419 lines
15 KiB
Python
419 lines
15 KiB
Python
import math
|
|
|
|
import torch
|
|
|
|
import comfy.k_diffusion.sampling as k_diffusion_sampling
|
|
import comfy.model_sampling
|
|
import comfy.samplers
|
|
import comfy.utils
|
|
|
|
|
|
MOTION_PULSE_MODES = ["off", "balanced", "aggressive", "custom"]
|
|
|
|
|
|
def _sigma_fn(t):
|
|
return t.neg().exp()
|
|
|
|
|
|
def _t_fn(sigma):
|
|
return sigma.log().neg()
|
|
|
|
|
|
def _hybrid_ode_step(model, x, sigma, sigma_next, s_in, extra_args, correction_blend, denoised=None):
|
|
if denoised is None:
|
|
denoised = model(x, sigma * s_in, **extra_args)
|
|
|
|
if sigma_next == 0:
|
|
return denoised, denoised
|
|
|
|
d = k_diffusion_sampling.to_d(x, sigma, denoised)
|
|
dt = sigma_next - sigma
|
|
x_euler = x + d * dt
|
|
|
|
if correction_blend <= 0.0:
|
|
return x_euler, denoised
|
|
|
|
t = _t_fn(sigma)
|
|
t_next = _t_fn(sigma_next)
|
|
h = t_next - t
|
|
r = 0.5
|
|
s = t + r * h
|
|
|
|
x_mid = (_sigma_fn(s) / _sigma_fn(t)) * x - torch.expm1(-h * r) * denoised
|
|
denoised_mid = model(x_mid, _sigma_fn(s) * s_in, **extra_args)
|
|
x_2s = (_sigma_fn(t_next) / _sigma_fn(t)) * x - torch.expm1(-h) * denoised_mid
|
|
|
|
if correction_blend >= 1.0:
|
|
return x_2s, denoised
|
|
return x_euler.lerp(x_2s, correction_blend), denoised
|
|
|
|
|
|
def _apply_motion_pulse(x, sigma, sigma_next, pulse_noise, noise_sampler):
|
|
if pulse_noise <= 0.0 or sigma_next >= sigma:
|
|
return x
|
|
|
|
sigma_delta_sq = max(0.0, float(sigma * sigma - sigma_next * sigma_next))
|
|
if sigma_delta_sq <= 0.0:
|
|
return x
|
|
|
|
sigma_delta = math.sqrt(sigma_delta_sq)
|
|
return x + noise_sampler(sigma, sigma_next) * (pulse_noise * sigma_delta)
|
|
|
|
|
|
def _find_schedule_anchor_index(sigmas, total_steps, schedule_progress):
|
|
if sigmas is None or total_steps <= 1:
|
|
return 0
|
|
|
|
schedule_progress = max(0.0, min(1.0, schedule_progress))
|
|
return min(total_steps - 1, max(0, int(round(schedule_progress * max(0, total_steps - 1)))))
|
|
|
|
|
|
def _resolve_motion_pulse_options(motion_pulse_mode, motion_pulse_start_pct,
|
|
motion_pulse_count, motion_pulse_spacing_pct,
|
|
motion_pulse_strength):
|
|
mode = (motion_pulse_mode or "off").lower()
|
|
if mode not in MOTION_PULSE_MODES:
|
|
mode = "off"
|
|
|
|
if mode == "off":
|
|
return {
|
|
"enabled": False,
|
|
"start_pct": 0.30,
|
|
"count": 0,
|
|
"spacing_pct": 0.22,
|
|
"strength": 0.0,
|
|
"noise": 0.0,
|
|
}
|
|
|
|
start_pct = 0.30 if motion_pulse_start_pct is None else float(motion_pulse_start_pct)
|
|
spacing_pct = 0.22 if motion_pulse_spacing_pct is None else float(motion_pulse_spacing_pct)
|
|
strength = 0.85 if motion_pulse_strength is None else float(motion_pulse_strength)
|
|
count = 2 if motion_pulse_count is None else int(motion_pulse_count)
|
|
|
|
start_pct = max(0.02, min(0.90, start_pct))
|
|
spacing_pct = max(0.04, min(0.45, spacing_pct))
|
|
strength = max(0.0, min(1.0, strength))
|
|
count = max(1, min(6, count))
|
|
|
|
if mode == "balanced":
|
|
count = min(count, 1)
|
|
strength = 0.55 if motion_pulse_strength is None else min(strength, 0.70)
|
|
elif mode == "aggressive":
|
|
count = max(2, count)
|
|
strength = max(strength, 0.85)
|
|
|
|
noise = 0.10 + strength * 0.55
|
|
|
|
return {
|
|
"enabled": True,
|
|
"start_pct": start_pct,
|
|
"count": count,
|
|
"spacing_pct": spacing_pct,
|
|
"strength": strength,
|
|
"noise": max(0.0, min(0.80, noise)),
|
|
}
|
|
|
|
|
|
def _get_late_start_index(total_steps, high_quality_pct):
|
|
high_quality_pct = max(0.0, min(1.0, float(high_quality_pct)))
|
|
late_steps = max(1, int(math.ceil(total_steps * high_quality_pct))) if high_quality_pct > 0.0 else 0
|
|
return max(0, total_steps - late_steps)
|
|
|
|
|
|
def _build_motion_pulse_steps(sigmas, total_steps, high_quality_pct,
|
|
motion_pulse_mode, motion_pulse_start_pct,
|
|
motion_pulse_count, motion_pulse_spacing_pct,
|
|
motion_pulse_strength):
|
|
options = _resolve_motion_pulse_options(
|
|
motion_pulse_mode,
|
|
motion_pulse_start_pct,
|
|
motion_pulse_count,
|
|
motion_pulse_spacing_pct,
|
|
motion_pulse_strength,
|
|
)
|
|
if not options["enabled"] or total_steps <= 2:
|
|
return [], options
|
|
|
|
late_start = _get_late_start_index(total_steps, high_quality_pct)
|
|
latest_anchor_pct = max(0.04, min(0.92, (late_start - 1) / max(1, total_steps - 1)))
|
|
pulse_steps = []
|
|
used_anchors = set()
|
|
|
|
for pulse_index in range(options["count"]):
|
|
trigger_pct = options["start_pct"] + options["spacing_pct"] * pulse_index
|
|
if trigger_pct >= latest_anchor_pct:
|
|
break
|
|
|
|
anchor_index = _find_schedule_anchor_index(sigmas, total_steps, trigger_pct)
|
|
anchor_index = min(total_steps - 1, max(0, anchor_index))
|
|
if anchor_index in used_anchors or anchor_index >= late_start:
|
|
continue
|
|
|
|
used_anchors.add(anchor_index)
|
|
pulse_steps.append({
|
|
"step_index": anchor_index,
|
|
"noise": options["noise"],
|
|
})
|
|
|
|
return pulse_steps, options
|
|
|
|
|
|
def _prepare_dynamic_sigmas(sigmas, high_quality_pct, motion_pulse_mode="off",
|
|
motion_pulse_start_pct=0.30, motion_pulse_count=2,
|
|
motion_pulse_spacing_pct=0.22, motion_pulse_strength=0.85):
|
|
if sigmas is None or not isinstance(sigmas, torch.Tensor):
|
|
return None, None, [], 0.0
|
|
|
|
base_sigmas = sigmas.detach().clone()
|
|
total_steps = max(0, int(base_sigmas.shape[0]) - 1)
|
|
if total_steps <= 0:
|
|
return base_sigmas, None, [], 0.0
|
|
|
|
late_start = _get_late_start_index(total_steps, high_quality_pct)
|
|
quality_sigma_start = None
|
|
if late_start < base_sigmas.shape[0]:
|
|
quality_sigma_start = float(base_sigmas[late_start].item())
|
|
|
|
pulse_steps, motion_pulse_options = _build_motion_pulse_steps(
|
|
base_sigmas,
|
|
total_steps,
|
|
high_quality_pct,
|
|
motion_pulse_mode,
|
|
motion_pulse_start_pct,
|
|
motion_pulse_count,
|
|
motion_pulse_spacing_pct,
|
|
motion_pulse_strength,
|
|
)
|
|
return base_sigmas, quality_sigma_start, pulse_steps, motion_pulse_options["noise"]
|
|
|
|
|
|
def sample_funpack_hybrid_euler_2s(model, x, sigmas, extra_args=None, callback=None,
|
|
disable=None, eta=1.0, s_noise=1.0,
|
|
high_quality_pct=0.35, correction_blend=1.0,
|
|
quality_sigma_start=None,
|
|
motion_pulse_mode="off",
|
|
motion_pulse_start_pct=0.30,
|
|
motion_pulse_count=2,
|
|
motion_pulse_spacing_pct=0.22,
|
|
motion_pulse_strength=0.85,
|
|
motion_pulse_noise=0.0,
|
|
motion_pulse_steps=None):
|
|
"""
|
|
Hybrid sampler:
|
|
- Early schedule: Euler ancestral for motion/anatomy buildup.
|
|
- Late schedule: deterministic Euler / DPM-Solver++(2S) ODE refinement for detail.
|
|
- Motion pulses: optional monotonic noise kicks before the late quality phase.
|
|
"""
|
|
if isinstance(model.inner_model.inner_model.model_sampling, comfy.model_sampling.CONST):
|
|
return k_diffusion_sampling.sample_euler_ancestral(
|
|
model, x, sigmas, extra_args=extra_args, callback=callback,
|
|
disable=disable, eta=eta, s_noise=s_noise
|
|
)
|
|
|
|
extra_args = {} if extra_args is None else extra_args
|
|
seed = extra_args.get("seed", None)
|
|
noise_sampler = k_diffusion_sampling.default_noise_sampler(x, seed=seed)
|
|
|
|
total_steps = max(0, len(sigmas) - 1)
|
|
if total_steps <= 0:
|
|
return x
|
|
|
|
high_quality_pct = max(0.0, min(1.0, float(high_quality_pct)))
|
|
correction_blend = max(0.0, min(1.0, float(correction_blend)))
|
|
if not motion_pulse_steps:
|
|
_, _, motion_pulse_steps, computed_motion_pulse_noise = _prepare_dynamic_sigmas(
|
|
sigmas,
|
|
high_quality_pct,
|
|
motion_pulse_mode,
|
|
motion_pulse_start_pct,
|
|
motion_pulse_count,
|
|
motion_pulse_spacing_pct,
|
|
motion_pulse_strength,
|
|
)
|
|
motion_pulse_noise = computed_motion_pulse_noise
|
|
motion_pulse_noise = max(0.0, float(motion_pulse_noise))
|
|
motion_step_noise = {
|
|
int(item.get("step_index", -1)): max(0.0, float(item.get("noise", motion_pulse_noise)))
|
|
for item in (motion_pulse_steps or [])
|
|
if isinstance(item, dict)
|
|
}
|
|
s_in = x.new_ones([x.shape[0]])
|
|
callback_step = 0
|
|
|
|
if quality_sigma_start is None:
|
|
late_steps = max(1, int(math.ceil(total_steps * high_quality_pct))) if high_quality_pct > 0.0 else 0
|
|
late_start = max(0, total_steps - late_steps)
|
|
if late_start < sigmas.shape[0]:
|
|
quality_sigma_start = float(sigmas[late_start].item())
|
|
else:
|
|
quality_sigma_start = float(quality_sigma_start)
|
|
|
|
for i in comfy.utils.model_trange(total_steps, disable=disable):
|
|
sigma = sigmas[i]
|
|
sigma_next = sigmas[i + 1]
|
|
in_quality_phase = quality_sigma_start is not None and float(sigma.item()) <= quality_sigma_start
|
|
|
|
if not in_quality_phase:
|
|
pulse_noise = motion_step_noise.get(int(i), 0.0)
|
|
if pulse_noise > 0.0:
|
|
x = _apply_motion_pulse(x, sigma, sigma_next, pulse_noise, noise_sampler)
|
|
|
|
denoised = model(x, sigma * s_in, **extra_args)
|
|
|
|
if callback is not None:
|
|
callback({
|
|
"x": x,
|
|
"i": callback_step,
|
|
"sigma": sigma,
|
|
"sigma_hat": sigma,
|
|
"denoised": denoised,
|
|
})
|
|
callback_step += 1
|
|
|
|
sigma_down, sigma_up = k_diffusion_sampling.get_ancestral_step(sigma, sigma_next, eta=eta)
|
|
|
|
if sigma_down == 0:
|
|
x = denoised
|
|
else:
|
|
d = k_diffusion_sampling.to_d(x, sigma, denoised)
|
|
dt = sigma_down - sigma
|
|
x = x + d * dt
|
|
|
|
if sigma_next > 0 and eta > 0 and s_noise > 0:
|
|
x = x + noise_sampler(sigma, sigma_next) * s_noise * sigma_up
|
|
else:
|
|
denoised = model(x, sigma * s_in, **extra_args)
|
|
|
|
if callback is not None:
|
|
callback({
|
|
"x": x,
|
|
"i": callback_step,
|
|
"sigma": sigma,
|
|
"sigma_hat": sigma,
|
|
"denoised": denoised,
|
|
})
|
|
callback_step += 1
|
|
|
|
x, _ = _hybrid_ode_step(
|
|
model,
|
|
x,
|
|
sigma,
|
|
sigma_next,
|
|
s_in,
|
|
extra_args,
|
|
correction_blend,
|
|
denoised=denoised,
|
|
)
|
|
|
|
return x
|
|
|
|
|
|
class FunPackHybridEuler2SSampler:
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"eta": ("FLOAT", {
|
|
"default": 1.0,
|
|
"min": 0.0,
|
|
"max": 1.0,
|
|
"step": 0.01,
|
|
"tooltip": "Ancestral stochasticity. Keep at 1.0 for classic ancestral behaviour."
|
|
}),
|
|
"s_noise": ("FLOAT", {
|
|
"default": 1.0,
|
|
"min": 0.0,
|
|
"max": 10.0,
|
|
"step": 0.01,
|
|
"tooltip": "Noise scale for ancestral noise injection."
|
|
}),
|
|
"high_quality_pct": ("FLOAT", {
|
|
"default": 0.35,
|
|
"min": 0.0,
|
|
"max": 1.0,
|
|
"step": 0.01,
|
|
"tooltip": "Fraction of late denoise steps that receive the slower 2S quality correction."
|
|
}),
|
|
"correction_blend": ("FLOAT", {
|
|
"default": 1.0,
|
|
"min": 0.0,
|
|
"max": 1.0,
|
|
"step": 0.01,
|
|
"tooltip": "Blend between Euler ancestral (0.0) and late-step 2S correction (1.0)."
|
|
}),
|
|
"motion_pulse_mode": (MOTION_PULSE_MODES, {
|
|
"default": "off",
|
|
"tooltip": "Adds early/mid anti-stiffness motion pulses. Off preserves legacy sampler behavior."
|
|
}),
|
|
"motion_pulse_start_pct": ("FLOAT", {
|
|
"default": 0.30,
|
|
"min": 0.0,
|
|
"max": 0.95,
|
|
"step": 0.01,
|
|
"tooltip": "Sampling progress where the first anti-stiffness pulse is applied."
|
|
}),
|
|
"motion_pulse_count": ("INT", {
|
|
"default": 2,
|
|
"min": 1,
|
|
"max": 6,
|
|
"step": 1,
|
|
"tooltip": "How many early/mid motion pulses to request before the late quality phase."
|
|
}),
|
|
"motion_pulse_spacing_pct": ("FLOAT", {
|
|
"default": 0.22,
|
|
"min": 0.04,
|
|
"max": 0.45,
|
|
"step": 0.01,
|
|
"tooltip": "Progress spacing between motion pulses."
|
|
}),
|
|
"motion_pulse_strength": ("FLOAT", {
|
|
"default": 0.85,
|
|
"min": 0.0,
|
|
"max": 1.0,
|
|
"step": 0.01,
|
|
"tooltip": "How strongly motion pulses add monotonic noise kicks. Higher values push harder against stale image references."
|
|
}),
|
|
},
|
|
"optional": {
|
|
"sigmas": ("SIGMAS",),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("SAMPLER", "SIGMAS")
|
|
RETURN_NAMES = ("sampler", "sigmas")
|
|
FUNCTION = "get_sampler"
|
|
CATEGORY = "FunPack/Sampling"
|
|
DESCRIPTION = "Hybrid sampler: early Euler ancestral for motion, late DPM-Solver++(2S) ODE refinement for quality, with optional anti-stiffness motion pulses."
|
|
|
|
def get_sampler(self, eta, s_noise, high_quality_pct, correction_blend,
|
|
motion_pulse_mode="off", motion_pulse_start_pct=0.30,
|
|
motion_pulse_count=2, motion_pulse_spacing_pct=0.22,
|
|
motion_pulse_strength=0.85, sigmas=None):
|
|
prepared_sigmas, quality_sigma_start, motion_pulse_steps, motion_pulse_noise = _prepare_dynamic_sigmas(
|
|
sigmas,
|
|
high_quality_pct,
|
|
motion_pulse_mode,
|
|
motion_pulse_start_pct,
|
|
motion_pulse_count,
|
|
motion_pulse_spacing_pct,
|
|
motion_pulse_strength,
|
|
)
|
|
sampler = comfy.samplers.KSAMPLER(
|
|
sample_funpack_hybrid_euler_2s,
|
|
extra_options={
|
|
"eta": eta,
|
|
"s_noise": s_noise,
|
|
"high_quality_pct": high_quality_pct,
|
|
"correction_blend": correction_blend,
|
|
"quality_sigma_start": quality_sigma_start,
|
|
"motion_pulse_mode": motion_pulse_mode,
|
|
"motion_pulse_start_pct": motion_pulse_start_pct,
|
|
"motion_pulse_count": motion_pulse_count,
|
|
"motion_pulse_spacing_pct": motion_pulse_spacing_pct,
|
|
"motion_pulse_strength": motion_pulse_strength,
|
|
"motion_pulse_noise": motion_pulse_noise,
|
|
"motion_pulse_steps": motion_pulse_steps,
|
|
}
|
|
)
|
|
return (sampler, prepared_sigmas)
|