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Merge pull request #7 from blepping/feat_ancestral

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Add ancestral sampling and advanced node
This commit is contained in:
Extraltodeus
2025-06-20 00:52:07 +02:00
committed by GitHub
parent ece7be1446 2ad92d1fa7
commit 49c35d09d9
2 changed files with 195 additions and 52 deletions
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__init__.py
+4 -3
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@@ -1,4 +1,4 @@
from .custom_samplers import SamplerDistanceAdvanced
from .custom_samplers import SamplerDistance, SamplerDistanceAdvanced
from .presets_to_add import extra_samplers
def add_samplers():
@@ -21,5 +21,6 @@ def add_samplers():
add_samplers()
NODE_CLASS_MAPPINGS = {
"SamplerDistance": SamplerDistanceAdvanced,
}
"SamplerDistance": SamplerDistance,
"SamplerDistanceAdvanced": SamplerDistanceAdvanced,
}
custom_samplers.py
+191 -49
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@@ -2,6 +2,8 @@ import torch
from comfy.k_diffusion.sampling import trange, to_d
import comfy.model_patcher
import comfy.samplers
from comfy.k_diffusion import sampling
from comfy import model_sampling
from math import pi
mmnorm = lambda x: (x - x.min()) / (x.max() - x.min())
selfnorm = lambda x: x / x.norm()
@@ -70,12 +72,60 @@ def normalize_adjust(a,b,strength=1):
a[~torch.isfinite(a)] = c[~torch.isfinite(a)]
return a
def get_ancestral_step_ext(sigma, sigma_next, eta=1.0, is_rf=False):
if sigma_next == 0 or eta == 0:
return sigma_next, sigma_next * 0.0, 1.0
if not is_rf:
return (*sampling.get_ancestral_step(sigma, sigma_next, eta=eta), 1.0)
# Referenced from ComfyUI.
downstep_ratio = 1.0 + (sigma_next / sigma - 1.0) * eta
sigma_down = sigma_next * downstep_ratio
alpha_ip1, alpha_down = 1.0 - sigma_next, 1.0 - sigma_down
sigma_up = (sigma_next**2 - sigma_down**2 * alpha_ip1**2 / alpha_down**2)**0.5
x_coeff = alpha_ip1 / alpha_down
return sigma_down, sigma_up, x_coeff
def internal_step(x, d, dt, sigma, sigma_next, sigma_up, x_coeff, noise_sampler):
x = x + d * dt
if sigma_up == 0 or noise_sampler is None:
return x
noise = noise_sampler(sigma, sigma_next).mul_(sigma_up)
if x_coeff != 1:
# x gets scaled for flow models.
x *= x_coeff
return x.add_(noise)
def fix_step_range(steps, start, end):
if start < 0:
start = steps + start
if end < 0:
end = steps + end
start = max(0, min(steps - 1, start))
end = max(0, min(steps - 1, end))
return (end, start) if start > end else (start, end)
# Euler and CFGpp part taken from comfy_extras/nodes_advanced_samplers
def distance_wrap(resample,resample_end=-1,cfgpp=False,sharpen=False,use_softmax=False,first_only=False,use_slerp=False,perp_step=False,smooth=False,use_negative=False):
def distance_wrap(
resample, resample_end=-1, cfgpp=False, sharpen=False, use_softmax=False,
distance_first=0, distance_last=-1, eta_first=0, eta_last=-1, distance_eta_first=0, distance_eta_last=-1,
use_slerp=False, perp_step=False, smooth=False, use_negative=False, eta=0.0, s_noise=1.0,
distance_step_eta=0.0, distance_step_s_noise=1.0, distance_step_seed_offset=42,
):
@torch.no_grad()
def sample_distance_advanced(model, x, sigmas, extra_args=None, callback=None, disable=None):
def sample_distance_advanced(model, x, sigmas, eta=eta, s_noise=s_noise, noise_sampler=None, distance_step_noise_sampler=None, extra_args=None, callback=None, disable=None):
nonlocal distance_first, distance_last, eta_first, eta_last, distance_eta_first, distance_eta_last
extra_args = {} if extra_args is None else extra_args
seed = extra_args.get("seed")
dstep_noise_sampler = None if distance_step_eta == 0 else distance_step_noise_sampler or noise_sampler or sampling.default_noise_sampler(x, seed=seed + distance_step_seed_offset if seed is not None else None)
noise_sampler = None if eta == 0 else noise_sampler or sampling.default_noise_sampler(x, seed=seed)
is_rf = isinstance(model.inner_model.inner_model.model_sampling, model_sampling.CONST)
uncond = None
steps = len(sigmas) - 1
distance_first, distance_last = fix_step_range(steps, distance_first, distance_last)
eta_first, eta_last = fix_step_range(steps, eta_first, eta_last)
distance_eta_first, distance_eta_last = fix_step_range(steps, distance_eta_first, distance_eta_last)
if cfgpp or use_negative:
uncond = None
@@ -96,58 +146,66 @@ def distance_wrap(resample,resample_end=-1,cfgpp=False,sharpen=False,use_softmax
current_resample = resample
total = 0
s_in = x.new_ones([x.shape[0]])
for i in trange(len(sigmas) - 1, disable=disable):
sigma_hat = sigmas[i]
for i in trange(steps, disable=disable):
use_distance = distance_first <= i <= distance_last
use_eta = eta_first <= i <= eta_last
use_distance_eta = distance_eta_first <= i <= distance_eta_last
sigma, sigma_next = sigmas[i:i + 2]
sigma_down, sigma_up, x_coeff = get_ancestral_step_ext(sigma, sigma_next, eta=eta if use_eta else 0.0, is_rf=is_rf)
sigma_up *= s_noise
dstep_sigma_down, dstep_sigma_up, dstep_x_coeff = get_ancestral_step_ext(sigma, sigma_next, eta=distance_step_eta if use_distance_eta else 0.0, is_rf=is_rf)
dstep_sigma_up *= distance_step_s_noise
res_mul = progression(sigma_hat)
res_mul = progression(sigma)
if resample_end >= 0:
resample_steps = max(min(current_resample,resample_end),min(max(current_resample,resample_end),int(current_resample * res_mul + resample_end * (1 - res_mul))))
else:
resample_steps = current_resample
denoised = model(x, sigma_hat * s_in, **extra_args)
denoised = model(x, sigma * s_in, **extra_args)
total += 1
if cfgpp and torch.any(uncond):
d = to_d(x - denoised + uncond, sigmas[i], denoised)
d = to_d(x - denoised + uncond, sigma, denoised)
else:
d = to_d(x, sigma_hat, denoised)
d = to_d(x, sigma, denoised)
if callback is not None:
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
dt = sigmas[i + 1] - sigma_hat
callback({'x': x, 'i': i, 'sigma': sigmas, 'sigma_hat': sigma, 'denoised': denoised})
dt = sigma_down - sigma
dstep_dt = dstep_sigma_down - sigma
if sigmas[i + 1] == 0 or resample_steps == 0 or (i > 0 and first_only):
if sigma_next == 0 or resample_steps == 0 or not use_distance:
# Euler method
x = x + d * dt
else:
# not Euler method
x_n = [d]
for re_step in range(resample_steps):
x_new = x + d * dt
new_denoised = model(x_new, sigmas[i + 1] * s_in, **extra_args)
if smooth:
new_denoised = new_denoised.abs().pow(1 / new_denoised.std().sqrt()) * new_denoised.sign()
new_denoised = new_denoised.div(new_denoised.std().sqrt())
total += 1
if cfgpp and torch.any(uncond):
new_d = to_d(x_new - new_denoised + uncond, sigmas[i + 1], new_denoised)
else:
new_d = to_d(x_new, sigmas[i + 1] * s_in, new_denoised)
x_n.append(new_d)
if re_step == 0:
d = (new_d + d) / 2
else:
u = uncond if (use_negative and uncond is not None and torch.any(uncond)) else None
d = fast_distance_weights(torch.stack(x_n), use_softmax=use_softmax, use_slerp=use_slerp, uncond=u)
if sharpen or perp_step:
if sharpen and d_prev is not None:
d = normalize_adjust(d, d_prev, 1)
elif perp_step and d_prev is not None:
d = diff_step(d, d_prev, 0.5)
d_prev = d.clone()
x_n.append(d)
x = x + d * dt
x = internal_step(x, d, dt, sigma, sigma_next, sigma_up, x_coeff, noise_sampler)
continue
# not Euler method
x_n = [d]
for re_step in trange(resample_steps, initial=1, disable=disable or resample_steps < 2, leave=False, desc=" Distance"):
x_new = internal_step(x, d, dstep_dt, sigma, sigma_next, dstep_sigma_up, dstep_x_coeff, dstep_noise_sampler)
new_denoised = model(x_new, sigma_next * s_in, **extra_args)
if smooth:
new_denoised = new_denoised.abs().pow(1 / new_denoised.std().sqrt()) * new_denoised.sign()
new_denoised = new_denoised.div(new_denoised.std().sqrt())
total += 1
if cfgpp and torch.any(uncond):
new_d = to_d(x_new - new_denoised + uncond, sigma_next, new_denoised)
else:
new_d = to_d(x_new, sigma_next * s_in, new_denoised)
x_n.append(new_d)
if re_step == 0:
d = (new_d + d) / 2
continue
u = uncond if (use_negative and uncond is not None and torch.any(uncond)) else None
d = fast_distance_weights(torch.stack(x_n), use_softmax=use_softmax, use_slerp=use_slerp, uncond=u)
if sharpen or perp_step:
if sharpen and d_prev is not None:
d = normalize_adjust(d, d_prev, 1)
elif perp_step and d_prev is not None:
d = diff_step(d, d_prev, 0.5)
d_prev = d.clone()
x_n.append(d)
x = internal_step(x, d, dt, sigma, sigma_next, sigma_up, x_coeff, noise_sampler)
return x
return sample_distance_advanced
@@ -202,19 +260,103 @@ def simplified_euler(model, x, sigmas, extra_args=None, callback=None, disable=N
x = x + d * dt
return x
class SamplerDistanceAdvanced:
class SamplerDistanceBase:
_DISTANCE_OPTIONS = None # All options by default.
_DISTANCE_PARAMS = {
"resample": ("INT", {
"default": 3, "min": -1, "max": 32, "step": 1,
"tooltip": "0 all along gives Euler. 1 gives Heun.\nAnything starting from 2 will use the distance method.\n-1 will do remaining steps + 1 as the resample value. This can be pretty slow.",
}),
"resample_end": ("INT", {
"default": -1, "min": -1, "max": 32, "step": 1,
"tooltip": "How many resamples for the end. -1 means constant.",
}),
"cfgpp": ("BOOLEAN", {
"default": True,
"tooltip": "Controls whether to use CFG++ sampling. When enabled, you should set CFG to a fairly low value.",
}),
"eta": ("FLOAT", {
"default": 0.0, "min": 0.0, "max": 32.0, "step": 0.01,
"tooltip": "Controls the ancestralness of the main sampler steps. 0.0 means to use non-ancestral sampling. Note: May not work well with some of the other options.",
}),
"s_noise": ("FLOAT", {
"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01,
"tooltip": "Scale factor for ancestral noise added during sampling. Generally should be left at 1.0 and only has an effect when ancestral sampling is used.",
}),
"distance_step_eta": ("FLOAT", {
"default": 0.0, "min": 0.0, "max": 32.0, "step": 0.01,
"tooltip": "Experimental option that allows using ancestral sampling for the internal distance steps. When used, should generally be a fairly low value such as 0.25. 0.0 means to use non-ancestral sampling for the internal distance steps.",
}),
"distance_step_s_noise": ("FLOAT", {
"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01,
"tooltip": "Scale factor for ancestral noise added in the internal distance steps. Generally should be left at 1.0 and only has an effect when distance_step_eta is non-zero.",
}),
"use_softmax": ("BOOLEAN", {
"default": False,
"tooltip": "Rather than using a min/max normalization and an exponent will use a softmax instead.",
}),
"use_slerp": ("BOOLEAN", {
"default": False,
"tooltip": "Will SLERP the predictions instead of doing a weighted average. The difference is more obvious when using use_negative.",
}),
"perp_step": ("BOOLEAN", {
"default": False,
"tooltip": "Experimental, not yet recommended.",
}),
"use_negative": ("BOOLEAN", {
"default": False,
"tooltip": "Will use the negative prediction to prepare the distance scores. This tends to give images with less errors from my testing.",
}),
"smooth": ("BOOLEAN", {
"default": False,
"tooltip": "Not recommended, will make everything brighter. Not smoother.",
}),
"sharpen": ("BOOLEAN", {
"default": False,
"tooltip": "Not recommended, attempts to sharpen the results but instead tends to make things fuzzy.",
}),
"distance_first": ("INT", {
"default": 0, "min": -10000, "max": 10000, "step": 1,
"tooltip": "First step to use distance sampling. You can use negative values to count from the end. Note: Steps are zero-based.",
}),
"distance_last": ("INT", {
"default": -1, "min": -10000, "max": 10000, "step": 1,
"tooltip": "Last step to use distance sampling. You can use negative values to count from the end. Note: Steps are zero-based.",
}),
"eta_first": ("INT", {
"default": 0, "min": -10000, "max": 10000, "step": 1,
"tooltip": "First step to use ancestral sampling. Only applies when ETA is non-zero. You can use negative values to count from the end. Note: Steps are zero-based.",
}),
"eta_last": ("INT", {
"default": -1, "min": -10000, "max": 10000, "step": 1,
"tooltip": "Last step to use ancestral sampling. Only applies when ETA is non-zero. You can use negative values to count from the end. Note: Steps are zero-based.",
}),
"distance_eta_first": ("INT", {
"default": 0, "min": -10000, "max": 10000, "step": 1,
"tooltip": "First step to use ancestral sampling for the distance steps. Only applies when distance ETA is non-zero. You can use negative values to count from the end. Note: Steps are zero-based.",
}),
"distance_eta_last": ("INT", {
"default": -1, "min": -10000, "max": 10000, "step": 1,
"tooltip": "Last step to use ancestral sampling for the distance steps. Only applies when distance ETA is non-zero. You can use negative values to count from the end. Note: Steps are zero-based.",
}),
}
@classmethod
def INPUT_TYPES(s):
return {"required": {"resample": ("INT", {"default": 3, "min": -1, "max": 32, "step": 1,
"tooltip":"0 all along gives Euler. 1 gives Heun.\nAnything starting from 2 will use the distance method.\n-1 will do remaining steps + 1 as the resample value. This can be pretty slow."}),
"resample_end": ("INT", {"default": -1, "min": -1, "max": 32, "step": 1, "tooltip":"How many resamples for the end. -1 means constant."}),
"cfgpp" : ("BOOLEAN", {"default": True}),
}}
if s._DISTANCE_OPTIONS is None:
return {"required": s._DISTANCE_PARAMS.copy()}
return {"required": {k: s._DISTANCE_PARAMS[k] for k in s._DISTANCE_OPTIONS}}
RETURN_TYPES = ("SAMPLER",)
CATEGORY = "sampling/custom_sampling/samplers"
FUNCTION = "get_sampler"
def get_sampler(self,resample,resample_end,cfgpp):
sampler = comfy.samplers.KSAMPLER(
distance_wrap(resample=resample,cfgpp=cfgpp,resample_end=resample_end))
def get_sampler(self, **kwargs):
sampler = comfy.samplers.KSAMPLER(distance_wrap(**kwargs))
return (sampler, )
class SamplerDistance(SamplerDistanceBase):
_DISTANCE_OPTIONS = ("resample", "resample_end", "cfgpp")
class SamplerDistanceAdvanced(SamplerDistanceBase):
pass # Includes all options by default.