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Menno van Leeuwen 2025-03-22 12:38:10 +01:00
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example/object_info.json
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{
"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
}
}