{
  "KSampler": {
    "input": {
      "required": {
        "model": [
          "MODEL",
          {
            "tooltip": "The model used for denoising the input latent."
          }
        ],
        "seed": [
          "INT",
          {
            "default": 0,
            "min": 0,
            "max": 18446744073709551615,
            "control_after_generate": true,
            "tooltip": "The random seed used for creating the noise."
          }
        ],
        "steps": [
          "INT",
          {
            "default": 20,
            "min": 1,
            "max": 10000,
            "tooltip": "The number of steps used in the denoising process."
          }
        ],
        "cfg": [
          "FLOAT",
          {
            "default": 8.0,
            "min": 0.0,
            "max": 100.0,
            "step": 0.1,
            "round": 0.01,
            "tooltip": "The Classifier-Free Guidance scale balances creativity and adherence to the prompt. Higher values result in images more closely matching the prompt however too high values will negatively impact quality."
          }
        ],
        "sampler_name": [
          [
            "euler",
            "euler_cfg_pp",
            "euler_ancestral",
            "euler_ancestral_cfg_pp",
            "heun",
            "heunpp2",
            "dpm_2",
            "dpm_2_ancestral",
            "lms",
            "dpm_fast",
            "dpm_adaptive",
            "dpmpp_2s_ancestral",
            "dpmpp_2s_ancestral_cfg_pp",
            "dpmpp_sde",
            "dpmpp_sde_gpu",
            "dpmpp_2m",
            "dpmpp_2m_cfg_pp",
            "dpmpp_2m_sde",
            "dpmpp_2m_sde_gpu",
            "dpmpp_3m_sde",
            "dpmpp_3m_sde_gpu",
            "ddpm",
            "lcm",
            "ipndm",
            "ipndm_v",
            "deis",
            "res_multistep",
            "res_multistep_cfg_pp",
            "res_multistep_ancestral",
            "res_multistep_ancestral_cfg_pp",
            "gradient_estimation",
            "er_sde",
            "ddim",
            "uni_pc",
            "uni_pc_bh2"
          ],
          {
            "tooltip": "The algorithm used when sampling, this can affect the quality, speed, and style of the generated output."
          }
        ],
        "scheduler": [
          [
            "normal",
            "karras",
            "exponential",
            "sgm_uniform",
            "simple",
            "ddim_uniform",
            "beta",
            "linear_quadratic",
            "kl_optimal"
          ],
          {
            "tooltip": "The scheduler controls how noise is gradually removed to form the image."
          }
        ],
        "positive": [
          "CONDITIONING",
          {
            "tooltip": "The conditioning describing the attributes you want to include in the image."
          }
        ],
        "negative": [
          "CONDITIONING",
          {
            "tooltip": "The conditioning describing the attributes you want to exclude from the image."
          }
        ],
        "latent_image": [
          "LATENT",
          {
            "tooltip": "The latent image to denoise."
          }
        ],
        "denoise": [
          "FLOAT",
          {
            "default": 1.0,
            "min": 0.0,
            "max": 1.0,
            "step": 0.01,
            "tooltip": "The amount of denoising applied, lower values will maintain the structure of the initial image allowing for image to image sampling."
          }
        ]
      }
    },
    "input_order": {
      "required": [
        "model",
        "seed",
        "steps",
        "cfg",
        "sampler_name",
        "scheduler",
        "positive",
        "negative",
        "latent_image",
        "denoise"
      ]
    },
    "output": [
      "LATENT"
    ],
    "output_is_list": [
      false
    ],
    "output_name": [
      "LATENT"
    ],
    "name": "KSampler",
    "display_name": "KSampler",
    "description": "Uses the provided model, positive and negative conditioning to denoise the latent image.",
    "python_module": "nodes",
    "category": "sampling",
    "output_node": false,
    "output_tooltips": [
      "The denoised latent."
    ]
  },
  "CheckpointLoaderSimple": {
    "input": {
      "required": {
        "ckpt_name": [
          [
            "Anime/autismmixSDXL_autismmixConfetti.safetensors",
            "Anime/autismmixSDXL_autismmixPony.safetensors",
            "Anime/ponyDiffusionV6XL_v6StartWithThisOne.safetensors",
            "Anime/prefectPonyXL_v50.safetensors",
            "Anime/waiANINSFWPONYXL_v11.safetensors",
            "Anime/waiANINSFWPONYXL_v130.safetensors",
            "Anime/waiNSFWIllustrious_v70.safetensors",
            "Babes/babesBYSTABLEYOGI_xlV2.safetensors",
            "Babes/babesByStableYogi_ponyV3VAE.safetensors",
            "FLUX/flux1-dev-fp8.safetensors",
            "RDXL/rdxlAnime_sdxlPony8.safetensors",
            "RDXL/rdxlPixelArt_pony2.safetensors",
            "RDXL/realDream_sdxlPony12.safetensors",
            "Realism/cyberrealisticPony_v70a.safetensors",
            "Realism/cyberrealisticPony_v8.safetensors",
            "Realism/realvisxlV50_v50Bakedvae.safetensors",
            "SD3.5/sd3.5_large_fp16.safetensors",
            "SD3.5/sd3.5_large_fp8_scaled.safetensors",
            "Semi-realism/bemypony_Semirealanime.safetensors",
            "Semi-realism/duchaitenPonyXLNo_v60.safetensors",
            "prefectPonyXL_v3.safetensors",
            "sd-v1-5-inpainting.ckpt",
            "v1-5-pruned-emaonly.ckpt"
          ],
          {
            "tooltip": "The name of the checkpoint (model) to load."
          }
        ]
      }
    },
    "input_order": {
      "required": [
        "ckpt_name"
      ]
    },
    "output": [
      "MODEL",
      "CLIP",
      "VAE"
    ],
    "output_is_list": [
      false,
      false,
      false
    ],
    "output_name": [
      "MODEL",
      "CLIP",
      "VAE"
    ],
    "name": "CheckpointLoaderSimple",
    "display_name": "Load Checkpoint",
    "description": "Loads a diffusion model checkpoint, diffusion models are used to denoise latents.",
    "python_module": "nodes",
    "category": "loaders",
    "output_node": false,
    "output_tooltips": [
      "The model used for denoising latents.",
      "The CLIP model used for encoding text prompts.",
      "The VAE model used for encoding and decoding images to and from latent space."
    ]
  },
  "CLIPTextEncode": {
    "input": {
      "required": {
        "text": [
          "STRING",
          {
            "multiline": true,
            "dynamicPrompts": true,
            "tooltip": "The text to be encoded."
          }
        ],
        "clip": [
          "CLIP",
          {
            "tooltip": "The CLIP model used for encoding the text."
          }
        ]
      }
    },
    "input_order": {
      "required": [
        "text",
        "clip"
      ]
    },
    "output": [
      "CONDITIONING"
    ],
    "output_is_list": [
      false
    ],
    "output_name": [
      "CONDITIONING"
    ],
    "name": "CLIPTextEncode",
    "display_name": "CLIP Text Encode (Prompt)",
    "description": "Encodes a text prompt using a CLIP model into an embedding that can be used to guide the diffusion model towards generating specific images.",
    "python_module": "nodes",
    "category": "conditioning",
    "output_node": false,
    "output_tooltips": [
      "A conditioning containing the embedded text used to guide the diffusion model."
    ]
  },
  "CLIPSetLastLayer": {
    "input": {
      "required": {
        "clip": [
          "CLIP"
        ],
        "stop_at_clip_layer": [
          "INT",
          {
            "default": -1,
            "min": -24,
            "max": -1,
            "step": 1
          }
        ]
      }
    },
    "input_order": {
      "required": [
        "clip",
        "stop_at_clip_layer"
      ]
    },
    "output": [
      "CLIP"
    ],
    "output_is_list": [
      false
    ],
    "output_name": [
      "CLIP"
    ],
    "name": "CLIPSetLastLayer",
    "display_name": "CLIP Set Last Layer",
    "description": "",
    "python_module": "nodes",
    "category": "conditioning",
    "output_node": false
  },
  "VAEDecode": {
    "input": {
      "required": {
        "samples": [
          "LATENT",
          {
            "tooltip": "The latent to be decoded."
          }
        ],
        "vae": [
          "VAE",
          {
            "tooltip": "The VAE model used for decoding the latent."
          }
        ]
      }
    },
    "input_order": {
      "required": [
        "samples",
        "vae"
      ]
    },
    "output": [
      "IMAGE"
    ],
    "output_is_list": [
      false
    ],
    "output_name": [
      "IMAGE"
    ],
    "name": "VAEDecode",
    "display_name": "VAE Decode",
    "description": "Decodes latent images back into pixel space images.",
    "python_module": "nodes",
    "category": "latent",
    "output_node": false,
    "output_tooltips": [
      "The decoded image."
    ]
  },
  "VAEEncode": {
    "input": {
      "required": {
        "pixels": [
          "IMAGE"
        ],
        "vae": [
          "VAE"
        ]
      }
    },
    "input_order": {
      "required": [
        "pixels",
        "vae"
      ]
    },
    "output": [
      "LATENT"
    ],
    "output_is_list": [
      false
    ],
    "output_name": [
      "LATENT"
    ],
    "name": "VAEEncode",
    "display_name": "VAE Encode",
    "description": "",
    "python_module": "nodes",
    "category": "latent",
    "output_node": false
  },
  "VAELoader": {
    "input": {
      "required": {
        "vae_name": [
          [
            "ae.safetensors",
            "sdxl_vae.safetensors",
            "vae-ft-mse-840000-ema-pruned.ckpt"
          ]
        ]
      }
    },
    "input_order": {
      "required": [
        "vae_name"
      ]
    },
    "output": [
      "VAE"
    ],
    "output_is_list": [
      false
    ],
    "output_name": [
      "VAE"
    ],
    "name": "VAELoader",
    "display_name": "Load VAE",
    "description": "",
    "python_module": "nodes",
    "category": "loaders",
    "output_node": false
  },
  "EmptyLatentImage": {
    "input": {
      "required": {
        "width": [
          "INT",
          {
            "default": 512,
            "min": 16,
            "max": 16384,
            "step": 8,
            "tooltip": "The width of the latent images in pixels."
          }
        ],
        "height": [
          "INT",
          {
            "default": 512,
            "min": 16,
            "max": 16384,
            "step": 8,
            "tooltip": "The height of the latent images in pixels."
          }
        ],
        "batch_size": [
          "INT",
          {
            "default": 1,
            "min": 1,
            "max": 4096,
            "tooltip": "The number of latent images in the batch."
          }
        ]
      }
    },
    "input_order": {
      "required": [
        "width",
        "height",
        "batch_size"
      ]
    },
    "output": [
      "LATENT"
    ],
    "output_is_list": [
      false
    ],
    "output_name": [
      "LATENT"
    ],
    "name": "EmptyLatentImage",
    "display_name": "Empty Latent Image",
    "description": "Create a new batch of empty latent images to be denoised via sampling.",
    "python_module": "nodes",
    "category": "latent",
    "output_node": false,
    "output_tooltips": [
      "The empty latent image batch."
    ]
  },
  "LatentUpscale": {
    "input": {
      "required": {
        "samples": [
          "LATENT"
        ],
        "upscale_method": [
          [
            "nearest-exact",
            "bilinear",
            "area",
            "bicubic",
            "bislerp"
          ]
        ],
        "width": [
          "INT",
          {
            "default": 512,
            "min": 0,
            "max": 16384,
            "step": 8
          }
        ],
        "height": [
          "INT",
          {
            "default": 512,
            "min": 0,
            "max": 16384,
            "step": 8
          }
        ],
        "crop": [
          [
            "disabled",
            "center"
          ]
        ]
      }
    },
    "input_order": {
      "required": [
        "samples",
        "upscale_method",
        "width",
        "height",
        "crop"
      ]
    },
    "output": [
      "LATENT"
    ],
    "output_is_list": [
      false
    ],
    "output_name": [
      "LATENT"
    ],
    "name": "LatentUpscale",
    "display_name": "Upscale Latent",
    "description": "",
    "python_module": "nodes",
    "category": "latent",
    "output_node": false
  },
  "LatentUpscaleBy": {
    "input": {
      "required": {
        "samples": [
          "LATENT"
        ],
        "upscale_method": [
          [
            "nearest-exact",
            "bilinear",
            "area",
            "bicubic",
            "bislerp"
          ]
        ],
        "scale_by": [
          "FLOAT",
          {
            "default": 1.5,
            "min": 0.01,
            "max": 8.0,
            "step": 0.01
          }
        ]
      }
    },
    "input_order": {
      "required": [
        "samples",
        "upscale_method",
        "scale_by"
      ]
    },
    "output": [
      "LATENT"
    ],
    "output_is_list": [
      false
    ],
    "output_name": [
      "LATENT"
    ],
    "name": "LatentUpscaleBy",
    "display_name": "Upscale Latent By",
    "description": "",
    "python_module": "nodes",
    "category": "latent",
    "output_node": false
  }
}