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Add TimeToMoveKSamplerAdvanced

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David Lee 2026-05-02 15:28:35 -04:00 committed by GitHub
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comfy_extras/nodes_custom_sampler.py
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@ -11,6 +11,87 @@ from typing_extensions import override
from comfy_api.latest import ComfyExtension, io
import re
def time_to_move_sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, reference_latent_image, reference_latent_mask, denoise=1.0, start_step=None, time_to_move_last_step=None, last_step=None, force_full_denoise=False, noise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None):
sampler = comfy.samplers.KSampler(model, steps=steps, device=model.load_device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options)
sigmas = sampler.sigmas
if last_step == None:
last_step = steps
if time_to_move_last_step == None:
time_to_move_last_step = last_step
if time_to_move_last_step > last_step:
time_to_move_last_step = last_step
if start_step == None:
start_step = 0
#during each step, composite the reference latent back onto the partially sampled latent using the reference latent mask
for i in range (min(last_step, len(sigmas) - 1) - start_step):
if i > 1:
#don't add new noise to samples after first loop iteration
noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
if i < last_step - 1:
temp_force_full_denoise = False
else:
temp_force_full_denoise = force_full_denoise
temp_start = start_step + i
samples = sampler.sample(noise, positive, negative, cfg=cfg, latent_image=latent_image, start_step=temp_start, last_step=temp_start + 1, force_full_denoise=temp_force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback, disable_pbar=disable_pbar, seed=seed)
#add noise to the reference latent image (referenced from AddNoise node)
if temp_start < time_to_move_last_step:
model_sampling = model.get_model_object("model_sampling")
process_latent_out = model.get_model_object("process_latent_out")
process_latent_in = model.get_model_object("process_latent_in")
scale = sigmas[temp_start + 1]
if torch.count_nonzero(reference_latent_image) > 0: #Don't shift the empty latent image.
reference_latent_image = process_latent_in(reference_latent_image)
noisy = model_sampling.noise_scaling(scale, noise, reference_latent_image)
noisy = process_latent_out(noisy)
noisy = torch.nan_to_num(noisy, nan=0.0, posinf=0.0, neginf=0.0)
samples = video_latent_composite(samples, noisy, 0, 0, reference_latent_mask, multiplier=8, resize_source=True)
samples = samples.to(device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype())
return samples
def time_to_move_common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, reference_latent, reference_latent_mask, denoise=1.0, disable_noise=False, start_step=None, time_to_move_last_step = None, last_step=None, force_full_denoise=False):
latent_image = latent["samples"]
latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image, latent.get("downscale_ratio_spacial", None))
reference_latent_image = reference_latent["samples"]
reference_latent_image = comfy.sample.fix_empty_latent_channels(model, reference_latent_image, reference_latent.get("downscale_ratio_spacial", None))
if disable_noise:
noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
else:
batch_inds = latent["batch_index"] if "batch_index" in latent else None
noise = comfy.sample.prepare_noise(latent_image, seed, batch_inds)
noise_mask = None
if "noise_mask" in latent:
noise_mask = latent["noise_mask"]
callback = latent_preview.prepare_callback(model, steps)
disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
samples = time_to_move_sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, reference_latent_image, reference_latent_mask,
denoise=denoise, start_step=start_step, time_to_move_last_step = time_to_move_last_step, last_step=last_step,
force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
out = latent.copy()
out.pop("downscale_ratio_spacial", None)
out["samples"] = samples
return (out, )
class BasicScheduler(io.ComfyNode):
@classmethod
@ -979,6 +1060,47 @@ class SamplerCustomAdvanced(io.ComfyNode):
sample = execute
class TimeToMoveKSamplerAdvanced(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="TimeToMoveKSamplerAdvanced",
category="sampling/time_to_move",
inputs=[
io.Model.Input("model"),
io.Combo.Input("add_noise", options=["enable", "disable"], advanced=True),
io.Int.Input("noise_seed", default=0, min=0, max=0xffffffffffffffff, control_after_generate=True),
io.Int.Input("steps", default=20, min=1, max=10000),
io.Float.Input("cfg", default=8.0, min=0.0, max=100.0, step=0.1, round=0.01),
io.Combo.Input("sampler_name", options = comfy.samplers.KSampler.SAMPLERS),
io.Combo.Input("scheduler", options = comfy.samplers.KSampler.SCHEDULERS),
io.Conditioning.Input("positive"),
io.Conditioning.Input("negative"),
io.Latent.Input("latent_image", tooltip = "Generally should be the same as reference_latent_image."),
io.Latent.Input("reference_latent_image"),
io.Mask.Input("reference_latent_mask", tooltip = "Make sure mask is the same length as the latents rather than the original video."),
io.Int.Input("start_at_step", default = 0, min = 0, max = 10000, advanced = True, tooltip = "Generally should set at a step greater than 0."),
io.Int.Input("time_to_move_end_at_step", default = 0, min = 0, max = 10000, advanced = True, tooltip = "Generally should set at a step greater than 0 and less than total number of steps."),
io.Int.Input("end_at_step", default = 10000, min = 0, max = 10000, advanced = True, tooltip = "Use just like typical end_at_step with normal KSamplerAdvanced"),
io.Combo.Input("return_with_leftover_noise", options=["disable", "enable"], advanced = True),
],
outputs=[
io.Latent.Output(display_name="latent"),
]
)
@classmethod
def execute(cls, model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, reference_latent_image, reference_latent_mask, start_at_step, time_to_move_end_at_step, end_at_step, return_with_leftover_noise, denoise=1.0) -> io.NodeOutput:
force_full_denoise = True
if return_with_leftover_noise == "enable":
force_full_denoise = False
disable_noise = False
if add_noise == "disable":
disable_noise = True
return time_to_move_common_ksampler(model, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, reference_latent_image, reference_latent_mask, denoise=denoise, disable_noise=disable_noise, start_step=start_at_step, time_to_move_last_step = time_to_move_end_at_step, last_step=end_at_step, force_full_denoise=force_full_denoise)
class AddNoise(io.ComfyNode):
@classmethod
def define_schema(cls):
@ -1087,6 +1209,7 @@ class CustomSamplersExtension(ComfyExtension):
DisableNoise,
AddNoise,
SamplerCustomAdvanced,
TimeToMoveKSamplerAdvanced,
ManualSigmas,
]