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chore: Various QoL updates of nodes display names, descriptions and categories (CORE-190, CORE-191) (#13830)

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* Move detection category under image category

* Add missing categories

* Move detection nodes to detection category

* Move save nodes to image root catefory

* Rename postprocessors

* Move mask category under image

* Move guiders category to parent level at root of sampling category

* Move custom_sampling category to parent level at the root of sampling category

* Modify description of LoRA loaders

* Fix node id SolidMask

* Move VOID Quadmask under image/mask

* Group compositing nodes under image/compositing

* Move load image as mask to image category for consistency with other load image nodes

* Align display name with Load Checkpoint

* Move dataset category under training category

* Rename Number Convert to Conver Number (verb first)

* Rename Canny node

* Revert wanBlockSwap + description

* Add description to RemoveBackground node

* Revert category update of dataset
This commit is contained in:
Alexis Rolland
2026-05-19 12:13:48 +08:00
committed by GitHub
parent 990a7ae7f2
commit d71cc1c8f2
23 changed files with 108 additions and 91 deletions
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65
comfy_extras/nodes_custom_sampler.py
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@@ -17,7 +17,7 @@ class BasicScheduler(io.ComfyNode):
def define_schema(cls):
return io.Schema(
node_id="BasicScheduler",
category="sampling/custom_sampling/schedulers",
category="sampling/schedulers",
inputs=[
io.Model.Input("model"),
io.Combo.Input("scheduler", options=comfy.samplers.SCHEDULER_NAMES),
@@ -47,7 +47,7 @@ class KarrasScheduler(io.ComfyNode):
def define_schema(cls):
return io.Schema(
node_id="KarrasScheduler",
category="sampling/custom_sampling/schedulers",
category="sampling/schedulers",
inputs=[
io.Int.Input("steps", default=20, min=1, max=10000),
io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
@@ -69,7 +69,7 @@ class ExponentialScheduler(io.ComfyNode):
def define_schema(cls):
return io.Schema(
node_id="ExponentialScheduler",
category="sampling/custom_sampling/schedulers",
category="sampling/schedulers",
inputs=[
io.Int.Input("steps", default=20, min=1, max=10000),
io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
@@ -90,7 +90,7 @@ class PolyexponentialScheduler(io.ComfyNode):
def define_schema(cls):
return io.Schema(
node_id="PolyexponentialScheduler",
category="sampling/custom_sampling/schedulers",
category="sampling/schedulers",
inputs=[
io.Int.Input("steps", default=20, min=1, max=10000),
io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
@@ -112,7 +112,7 @@ class LaplaceScheduler(io.ComfyNode):
def define_schema(cls):
return io.Schema(
node_id="LaplaceScheduler",
category="sampling/custom_sampling/schedulers",
category="sampling/schedulers",
inputs=[
io.Int.Input("steps", default=20, min=1, max=10000),
io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
@@ -136,7 +136,7 @@ class SDTurboScheduler(io.ComfyNode):
def define_schema(cls):
return io.Schema(
node_id="SDTurboScheduler",
category="sampling/custom_sampling/schedulers",
category="sampling/schedulers",
inputs=[
io.Model.Input("model"),
io.Int.Input("steps", default=1, min=1, max=10),
@@ -160,7 +160,7 @@ class BetaSamplingScheduler(io.ComfyNode):
def define_schema(cls):
return io.Schema(
node_id="BetaSamplingScheduler",
category="sampling/custom_sampling/schedulers",
category="sampling/schedulers",
inputs=[
io.Model.Input("model"),
io.Int.Input("steps", default=20, min=1, max=10000),
@@ -182,7 +182,7 @@ class VPScheduler(io.ComfyNode):
def define_schema(cls):
return io.Schema(
node_id="VPScheduler",
category="sampling/custom_sampling/schedulers",
category="sampling/schedulers",
inputs=[
io.Int.Input("steps", default=20, min=1, max=10000),
io.Float.Input("beta_d", default=19.9, min=0.0, max=5000.0, step=0.01, round=False, advanced=True), #TODO: fix default values
@@ -204,7 +204,7 @@ class SplitSigmas(io.ComfyNode):
def define_schema(cls):
return io.Schema(
node_id="SplitSigmas",
category="sampling/custom_sampling/sigmas",
category="sampling/sigmas",
inputs=[
io.Sigmas.Input("sigmas"),
io.Int.Input("step", default=0, min=0, max=10000),
@@ -228,7 +228,7 @@ class SplitSigmasDenoise(io.ComfyNode):
def define_schema(cls):
return io.Schema(
node_id="SplitSigmasDenoise",
category="sampling/custom_sampling/sigmas",
category="sampling/sigmas",
inputs=[
io.Sigmas.Input("sigmas"),
io.Float.Input("denoise", default=1.0, min=0.0, max=1.0, step=0.01),
@@ -254,7 +254,7 @@ class FlipSigmas(io.ComfyNode):
def define_schema(cls):
return io.Schema(
node_id="FlipSigmas",
category="sampling/custom_sampling/sigmas",
category="sampling/sigmas",
inputs=[io.Sigmas.Input("sigmas")],
outputs=[io.Sigmas.Output()]
)
@@ -276,7 +276,7 @@ class SetFirstSigma(io.ComfyNode):
def define_schema(cls):
return io.Schema(
node_id="SetFirstSigma",
category="sampling/custom_sampling/sigmas",
category="sampling/sigmas",
inputs=[
io.Sigmas.Input("sigmas"),
io.Float.Input("sigma", default=136.0, min=0.0, max=20000.0, step=0.001, round=False),
@@ -298,7 +298,7 @@ class ExtendIntermediateSigmas(io.ComfyNode):
return io.Schema(
node_id="ExtendIntermediateSigmas",
search_aliases=["interpolate sigmas"],
category="sampling/custom_sampling/sigmas",
category="sampling/sigmas",
inputs=[
io.Sigmas.Input("sigmas"),
io.Int.Input("steps", default=2, min=1, max=100),
@@ -351,7 +351,7 @@ class SamplingPercentToSigma(io.ComfyNode):
def define_schema(cls):
return io.Schema(
node_id="SamplingPercentToSigma",
category="sampling/custom_sampling/sigmas",
category="sampling/sigmas",
inputs=[
io.Model.Input("model"),
io.Float.Input("sampling_percent", default=0.0, min=0.0, max=1.0, step=0.0001),
@@ -379,7 +379,7 @@ class KSamplerSelect(io.ComfyNode):
def define_schema(cls):
return io.Schema(
node_id="KSamplerSelect",
category="sampling/custom_sampling/samplers",
category="sampling/samplers",
inputs=[io.Combo.Input("sampler_name", options=comfy.samplers.SAMPLER_NAMES)],
outputs=[io.Sampler.Output()]
)
@@ -396,7 +396,7 @@ class SamplerDPMPP_3M_SDE(io.ComfyNode):
def define_schema(cls):
return io.Schema(
node_id="SamplerDPMPP_3M_SDE",
category="sampling/custom_sampling/samplers",
category="sampling/samplers",
inputs=[
io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
@@ -421,7 +421,7 @@ class SamplerDPMPP_2M_SDE(io.ComfyNode):
def define_schema(cls):
return io.Schema(
node_id="SamplerDPMPP_2M_SDE",
category="sampling/custom_sampling/samplers",
category="sampling/samplers",
inputs=[
io.Combo.Input("solver_type", options=['midpoint', 'heun']),
io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
@@ -448,7 +448,7 @@ class SamplerDPMPP_SDE(io.ComfyNode):
def define_schema(cls):
return io.Schema(
node_id="SamplerDPMPP_SDE",
category="sampling/custom_sampling/samplers",
category="sampling/samplers",
inputs=[
io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
@@ -474,7 +474,7 @@ class SamplerDPMPP_2S_Ancestral(io.ComfyNode):
def define_schema(cls):
return io.Schema(
node_id="SamplerDPMPP_2S_Ancestral",
category="sampling/custom_sampling/samplers",
category="sampling/samplers",
inputs=[
io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
@@ -494,7 +494,7 @@ class SamplerEulerAncestral(io.ComfyNode):
def define_schema(cls):
return io.Schema(
node_id="SamplerEulerAncestral",
category="sampling/custom_sampling/samplers",
category="sampling/samplers",
inputs=[
io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
@@ -515,7 +515,7 @@ class SamplerEulerAncestralCFGPP(io.ComfyNode):
return io.Schema(
node_id="SamplerEulerAncestralCFGPP",
display_name="SamplerEulerAncestralCFG++",
category="sampling/custom_sampling/samplers",
category="sampling/samplers",
inputs=[
io.Float.Input("eta", default=1.0, min=0.0, max=1.0, step=0.01, round=False),
io.Float.Input("s_noise", default=1.0, min=0.0, max=10.0, step=0.01, round=False),
@@ -537,7 +537,7 @@ class SamplerLMS(io.ComfyNode):
def define_schema(cls):
return io.Schema(
node_id="SamplerLMS",
category="sampling/custom_sampling/samplers",
category="sampling/samplers",
inputs=[io.Int.Input("order", default=4, min=1, max=100, advanced=True)],
outputs=[io.Sampler.Output()]
)
@@ -554,7 +554,7 @@ class SamplerDPMAdaptative(io.ComfyNode):
def define_schema(cls):
return io.Schema(
node_id="SamplerDPMAdaptative",
category="sampling/custom_sampling/samplers",
category="sampling/samplers",
inputs=[
io.Int.Input("order", default=3, min=2, max=3, advanced=True),
io.Float.Input("rtol", default=0.05, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
@@ -585,7 +585,7 @@ class SamplerER_SDE(io.ComfyNode):
def define_schema(cls):
return io.Schema(
node_id="SamplerER_SDE",
category="sampling/custom_sampling/samplers",
category="sampling/samplers",
inputs=[
io.Combo.Input("solver_type", options=["ER-SDE", "Reverse-time SDE", "ODE"]),
io.Int.Input("max_stage", default=3, min=1, max=3, advanced=True),
@@ -623,7 +623,7 @@ class SamplerSASolver(io.ComfyNode):
return io.Schema(
node_id="SamplerSASolver",
search_aliases=["sde"],
category="sampling/custom_sampling/samplers",
category="sampling/samplers",
inputs=[
io.Model.Input("model"),
io.Float.Input("eta", default=1.0, min=0.0, max=10.0, step=0.01, round=False, advanced=True),
@@ -668,7 +668,7 @@ class SamplerSEEDS2(io.ComfyNode):
return io.Schema(
node_id="SamplerSEEDS2",
search_aliases=["sde", "exp heun"],
category="sampling/custom_sampling/samplers",
category="sampling/samplers",
inputs=[
io.Combo.Input("solver_type", options=["phi_1", "phi_2"]),
io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False, tooltip="Stochastic strength", advanced=True),
@@ -794,7 +794,8 @@ class BasicGuider(io.ComfyNode):
def define_schema(cls):
return io.Schema(
node_id="BasicGuider",
category="sampling/custom_sampling/guiders",
display_name="Basic Guider",
category="sampling/guiders",
inputs=[
io.Model.Input("model"),
io.Conditioning.Input("conditioning"),
@@ -815,7 +816,8 @@ class CFGGuider(io.ComfyNode):
def define_schema(cls):
return io.Schema(
node_id="CFGGuider",
category="sampling/custom_sampling/guiders",
display_name="CFG Guider",
category="sampling/guiders",
inputs=[
io.Model.Input("model"),
io.Conditioning.Input("positive"),
@@ -869,7 +871,8 @@ class DualCFGGuider(io.ComfyNode):
return io.Schema(
node_id="DualCFGGuider",
search_aliases=["dual prompt guidance"],
category="sampling/custom_sampling/guiders",
display_name="Dual CFG Guider",
category="sampling/guiders",
inputs=[
io.Model.Input("model"),
io.Conditioning.Input("cond1"),
@@ -897,7 +900,7 @@ class DisableNoise(io.ComfyNode):
return io.Schema(
node_id="DisableNoise",
search_aliases=["zero noise"],
category="sampling/custom_sampling/noise",
category="sampling/noise",
inputs=[],
outputs=[io.Noise.Output()]
)
@@ -914,7 +917,7 @@ class RandomNoise(io.ComfyNode):
def define_schema(cls):
return io.Schema(
node_id="RandomNoise",
category="sampling/custom_sampling/noise",
category="sampling/noise",
inputs=[io.Int.Input("noise_seed", default=0, min=0, max=0xffffffffffffffff, control_after_generate=True)],
outputs=[io.Noise.Output()]
)