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Noise improvements phase 1

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This commit is contained in:
blepping
2024-03-25 07:12:11 -06:00
parent ecadbfcd19
commit 81a163fcf0
6 changed files with 89 additions and 73 deletions
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README.md
+2 -4
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@@ -57,8 +57,6 @@ If you want to create noise for initial sampling, connect model and sigmas to th
can be used to override configuration settings for other samplers, including the noise type. For example, you could force `euler_ancestral` to use a different noise type. It's also possible to override other settings like `s_noise`, etc. *Note*: The wrapper inspects the sampling function's arguments to see what it supports, so you should connect the sampler directly to this rather than having other nodes (like a different sampler wrapper) in between.
**Note**: If you are using this with Sonar samplers, make sure you set the noise type in the sampler to `gaussian` as the Sonar samplers only allow overriding noise types in that case.
### `SonarCustomNoise`
See the [Noise](#noise) section below for information on noise types.
@@ -146,9 +144,9 @@ I also have some other ComfyUI nodes here: https://github.com/blepping/ComfyUI-b
Original Sonar Sampler implementation (for A1111): https://github.com/Kahsolt/stable-diffusion-webui-sonar
My version basically just rips off this Sonar sampler implementation for Diffusers: https://github.com/alexblattner/modified-euler-samplers-for-sonar-diffusers/
My version was initially based on this Sonar sampler implementation for Diffusers: https://github.com/alexblattner/modified-euler-samplers-for-sonar-diffusers/
Noise generation functions copied from https://github.com/Clybius/ComfyUI-Extra-Samplers with only minor modifications. I may have broken some of them in the process _or_ they may not have been suitable for use and I took them anyway. If they don't work it is not a reflection on the original source.
Many noise generation functions copied from https://github.com/Clybius/ComfyUI-Extra-Samplers with only minor modifications. I may have broken some of them in the process _or_ they may not have been suitable for use and I took them anyway. If they don't work it is not a reflection on the original source.
`SonarPowerNoise` contributed by [elias-gaeros](https://github.com/elias-gaeros/). Thanks!
changelog.md
+8
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@@ -2,6 +2,14 @@
Note, only relatively significant changes to user-visible functionality will be included here. Most recent changes at the top.
## 20240325
* Fixed issue when using Sonar samplers in normal sampling nodes/via stuff like `KSamplerSelect`.
* Add `pyramid` (non-high-res) noise type.
* Allow selecting `brownian` noise in custom noise nodes (but it won't work with `NoisyLatentLike`).
* Use `brownian` as the default noise type for `SamplerSonarDPMPP`.
* Make overriding the selected noise type in Sonar samplers a warning instead of a hard error.
## 20240320
* `NoisyLatentLike` node improved to allow calculating strength with sigmas and injecting noise itself.
py/nodes.py
+14 -29
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@@ -8,6 +8,7 @@ import torch
from comfy import samplers
from . import noise
from .noise import NoiseType
from .sonar import (
GuidanceConfig,
GuidanceType,
@@ -24,13 +25,7 @@ class NoisyLatentLikeNode:
def INPUT_TYPES(cls):
return {
"required": {
"noise_type": (
tuple(
t.name.lower()
for t in noise.NoiseType
if t is not noise.NoiseType.BROWNIAN
),
),
"noise_type": (tuple(NoiseType.get_names(skip=(NoiseType.BROWNIAN,))),),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFFFFFFFFFFFF}),
"latent": ("LATENT",),
"multiplier": ("FLOAT", {"default": 1.0}),
@@ -83,7 +78,7 @@ class NoisyLatentLikeNode:
ns = custom_noise_opt.make_noise_sampler(latent_samples)
else:
ns = noise.get_noise_sampler(
noise.NoiseType[noise_type.upper()],
NoiseType[noise_type.upper()],
latent_samples,
None,
None,
@@ -164,13 +159,7 @@ class SonarCustomNoiseNode(SonarCustomNoiseNodeBase):
def INPUT_TYPES(cls):
result = super().INPUT_TYPES()
result["required"] |= {
"noise_type": (
tuple(
t.name.lower()
for t in noise.NoiseType
if t is not noise.NoiseType.BROWNIAN
),
),
"noise_type": (tuple(NoiseType.get_names()),),
}
return result
@@ -261,11 +250,7 @@ class SamplerNodeSonarBase:
},
),
"rand_init_noise_type": (
tuple(
t.name.lower()
for t in noise.NoiseType
if t is not noise.NoiseType.BROWNIAN
),
tuple(NoiseType.get_names(skip=(NoiseType.BROWNIAN,))),
),
},
"optional": {
@@ -317,7 +302,7 @@ class SamplerNodeSonarEuler(SamplerNodeSonarBase):
init=HistoryType[momentum_init.upper()],
momentum_hist=momentum_hist,
direction=direction,
rand_init_noise_type=noise.NoiseType[rand_init_noise_type.upper()],
rand_init_noise_type=NoiseType[rand_init_noise_type.upper()],
guidance=guidance_cfg_opt,
)
return (
@@ -347,7 +332,7 @@ class SamplerNodeSonarEulerAncestral(SamplerNodeSonarEuler):
"round": False,
},
),
"noise_type": (tuple(t.name.lower() for t in noise.NoiseType),),
"noise_type": (tuple(NoiseType.get_names()),),
},
)
result["optional"].update(
@@ -375,8 +360,8 @@ class SamplerNodeSonarEulerAncestral(SamplerNodeSonarEuler):
init=HistoryType[momentum_init.upper()],
momentum_hist=momentum_hist,
direction=direction,
rand_init_noise_type=noise.NoiseType[rand_init_noise_type.upper()],
noise_type=noise.NoiseType[noise_type.upper()],
rand_init_noise_type=NoiseType[rand_init_noise_type.upper()],
noise_type=NoiseType[noise_type.upper()],
custom_noise=custom_noise_opt.clone() if custom_noise_opt else None,
guidance=guidance_cfg_opt,
)
@@ -408,7 +393,7 @@ class SamplerNodeSonarDPMPPSDE(SamplerNodeSonarEuler):
"round": False,
},
),
"noise_type": (tuple(t.name.lower() for t in noise.NoiseType),),
"noise_type": (tuple(NoiseType.get_names(default=NoiseType.BROWNIAN)),),
},
)
result["optional"].update(
@@ -436,8 +421,8 @@ class SamplerNodeSonarDPMPPSDE(SamplerNodeSonarEuler):
init=HistoryType[momentum_init.upper()],
momentum_hist=momentum_hist,
direction=direction,
rand_init_noise_type=noise.NoiseType[rand_init_noise_type.upper()],
noise_type=noise.NoiseType[noise_type.upper()],
rand_init_noise_type=NoiseType[rand_init_noise_type.upper()],
noise_type=NoiseType[noise_type.upper()],
custom_noise=custom_noise_opt.clone() if custom_noise_opt else None,
guidance=guidance_cfg_opt,
)
@@ -497,7 +482,7 @@ class SamplerNodeConfigOverride:
"sde_solver": (("midpoint", "heun"),),
},
"optional": {
"noise_type": (tuple(t.name.lower() for t in noise.NoiseType),),
"noise_type": (tuple(NoiseType.get_names()),),
"custom_noise_opt": ("SONAR_CUSTOM_NOISE",),
},
}
@@ -525,7 +510,7 @@ class SamplerNodeConfigOverride:
| {
"override_sampler_cfg": {
"sampler": sampler,
"noise_type": noise.NoiseType[noise_type.upper()]
"noise_type": NoiseType[noise_type.upper()]
if noise_type is not None
else None,
"custom_noise": custom_noise_opt,
py/noise.py
+52 -16
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@@ -15,9 +15,16 @@ from torch import FloatTensor, Generator, Tensor
# ruff: noqa: D412, D413, D417, D212, D407, ANN002, ANN003, FBT001, FBT002, S311
def scale_noise(noise, factor=1.0):
mean, std = noise.mean(), noise.std()
return (noise - mean).div_(std).mul_(factor)
def scale_noise(noise, factor=1.0, threshold_std_devs=2.5):
mean, std = noise.mean().item(), noise.std().item()
threshold = threshold_std_devs / math.sqrt(noise.numel())
if abs(mean) > threshold:
noise -= mean
if abs(1.0 - std) > threshold:
noise /= std
if factor != 1.0:
noise *= factor
return noise
class NoiseType(Enum):
@@ -27,6 +34,7 @@ class NoiseType(Enum):
PERLIN = auto()
STUDENTT = auto()
HIGHRES_PYRAMID = auto()
PYRAMID = auto()
PINK = auto()
LAPLACIAN = auto()
POWER = auto()
@@ -37,6 +45,15 @@ class NoiseType(Enum):
# RAINBOW_INTENSE3 = auto()
GREEN_TEST = auto()
@classmethod
def get_names(cls, default=None, skip=None):
if default is not None:
yield default.name.lower()
for nt in cls:
if nt == default or (skip and nt in skip):
continue
yield nt.name.lower()
class NoiseError(Exception):
pass
@@ -357,6 +374,36 @@ def highres_pyramid_noise_like(x, discount=0.7):
return noise / noise.std() # Scaled back to roughly unit variance
def pyramid_noise_like(x, generator=None, device="cpu", discount=0.8):
size = x.size()
b, c, h, w = size
orig_h = h
orig_w = w
noise = torch.zeros(size=size, dtype=x.dtype, layout=x.layout, device=device)
r = 1
for i in range(5):
r *= 2 # Rather than always going 2x,
noise += (
torch.nn.functional.interpolate(
(
torch.normal(
mean=0,
std=0.5**i,
size=(b, c, h * r, w * r),
dtype=x.dtype,
layout=x.layout,
generator=generator,
device=device,
)
),
size=(orig_h, orig_w),
mode="nearest-exact",
)
* discount**i
)
return noise.to(device=x.device)
def studentt_noise_like(x):
from torch.distributions import StudentT
@@ -501,7 +548,7 @@ class NoiseSampler:
else noise.mul_(self.factor)
)
if hasattr(noise, "to"):
return noise.to(dtype=self.dtype, device=self.device)
noise = noise.to(dtype=self.dtype, device=self.device)
return noise
@@ -513,6 +560,7 @@ NOISE_SAMPLERS: dict[NoiseType, Callable] = {
NoiseType.STUDENTT: NoiseSampler.simple(studentt_noise_like),
NoiseType.PINK: NoiseSampler.simple(pink_noise_like),
NoiseType.HIGHRES_PYRAMID: NoiseSampler.simple(highres_pyramid_noise_like),
NoiseType.PYRAMID: NoiseSampler.simple(pyramid_noise_like),
NoiseType.RAINBOW_MILD: NoiseSampler.simple(
lambda x: (green_noise_like(x) * 0.55 + rand_perlin_like(x) * 0.7) * 1.15,
),
@@ -522,18 +570,6 @@ NOISE_SAMPLERS: dict[NoiseType, Callable] = {
NoiseType.LAPLACIAN: NoiseSampler.simple(laplacian_noise_like),
NoiseType.POWER: NoiseSampler.simple(power_noise_like),
NoiseType.GREEN_TEST: NoiseSampler.simple(green_noise_like),
# NoiseType.RAINBOW_MILD2: lambda x: lambda _s, _sn: (
# green_noise_like(x) * 0.55 + uniform_noise_like(x) * 0.7
# )
# * 1.15,
# NoiseType.RAINBOW_INTENSE2: lambda x: lambda _s, _sn: (
# green_noise_like(x) * 0.75 + uniform_noise_like(x) * 0.5
# )
# * 1.15,
# NoiseType.RAINBOW_INTENSE3: lambda x: lambda _s, _sn: (
# green_noise_like(x) * 0.75 + highres_pyramid_noise_like(x) * 0.5
# )
# * 1.15,
}
py/powernoise.py
+2 -1
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@@ -314,7 +314,8 @@ class SonarPowerNoiseNode(SonarCustomNoiseNodeBase):
output_dir = folder_paths.get_temp_directory()
prefix_append = "sonar_temp_" + "".join(
random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5) # noqa: S311
random.choice("abcdefghijklmnopqrstupvxyz") # noqa: S311
for x in range(5)
)
full_output_folder, filename, counter, subfolder, _ = (
folder_paths.get_save_image_path(prefix_append, output_dir)
py/sonar.py
+11 -23
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@@ -3,6 +3,7 @@
from __future__ import annotations
from enum import Enum, auto
from sys import stderr
from typing import Any, Callable, NamedTuple
import torch
@@ -44,6 +45,8 @@ class SonarConfig(NamedTuple):
class SonarBase:
DEFAULT_NOISE_TYPE = noise.NoiseType.GAUSSIAN
def __init__(self, cfg: SonarConfig) -> None:
self.history_d = None
self.cfg = cfg
@@ -59,11 +62,11 @@ class SonarBase:
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
if noise_sampler is not None and self.cfg.noise_type not in (
None,
noise.NoiseType.GAUSSIAN,
self.DEFAULT_NOISE_TYPE,
):
# Possibly we should just use the supplied already-created noise sampler here.
raise ValueError(
"Unexpected noise_sampler presence with non-default noise type requested",
print(
"Sonar: Warning: Noise sampler supplied, overriding noise type from settings",
file=stderr,
)
if self.cfg.custom_noise:
noise_sampler = self.cfg.custom_noise.make_noise_sampler(
@@ -72,9 +75,9 @@ class SonarBase:
sigma_max,
seed=seed,
)
elif noise_sampler is None and self.cfg.noise_type:
elif noise_sampler is None:
noise_sampler = noise.get_noise_sampler(
self.cfg.noise_type,
self.cfg.noise_type or self.DEFAULT_NOISE_TYPE,
x,
sigma_min,
sigma_max,
@@ -386,14 +389,6 @@ class SonarEulerAncestral(SonarSampler):
):
if sonar_config is None:
sonar_config = SonarConfig()
if (
noise_sampler is not None
and sonar_config.noise_type != noise.NoiseType.GAUSSIAN
):
# Possibly we should just use the supplied already-created noise sampler here.
raise ValueError(
"Unexpected noise_sampler presence with non-default noise type requested",
)
s_in = x.new_ones([x.shape[0]])
sonar = cls(
eta,
@@ -430,6 +425,8 @@ class SonarEulerAncestral(SonarSampler):
class SonarDPMPPSDE(SonarSampler):
DEFAULT_NOISE_TYPE = noise.NoiseType.BROWNIAN
def __init__(
self,
eta: float = 1.0,
@@ -560,15 +557,6 @@ class SonarDPMPPSDE(SonarSampler):
):
if sonar_config is None:
sonar_config = SonarConfig()
if (
noise_sampler is not None
and sonar_config.noise_type != noise.NoiseType.GAUSSIAN
):
# Possibly we should just use the supplied already-created noise sampler here.
raise ValueError(
"Unexpected noise_sampler presence with non-default noise type requested",
)
s_in = x.new_ones([x.shape[0]])
sonar = cls(
eta,