feat: mark 429 widgets as advanced for collapsible UI (#12197)
* 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
GitHub
@@ -50,9 +50,9 @@ class KarrasScheduler(io.ComfyNode):
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category="sampling/custom_sampling/schedulers",
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inputs=[
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io.Int.Input("steps", default=20, min=1, max=10000),
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io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False),
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io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False),
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io.Float.Input("rho", default=7.0, min=0.0, max=100.0, step=0.01, round=False),
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io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
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io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
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io.Float.Input("rho", default=7.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
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],
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outputs=[io.Sigmas.Output()]
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)
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@@ -72,8 +72,8 @@ class ExponentialScheduler(io.ComfyNode):
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category="sampling/custom_sampling/schedulers",
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inputs=[
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io.Int.Input("steps", default=20, min=1, max=10000),
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io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False),
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io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False),
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io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
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io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
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],
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outputs=[io.Sigmas.Output()]
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)
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@@ -93,9 +93,9 @@ class PolyexponentialScheduler(io.ComfyNode):
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category="sampling/custom_sampling/schedulers",
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inputs=[
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io.Int.Input("steps", default=20, min=1, max=10000),
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io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False),
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io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False),
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io.Float.Input("rho", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
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io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
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io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
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io.Float.Input("rho", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
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],
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outputs=[io.Sigmas.Output()]
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)
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@@ -115,10 +115,10 @@ class LaplaceScheduler(io.ComfyNode):
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category="sampling/custom_sampling/schedulers",
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inputs=[
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io.Int.Input("steps", default=20, min=1, max=10000),
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io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False),
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io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False),
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io.Float.Input("mu", default=0.0, min=-10.0, max=10.0, step=0.1, round=False),
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io.Float.Input("beta", default=0.5, min=0.0, max=10.0, step=0.1, round=False),
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io.Float.Input("sigma_max", default=14.614642, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
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io.Float.Input("sigma_min", default=0.0291675, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
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io.Float.Input("mu", default=0.0, min=-10.0, max=10.0, step=0.1, round=False, advanced=True),
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io.Float.Input("beta", default=0.5, min=0.0, max=10.0, step=0.1, round=False, advanced=True),
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],
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outputs=[io.Sigmas.Output()]
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)
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@@ -164,8 +164,8 @@ class BetaSamplingScheduler(io.ComfyNode):
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inputs=[
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io.Model.Input("model"),
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io.Int.Input("steps", default=20, min=1, max=10000),
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io.Float.Input("alpha", default=0.6, min=0.0, max=50.0, step=0.01, round=False),
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io.Float.Input("beta", default=0.6, min=0.0, max=50.0, step=0.01, round=False),
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io.Float.Input("alpha", default=0.6, min=0.0, max=50.0, step=0.01, round=False, advanced=True),
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io.Float.Input("beta", default=0.6, min=0.0, max=50.0, step=0.01, round=False, advanced=True),
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],
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outputs=[io.Sigmas.Output()]
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)
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@@ -185,9 +185,9 @@ class VPScheduler(io.ComfyNode):
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category="sampling/custom_sampling/schedulers",
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inputs=[
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io.Int.Input("steps", default=20, min=1, max=10000),
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io.Float.Input("beta_d", default=19.9, min=0.0, max=5000.0, step=0.01, round=False), #TODO: fix default values
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io.Float.Input("beta_min", default=0.1, min=0.0, max=5000.0, step=0.01, round=False),
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io.Float.Input("eps_s", default=0.001, min=0.0, max=1.0, step=0.0001, round=False),
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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
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io.Float.Input("beta_min", default=0.1, min=0.0, max=5000.0, step=0.01, round=False, advanced=True),
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io.Float.Input("eps_s", default=0.001, min=0.0, max=1.0, step=0.0001, round=False, advanced=True),
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],
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outputs=[io.Sigmas.Output()]
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)
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@@ -398,9 +398,9 @@ class SamplerDPMPP_3M_SDE(io.ComfyNode):
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node_id="SamplerDPMPP_3M_SDE",
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category="sampling/custom_sampling/samplers",
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inputs=[
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io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
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io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
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io.Combo.Input("noise_device", options=['gpu', 'cpu']),
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io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
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io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
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io.Combo.Input("noise_device", options=['gpu', 'cpu'], advanced=True),
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],
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outputs=[io.Sampler.Output()]
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)
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@@ -424,9 +424,9 @@ class SamplerDPMPP_2M_SDE(io.ComfyNode):
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category="sampling/custom_sampling/samplers",
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inputs=[
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io.Combo.Input("solver_type", options=['midpoint', 'heun']),
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io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
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io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
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io.Combo.Input("noise_device", options=['gpu', 'cpu']),
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io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
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io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
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io.Combo.Input("noise_device", options=['gpu', 'cpu'], advanced=True),
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],
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outputs=[io.Sampler.Output()]
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)
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@@ -450,10 +450,10 @@ class SamplerDPMPP_SDE(io.ComfyNode):
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node_id="SamplerDPMPP_SDE",
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category="sampling/custom_sampling/samplers",
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inputs=[
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io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
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io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
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io.Float.Input("r", default=0.5, min=0.0, max=100.0, step=0.01, round=False),
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io.Combo.Input("noise_device", options=['gpu', 'cpu']),
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io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
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io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
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io.Float.Input("r", default=0.5, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
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io.Combo.Input("noise_device", options=['gpu', 'cpu'], advanced=True),
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],
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outputs=[io.Sampler.Output()]
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)
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@@ -496,8 +496,8 @@ class SamplerEulerAncestral(io.ComfyNode):
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node_id="SamplerEulerAncestral",
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category="sampling/custom_sampling/samplers",
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inputs=[
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io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
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io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
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io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
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io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
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],
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outputs=[io.Sampler.Output()]
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)
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@@ -538,7 +538,7 @@ class SamplerLMS(io.ComfyNode):
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return io.Schema(
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node_id="SamplerLMS",
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category="sampling/custom_sampling/samplers",
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inputs=[io.Int.Input("order", default=4, min=1, max=100)],
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inputs=[io.Int.Input("order", default=4, min=1, max=100, advanced=True)],
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outputs=[io.Sampler.Output()]
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)
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@@ -556,16 +556,16 @@ class SamplerDPMAdaptative(io.ComfyNode):
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node_id="SamplerDPMAdaptative",
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category="sampling/custom_sampling/samplers",
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inputs=[
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io.Int.Input("order", default=3, min=2, max=3),
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io.Float.Input("rtol", default=0.05, min=0.0, max=100.0, step=0.01, round=False),
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io.Float.Input("atol", default=0.0078, min=0.0, max=100.0, step=0.01, round=False),
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io.Float.Input("h_init", default=0.05, min=0.0, max=100.0, step=0.01, round=False),
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io.Float.Input("pcoeff", default=0.0, min=0.0, max=100.0, step=0.01, round=False),
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io.Float.Input("icoeff", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
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io.Float.Input("dcoeff", default=0.0, min=0.0, max=100.0, step=0.01, round=False),
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io.Float.Input("accept_safety", default=0.81, min=0.0, max=100.0, step=0.01, round=False),
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io.Float.Input("eta", default=0.0, min=0.0, max=100.0, step=0.01, round=False),
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io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
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io.Int.Input("order", default=3, min=2, max=3, advanced=True),
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io.Float.Input("rtol", default=0.05, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
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io.Float.Input("atol", default=0.0078, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
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io.Float.Input("h_init", default=0.05, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
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io.Float.Input("pcoeff", default=0.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
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io.Float.Input("icoeff", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
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io.Float.Input("dcoeff", default=0.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
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io.Float.Input("accept_safety", default=0.81, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
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io.Float.Input("eta", default=0.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
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io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
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],
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outputs=[io.Sampler.Output()]
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)
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@@ -588,9 +588,9 @@ class SamplerER_SDE(io.ComfyNode):
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category="sampling/custom_sampling/samplers",
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inputs=[
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io.Combo.Input("solver_type", options=["ER-SDE", "Reverse-time SDE", "ODE"]),
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io.Int.Input("max_stage", default=3, min=1, max=3),
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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."),
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io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
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io.Int.Input("max_stage", default=3, min=1, max=3, advanced=True),
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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),
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io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
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],
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outputs=[io.Sampler.Output()]
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)
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@@ -626,14 +626,14 @@ class SamplerSASolver(io.ComfyNode):
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category="sampling/custom_sampling/samplers",
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inputs=[
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io.Model.Input("model"),
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io.Float.Input("eta", default=1.0, min=0.0, max=10.0, step=0.01, round=False),
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io.Float.Input("sde_start_percent", default=0.2, min=0.0, max=1.0, step=0.001),
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io.Float.Input("sde_end_percent", default=0.8, min=0.0, max=1.0, step=0.001),
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io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False),
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io.Int.Input("predictor_order", default=3, min=1, max=6),
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io.Int.Input("corrector_order", default=4, min=0, max=6),
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io.Boolean.Input("use_pece"),
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io.Boolean.Input("simple_order_2"),
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io.Float.Input("eta", default=1.0, min=0.0, max=10.0, step=0.01, round=False, advanced=True),
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io.Float.Input("sde_start_percent", default=0.2, min=0.0, max=1.0, step=0.001, advanced=True),
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io.Float.Input("sde_end_percent", default=0.8, min=0.0, max=1.0, step=0.001, advanced=True),
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io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, advanced=True),
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io.Int.Input("predictor_order", default=3, min=1, max=6, advanced=True),
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io.Int.Input("corrector_order", default=4, min=0, max=6, advanced=True),
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io.Boolean.Input("use_pece", advanced=True),
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io.Boolean.Input("simple_order_2", advanced=True),
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],
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outputs=[io.Sampler.Output()]
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)
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@@ -671,9 +671,9 @@ class SamplerSEEDS2(io.ComfyNode):
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category="sampling/custom_sampling/samplers",
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inputs=[
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io.Combo.Input("solver_type", options=["phi_1", "phi_2"]),
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io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False, tooltip="Stochastic strength"),
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io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, tooltip="SDE noise multiplier"),
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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)"),
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io.Float.Input("eta", default=1.0, min=0.0, max=100.0, step=0.01, round=False, tooltip="Stochastic strength", advanced=True),
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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),
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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),
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],
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outputs=[io.Sampler.Output()],
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description=(
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@@ -730,7 +730,7 @@ class SamplerCustom(io.ComfyNode):
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category="sampling/custom_sampling",
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inputs=[
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io.Model.Input("model"),
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io.Boolean.Input("add_noise", default=True),
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io.Boolean.Input("add_noise", default=True, advanced=True),
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io.Int.Input("noise_seed", default=0, min=0, max=0xffffffffffffffff, control_after_generate=True),
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io.Float.Input("cfg", default=8.0, min=0.0, max=100.0, step=0.1, round=0.01),
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io.Conditioning.Input("positive"),
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