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DemonGatanjieu-Anomalous_Mo…/api/blocks/upscale_tile.json
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DemonGatanjieuandClaude Opus 5.5 8ecc261949 Let ready-made blocks say what they hand the next one, and build them as a chain
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>
2026-11-09 00:42:56 +08:00

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{
"job": "upscale",
"want": "detail",
"rank": 2,
"short": "held by Tile, own model",
"title": {
"zh": "Tile 锁图放大",
"en": "Upscale held by Tile"
},
"about": {
"zh": "放大时用 Tile 钉住画面,重画得更狠、细节更多。放大这一段有自己的一份模型(默认和底图同一个,LoRA 一起带上),想换第二个模型只要换这一格;Anima 上用 Anima 的 Tile LLLite。",
"en": "A Tile holds the picture while it is enlarged and redrawn, so the redraw can go stronger: more detail. The redraw has its own copy of the model (the base's, its LoRAs too); a second model is one swap of that loader. On Anima, Anima's Tile LLLite holds it."
},
"source": {
"zh": "参考工作流的「通用放大」组(平铺控制网 0.6,到 80% 的步数)和它的放大加载器;Anima 是你验证过的两段放大卡(潜空间 1.22 倍 + Tile LLLite,降噪 0.85)",
"en": "The reference workflow's general upscale (tile ControlNet 0.6, to 80% of the steps) and its upscale loader; on Anima the user's tested two-pass card (latent 1.22x, Tile LLLite, denoise 0.85)"
},
"packs": {},
"upscaler": true,
"upscale_by": 1.25,
"control": "tile",
"extras": {
"ColorMatch": "ComfyUI-KJNodes"
},
"nodes": {
"own": {
"class_type": "CheckpointLoaderSimple",
"copy_of": "$sampler.model",
"_meta": {
"title": "Upscale model"
},
"inputs": {}
},
"upscaler": {
"class_type": "UpscaleModelLoader",
"inputs": {
"model_name": "$upscaler"
}
},
"enlarge": {
"class_type": "ImageUpscaleWithModel",
"inputs": {
"upscale_model": [
"upscaler",
0
],
"image": [
"$save.images",
"*"
]
}
},
"fit": {
"class_type": "ImageScaleBy",
"inputs": {
"image": [
"enlarge",
0
],
"upscale_method": "lanczos",
"scale_by": "$fit"
}
},
"controlnet": {
"class_type": "ControlNetLoader",
"inputs": {
"control_net_name": "$controlnet"
}
},
"hold": {
"class_type": "ControlNetApplyAdvanced",
"inputs": {
"positive": [
"$sampler.positive",
"*"
],
"negative": [
"$sampler.negative",
"*"
],
"control_net": [
"controlnet",
0
],
"image": [
"fit",
0
],
"vae": [
"$decode.vae",
"*"
],
"strength": 0.6,
"start_percent": 0.0,
"end_percent": 0.8
}
},
"encode": {
"class_type": "VAEEncode",
"inputs": {
"pixels": [
"fit",
0
],
"vae": [
"$decode.vae",
"*"
]
}
},
"redraw": {
"class_type": "KSampler",
"like": "$sampler",
"inputs": {
"model": [
"own",
0
],
"positive": [
"hold",
0
],
"negative": [
"hold",
1
],
"latent_image": [
"encode",
0
],
"denoise": 0.5
}
},
"decode": {
"class_type": "VAEDecode",
"inputs": {
"samples": [
"redraw",
0
],
"vae": [
"$decode.vae",
"*"
]
}
},
"match": {
"class_type": "ColorMatch",
"optional": true,
"inputs": {
"image_ref": [
"fit",
0
],
"image_target": [
"decode",
0
],
"method": "mkl",
"strength": 1.0,
"multithread": true
}
},
"save": {
"class_type": "SaveImage",
"inputs": {
"images": [
"match|decode",
0
],
"filename_prefix": "upscaled"
}
}
},
"variants": {
"anima": {
"packs": {
"AnimaLLLiteApply_sdscripts": "ComfyUI-Anima-LLLite"
},
"upscaler": false,
"control": null,
"lllite": "tile",
"nodes": {
"own": {
"class_type": "UNETLoader",
"copy_of": "$sampler.model",
"_meta": {
"title": "Upscale model"
},
"inputs": {}
},
"encode": {
"class_type": "VAEEncodeTiled",
"inputs": {
"pixels": [
"$save.images",
"*"
],
"vae": [
"$decode.vae",
"*"
],
"tile_size": 512,
"overlap": 64,
"temporal_size": 64,
"temporal_overlap": 8
}
},
"enlarge": {
"class_type": "LatentUpscaleBy",
"inputs": {
"samples": [
"encode",
0
],
"upscale_method": "bislerp",
"scale_by": 1.22
}
},
"tile": {
"class_type": "AnimaLLLiteApply_sdscripts",
"inputs": {
"model": [
"own",
0
],
"lllite_name": "$lllite",
"image": [
"$save.images",
"*"
],
"strength": 0.7,
"start_percent": 0.0,
"end_percent": 0.8,
"preserve_wrapper": true
}
},
"redraw": {
"class_type": "KSampler",
"like": "$sampler",
"inputs": {
"model": [
"tile",
0
],
"positive": [
"$sampler.positive",
"*"
],
"negative": [
"$sampler.negative",
"*"
],
"latent_image": [
"enlarge",
0
],
"steps": 30,
"cfg": 4.4,
"sampler_name": "dpmpp_2m_sde_gpu",
"scheduler": "sgm_uniform",
"denoise": 0.85
}
},
"decode": {
"class_type": "VAEDecodeTiled",
"inputs": {
"samples": [
"redraw",
0
],
"vae": [
"$decode.vae",
"*"
],
"tile_size": 512,
"overlap": 64,
"temporal_size": 64,
"temporal_overlap": 8
}
},
"match": {
"class_type": "ColorMatch",
"optional": true,
"inputs": {
"image_ref": [
"$save.images",
"*"
],
"image_target": [
"decode",
0
],
"method": "mkl",
"strength": 1.0,
"multithread": true
}
},
"save": {
"class_type": "SaveImage",
"inputs": {
"images": [
"match|decode",
0
],
"filename_prefix": "upscaled"
}
}
},
"after": "On this Anima model the picture is enlarged 1.22x in latent space and redrawn at denoise 0.85, held by Anima's Tile LLLite (0.7, to 80% of the steps), with its own copy of the canvas's model and LoRAs (the loader titled Upscale model). For a second model (steadier, other skin or texture), set_model that loader to another Anima model; the LoRAs stay. Then run_workflow; the larger one is saved beside it (prefix upscaled). Compare face, hands and clothes with the base: Tile guides, it does not guarantee."
}
},
"after": "run_workflow saves the picture as before and the 1.25x one beside it (prefix upscaled): a tile ControlNet holds it (strength 0.6, to 80% of the steps) while it is redrawn at denoise 0.5 with its own copy of the canvas's model and LoRAs (the loader titled Upscale model). For a second model (steadier, another look), set_model that loader to one of the same family (it reuses the first model's prompts); the LoRAs stay. Lower the denoise if it changes too much, raise the strength to hold it closer.",
"gives": {
"sampler": "redraw",
"save": "save"
},
"own_model": {
"nodes": [
"own_1",
"own"
],
"copies_loras": true
},
"notes": "The source's tile values; denoise 0.5 because the tile holds the picture. The redraw's own model line replaced the separate second-model way (2026-11-04): same model by default, one swap for another. On Anima the user's tested route: Harem 1.5 base, Realskin 1.3 for the Tile upscale, its style LoRA on both, 832x1216 to 1016x1480, 116 s for the upscale on 6 GB.",
"models": [
"union_promax_sdxl",
"noob_tile",
"anima_tile_repair",
"2x_animesharp_v4",
"4x_ultrasharp"
],
"tested": {
"gpu": "RTX 4050 Laptop",
"vram_gb": 6,
"date": "2026-11-02",
"runs": [
{
"model": "SDXL (Illustrious)",
"size": "832×1216 → 1.25×",
"seconds": 77
},
{
"model": "Anima (Kirazuri → Realskin 1.3, Tile)",
"size": "1216×832 → 1.22×",
"seconds": 127
}
]
}
}