nodes/blepping-ComfyUI-sonar
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Internal cleanups and refactoring.

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Some integration improvements.
Bump date in changelog
This commit is contained in:
blepping
2025-01-30 06:10:36 -07:00
parent a31eb6940b
commit ca3ee58750
3 changed files with 93 additions and 81 deletions
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changelog.md
+1 -1
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@@ -2,7 +2,7 @@
Note, only relatively significant changes to user-visible functionality will be included here. Most recent changes at the top.
## 20241219
## 20250130
*Note*: May change seeds.
py/nodes.py
+88 -80
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@@ -2,8 +2,8 @@ from __future__ import annotations
import abc
import inspect
import math
import random
from types import SimpleNamespace
from typing import Any, Callable
import numpy as np
@@ -158,7 +158,7 @@ class NoisyLatentLikeNode(metaclass=IntegratedNode):
}
@classmethod
def go(
def go( # noqa: PLR0914
cls,
*,
noise_type: str,
@@ -182,9 +182,11 @@ class NoisyLatentLikeNode(metaclass=IntegratedNode):
while hasattr(model, "model"):
model = model.model
latent_scale_factor = model.latent_format.scale_factor
max_denoise = samplers.Sampler().max_denoise(
SimpleNamespace(inner_model=model),
sigmas,
model_sigma_max = float(model.model_sampling.sigma_max)
first_sigma = float(sigmas[0])
max_denoise = (
math.isclose(model_sigma_max, first_sigma, rel_tol=1e-05)
or first_sigma > model_sigma_max
)
multiplier *= (
float(
@@ -1404,68 +1406,6 @@ class SonarAdvancedDistroNoiseNode(SonarCustomNoiseNodeBase):
)
class CustomNOISE:
def __init__(
self,
custom_noise,
seed,
*,
cpu_noise=True,
normalize=True,
multiplier=1.0,
):
self.custom_noise = custom_noise
self.seed = seed
self.cpu_noise = cpu_noise
self.normalize = normalize
self.multiplier = multiplier
def _sample_noise(self, latent_image, seed):
result = self.custom_noise.make_noise_sampler(
latent_image,
None,
None,
seed=seed,
cpu=self.cpu_noise,
normalized=self.normalize,
)(None, None).to(
device="cpu",
dtype=latent_image.dtype,
)
if result.layout != latent_image.layout:
if latent_image.layout == torch.sparse_coo:
return result.to_sparse()
errstr = f"Cannot handle latent layout {type(latent_image.layout).__name__}"
raise NotImplementedError(errstr)
return result if self.multiplier == 1.0 else result.mul_(self.multiplier)
def generate_noise(self, input_latent):
latent_image = input_latent["samples"]
batch_inds = input_latent.get("batch_index")
torch.manual_seed(self.seed)
random.seed(self.seed)
if self.multiplier == 0.0:
return torch.zeros(
latent_image.shape,
dtype=latent_image.dtype,
layout=latent_image.layout,
device="cpu",
)
if batch_inds is None:
return self._sample_noise(latent_image, self.seed)
unique_inds, inverse_inds = np.unique(batch_inds, return_inverse=True)
result = []
batch_size = latent_image.shape[0]
for idx in range(unique_inds[-1] + 1):
noise = self._sample_noise(
latent_image[idx % batch_size].unsqueeze(0),
self.seed + idx,
)
if idx in unique_inds:
result.append(noise)
return torch.cat(tuple(result[i] for i in inverse_inds), axis=0)
class SonarWaveletFilteredNoiseNode(
SonarCustomNoiseNodeBase,
SonarNormalizeNoiseNodeMixin,
@@ -1535,6 +1475,68 @@ class SonarWaveletFilteredNoiseNode(
)
class CustomNOISE:
def __init__(
self,
custom_noise,
seed,
*,
cpu_noise=True,
normalize=True,
multiplier=1.0,
):
self.custom_noise = custom_noise
self.seed = seed
self.cpu_noise = cpu_noise
self.normalize = normalize
self.multiplier = multiplier
def _sample_noise(self, latent_image, seed):
result = self.custom_noise.make_noise_sampler(
latent_image,
None,
None,
seed=seed,
cpu=self.cpu_noise,
normalized=self.normalize,
)(None, None).to(
device="cpu",
dtype=latent_image.dtype,
)
if result.layout != latent_image.layout:
if latent_image.layout == torch.sparse_coo:
return result.to_sparse()
errstr = f"Cannot handle latent layout {type(latent_image.layout).__name__}"
raise NotImplementedError(errstr)
return result if self.multiplier == 1.0 else result.mul_(self.multiplier)
def generate_noise(self, input_latent):
latent_image = input_latent["samples"]
batch_inds = input_latent.get("batch_index")
torch.manual_seed(self.seed)
random.seed(self.seed)
if self.multiplier == 0.0:
return torch.zeros(
latent_image.shape,
dtype=latent_image.dtype,
layout=latent_image.layout,
device="cpu",
)
if batch_inds is None:
return self._sample_noise(latent_image, self.seed)
unique_inds, inverse_inds = np.unique(batch_inds, return_inverse=True)
result = []
batch_size = latent_image.shape[0]
for idx in range(unique_inds[-1] + 1):
noise = self._sample_noise(
latent_image[idx % batch_size].unsqueeze(0),
self.seed + idx,
)
if idx in unique_inds:
result.append(noise)
return torch.cat(tuple(result[i] for i in inverse_inds), axis=0)
class SonarToComfyNOISENode(metaclass=IntegratedNode):
DESCRIPTION = "Allows converting SONAR_CUSTOM_NOISE to NOISE (used by SamplerCustomAdvanced and possibly other custom samplers). NOTE: Does not work with noise types that depend on sigma (Brownian, ScheduledNoise, etc)."
RETURN_TYPES = ("NOISE",)
@@ -2244,6 +2246,8 @@ class SonarBlendFilterNoiseNode(
normalize_result,
normalize_noise,
):
if bleh is None:
raise RuntimeError("bleh not available")
import ast # noqa: PLC0415
ffilter_custom = ffilter_custom.strip()
@@ -2321,6 +2325,8 @@ class SonarBlehOpsNoiseNode(
rules,
normalize,
):
if bleh is None:
raise RuntimeError("bleh not available")
return super().go(
factor,
noise=sonar_custom_noise.clone(),
@@ -2384,11 +2390,11 @@ class KRestartSamplerCustomNoise(metaclass=IntegratedNode):
RETURN_TYPES = ("LATENT", "LATENT")
RETURN_NAMES = ("output", "denoised_output")
FUNCTION = "sample"
FUNCTION = "go"
CATEGORY = "sampling"
@classmethod
def sample(
def go(
cls,
*,
model,
@@ -2409,6 +2415,8 @@ class KRestartSamplerCustomNoise(metaclass=IntegratedNode):
chunked_mode=False,
custom_noise_opt=None,
):
if restart is None:
raise RuntimeError("Restart not available")
return restart.restart_sampling.restart_sampling(
model,
noise_seed,
@@ -2458,6 +2466,8 @@ class RestartSamplerCustomNoise(metaclass=IntegratedNode):
@classmethod
def go(cls, sampler, chunked_mode, custom_noise_opt=None):
if restart is None or not hasattr(restart.restart_sampling, "RestartSampler"):
raise RuntimeError("Restart not available")
restart_options = {
"restart_chunked": chunked_mode,
"restart_wrapped_sampler": sampler,
@@ -2473,20 +2483,18 @@ class RestartSamplerCustomNoise(metaclass=IntegratedNode):
return (restart_sampler,)
NODE_CLASS_MAPPINGS |= {
"KRestartSamplerCustomNoise": KRestartSamplerCustomNoise,
"RestartSamplerCustomNoise": RestartSamplerCustomNoise,
"SonarBlendFilterNoise": SonarBlendFilterNoiseNode,
"SonarBlehOpsNoise": SonarBlehOpsNoiseNode,
}
def init_integrations(integrations):
global NODE_CLASS_MAPPINGS, restart, bleh # noqa: PLW0603
global restart, bleh # noqa: PLW0603
restart = integrations.restart
if restart is not None:
NODE_CLASS_MAPPINGS["KRestartSamplerCustomNoise"] = KRestartSamplerCustomNoise
if hasattr(restart.restart_sampling, "RestartSampler"):
NODE_CLASS_MAPPINGS["RestartSamplerCustomNoise"] = RestartSamplerCustomNoise
bleh = integrations.bleh
if bleh is None:
return
NODE_CLASS_MAPPINGS |= {
"SonarBlendFilterNoise": SonarBlendFilterNoiseNode,
"SonarBlehOpsNoise": SonarBlehOpsNoiseNode,
}
external.MODULES.register_init_handler(init_integrations)
py/noise.py
+4
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@@ -489,6 +489,10 @@ class ScheduledNoise(CustomNoiseItemBase):
return torch.zeros_like(x)
def noise_sampler(s, sn):
if s is None or sn is None:
raise ValueError(
"ScheduledNoise requires sigma, sigma_next to be passed",
)
noise = (ns if end_sigma <= s <= start_sigma else nsa)(s, sn)
return scale_noise(noise, factor, normalized=normalize)