andreas/ComfyUI
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

feat: mark 429 widgets as advanced for collapsible UI (#12197)

Browse Source 
* feat: mark 429 widgets as advanced for collapsible UI

Mark widgets as advanced across core, comfy_extras, and comfy_api_nodes
to support the new collapsible advanced inputs section in the frontend.

Changes:
- 267 advanced markers in comfy_extras/
- 162 advanced markers in comfy_api_nodes/
- All files pass python3 -m py_compile verification

Widgets marked advanced (hidden by default):
- Scheduler internals: sigma_max, sigma_min, rho, mu, beta, alpha
- Sampler internals: eta, s_noise, order, rtol, atol, h_init, pcoeff, etc.
- Memory optimization: tile_size, overlap, temporal_size, temporal_overlap
- Pipeline controls: add_noise, start_at_step, end_at_step
- Timing controls: start_percent, end_percent
- Layer selection: stop_at_clip_layer, layers, block_number
- Video encoding: codec, crf, format
- Device/dtype: device, noise_device, dtype, weight_dtype

Widgets kept basic (always visible):
- Core params: strength, steps, cfg, denoise, seed, width, height
- Model selectors: ckpt_name, lora_name, vae_name, sampler_name
- Common controls: upscale_method, crop, batch_size, fps, opacity

Related: frontend PR #11939
Amp-Thread-ID: https://ampcode.com/threads/T-019c1734-6b61-702e-b333-f02c399963fc

* fix: remove advanced=True from DynamicCombo.Input (unsupported)

Amp-Thread-ID: https://ampcode.com/threads/T-019c1734-6b61-702e-b333-f02c399963fc

* fix: address review - un-mark model merge, video, image, and training node widgets as advanced

Per comfyanonymous review:
- Model merge arguments should not be advanced (all 14 model-specific merge classes)
- SaveAnimatedWEBP lossless/quality/method should not be advanced
- SaveWEBM/SaveVideo codec/crf/format should not be advanced
- TrainLoraNode options should not be advanced (7 inputs)

Amp-Thread-ID: https://ampcode.com/threads/T-019c322b-a3a8-71b7-9962-d44573ca6352

* fix: un-mark batch_size and webcam width/height as advanced (should stay basic)

Amp-Thread-ID: https://ampcode.com/threads/T-019c3236-1417-74aa-82a3-bcb365fbe9d1

---------

Co-authored-by: Jedrzej Kosinski <kosinkadink1@gmail.com>
This commit is contained in:
Christian Byrne
2026-02-19 19:20:02 -08:00
committed by GitHub
parent 5632b2df9d
commit 4d172e9ad7
65 changed files with 407 additions and 267 deletions
Download Patch File Download Diff File Expand all files Collapse all files
comfy_extras/nodes_advanced_samplers.py
+3 -3
View File
@@ -47,8 +47,8 @@ class SamplerLCMUpscale(io.ComfyNode):
node_id="SamplerLCMUpscale",
category="sampling/custom_sampling/samplers",
inputs=[
io.Float.Input("scale_ratio", default=1.0, min=0.1, max=20.0, step=0.01),
io.Int.Input("scale_steps", default=-1, min=-1, max=1000, step=1),
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),
io.Combo.Input("upscale_method", options=cls.UPSCALE_METHODS),
],
outputs=[io.Sampler.Output()],
@@ -94,7 +94,7 @@ class SamplerEulerCFGpp(io.ComfyNode):
display_name="SamplerEulerCFG++",
category="_for_testing", # "sampling/custom_sampling/samplers"
inputs=[
io.Combo.Input("version", options=["regular", "alternative"]),
io.Combo.Input("version", options=["regular", "alternative"], advanced=True),
],
outputs=[io.Sampler.Output()],
is_experimental=True,
comfy_extras/nodes_apg.py
+3
View File
@@ -26,6 +26,7 @@ class APG(io.ComfyNode):
max=10.0,
step=0.01,
tooltip="Controls the scale of the parallel guidance vector. Default CFG behavior at a setting of 1.",
advanced=True,
),
io.Float.Input(
"norm_threshold",
@@ -34,6 +35,7 @@ class APG(io.ComfyNode):
max=50.0,
step=0.1,
tooltip="Normalize guidance vector to this value, normalization disable at a setting of 0.",
advanced=True,
),
io.Float.Input(
"momentum",
@@ -42,6 +44,7 @@ class APG(io.ComfyNode):
max=1.0,
step=0.01,
tooltip="Controls a running average of guidance during diffusion, disabled at a setting of 0.",
advanced=True,
),
],
outputs=[io.Model.Output()],
comfy_extras/nodes_attention_multiply.py
+16 -16
View File
@@ -28,10 +28,10 @@ class UNetSelfAttentionMultiply(io.ComfyNode):
category="_for_testing/attention_experiments",
inputs=[
io.Model.Input("model"),
io.Float.Input("q", default=1.0, min=0.0, max=10.0, step=0.01),
io.Float.Input("k", default=1.0, min=0.0, max=10.0, step=0.01),
io.Float.Input("v", default=1.0, min=0.0, max=10.0, step=0.01),
io.Float.Input("out", default=1.0, min=0.0, max=10.0, step=0.01),
io.Float.Input("q", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
io.Float.Input("k", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
io.Float.Input("v", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
io.Float.Input("out", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
],
outputs=[io.Model.Output()],
is_experimental=True,
@@ -51,10 +51,10 @@ class UNetCrossAttentionMultiply(io.ComfyNode):
category="_for_testing/attention_experiments",
inputs=[
io.Model.Input("model"),
io.Float.Input("q", default=1.0, min=0.0, max=10.0, step=0.01),
io.Float.Input("k", default=1.0, min=0.0, max=10.0, step=0.01),
io.Float.Input("v", default=1.0, min=0.0, max=10.0, step=0.01),
io.Float.Input("out", default=1.0, min=0.0, max=10.0, step=0.01),
io.Float.Input("q", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
io.Float.Input("k", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
io.Float.Input("v", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
io.Float.Input("out", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
],
outputs=[io.Model.Output()],
is_experimental=True,
@@ -75,10 +75,10 @@ class CLIPAttentionMultiply(io.ComfyNode):
category="_for_testing/attention_experiments",
inputs=[
io.Clip.Input("clip"),
io.Float.Input("q", default=1.0, min=0.0, max=10.0, step=0.01),
io.Float.Input("k", default=1.0, min=0.0, max=10.0, step=0.01),
io.Float.Input("v", default=1.0, min=0.0, max=10.0, step=0.01),
io.Float.Input("out", default=1.0, min=0.0, max=10.0, step=0.01),
io.Float.Input("q", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
io.Float.Input("k", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
io.Float.Input("v", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
io.Float.Input("out", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
],
outputs=[io.Clip.Output()],
is_experimental=True,
@@ -109,10 +109,10 @@ class UNetTemporalAttentionMultiply(io.ComfyNode):
category="_for_testing/attention_experiments",
inputs=[
io.Model.Input("model"),
io.Float.Input("self_structural", default=1.0, min=0.0, max=10.0, step=0.01),
io.Float.Input("self_temporal", default=1.0, min=0.0, max=10.0, step=0.01),
io.Float.Input("cross_structural", default=1.0, min=0.0, max=10.0, step=0.01),
io.Float.Input("cross_temporal", default=1.0, min=0.0, max=10.0, step=0.01),
io.Float.Input("self_structural", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
io.Float.Input("self_temporal", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
io.Float.Input("cross_structural", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
io.Float.Input("cross_temporal", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
],
outputs=[io.Model.Output()],
is_experimental=True,
comfy_extras/nodes_audio.py
+3 -1
View File
@@ -22,7 +22,7 @@ class EmptyLatentAudio(IO.ComfyNode):
inputs=[
IO.Float.Input("seconds", default=47.6, min=1.0, max=1000.0, step=0.1),
IO.Int.Input(
"batch_size", default=1, min=1, max=4096, tooltip="The number of latent images in the batch."
"batch_size", default=1, min=1, max=4096, tooltip="The number of latent images in the batch.",
),
],
outputs=[IO.Latent.Output()],
@@ -679,6 +679,7 @@ class EmptyAudio(IO.ComfyNode):
tooltip="Sample rate of the empty audio clip.",
min=1,
max=192000,
advanced=True,
),
IO.Int.Input(
"channels",
@@ -686,6 +687,7 @@ class EmptyAudio(IO.ComfyNode):
min=1,
max=2,
tooltip="Number of audio channels (1 for mono, 2 for stereo).",
advanced=True,
),
],
outputs=[IO.Audio.Output()],
comfy_extras/nodes_camera_trajectory.py
+4 -4
View File
@@ -174,10 +174,10 @@ class WanCameraEmbedding(io.ComfyNode):
io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4),
io.Float.Input("speed", default=1.0, min=0, max=10.0, step=0.1, optional=True),
io.Float.Input("fx", default=0.5, min=0, max=1, step=0.000000001, optional=True),
io.Float.Input("fy", default=0.5, min=0, max=1, step=0.000000001, optional=True),
io.Float.Input("cx", default=0.5, min=0, max=1, step=0.01, optional=True),
io.Float.Input("cy", default=0.5, min=0, max=1, step=0.01, optional=True),
io.Float.Input("fx", default=0.5, min=0, max=1, step=0.000000001, optional=True, advanced=True),
io.Float.Input("fy", default=0.5, min=0, max=1, step=0.000000001, optional=True, advanced=True),
io.Float.Input("cx", default=0.5, min=0, max=1, step=0.01, optional=True, advanced=True),
io.Float.Input("cy", default=0.5, min=0, max=1, step=0.01, optional=True, advanced=True),
],
outputs=[
io.WanCameraEmbedding.Output(display_name="camera_embedding"),
comfy_extras/nodes_chroma_radiance.py
+3
View File
@@ -48,6 +48,7 @@ class ChromaRadianceOptions(io.ComfyNode):
min=0.0,
max=1.0,
tooltip="First sigma that these options will be in effect.",
advanced=True,
),
io.Float.Input(
id="end_sigma",
@@ -55,12 +56,14 @@ class ChromaRadianceOptions(io.ComfyNode):
min=0.0,
max=1.0,
tooltip="Last sigma that these options will be in effect.",
advanced=True,
),
io.Int.Input(
id="nerf_tile_size",
default=-1,
min=-1,
tooltip="Allows overriding the default NeRF tile size. -1 means use the default (32). 0 means use non-tiling mode (may require a lot of VRAM).",
advanced=True,
),
],
outputs=[io.Model.Output()],
comfy_extras/nodes_clip_sdxl.py
+2 -2
View File
@@ -35,8 +35,8 @@ class CLIPTextEncodeSDXL(io.ComfyNode):
io.Clip.Input("clip"),
io.Int.Input("width", default=1024, min=0, max=nodes.MAX_RESOLUTION),
io.Int.Input("height", default=1024, min=0, max=nodes.MAX_RESOLUTION),
io.Int.Input("crop_w", default=0, min=0, max=nodes.MAX_RESOLUTION),
io.Int.Input("crop_h", default=0, min=0, max=nodes.MAX_RESOLUTION),
io.Int.Input("crop_w", default=0, min=0, max=nodes.MAX_RESOLUTION, advanced=True),
io.Int.Input("crop_h", default=0, min=0, max=nodes.MAX_RESOLUTION, advanced=True),
io.Int.Input("target_width", default=1024, min=0, max=nodes.MAX_RESOLUTION),
io.Int.Input("target_height", default=1024, min=0, max=nodes.MAX_RESOLUTION),
io.String.Input("text_g", multiline=True, dynamic_prompts=True),
comfy_extras/nodes_cond.py
+2 -2
View File
@@ -38,8 +38,8 @@ class T5TokenizerOptions(io.ComfyNode):
category="_for_testing/conditioning",
inputs=[
io.Clip.Input("clip"),
io.Int.Input("min_padding", default=0, min=0, max=10000, step=1),
io.Int.Input("min_length", default=0, min=0, max=10000, step=1),
io.Int.Input("min_padding", default=0, min=0, max=10000, step=1, advanced=True),
io.Int.Input("min_length", default=0, min=0, max=10000, step=1, advanced=True),
],
outputs=[io.Clip.Output()],
is_experimental=True,
comfy_extras/nodes_context_windows.py
+6 -6
View File
@@ -14,15 +14,15 @@ class ContextWindowsManualNode(io.ComfyNode):
description="Manually set context windows.",
inputs=[
io.Model.Input("model", tooltip="The model to apply context windows to during sampling."),
io.Int.Input("context_length", min=1, default=16, tooltip="The length of the context window."),
io.Int.Input("context_overlap", min=0, default=4, tooltip="The overlap of the context window."),
io.Int.Input("context_length", min=1, default=16, tooltip="The length of the context window.", advanced=True),
io.Int.Input("context_overlap", min=0, default=4, tooltip="The overlap of the context window.", advanced=True),
io.Combo.Input("context_schedule", options=[
comfy.context_windows.ContextSchedules.STATIC_STANDARD,
comfy.context_windows.ContextSchedules.UNIFORM_STANDARD,
comfy.context_windows.ContextSchedules.UNIFORM_LOOPED,
comfy.context_windows.ContextSchedules.BATCHED,
], tooltip="The stride of the context window."),
io.Int.Input("context_stride", min=1, default=1, tooltip="The stride of the context window; only applicable to uniform schedules."),
io.Int.Input("context_stride", min=1, default=1, tooltip="The stride of the context window; only applicable to uniform schedules.", advanced=True),
io.Boolean.Input("closed_loop", default=False, tooltip="Whether to close the context window loop; only applicable to looped schedules."),
io.Combo.Input("fuse_method", options=comfy.context_windows.ContextFuseMethods.LIST_STATIC, default=comfy.context_windows.ContextFuseMethods.PYRAMID, tooltip="The method to use to fuse the context windows."),
io.Int.Input("dim", min=0, max=5, default=0, tooltip="The dimension to apply the context windows to."),
@@ -67,15 +67,15 @@ class WanContextWindowsManualNode(ContextWindowsManualNode):
schema.description = "Manually set context windows for WAN-like models (dim=2)."
schema.inputs = [
io.Model.Input("model", tooltip="The model to apply context windows to during sampling."),
io.Int.Input("context_length", min=1, max=nodes.MAX_RESOLUTION, step=4, default=81, tooltip="The length of the context window."),
io.Int.Input("context_overlap", min=0, default=30, tooltip="The overlap of the context window."),
io.Int.Input("context_length", min=1, max=nodes.MAX_RESOLUTION, step=4, default=81, tooltip="The length of the context window.", advanced=True),
io.Int.Input("context_overlap", min=0, default=30, tooltip="The overlap of the context window.", advanced=True),
io.Combo.Input("context_schedule", options=[
comfy.context_windows.ContextSchedules.STATIC_STANDARD,
comfy.context_windows.ContextSchedules.UNIFORM_STANDARD,
comfy.context_windows.ContextSchedules.UNIFORM_LOOPED,
comfy.context_windows.ContextSchedules.BATCHED,
], tooltip="The stride of the context window."),
io.Int.Input("context_stride", min=1, default=1, tooltip="The stride of the context window; only applicable to uniform schedules."),
io.Int.Input("context_stride", min=1, default=1, tooltip="The stride of the context window; only applicable to uniform schedules.", advanced=True),
io.Boolean.Input("closed_loop", default=False, tooltip="Whether to close the context window loop; only applicable to looped schedules."),
io.Combo.Input("fuse_method", options=comfy.context_windows.ContextFuseMethods.LIST_STATIC, default=comfy.context_windows.ContextFuseMethods.PYRAMID, tooltip="The method to use to fuse the context windows."),
io.Boolean.Input("freenoise", default=False, tooltip="Whether to apply FreeNoise noise shuffling, improves window blending."),
comfy_extras/nodes_controlnet.py
+2 -2
View File
@@ -48,8 +48,8 @@ class ControlNetInpaintingAliMamaApply(io.ComfyNode):
io.Image.Input("image"),
io.Mask.Input("mask"),
io.Float.Input("strength", default=1.0, min=0.0, max=10.0, step=0.01),
io.Float.Input("start_percent", default=0.0, min=0.0, max=1.0, step=0.001),
io.Float.Input("end_percent", default=1.0, min=0.0, max=1.0, step=0.001),
io.Float.Input("start_percent", default=0.0, min=0.0, max=1.0, step=0.001, advanced=True),
io.Float.Input("end_percent", default=1.0, min=0.0, max=1.0, step=0.001, advanced=True),
],
outputs=[
io.Conditioning.Output(display_name="positive"),
comfy_extras/nodes_custom_sampler.py
+55 -55
View File
@@ -50,9 +50,9 @@ class KarrasScheduler(io.ComfyNode):
category="sampling/custom_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),
io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False),
io.Float.Input("rho", default=7.0, min=0.0, max=100.0, step=0.01, round=False),
io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
io.Float.Input("rho", default=7.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
],
outputs=[io.Sigmas.Output()]
)
@@ -72,8 +72,8 @@ class ExponentialScheduler(io.ComfyNode):
category="sampling/custom_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),
io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False),
io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
],
outputs=[io.Sigmas.Output()]
)
@@ -93,9 +93,9 @@ class PolyexponentialScheduler(io.ComfyNode):
category="sampling/custom_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),
io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False),
io.Float.Input("rho", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
io.Float.Input("rho", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
],
outputs=[io.Sigmas.Output()]
)
@@ -115,10 +115,10 @@ class LaplaceScheduler(io.ComfyNode):
category="sampling/custom_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),
io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False),
io.Float.Input("mu", default=0.0, min=-10.0, max=10.0, step=0.1, round=False),
io.Float.Input("beta", default=0.5, min=0.0, max=10.0, step=0.1, round=False),
io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
io.Float.Input("mu", default=0.0, min=-10.0, max=10.0, step=0.1, round=False, advanced=True),
io.Float.Input("beta", default=0.5, min=0.0, max=10.0, step=0.1, round=False, advanced=True),
],
outputs=[io.Sigmas.Output()]
)
@@ -164,8 +164,8 @@ class BetaSamplingScheduler(io.ComfyNode):
inputs=[
io.Model.Input("model"),
io.Int.Input("steps", default=20, min=1, max=10000),
io.Float.Input("alpha", default=0.6, min=0.0, max=50.0, step=0.01, round=False),
io.Float.Input("beta", default=0.6, min=0.0, max=50.0, step=0.01, round=False),
io.Float.Input("alpha", default=0.6, min=0.0, max=50.0, step=0.01, round=False, advanced=True),
io.Float.Input("beta", default=0.6, min=0.0, max=50.0, step=0.01, round=False, advanced=True),
],
outputs=[io.Sigmas.Output()]
)
@@ -185,9 +185,9 @@ class VPScheduler(io.ComfyNode):
category="sampling/custom_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), #TODO: fix default values
io.Float.Input("beta_min", default=0.1, min=0.0, max=5000.0, step=0.01, round=False),
io.Float.Input("eps_s", default=0.001, min=0.0, max=1.0, step=0.0001, round=False),
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
io.Float.Input("beta_min", default=0.1, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
io.Float.Input("eps_s", default=0.001, min=0.0, max=1.0, step=0.0001, round=False, advanced=True),
],
outputs=[io.Sigmas.Output()]
)
@@ -398,9 +398,9 @@ class SamplerDPMPP_3M_SDE(io.ComfyNode):
node_id="SamplerDPMPP_3M_SDE",
category="sampling/custom_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),
io.Combo.Input("noise_device", options=['gpu', 'cpu']),
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),
io.Combo.Input("noise_device", options=['gpu', 'cpu'], advanced=True),
],
outputs=[io.Sampler.Output()]
)
@@ -424,9 +424,9 @@ class SamplerDPMPP_2M_SDE(io.ComfyNode):
category="sampling/custom_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),
io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
io.Combo.Input("noise_device", options=['gpu', 'cpu']),
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),
io.Combo.Input("noise_device", options=['gpu', 'cpu'], advanced=True),
],
outputs=[io.Sampler.Output()]
)
@@ -450,10 +450,10 @@ class SamplerDPMPP_SDE(io.ComfyNode):
node_id="SamplerDPMPP_SDE",
category="sampling/custom_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),
io.Float.Input("r", default=0.5, min=0.0, max=100.0, step=0.01, round=False),
io.Combo.Input("noise_device", options=['gpu', 'cpu']),
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),
io.Float.Input("r", default=0.5, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
io.Combo.Input("noise_device", options=['gpu', 'cpu'], advanced=True),
],
outputs=[io.Sampler.Output()]
)
@@ -496,8 +496,8 @@ class SamplerEulerAncestral(io.ComfyNode):
node_id="SamplerEulerAncestral",
category="sampling/custom_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),
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),
],
outputs=[io.Sampler.Output()]
)
@@ -538,7 +538,7 @@ class SamplerLMS(io.ComfyNode):
return io.Schema(
node_id="SamplerLMS",
category="sampling/custom_sampling/samplers",
inputs=[io.Int.Input("order", default=4, min=1, max=100)],
inputs=[io.Int.Input("order", default=4, min=1, max=100, advanced=True)],
outputs=[io.Sampler.Output()]
)
@@ -556,16 +556,16 @@ class SamplerDPMAdaptative(io.ComfyNode):
node_id="SamplerDPMAdaptative",
category="sampling/custom_sampling/samplers",
inputs=[
io.Int.Input("order", default=3, min=2, max=3),
io.Float.Input("rtol", default=0.05, min=0.0, max=100.0, step=0.01, round=False),
io.Float.Input("atol", default=0.0078, min=0.0, max=100.0, step=0.01, round=False),
io.Float.Input("h_init", default=0.05, min=0.0, max=100.0, step=0.01, round=False),
io.Float.Input("pcoeff", default=0.0, min=0.0, max=100.0, step=0.01, round=False),
io.Float.Input("icoeff", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
io.Float.Input("dcoeff", default=0.0, min=0.0, max=100.0, step=0.01, round=False),
io.Float.Input("accept_safety", default=0.81, min=0.0, max=100.0, step=0.01, round=False),
io.Float.Input("eta", default=0.0, 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),
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),
io.Float.Input("atol", default=0.0078, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
io.Float.Input("h_init", default=0.05, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
io.Float.Input("pcoeff", default=0.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
io.Float.Input("icoeff", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
io.Float.Input("dcoeff", default=0.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
io.Float.Input("accept_safety", default=0.81, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
io.Float.Input("eta", default=0.0, 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),
],
outputs=[io.Sampler.Output()]
)
@@ -588,9 +588,9 @@ class SamplerER_SDE(io.ComfyNode):
category="sampling/custom_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),
io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False, tooltip="Stochastic strength of reverse-time SDE.\nWhen eta=0, it reduces to deterministic ODE. This setting doesn't apply to ER-SDE solver type."),
io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
io.Int.Input("max_stage", default=3, min=1, max=3, advanced=True),
io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False, tooltip="Stochastic strength of reverse-time SDE.\nWhen eta=0, it reduces to deterministic ODE. This setting doesn't apply to ER-SDE solver type.", advanced=True),
io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
],
outputs=[io.Sampler.Output()]
)
@@ -626,14 +626,14 @@ class SamplerSASolver(io.ComfyNode):
category="sampling/custom_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),
io.Float.Input("sde_start_percent", default=0.2, min=0.0, max=1.0, step=0.001),
io.Float.Input("sde_end_percent", default=0.8, min=0.0, max=1.0, step=0.001),
io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
io.Int.Input("predictor_order", default=3, min=1, max=6),
io.Int.Input("corrector_order", default=4, min=0, max=6),
io.Boolean.Input("use_pece"),
io.Boolean.Input("simple_order_2"),
io.Float.Input("eta", default=1.0, min=0.0, max=10.0, step=0.01, round=False, advanced=True),
io.Float.Input("sde_start_percent", default=0.2, min=0.0, max=1.0, step=0.001, advanced=True),
io.Float.Input("sde_end_percent", default=0.8, min=0.0, max=1.0, step=0.001, advanced=True),
io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
io.Int.Input("predictor_order", default=3, min=1, max=6, advanced=True),
io.Int.Input("corrector_order", default=4, min=0, max=6, advanced=True),
io.Boolean.Input("use_pece", advanced=True),
io.Boolean.Input("simple_order_2", advanced=True),
],
outputs=[io.Sampler.Output()]
)
@@ -671,9 +671,9 @@ class SamplerSEEDS2(io.ComfyNode):
category="sampling/custom_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"),
io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, tooltip="SDE noise multiplier"),
io.Float.Input("r", default=0.5, min=0.01, max=1.0, step=0.01, round=False, tooltip="Relative step size for the intermediate stage (c2 node)"),
io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False, tooltip="Stochastic strength", advanced=True),
io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, tooltip="SDE noise multiplier", advanced=True),
io.Float.Input("r", default=0.5, min=0.01, max=1.0, step=0.01, round=False, tooltip="Relative step size for the intermediate stage (c2 node)", advanced=True),
],
outputs=[io.Sampler.Output()],
description=(
@@ -730,7 +730,7 @@ class SamplerCustom(io.ComfyNode):
category="sampling/custom_sampling",
inputs=[
io.Model.Input("model"),
io.Boolean.Input("add_noise", default=True),
io.Boolean.Input("add_noise", default=True, advanced=True),
io.Int.Input("noise_seed", default=0, min=0, max=0xffffffffffffffff, control_after_generate=True),
io.Float.Input("cfg", default=8.0, min=0.0, max=100.0, step=0.1, round=0.01),
io.Conditioning.Input("positive"),
comfy_extras/nodes_dataset.py
+9 -1
View File
@@ -222,6 +222,7 @@ class SaveImageDataSetToFolderNode(io.ComfyNode):
"filename_prefix",
default="image",
tooltip="Prefix for saved image filenames.",
advanced=True,
),
],
outputs=[],
@@ -262,6 +263,7 @@ class SaveImageTextDataSetToFolderNode(io.ComfyNode):
"filename_prefix",
default="image",
tooltip="Prefix for saved image filenames.",
advanced=True,
),
],
outputs=[],
@@ -741,6 +743,7 @@ class NormalizeImagesNode(ImageProcessingNode):
min=0.0,
max=1.0,
tooltip="Mean value for normalization.",
advanced=True,
),
io.Float.Input(
"std",
@@ -748,6 +751,7 @@ class NormalizeImagesNode(ImageProcessingNode):
min=0.001,
max=1.0,
tooltip="Standard deviation for normalization.",
advanced=True,
),
]
@@ -961,6 +965,7 @@ class ImageDeduplicationNode(ImageProcessingNode):
min=0.0,
max=1.0,
tooltip="Similarity threshold (0-1). Higher means more similar. Images above this threshold are considered duplicates.",
advanced=True,
),
]
@@ -1039,6 +1044,7 @@ class ImageGridNode(ImageProcessingNode):
min=32,
max=2048,
tooltip="Width of each cell in the grid.",
advanced=True,
),
io.Int.Input(
"cell_height",
@@ -1046,9 +1052,10 @@ class ImageGridNode(ImageProcessingNode):
min=32,
max=2048,
tooltip="Height of each cell in the grid.",
advanced=True,
),
io.Int.Input(
"padding", default=4, min=0, max=50, tooltip="Padding between images."
"padding", default=4, min=0, max=50, tooltip="Padding between images.", advanced=True
),
]
@@ -1339,6 +1346,7 @@ class SaveTrainingDataset(io.ComfyNode):
min=1,
max=100000,
tooltip="Number of samples per shard file.",
advanced=True,
),
],
outputs=[],
comfy_extras/nodes_easycache.py
+8 -8
View File
@@ -367,10 +367,10 @@ class EasyCacheNode(io.ComfyNode):
is_experimental=True,
inputs=[
io.Model.Input("model", tooltip="The model to add EasyCache to."),
io.Float.Input("reuse_threshold", min=0.0, default=0.2, max=3.0, step=0.01, tooltip="The threshold for reusing cached steps."),
io.Float.Input("start_percent", min=0.0, default=0.15, max=1.0, step=0.01, tooltip="The relative sampling step to begin use of EasyCache."),
io.Float.Input("end_percent", min=0.0, default=0.95, max=1.0, step=0.01, tooltip="The relative sampling step to end use of EasyCache."),
io.Boolean.Input("verbose", default=False, tooltip="Whether to log verbose information."),
io.Float.Input("reuse_threshold", min=0.0, default=0.2, max=3.0, step=0.01, tooltip="The threshold for reusing cached steps.", advanced=True),
io.Float.Input("start_percent", min=0.0, default=0.15, max=1.0, step=0.01, tooltip="The relative sampling step to begin use of EasyCache.", advanced=True),
io.Float.Input("end_percent", min=0.0, default=0.95, max=1.0, step=0.01, tooltip="The relative sampling step to end use of EasyCache.", advanced=True),
io.Boolean.Input("verbose", default=False, tooltip="Whether to log verbose information.", advanced=True),
],
outputs=[
io.Model.Output(tooltip="The model with EasyCache."),
@@ -500,10 +500,10 @@ class LazyCacheNode(io.ComfyNode):
is_experimental=True,
inputs=[
io.Model.Input("model", tooltip="The model to add LazyCache to."),
io.Float.Input("reuse_threshold", min=0.0, default=0.2, max=3.0, step=0.01, tooltip="The threshold for reusing cached steps."),
io.Float.Input("start_percent", min=0.0, default=0.15, max=1.0, step=0.01, tooltip="The relative sampling step to begin use of LazyCache."),
io.Float.Input("end_percent", min=0.0, default=0.95, max=1.0, step=0.01, tooltip="The relative sampling step to end use of LazyCache."),
io.Boolean.Input("verbose", default=False, tooltip="Whether to log verbose information."),
io.Float.Input("reuse_threshold", min=0.0, default=0.2, max=3.0, step=0.01, tooltip="The threshold for reusing cached steps.", advanced=True),
io.Float.Input("start_percent", min=0.0, default=0.15, max=1.0, step=0.01, tooltip="The relative sampling step to begin use of LazyCache.", advanced=True),
io.Float.Input("end_percent", min=0.0, default=0.95, max=1.0, step=0.01, tooltip="The relative sampling step to end use of LazyCache.", advanced=True),
io.Boolean.Input("verbose", default=False, tooltip="Whether to log verbose information.", advanced=True),
],
outputs=[
io.Model.Output(tooltip="The model with LazyCache."),
comfy_extras/nodes_eps.py
+3
View File
@@ -28,6 +28,7 @@ class EpsilonScaling(io.ComfyNode):
max=1.5,
step=0.001,
display_mode=io.NumberDisplay.number,
advanced=True,
),
],
outputs=[
@@ -97,6 +98,7 @@ class TemporalScoreRescaling(io.ComfyNode):
max=100.0,
step=0.001,
display_mode=io.NumberDisplay.number,
advanced=True,
),
io.Float.Input(
"tsr_sigma",
@@ -109,6 +111,7 @@ class TemporalScoreRescaling(io.ComfyNode):
max=100.0,
step=0.001,
display_mode=io.NumberDisplay.number,
advanced=True,
),
],
outputs=[
comfy_extras/nodes_flux.py
+1
View File
@@ -161,6 +161,7 @@ class FluxKontextMultiReferenceLatentMethod(io.ComfyNode):
io.Combo.Input(
"reference_latents_method",
options=["offset", "index", "uxo/uno", "index_timestep_zero"],
advanced=True,
),
],
outputs=[
comfy_extras/nodes_freelunch.py
+8 -8
View File
@@ -32,10 +32,10 @@ class FreeU(IO.ComfyNode):
category="model_patches/unet",
inputs=[
IO.Model.Input("model"),
IO.Float.Input("b1", default=1.1, min=0.0, max=10.0, step=0.01),
IO.Float.Input("b2", default=1.2, min=0.0, max=10.0, step=0.01),
IO.Float.Input("s1", default=0.9, min=0.0, max=10.0, step=0.01),
IO.Float.Input("s2", default=0.2, min=0.0, max=10.0, step=0.01),
IO.Float.Input("b1", default=1.1, min=0.0, max=10.0, step=0.01, advanced=True),
IO.Float.Input("b2", default=1.2, min=0.0, max=10.0, step=0.01, advanced=True),
IO.Float.Input("s1", default=0.9, min=0.0, max=10.0, step=0.01, advanced=True),
IO.Float.Input("s2", default=0.2, min=0.0, max=10.0, step=0.01, advanced=True),
],
outputs=[
IO.Model.Output(),
@@ -79,10 +79,10 @@ class FreeU_V2(IO.ComfyNode):
category="model_patches/unet",
inputs=[
IO.Model.Input("model"),
IO.Float.Input("b1", default=1.3, min=0.0, max=10.0, step=0.01),
IO.Float.Input("b2", default=1.4, min=0.0, max=10.0, step=0.01),
IO.Float.Input("s1", default=0.9, min=0.0, max=10.0, step=0.01),
IO.Float.Input("s2", default=0.2, min=0.0, max=10.0, step=0.01),
IO.Float.Input("b1", default=1.3, min=0.0, max=10.0, step=0.01, advanced=True),
IO.Float.Input("b2", default=1.4, min=0.0, max=10.0, step=0.01, advanced=True),
IO.Float.Input("s1", default=0.9, min=0.0, max=10.0, step=0.01, advanced=True),
IO.Float.Input("s2", default=0.2, min=0.0, max=10.0, step=0.01, advanced=True),
],
outputs=[
IO.Model.Output(),
comfy_extras/nodes_fresca.py
+3 -3
View File
@@ -65,11 +65,11 @@ class FreSca(io.ComfyNode):
inputs=[
io.Model.Input("model"),
io.Float.Input("scale_low", default=1.0, min=0, max=10, step=0.01,
tooltip="Scaling factor for low-frequency components"),
tooltip="Scaling factor for low-frequency components", advanced=True),
io.Float.Input("scale_high", default=1.25, min=0, max=10, step=0.01,
tooltip="Scaling factor for high-frequency components"),
tooltip="Scaling factor for high-frequency components", advanced=True),
io.Int.Input("freq_cutoff", default=20, min=1, max=10000, step=1,
tooltip="Number of frequency indices around center to consider as low-frequency"),
tooltip="Number of frequency indices around center to consider as low-frequency", advanced=True),
],
outputs=[
io.Model.Output(),
comfy_extras/nodes_gits.py
+1 -1
View File
@@ -342,7 +342,7 @@ class GITSScheduler(io.ComfyNode):
node_id="GITSScheduler",
category="sampling/custom_sampling/schedulers",
inputs=[
io.Float.Input("coeff", default=1.20, min=0.80, max=1.50, step=0.05),
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),
io.Float.Input("denoise", default=1.0, min=0.0, max=1.0, step=0.01),
],
comfy_extras/nodes_hooks.py
+4 -4
View File
@@ -233,8 +233,8 @@ class SetClipHooks:
return {
"required": {
"clip": ("CLIP",),
"apply_to_conds": ("BOOLEAN", {"default": True}),
"schedule_clip": ("BOOLEAN", {"default": False})
"apply_to_conds": ("BOOLEAN", {"default": True, "advanced": True}),
"schedule_clip": ("BOOLEAN", {"default": False, "advanced": True})
},
"optional": {
"hooks": ("HOOKS",)
@@ -512,7 +512,7 @@ class CreateHookKeyframesInterpolated:
"start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
"end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}),
"keyframes_count": ("INT", {"default": 5, "min": 2, "max": 100, "step": 1}),
"print_keyframes": ("BOOLEAN", {"default": False}),
"print_keyframes": ("BOOLEAN", {"default": False, "advanced": True}),
},
"optional": {
"prev_hook_kf": ("HOOK_KEYFRAMES",),
@@ -557,7 +557,7 @@ class CreateHookKeyframesFromFloats:
"floats_strength": ("FLOATS", {"default": -1, "min": -1, "step": 0.001, "forceInput": True}),
"start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
"end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}),
"print_keyframes": ("BOOLEAN", {"default": False}),
"print_keyframes": ("BOOLEAN", {"default": False, "advanced": True}),
},
"optional": {
"prev_hook_kf": ("HOOK_KEYFRAMES",),
comfy_extras/nodes_hunyuan.py
+4 -3
View File
@@ -138,7 +138,7 @@ class HunyuanVideo15SuperResolution(io.ComfyNode):
io.Image.Input("start_image", optional=True),
io.ClipVisionOutput.Input("clip_vision_output", optional=True),
io.Latent.Input("latent"),
io.Float.Input("noise_augmentation", default=0.70, min=0.0, max=1.0, step=0.01),
io.Float.Input("noise_augmentation", default=0.70, min=0.0, max=1.0, step=0.01, advanced=True),
],
outputs=[
@@ -285,6 +285,7 @@ class TextEncodeHunyuanVideo_ImageToVideo(io.ComfyNode):
min=1,
max=512,
tooltip="How much the image influences things vs the text prompt. Higher number means more influence from the text prompt.",
advanced=True,
),
],
outputs=[
@@ -313,7 +314,7 @@ class HunyuanImageToVideo(io.ComfyNode):
io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
io.Int.Input("length", default=53, min=1, max=nodes.MAX_RESOLUTION, step=4),
io.Int.Input("batch_size", default=1, min=1, max=4096),
io.Combo.Input("guidance_type", options=["v1 (concat)", "v2 (replace)", "custom"]),
io.Combo.Input("guidance_type", options=["v1 (concat)", "v2 (replace)", "custom"], advanced=True),
io.Image.Input("start_image", optional=True),
],
outputs=[
@@ -384,7 +385,7 @@ class HunyuanRefinerLatent(io.ComfyNode):
io.Conditioning.Input("positive"),
io.Conditioning.Input("negative"),
io.Latent.Input("latent"),
io.Float.Input("noise_augmentation", default=0.10, min=0.0, max=1.0, step=0.01),
io.Float.Input("noise_augmentation", default=0.10, min=0.0, max=1.0, step=0.01, advanced=True),
],
outputs=[
comfy_extras/nodes_hunyuan3d.py
+3 -3
View File
@@ -106,8 +106,8 @@ class VAEDecodeHunyuan3D(IO.ComfyNode):
inputs=[
IO.Latent.Input("samples"),
IO.Vae.Input("vae"),
IO.Int.Input("num_chunks", default=8000, min=1000, max=500000),
IO.Int.Input("octree_resolution", default=256, min=16, max=512),
IO.Int.Input("num_chunks", default=8000, min=1000, max=500000, advanced=True),
IO.Int.Input("octree_resolution", default=256, min=16, max=512, advanced=True),
],
outputs=[
IO.Voxel.Output(),
@@ -456,7 +456,7 @@ class VoxelToMesh(IO.ComfyNode):
category="3d",
inputs=[
IO.Voxel.Input("voxel"),
IO.Combo.Input("algorithm", options=["surface net", "basic"]),
IO.Combo.Input("algorithm", options=["surface net", "basic"], advanced=True),
IO.Float.Input("threshold", default=0.6, min=-1.0, max=1.0, step=0.01),
],
outputs=[
comfy_extras/nodes_hypertile.py
+4 -4
View File
@@ -30,10 +30,10 @@ class HyperTile(io.ComfyNode):
category="model_patches/unet",
inputs=[
io.Model.Input("model"),
io.Int.Input("tile_size", default=256, min=1, max=2048),
io.Int.Input("swap_size", default=2, min=1, max=128),
io.Int.Input("max_depth", default=0, min=0, max=10),
io.Boolean.Input("scale_depth", default=False),
io.Int.Input("tile_size", default=256, min=1, max=2048, advanced=True),
io.Int.Input("swap_size", default=2, min=1, max=128, advanced=True),
io.Int.Input("max_depth", default=0, min=0, max=10, advanced=True),
io.Boolean.Input("scale_depth", default=False, advanced=True),
],
outputs=[
io.Model.Output(),
comfy_extras/nodes_images.py
+5 -5
View File
@@ -228,7 +228,7 @@ class SaveAnimatedPNG(IO.ComfyNode):
IO.Image.Input("images"),
IO.String.Input("filename_prefix", default="ComfyUI"),
IO.Float.Input("fps", default=6.0, min=0.01, max=1000.0, step=0.01),
IO.Int.Input("compress_level", default=4, min=0, max=9),
IO.Int.Input("compress_level", default=4, min=0, max=9, advanced=True),
],
hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo],
is_output_node=True,
@@ -265,8 +265,8 @@ class ImageStitch(IO.ComfyNode):
IO.Image.Input("image1"),
IO.Combo.Input("direction", options=["right", "down", "left", "up"], default="right"),
IO.Boolean.Input("match_image_size", default=True),
IO.Int.Input("spacing_width", default=0, min=0, max=1024, step=2),
IO.Combo.Input("spacing_color", options=["white", "black", "red", "green", "blue"], default="white"),
IO.Int.Input("spacing_width", default=0, min=0, max=1024, step=2, advanced=True),
IO.Combo.Input("spacing_color", options=["white", "black", "red", "green", "blue"], default="white", advanced=True),
IO.Image.Input("image2", optional=True),
],
outputs=[IO.Image.Output()],
@@ -436,8 +436,8 @@ class ResizeAndPadImage(IO.ComfyNode):
IO.Image.Input("image"),
IO.Int.Input("target_width", default=512, min=1, max=nodes.MAX_RESOLUTION, step=1),
IO.Int.Input("target_height", default=512, min=1, max=nodes.MAX_RESOLUTION, step=1),
IO.Combo.Input("padding_color", options=["white", "black"]),
IO.Combo.Input("interpolation", options=["area", "bicubic", "nearest-exact", "bilinear", "lanczos"]),
IO.Combo.Input("padding_color", options=["white", "black"], advanced=True),
IO.Combo.Input("interpolation", options=["area", "bicubic", "nearest-exact", "bilinear", "lanczos"], advanced=True),
],
outputs=[IO.Image.Output()],
)
comfy_extras/nodes_latent.py
+3 -3
View File
@@ -413,9 +413,9 @@ class LatentOperationSharpen(io.ComfyNode):
category="latent/advanced/operations",
is_experimental=True,
inputs=[
io.Int.Input("sharpen_radius", default=9, min=1, max=31, step=1),
io.Float.Input("sigma", default=1.0, min=0.1, max=10.0, step=0.1),
io.Float.Input("alpha", default=0.1, min=0.0, max=5.0, step=0.01),
io.Int.Input("sharpen_radius", default=9, min=1, max=31, step=1, advanced=True),
io.Float.Input("sigma", default=1.0, min=0.1, max=10.0, step=0.1, advanced=True),
io.Float.Input("alpha", default=0.1, min=0.0, max=5.0, step=0.01, advanced=True),
],
outputs=[
io.LatentOperation.Output(),
comfy_extras/nodes_load_3d.py
+2 -2
View File
@@ -98,8 +98,8 @@ class Preview3D(IO.ComfyNode):
],
tooltip="3D model file or path string",
),
IO.Load3DCamera.Input("camera_info", optional=True),
IO.Image.Input("bg_image", optional=True),
IO.Load3DCamera.Input("camera_info", optional=True, advanced=True),
IO.Image.Input("bg_image", optional=True, advanced=True),
],
outputs=[],
)
comfy_extras/nodes_lora_extract.py
+3 -3
View File
@@ -94,9 +94,9 @@ class LoraSave(io.ComfyNode):
category="_for_testing",
inputs=[
io.String.Input("filename_prefix", default="loras/ComfyUI_extracted_lora"),
io.Int.Input("rank", default=8, min=1, max=4096, step=1),
io.Combo.Input("lora_type", options=tuple(LORA_TYPES.keys())),
io.Boolean.Input("bias_diff", default=True),
io.Int.Input("rank", default=8, min=1, max=4096, step=1, advanced=True),
io.Combo.Input("lora_type", options=tuple(LORA_TYPES.keys()), advanced=True),
io.Boolean.Input("bias_diff", default=True, advanced=True),
io.Model.Input(
"model_diff",
tooltip="The ModelSubtract output to be converted to a lora.",
comfy_extras/nodes_lt.py
+2
View File
@@ -450,6 +450,7 @@ class LTXVScheduler(io.ComfyNode):
id="stretch",
default=True,
tooltip="Stretch the sigmas to be in the range [terminal, 1].",
advanced=True,
),
io.Float.Input(
id="terminal",
@@ -458,6 +459,7 @@ class LTXVScheduler(io.ComfyNode):
max=0.99,
step=0.01,
tooltip="The terminal value of the sigmas after stretching.",
advanced=True,
),
io.Latent.Input("latent", optional=True),
],
comfy_extras/nodes_lt_audio.py
+1
View File
@@ -189,6 +189,7 @@ class LTXAVTextEncoderLoader(io.ComfyNode):
io.Combo.Input(
"device",
options=["default", "cpu"],
advanced=True,
)
],
outputs=[io.Clip.Output()],
comfy_extras/nodes_lumina2.py
+2 -2
View File
@@ -12,8 +12,8 @@ class RenormCFG(io.ComfyNode):
category="advanced/model",
inputs=[
io.Model.Input("model"),
io.Float.Input("cfg_trunc", default=100, min=0.0, max=100.0, step=0.01),
io.Float.Input("renorm_cfg", default=1.0, min=0.0, max=100.0, step=0.01),
io.Float.Input("cfg_trunc", default=100, min=0.0, max=100.0, step=0.01, advanced=True),
io.Float.Input("renorm_cfg", default=1.0, min=0.0, max=100.0, step=0.01, advanced=True),
],
outputs=[
io.Model.Output(),
comfy_extras/nodes_mask.py
+1 -1
View File
@@ -348,7 +348,7 @@ class GrowMask(IO.ComfyNode):
inputs=[
IO.Mask.Input("mask"),
IO.Int.Input("expand", default=0, min=-nodes.MAX_RESOLUTION, max=nodes.MAX_RESOLUTION, step=1),
IO.Boolean.Input("tapered_corners", default=True),
IO.Boolean.Input("tapered_corners", default=True, advanced=True),
],
outputs=[IO.Mask.Output()],
)
comfy_extras/nodes_model_advanced.py
+8 -8
View File
@@ -53,7 +53,7 @@ class ModelSamplingDiscrete:
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"sampling": (["eps", "v_prediction", "lcm", "x0", "img_to_img"],),
"zsnr": ("BOOLEAN", {"default": False}),
"zsnr": ("BOOLEAN", {"default": False, "advanced": True}),
}}
RETURN_TYPES = ("MODEL",)
@@ -153,8 +153,8 @@ class ModelSamplingFlux:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"max_shift": ("FLOAT", {"default": 1.15, "min": 0.0, "max": 100.0, "step":0.01}),
"base_shift": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 100.0, "step":0.01}),
"max_shift": ("FLOAT", {"default": 1.15, "min": 0.0, "max": 100.0, "step":0.01, "advanced": True}),
"base_shift": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 100.0, "step":0.01, "advanced": True}),
"width": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
"height": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
}}
@@ -190,8 +190,8 @@ class ModelSamplingContinuousEDM:
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"sampling": (["v_prediction", "edm", "edm_playground_v2.5", "eps", "cosmos_rflow"],),
"sigma_max": ("FLOAT", {"default": 120.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
"sigma_min": ("FLOAT", {"default": 0.002, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
"sigma_max": ("FLOAT", {"default": 120.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False, "advanced": True}),
"sigma_min": ("FLOAT", {"default": 0.002, "min": 0.0, "max": 1000.0, "step":0.001, "round": False, "advanced": True}),
}}
RETURN_TYPES = ("MODEL",)
@@ -235,8 +235,8 @@ class ModelSamplingContinuousV:
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"sampling": (["v_prediction"],),
"sigma_max": ("FLOAT", {"default": 500.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
"sigma_min": ("FLOAT", {"default": 0.03, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
"sigma_max": ("FLOAT", {"default": 500.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False, "advanced": True}),
"sigma_min": ("FLOAT", {"default": 0.03, "min": 0.0, "max": 1000.0, "step":0.001, "round": False, "advanced": True}),
}}
RETURN_TYPES = ("MODEL",)
@@ -303,7 +303,7 @@ class ModelComputeDtype:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"dtype": (["default", "fp32", "fp16", "bf16"],),
"dtype": (["default", "fp32", "fp16", "bf16"], {"advanced": True}),
}}
RETURN_TYPES = ("MODEL",)
comfy_extras/nodes_model_downscale.py
+4 -4
View File
@@ -13,11 +13,11 @@ class PatchModelAddDownscale(io.ComfyNode):
category="model_patches/unet",
inputs=[
io.Model.Input("model"),
io.Int.Input("block_number", default=3, min=1, max=32, step=1),
io.Int.Input("block_number", default=3, min=1, max=32, step=1, advanced=True),
io.Float.Input("downscale_factor", default=2.0, min=0.1, max=9.0, step=0.001),
io.Float.Input("start_percent", default=0.0, min=0.0, max=1.0, step=0.001),
io.Float.Input("end_percent", default=0.35, min=0.0, max=1.0, step=0.001),
io.Boolean.Input("downscale_after_skip", default=True),
io.Float.Input("start_percent", default=0.0, min=0.0, max=1.0, step=0.001, advanced=True),
io.Float.Input("end_percent", default=0.35, min=0.0, max=1.0, step=0.001, advanced=True),
io.Boolean.Input("downscale_after_skip", default=True, advanced=True),
io.Combo.Input("downscale_method", options=cls.UPSCALE_METHODS),
io.Combo.Input("upscale_method", options=cls.UPSCALE_METHODS),
],
comfy_extras/nodes_perpneg.py
+2 -2
View File
@@ -29,7 +29,7 @@ class PerpNeg(io.ComfyNode):
inputs=[
io.Model.Input("model"),
io.Conditioning.Input("empty_conditioning"),
io.Float.Input("neg_scale", default=1.0, min=0.0, max=100.0, step=0.01),
io.Float.Input("neg_scale", default=1.0, min=0.0, max=100.0, step=0.01, advanced=True),
],
outputs=[
io.Model.Output(),
@@ -134,7 +134,7 @@ class PerpNegGuider(io.ComfyNode):
io.Conditioning.Input("negative"),
io.Conditioning.Input("empty_conditioning"),
io.Float.Input("cfg", default=8.0, min=0.0, max=100.0, step=0.1, round=0.01),
io.Float.Input("neg_scale", default=1.0, min=0.0, max=100.0, step=0.01),
io.Float.Input("neg_scale", default=1.0, min=0.0, max=100.0, step=0.01, advanced=True),
],
outputs=[
io.Guider.Output(),
comfy_extras/nodes_post_processing.py
+4 -4
View File
@@ -180,9 +180,9 @@ class Sharpen(io.ComfyNode):
category="image/postprocessing",
inputs=[
io.Image.Input("image"),
io.Int.Input("sharpen_radius", default=1, min=1, max=31, step=1),
io.Float.Input("sigma", default=1.0, min=0.1, max=10.0, step=0.01),
io.Float.Input("alpha", default=1.0, min=0.0, max=5.0, step=0.01),
io.Int.Input("sharpen_radius", default=1, min=1, max=31, step=1, advanced=True),
io.Float.Input("sigma", default=1.0, min=0.1, max=10.0, step=0.01, advanced=True),
io.Float.Input("alpha", default=1.0, min=0.0, max=5.0, step=0.01, advanced=True),
],
outputs=[
io.Image.Output(),
@@ -226,7 +226,7 @@ class ImageScaleToTotalPixels(io.ComfyNode):
io.Image.Input("image"),
io.Combo.Input("upscale_method", options=cls.upscale_methods),
io.Float.Input("megapixels", default=1.0, min=0.01, max=16.0, step=0.01),
io.Int.Input("resolution_steps", default=1, min=1, max=256),
io.Int.Input("resolution_steps", default=1, min=1, max=256, advanced=True),
],
outputs=[
io.Image.Output(),
comfy_extras/nodes_qwen.py
+1 -1
View File
@@ -116,7 +116,7 @@ class EmptyQwenImageLayeredLatentImage(io.ComfyNode):
inputs=[
io.Int.Input("width", default=640, min=16, max=nodes.MAX_RESOLUTION, step=16),
io.Int.Input("height", default=640, min=16, max=nodes.MAX_RESOLUTION, step=16),
io.Int.Input("layers", default=3, min=0, max=nodes.MAX_RESOLUTION, step=1),
io.Int.Input("layers", default=3, min=0, max=nodes.MAX_RESOLUTION, step=1, advanced=True),
io.Int.Input("batch_size", default=1, min=1, max=4096),
],
outputs=[
comfy_extras/nodes_rope.py
+6 -6
View File
@@ -12,14 +12,14 @@ class ScaleROPE(io.ComfyNode):
is_experimental=True,
inputs=[
io.Model.Input("model"),
io.Float.Input("scale_x", default=1.0, min=0.0, max=100.0, step=0.1),
io.Float.Input("shift_x", default=0.0, min=-256.0, max=256.0, step=0.1),
io.Float.Input("scale_x", default=1.0, min=0.0, max=100.0, step=0.1, advanced=True),
io.Float.Input("shift_x", default=0.0, min=-256.0, max=256.0, step=0.1, advanced=True),
io.Float.Input("scale_y", default=1.0, min=0.0, max=100.0, step=0.1),
io.Float.Input("shift_y", default=0.0, min=-256.0, max=256.0, step=0.1),
io.Float.Input("scale_y", default=1.0, min=0.0, max=100.0, step=0.1, advanced=True),
io.Float.Input("shift_y", default=0.0, min=-256.0, max=256.0, step=0.1, advanced=True),
io.Float.Input("scale_t", default=1.0, min=0.0, max=100.0, step=0.1),
io.Float.Input("shift_t", default=0.0, min=-256.0, max=256.0, step=0.1),
io.Float.Input("scale_t", default=1.0, min=0.0, max=100.0, step=0.1, advanced=True),
io.Float.Input("shift_t", default=0.0, min=-256.0, max=256.0, step=0.1, advanced=True),
],
comfy_extras/nodes_sag.py
+1 -1
View File
@@ -117,7 +117,7 @@ class SelfAttentionGuidance(io.ComfyNode):
inputs=[
io.Model.Input("model"),
io.Float.Input("scale", default=0.5, min=-2.0, max=5.0, step=0.01),
io.Float.Input("blur_sigma", default=2.0, min=0.0, max=10.0, step=0.1),
io.Float.Input("blur_sigma", default=2.0, min=0.0, max=10.0, step=0.1, advanced=True),
],
outputs=[
io.Model.Output(),
comfy_extras/nodes_sd3.py
+4 -4
View File
@@ -72,7 +72,7 @@ class CLIPTextEncodeSD3(io.ComfyNode):
io.String.Input("clip_l", multiline=True, dynamic_prompts=True),
io.String.Input("clip_g", multiline=True, dynamic_prompts=True),
io.String.Input("t5xxl", multiline=True, dynamic_prompts=True),
io.Combo.Input("empty_padding", options=["none", "empty_prompt"]),
io.Combo.Input("empty_padding", options=["none", "empty_prompt"], advanced=True),
],
outputs=[
io.Conditioning.Output(),
@@ -179,10 +179,10 @@ class SkipLayerGuidanceSD3(io.ComfyNode):
description="Generic version of SkipLayerGuidance node that can be used on every DiT model.",
inputs=[
io.Model.Input("model"),
io.String.Input("layers", default="7, 8, 9", multiline=False),
io.String.Input("layers", default="7, 8, 9", multiline=False, advanced=True),
io.Float.Input("scale", default=3.0, min=0.0, max=10.0, step=0.1),
io.Float.Input("start_percent", default=0.01, min=0.0, max=1.0, step=0.001),
io.Float.Input("end_percent", default=0.15, min=0.0, max=1.0, step=0.001),
io.Float.Input("start_percent", default=0.01, min=0.0, max=1.0, step=0.001, advanced=True),
io.Float.Input("end_percent", default=0.15, min=0.0, max=1.0, step=0.001, advanced=True),
],
outputs=[
io.Model.Output(),
comfy_extras/nodes_sdupscale.py
+1 -1
View File
@@ -15,7 +15,7 @@ class SD_4XUpscale_Conditioning(io.ComfyNode):
io.Conditioning.Input("positive"),
io.Conditioning.Input("negative"),
io.Float.Input("scale_ratio", default=4.0, min=0.0, max=10.0, step=0.01),
io.Float.Input("noise_augmentation", default=0.0, min=0.0, max=1.0, step=0.001),
io.Float.Input("noise_augmentation", default=0.0, min=0.0, max=1.0, step=0.001, advanced=True),
],
outputs=[
io.Conditioning.Output(display_name="positive"),
comfy_extras/nodes_slg.py
+8 -8
View File
@@ -21,11 +21,11 @@ class SkipLayerGuidanceDiT(io.ComfyNode):
is_experimental=True,
inputs=[
io.Model.Input("model"),
io.String.Input("double_layers", default="7, 8, 9"),
io.String.Input("single_layers", default="7, 8, 9"),
io.String.Input("double_layers", default="7, 8, 9", advanced=True),
io.String.Input("single_layers", default="7, 8, 9", advanced=True),
io.Float.Input("scale", default=3.0, min=0.0, max=10.0, step=0.1),
io.Float.Input("start_percent", default=0.01, min=0.0, max=1.0, step=0.001),
io.Float.Input("end_percent", default=0.15, min=0.0, max=1.0, step=0.001),
io.Float.Input("start_percent", default=0.01, min=0.0, max=1.0, step=0.001, advanced=True),
io.Float.Input("end_percent", default=0.15, min=0.0, max=1.0, step=0.001, advanced=True),
io.Float.Input("rescaling_scale", default=0.0, min=0.0, max=10.0, step=0.01),
],
outputs=[
@@ -101,10 +101,10 @@ class SkipLayerGuidanceDiTSimple(io.ComfyNode):
is_experimental=True,
inputs=[
io.Model.Input("model"),
io.String.Input("double_layers", default="7, 8, 9"),
io.String.Input("single_layers", default="7, 8, 9"),
io.Float.Input("start_percent", default=0.0, min=0.0, max=1.0, step=0.001),
io.Float.Input("end_percent", default=1.0, min=0.0, max=1.0, step=0.001),
io.String.Input("double_layers", default="7, 8, 9", advanced=True),
io.String.Input("single_layers", default="7, 8, 9", advanced=True),
io.Float.Input("start_percent", default=0.0, min=0.0, max=1.0, step=0.001, advanced=True),
io.Float.Input("end_percent", default=1.0, min=0.0, max=1.0, step=0.001, advanced=True),
],
outputs=[
io.Model.Output(),
comfy_extras/nodes_stable3d.py
+2 -2
View File
@@ -75,8 +75,8 @@ class StableZero123_Conditioning_Batched(io.ComfyNode):
io.Int.Input("batch_size", default=1, min=1, max=4096),
io.Float.Input("elevation", default=0.0, min=-180.0, max=180.0, step=0.1, round=False),
io.Float.Input("azimuth", default=0.0, min=-180.0, max=180.0, step=0.1, round=False),
io.Float.Input("elevation_batch_increment", default=0.0, min=-180.0, max=180.0, step=0.1, round=False),
io.Float.Input("azimuth_batch_increment", default=0.0, min=-180.0, max=180.0, step=0.1, round=False)
io.Float.Input("elevation_batch_increment", default=0.0, min=-180.0, max=180.0, step=0.1, round=False, advanced=True),
io.Float.Input("azimuth_batch_increment", default=0.0, min=-180.0, max=180.0, step=0.1, round=False, advanced=True)
],
outputs=[
io.Conditioning.Output(display_name="positive"),
comfy_extras/nodes_stable_cascade.py
+2 -2
View File
@@ -33,7 +33,7 @@ class StableCascade_EmptyLatentImage(io.ComfyNode):
inputs=[
io.Int.Input("width", default=1024, min=256, max=nodes.MAX_RESOLUTION, step=8),
io.Int.Input("height", default=1024, min=256, max=nodes.MAX_RESOLUTION, step=8),
io.Int.Input("compression", default=42, min=4, max=128, step=1),
io.Int.Input("compression", default=42, min=4, max=128, step=1, advanced=True),
io.Int.Input("batch_size", default=1, min=1, max=4096),
],
outputs=[
@@ -62,7 +62,7 @@ class StableCascade_StageC_VAEEncode(io.ComfyNode):
inputs=[
io.Image.Input("image"),
io.Vae.Input("vae"),
io.Int.Input("compression", default=42, min=4, max=128, step=1),
io.Int.Input("compression", default=42, min=4, max=128, step=1, advanced=True),
],
outputs=[
io.Latent.Output(display_name="stage_c"),
comfy_extras/nodes_string.py
+13 -13
View File
@@ -169,7 +169,7 @@ class StringContains(io.ComfyNode):
inputs=[
io.String.Input("string", multiline=True),
io.String.Input("substring", multiline=True),
io.Boolean.Input("case_sensitive", default=True),
io.Boolean.Input("case_sensitive", default=True, advanced=True),
],
outputs=[
io.Boolean.Output(display_name="contains"),
@@ -198,7 +198,7 @@ class StringCompare(io.ComfyNode):
io.String.Input("string_a", multiline=True),
io.String.Input("string_b", multiline=True),
io.Combo.Input("mode", options=["Starts With", "Ends With", "Equal"]),
io.Boolean.Input("case_sensitive", default=True),
io.Boolean.Input("case_sensitive", default=True, advanced=True),
],
outputs=[
io.Boolean.Output(),
@@ -233,9 +233,9 @@ class RegexMatch(io.ComfyNode):
inputs=[
io.String.Input("string", multiline=True),
io.String.Input("regex_pattern", multiline=True),
io.Boolean.Input("case_insensitive", default=True),
io.Boolean.Input("multiline", default=False),
io.Boolean.Input("dotall", default=False),
io.Boolean.Input("case_insensitive", default=True, advanced=True),
io.Boolean.Input("multiline", default=False, advanced=True),
io.Boolean.Input("dotall", default=False, advanced=True),
],
outputs=[
io.Boolean.Output(display_name="matches"),
@@ -275,10 +275,10 @@ class RegexExtract(io.ComfyNode):
io.String.Input("string", multiline=True),
io.String.Input("regex_pattern", multiline=True),
io.Combo.Input("mode", options=["First Match", "All Matches", "First Group", "All Groups"]),
io.Boolean.Input("case_insensitive", default=True),
io.Boolean.Input("multiline", default=False),
io.Boolean.Input("dotall", default=False),
io.Int.Input("group_index", default=1, min=0, max=100),
io.Boolean.Input("case_insensitive", default=True, advanced=True),
io.Boolean.Input("multiline", default=False, advanced=True),
io.Boolean.Input("dotall", default=False, advanced=True),
io.Int.Input("group_index", default=1, min=0, max=100, advanced=True),
],
outputs=[
io.String.Output(),
@@ -351,10 +351,10 @@ class RegexReplace(io.ComfyNode):
io.String.Input("string", multiline=True),
io.String.Input("regex_pattern", multiline=True),
io.String.Input("replace", multiline=True),
io.Boolean.Input("case_insensitive", default=True, optional=True),
io.Boolean.Input("multiline", default=False, optional=True),
io.Boolean.Input("dotall", default=False, optional=True, tooltip="When enabled, the dot (.) character will match any character including newline characters. When disabled, dots won't match newlines."),
io.Int.Input("count", default=0, min=0, max=100, optional=True, tooltip="Maximum number of replacements to make. Set to 0 to replace all occurrences (default). Set to 1 to replace only the first match, 2 for the first two matches, etc."),
io.Boolean.Input("case_insensitive", default=True, optional=True, advanced=True),
io.Boolean.Input("multiline", default=False, optional=True, advanced=True),
io.Boolean.Input("dotall", default=False, optional=True, advanced=True, tooltip="When enabled, the dot (.) character will match any character including newline characters. When disabled, dots won't match newlines."),
io.Int.Input("count", default=0, min=0, max=100, optional=True, advanced=True, tooltip="Maximum number of replacements to make. Set to 0 to replace all occurrences (default). Set to 1 to replace only the first match, 2 for the first two matches, etc."),
],
outputs=[
io.String.Output(),
comfy_extras/nodes_torch_compile.py
+1
View File
@@ -16,6 +16,7 @@ class TorchCompileModel(io.ComfyNode):
io.Combo.Input(
"backend",
options=["inductor", "cudagraphs"],
advanced=True,
),
],
outputs=[io.Model.Output()],
comfy_extras/nodes_video_model.py
+4 -4
View File
@@ -32,9 +32,9 @@ class SVD_img2vid_Conditioning:
"width": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
"height": ("INT", {"default": 576, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
"video_frames": ("INT", {"default": 14, "min": 1, "max": 4096}),
"motion_bucket_id": ("INT", {"default": 127, "min": 1, "max": 1023}),
"motion_bucket_id": ("INT", {"default": 127, "min": 1, "max": 1023, "advanced": True}),
"fps": ("INT", {"default": 6, "min": 1, "max": 1024}),
"augmentation_level": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.01})
"augmentation_level": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.01, "advanced": True})
}}
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
RETURN_NAMES = ("positive", "negative", "latent")
@@ -60,7 +60,7 @@ class VideoLinearCFGGuidance:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"min_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}),
"min_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01, "advanced": True}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
@@ -84,7 +84,7 @@ class VideoTriangleCFGGuidance:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"min_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}),
"min_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01, "advanced": True}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
comfy_extras/nodes_wan.py
+3 -3
View File
@@ -717,8 +717,8 @@ class WanTrackToVideo(io.ComfyNode):
io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4),
io.Int.Input("batch_size", default=1, min=1, max=4096),
io.Float.Input("temperature", default=220.0, min=1.0, max=1000.0, step=0.1),
io.Int.Input("topk", default=2, min=1, max=10),
io.Float.Input("temperature", default=220.0, min=1.0, max=1000.0, step=0.1, advanced=True),
io.Int.Input("topk", default=2, min=1, max=10, advanced=True),
io.Image.Input("start_image"),
io.ClipVisionOutput.Input("clip_vision_output", optional=True),
],
@@ -1323,7 +1323,7 @@ class WanInfiniteTalkToVideo(io.ComfyNode):
io.ClipVisionOutput.Input("clip_vision_output", optional=True),
io.Image.Input("start_image", optional=True),
io.AudioEncoderOutput.Input("audio_encoder_output_1"),
io.Int.Input("motion_frame_count", default=9, min=1, max=33, step=1, tooltip="Number of previous frames to use as motion context."),
io.Int.Input("motion_frame_count", default=9, min=1, max=33, step=1, tooltip="Number of previous frames to use as motion context.", advanced=True),
io.Float.Input("audio_scale", default=1.0, min=-10.0, max=10.0, step=0.01),
io.Image.Input("previous_frames", optional=True),
],
comfy_extras/nodes_wanmove.py
+2 -2
View File
@@ -252,9 +252,9 @@ class WanMoveVisualizeTracks(io.ComfyNode):
io.Image.Input("images"),
io.Tracks.Input("tracks", optional=True),
io.Int.Input("line_resolution", default=24, min=1, max=1024),
io.Int.Input("circle_size", default=12, min=1, max=128),
io.Int.Input("circle_size", default=12, min=1, max=128, advanced=True),
io.Float.Input("opacity", default=0.75, min=0.0, max=1.0, step=0.01),
io.Int.Input("line_width", default=16, min=1, max=128),
io.Int.Input("line_width", default=16, min=1, max=128, advanced=True),
],
outputs=[
io.Image.Output(),
comfy_extras/nodes_zimage.py
+1 -1
View File
@@ -16,7 +16,7 @@ class TextEncodeZImageOmni(io.ComfyNode):
io.Clip.Input("clip"),
io.ClipVision.Input("image_encoder", optional=True),
io.String.Input("prompt", multiline=True, dynamic_prompts=True),
io.Boolean.Input("auto_resize_images", default=True),
io.Boolean.Input("auto_resize_images", default=True, advanced=True),
io.Vae.Input("vae", optional=True),
io.Image.Input("image1", optional=True),
io.Image.Input("image2", optional=True),