DemonGatanjieuandClaude Opus 5.5
dd9c3ee2f5
- tags_from_picture: WD14 tagger (the source's model and thresholds) on a picture the user handed; run_status now carries what nodes showed as words (texts: a tagger's tags), with a summary and next step for a run that made words but no pictures. - Optional nodes: a block node marked optional goes when its pack is missing, and a link "match|decode" takes the first one there. The four upscales end in KJNodes' ColorMatch (mkl, the source's) when it is installed; the panel says "better with" it otherwise. - LanPaint's next step keeps the prompt about the whole picture; Follow a pose says OpenPose misreads anime (use control_depth), as the source's author did. - The directory has a size cap: past it, block names alone, so collected ways never make begin_task long; shorter headings and few-word lines. Live-tested on the isolated 8199 ComfyUI: LanPaint (area changed 14.6, outside 0.0, 82 s), tiled 2x (167 s), depth ControlNet (follows the layout), OpenPose (misreads an anime picture), background removal (8 s), tags (166 s with the model download), upscale with ColorMatch (73 s). Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
80 lines
2.4 KiB
JSON
80 lines
2.4 KiB
JSON
{
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"job": "follow",
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"rank": 1,
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"short": "its pose, for photos and 3D",
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"title": {
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"zh": "照着姿势画",
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"en": "Follow a pose"
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},
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"about": {
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"zh": "你递给 AI 一张图，AMB 读出人物姿势（OpenPose），用和你底模对得上的控制网让新图摆出这个姿势。 OpenPose 认真人照片和 3D 图准，认二次元人物常常认错，二次元图用「照着构图画」更稳（参考工作流作者也这么说）。",
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"en": "From a picture you hand the AI, Anomalous reads the pose (OpenPose) and steers the picture with a ControlNet that fits your model. OpenPose reads photos and 3D well and anime characters often wrongly; for anime, Follow a layout is steadier (as the source's author says)."
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},
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"source": {
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"zh": "参考工作流的「底图控制网」组",
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"en": "The reference workflow's ControlNet group"
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},
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"packs": {
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"OpenposePreprocessor": "comfyui_controlnet_aux"
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},
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"takes": "reference",
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"control": "pose",
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"nodes": {
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"reference": {
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"class_type": "LoadImage",
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"inputs": {
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"image": "$image"
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}
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},
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"pose": {
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"class_type": "OpenposePreprocessor",
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"inputs": {
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"image": [
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"reference",
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0
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],
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"detect_hand": "enable",
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"detect_body": "enable",
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"detect_face": "enable",
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"resolution": 1024
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}
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},
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"controlnet": {
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"class_type": "ControlNetLoader",
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"inputs": {
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"control_net_name": "$controlnet"
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}
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},
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"steer": {
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"class_type": "ControlNetApplyAdvanced",
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"inputs": {
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"positive": [
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"$sampler.positive",
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"*"
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],
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"negative": [
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"$sampler.negative",
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"*"
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],
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"control_net": [
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"controlnet",
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0
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],
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"image": [
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"pose",
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0
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],
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"vae": [
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"$decode.vae",
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"*"
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],
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"strength": 0.8,
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"start_percent": 0.0,
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"end_percent": 0.8
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},
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"wire_to": "$sampler"
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}
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},
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"after": "run_workflow: the picture now takes that pose (strength 0.8, for the first 80% of the steps; lower either for a looser pose). The prompt still says who and what: change it if it fights the pose. If the picture came out ignoring the pose, OpenPose likely misread an anime picture: try control_depth."
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}
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