前回の続きで、今回は配布ワークフローの右半分です。
ワークフローは初級編で配布しています。
EasyUseにビルドインされている機能を使う事にフォーカスしているので、個別の機能に対する細かい説明はありません。
Controlnet
Stack
最もシンプル。Loaderに繋ぎます。
最大３で更に必要な場合は同じものを直列でいいはずです。
３で足りないことある…？
途中に挟む場合はeasy controlnetStackApplyをPipeで繋いでStackをつないでください。
HiresFixだけにAnytestとかを効かせる時は途中繋ぎですね。
EasyUseのCNはSoftWeightがデフォで入っているのがいいですね。最高です。
■サンプルワークフロー
{
"last_node_id": 7,
"last_link_id": 4,
"nodes": [
{
"id": 1,
"type": "easy comfyLoader",
"pos": {
"0": -1721.841552734375,
"1": -677.8846435546875
},
"size": [
262,
360
],
"flags": {},
"order": 4,
"mode": 0,
"inputs": [
{
"name": "optional_lora_stack",
"type": "LORA_STACK",
"link": null,
"shape": 7
},
{
"name": "optional_controlnet_stack",
"type": "CONTROL_NET_STACK",
"link": 1,
"shape": 7
}
],
"outputs": [
{
"name": "pipe",
"type": "PIPE_LINE",
"links": [
2
],
"slot_index": 0
},
{
"name": "model",
"type": "MODEL",
"links": null
},
{
"name": "vae",
"type": "VAE",
"links": null
}
],
"title": "easy comfyLoader",
"properties": {
"Node name for S&R": "easy comfyLoader"
},
"widgets_values": [
"SDXL\\osukinimixXL_v10.safetensors",
"Baked VAE",
-2,
"None",
1,
1,
"1280 x 768",
512,
512,
"1girl,multiple views",
"worst quality,low quality,normal quality,oldest,early,mid",
1
]
},
{
"id": 2,
"type": "easy preSampling",
"pos": {
"0": -1434.842041015625,
"1": -672.8846435546875
},
"size": [
210,
242
],
"flags": {},
"order": 5,
"mode": 0,
"inputs": [
{
"name": "pipe",
"type": "PIPE_LINE",
"link": 2
},
{
"name": "image_to_latent",
"type": "IMAGE",
"link": null,
"shape": 7
},
{
"name": "latent",
"type": "LATENT",
"link": null,
"shape": 7
}
],
"outputs": [
{
"name": "pipe",
"type": "PIPE_LINE",
"links": [
3
],
"slot_index": 0
}
],
"title": "easy preSampling",
"properties": {
"Node name for S&R": "easy preSampling"
},
"widgets_values": [
20,
8,
"euler_ancestral",
"karras",
1,
273368864666126,
"randomize"
]
},
{
"id": 3,
"type": "easy kSampler",
"pos": {
"0": -1200.842041015625,
"1": -669.8846435546875
},
"size": [
320,
290
],
"flags": {},
"order": 6,
"mode": 0,
"inputs": [
{
"name": "pipe",
"type": "PIPE_LINE",
"link": 3
},
{
"name": "model",
"type": "MODEL",
"link": null,
"shape": 7
}
],
"outputs": [
{
"name": "pipe",
"type": "PIPE_LINE",
"links": null
},
{
"name": "image",
"type": "IMAGE",
"links": null
}
],
"title": "easy kSampler",
"properties": {
"Node name for S&R": "easy kSampler"
},
"widgets_values": [
"Preview",
0,
"ComfyUI"
]
},
{
"id": 4,
"type": "easy controlnetStack",
"pos": {
"0": -2101.841552734375,
"1": -657.8845825195312
},
"size": [
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"flags": {},
"order": 3,
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"inputs": [
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{
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"link": 4,
"shape": 7
},
{
"name": "image_2",
"type": "IMAGE",
"link": null,
"shape": 7
},
{
"name": "image_3",
"type": "IMAGE",
"link": null,
"shape": 7
}
],
"outputs": [
{
"name": "controlnet_stack",
"type": "CONTROL_NET_STACK",
"links": [
1
],
"slot_index": 0
}
],
"title": "easy controlnetStack",
"properties": {
"Node name for S&R": "easy controlnetStack"
},
"widgets_values": [
true,
"advanced",
1,
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0,
1,
1,
"None",
1,
0,
1,
1,
"None",
1,
0,
1,
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},
{
"id": 5,
"type": "LoadImage",
"pos": {
"0": -2370.841796875,
"1": -638.8845825195312
},
"size": {
"0": 240,
"1": 314
},
"flags": {},
"order": 0,
"mode": 0,
"inputs": [],
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"links": [
4
],
"slot_index": 0
},
{
"name": "MASK",
"type": "MASK",
"links": null
}
],
"properties": {
"Node name for S&R": "LoadImage"
},
"widgets_values": [
"ComfyUI_temp_jlvih_00003_.png",
"image"
]
},
{
"id": 6,
"type": "easy controlnetStackApply",
"pos": {
"0": -1433.350830078125,
"1": -368.8717956542969
},
"size": {
"0": 210,
"1": 50
},
"flags": {},
"order": 1,
"mode": 0,
"inputs": [
{
"name": "controlnet_stack",
"type": "CONTROL_NET_STACK",
"link": null
},
{
"name": "pipe",
"type": "PIPE_LINE",
"link": null
}
],
"outputs": [
{
"name": "pipe",
"type": "PIPE_LINE",
"links": null
}
],
"title": "easy controlnetStackApply",
"properties": {
"Node name for S&R": "easy controlnetStackApply"
}
},
{
"id": 7,
"type": "Note",
"pos": {
"0": -1362,
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},
"size": [
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],
"flags": {},
"order": 2,
"mode": 0,
"inputs": [],
"outputs": [],
"properties": {},
"widgets_values": [
"↑間に挟む場合はこっち"
],
"color": "#432",
"bgcolor": "#653"
}
],
"links": [
[
1,
4,
0,
1,
1,
"CONTROL_NET_STACK"
],
[
2,
1,
0,
2,
0,
"PIPE_LINE"
],
[
3,
2,
0,
3,
0,
"PIPE_LINE"
],
[
4,
5,
0,
4,
1,
"IMAGE"
]
],
"groups": [],
"config": {},
"extra": {
"0246.VERSION": [
0,
0,
4
]
},
"version": 0.4
}シングル
途中Pipe接続。こちらはシングル接続のみです。直列もできません。
Stackだとデカい…とか別のノードにConditioning渡すって時に。
経験上１つでいいかなって思って繋いだけど２つ欲しいなってなる時は割とありますｗ
CN-i2iをしたい場合はPreSamplingにImageを繋ぎ、
サンプルはデノイズ１なので適宜下げてください。
■サンプルワークフロー
{
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"last_link_id": 23,
"nodes": [
{
"id": 9,
"type": "easy comfyLoader",
"pos": {
"0": -2246.733642578125,
"1": -888.7149658203125
},
"size": [
262,
360
],
"flags": {},
"order": 0,
"mode": 0,
"inputs": [
{
"name": "optional_lora_stack",
"type": "LORA_STACK",
"link": null,
"shape": 7
},
{
"name": "optional_controlnet_stack",
"type": "CONTROL_NET_STACK",
"link": null,
"shape": 7
}
],
"outputs": [
{
"name": "pipe",
"type": "PIPE_LINE",
"links": [
5
],
"slot_index": 0
},
{
"name": "model",
"type": "MODEL",
"links": null
},
{
"name": "vae",
"type": "VAE",
"links": null
}
],
"title": "easy comfyLoader",
"properties": {
"Node name for S&R": "easy comfyLoader"
},
"widgets_values": [
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"XL\\sdxl-vae-fp16-fix.safetensors",
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512,
512,
"1girl,chibi",
"worst quality,low quality,normal quality,oldest,early,mid",
1
]
},
{
"id": 10,
"type": "easy preSampling",
"pos": {
"0": -1316.3780517578125,
"1": -891.9578857421875
},
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210,
242
],
"flags": {},
"order": 11,
"mode": 0,
"inputs": [
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"link": 23
},
{
"name": "image_to_latent",
"type": "IMAGE",
"link": null,
"shape": 7
},
{
"name": "latent",
"type": "LATENT",
"link": null,
"shape": 7
}
],
"outputs": [
{
"name": "pipe",
"type": "PIPE_LINE",
"links": [
8
],
"slot_index": 0
}
],
"title": "easy preSampling",
"properties": {
"Node name for S&R": "easy preSampling"
},
"widgets_values": [
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8,
"euler_ancestral",
"karras",
1,
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"randomize"
]
},
{
"id": 11,
"type": "easy kSampler",
"pos": {
"0": -1082.3780517578125,
"1": -888.9578857421875
},
"size": [
320,
290
],
"flags": {},
"order": 12,
"mode": 0,
"inputs": [
{
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},
{
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}
],
"outputs": [
{
"name": "pipe",
"type": "PIPE_LINE",
"links": null
},
{
"name": "image",
"type": "IMAGE",
"links": null
}
],
"title": "easy kSampler",
"properties": {
"Node name for S&R": "easy kSampler"
},
"widgets_values": [
"Preview",
0,
"ComfyUI"
]
},
{
"id": 14,
"type": "easy pipeIn",
"pos": {
"0": -1450.3785400390625,
"1": -449.9580078125
},
"size": {
"0": 140,
"1": 190
},
"flags": {},
"order": 9,
"mode": 0,
"inputs": [
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{
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},
{
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{
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{
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},
{
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},
{
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"type": "CLIP",
"link": null,
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},
{
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"link": null,
"shape": 7
},
{
"name": "xyPlot",
"type": "XYPLOT",
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"shape": 7
}
],
"outputs": [
{
"name": "pipe",
"type": "PIPE_LINE",
"links": null
}
],
"title": "easy pipeIn",
"properties": {
"Node name for S&R": "easy pipeIn"
}
},
{
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"type": "Note",
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"flags": {},
"order": 1,
"mode": 0,
"inputs": [],
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"properties": {},
"widgets_values": [
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],
"color": "#432",
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},
{
"id": 16,
"type": "ACN_AdvancedControlNetApply",
"pos": {
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"size": {
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"mode": 0,
"inputs": [
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},
{
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{
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{
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{
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{
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{
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"type": "LATENT_KEYFRAME",
"link": null,
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{
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{
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{
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1,
""
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},
{
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"flags": {},
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{
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"title": "ControlNetLoaderAdvanced",
"properties": {
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},
{
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{
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},
{
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"links": [
13
],
"slot_index": 2
},
{
"name": "neg",
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"links": [
14
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"slot_index": 3
},
{
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},
{
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{
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"type": "CLIP",
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{
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"type": "IMAGE",
"links": null
},
{
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}
],
"title": "easy pipeOut",
"properties": {
"Node name for S&R": "easy pipeOut"
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},
{
"id": 19,
"type": "ACN_ScaledSoftControlNetWeights",
"pos": {
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"inputs": [
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"inputs": [],
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"color": "#432",
"bgcolor": "#653"
},
{
"id": 12,
"type": "LoadImage",
"pos": {
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{
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},
{
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{
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],
"outputs": [
{
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{
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0,
4
]
},
"version": 0.4
}ACNなどベーシックなCNノードを使いたい場合はPipeIN-OUTなどで接続します。
Controlnet++（CN-Union）
なんで名前ばらけてん…というのは置いといて、これは何かというと１つのモデルに複数のCNを学習させてあるもので精度は個別のものと遜色ない（と書かれている）らしいです。
モデルはこちら
xinsir/controlnet-union-sdxl-1.0 at main
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
huggingface.co
自分の用途ではAnytestで割と何でも処理できてしまうため、テスト出力程度しかしていないので詳しい所はよくわかりません。
Type指定で何をさせるかを明確に選ぶこともできるし、autoでお任せにすることも可能です。
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}IPAdapter
シングル
地味に繋ぐの面倒なやつ。
でもEasyUseさんにかかれば１ノードPipeするだけ。ﾜｧ…
■サンプルワークフロー
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"worst quality,low quality,normal quality,oldest,early,mid",
1
]
}
],
"links": [
[
31,
31,
0,
32,
0,
"PIPE_LINE"
],
[
32,
35,
1,
32,
1,
"IMAGE"
],
[
33,
32,
0,
33,
0,
"PIPE_LINE"
],
[
34,
35,
0,
33,
1,
"MODEL"
],
[
35,
31,
1,
35,
0,
"MODEL"
],
[
36,
34,
0,
35,
1,
"IMAGE"
],
[
42,
39,
0,
40,
0,
"PIPE_LINE"
],
[
43,
37,
0,
40,
1,
"MODEL"
],
[
70,
41,
0,
47,
3,
"IMAGE"
],
[
71,
42,
0,
47,
4,
"IMAGE"
],
[
72,
47,
0,
37,
0,
"MODEL"
],
[
73,
47,
1,
37,
1,
"CLIP_VISION"
],
[
74,
47,
2,
37,
2,
"IPADAPTER"
],
[
75,
47,
3,
37,
3,
"EMBEDS"
],
[
76,
47,
4,
37,
4,
"EMBEDS"
],
[
77,
45,
0,
47,
1,
"CLIP_VISION"
],
[
78,
49,
0,
47,
2,
"IMAGE"
],
[
79,
48,
1,
47,
0,
"MODEL"
],
[
80,
48,
0,
39,
0,
"PIPE_LINE"
]
],
"groups": [],
"config": {},
"extra": {
"0246.VERSION": [
0,
0,
4
]
},
"version": 0.4
}複数参照
少し複雑になりますが、複数参照もかなりスッキリ。
Detailer
手とか顔とかをBBOXで自動選択してそこだけi2iする機能です。
A1111だとAdetailerとかが類似のものです。
ImpactPackの一部を簡略化して持ってきた感じですね。
これもPipeで繋ぐだけで済むからかなり楽です。
■サンプルワークフロー
{
"last_node_id": 57,
"last_link_id": 88,
"nodes": [
{
"id": 50,
"type": "easy comfyLoader",
"pos": {
"0": -1585.3148193359375,
"1": -624.2964477539062
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"size": [
262,
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"flags": {},
"order": 0,
"mode": 0,
"inputs": [
{
"name": "optional_lora_stack",
"type": "LORA_STACK",
"link": null,
"shape": 7
},
{
"name": "optional_controlnet_stack",
"type": "CONTROL_NET_STACK",
"link": null,
"shape": 7
}
],
"outputs": [
{
"name": "pipe",
"type": "PIPE_LINE",
"links": [
83
],
"slot_index": 0
},
{
"name": "model",
"type": "MODEL",
"links": null
},
{
"name": "vae",
"type": "VAE",
"links": null
}
],
"title": "easy comfyLoader",
"properties": {
"Node name for S&R": "easy comfyLoader"
},
"widgets_values": [
"SDXL\\osukinimixXL_v10.safetensors",
"Baked VAE",
-2,
"None",
1,
1,
"1024 x 1024",
512,
512,
"1girl",
"worst quality,low quality,normal quality,oldest,early,mid",
1
]
},
{
"id": 51,
"type": "easy preSampling",
"pos": {
"0": -1307.6539306640625,
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},
"size": [
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"flags": {},
"order": 4,
"mode": 0,
"inputs": [
{
"name": "pipe",
"type": "PIPE_LINE",
"link": 83
},
{
"name": "image_to_latent",
"type": "IMAGE",
"link": null,
"shape": 7
},
{
"name": "latent",
"type": "LATENT",
"link": null,
"shape": 7
}
],
"outputs": [
{
"name": "pipe",
"type": "PIPE_LINE",
"links": [
84
],
"slot_index": 0
}
],
"title": "easy preSampling",
"properties": {
"Node name for S&R": "easy preSampling"
},
"widgets_values": [
20,
8,
"euler_ancestral",
"karras",
1,
645827961295857,
"randomize"
]
},
{
"id": 52,
"type": "easy kSampler",
"pos": {
"0": -1077.6544189453125,
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},
"size": [
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242.68160758406248
],
"flags": {},
"order": 5,
"mode": 0,
"inputs": [
{
"name": "pipe",
"type": "PIPE_LINE",
"link": 84
},
{
"name": "model",
"type": "MODEL",
"link": null,
"shape": 7
}
],
"outputs": [
{
"name": "pipe",
"type": "PIPE_LINE",
"links": [
86
],
"slot_index": 0
},
{
"name": "image",
"type": "IMAGE",
"links": null
}
],
"title": "easy kSampler",
"properties": {
"Node name for S&R": "easy kSampler"
},
"widgets_values": [
"Preview",
0,
"ComfyUI"
]
},
{
"id": 54,
"type": "easy samLoaderPipe",
"pos": {
"0": -820,
"1": -450
},
"size": [
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],
"flags": {},
"order": 1,
"mode": 0,
"inputs": [],
"outputs": [
{
"name": "sam_pipe",
"type": "PIPE_LINE",
"links": [
88
],
"slot_index": 0
}
],
"title": "easy samLoaderPipe",
"properties": {
"Node name for S&R": "easy samLoaderPipe"
},
"widgets_values": [
"sam_vit_b_01ec64.pth",
"AUTO",
"center-1",
0,
0.93,
0,
0.7,
"False"
]
},
{
"id": 55,
"type": "easy ultralyticsDetectorPipe",
"pos": {
"0": -820,
"1": -620
},
"size": [
296.22297775618244,
130
],
"flags": {},
"order": 2,
"mode": 0,
"inputs": [],
"outputs": [
{
"name": "bbox_segm_pipe",
"type": "PIPE_LINE",
"links": [
87
],
"slot_index": 0
}
],
"title": "easy ultralyticsDetectorPipe",
"properties": {
"Node name for S&R": "easy ultralyticsDetectorPipe"
},
"widgets_values": [
"bbox/face_yolov8m.pt",
0.5,
10,
3
]
},
{
"id": 57,
"type": "Note",
"pos": {
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},
"size": [
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],
"flags": {},
"order": 3,
"mode": 2,
"inputs": [],
"outputs": [],
"properties": {},
"widgets_values": [
"←自分でマスクを設定する場合はpreMaskDetailerFixの方を使用"
],
"color": "#432",
"bgcolor": "#653"
},
{
"id": 53,
"type": "easy detailerFix",
"pos": {
"0": -180,
"1": -620
},
"size": [
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265.1361538248483
],
"flags": {},
"order": 7,
"mode": 0,
"inputs": [
{
"name": "pipe",
"type": "PIPE_LINE",
"link": 85
},
{
"name": "model",
"type": "MODEL",
"link": null,
"shape": 7
}
],
"outputs": [
{
"name": "pipe",
"type": "PIPE_LINE",
"links": null
},
{
"name": "image",
"type": "IMAGE",
"links": null
},
{
"name": "cropped_refined",
"type": "IMAGE",
"links": null,
"shape": 6
},
{
"name": "cropped_enhanced_alpha",
"type": "IMAGE",
"links": null,
"shape": 6
}
],
"title": "easy detailerFix",
"properties": {
"Node name for S&R": "easy detailerFix"
},
"widgets_values": [
"Preview",
0,
"ComfyUI"
]
},
{
"id": 56,
"type": "easy preDetailerFix",
"pos": {
"0": -500,
"1": -620
},
"size": [
298.7221586076553,
498.11690332308217
],
"flags": {},
"order": 6,
"mode": 0,
"inputs": [
{
"name": "pipe",
"type": "PIPE_LINE",
"link": 86
},
{
"name": "bbox_segm_pipe",
"type": "PIPE_LINE",
"link": 87,
"shape": 7
},
{
"name": "sam_pipe",
"type": "PIPE_LINE",
"link": 88,
"shape": 7
},
{
"name": "optional_image",
"type": "IMAGE",
"link": null,
"shape": 7
}
],
"outputs": [
{
"name": "pipe",
"type": "PIPE_LINE",
"links": [
85
],
"slot_index": 0
}
],
"title": "easy preDetailerFix",
"properties": {
"Node name for S&R": "easy preDetailerFix"
},
"widgets_values": [
768,
true,
1600,
119356943372838,
"randomize",
20,
7.5,
"euler_ancestral",
"normal",
0.5,
5,
true,
true,
10,
"",
1
]
}
],
"links": [
[
83,
50,
0,
51,
0,
"PIPE_LINE"
],
[
84,
51,
0,
52,
0,
"PIPE_LINE"
],
[
85,
56,
0,
53,
0,
"PIPE_LINE"
],
[
86,
52,
0,
56,
0,
"PIPE_LINE"
],
[
87,
55,
0,
56,
1,
"PIPE_LINE"
],
[
88,
54,
0,
56,
2,
"PIPE_LINE"
]
],
"groups": [],
"config": {},
"extra": {
"0246.VERSION": [
0,
0,
4
]
},
"version": 0.4
}Fooocus inpaint
いつもoが何個かわからなくなるFooooooooooocusさんです。
通常インペイントよりも強力（場合による）かつインペイントモデルを用意せず普段遣いのモデルでいけます。（インペイントに強いモデル、弱いモデルはあります）
専用モデルが必要なので、下記からDLしてください。（今は自動DLされるんだっけ…？）
fooocus_inpaint_head,inpaint_v26か25の２つが必要です。
GitHub - Acly/comfyui-inpaint-nodes: Nodes for better inpainting with ComfyUI: Fooocus inpaint model for SDXL, LaMa, MAT, and various other tools for pre-filling inpaint & outpaint areas.
Nodes for better inpainting with ComfyUI: Fooocus inpaint model for SDXL, LaMa, MAT, and various other tools for pre-fil...
github.com
{
"last_node_id": 58,
"last_link_id": 97,
"nodes": [
{
"id": 50,
"type": "easy comfyLoader",
"pos": {
"0": -1445.116943359375,
"1": -310.546630859375
},
"size": [
262,
360
],
"flags": {},
"order": 0,
"mode": 0,
"inputs": [
{
"name": "optional_lora_stack",
"type": "LORA_STACK",
"link": null,
"shape": 7
},
{
"name": "optional_controlnet_stack",
"type": "CONTROL_NET_STACK",
"link": null,
"shape": 7
}
],
"outputs": [
{
"name": "pipe",
"type": "PIPE_LINE",
"links": [
92
],
"slot_index": 0
},
{
"name": "model",
"type": "MODEL",
"links": [],
"slot_index": 1
},
{
"name": "vae",
"type": "VAE",
"links": [],
"slot_index": 2
}
],
"title": "easy comfyLoader",
"properties": {
"Node name for S&R": "easy comfyLoader"
},
"widgets_values": [
"SDXL\\bluePencilXL_v700.safetensors",
"Baked VAE",
-2,
"None",
1,
1,
"1024 x 1024",
512,
512,
"1girl,closed mouth",
"worst quality,low quality,normal quality,oldest,early,mid",
1
]
},
{
"id": 51,
"type": "easy preSampling",
"pos": {
"0": -99.88481903076172,
"1": -311.865478515625
},
"size": [
210,
242
],
"flags": {},
"order": 4,
"mode": 0,
"inputs": [
{
"name": "pipe",
"type": "PIPE_LINE",
"link": 83
},
{
"name": "image_to_latent",
"type": "IMAGE",
"link": null,
"shape": 7
},
{
"name": "latent",
"type": "LATENT",
"link": null,
"shape": 7
}
],
"outputs": [
{
"name": "pipe",
"type": "PIPE_LINE",
"links": [
93
],
"slot_index": 0
}
],
"title": "easy preSampling",
"properties": {
"Node name for S&R": "easy preSampling"
},
"widgets_values": [
20,
8,
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0.538,
604749649137755,
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},
{
"id": 52,
"type": "LoadImage",
"pos": {
"0": -1160.5615234375,
"1": -253.741943359375
},
"size": [
320,
314
],
"flags": {},
"order": 1,
"mode": 0,
"inputs": [],
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"links": [
89
],
"slot_index": 0
},
{
"name": "MASK",
"type": "MASK",
"links": [
90
],
"slot_index": 1
}
],
"properties": {
"Node name for S&R": "LoadImage"
},
"widgets_values": [
"clipspace/clipspace-mask-1837733.2999999523.png [input]",
"image"
]
},
{
"id": 54,
"type": "INPAINT_VAEEncodeInpaintConditioning",
"pos": {
"0": -664.8848266601562,
"1": -290.865478515625
},
"size": {
"0": 280,
"1": 110
},
"flags": {},
"order": 5,
"mode": 0,
"inputs": [
{
"name": "positive",
"type": "CONDITIONING",
"link": 86
},
{
"name": "negative",
"type": "CONDITIONING",
"link": 87
},
{
"name": "vae",
"type": "VAE",
"link": 88
},
{
"name": "pixels",
"type": "IMAGE",
"link": 89
},
{
"name": "mask",
"type": "MASK",
"link": 90
}
],
"outputs": [
{
"name": "positive",
"type": "CONDITIONING",
"links": [
95
],
"slot_index": 0,
"shape": 3
},
{
"name": "negative",
"type": "CONDITIONING",
"links": [
96
],
"slot_index": 1,
"shape": 3
},
{
"name": "latent_inpaint",
"type": "LATENT",
"links": [
85
],
"slot_index": 2,
"shape": 3
},
{
"name": "latent_samples",
"type": "LATENT",
"links": [
97
],
"slot_index": 3,
"shape": 3
}
],
"properties": {
"Node name for S&R": "INPAINT_VAEEncodeInpaintConditioning"
}
},
{
"id": 55,
"type": "DifferentialDiffusion",
"pos": {
"0": -92.88481903076172,
"1": -31.8654842376709
},
"size": {
"0": 184.8000030517578,
"1": 30
},
"flags": {
"collapsed": false
},
"order": 7,
"mode": 0,
"inputs": [
{
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"link": 91,
"slot_index": 0
}
],
"outputs": [
{
"name": "MODEL",
"type": "MODEL",
"links": [
94
],
"slot_index": 0,
"shape": 3
}
],
"properties": {
"Node name for S&R": "DifferentialDiffusion"
},
"color": "#323",
"bgcolor": "#535"
},
{
"id": 56,
"type": "easy pipeOut",
"pos": {
"0": -831.8848266601562,
"1": -334.865478515625
},
"size": {
"0": 140,
"1": 190
},
"flags": {},
"order": 3,
"mode": 0,
"inputs": [
{
"name": "pipe",
"type": "PIPE_LINE",
"link": 92
}
],
"outputs": [
{
"name": "pipe",
"type": "PIPE_LINE",
"links": [
83
],
"slot_index": 0,
"shape": 3
},
{
"name": "model",
"type": "MODEL",
"links": [
84
],
"slot_index": 1,
"shape": 3
},
{
"name": "pos",
"type": "CONDITIONING",
"links": [
86
],
"slot_index": 2,
"shape": 3
},
{
"name": "neg",
"type": "CONDITIONING",
"links": [
87
],
"slot_index": 3,
"shape": 3
},
{
"name": "latent",
"type": "LATENT",
"links": null,
"shape": 3
},
{
"name": "vae",
"type": "VAE",
"links": [
88
],
"slot_index": 5,
"shape": 3
},
{
"name": "clip",
"type": "CLIP",
"links": null,
"shape": 3
},
{
"name": "image",
"type": "IMAGE",
"links": null,
"shape": 3
},
{
"name": "seed",
"type": "INT",
"links": null,
"shape": 3
}
],
"title": "easy pipeOut",
"properties": {
"Node name for S&R": "easy pipeOut"
}
},
{
"id": 57,
"type": "easy fullkSampler",
"pos": {
"0": 155.1151885986328,
"1": -309.865478515625
},
"size": [
270,
386
],
"flags": {},
"order": 8,
"mode": 0,
"inputs": [
{
"name": "pipe",
"type": "PIPE_LINE",
"link": 93
},
{
"name": "model",
"type": "MODEL",
"link": 94,
"shape": 7
},
{
"name": "positive",
"type": "CONDITIONING",
"link": 95,
"shape": 7
},
{
"name": "negative",
"type": "CONDITIONING",
"link": 96,
"shape": 7
},
{
"name": "latent",
"type": "LATENT",
"link": 97,
"shape": 7
},
{
"name": "vae",
"type": "VAE",
"link": null,
"shape": 7
},
{
"name": "clip",
"type": "CLIP",
"link": null,
"shape": 7
},
{
"name": "xyPlot",
"type": "XYPLOT",
"link": null,
"shape": 7
},
{
"name": "image",
"type": "IMAGE",
"link": null,
"shape": 7
}
],
"outputs": [
{
"name": "pipe",
"type": "PIPE_LINE",
"links": [],
"slot_index": 0,
"shape": 3
},
{
"name": "image",
"type": "IMAGE",
"links": [],
"slot_index": 1,
"shape": 3
},
{
"name": "model",
"type": "MODEL",
"links": null,
"shape": 3
},
{
"name": "positive",
"type": "CONDITIONING",
"links": null,
"shape": 3
},
{
"name": "negative",
"type": "CONDITIONING",
"links": null,
"shape": 3
},
{
"name": "latent",
"type": "LATENT",
"links": null,
"shape": 3
},
{
"name": "vae",
"type": "VAE",
"links": null,
"shape": 3
},
{
"name": "clip",
"type": "CLIP",
"links": null,
"shape": 3
},
{
"name": "seed",
"type": "INT",
"links": null,
"shape": 3
}
],
"title": "easy fullkSampler",
"properties": {
"Node name for S&R": "easy fullkSampler"
},
"widgets_values": [
16,
1,
"dpmpp_sde",
"karras",
1,
"Preview",
0,
"ComfyUI",
787926129322897,
"randomize"
]
},
{
"id": 58,
"type": "Note",
"pos": {
"0": -813.8848266601562,
"1": -93.86548614501953
},
"size": [
416.2190856933594,
164.23165893554688
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"flags": {},
"order": 2,
"mode": 2,
"inputs": [],
"outputs": [],
"properties": {},
"widgets_values": [
"こちらはインペイントモデルを使いません。\n\n別途Fooocus inpaintモデルをDLする必要があります。\n\nhttps://github.com/Acly/comfyui-inpaint-nodes"
],
"color": "#432",
"bgcolor": "#653"
},
{
"id": 53,
"type": "easy applyFooocusInpaint",
"pos": {
"0": -368.88482666015625,
"1": -31.8654842376709
},
"size": {
"0": 260,
"1": 102
},
"flags": {},
"order": 6,
"mode": 0,
"inputs": [
{
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"type": "MODEL",
"link": 84
},
{
"name": "latent",
"type": "LATENT",
"link": 85
}
],
"outputs": [
{
"name": "model",
"type": "MODEL",
"links": [
91
],
"slot_index": 0
}
],
"title": "easy applyFooocusInpaint",
"properties": {
"Node name for S&R": "easy applyFooocusInpaint"
},
"widgets_values": [
"fooocus_inpaint_head",
"inpaint_v26 (1.32GB)"
]
}
],
"links": [
[
83,
56,
0,
51,
0,
"PIPE_LINE"
],
[
84,
56,
1,
53,
0,
"MODEL"
],
[
85,
54,
2,
53,
1,
"LATENT"
],
[
86,
56,
2,
54,
0,
"CONDITIONING"
],
[
87,
56,
3,
54,
1,
"CONDITIONING"
],
[
88,
56,
5,
54,
2,
"VAE"
],
[
89,
52,
0,
54,
3,
"IMAGE"
],
[
90,
52,
1,
54,
4,
"MASK"
],
[
91,
53,
0,
55,
0,
"MODEL"
],
[
92,
50,
0,
56,
0,
"PIPE_LINE"
],
[
93,
51,
0,
57,
0,
"PIPE_LINE"
],
[
94,
55,
0,
57,
1,
"MODEL"
],
[
95,
54,
0,
57,
2,
"CONDITIONING"
],
[
96,
54,
1,
57,
3,
"CONDITIONING"
],
[
97,
54,
3,
57,
4,
"LATENT"
]
],
"groups": [],
"config": {},
"extra": {
"0246.VERSION": [
0,
0,
4
]
},
"version": 0.4
}他にもまだあるのですが、普段あまりにも使わなすぎて説明できるほど知識ないので省略…
次回はEasyUse+0246やList-Utilsを使った構築かXYかEff-EDかはたまた別のなにかか…
今一番ほしいのは複数のワークフローをモジュール形式で読み込んで、同じフローは別の環境でも変更が適用されるような機能です。だれかー
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