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Upscaler (SEGS/pipe)

ImpactPack/Upscale
SEGSUpscalerPipe

Example

JSON Example
{
  "class_type": "SEGSUpscalerPipe",
  "inputs": {
    "image": [
      "node_id",
      0
    ],
    "segs": [
      "node_id",
      0
    ],
    "basic_pipe": [
      "node_id",
      0
    ],
    "rescale_factor": 2,
    "resampling_method": "lanczos",
    "supersample": "true",
    "rounding_modulus": 8,
    "seed": 0,
    "steps": 20,
    "cfg": 8,
    "sampler_name": "euler",
    "scheduler": "simple",
    "denoise": 0.5,
    "feather": 5,
    "inpaint_model": false,
    "noise_mask_feather": 20
  }
}

This example shows required inputs only. Connection values like ["node_id", 0] should reference actual node IDs from your workflow.

Inputs

NameTypeStatusConstraintsDefault
imageIMAGErequired--
segsSEGSrequired--
basic_pipeBASIC_PIPErequired--
rescale_factorFLOATrequiredmin: 0.01, max: 100, step: 0.012
resampling_methodENUM
4 options
  • lanczos
  • nearest
  • bilinear
  • bicubic
required--
supersampleENUM
2 options
  • true
  • false
required--
rounding_modulusINTrequiredmin: 8, max: 1024, step: 88
seedINTrequiredmin: 0, max: 1.84e+190
stepsINTrequiredmin: 1, max: 1000020
cfgFLOATrequiredmin: 0, max: 1008
sampler_nameENUM
44 options
  • euler
  • euler_cfg_pp
  • euler_ancestral
  • euler_ancestral_cfg_pp
  • heun
  • heunpp2
  • exp_heun_2_x0
  • exp_heun_2_x0_sde
  • 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_2m_sde_heun
  • dpmpp_2m_sde_heun_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
  • gradient_estimation_cfg_pp
  • er_sde
  • seeds_2
  • seeds_3
  • sa_solver
  • sa_solver_pece
  • ddim
  • uni_pc
  • uni_pc_bh2
required--
schedulerENUM
17 options
  • simple
  • sgm_uniform
  • karras
  • exponential
  • ddim_uniform
  • beta
  • normal
  • linear_quadratic
  • kl_optimal
  • AYS SDXL
  • AYS SD1
  • AYS SVD
  • GITS[coeff=1.2]
  • LTXV[default]
  • OSS FLUX
  • OSS Wan
  • OSS Chroma
required--
denoiseFLOATrequiredmin: 0.0001, max: 1, step: 0.010.5
featherINTrequiredmin: 0, max: 100, step: 15
inpaint_modelBOOLEANrequired-false
noise_mask_featherINTrequiredmin: 0, max: 100, step: 120
upscale_model_optUPSCALE_MODELoptional--
upscaler_hook_optUPSCALER_HOOKoptional--
scheduler_func_optSCHEDULER_FUNCoptional--

Outputs

IndexNameTypeIs ListConnection Reference
0IMAGEIMAGENo["{node_id}", 0]
How to connect to these outputs

To connect another node's input to an output from this node, use the connection reference format:

["node_id", output_index]

Where node_id is the ID of this SEGSUpscalerPipe node in your workflow, and output_index is the index from the table above.

Example

If this node has ID "5" in your workflow:

  • IMAGE (IMAGE): ["5", 0]
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