{
  "job": "upscale",
  "want": "detail",
  "rank": 2,
  "short": "tile-held",
  "title": {
    "zh": "平铺放大",
    "en": "Upscale held by tile"
  },
  "about": {
    "zh": "放大时用平铺控制网钉住画面，可以重画得更狠，细节最多。",
    "en": "A tile ControlNet holds the picture so the redraw can be stronger: the most detail."
  },
  "source": {
    "zh": "参考工作流的「通用放大」组（作者默认开着平铺控制网：强度 0.6，到 80% 的步数）",
    "en": "The reference workflow's general upscale, with its tile ControlNet on (strength 0.6, to 80% of the steps) as its author sets it"
  },
  "packs": {},
  "upscaler": true,
  "upscale_by": 1.25,
  "control": "tile",
  "extras": {
    "ColorMatch": "ComfyUI-KJNodes"
  },
  "nodes": {
    "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": "$sampler",
      "inputs": {
        "model": [
          "$sampler.model",
          "*"
        ],
        "positive": [
          "hold",
          0
        ],
        "negative": [
          "hold",
          1
        ],
        "latent_image": [
          "encode",
          0
        ],
        "denoise": 0.5
      }
    },
    "decode": {
      "class_type": "VAEDecode",
      "inputs": {
        "samples": [
          "redraw",
          0
        ],
        "vae": [
          "$decode.vae",
          "*"
        ]
      }
    },
    "match": {
      "class_type": "ColorMatch",
      "optional": true,
      "inputs": {
        "image_ref": [
          "fit",
          0
        ],
        "image_target": [
          "decode",
          0
        ],
        "method": "mkl",
        "strength": 1.0,
        "multithread": true
      }
    },
    "save": {
      "class_type": "SaveImage",
      "inputs": {
        "images": [
          "match|decode",
          0
        ],
        "filename_prefix": "upscaled"
      }
    }
  },
  "variants": {
    "anima": {
      "packs": {
        "AnimaLLLiteApply_sdscripts": "ComfyUI-Anima-LLLite"
      },
      "control": null,
      "lllite": "tile",
      "nodes": {
        "upscaler": {
          "class_type": "UpscaleModelLoader",
          "inputs": {
            "model_name": "$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"
          }
        },
        "encode": {
          "class_type": "VAEEncode",
          "inputs": {
            "pixels": [
              "fit",
              0
            ],
            "vae": [
              "$decode.vae",
              "*"
            ]
          }
        },
        "redraw": {
          "class_type": "KSampler",
          "like": "$sampler",
          "inputs": {
            "model": [
              "hold",
              0
            ],
            "positive": [
              "$sampler.positive",
              "*"
            ],
            "negative": [
              "$sampler.negative",
              "*"
            ],
            "latent_image": [
              "encode",
              0
            ],
            "denoise": 0.5
          }
        },
        "decode": {
          "class_type": "VAEDecode",
          "inputs": {
            "samples": [
              "redraw",
              0
            ],
            "vae": [
              "$decode.vae",
              "*"
            ]
          }
        },
        "match": {
          "class_type": "ColorMatch",
          "optional": true,
          "inputs": {
            "image_ref": [
              "fit",
              0
            ],
            "image_target": [
              "decode",
              0
            ],
            "method": "mkl",
            "strength": 1.0,
            "multithread": true
          }
        },
        "save": {
          "class_type": "SaveImage",
          "inputs": {
            "images": [
              "match|decode",
              0
            ],
            "filename_prefix": "upscaled"
          }
        },
        "hold": {
          "class_type": "AnimaLLLiteApply_sdscripts",
          "inputs": {
            "model": [
              "$sampler.model",
              "*"
            ],
            "lllite_name": "$lllite",
            "image": [
              "fit",
              0
            ],
            "strength": 0.9,
            "start_percent": 0.0,
            "end_percent": 0.8,
            "preserve_wrapper": true
          }
        }
      },
      "after": "On this Anima model Anima's tile-repair LLLite holds the picture (0.9, to 80% of the steps) while it is redrawn at denoise 0.5. run_workflow; the larger one is saved beside it (prefix upscaled). It is slow on a small card (about 10 minutes on 6 GB): say so before running it."
    }
  },
  "after": "run_workflow saves the picture as before and the 1.25× 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. Lower the denoise if it changes too much, raise the strength to hold it closer.",
  "notes": "The source's tile values; denoise 0.5 because the tile holds the picture (the source went to 0.85 with a latent upscale).",
  "models": [
    "union_promax_sdxl",
    "noob_tile",
    "anima_tile_repair",
    "2x_animesharp_v4"
  ],
  "tested": {
    "gpu": "RTX 4050 Laptop",
    "vram_gb": 6,
    "date": "2026-10-27",
    "runs": [
      {
        "model": "SDXL (Illustrious)",
        "size": "832×1216 → 1.25×",
        "seconds": 77
      },
      {
        "model": "Anima",
        "size": "1216×832 → 1.25×",
        "seconds": 710
      }
    ]
  }
}