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converted extra nodes files starting with "f,g"

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7 changed files with 748 additions and 2 deletions
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1
comfy_extras/v3/nodes_cfg.py
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@ -32,6 +32,7 @@ class CFGNorm(io.ComfyNodeV3):
io.Float.Input("strength", default=1.0, min=0.0, max=100.0, step=0.01), io.Float.Input("strength", default=1.0, min=0.0, max=100.0, step=0.01),
], ],
outputs=[io.Model.Output("patched_model", display_name="patched_model")], outputs=[io.Model.Output("patched_model", display_name="patched_model")],
is_experimental=True,
) )
@classmethod @classmethod

125
comfy_extras/v3/nodes_flux.py Normal file
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@ -0,0 +1,125 @@
from __future__ import annotations
import comfy.utils
import node_helpers
from comfy_api.v3 import io
PREFERED_KONTEXT_RESOLUTIONS = [
(672, 1568),
(688, 1504),
(720, 1456),
(752, 1392),
(800, 1328),
(832, 1248),
(880, 1184),
(944, 1104),
(1024, 1024),
(1104, 944),
(1184, 880),
(1248, 832),
(1328, 800),
(1392, 752),
(1456, 720),
(1504, 688),
(1568, 672),
]
class CLIPTextEncodeFlux(io.ComfyNodeV3):
@classmethod
def define_schema(cls):
return io.SchemaV3(
node_id="CLIPTextEncodeFlux_V3",
category="advanced/conditioning/flux",
inputs=[
io.Clip.Input(id="clip"),
io.String.Input(id="clip_l", multiline=True, dynamic_prompts=True),
io.String.Input(id="t5xxl", multiline=True, dynamic_prompts=True),
io.Float.Input(id="guidance", default=3.5, min=0.0, max=100.0, step=0.1),
],
outputs=[
io.Conditioning.Output(),
],
)
@classmethod
def execute(cls, clip, clip_l, t5xxl, guidance):
tokens = clip.tokenize(clip_l)
tokens["t5xxl"] = clip.tokenize(t5xxl)["t5xxl"]
return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens, add_dict={"guidance": guidance}))
class FluxDisableGuidance(io.ComfyNodeV3):
@classmethod
def define_schema(cls):
return io.SchemaV3(
node_id="FluxDisableGuidance_V3",
category="advanced/conditioning/flux",
description="This node completely disables the guidance embed on Flux and Flux like models",
inputs=[
io.Conditioning.Input(id="conditioning"),
],
outputs=[
io.Conditioning.Output(),
],
)
@classmethod
def execute(cls, conditioning):
c = node_helpers.conditioning_set_values(conditioning, {"guidance": None})
return io.NodeOutput(c)
class FluxGuidance(io.ComfyNodeV3):
@classmethod
def define_schema(cls):
return io.SchemaV3(
node_id="FluxGuidance_V3",
category="advanced/conditioning/flux",
inputs=[
io.Conditioning.Input(id="conditioning"),
io.Float.Input(id="guidance", default=3.5, min=0.0, max=100.0, step=0.1),
],
outputs=[
io.Conditioning.Output(),
],
)
@classmethod
def execute(cls, conditioning, guidance):
c = node_helpers.conditioning_set_values(conditioning, {"guidance": guidance})
return io.NodeOutput(c)
class FluxKontextImageScale(io.ComfyNodeV3):
@classmethod
def define_schema(cls):
return io.SchemaV3(
node_id="FluxKontextImageScale_V3",
category="advanced/conditioning/flux",
description="This node resizes the image to one that is more optimal for flux kontext.",
inputs=[
io.Image.Input(id="image"),
],
outputs=[
io.Image.Output(),
],
)
@classmethod
def execute(cls, image):
width = image.shape[2]
height = image.shape[1]
aspect_ratio = width / height
_, width, height = min((abs(aspect_ratio - w / h), w, h) for w, h in PREFERED_KONTEXT_RESOLUTIONS)
image = comfy.utils.common_upscale(image.movedim(-1, 1), width, height, "lanczos", "center").movedim(1, -1)
return io.NodeOutput(image)
NODES_LIST = [
CLIPTextEncodeFlux,
FluxDisableGuidance,
FluxGuidance,
FluxKontextImageScale,
]

131
comfy_extras/v3/nodes_freelunch.py Normal file
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@ -0,0 +1,131 @@
#code originally taken from: https://github.com/ChenyangSi/FreeU (under MIT License)
from __future__ import annotations
import logging
import torch
from comfy_api.v3 import io
def Fourier_filter(x, threshold, scale):
# FFT
x_freq = torch.fft.fftn(x.float(), dim=(-2, -1))
x_freq = torch.fft.fftshift(x_freq, dim=(-2, -1))
B, C, H, W = x_freq.shape
mask = torch.ones((B, C, H, W), device=x.device)
crow, ccol = H // 2, W //2
mask[..., crow - threshold:crow + threshold, ccol - threshold:ccol + threshold] = scale
x_freq = x_freq * mask
# IFFT
x_freq = torch.fft.ifftshift(x_freq, dim=(-2, -1))
x_filtered = torch.fft.ifftn(x_freq, dim=(-2, -1)).real
return x_filtered.to(x.dtype)
class FreeU(io.ComfyNodeV3):
@classmethod
def define_schema(cls):
return io.SchemaV3(
node_id="FreeU_V3",
category="model_patches/unet",
inputs=[
io.Model.Input(id="model"),
io.Float.Input(id="b1", default=1.1, min=0.0, max=10.0, step=0.01),
io.Float.Input(id="b2", default=1.2, min=0.0, max=10.0, step=0.01),
io.Float.Input(id="s1", default=0.9, min=0.0, max=10.0, step=0.01),
io.Float.Input(id="s2", default=0.2, min=0.0, max=10.0, step=0.01),
],
outputs=[
io.Model.Output(),
],
)
@classmethod
def execute(cls, model, b1, b2, s1, s2):
model_channels = model.model.model_config.unet_config["model_channels"]
scale_dict = {model_channels * 4: (b1, s1), model_channels * 2: (b2, s2)}
on_cpu_devices = {}
def output_block_patch(h, hsp, transformer_options):
scale = scale_dict.get(int(h.shape[1]), None)
if scale is not None:
h[:,:h.shape[1] // 2] = h[:,:h.shape[1] // 2] * scale[0]
if hsp.device not in on_cpu_devices:
try:
hsp = Fourier_filter(hsp, threshold=1, scale=scale[1])
except Exception:
logging.warning("Device {} does not support the torch.fft functions used in the FreeU node, switching to CPU.".format(hsp.device))
on_cpu_devices[hsp.device] = True
hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device)
else:
hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device)
return h, hsp
m = model.clone()
m.set_model_output_block_patch(output_block_patch)
return io.NodeOutput(m)
class FreeU_V2(io.ComfyNodeV3):
@classmethod
def define_schema(cls):
return io.SchemaV3(
node_id="FreeU_V2_V3",
category="model_patches/unet",
inputs=[
io.Model.Input(id="model"),
io.Float.Input(id="b1", default=1.3, min=0.0, max=10.0, step=0.01),
io.Float.Input(id="b2", default=1.4, min=0.0, max=10.0, step=0.01),
io.Float.Input(id="s1", default=0.9, min=0.0, max=10.0, step=0.01),
io.Float.Input(id="s2", default=0.2, min=0.0, max=10.0, step=0.01),
],
outputs=[
io.Model.Output(),
],
)
@classmethod
def execute(cls, model, b1, b2, s1, s2):
model_channels = model.model.model_config.unet_config["model_channels"]
scale_dict = {model_channels * 4: (b1, s1), model_channels * 2: (b2, s2)}
on_cpu_devices = {}
def output_block_patch(h, hsp, transformer_options):
scale = scale_dict.get(int(h.shape[1]), None)
if scale is not None:
hidden_mean = h.mean(1).unsqueeze(1)
B = hidden_mean.shape[0]
hidden_max, _ = torch.max(hidden_mean.view(B, -1), dim=-1, keepdim=True)
hidden_min, _ = torch.min(hidden_mean.view(B, -1), dim=-1, keepdim=True)
hidden_mean = (hidden_mean - hidden_min.unsqueeze(2).unsqueeze(3)) / (hidden_max - hidden_min).unsqueeze(2).unsqueeze(3)
h[:,:h.shape[1] // 2] = h[:,:h.shape[1] // 2] * ((scale[0] - 1 ) * hidden_mean + 1)
if hsp.device not in on_cpu_devices:
try:
hsp = Fourier_filter(hsp, threshold=1, scale=scale[1])
except Exception:
logging.warning("Device {} does not support the torch.fft functions used in the FreeU node, switching to CPU.".format(hsp.device))
on_cpu_devices[hsp.device] = True
hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device)
else:
hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device)
return h, hsp
m = model.clone()
m.set_model_output_block_patch(output_block_patch)
return io.NodeOutput(m)
NODES_LIST = [
FreeU,
FreeU_V2,
]

110
comfy_extras/v3/nodes_fresca.py Normal file
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@ -0,0 +1,110 @@
# Code based on https://github.com/WikiChao/FreSca (MIT License)
from __future__ import annotations
import torch
import torch.fft as fft
from comfy_api.v3 import io
def Fourier_filter(x, scale_low=1.0, scale_high=1.5, freq_cutoff=20):
"""
Apply frequency-dependent scaling to an image tensor using Fourier transforms.
Parameters:
x: Input tensor of shape (B, C, H, W)
scale_low: Scaling factor for low-frequency components (default: 1.0)
scale_high: Scaling factor for high-frequency components (default: 1.5)
freq_cutoff: Number of frequency indices around center to consider as low-frequency (default: 20)
Returns:
x_filtered: Filtered version of x in spatial domain with frequency-specific scaling applied.
"""
# Preserve input dtype and device
dtype, device = x.dtype, x.device
# Convert to float32 for FFT computations
x = x.to(torch.float32)
# 1) Apply FFT and shift low frequencies to center
x_freq = fft.fftn(x, dim=(-2, -1))
x_freq = fft.fftshift(x_freq, dim=(-2, -1))
# Initialize mask with high-frequency scaling factor
mask = torch.ones(x_freq.shape, device=device) * scale_high
m = mask
for d in range(len(x_freq.shape) - 2):
dim = d + 2
cc = x_freq.shape[dim] // 2
f_c = min(freq_cutoff, cc)
m = m.narrow(dim, cc - f_c, f_c * 2)
# Apply low-frequency scaling factor to center region
m[:] = scale_low
# 3) Apply frequency-specific scaling
x_freq = x_freq * mask
# 4) Convert back to spatial domain
x_freq = fft.ifftshift(x_freq, dim=(-2, -1))
x_filtered = fft.ifftn(x_freq, dim=(-2, -1)).real
# 5) Restore original dtype
x_filtered = x_filtered.to(dtype)
return x_filtered
class FreSca(io.ComfyNodeV3):
@classmethod
def define_schema(cls):
return io.SchemaV3(
node_id="FreSca_V3",
display_name="FreSca _V3",
category="_for_testing",
description="Applies frequency-dependent scaling to the guidance",
inputs=[
io.Model.Input(id="model"),
io.Float.Input(id="scale_low", default=1.0, min=0, max=10, step=0.01,
tooltip="Scaling factor for low-frequency components"),
io.Float.Input(id="scale_high", default=1.25, min=0, max=10, step=0.01,
tooltip="Scaling factor for high-frequency components"),
io.Int.Input(id="freq_cutoff", default=20, min=1, max=10000, step=1,
tooltip="Number of frequency indices around center to consider as low-frequency"),
],
outputs=[
io.Model.Output(),
],
is_experimental=True,
)
@classmethod
def execute(cls, model, scale_low, scale_high, freq_cutoff):
def custom_cfg_function(args):
conds_out = args["conds_out"]
if len(conds_out) <= 1 or None in args["conds"][:2]:
return conds_out
cond = conds_out[0]
uncond = conds_out[1]
guidance = cond - uncond
filtered_guidance = Fourier_filter(
guidance,
scale_low=scale_low,
scale_high=scale_high,
freq_cutoff=freq_cutoff,
)
filtered_cond = filtered_guidance + uncond
return [filtered_cond, uncond] + conds_out[2:]
m = model.clone()
m.set_model_sampler_pre_cfg_function(custom_cfg_function)
return io.NodeOutput(m)
NODES_LIST = [
FreSca,
]

376
comfy_extras/v3/nodes_gits.py Normal file
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@ -0,0 +1,376 @@
from __future__ import annotations
import numpy as np
import torch
from comfy_api.v3 import io
def loglinear_interp(t_steps, num_steps):
"""Performs log-linear interpolation of a given array of decreasing numbers."""
xs = np.linspace(0, 1, len(t_steps))
ys = np.log(t_steps[::-1])
new_xs = np.linspace(0, 1, num_steps)
new_ys = np.interp(new_xs, xs, ys)
return np.exp(new_ys)[::-1].copy()
NOISE_LEVELS = {
0.80: [
[14.61464119, 7.49001646, 0.02916753],
[14.61464119, 11.54541874, 6.77309084, 0.02916753],
[14.61464119, 11.54541874, 7.49001646, 3.07277966, 0.02916753],
[14.61464119, 11.54541874, 7.49001646, 5.85520077, 2.05039096, 0.02916753],
[14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 2.05039096, 0.02916753],
[14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753],
[14.61464119, 12.96784878, 11.54541874, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753],
[14.61464119, 13.76078796, 12.2308979, 10.90732002, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.1956799, 1.98035145, 0.86115354, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.1956799, 1.98035145, 0.86115354, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.07277966, 1.84880662, 0.83188516, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.88507891, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.07277966, 1.84880662, 0.83188516, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.88507891, 7.49001646, 6.77309084, 5.85520077, 4.86714602, 3.75677586, 2.84484982, 1.78698075, 0.803307, 0.02916753],
],
0.85: [
[14.61464119, 7.49001646, 0.02916753],
[14.61464119, 7.49001646, 1.84880662, 0.02916753],
[14.61464119, 11.54541874, 6.77309084, 1.56271636, 0.02916753],
[14.61464119, 11.54541874, 7.11996698, 3.07277966, 1.24153244, 0.02916753],
[14.61464119, 11.54541874, 7.49001646, 5.09240818, 2.84484982, 0.95350921, 0.02916753],
[14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.09240818, 2.84484982, 0.95350921, 0.02916753],
[14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.58536053, 3.1956799, 1.84880662, 0.803307, 0.02916753],
[14.61464119, 12.96784878, 11.54541874, 8.75849152, 7.49001646, 5.58536053, 3.1956799, 1.84880662, 0.803307, 0.02916753],
[14.61464119, 12.96784878, 11.54541874, 8.75849152, 7.49001646, 6.14220476, 4.65472794, 3.07277966, 1.84880662, 0.803307, 0.02916753],
[14.61464119, 13.76078796, 12.2308979, 10.90732002, 8.75849152, 7.49001646, 6.14220476, 4.65472794, 3.07277966, 1.84880662, 0.803307, 0.02916753],
[14.61464119, 13.76078796, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.65472794, 3.07277966, 1.84880662, 0.803307, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.65472794, 3.07277966, 1.84880662, 0.803307, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.65472794, 3.07277966, 1.84880662, 0.803307, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.60512662, 2.6383388, 1.56271636, 0.72133851, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.46139455, 2.45070267, 1.56271636, 0.72133851, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.46139455, 2.45070267, 1.56271636, 0.72133851, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.46139455, 2.45070267, 1.56271636, 0.72133851, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.46139455, 2.45070267, 1.56271636, 0.72133851, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.88507891, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.46139455, 2.45070267, 1.56271636, 0.72133851, 0.02916753],
],
0.90: [
[14.61464119, 6.77309084, 0.02916753],
[14.61464119, 7.49001646, 1.56271636, 0.02916753],
[14.61464119, 7.49001646, 3.07277966, 0.95350921, 0.02916753],
[14.61464119, 7.49001646, 4.86714602, 2.54230714, 0.89115214, 0.02916753],
[14.61464119, 11.54541874, 7.49001646, 4.86714602, 2.54230714, 0.89115214, 0.02916753],
[14.61464119, 11.54541874, 7.49001646, 5.09240818, 3.07277966, 1.61558151, 0.69515091, 0.02916753],
[14.61464119, 12.2308979, 8.75849152, 7.11996698, 4.86714602, 3.07277966, 1.61558151, 0.69515091, 0.02916753],
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[14.61464119, 2.84484982, 1.61558151, 1.08895338, 0.803307, 0.64427125, 0.52423614, 0.45573691, 0.41087446, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
],
1.45: [
[14.61464119, 0.59516323, 0.02916753],
[14.61464119, 0.803307, 0.25053367, 0.02916753],
[14.61464119, 0.95350921, 0.34370604, 0.09824532, 0.02916753],
[14.61464119, 1.24153244, 0.54755926, 0.25053367, 0.09824532, 0.02916753],
[14.61464119, 1.56271636, 0.72133851, 0.36617002, 0.19894916, 0.09824532, 0.02916753],
[14.61464119, 1.61558151, 0.803307, 0.45573691, 0.27464288, 0.17026083, 0.09824532, 0.02916753],
[14.61464119, 1.91321158, 0.95350921, 0.57119018, 0.36617002, 0.25053367, 0.17026083, 0.09824532, 0.02916753],
[14.61464119, 2.19988537, 1.08895338, 0.64427125, 0.41087446, 0.27464288, 0.19894916, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.45070267, 1.24153244, 0.74807048, 0.50118381, 0.34370604, 0.25053367, 0.19894916, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.45070267, 1.24153244, 0.74807048, 0.50118381, 0.36617002, 0.27464288, 0.22545385, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.45070267, 1.28281462, 0.803307, 0.54755926, 0.41087446, 0.32104823, 0.25053367, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.45070267, 1.28281462, 0.803307, 0.57119018, 0.43325692, 0.34370604, 0.27464288, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.45070267, 1.28281462, 0.83188516, 0.59516323, 0.45573691, 0.36617002, 0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.45070267, 1.28281462, 0.83188516, 0.59516323, 0.45573691, 0.36617002, 0.32104823, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.84484982, 1.51179266, 0.95350921, 0.69515091, 0.52423614, 0.41087446, 0.34370604, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.84484982, 1.51179266, 0.95350921, 0.69515091, 0.52423614, 0.43325692, 0.36617002, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.84484982, 1.56271636, 0.98595673, 0.72133851, 0.54755926, 0.45573691, 0.38853383, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.84484982, 1.56271636, 1.01931262, 0.74807048, 0.57119018, 0.4783645, 0.41087446, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.84484982, 1.56271636, 1.01931262, 0.74807048, 0.59516323, 0.50118381, 0.43325692, 0.38853383, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
],
1.50: [
[14.61464119, 0.54755926, 0.02916753],
[14.61464119, 0.803307, 0.25053367, 0.02916753],
[14.61464119, 0.86115354, 0.32104823, 0.09824532, 0.02916753],
[14.61464119, 1.24153244, 0.54755926, 0.25053367, 0.09824532, 0.02916753],
[14.61464119, 1.56271636, 0.72133851, 0.36617002, 0.19894916, 0.09824532, 0.02916753],
[14.61464119, 1.61558151, 0.803307, 0.45573691, 0.27464288, 0.17026083, 0.09824532, 0.02916753],
[14.61464119, 1.61558151, 0.83188516, 0.52423614, 0.34370604, 0.25053367, 0.17026083, 0.09824532, 0.02916753],
[14.61464119, 1.84880662, 0.95350921, 0.59516323, 0.38853383, 0.27464288, 0.19894916, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 1.84880662, 0.95350921, 0.59516323, 0.41087446, 0.29807833, 0.22545385, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 1.84880662, 0.95350921, 0.61951244, 0.43325692, 0.32104823, 0.25053367, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.19988537, 1.12534678, 0.72133851, 0.50118381, 0.36617002, 0.27464288, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.19988537, 1.12534678, 0.72133851, 0.50118381, 0.36617002, 0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.36326075, 1.24153244, 0.803307, 0.57119018, 0.43325692, 0.34370604, 0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.36326075, 1.24153244, 0.803307, 0.57119018, 0.43325692, 0.34370604, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.36326075, 1.24153244, 0.803307, 0.59516323, 0.45573691, 0.36617002, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.36326075, 1.24153244, 0.803307, 0.59516323, 0.45573691, 0.38853383, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.45070267, 1.32549286, 0.86115354, 0.64427125, 0.50118381, 0.41087446, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.45070267, 1.36964464, 0.92192322, 0.69515091, 0.54755926, 0.45573691, 0.41087446, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.45070267, 1.41535246, 0.95350921, 0.72133851, 0.57119018, 0.4783645, 0.43325692, 0.38853383, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
],
}
class GITSScheduler(io.ComfyNodeV3):
@classmethod
def define_schema(cls):
return io.SchemaV3(
node_id="GITSScheduler_V3",
category="sampling/custom_sampling/schedulers",
inputs=[
io.Float.Input(id="coeff", default=1.20, min=0.80, max=1.50, step=0.05),
io.Int.Input(id="steps", default=10, min=2, max=1000),
io.Float.Input(id="denoise", default=1.0, min=0.0, max=1.0, step=0.01),
],
outputs=[
io.Sigmas.Output(),
],
)
@classmethod
def execute(cls, coeff, steps, denoise):
total_steps = steps
if denoise < 1.0:
if denoise <= 0.0:
return io.NodeOutput(torch.FloatTensor([]))
total_steps = round(steps * denoise)
if steps <= 20:
sigmas = NOISE_LEVELS[round(coeff, 2)][steps-2][:]
else:
sigmas = NOISE_LEVELS[round(coeff, 2)][-1][:]
sigmas = loglinear_interp(sigmas, steps + 1)
sigmas = sigmas[-(total_steps + 1):]
sigmas[-1] = 0
return io.NodeOutput(torch.FloatTensor(sigmas))
NODES_LIST = [
GITSScheduler,
]

4
nodes.py
View File

@ -2313,6 +2313,10 @@ def init_builtin_extra_nodes():
"v3/nodes_cond.py", "v3/nodes_cond.py",
"v3/nodes_controlnet.py", "v3/nodes_controlnet.py",
"v3/nodes_cosmos.py", "v3/nodes_cosmos.py",
"v3/nodes_flux.py",
"v3/nodes_freelunch.py",
"v3/nodes_fresca.py",
"v3/nodes_gits.py",
"v3/nodes_images.py", "v3/nodes_images.py",
"v3/nodes_mask.py", "v3/nodes_mask.py",
"v3/nodes_preview_any.py", "v3/nodes_preview_any.py",

3
pyproject.toml
View File

@ -23,9 +23,8 @@ lint.select = [
# See all rules here: https://docs.astral.sh/ruff/rules/#pyflakes-f # See all rules here: https://docs.astral.sh/ruff/rules/#pyflakes-f
"F", "F",
] ]
ignore = ["E501"] # disable line-length checking
exclude = ["*.ipynb"] exclude = ["*.ipynb"]
line-length = 144
lint.pycodestyle.ignore-overlong-task-comments = true
[tool.ruff.lint.per-file-ignores] [tool.ruff.lint.per-file-ignores]
"!comfy_extras/v3/*" = ["E", "I"] # enable these rules only for V3 nodes "!comfy_extras/v3/*" = ["E", "I"] # enable these rules only for V3 nodes