{
  "prompt": {
    "1": {
      "class_type": "UNETLoader",
      "inputs": {
        "unet_name": "flux-2-klein-4b-fp8.safetensors",
        "weight_dtype": "default"
      }
    },
    "2": {
      "class_type": "CLIPLoader",
      "inputs": {
        "clip_name": "qwen_3_4b.safetensors",
        "type": "flux2",
        "device": "default"
      }
    },
    "3": {
      "class_type": "VAELoader",
      "inputs": {
        "vae_name": "flux2-vae.safetensors"
      }
    },
    "4": {
      "class_type": "CLIPTextEncode",
      "inputs": {
        "text": "Create a new professionally art-directed vertical poster from the supplied character reference, not a simple recoloring of the source. Keep the woman / black hair / red coat / umbrella motif, adapting the rendering to the medium. The only writing allowed is the exact title AFTER THE RAIN. No credits, taglines, logos, watermarks or invented writing. Design a convincing photographed physical paper-cut theater poster with deep layered construction. A tiny red-paper-coated woman holding a translucent vellum umbrella stands in the lower center. Six nested cut-paper city arches recede behind her to a small warm glowing window, with clear perspective and asymmetrical teal building silhouettes. Make every surface visibly paper: thick cream cut edges, slight curling, soft contact shadows, tiny fiber texture; raindrops are separate narrow cream paper strips suspended between layers. Controlled petrol teal, pale cream and coral red palette, warm light from the far background and cool soft frontal light. Leave a substantial flat cream foreground plinth for one bold dark-teal title that appears cut from paper. No extra text, no realistic human portrait pasted on, no plastic or clay appearance.",
        "clip": [
          "2",
          0
        ]
      }
    },
    "5": {
      "class_type": "ConditioningZeroOut",
      "inputs": {
        "conditioning": [
          "4",
          0
        ]
      }
    },
    "6": {
      "class_type": "EmptyFlux2LatentImage",
      "inputs": {
        "width": 768,
        "height": 1152,
        "batch_size": 1
      }
    },
    "7": {
      "class_type": "RandomNoise",
      "inputs": {
        "noise_seed": 20262008
      }
    },
    "8": {
      "class_type": "KSamplerSelect",
      "inputs": {
        "sampler_name": "euler"
      }
    },
    "9": {
      "class_type": "Flux2Scheduler",
      "inputs": {
        "steps": 4,
        "width": 768,
        "height": 1152
      }
    },
    "10": {
      "class_type": "CFGGuider",
      "inputs": {
        "model": [
          "1",
          0
        ],
        "positive": [
          "22",
          0
        ],
        "negative": [
          "23",
          0
        ],
        "cfg": 1.0
      }
    },
    "11": {
      "class_type": "SamplerCustomAdvanced",
      "inputs": {
        "noise": [
          "7",
          0
        ],
        "guider": [
          "10",
          0
        ],
        "sampler": [
          "8",
          0
        ],
        "sigmas": [
          "9",
          0
        ],
        "latent_image": [
          "6",
          0
        ]
      }
    },
    "12": {
      "class_type": "VAEDecode",
      "inputs": {
        "samples": [
          "11",
          0
        ],
        "vae": [
          "3",
          0
        ]
      }
    },
    "13": {
      "class_type": "SaveImage",
      "inputs": {
        "images": [
          "12",
          0
        ],
        "filename_prefix": "spark_demos/posters_10_refined/09_papercut"
      }
    },
    "20": {
      "class_type": "LoadImage",
      "inputs": {
        "image": "ref_01.png"
      }
    },
    "21": {
      "class_type": "VAEEncode",
      "inputs": {
        "pixels": [
          "20",
          0
        ],
        "vae": [
          "3",
          0
        ]
      }
    },
    "22": {
      "class_type": "ReferenceLatent",
      "inputs": {
        "conditioning": [
          "4",
          0
        ],
        "latent": [
          "21",
          0
        ]
      }
    },
    "23": {
      "class_type": "ReferenceLatent",
      "inputs": {
        "conditioning": [
          "5",
          0
        ],
        "latent": [
          "21",
          0
        ]
      }
    }
  }
}