DemonGatanjieuandClaude Opus 5.5
8ecc261949
Every block in api/blocks names what it gives a block after it ("gives": its sampler and its
output node; {} for one that changes the step it is put on) and its own model loader
("own_model"). api/block_chain.py builds a list of steps on a new tab: the picture first, each
next block anchored to what the step before gave, by near_node_id and a new page-only
save_node_id (canvas_roles.js findRoles pins the output node), so a canvas with several
samplers and outputs is no guess. add_nodes' answer names the block it built.
Redraw with another model and face/hand detailing now take the step before's picture
("$save.images") instead of the sampler's decode, which after an enlargement is the older
picture. The two-model routes are built as two-step chains; routes.json no longer names the
loaders.
Checked through the real page on the test ComfyUI: the Tile route on two Anima models (base on
Harem 1.5, Tile redraw on Kirazuri v4, from the base's picture and prompts), then face and hand
detailing after it took the Tile step's colour-matched picture.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
493 lines
15 KiB
JSON
493 lines
15 KiB
JSON
{
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"job": "detail",
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"want": "hands",
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"rank": 2,
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"short": "enlarge, then hands (fallback)",
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"enlarge": {
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"megapixels": 4.2,
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"most": 2.0,
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"unknown": 1.5
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},
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"title": {
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"zh": "修手·先放大再修（备选）",
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"en": "Fix hands: enlarge, then fix (fallback)"
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},
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"about": {
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"zh": "整张图放大到约 400 万像素（小图 2 倍）轻轻重画，让手变大；再把每只手放大重画后贴回去：有 SAM3 模型时先按文字找出贴着手形的轮廓重画一遍，再用手部检测框补修一遍。",
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"en": "The whole picture is enlarged to about 4 megapixels (2× for a small one) and redrawn lightly, so the hands grow; then each hand is redrawn larger and pasted back: with the SAM3 model here, first inside the outline it finds by the word, then once more in the box the hand detector finds."
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},
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"source": {
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"zh": "参考作者的「先放大重画」和「手部精修」两步合在一起，2026-10-29 在 RTX 4050 上试出这组数；SAM3 按文字找手那一轮照他的连法，2026-11-03 加入",
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"en": "The reference author's light redraw after enlarging and his hand detailing, put together; these numbers tried on an RTX 4050 on 2026-10-29; the SAM3 pass finding hands by the word follows his wiring, added on 2026-11-03"
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},
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"packs": {
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"FaceDetailer": "ComfyUI-Impact-Pack",
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"UltralyticsDetectorProvider": "ComfyUI-Impact-Subpack"
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},
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"takes": "picture",
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"detectors": {
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"hand": "hand_yolov8"
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},
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"checkpoints": {
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"sam3": "sam3"
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},
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"roles": {
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"sampler": {
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"denoise": 0.4
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}
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},
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"nodes": {
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"picture": {
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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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"bigger": {
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"class_type": "ImageScaleBy",
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"inputs": {
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"image": [
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"picture",
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0
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],
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"upscale_method": "lanczos",
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"scale_by": "$enlarge"
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}
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},
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"encode": {
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"class_type": "VAEEncode",
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"inputs": {
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"pixels": [
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"bigger",
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0
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],
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"vae": [
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"$decode.vae",
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"*"
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]
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},
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"wire_to": "$sampler"
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},
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"hand_seeker": {
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"class_type": "CheckpointLoaderSimple",
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"optional": true,
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"_meta": {
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"title": "SAM3"
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},
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"inputs": {
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"ckpt_name": "$sam3"
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}
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},
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"hand_words": {
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"class_type": "CLIPTextEncode",
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"optional": true,
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"_meta": {
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"title": "SAM3: what to find"
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},
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"inputs": {
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"text": "hand:8",
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"clip": [
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"hand_seeker",
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1
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]
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}
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},
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"hand_shapes": {
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"class_type": "SAM3_Detect",
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"optional": true,
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"inputs": {
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"model": [
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"hand_seeker",
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0
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],
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"image": [
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"$decode",
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0
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],
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"conditioning": [
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"hand_words",
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0
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],
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"threshold": 0.5,
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"refine_iterations": 2,
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"individual_masks": false
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}
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},
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"hand_areas": {
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"class_type": "MaskToSEGS",
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"optional": true,
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"inputs": {
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"mask": [
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"hand_shapes",
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0
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],
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"combined": false,
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"crop_factor": 2.0,
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"bbox_fill": false,
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"drop_size": 10,
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"contour_fill": false
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}
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},
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"shaped_hands": {
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"class_type": "DetailerForEach",
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"like": "$sampler",
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"optional": true,
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"inputs": {
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"image": [
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"$decode",
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0
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],
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"segs": [
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"hand_areas",
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0
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],
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"model": [
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"$sampler.model",
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"*"
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],
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"clip": [
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"$sampler.positive.clip",
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"*"
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],
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"vae": [
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"$decode.vae",
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"*"
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],
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"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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"guide_size": 768,
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"max_size": 1024,
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"denoise": 0.45,
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"feather": 5,
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"noise_mask": true,
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"force_inpaint": true,
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"noise_mask_feather": 10,
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"wildcard": "detailed hand"
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}
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},
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"hand_finder": {
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"class_type": "UltralyticsDetectorProvider",
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"inputs": {
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"model_name": "$hand"
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}
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},
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"hands": {
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"class_type": "FaceDetailer",
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"like": "$sampler",
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"inputs": {
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"image": [
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"shaped_hands|$decode",
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0
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],
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"model": [
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"$sampler.model",
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"*"
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],
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"clip": [
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"$sampler.positive.clip",
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"*"
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],
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"vae": [
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"$decode.vae",
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"*"
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],
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"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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"bbox_detector": [
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"hand_finder",
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0
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],
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"guide_size": 768,
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"max_size": 1024,
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"denoise": 0.45,
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"bbox_threshold": 0.3,
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"bbox_crop_factor": 2.0,
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"feather": 5,
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"drop_size": 10,
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"wildcard": "detailed hand"
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}
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},
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"save": {
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"class_type": "SaveImage",
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"inputs": {
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"images": [
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"hands",
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"image"
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],
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"filename_prefix": "hands_fixed"
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}
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}
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},
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"after": "A fallback: a FLUX-Fill redraw of one square crop around each hand did better in the user's tests; use that when you have it. The picture is enlarged to about 4 megapixels (2× for one of about 1 megapixel, at most 2×, not at all for a large one) and drawn again lightly (the sampler now takes it, at denoise 0.4), then the hands are redrawn larger: with SAM3 here, first each hand it finds by the word inside its own outline (shaped_hands), then each hand the detector finds, in its box (hands, denoise 0.45), saved with prefix hands_fixed (without SAM3, which the user can get in the ways panel, only the detector's pass); the enlarged one is saved too, without the hand pass. About 3 tries in 4 gave a good hand in the pictures tried (one or two people, simple poses), no rate for crowds or hard poses: run_workflow with batch 4 (each try its own seed), then check them before the user does: zoom_image each hands_fixed picture on the hand, compare_with the enlarged one (the light redraw alone already changes faces and hands), and drop a hand with the wrong number of fingers, fused or melted fingers, one far bigger or smaller than its arm, a seam at its edge, or a hand that was fine made worse; say if a hand was missed. None good: run_workflow batch 4 again, at most twice. Then a picks panel zoomed on the hand lets the user choose among the good ones.",
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"gives": {
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"save": "save"
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},
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"notes": "Tried on the Codex test picture (1280×832, three characters, the hand about 110×78 px): the hand pass alone gave a good hand in about half the seeds whatever its size (512 or 768) and denoise (0.45 or 0.55); enlarging 1.5× first gave good hands in 3 of 4, 2× gave sharper fingers in the same seeds (about 105 s instead of 75 s). Denoise 0.4 for the enlarged redraw kept the layout; faces and splashes change a little. The SAM3 pass (2026-11-03): its word says how many it takes, \"hand\" alone only the likeliest hand, \"hand:8\" up to eight; on a 832×1216 picture of a girl waving both hands (enlarged 2×) it outlined both hands closely at threshold 0.5 (0.2 found no more), about 112 s with both passes on Anima with the Turbo LoRA. GPT's check the same day (two people, one hand with fused fingers, two seeds): SAM3 found both hands (one outline took in the wrist), both versions gave acceptable hands; on that one picture the SAM3 pass was not shown better than the detector alone.",
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"models": [
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"hand_yolov8s",
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"sam3_1",
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"anima_turbo_lora"
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],
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"tested": {
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"gpu": "RTX 4050 Laptop",
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"vram_gb": 6,
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"date": "2026-11-03",
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"runs": [
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{
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"model": "Anima (Kirazuri v4, Turbo LoRA)",
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"size": "1280×832 → 2560×1664",
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"seconds": 105
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},
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{
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"model": "Anima (MiaoMiao Harem 1.5, Turbo LoRA, SAM3 pass)",
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"size": "832×1216 → 1664×2432",
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"seconds": 112
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}
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]
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},
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"variants": {
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"anima": {
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"loras": {
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"turbo": "anima-turbo-lora"
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},
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"without_loras": "On an Anima model the Anima Turbo LoRA (anima-turbo-lora-v0.2, the user can get it in the ways panel) makes this about three times faster: 10 steps at CFG 1.",
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"roles": {
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"sampler": {
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"denoise": 0.4,
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"steps": 10,
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"cfg": 1.0,
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"sampler_name": "euler_ancestral",
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"scheduler": "normal"
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}
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},
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"nodes": {
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"picture": {
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"class_type": "LoadImage",
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"inputs": {
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"image": "$image"
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}
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},
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"turbo": {
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"class_type": "LoraLoaderModelOnly",
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"inputs": {
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"model": [
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"$sampler.model",
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"*"
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],
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"lora_name": "$turbo",
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"strength_model": 0.7
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},
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"wire_to": "$sampler"
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},
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"bigger": {
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"class_type": "ImageScaleBy",
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"inputs": {
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"image": [
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"picture",
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0
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],
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"upscale_method": "lanczos",
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"scale_by": "$enlarge"
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}
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},
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"encode": {
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"class_type": "VAEEncode",
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"inputs": {
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"pixels": [
|
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"bigger",
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0
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],
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"vae": [
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"$decode.vae",
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"*"
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]
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},
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"wire_to": "$sampler"
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},
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"hand_seeker": {
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"class_type": "CheckpointLoaderSimple",
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"optional": true,
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"_meta": {
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"title": "SAM3"
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},
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"inputs": {
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"ckpt_name": "$sam3"
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}
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},
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"hand_words": {
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"class_type": "CLIPTextEncode",
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"optional": true,
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"_meta": {
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"title": "SAM3: what to find"
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},
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"inputs": {
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"text": "hand:8",
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"clip": [
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"hand_seeker",
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1
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]
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}
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},
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"hand_shapes": {
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"class_type": "SAM3_Detect",
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"optional": true,
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"inputs": {
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"model": [
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"hand_seeker",
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0
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],
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"image": [
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"$decode",
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0
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],
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"conditioning": [
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"hand_words",
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0
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],
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"threshold": 0.5,
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"refine_iterations": 2,
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"individual_masks": false
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}
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},
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"hand_areas": {
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"class_type": "MaskToSEGS",
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"optional": true,
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"inputs": {
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"mask": [
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"hand_shapes",
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0
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],
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"combined": false,
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"crop_factor": 2.0,
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"bbox_fill": false,
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"drop_size": 10,
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"contour_fill": false
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}
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},
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"shaped_hands": {
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"class_type": "DetailerForEach",
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"like": "$sampler",
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"optional": true,
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"inputs": {
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"image": [
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"$decode",
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0
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],
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"segs": [
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"hand_areas",
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0
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],
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"model": [
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"turbo",
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0
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],
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"clip": [
|
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"$sampler.positive.clip",
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"*"
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||
],
|
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"vae": [
|
||
"$decode.vae",
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"*"
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],
|
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"positive": [
|
||
"$sampler.positive",
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"*"
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],
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"negative": [
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"$sampler.negative",
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"*"
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],
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"guide_size": 768,
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"max_size": 1024,
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"steps": 10,
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"cfg": 1.0,
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"sampler_name": "euler_ancestral",
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"scheduler": "normal",
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"denoise": 0.45,
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"feather": 5,
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"noise_mask": true,
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"force_inpaint": true,
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"noise_mask_feather": 10,
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"wildcard": "detailed hand"
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}
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},
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"hand_finder": {
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"class_type": "UltralyticsDetectorProvider",
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"inputs": {
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"model_name": "$hand"
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}
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},
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"hands": {
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"class_type": "FaceDetailer",
|
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"like": "$sampler",
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"inputs": {
|
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"image": [
|
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"shaped_hands|$decode",
|
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0
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||
],
|
||
"model": [
|
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"turbo",
|
||
0
|
||
],
|
||
"clip": [
|
||
"$sampler.positive.clip",
|
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"*"
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||
],
|
||
"vae": [
|
||
"$decode.vae",
|
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"*"
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],
|
||
"positive": [
|
||
"$sampler.positive",
|
||
"*"
|
||
],
|
||
"negative": [
|
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"$sampler.negative",
|
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"*"
|
||
],
|
||
"bbox_detector": [
|
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"hand_finder",
|
||
0
|
||
],
|
||
"guide_size": 768,
|
||
"max_size": 1024,
|
||
"steps": 10,
|
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"cfg": 1.0,
|
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"sampler_name": "euler_ancestral",
|
||
"scheduler": "normal",
|
||
"denoise": 0.45,
|
||
"bbox_threshold": 0.3,
|
||
"bbox_crop_factor": 2.0,
|
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"feather": 5,
|
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"drop_size": 10,
|
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"wildcard": "detailed hand"
|
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}
|
||
},
|
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"save": {
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"class_type": "SaveImage",
|
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"inputs": {
|
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"images": [
|
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"hands",
|
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"image"
|
||
],
|
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"filename_prefix": "hands_fixed"
|
||
}
|
||
}
|
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},
|
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"after": "A fallback: a FLUX-Fill redraw of one square crop around each hand did better in the user's tests; use that when you have it. On this Anima model the Anima Turbo LoRA (0.7) makes both passes 10 steps at CFG 1, about 105 s for 1280×832 (2×) on a 6 GB card. The picture is enlarged to about 4 megapixels (2× for one of about 1 megapixel, at most 2×, not at all for a large one) and drawn again lightly (the sampler now takes it, at denoise 0.4), then the hands are redrawn larger: with SAM3 here, first each hand it finds by the word inside its own outline (shaped_hands), then each hand the detector finds, in its box (hands, denoise 0.45), saved with prefix hands_fixed (without SAM3, which the user can get in the ways panel, only the detector's pass); the enlarged one is saved too, without the hand pass. About 3 tries in 4 gave a good hand in the pictures tried (one or two people, simple poses), no rate for crowds or hard poses: run_workflow with batch 4 (each try its own seed), then check them before the user does: zoom_image each hands_fixed picture on the hand, compare_with the enlarged one (the light redraw alone already changes faces and hands), and drop a hand with the wrong number of fingers, fused or melted fingers, one far bigger or smaller than its arm, a seam at its edge, or a hand that was fine made worse; say if a hand was missed. None good: run_workflow batch 4 again, at most twice. Then a picks panel zoomed on the hand lets the user choose among the good ones."
|
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}
|
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}
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}
|