wangbo/ComfyUI
1
0
Fork 0
mirror of https://github.com/comfyanonymous/ComfyUI.git synced 2026-05-27 17:37:39 +08:00
Code Issues Actions 17 Packages Projects Releases Wiki Activity

Merge branch 'master' into blueprints-0519

Browse Source
This commit is contained in:
Daxiong (Lin) 2026-05-19 18:29:48 +08:00 committed by GitHub
commit 0ca51a06e9
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
28 changed files with 137 additions and 100 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
comfy/latent_formats.py
View File

@ -150,6 +150,7 @@ class SD3(LatentFormat):
class StableAudio1(LatentFormat):
latent_channels = 64
latent_dimensions = 1
temporal_downscale_ratio = 2048
class Flux(SD3):
latent_channels = 16
@ -766,6 +767,7 @@ class ACEAudio(LatentFormat):
class ACEAudio15(LatentFormat):
latent_channels = 64
latent_dimensions = 1
temporal_downscale_ratio = 1764
class ChromaRadiance(LatentFormat):
latent_channels = 3

12
comfy/sample.py
View File

@ -37,11 +37,12 @@ def prepare_noise(latent_image, seed, noise_inds=None):
return noises
def fix_empty_latent_channels(model, latent_image, downscale_ratio_spacial=None):
def fix_empty_latent_channels(model, latent_image, downscale_ratio_spacial=None, downscale_ratio_temporal=None):
if latent_image.is_nested:
return latent_image
latent_format = model.get_model_object("latent_format") #Resize the empty latent image so it has the right number of channels
if torch.count_nonzero(latent_image) == 0:
is_empty = torch.count_nonzero(latent_image) == 0
if is_empty:
if latent_format.latent_channels != latent_image.shape[1]:
latent_image = comfy.utils.repeat_to_batch_size(latent_image, latent_format.latent_channels, dim=1)
if downscale_ratio_spacial is not None:
@ -51,6 +52,13 @@ def fix_empty_latent_channels(model, latent_image, downscale_ratio_spacial=None)
if latent_format.latent_dimensions == 3 and latent_image.ndim == 4:
latent_image = latent_image.unsqueeze(2)
if is_empty and downscale_ratio_temporal is not None:
if downscale_ratio_temporal != latent_format.temporal_downscale_ratio:
ratio = downscale_ratio_temporal / latent_format.temporal_downscale_ratio
new_t = max(1, round(latent_image.shape[2] * ratio))
latent_image = comfy.utils.repeat_to_batch_size(latent_image, new_t, dim=2)
return latent_image
def prepare_sampling(model, noise_shape, positive, negative, noise_mask):

2
comfy_extras/nodes_ace.py
View File

@ -104,7 +104,7 @@ class EmptyAceStep15LatentAudio(IO.ComfyNode):
def execute(cls, seconds, batch_size) -> IO.NodeOutput:
length = round((seconds * 48000 / 1920))
latent = torch.zeros([batch_size, 64, length], device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype())
return IO.NodeOutput({"samples": latent, "type": "audio"})
return IO.NodeOutput({"samples": latent, "type": "audio", "downscale_ratio_temporal": 1764})
class ReferenceAudio(IO.ComfyNode):
@classmethod

4
comfy_extras/nodes_advanced_samplers.py
View File

@ -45,7 +45,7 @@ class SamplerLCMUpscale(io.ComfyNode):
def define_schema(cls) -> io.Schema:
return io.Schema(
node_id="SamplerLCMUpscale",
category="sampling/custom_sampling/samplers",
category="sampling/samplers",
inputs=[
io.Float.Input("scale_ratio", default=1.0, min=0.1, max=20.0, step=0.01, advanced=True),
io.Int.Input("scale_steps", default=-1, min=-1, max=1000, step=1, advanced=True),
@ -123,7 +123,7 @@ class SamplerEulerCFGpp(io.ComfyNode):
return io.Schema(
node_id="SamplerEulerCFGpp",
display_name="SamplerEulerCFG++",
category="experimental", # "sampling/custom_sampling/samplers"
category="experimental", # "sampling/samplers"
inputs=[
io.Combo.Input("version", options=["regular", "alternative"], advanced=True),
],

2
comfy_extras/nodes_align_your_steps.py
View File

@ -29,7 +29,7 @@ class AlignYourStepsScheduler(io.ComfyNode):
return io.Schema(
node_id="AlignYourStepsScheduler",
search_aliases=["AYS scheduler"],
category="sampling/custom_sampling/schedulers",
category="sampling/schedulers",
inputs=[
io.Combo.Input("model_type", options=["SD1", "SDXL", "SVD"]),
io.Int.Input("steps", default=10, min=1, max=10000),

2
comfy_extras/nodes_ar_video.py
View File

@ -53,7 +53,7 @@ class SamplerARVideo(io.ComfyNode):
return io.Schema(
node_id="SamplerARVideo",
display_name="Sampler AR Video",
category="sampling/custom_sampling/samplers",
category="sampling/samplers",
inputs=[
io.Int.Input(
"num_frame_per_block",

2
comfy_extras/nodes_audio.py
View File

@ -33,7 +33,7 @@ class EmptyLatentAudio(IO.ComfyNode):
def execute(cls, seconds, batch_size) -> IO.NodeOutput:
length = round((seconds * 44100 / 2048) / 2) * 2
latent = torch.zeros([batch_size, 64, length], device=comfy.model_management.intermediate_device())
return IO.NodeOutput({"samples":latent, "type": "audio"})
return IO.NodeOutput({"samples": latent, "type": "audio", "downscale_ratio_temporal": 2048})
generate = execute # TODO: remove

1
comfy_extras/nodes_bg_removal.py
View File

@ -34,6 +34,7 @@ class RemoveBackground(IO.ComfyNode):
node_id="RemoveBackground",
display_name="Remove Background",
category="image/background removal",
description="Generates a foreground mask to remove the background from an image using a background removal model.",
inputs=[
IO.Image.Input("image", tooltip="Input image to remove the background from"),
IO.BackgroundRemoval.Input("bg_removal_model", tooltip="Background removal model used to generate the mask")

4
comfy_extras/nodes_canny.py
View File

@ -11,9 +11,9 @@ class Canny(io.ComfyNode):
def define_schema(cls):
return io.Schema(
node_id="Canny",
display_name="Canny",
display_name="Detect Edges (Canny)",
search_aliases=["edge detection", "outline", "contour detection", "line art"],
category="image/preprocessors",
category="image/filters",
essentials_category="Image Tools",
inputs=[
io.Image.Input("image"),

6
comfy_extras/nodes_compositing.py
View File

@ -111,7 +111,7 @@ class PorterDuffImageComposite(io.ComfyNode):
node_id="PorterDuffImageComposite",
search_aliases=["alpha composite", "blend modes", "layer blend", "transparency blend"],
display_name="Porter-Duff Image Composite",
category="mask/compositing",
category="image/compositing",
inputs=[
io.Image.Input("source"),
io.Mask.Input("source_alpha"),
@ -168,7 +168,7 @@ class SplitImageWithAlpha(io.ComfyNode):
node_id="SplitImageWithAlpha",
search_aliases=["extract alpha", "separate transparency", "remove alpha"],
display_name="Split Image with Alpha",
category="mask/compositing",
category="image/compositing",
inputs=[
io.Image.Input("image"),
],
@ -192,7 +192,7 @@ class JoinImageWithAlpha(io.ComfyNode):
node_id="JoinImageWithAlpha",
search_aliases=["add transparency", "apply alpha", "composite alpha", "RGBA"],
display_name="Join Image with Alpha",
category="mask/compositing",
category="image/compositing",
inputs=[
io.Image.Input("image"),
io.Mask.Input("alpha"),

71
comfy_extras/nodes_custom_sampler.py
View File

@ -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),
@ -750,7 +750,7 @@ class SamplerCustom(io.ComfyNode):
latent = latent_image
latent_image = latent["samples"]
latent = latent.copy()
latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image, latent.get("downscale_ratio_spacial", None))
latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image, latent.get("downscale_ratio_spacial", None), latent.get("downscale_ratio_temporal", None))
latent["samples"] = latent_image
if not add_noise:
@ -770,6 +770,7 @@ class SamplerCustom(io.ComfyNode):
out = latent.copy()
out.pop("downscale_ratio_spacial", None)
out.pop("downscale_ratio_temporal", None)
out["samples"] = samples
if "x0" in x0_output:
x0_out = model.model.process_latent_out(x0_output["x0"].cpu())
@ -793,7 +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"),
@ -814,7 +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"),
@ -868,7 +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"),
@ -896,7 +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()]
)
@ -913,7 +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()]
)
@ -949,7 +953,7 @@ class SamplerCustomAdvanced(io.ComfyNode):
latent = latent_image
latent_image = latent["samples"]
latent = latent.copy()
latent_image = comfy.sample.fix_empty_latent_channels(guider.model_patcher, latent_image, latent.get("downscale_ratio_spacial", None))
latent_image = comfy.sample.fix_empty_latent_channels(guider.model_patcher, latent_image, latent.get("downscale_ratio_spacial", None), latent.get("downscale_ratio_temporal", None))
latent["samples"] = latent_image
noise_mask = None
@ -965,6 +969,7 @@ class SamplerCustomAdvanced(io.ComfyNode):
out = latent.copy()
out.pop("downscale_ratio_spacial", None)
out.pop("downscale_ratio_temporal", None)
out["samples"] = samples
if "x0" in x0_output:
x0_out = guider.model_patcher.model.process_latent_out(x0_output["x0"].cpu())

4
comfy_extras/nodes_flux.py
View File

@ -215,7 +215,7 @@ class Flux2Scheduler(io.ComfyNode):
def define_schema(cls):
return io.Schema(
node_id="Flux2Scheduler",
category="sampling/custom_sampling/schedulers",
category="sampling/schedulers",
inputs=[
io.Int.Input("steps", default=20, min=1, max=4096),
io.Int.Input("width", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=1),
@ -263,7 +263,7 @@ class FluxKVCache(io.ComfyNode):
node_id="FluxKVCache",
display_name="Flux KV Cache",
description="Enables KV Cache optimization for reference images on Flux family models.",
category="",
category="experimental",
is_experimental=True,
inputs=[
io.Model.Input("model", tooltip="The model to use KV Cache on."),

2
comfy_extras/nodes_gits.py
View File

@ -340,7 +340,7 @@ class GITSScheduler(io.ComfyNode):
def define_schema(cls):
return io.Schema(
node_id="GITSScheduler",
category="sampling/custom_sampling/schedulers",
category="sampling/schedulers",
inputs=[
io.Float.Input("coeff", default=1.20, min=0.80, max=1.50, step=0.05, advanced=True),
io.Int.Input("steps", default=10, min=2, max=1000),

10
comfy_extras/nodes_images.py
View File

@ -162,7 +162,7 @@ class ImageAddNoise(IO.ComfyNode):
node_id="ImageAddNoise",
search_aliases=["film grain"],
display_name="Add Noise to Image",
category="image/postprocessing",
category="image/filters",
inputs=[
IO.Image.Input("image"),
IO.Int.Input(
@ -194,7 +194,8 @@ class SaveAnimatedWEBP(IO.ComfyNode):
def define_schema(cls):
return IO.Schema(
node_id="SaveAnimatedWEBP",
category="image/animation",
display_name="Save Animated WEBP",
category="image",
inputs=[
IO.Image.Input("images"),
IO.String.Input("filename_prefix", default="ComfyUI"),
@ -231,7 +232,8 @@ class SaveAnimatedPNG(IO.ComfyNode):
def define_schema(cls):
return IO.Schema(
node_id="SaveAnimatedPNG",
category="image/animation",
display_name="Save Animated PNG",
category="image",
inputs=[
IO.Image.Input("images"),
IO.String.Input("filename_prefix", default="ComfyUI"),
@ -493,7 +495,7 @@ class SaveSVGNode(IO.ComfyNode):
search_aliases=["export vector", "save vector graphics"],
display_name="Save SVG",
description="Save SVG files on disk.",
category="image/save",
category="image",
inputs=[
IO.SVG.Input("svg"),
IO.String.Input(

2
comfy_extras/nodes_lt.py
View File

@ -601,7 +601,7 @@ class LTXVScheduler(io.ComfyNode):
def define_schema(cls):
return io.Schema(
node_id="LTXVScheduler",
category="sampling/custom_sampling/schedulers",
category="sampling/schedulers",
inputs=[
io.Int.Input("steps", default=20, min=1, max=10000),
io.Float.Input("max_shift", default=2.05, min=0.0, max=100.0, step=0.01),

27
comfy_extras/nodes_mask.py
View File

@ -83,7 +83,7 @@ class ImageCompositeMasked(IO.ComfyNode):
node_id="ImageCompositeMasked",
search_aliases=["overlay", "layer", "paste image", "images composition"],
display_name="Image Composite Masked",
category="image",
category="image/compositing",
inputs=[
IO.Image.Input("destination"),
IO.Image.Input("source"),
@ -112,7 +112,7 @@ class MaskToImage(IO.ComfyNode):
node_id="MaskToImage",
search_aliases=["convert mask"],
display_name="Convert Mask to Image",
category="mask",
category="image/mask",
inputs=[
IO.Mask.Input("mask"),
],
@ -134,7 +134,7 @@ class ImageToMask(IO.ComfyNode):
node_id="ImageToMask",
search_aliases=["extract channel", "channel to mask"],
display_name="Convert Image to Mask",
category="mask",
category="image/mask",
inputs=[
IO.Image.Input("image"),
IO.Combo.Input("channel", options=["red", "green", "blue", "alpha"]),
@ -157,7 +157,8 @@ class ImageColorToMask(IO.ComfyNode):
return IO.Schema(
node_id="ImageColorToMask",
search_aliases=["color keying", "chroma key"],
category="mask",
display_name="Convert Image Color to Mask",
category="image/mask",
inputs=[
IO.Image.Input("image"),
IO.Int.Input("color", default=0, min=0, max=0xFFFFFF, step=1, display_mode=IO.NumberDisplay.number),
@ -180,7 +181,8 @@ class SolidMask(IO.ComfyNode):
def define_schema(cls):
return IO.Schema(
node_id="SolidMask",
category="mask",
display_name="Create Solid Mask",
category="image/mask",
inputs=[
IO.Float.Input("value", default=1.0, min=0.0, max=1.0, step=0.01),
IO.Int.Input("width", default=512, min=1, max=nodes.MAX_RESOLUTION, step=1),
@ -204,7 +206,7 @@ class InvertMask(IO.ComfyNode):
node_id="InvertMask",
search_aliases=["reverse mask", "flip mask"],
display_name="Invert Mask",
category="mask",
category="image/mask",
inputs=[
IO.Mask.Input("mask"),
],
@ -226,7 +228,7 @@ class CropMask(IO.ComfyNode):
node_id="CropMask",
search_aliases=["cut mask", "extract mask region", "mask slice"],
display_name="Crop Mask",
category="mask",
category="image/mask",
inputs=[
IO.Mask.Input("mask"),
IO.Int.Input("x", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
@ -253,7 +255,7 @@ class MaskComposite(IO.ComfyNode):
node_id="MaskComposite",
search_aliases=["combine masks", "blend masks", "layer masks", "masks composition"],
display_name="Combine Masks",
category="mask",
category="image/mask",
inputs=[
IO.Mask.Input("destination"),
IO.Mask.Input("source"),
@ -304,7 +306,7 @@ class FeatherMask(IO.ComfyNode):
node_id="FeatherMask",
search_aliases=["soft edge mask", "blur mask edges", "gradient mask edge"],
display_name="Feather Mask",
category="mask",
category="image/mask",
inputs=[
IO.Mask.Input("mask"),
IO.Int.Input("left", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
@ -352,7 +354,7 @@ class GrowMask(IO.ComfyNode):
node_id="GrowMask",
search_aliases=["expand mask", "shrink mask"],
display_name="Grow Mask",
category="mask",
category="image/mask",
inputs=[
IO.Mask.Input("mask"),
IO.Int.Input("expand", default=0, min=-nodes.MAX_RESOLUTION, max=nodes.MAX_RESOLUTION, step=1),
@ -388,7 +390,8 @@ class ThresholdMask(IO.ComfyNode):
return IO.Schema(
node_id="ThresholdMask",
search_aliases=["binary mask"],
category="mask",
display_name="Threshold Mask",
category="image/mask",
inputs=[
IO.Mask.Input("mask"),
IO.Float.Input("value", default=0.5, min=0.0, max=1.0, step=0.01),
@ -414,7 +417,7 @@ class MaskPreview(IO.ComfyNode):
node_id="MaskPreview",
search_aliases=["show mask", "view mask", "inspect mask", "debug mask"],
display_name="Preview Mask",
category="mask",
category="image/mask",
description="Saves the input images to your ComfyUI output directory.",
inputs=[
IO.Mask.Input("mask"),

4
comfy_extras/nodes_morphology.py
View File

@ -13,8 +13,8 @@ class Morphology(io.ComfyNode):
return io.Schema(
node_id="Morphology",
search_aliases=["erode", "dilate"],
display_name="ImageMorphology",
category="image/postprocessing",
display_name="Apply Morphology",
category="image/filters",
inputs=[
io.Image.Input("image"),
io.Combo.Input(

2
comfy_extras/nodes_nop.py
View File

@ -13,7 +13,7 @@ class wanBlockSwap(io.ComfyNode):
return io.Schema(
node_id="wanBlockSwap",
category="",
description="NOP",
description="Intercept wanBlockSwap custom node that causes major instability and make it no-op.",
inputs=[
io.Model.Input("model"),
],

2
comfy_extras/nodes_number_convert.py
View File

@ -20,7 +20,7 @@ class NumberConvertNode(io.ComfyNode):
def define_schema(cls) -> io.Schema:
return io.Schema(
node_id="ComfyNumberConvert",
display_name="Number Convert",
display_name="Convert Number",
category="utils",
search_aliases=[
"int to float", "float to int", "number convert",

2
comfy_extras/nodes_optimalsteps.py
View File

@ -31,7 +31,7 @@ class OptimalStepsScheduler(io.ComfyNode):
def define_schema(cls):
return io.Schema(
node_id="OptimalStepsScheduler",
category="sampling/custom_sampling/schedulers",
category="sampling/schedulers",
inputs=[
io.Combo.Input("model_type", options=["FLUX", "Wan", "Chroma"]),
io.Int.Input("steps", default=20, min=3, max=1000),

16
comfy_extras/nodes_post_processing.py
View File

@ -22,7 +22,7 @@ class Blend(io.ComfyNode):
node_id="ImageBlend",
search_aliases=["mix images"],
display_name="Blend Images",
category="image/postprocessing",
category="image/filters",
essentials_category="Image Tools",
inputs=[
io.Image.Input("image1"),
@ -80,8 +80,8 @@ class Blur(io.ComfyNode):
def define_schema(cls):
return io.Schema(
node_id="ImageBlur",
display_name="Image Blur",
category="image/postprocessing",
display_name="Blur Image",
category="image/filters",
inputs=[
io.Image.Input("image"),
io.Int.Input("blur_radius", default=1, min=1, max=31, step=1),
@ -117,7 +117,7 @@ class Quantize(io.ComfyNode):
return io.Schema(
node_id="ImageQuantize",
display_name="Quantize Image",
category="image/postprocessing",
category="image/filters",
inputs=[
io.Image.Input("image"),
io.Int.Input("colors", default=256, min=1, max=256, step=1),
@ -183,7 +183,7 @@ class Sharpen(io.ComfyNode):
return io.Schema(
node_id="ImageSharpen",
display_name="Sharpen Image",
category="image/postprocessing",
category="image/filters",
inputs=[
io.Image.Input("image"),
io.Int.Input("sharpen_radius", default=1, min=1, max=31, step=1, advanced=True),
@ -595,7 +595,7 @@ class BatchMasksNode(io.ComfyNode):
node_id="BatchMasksNode",
search_aliases=["combine masks", "stack masks", "merge masks"],
display_name="Batch Masks",
category="mask",
category="image/mask",
inputs=[
io.Autogrow.Input("masks", template=autogrow_template)
],
@ -670,8 +670,8 @@ class ColorTransfer(io.ComfyNode):
def define_schema(cls):
return io.Schema(
node_id="ColorTransfer",
display_name="Color Transfer",
category="image/postprocessing",
display_name="Transfer Color",
category="image/filters",
description="Match the colors of one image to another using various algorithms.",
search_aliases=["color match", "color grading", "color correction", "match colors", "color transform", "mkl", "reinhard", "histogram"],
inputs=[

4
comfy_extras/nodes_rtdetr.py
View File

@ -15,7 +15,7 @@ class RTDETR_detect(io.ComfyNode):
return io.Schema(
node_id="RTDETR_detect",
display_name="RT-DETR Detect",
category="detection",
category="image/detection",
search_aliases=["bbox", "bounding box", "object detection", "coco"],
inputs=[
io.Model.Input("model", display_name="model"),
@ -71,7 +71,7 @@ class DrawBBoxes(io.ComfyNode):
return io.Schema(
node_id="DrawBBoxes",
display_name="Draw BBoxes",
category="detection",
category="image/detection",
search_aliases=["bbox", "bounding box", "object detection", "rt_detr", "visualize detections", "coco"],
inputs=[
io.Image.Input("image", optional=True),

8
comfy_extras/nodes_sam3.py
View File

@ -93,7 +93,7 @@ class SAM3_Detect(io.ComfyNode):
return io.Schema(
node_id="SAM3_Detect",
display_name="SAM3 Detect",
category="detection",
category="image/detection",
search_aliases=["sam3", "segment anything", "open vocabulary", "text detection", "segment"],
inputs=[
io.Model.Input("model", display_name="model"),
@ -265,7 +265,7 @@ class SAM3_VideoTrack(io.ComfyNode):
return io.Schema(
node_id="SAM3_VideoTrack",
display_name="SAM3 Video Track",
category="detection",
category="image/detection",
search_aliases=["sam3", "video", "track", "propagate"],
inputs=[
io.Image.Input("images", display_name="images", tooltip="Video frames as batched images"),
@ -320,7 +320,7 @@ class SAM3_TrackPreview(io.ComfyNode):
return io.Schema(
node_id="SAM3_TrackPreview",
display_name="SAM3 Track Preview",
category="detection",
category="image/detection",
inputs=[
SAM3TrackData.Input("track_data", display_name="track_data"),
io.Image.Input("images", display_name="images", optional=True),
@ -478,7 +478,7 @@ class SAM3_TrackToMask(io.ComfyNode):
return io.Schema(
node_id="SAM3_TrackToMask",
display_name="SAM3 Track to Mask",
category="detection",
category="image/detection",
inputs=[
SAM3TrackData.Input("track_data", display_name="track_data"),
io.String.Input("object_indices", display_name="object_indices", default="",

12
comfy_extras/nodes_sdpose.py
View File

@ -353,7 +353,8 @@ class SDPoseDrawKeypoints(io.ComfyNode):
def define_schema(cls):
return io.Schema(
node_id="SDPoseDrawKeypoints",
category="image/preprocessors",
display_name="SDPose Draw Keypoints",
category="image/detection",
search_aliases=["openpose", "pose detection", "preprocessor", "keypoints", "pose"],
inputs=[
io.Custom("POSE_KEYPOINT").Input("keypoints"),
@ -421,7 +422,8 @@ class SDPoseKeypointExtractor(io.ComfyNode):
def define_schema(cls):
return io.Schema(
node_id="SDPoseKeypointExtractor",
category="image/preprocessors",
display_name="SDPose Keypoint Extractor",
category="image/detection",
search_aliases=["openpose", "pose detection", "preprocessor", "keypoints", "sdpose"],
description="Extract pose keypoints from images using the SDPose model: https://huggingface.co/Comfy-Org/SDPose/tree/main/checkpoints",
inputs=[
@ -595,7 +597,8 @@ class SDPoseFaceBBoxes(io.ComfyNode):
def define_schema(cls):
return io.Schema(
node_id="SDPoseFaceBBoxes",
category="image/preprocessors",
display_name="SDPose Face Bounding Boxes",
category="image/detection",
search_aliases=["face bbox", "face bounding box", "pose", "keypoints"],
inputs=[
io.Custom("POSE_KEYPOINT").Input("keypoints"),
@ -652,7 +655,8 @@ class CropByBBoxes(io.ComfyNode):
def define_schema(cls):
return io.Schema(
node_id="CropByBBoxes",
category="image/preprocessors",
display_name="Crop By Bounding Boxes",
category="image/transform",
search_aliases=["crop", "face crop", "bbox crop", "pose", "bounding box"],
description="Crop and resize regions from the input image batch based on provided bounding boxes.",
inputs=[

8
comfy_extras/nodes_video_model.py
View File

@ -65,7 +65,7 @@ class VideoLinearCFGGuidance:
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "sampling/video_models"
CATEGORY = "sampling/guiders"
def patch(self, model, min_cfg):
def linear_cfg(args):
@ -89,7 +89,7 @@ class VideoTriangleCFGGuidance:
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "sampling/video_models"
CATEGORY = "sampling/guiders"
def patch(self, model, min_cfg):
def linear_cfg(args):
@ -157,5 +157,7 @@ NODE_CLASS_MAPPINGS = {
}
NODE_DISPLAY_NAME_MAPPINGS = {
"ImageOnlyCheckpointLoader": "Image Only Checkpoint Loader (img2vid model)",
"ImageOnlyCheckpointLoader": "Load Checkpoint Image Only (img2vid model)",
"VideoLinearCFGGuidance": "Video Linear CFG Guidance",
"VideoTriangleCFGGuidance": "Video Triangle CFG Guidance",
}

7
comfy_extras/nodes_void.py
View File

@ -122,7 +122,8 @@ class VOIDQuadmaskPreprocess(io.ComfyNode):
def define_schema(cls):
return io.Schema(
node_id="VOIDQuadmaskPreprocess",
category="mask/video",
display_name="VOID Quadmask Preprocessor",
category="image/mask",
inputs=[
io.Mask.Input("mask"),
io.Int.Input("dilate_width", default=0, min=0, max=50, step=1,
@ -392,7 +393,7 @@ class VOIDWarpedNoiseSource(io.ComfyNode):
def define_schema(cls):
return io.Schema(
node_id="VOIDWarpedNoiseSource",
category="sampling/custom_sampling/noise",
category="sampling/noise",
inputs=[
io.Latent.Input("warped_noise",
tooltip="Warped noise latent from VOIDWarpedNoise"),
@ -454,7 +455,7 @@ class VOIDSampler(io.ComfyNode):
def define_schema(cls):
return io.Schema(
node_id="VOIDSampler",
category="sampling/custom_sampling/samplers",
category="sampling/samplers",
inputs=[],
outputs=[io.Sampler.Output()],
)

8
nodes.py
View File

@ -691,7 +691,7 @@ class LoraLoader:
FUNCTION = "load_lora"
CATEGORY = "loaders"
DESCRIPTION = "LoRAs are used to modify diffusion and CLIP models, altering the way in which latents are denoised such as applying styles. Multiple LoRA nodes can be linked together."
DESCRIPTION = "This LoRA loader is used to modify both diffusion and CLIP models, altering the way in which latents are denoised such as applying styles. Multiple LoRA nodes can be linked together."
SEARCH_ALIASES = ["lora", "load lora", "apply lora", "lora loader", "lora model"]
def load_lora(self, model, clip, lora_name, strength_model, strength_clip):
@ -723,6 +723,7 @@ class LoraLoaderModelOnly(LoraLoader):
"strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01}),
}}
RETURN_TYPES = ("MODEL",)
DESCRIPTION = "This LoRAs loader is used to modify the diffusion model, altering the way in which latents are denoised such as applying styles. Multiple LoRA nodes can be linked together."
FUNCTION = "load_lora_model_only"
def load_lora_model_only(self, model, lora_name, strength_model):
@ -1524,7 +1525,7 @@ class SetLatentNoiseMask:
def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False):
latent_image = latent["samples"]
latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image, latent.get("downscale_ratio_spacial", None))
latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image, latent.get("downscale_ratio_spacial", None), latent.get("downscale_ratio_temporal", None))
if disable_noise:
noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
@ -1543,6 +1544,7 @@ def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive,
force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
out = latent.copy()
out.pop("downscale_ratio_spacial", None)
out.pop("downscale_ratio_temporal", None)
out["samples"] = samples
return (out, )
@ -1775,7 +1777,7 @@ class LoadImageMask(LoadImage):
}
}
CATEGORY = "mask"
CATEGORY = "image"
RETURN_TYPES = ("MASK",)
FUNCTION = "load_image_mask"

11
openapi.yaml
View File

@ -485,8 +485,15 @@ paths:
post:
operationId: uploadMask
tags: [upload]
summary: Upload a mask image
description: Uploads a mask image associated with a previously-uploaded reference image.
deprecated: true
summary: Upload a mask image (deprecated)
description: |
Deprecated. Clients should composite the mask onto the source image
client-side and upload the resulting image via POST /api/upload/image
instead. This endpoint will continue to function for older clients,
but will not receive new features.
Uploads a mask image associated with a previously-uploaded reference image.
requestBody:
required: true
content: