2026/04/11(土)ComfyUIを使ってみる2
投稿日:
去年の六月にComfyUIを使ってみるという記事を書いたが、当時はAUTOMATIC1111との差を覆せず、イマイチという結論に終わった。その後AUTOMATIC1111からreForgeに乗り換えたものの、ComfyUIへの未練は残っていた。そこで今回あらためて挑戦してみたところ上手くいったので、その記録を残しておく。
確認環境
ソフトウェア
ComfyUI v0.18.5
ハードウェア
前回とマザボが変わっているが、これによる差はないだろう。
| デバイス | 製品 |
|---|---|
| CPU | Intel Core Ultra 7 265F |
| GPU | GeForce RTX 5070 Ti |
| MEM | Crucial CT2K16G56C46U5 * 4 |
| M/B | ASRock Z890 Pro RS |
導入方法
インストール
公式サイトからインストーラーをダウンロードして実行するだけ。
インストール先は変えてはならない。もし変えると起動してもフリーズするようになったりする。
各種設定
ComfyUI-Managerはデフォルトで入ってるので入れなくてよい。
- 設定>Comfyから言語を英語にする[1]
- Settings>Server-Configを開き、一番下までスクロールし、DirectoriesにあるOutput directoryを適当に変える
- ここが生成した画像の出力先になる
- Settings>Graph>Link Render ModeでStraight
- ノードを繋ぐ線が直線になり、見やすくなる
- Settings>Graph>Always snap to gridをON
- ノードがグリッドにスナップするようになる
- Extentionsを開きComfyUI-Custom-Scriptsを入れておくと幸せになるらしいので入れる
資材パスの変更方法
モデルなどは容量を食って重いので別のドライブに移動させる方法。
- 別ドライブのどこか適当な場所に
%HOMEPATH%\Documents\ComfyUI配下のフォルダを丸ごと移動する %HOMEPATH%\AppData\Roaming\ComfyUI\extra_models_config.yamlを開く- 取り敢えずこんくらいの設定にしておけばよいと思う。パスの尻に``が入ってると起動しなくなるので注意
comfyui_desktop: is_default: "true" custom_nodes: custom_nodes download_model_base: models base_path: E:\path\to\ComfyUI # models\Stable-diffusion checkpoints: models\checkpoints # models\ControlNet controlnet: models\controlnet # embeddings\ embeddings: models\embeddings # models\Lora loras: models\loras # models\VAE vae: models\vae desktop_extensions: # 元から記述がある部分、変える必要はない custom_nodes: C:\Users\hoge\AppData\Local\Programs\ComfyUI\resources\ComfyUI\custom_nodes
AUTOMATIC1111やreForgeの設定をワークロードに反映させる方法
一例として、りこベンチの設定の再現方法。ComfyUIはアップスケーラー周りの設定が分かってないとゴミ画像が出てくるので、アップスケーラーの設定が重要になる。
全体像としてはこんな感じで、以前書いたComfyUIを使ってみると構成するノードには大きな変わりはない。
ポイントは一段目のKSamplerでdenoiseを1.00にし、二段目で0.70にする部分だ。両方を0.70にすると画質が大幅に劣化する。
ベンチスコア
りこベンチ設定では64.76秒を記録し、AUTOMATIC1111からreForgeに乗り換えた時のスコアである81秒と比べると16.24秒も早くなっている。
使用したワークフロー
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"last_link_id": 25,
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"outputs": [
{
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{
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"name": "CLIP",
"type": "CLIP",
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"links": [11]
},
{
"localized_name": "VAE",
"name": "VAE",
"type": "VAE",
"slot_index": 2,
"links": [8]
}
],
"properties": { "Node name for S&R": "CheckpointLoaderSimple" },
"widgets_values": ["waiNSFWIllustrious_v150.safetensors"]
},
{
"id": 11,
"type": "CLIPSetLastLayer",
"pos": [-260, 380],
"size": [270, 60],
"flags": {},
"order": 2,
"mode": 0,
"inputs": [
{
"localized_name": "clip",
"name": "clip",
"type": "CLIP",
"link": 11
},
{
"localized_name": "stop_at_clip_layer",
"name": "stop_at_clip_layer",
"type": "INT",
"widget": { "name": "stop_at_clip_layer" },
"link": null
}
],
"outputs": [
{
"localized_name": "CLIP",
"name": "CLIP",
"type": "CLIP",
"links": [12, 13]
}
],
"properties": { "Node name for S&R": "CLIPSetLastLayer" },
"widgets_values": [-2]
},
{
"id": 3,
"type": "KSampler",
"pos": [490, 240],
"size": [320, 270],
"flags": {},
"order": 5,
"mode": 0,
"inputs": [
{
"localized_name": "model",
"name": "model",
"type": "MODEL",
"link": 1
},
{
"localized_name": "positive",
"name": "positive",
"type": "CONDITIONING",
"link": 15
},
{
"localized_name": "negative",
"name": "negative",
"type": "CONDITIONING",
"link": 14
},
{
"localized_name": "latent_image",
"name": "latent_image",
"type": "LATENT",
"link": 22
},
{
"localized_name": "seed",
"name": "seed",
"type": "INT",
"widget": { "name": "seed" },
"link": null
},
{
"localized_name": "steps",
"name": "steps",
"type": "INT",
"widget": { "name": "steps" },
"link": null
},
{
"localized_name": "cfg",
"name": "cfg",
"type": "FLOAT",
"widget": { "name": "cfg" },
"link": null
},
{
"localized_name": "sampler_name",
"name": "sampler_name",
"type": "COMBO",
"widget": { "name": "sampler_name" },
"link": null
},
{
"localized_name": "scheduler",
"name": "scheduler",
"type": "COMBO",
"widget": { "name": "scheduler" },
"link": null
},
{
"localized_name": "denoise",
"name": "denoise",
"type": "FLOAT",
"widget": { "name": "denoise" },
"link": null
}
],
"outputs": [
{
"localized_name": "LATENT",
"name": "LATENT",
"type": "LATENT",
"slot_index": 0,
"links": [24]
}
],
"properties": { "Node name for S&R": "KSampler" },
"widgets_values": [
735067360423163,
"randomize",
20,
7,
"dpmpp_2m",
"karras",
1
]
},
{
"id": 17,
"type": "LatentUpscaleBy",
"pos": [60, 100],
"size": [270, 90],
"flags": {},
"order": 6,
"mode": 0,
"inputs": [
{
"localized_name": "samples",
"name": "samples",
"type": "LATENT",
"link": 24
},
{
"localized_name": "upscale_method",
"name": "upscale_method",
"type": "COMBO",
"widget": { "name": "upscale_method" },
"link": null
},
{
"localized_name": "scale_by",
"name": "scale_by",
"type": "FLOAT",
"widget": { "name": "scale_by" },
"link": null
}
],
"outputs": [
{
"localized_name": "LATENT",
"name": "LATENT",
"type": "LATENT",
"links": [25]
}
],
"properties": { "Node name for S&R": "LatentUpscaleBy" },
"widgets_values": ["nearest-exact", 2]
},
{
"id": 15,
"type": "KSampler",
"pos": [490, 550],
"size": [320, 270],
"flags": {},
"order": 7,
"mode": 0,
"inputs": [
{
"localized_name": "model",
"name": "model",
"type": "MODEL",
"link": 18
},
{
"localized_name": "positive",
"name": "positive",
"type": "CONDITIONING",
"link": 20
},
{
"localized_name": "negative",
"name": "negative",
"type": "CONDITIONING",
"link": 21
},
{
"localized_name": "latent_image",
"name": "latent_image",
"type": "LATENT",
"link": 25
},
{
"localized_name": "seed",
"name": "seed",
"type": "INT",
"widget": { "name": "seed" },
"link": null
},
{
"localized_name": "steps",
"name": "steps",
"type": "INT",
"widget": { "name": "steps" },
"link": null
},
{
"localized_name": "cfg",
"name": "cfg",
"type": "FLOAT",
"widget": { "name": "cfg" },
"link": null
},
{
"localized_name": "sampler_name",
"name": "sampler_name",
"type": "COMBO",
"widget": { "name": "sampler_name" },
"link": null
},
{
"localized_name": "scheduler",
"name": "scheduler",
"type": "COMBO",
"widget": { "name": "scheduler" },
"link": null
},
{
"localized_name": "denoise",
"name": "denoise",
"type": "FLOAT",
"widget": { "name": "denoise" },
"link": null
}
],
"outputs": [
{
"localized_name": "LATENT",
"name": "LATENT",
"type": "LATENT",
"slot_index": 0,
"links": [19]
}
],
"properties": { "Node name for S&R": "KSampler" },
"widgets_values": [
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"randomize",
20,
7,
"dpmpp_2m",
"karras",
0.7
]
},
{
"id": 8,
"type": "VAEDecode",
"pos": [490, 140],
"size": [210, 50],
"flags": {},
"order": 8,
"mode": 0,
"inputs": [
{
"localized_name": "samples",
"name": "samples",
"type": "LATENT",
"link": 19
},
{ "localized_name": "vae", "name": "vae", "type": "VAE", "link": 8 }
],
"outputs": [
{
"localized_name": "IMAGE",
"name": "IMAGE",
"type": "IMAGE",
"slot_index": 0,
"links": [9]
}
],
"properties": { "Node name for S&R": "VAEDecode" },
"widgets_values": []
},
{
"id": 9,
"type": "SaveImage",
"pos": [830, 140],
"size": [260, 270],
"flags": {},
"order": 9,
"mode": 0,
"inputs": [
{
"localized_name": "images",
"name": "images",
"type": "IMAGE",
"link": 9
},
{
"localized_name": "filename_prefix",
"name": "filename_prefix",
"type": "STRING",
"widget": { "name": "filename_prefix" },
"link": null
}
],
"outputs": [],
"properties": {},
"widgets_values": ["ComfyUI"]
},
{
"id": 14,
"type": "CLIPTextEncode",
"pos": [50, 460],
"size": [430, 190],
"flags": {},
"order": 4,
"mode": 0,
"inputs": [
{
"localized_name": "clip",
"name": "clip",
"type": "CLIP",
"link": 13
},
{
"localized_name": "text",
"name": "text",
"type": "STRING",
"widget": { "name": "text" },
"link": null
}
],
"outputs": [
{
"localized_name": "CONDITIONING",
"name": "CONDITIONING",
"type": "CONDITIONING",
"slot_index": 0,
"links": [14, 21]
}
],
"properties": { "Node name for S&R": "CLIPTextEncode" },
"widgets_values": [
"nsfw, (worst quality, low quality:1.4), (depth of field, blurry, bokeh:1.5), (greyscale, monochrome:1.0), multiple views, text, title, logo, signature, (tooth, lip, nose, 3d, realistic:1.0), dutch angle,(cropped:1.4), text, title, signature, logo, (loli:1.2), school satchel, pink, school bag, school uniform, from behind"
]
},
{
"id": 7,
"type": "CLIPTextEncode",
"pos": [50, 240],
"size": [430, 190],
"flags": {},
"order": 3,
"mode": 0,
"inputs": [
{
"localized_name": "clip",
"name": "clip",
"type": "CLIP",
"link": 12
},
{
"localized_name": "text",
"name": "text",
"type": "STRING",
"widget": { "name": "text" },
"link": null
}
],
"outputs": [
{
"localized_name": "CONDITIONING",
"name": "CONDITIONING",
"type": "CONDITIONING",
"slot_index": 0,
"links": [15, 20]
}
],
"properties": { "Node name for S&R": "CLIPTextEncode" },
"widgets_values": [
"(illustration:1.0), masterpiece, best quality, 1girl, solo, happy, smile, theater, (perspective:1.3), from below, (looking away:1.2), (from side:1.0), (shot_hair:1.1), smile, bangs, shaggy, (brown hair:1.1), swept_bangs, thick_eyebrows, skin_fang, closed mouth, (purple eyes:1.1), gray (jacket:1.1), white shirt, glasses, (small breasts:1.1),\n"
]
},
{
"id": 16,
"type": "EmptyLatentImage",
"pos": [-250, 90],
"size": [270, 110],
"flags": {},
"order": 1,
"mode": 0,
"inputs": [
{
"localized_name": "width",
"name": "width",
"type": "INT",
"widget": { "name": "width" },
"link": null
},
{
"localized_name": "height",
"name": "height",
"type": "INT",
"widget": { "name": "height" },
"link": null
},
{
"localized_name": "batch_size",
"name": "batch_size",
"type": "INT",
"widget": { "name": "batch_size" },
"link": null
}
],
"outputs": [
{
"localized_name": "LATENT",
"name": "LATENT",
"type": "LATENT",
"links": [22]
}
],
"properties": { "Node name for S&R": "EmptyLatentImage" },
"widgets_values": [768, 768, 1]
}
],
"links": [
[1, 4, 0, 3, 0, "MODEL"],
[8, 4, 2, 8, 1, "VAE"],
[9, 8, 0, 9, 0, "IMAGE"],
[11, 4, 1, 11, 0, "CLIP"],
[12, 11, 0, 7, 0, "CLIP"],
[13, 11, 0, 14, 0, "CLIP"],
[14, 14, 0, 3, 2, "CONDITIONING"],
[15, 7, 0, 3, 1, "CONDITIONING"],
[18, 4, 0, 15, 0, "MODEL"],
[19, 15, 0, 8, 0, "LATENT"],
[20, 7, 0, 15, 1, "CONDITIONING"],
[21, 14, 0, 15, 2, "CONDITIONING"],
[22, 16, 0, 3, 3, "LATENT"],
[24, 3, 0, 17, 0, "LATENT"],
[25, 17, 0, 15, 3, "LATENT"]
],
"groups": [],
"config": {},
"extra": {
"ds": {
"scale": 1.1284491351375,
"offset": [757.6336710071444, 145.5718113186996]
}
},
"version": 0.4
}
生成された画像
reForgeと変わらない、ちゃんとした品質のものを出すことができた。
あとがき
reForgeと同じ品質で、更に早く出せることが分かったので、これは乗り換えができそうだ。
Civitaiで配布されているワークフローを見る感じ、同じ絵柄で別の構図を出すワークフローなどもあるようなので、非常に期待できる。
他にもForge Coupleのような機能もあり、全体的にreForgeより高機能で、使い方次第では何でもできそうな気配を感じるので、今回、無事ComfyUIに乗り換えられたのはとてもよかった。
今回行った調査にはClaude Opus 4.6を使い、前回の問題点の調査と、その対策方法を調べたが、非常に有益な情報が得られたので、こういったことをするにもLLMも必須の存在といえることがよく分かった。
- 日本語だと機能を探しづらいため ↩




