Tinytterra（ttN）やImpactPackも似た系列ですが、この２つのノードのある程度の部分はEasyUseにビルドインされています。また、ttNは併用の親和性がとても高いので入れておくと良いでしょう。
ワークフローDL（全部一括）
チートシート的に使う用。今回の解説は左半分です。
でかいのくると目が泳いじゃう！
て方は解説の各項目に個別のワークフローも置いてあるので、そっちを使ってください。
試したい場合は下のJSONデータをコピー（右のボタンクリック）してComfyUIが起動しているブラウザ画面で貼り付け（Ctrl+V）してください。もしくは上の画像をDLしてワークフローとして読み込んでください。
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[
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[
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[
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],
[
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]
],
"groups": [
{
"title": "アップスケール（モデル）",
"bounding": [
54,
1922,
1516,
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],
"color": "#3f789e",
"font_size": 24,
"flags": {}
},
{
"title": "Controlnet",
"bounding": [
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],
"color": "#8AA",
"font_size": 24,
"flags": {}
},
{
"title": "シンプルi2i",
"bounding": [
53,
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],
"color": "#3f789e",
"font_size": 24,
"flags": {}
},
{
"title": "シンプルt2i",
"bounding": [
55,
112,
1228,
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],
"color": "#3f789e",
"font_size": 24,
"flags": {}
},
{
"title": "Cntrolnet（Stack）",
"bounding": [
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116,
1523,
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],
"color": "#8AA",
"font_size": 24,
"flags": {}
},
{
"title": "IpAdapter",
"bounding": [
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1547,
1423,
838
],
"color": "#8AA",
"font_size": 24,
"flags": {}
},
{
"title": "Lora-Stack",
"bounding": [
54,
1118,
1303,
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],
"color": "#3f789e",
"font_size": 24,
"flags": {}
},
{
"title": "アップスケール（Latent）",
"bounding": [
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1512,
622
],
"color": "#3f789e",
"font_size": 24,
"flags": {}
},
{
"title": "DetailerFix（Adetailerみたいなやつ）",
"bounding": [
1681,
2406,
2151,
623
],
"color": "#8AA",
"font_size": 24,
"flags": {}
},
{
"title": "シンプルインペイント",
"bounding": [
57,
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1507,
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],
"color": "#3f789e",
"font_size": 24,
"flags": {}
},
{
"title": "Foooooocus インペイント",
"bounding": [
1682,
3082,
1934,
488
],
"color": "#8AA",
"font_size": 24,
"flags": {}
},
{
"title": "Controlnet++ （Union）",
"bounding": [
3253,
685,
1616,
624
],
"color": "#8AA",
"font_size": 24,
"flags": {}
}
],
"config": {},
"extra": {
"workspace_info": {
"id": "3781VAsBbjEG8Fcs3NQ1I",
"saveLock": false,
"cloudID": null,
"coverMediaPath": null
},
"0246.VERSION": [
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0,
4
]
},
"version": 0.4
}Pipeの概念を知ろう
Pipeとは複数のデータをまとめて１本の線で運べるようにしたものです。
正直Pipeじゃなくてもこれだけで済むなら楽やろがいって感じですが、Pipeなら１本繋げば終わりなんですね。ワァ簡単…
途中で別の接続を合流させたい時はPipeInやEdit系で必要な所だけ追加してあげればOKです。
まぁ下の例だとPipeIn使わなくても直接Samplerに渡せばいいんですが、フローの途中で差し込みたい時というのは必ずあるので覚えておくと楽できるかもしれません。
繋げるPipeと繋げないPipe
えーPipeは種類が乱立しています……
基本的に種類が違うものには変換を噛ませないと繋がりません。
Pipe（紫色）
まずEasyUseやttNで使われている先述のやつですね。
Comfyroll Studioも多分これです。
BasicPipe、DetailerPipe
ImpactPackなどで使われているものです。
なおBasicとDetailerは変換しないと繋がりません（なんで…）
Context（ctx）
Rgthree、Eff-EDのctx。ここは接続可能です。
他個別っぽいもの。CPipe、Pipe（灰色）
CrystoolsとArtVentrure。
これもこのノードだけで使えるオリジナル…？
ArtVentrureは名前がただのPipeですが、ttNとかに繋がりません。
個人的に大好きノード0246です。
INに何でも接続できてOutも好きに出せる、かゆい所に手が届く系ノードです。
いつか紹介したいけど説明が難しい…。
基本のつなぎ方
text to image（t2i）
この３つを繋ぐだけです。
Fullノード同士で繋げば２つで済むのですが、3接続の場合はKsamplerを実質Previewとして使えるので基本的には上パターンのほうが便利です。
下パターンだとPreviewをデカく見ようと拡大すると接続の取り回しが悪くなります。
■サンプルワークフロー
{
"last_node_id": 4,
"last_link_id": 2,
"nodes": [
{
"id": 2,
"type": "easy comfyLoader",
"pos": {
"0": 44,
"1": 68
},
"size": [
262,
363.2813720703125
],
"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": [
1
],
"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": 3,
"type": "easy preSampling",
"pos": {
"0": 336,
"1": 70
},
"size": [
210,
242
],
"flags": {},
"order": 1,
"mode": 0,
"inputs": [
{
"name": "pipe",
"type": "PIPE_LINE",
"link": 1
},
{
"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": [
2
],
"slot_index": 0
}
],
"title": "easy preSampling",
"properties": {
"Node name for S&R": "easy preSampling"
},
"widgets_values": [
20,
8,
"euler_ancestral",
"karras",
1,
14766764379128,
"randomize"
]
},
{
"id": 4,
"type": "easy kSampler",
"pos": {
"0": 575,
"1": 69
},
"size": [
315,
126
],
"flags": {},
"order": 2,
"mode": 0,
"inputs": [
{
"name": "pipe",
"type": "PIPE_LINE",
"link": 2
},
{
"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"
]
}
],
"links": [
[
1,
2,
0,
3,
0,
"PIPE_LINE"
],
[
2,
3,
0,
4,
0,
"PIPE_LINE"
]
],
"groups": [],
"config": {},
"extra": {
"0246.VERSION": [
0,
0,
4
]
},
"version": 0.4
}image to image（i2i）
t2iのpreSamplingにLoadImageを繋ぐだけです。これは自動でLatent化してくれるので、VaeEncodeを繋ぐ必要もありません。
Inpaint
一番シンプルなインペイント。こちらはインペイントモデルを使う必要があります。
applyInpaintでinpaint_model_conditioningを選びます。
マスクはLoadImageを右クリックしてOpen in MaskEditorで塗りつぶしてSaveをおします。
■サンプルワークフロー
{
"last_node_id": 10,
"last_link_id": 8,
"nodes": [
{
"id": 5,
"type": "easy comfyLoader",
"pos": {
"0": 39.159942626953125,
"1": 59.10161209106445
},
"size": [
262,
364.93817138671875
],
"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": [
6
],
"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": [
"inpaint\\animaginexl_v31Inpainting.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": 6,
"type": "easy preSampling",
"pos": {
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"1": 61.90629959106445
},
"size": [
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],
"flags": {},
"order": 4,
"mode": 0,
"inputs": [
{
"name": "pipe",
"type": "PIPE_LINE",
"link": 4
},
{
"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": [
5
],
"slot_index": 0
}
],
"title": "easy preSampling",
"properties": {
"Node name for S&R": "easy preSampling"
},
"widgets_values": [
20,
8,
"euler_ancestral",
"karras",
0.538,
66955692789861,
"randomize"
]
},
{
"id": 7,
"type": "easy kSampler",
"pos": {
"0": 1206.7156982421875,
"1": 60.90629959106445
},
"size": [
315,
290
],
"flags": {},
"order": 5,
"mode": 0,
"inputs": [
{
"name": "pipe",
"type": "PIPE_LINE",
"link": 5
},
{
"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": 8,
"type": "LoadImage",
"pos": {
"0": 323.7157287597656,
"1": 115.90629577636719
},
"size": {
"0": 315,
"1": 314
},
"flags": {},
"order": 1,
"mode": 0,
"inputs": [],
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"links": [
7
],
"slot_index": 0
},
{
"name": "MASK",
"type": "MASK",
"links": [
8
],
"slot_index": 1
}
],
"properties": {
"Node name for S&R": "LoadImage"
},
"widgets_values": [
"clipspace/clipspace-mask-1837733.2999999523.png [input]",
"image"
]
},
{
"id": 9,
"type": "easy applyInpaint",
"pos": {
"0": 679.7156982421875,
"1": 60.90629959106445
},
"size": [
272.54833984375,
146
],
"flags": {},
"order": 3,
"mode": 0,
"inputs": [
{
"name": "pipe",
"type": "PIPE_LINE",
"link": 6
},
{
"name": "image",
"type": "IMAGE",
"link": 7
},
{
"name": "mask",
"type": "MASK",
"link": 8
}
],
"outputs": [
{
"name": "pipe",
"type": "PIPE_LINE",
"links": [
4
],
"slot_index": 0
}
],
"title": "easy applyInpaint",
"properties": {
"Node name for S&R": "easy applyInpaint"
},
"widgets_values": [
"normal",
"inpaint_model_conditioning",
6,
"float16",
1,
"text guided",
1,
0,
10000
]
},
{
"id": 10,
"type": "Note",
"pos": {
"0": 656.7156982421875,
"1": 352.90631103515625
},
"size": [
348.364013671875,
79.45137023925781
],
"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用する必要があります。"
],
"color": "#432",
"bgcolor": "#653"
}
],
"links": [
[
4,
9,
0,
6,
0,
"PIPE_LINE"
],
[
5,
6,
0,
7,
0,
"PIPE_LINE"
],
[
6,
5,
0,
9,
0,
"PIPE_LINE"
],
[
7,
8,
0,
9,
1,
"IMAGE"
],
[
8,
8,
1,
9,
2,
"MASK"
]
],
"groups": [],
"config": {},
"extra": {
"0246.VERSION": [
0,
0,
4
]
},
"version": 0.4
}LoRAを使う
一種類だけ使う
LoaderにあるLoRAでセットするだけです。
複数使う_１
LoraStackをLoaderに繋ぎます。（これはEasyUseのStackerじゃなくてもOK）
複数使う_２
ttNのPipeLoraStackを繋ぎます。
こちらは好きな位置で繋ぎこみできるので、例えばLatentUpscale処理をかける時だけLoRAを効かせたい時やXYするほどでもないけどLora有り無しを比較したい時などに役立ちます。
■サンプルワークフロー
{
"last_node_id": 20,
"last_link_id": 10,
"nodes": [
{
"id": 5,
"type": "easy comfyLoader",
"pos": {
"0": 389.1976318359375,
"1": 103.79444885253906
},
"size": [
262,
363.2813720703125
],
"flags": {},
"order": 2,
"mode": 0,
"inputs": [
{
"name": "optional_lora_stack",
"type": "LORA_STACK",
"link": 3,
"shape": 7
},
{
"name": "optional_controlnet_stack",
"type": "CONTROL_NET_STACK",
"link": null,
"shape": 7
}
],
"outputs": [
{
"name": "pipe",
"type": "PIPE_LINE",
"links": [
4
],
"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": 6,
"type": "easy preSampling",
"pos": {
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"1": 101.79444885253906
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"size": [
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242
],
"flags": {},
"order": 4,
"mode": 0,
"inputs": [
{
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"type": "PIPE_LINE",
"link": 4
},
{
"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": [
5
],
"slot_index": 0
}
],
"title": "easy preSampling",
"properties": {
"Node name for S&R": "easy preSampling"
},
"widgets_values": [
20,
8,
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"karras",
1,
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"randomize"
]
},
{
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"type": "easy kSampler",
"pos": {
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"size": [
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],
"flags": {},
"order": 6,
"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": 9,
"type": "easy loraStack",
"pos": {
"0": 100.1976318359375,
"1": 107.79444885253906
},
"size": [
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178
],
"flags": {},
"order": 0,
"mode": 0,
"inputs": [
{
"name": "optional_lora_stack",
"type": "LORA_STACK",
"link": null,
"shape": 7
}
],
"outputs": [
{
"name": "lora_stack",
"type": "LORA_STACK",
"links": [
3
],
"slot_index": 0
}
],
"title": "easy loraStack",
"properties": {
"Node name for S&R": "easy loraStack"
},
"widgets_values": [
true,
"advanced",
1,
"None",
1,
1,
1,
"None",
1,
1,
1,
"None",
1,
1,
1,
"None",
1,
1,
1,
"None",
1,
1,
1,
"None",
1,
1,
1,
"None",
1,
1,
1,
"None",
1,
1,
1,
"None",
1,
1,
1,
"None",
1,
1,
1
]
},
{
"id": 13,
"type": "easy comfyLoader",
"pos": {
"0": 66,
"1": 593
},
"size": [
262,
363.2813720703125
],
"flags": {},
"order": 1,
"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": [
9
],
"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": 20,
"type": "ttN pipeLoraStack",
"pos": {
"0": 359,
"1": 592
},
"size": [
281.84882812499995,
214
],
"flags": {},
"order": 3,
"mode": 0,
"inputs": [
{
"name": "optional_pipe",
"type": "PIPE_LINE",
"link": 9,
"shape": 7
},
{
"name": "model_override",
"type": "MODEL",
"link": null,
"shape": 7
},
{
"name": "clip_override",
"type": "CLIP",
"link": null,
"shape": 7
},
{
"name": "optional_lora_stack",
"type": "LORA_STACK",
"link": null,
"shape": 7
}
],
"outputs": [
{
"name": "optional_pipe",
"type": "PIPE_LINE",
"links": [
10
],
"slot_index": 0
},
{
"name": "lora_stack",
"type": "LORA_STACK",
"links": null
}
],
"title": "ttN pipeLoraStack",
"properties": {
"Node name for S&R": "ttN pipeLoraStack",
"ttNnodeVersion": "1.1.1"
},
"widgets_values": [
true,
"simple",
1,
"None",
1,
1,
1,
"None",
1,
1,
1,
"None",
1,
1,
1,
"None",
1,
1,
1,
"None",
1,
1,
1,
"None",
1,
1,
1,
"None",
1,
1,
1,
"None",
1,
1,
1,
"None",
1,
1,
1,
"None",
1,
1,
1,
"None",
1,
1,
1,
"None",
1,
1,
1,
"None",
1,
1,
1,
"None",
1,
1,
1,
"None",
1,
1,
1,
"None",
1,
1,
1,
"None",
1,
1,
1,
"None",
1,
1,
1,
"None",
1,
1,
1,
"None",
1,
1,
1
]
},
{
"id": 14,
"type": "easy preSampling",
"pos": {
"0": 669,
"1": 590
},
"size": [
210,
242
],
"flags": {},
"order": 5,
"mode": 0,
"inputs": [
{
"name": "pipe",
"type": "PIPE_LINE",
"link": 10
},
{
"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": [
20,
8,
"euler_ancestral",
"karras",
1,
983795126299274,
"randomize"
]
},
{
"id": 15,
"type": "easy kSampler",
"pos": {
"0": 902,
"1": 590
},
"size": [
315,
126
],
"flags": {},
"order": 7,
"mode": 0,
"inputs": [
{
"name": "pipe",
"type": "PIPE_LINE",
"link": 8
},
{
"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"
]
}
],
"links": [
[
3,
9,
0,
5,
0,
"LORA_STACK"
],
[
4,
5,
0,
6,
0,
"PIPE_LINE"
],
[
5,
6,
0,
7,
0,
"PIPE_LINE"
],
[
8,
14,
0,
15,
0,
"PIPE_LINE"
],
[
9,
13,
0,
20,
0,
"PIPE_LINE"
],
[
10,
20,
0,
14,
0,
"PIPE_LINE"
]
],
"groups": [
{
"title": "Loaderにつなぐ",
"bounding": [
31,
18,
1210,
466
],
"color": "#3f789e",
"font_size": 24,
"flags": {}
},
{
"title": "途中で繋ぐ",
"bounding": [
34,
506,
1209,
471
],
"color": "#3f789e",
"font_size": 24,
"flags": {}
}
],
"config": {},
"extra": {
"0246.VERSION": [
0,
0,
4
]
},
"version": 0.4
}アップスケール
モデル使用
Ksamplerの後ろにHiresFixを繋いでモデルを指定します。
ビルドイン以外のアップスケールモデルも使用できます。
アップスケールモデルはManagerのModel ManagerからTypeをUpscaleを選べばインストールできます。
アップスケールモデルDBから探して自分でフォルダに入れるでもOK。
ComfyUI単独インストールならmodels/upscale_modelsへ
StabilityMatrixインストールならModels/ESRGANへ
OpenModelDB
LatentUpscale
EasyUseだけでやろうと思うと間にLatentUpscaleノードを挟んだりする必要があるのでttNのSamplerを繋げるのが一番手軽です。
■サンプルワークフロー
{
"last_node_id": 4,
"last_link_id": 3,
"nodes": [
{
"id": 1,
"type": "easy comfyLoader",
"pos": {
"0": 100.64781951904297,
"1": 113.9732666015625
},
"size": [
262,
363.2813720703125
],
"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": [
1
],
"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": 2,
"type": "easy preSampling",
"pos": {
"0": 390.5852966308594,
"1": 112.4625244140625
},
"size": [
210,
242
],
"flags": {},
"order": 1,
"mode": 0,
"inputs": [
{
"name": "pipe",
"type": "PIPE_LINE",
"link": 1
},
{
"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": [
2
],
"slot_index": 0
}
],
"title": "easy preSampling",
"properties": {
"Node name for S&R": "easy preSampling"
},
"widgets_values": [
20,
8,
"euler_ancestral",
"karras",
1,
126449865765594,
"randomize"
]
},
{
"id": 3,
"type": "easy kSampler",
"pos": {
"0": 622.5852661132812,
"1": 113.4625244140625
},
"size": [
225.57479858398438,
290
],
"flags": {},
"order": 2,
"mode": 0,
"inputs": [
{
"name": "pipe",
"type": "PIPE_LINE",
"link": 2
},
{
"name": "model",
"type": "MODEL",
"link": null,
"shape": 7
}
],
"outputs": [
{
"name": "pipe",
"type": "PIPE_LINE",
"links": [
3
],
"slot_index": 0
},
{
"name": "image",
"type": "IMAGE",
"links": [],
"slot_index": 1
}
],
"title": "easy kSampler",
"properties": {
"Node name for S&R": "easy kSampler"
},
"widgets_values": [
"Preview",
0,
"ComfyUI"
]
},
{
"id": 4,
"type": "ttN pipeKSampler_v2",
"pos": {
"0": 866.5852661132812,
"1": 115.4625244140625
},
"size": [
304.80712890625,
526
],
"flags": {},
"order": 3,
"mode": 0,
"inputs": [
{
"name": "pipe",
"type": "PIPE_LINE",
"link": 3
},
{
"name": "optional_model",
"type": "MODEL",
"link": null,
"shape": 7
},
{
"name": "optional_positive",
"type": "CONDITIONING",
"link": null,
"shape": 7
},
{
"name": "optional_negative",
"type": "CONDITIONING",
"link": null,
"shape": 7
},
{
"name": "optional_latent",
"type": "LATENT",
"link": null,
"shape": 7
},
{
"name": "optional_vae",
"type": "VAE",
"link": null,
"shape": 7
},
{
"name": "optional_clip",
"type": "CLIP",
"link": null,
"shape": 7
},
{
"name": "input_image_override",
"type": "IMAGE",
"link": null,
"shape": 7
},
{
"name": "adv_xyPlot",
"type": "ADV_XYPLOT",
"link": null,
"shape": 7
}
],
"outputs": [
{
"name": "pipe",
"type": "PIPE_LINE",
"links": null
},
{
"name": "model",
"type": "MODEL",
"links": null
},
{
"name": "positive",
"type": "CONDITIONING",
"links": null
},
{
"name": "negative",
"type": "CONDITIONING",
"links": null
},
{
"name": "latent",
"type": "LATENT",
"links": null
},
{
"name": "vae",
"type": "VAE",
"links": null
},
{
"name": "clip",
"type": "CLIP",
"links": null
},
{
"name": "images",
"type": "IMAGE",
"links": [],
"slot_index": 7
},
{
"name": "seed",
"type": "INT",
"links": null
},
{
"name": "plot_image",
"type": "IMAGE",
"links": null
}
],
"title": "ttN pipeKSampler_v2",
"properties": {
"Node name for S&R": "ttN pipeKSampler_v2",
"ttNnodeVersion": "2.3.1"
},
"widgets_values": [
"None",
1,
"[latent] nearest-exact",
"2x_NMKD-YandereNeo-Lite_320k-10k.pth",
1.5,
"by percentage",
50,
1024,
1024,
1024,
"disabled",
20,
8,
"euler_ancestral",
"karras",
0.552,
"Preview",
"ComfyUI",
"png",
true,
962680672102791,
"randomize",
null
]
}
],
"links": [
[
1,
1,
0,
2,
0,
"PIPE_LINE"
],
[
2,
2,
0,
3,
0,
"PIPE_LINE"
],
[
3,
3,
0,
4,
0,
"PIPE_LINE"
]
],
"groups": [],
"config": {},
"extra": {
"0246.VERSION": [
0,
0,
4
]
},
"version": 0.4
}Pipe変換
BasicPipe ー＞Pipe変換は無いっぽい…？
やりたい場面はあんまりない気はしますが、やる場合はPipeInとかで拾うしか無いですね。
類似ノード
下記２つですが実質１つ。
Eff-V2とEff-ED（Eff拡張ノードみたいなやつ）があります。
こちらもそのうち使い方紹介作るかもしれません。
GitHub - jags111/efficiency-nodes-comfyui: A collection of ComfyUI custom nodes.- Awesome smart way to work with nodes!
A collection of ComfyUI custom nodes.- Awesome smart way to work with nodes! - jags111/efficiency-nodes-comfyui
github.com
GitHub - NyaamZ/efficiency-nodes-ED: ▷Efficiency Nodes 💬ExtendeD - Expansion of Efficiency Nodes for ComfyUI. Significant UX improvements.
▷Efficiency Nodes 💬ExtendeD - Expansion of Efficiency Nodes for ComfyUI. Significant UX improvements. - NyaamZ/efficienc...
github.com
次は中級編。ビルドイン系ノードを使って圧縮しよう。
