one ModernVLM worker per ordered image list, reuses its model sequentially, and clears its private model handle in a finally block. This removes the repeated reload/quantization caused by ordinary Comfy list mapping with unload_after=True. It does not retain a separate model cache per graph node. Empty lists load nothing; mismatched prompts fail before loading. Progress and per-image elapsed times are logged. Cancellation is checked before/after each image; an already-running ModernVLM generation must return before the next check.

All 16 list-fed audit/caption nodes use the adapter. The single-image planner retains its ordinary unload behavior. Audit reasons are capped at 32 tokens and requested in six words. File selectors, diffusion settings, candidate counts and segmentation parameters are preserved. Requires the existing comfyui_vlm_nodes ModernVLM with clear_model API; no additional models. Publish this release, update DOGMA via Manager, restart ComfyUI, and load V56.19.

59 CPU regression tests passed. Frontend import/export and graph wiring validated offline. No GPU benchmark or visual generation has been performed on the pod; further hardware/quantization bottlenecks remain possible.

DOGMA Nodes 1.0.6 — instance review and local recovery

Workflow V56.18 keeps the supplied SimplePod model filenames and uses KJNodes for explicit memory cleanup. ModernVLM retains its own private-model lifecycle. Update through ComfyUI-Manager after this release is available in the Registry.

Phase 3 detects raw SAM masks with paired boxes, removes speckles without growing ownership, reviews each candidate separately, and searches four overlapping local views to recover missed targets. Valid detections survive rejection of unrelated candidates. Duplicate detections and cross-category overlaps are removed. Captions describe actual crops. Inactive slots do not execute caption or diffusion; a selected visible category with no verified masks after both searches raises an explicit diagnostic instead of silently returning an empty result.

This adds CPU-tested processing and workflow wiring; actual segmentation quality and GPU runtime still require validation on the user's pod. SAM/Qwen can fail. Instance-level review and local search require more analysis than a single category-wide verdict.

DOGMA Nodes 1.0.5 — Phase 3 mask and caption correction

New opt-in V566 nodes provide per-crop declarative prompts, SAM box ownership cleanup, per-category visual audits, masked img2img latent encoding, and distance-feathered compositing with low-frequency color protection. Existing node IDs retain their behaviour. Use the DOGMA V56.17 workflow. CPU regression tests: python tests/run.py (requires pytest).

DOGMA Nodes

Custom ComfyUI nodes for DOGMA AI video workflows.

Nodes

WAN VACE Keyframe Control Prep

Category:

video/WAN VACE

This node prepares a WAN VACE control video and control mask video from:

video             IMAGE batch
mask_video        IMAGE batch
reference_frames  IMAGE batch
keyframe_indices  STRING

It replaces selected video frames with the corresponding reference frames, turns the mask fully black at those same frames, and pads the result to a WAN VACE-compatible frame count.

WAN VACE expects frame counts in the form:

4n + 1

So the node pads the sequence by duplicating frames at the beginning and end, symmetrically, with preference for the beginning when the padding count is odd.

keyframe_indices supports comma, space, or semicolon separated values.

Examples:

start, 25, end
0 24 48
first; 32; last

Indexing is 0-based.

0 = first frame
24 = 25th frame
end = last frame

Outputs:

control_video       IMAGE batch
control_mask_video  IMAGE batch
frame_count         INT
padding_info        WANVACE_PAD_INFO

WAN VACE Remove Added Padding

Category:

video/WAN VACE

This node removes the replicated start/end frames that were added by WAN VACE Keyframe Control Prep.

Use it after WAN VACE generation when a later crop-and-stitch step needs the generated video to return to the original unpadded frame count.

Inputs:

video         IMAGE batch
padding_info  WANVACE_PAD_INFO

Output:

video        IMAGE batch
frame_count  INT

Typical use:

WAN VACE Keyframe Control Prep → padding_info
WAN generated video            → WAN VACE Remove Added Padding

DOGMA Sampler Select

Category:

sampling/custom_sampling/samplers

This node returns a DOGMA sampler as a SAMPLER object. Use it with SamplerCustomAdvanced and any SIGMAS source, including custom hand-drawn sigma curves.

The same DOGMA samplers are also registered into normal ComfyUI sampler menus after restart, so they can appear in ordinary KSampler, KSampler Advanced, and KSamplerSelect dropdowns.

Available samplers:

Sampler	Intended use	Approximate model calls
DOGMA_klein_distilled_REBUILD	T2I, strong edit, heavily damaged upscale tile	2 × non-final steps + 1
DOGMA_klein_distilled_BALANCED	General T2I / i2i / edit	2 × non-final steps + 1
DOGMA_klein_distilled_DETAIL	Soft edit, good upscale tile, fine reconstruction	3 × non-final steps + 1
DOGMA_klein_basemodel_REBUILD	Fast strong reconstruction, bad source anatomy or structure	1 × steps
DOGMA_klein_basemodel_BALANCED	General base-model work with selective correction	Usually 1.2-1.35 × steps
DOGMA_klein_basemodel_DETAIL	Soft edit and upscale refinement; extra work at low sigma	Usually 1.4-1.5 × steps

These are experimental ODE samplers designed around FLUX.2 Klein 9B workflows: three for the 4-6 step distilled model and three for the 20-50 step base model. No LoRA is included or required.

Install

Install through ComfyUI Manager as DOGMA Nodes, or with:

comfy node install comfyui-dogma-nodes

Notes

The nodes use only PyTorch and ComfyUI's built-in sampler APIs. No extra Python dependencies are required.