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