November 2024 mega update, see changelog for details

Fix an issue with NoisyLatentLike where it didn't correctly pass a seed to connected custom noise nodes (probably only affected Brownian)
Add normalize and cpu_noise parameters to NoisyLatentLike
2024-11-30 00:59:08 -07:00 · 2024-10-10 16:09:54 -06:00 · 2024-09-02 10:20:25 -06:00 · 2024-09-02 19:08:31 +03:00 · 2024-09-01 18:11:21 -06:00 · 2024-08-23 09:26:00 -06:00
89 changed files with 5743 additions and 741 deletions
@@ -1,13 +1,33 @@
 # ComfyUI-sonar

-A janky implementation of Sonar sampling (momentum-based sampling) for [ComfyUI](https://github.com/comfyanonymous/ComfyUI). It may or may not be working _properly_ but it does produce pretty reasonable results. I am using it personally. At this point, I would say it's suitable for general use with the caveat that it's very likely stuff like implementation and inputs to nodes will still be changing fairly frequently. In other words, don't depend on reproduceable generations with this unless you're willing to keep track of the git revision something was generated with.
+A janky implementation of Sonar sampling (momentum-based sampling) for [ComfyUI](https://github.com/comfyanonymous/ComfyUI) as well as an assortment of advanced noise tools.

-Currently supports Euler, Euler Ancestral, and DPM++ SDE sampling.
+Disclaimer: It's very likely stuff like implementation and inputs to nodes will still be changing fairly frequently. In other words, don't depend on reproduceable generations with this unless you're willing to keep track of the git revision something was generated with.
+
+Momentum based sampling currently supports Euler, Euler Ancestral, and DPM++ SDE sampling.

 See the [ChangeLog](changelog.md) for recent user-visible changes.

 ## Description

+This started out as an implementation of Sonar sampling and has evolved into something more like a noise toybox.
+
+Please note that while a lot of the nodes in here have a `Sonar` prefix, that doesn't indicate a relation with
+the original Sonar sampling implementation. Why is there random noise stuff in this repo? Mainly because it gets
+very awkward having node collections depending on other node collections.
+
+Keep reading below this section for information on Sonar sampling and associated nodes.
+
+For information on the advanced noise tools which include many different noise types, nodes to schedule,
+composite and otherwise manipulate noise see:
+
+* [Base Noise Types](docs/base_noise_types.md) - examples and descriptions of the base noise types.
+* [Advanced Power Noise](docs/advanced_power_noise.md) - examples and descriptions of the advanced power noise node.
+* [Advanced Noise Nodes](docs/advanced_noise_nodes.md) - examples and descriptions of advanced noise nodes (schedule, composite, etc).
+* [FreeU Extreme](docs/frux.md) - a build your own FreeU kit that allows advanced filtering, blending, scheduling of effects as well as targetting input and middle blocks.
+
+## Sonar Description
+
 See https://github.com/Kahsolt/stable-diffusion-webui-sonar for a more in-depth explanation.

 The `direction` parameter should (unless I screwed it up) work like setting sign to positive or negative: `1.0` is positive, `-1.0` is negative. You can also potentially play with fractional values.
@@ -24,17 +44,23 @@ You can also just choose `sonar_euler`, `sonar_euler_ancestral` or `sonar_dpmpp_

 ## Nodes

-* `SamplerSonarEuler` — Custom sampler node that combines Euler sampling and momentum and optionally guidance. A bit boring compared to the ancestral version but it has predictability going for it. You can possibly try setting init type to `RAND` and using different noise types, however this sampler seems _very_ sensitive to that init type. You may want to set direction to a very low value like `0.05` or `-0.15` when using the `RAND` init type. Setting `momentum=1` is the same as disabling momentum, so this sampler with `momentum=1` is basically the same as the basic `euler` sampler.
-* `SamplerSonarEulerAncestral` — Ancestral version of the above. Same features, just with ancestral Euler.
-* `SonarGuidanceConfig` — You can optionally plug this into the Sonar sampler nodes. See the [Guidance](#guidance) section below.
-* `NoisyLatentLike` — If you give it a latent (or latent batch) it'll return a noisy latent of the same shape. Allows specifying all the custom noise types except `brownian` which has some special requirements. Provided just because the noise generation functions are conveniently available. You can also use this as a reference latent with `SonarGuidanceConfig` node and depending on the strength it can act like variation seed (you'd change the seed in the `NoisyLatentLike` node). *Note*: The seed stuff may or may not work correctly.
-* `SamplerSonarDPMPPSDE` — This one is extra experimental but it is an attempt to add moment and guidance to the DPM++ SDE sampler. It may not work correctly but you can sample stuff with it and get interesting results. I actually really like this one, and you can get away with more extreme stuff like `green_test` noise and still produce reasonable results. You may want to use the `BlehDiscardPenultimateSigma` node from my [ComfyUI-bleh](https://github.com/blepping/ComfyUI-bleh) collection if you find the result seems a bit washed out and blurry.
-* `SamplerConfigOverride` — 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.
-* `SonarCustomNoise` — See the [Noise](#noise) section below.
+### `SamplerSonarEuler`

-*Note*: `NoisyLatentLike` and `SamplerConfigOverride` are candidates for moving to a different project. They're just here at the moment because the noise generation functions are readily available.
+Custom sampler node that combines Euler sampling and momentum and optionally guidance. A bit boring compared to the ancestral version but it has predictability going for it. You can possibly try setting init type to `RAND` and using different noise types, however this sampler seems _very_ sensitive to that init type. You may want to set direction to a very low value like `0.05` or `-0.15` when using the `RAND` init type. Setting `momentum=1` is the same as disabling momentum, so this sampler with `momentum=1` is basically the same as the basic `euler` sampler.

-## Parameters
+### `SamplerSonarEulerAncestral`
+
+Ancestral version of the above. Same features, just with ancestral Euler.
+
+### `SamplerSonarDPMPPSDE`
+
+Attempt to add momentum and guidance to the DPM++ SDE sampler. It may not work correctly but you can sample stuff with it and get interesting results. I actually really like this one, and you can get away with more extreme stuff like `green_test` noise and still produce reasonable results. You may want to use the `BlehDiscardPenultimateSigma` node from my [ComfyUI-bleh](https://github.com/blepping/ComfyUI-bleh) collection if you find the result seems a bit washed out and blurry.
+
+### `SonarGuidanceConfig`
+
+You can optionally plug this into the Sonar sampler nodes. See the [Guidance](#guidance) section below.
+
+## Sonar Sampler Parameters

 Very abbreviated section. The init type can make a big difference. If you use `RANDOM` you can get away with setting `direction` to high values (like up to `2.25` or so) and absurdly low values (like `-30.0`). It's also possible to set `momentum` and `momentum_hist` to negative values, although whether it's a good idea...

@@ -50,26 +76,27 @@ Without guidance it should basically work the same as the ancestral Euler versio

 ## Noise

-I basically just copied a bunch of noise functions without really knowing what they do. The main thing I can say is they produce a semi-reasonable result and it's different from the other noise samplers. See [Credits](#credits) below.
+See [Base Noise Types](docs/base_noise_types.md) for examples.

-1. `gaussian`: This is the default noise type.
-2. `uniform`: Might enhance background details?
-3. `brownian`: This is the noise type SDE samplers use.
-4. `perlin`
-5. `studentt`: There's a comment that says it may enhance subject details. It seemed to produce a fairly dark result.
-6. `pink`
-7. `highres_pyramid`: Not extensively tested, but it is slower than the other noise types. I would guess it does something like enhance details.
-8. `laplacian`
-9. `power`
-10. `rainbow_mild` and `rainbow_intense`: A combination of green (-ish, the implementation may be broken) noise plus perlin noise. Very colorful results.
-11. `green_test`: Even more rainbow-y than the rainbow noise types. It _probably_ isn't working correctly, but the results are very interesting and colorful. Depending on the model, it may not work well for an initial generation but may be worth trying with img2img type workflows.
-
-You can scroll down to the the [Examples](#examples) section near the bottom to see some example generations with different noise types.
-
-The sampler and `NoisyLatentLike` nodes now take an optional `SonarCustomNoise` input. You can chain `SonarCustomNoise` nodes together to mix different types of noise, similar to how some of the built in ones. It shouldn't matter what order the noise types are chained. If `rescale` is set to `0.0` no rescaling will occur. `factor` is the proportion of that type of noise you want. If you want to use `rescale` it should be on the node that you are plugging into a sampler. Just for example if you had two `SonarCustomNoise` nodes both with `factor=0.7` and `rescale=1.0` on the last one, it would be effectively the same as if you'd used `factor=0.5` and `rescale=1.0` doesn't actually do anything. You can also rescale to values above `1.0` — the result is more noise, similar to increasing `s_noise` above `1.0` on a sampler. The simple explanation is `rescale` means you don't have to make sure the `factor`s add up to the scale you want (which normally would be `1.0`).
+The sampler and `NoisyLatentLike` nodes now take an optional `SonarCustomNoise` input.

 **Note**: If you connect the optional `SonarCustomNoise` node to a Sonar sampler, the `NoisyLatentLike` node or the `SamplerConfigOverride` node, it will override the noise type selected in the node.

+## Integrations
+
+You'll get some bonus features if you have some other node collections installed:
+
+### `KRestartSamplerCustomNoise`
+
+If you have a recent enough version of [ComfyUI_restart_sampling](https://github.com/ssitu/ComfyUI_restart_sampling/)
+installed, you'll also get the `KRestartSamplerCustomNoise` node which is exactly the same as `KRestartSamplerCustom`
+except for adding an optional custom noise input.
+See the restart sampling repo for more information: https://github.com/ssitu/ComfyUI_restart_sampling
+
+### `RestartSamplerCustomNoise`
+
+As above, except this is the custom sampler version.
+
 ## Related

 I also have some other ComfyUI nodes here: https://github.com/blepping/ComfyUI-bleh/
@@ -78,11 +105,21 @@ 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.
+* New pyramid noise based on implementation in [Jonathan Whitaker](https://wandb.ai/johnowhitaker/multires_noise/reports/Multi-Resolution-Noise-for-Diffusion-Model-Training--VmlldzozNjYyOTU2)'s article on multi-resolution noise.
+* Original `SonarPowerNoise` contributed by [elias-gaeros](https://github.com/elias-gaeros/). Additionally, he provided a lot of guidance with refactoring it to allow separate filtering and other enhancements and answered a multitude of dumb questions. To say those changes are only co-authored is probably giving myself too much credit. Thank you! Your patience and help is very much appreciated.
+* New 1/f (onef) and power law (white, grey, violet, velvet) noise types referenced from https://github.com/WASasquatch/PowerNoiseSuite

-## Examples
+## Errata
+
+* The noise types might not actually do what they claim. In that, I mean something I called "pink" noise might not be what is technically known as "pink noise". My implementations are best-effort. Bug reports and contributions to improve this repo are always welcome!
+* Whether noise gets generated on GPU or CPU is probably inconsistent. This means changing GPU types may change seeds, also when this eventually gets fixed it will probably also change seeds.
+
+## Sonar Examples
+
+Unfortunately, right now these examples are somewhat incomplete and out of date. I hope to update them when I get the time.

 ### Guidance

@@ -103,149 +140,10 @@ Using the `linear` guidance type and `guidance_factor=-0.015`. The reference ima

 </details>

+### Noise Types

-### Noise Types (img2img)
+See:

-These were generated with `s_noise=1.05` to make the noise effect more pronounced, 30 steps at `0.66` denoise, sonar settings increased slightly to enhance the effect (`momentum=0.9, momentum_hist=0.85, direction=1.0, momentum_init=ZERO`). It is probably easier to compare using these as the image _mostly_ stays the same as the sonar sampler settings change.
-
-<details>
-<summary>Expand renoise example images</summary>
-
-#### Base
-
-Base image - no Sonar Sampler steps.
-
-![Base](assets/example_images/noise/renoise_base.png)
-
-#### Euler A
-
-Normal (non-sonar) Eular A. Not really a comparison with noise (think it would use gaussian) but with the difference in effect from momentum.
-
-![Euler A](assets/example_images/noise/renoise_eulera.png)
-
-
-#### Gaussian
-
-![Gaussian](assets/example_images/noise/renoise_gaussian.png)
-
-#### Brownian
-
-![Brownian](assets/example_images/noise/renoise_brownian.png)
-
-#### Perlin
-
-![Perlin](assets/example_images/noise/renoise_perlin.png)
-
-#### Uniform
-
-![Uniform](assets/example_images/noise/renoise_uniform.png)
-
-#### Highres Pyramid
-
-![Highres_pyramid](assets/example_images/noise/renoise_highres_pyramid.png)
-
-#### Pink
-
-![Pink](assets/example_images/noise/renoise_pink.png)
-
-#### StudentT
-
-**outdated**
-
-![StudentT](assets/example_images/noise/renoise_studentt.png)
-
-
-#### StudentT_test
-
-**outdated**
-
-![StudentT_test](assets/example_images/noise/renoise_studentt_test.png)
-
-#### Laplacian
-
-![Laplacian](assets/example_images/noise/renoise_laplacian.png)
-
-#### Power
-
-![Power](assets/example_images/noise/renoise_power.png)
-
-#### Rainbow Mild
-
-![Rainbow Mild](assets/example_images/noise/renoise_rainbow_mild.png)
-
-#### Rainbow Intense
-
-![Rainbow Intense](assets/example_images/noise/renoise_rainbow_intense.png)
-
-#### Green_test
-
-![Green_test](assets/example_images/noise/renoise_green_test.png)
-
-</details>
-
-### Noise Types (Initial Generations)
-
-These were generated with `s_noise=1.1` to make the noise effect more pronounced, default sonar settings (`momentum=0.95, momentum_hist=0.75, direction=1.0, momentum_init=ZERO`). It may be harder to see the noise effects since the composition can change a lot in initial generations.
-
-<details>
-<summary>Expand initial generation example images</summary>
-
-#### Gaussian
-
-![Gaussian](assets/example_images/noise/noise_gaussian.png)
-
-#### Brownian
-
-![Brownian](assets/example_images/noise/noise_brownian.png)
-
-#### Perlin
-
-![Perlin](assets/example_images/noise/noise_perlin.png)
-
-#### Uniform
-
-![Uniform](assets/example_images/noise/noise_uniform.png)
-
-#### Highres Pyramid
-
-![Highres_pyramid](assets/example_images/noise/noise_highres_pyramid.png)
-
-#### Pink
-
-![Pink](assets/example_images/noise/noise_pink.png)
-
-#### StudentT
-
-**outdated**
-
-![StudentT](assets/example_images/noise/noise_studentt.png)
-
-#### StudentT_test
-
-**outdated**
-
-![StudentT_test](assets/example_images/noise/noise_studentt_test.png)
-
-#### Laplacian
-
-![Laplacian](assets/example_images/noise/noise_laplacian.png)
-
-#### Power
-
-![Power](assets/example_images/noise/noise_power.png)
-
-#### Rainbow Mild
-
-![Rainbow Mild](assets/example_images/noise/noise_rainbow_mild.png)
-
-#### Rainbow Intense
-
-![Rainbow Intense](assets/example_images/noise/noise_rainbow_intense.png)
-
-#### Green_test
-
-This might seem too crazy for actual use, but you can actually get decent results using the DPMPP Sonar sampler and a relatively high step count.
-
-![Green_test](assets/example_images/noise/noise_green_test.png)
-
-</details>
+* [Base Noise Types](docs/base_noise_types.md)
+* [Advanced Power Noise](docs/advanced_power_noise.md)
+* [Advanced Noise Nodes](docs/advanced_noise_nodes.md)
@@ -1,17 +1,15 @@
-from .py import nodes, sonar
+from .py import freeu_extreme, nodes, powernoise, sonar

 sonar.add_samplers()

-NODE_CLASS_MAPPINGS = {
-    "SamplerSonarEuler": nodes.SamplerNodeSonarEuler,
-    "SamplerSonarEulerA": nodes.SamplerNodeSonarEulerAncestral,
-    "SamplerSonarDPMPPSDE": nodes.SamplerNodeSonarDPMPPSDE,
-    "SamplerConfigOverride": nodes.SamplerNodeConfigOverride,
-    "NoisyLatentLike": nodes.NoisyLatentLikeNode,
-    "SonarCustomNoise": nodes.SonarCustomNoiseNode,
-    "SonarGuidanceConfig": nodes.GuidanceConfigNode,
+NODE_CLASS_MAPPINGS = nodes.NODE_CLASS_MAPPINGS | {
+    "SonarPowerNoise": powernoise.SonarPowerNoiseNode,
+    "SonarPowerFilterNoise": powernoise.SonarPowerFilterNoiseNode,
+    "SonarPowerFilter": powernoise.SonarPowerFilterNode,
+    "SonarPreviewFilter": powernoise.SonarPreviewFilterNode,
+    "FreeUExtremeConfig": freeu_extreme.FreeUExtremeConfigNode,
+    "FreeUExtreme": freeu_extreme.FreeUExtremeNode,
 }
-
-NODE_DISPLAY_NAME_MAPPINGS = {}
+NODE_DISPLAY_NAME_MAPPINGS = nodes.NODE_DISPLAY_NAME_MAPPINGS

 __all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
@@ -2,6 +2,61 @@

 Note, only relatively significant changes to user-visible functionality will be included here. Most recent changes at the top.

+## 20241129
+
+*Note*: Contains some potentially workflow-breaking changes.
+
+* `pink` noise type renamed to `pink_old` - the implementation was incorrect.
+* `power` noise type renamed to `power_old` - the implementation was incorrect.
+* Added `onef_pinkish` (higher frequencye) and `onef_greenish` (lower frequency) noise types.
+* Added `SonarAdvanced1fNoise` node and `onef_pinkish`, `onef_greenish`, `onef_pinkish_mix`, `onef_greenish_mix`, and `onef_pinkishgreenish` noise types.
+* Added `SonarAdvancedPowerLawNoise` node and `grey`, `white`, `violet` and `velvet` noise types.
+* The `SonarAdvancedPyramidNoise` node can now use upscale methods from my [ComfyUI-bleh](https://github.com/blepping/ComfyUI-bleh) node pack if it is available.
+* Added the `SonarChannelNoise` and `SonarBlendedNoise` nodes.
+* Added the `SonarBlehOpsNoise` node.
+* Added advanced parameter input to the SampleConfigOverride node, you can now pass options directly to the wrapped sampler function.
+* Custom noise inputs now are semi-wildcard and will accept `OCS_NOISE` or `SONAR_CUSTOM_NOISE` interchangeably.
+
+## 20240823
+
+* Added descriptions and tooltips for most nodes.
+* Added `repeat_batch` parameter to `NoisyLatentLike` node.
+* Added a `SONAR_CUSTOM_NOISE to NOISE` node to allow converting from Sonar's custom noise type to the built in ComfyUI `NOISE` (used by `SamplerCustomAdvanced` and possibly other nodes).
+* Added a `SonarAdvancedPyramidNoise` node that allows setting parameters for the pyramid noise variants.
+
+## 20240521
+
+Mega update! Many new features, documentation reorganized.
+
+* Add `SonarScheduledNoise`, `SonarCompositeNoise`, `SonarGuidedNoise`, `SonarRandomNoise` nodes. See [Advanced Noise Nodes](docs/advanced_noise_nodes.md).
+* Add `SonarPowerFilterNoise`, `SonarPowerFilter`, `SonarPreviewFilter` nodes. See [Advanced Power Noise](docs/advanced_power_noise.md).
+* Add `FreeUExtreme`, `FreeUExtremeConfig` nodes. See [FreeU Extreme](docs/frux.md).
+* Replace `pyramid` noise type with a (hopefully) more correct implementation. You can use `pyramid_old` for the previous behavior.
+* Add more noise types and variations.
+* The `NoisyLatentLike` node now allows using brownian noise if you connect a model and sigmas.
+
+## 20240506
+
+* Add `SonarModulatedNoise` and `SonarRepeatedNoise` nodes.
+
+## 20240327
+
+* 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.
+* Improve noise scaling (may change seeds).
+* Add `KRestartSamplerCustomNoise` if the user has a recent enough version of ComfyUI_restart_sampling installed.
+
+## 20240320
+
+* `NoisyLatentLike` node improved to allow calculating strength with sigmas and injecting noise itself.
+
+## 20240314
+
+* `SonarPowerNoise` node added.
+
 ## 20240227

 * Refactored noise generation functions (will break seeds).
@@ -0,0 +1,376 @@
+# Advanced Nodes
+
+## Normalization
+
+Normalization essentially rebalances the noise (or mixture of noise) to 1.0 strength and then scales based
+on the factor of the node. Most nodes will allow you to set three values:
+
+* `default`: By default, noise will be normalized only just before it's used. So you could consider this setting to be false except for where it is connected to an actual noise consumer (i.e. a `SamplerConfigOverride` node).
+* `forced`: Will always normalize.
+* `disabled`: Will never normalize.
+
+## `SONAR_CUSTOM_NOISE`
+
+This node output type actually constitutes a chain of noise items. For most nodes, when you use it as input,
+they will add an item to the chain. There are some exceptions that treat the `SONAR_CUSTOM_NOISE` input as a list:
+
+* `SonarRepeatedNoise`
+* `SonarRandomNoise`
+
+There are also some exceptions that will consume the list rather than adding an item to it:
+
+* `SonarModulatedNoise`
+* `SonarCompositeNoise`
+* `SonarScheduledNoise`
+* `SonarGuidedNoise`
+
+The distinction is mainly only important when setting `rescale`. Visual example:
+
+![Chain example](../assets/example_images/noise_adv/noise_chain_example.png)
+
+It may be counter intuitive that there are actually two separate chains here.
+
+## Examples
+
+Note on the examples included for some of these nodes:
+
+The example images included for some of these nodes all have metadata and can be loaded in ComfyUI.
+Generated using `dpmpp_2s_ancestral`, Karras scheduler and starting out with gaussian noise then switching
+to the custom noise type at the 35% mark.
+
+***
+
+### `SonarCustomNoise`
+
+You can chain `SonarCustomNoise` nodes together to mix different types of noise. The order of `SonarCustomNoise` nodes is not important.
+
+Parameters:
+
+- `factor` controls the strength of the noise.
+- `rescale` controls rebalancing `factor` for nodes in the chain. When `rescale` is set to `0.0`, no rebalancing will occur. Otherwise the current node as well as the nodes connect to it will have their `factor` adjusted to add up to the rescale value. For example, if you have three nodes with `factor` 1.0 and the last with `rescale` 1.0, then the `factor` value will be adjusted to `1/3 = 0.3333...`. *Note*: Rescaling uses the `factor` absolute value.
+- `noise_type` allows you to select the built-in noise type.
+
+***
+
+### `NoisyLatentLike`
+
+This node takes a reference latent and generates noise of the same shape. The one required input is `latent`.
+
+You can connect a `SonarCustomNoise` or `SonerPowerNoise` node to the `custom_noise_opt` input: if that is attached, the built in noise type selector is ignored. The generated noise will be multiplied by the `multiplier` value. **Note**: If you select `brownian` noise (either through the dropdown or by connecting custom noise nodes) you must connect a model and sigmas.
+
+The node has two main modes: simply generate and scale the noise by the multiplier and return or add it to the input latent. In this mode, you don't connect anything to the `mul_by_sigmas_opt` or `model_opt` inputs and you would use other nodes to calculate the correct strength.
+
+In the second mode you must connect sigmas (for example from a `BasicScheduler` node) to the `mul_by_sigmas_opt` input and connect a model to the `model_opt` input. It will calculate the strength based on the first item in the list of sigmas (so you could use something like a `SplitSigmas` node to slice them as needed). Note that `multiplier` still applies: the calculated strength will be scaled by it. This second mode is generally this is the most convenient way to use the node since the two main uses cases are: making a latent with initial noise or adding noise to a latent (for img2img type stuff).
+
+If you want to create noise for initial sampling, connect model and sigmas to the node, connect an empty latent (or one of the appropriate size) to it and that is basically all you need to do (aside from configuring the noise types). For img2img (upscaling, etc), either slice the sigmas at the appropriate or set a denoise in something like the `BasicScheduler` node. *Note*: For img2img, you also need to turn on the `add_to_latent` toggle. Turning this on doesn't matter for initial noise since an empty latent is all zeros.
+
+**Note**: This node does not currently respect the latent noise mask.
+
+***
+
+### `SamplerConfigOverride`
+
+This node 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.
+
+You can enter YAML parameters in the text input, these arguments are passed directly to the sampler function without any error checking. If the same key exists in the node itself (i.e. `s_noise`) the one in the text input will take precedence. Note that these are based on the internal sampler function so the names of the arguments won't necessarily be the same as the sampler node (but they often are). You may need to check the source code for the sampler.
+
+***
+
+### `SONAR_CUSTOM_NOISE to NOISE`
+
+This node can be used to convert Sonar custom noise to the `NOISE` type used by the builtin `SamplerCustomAdvanced` (and any other nodes that take a `NOISE` input).
+
+***
+
+### `SonarAdvancedPyramidNoise`
+
+Allows setting some parameters for the pyramid noise variants (`pyramid`, `highres_pyramid` and `pyramid_old`). `discount` further from zero generally results in a more extreme colorful effect (can also be set to negative values). Higher `iterations` also tends to make the effect more extreme - zero iterations will just return normal Gaussian noise. You can also experiment with the `upscale_mode` for different effects.
+
+### `SonarAdvanced1fNoise`
+
+More extensive documentation TBD (hopefully). For now, a few recipes:
+
+These differ differ only in alpha. For the other parameters, use `k=1, vf=1, hf=1, use_sqrt=true` to start.
+
+* `blue`: `alpha=1`
+* `green`: `alpha=0.75`
+* `pink`: `alpha=0.5`
+*
+
+### `SonarAdvancedPowerLawNoise`
+
+More extensive documentation TBD (hopefully). For now, a few recipes:
+
+* `white`: `alpha=0, use_sign=true, div_max_dims=none`
+* `grey`: `alpha=0, use_sign=false, div_max_dims=none`
+* `velvet`: `alpha=1, use_sign=true, div_max_dims=all, use_div_max_abs=true`
+* `violet`: `alpha=0.5, use_sign=true, div_max_dims=all, use_div_max_abs=true`
+
+***
+
+### `SonarModulatedNoise`
+
+Experimental noise modulation based on code stolen from
+[ComfyUI-Extra-Samplers](https://github.com/Clybius/ComfyUI-Extra-Samplers). `intensity` and `frequency` modulation
+types _probably_ do not work correctly for normal sampling — I expect the modulation will be based on the tensor
+where the noise sampler was created rather than each step. However it may be useful for something like restart sampling
+noise (see `KRestartSamplerCustomNoise` below). You can also pass it a reference latent to modulate based on
+instead (only used for `intensity` and `frequency` modulation types).
+
+*Note*: It's likely this node will be changed in the future.
+
+<details>
+
+<summary>⭐ Expand Example Images ⭐</summary>
+
+<br/>
+
+These examples all use the `spectral_signum` modulation type as it doesn't depend on a reference.
+
+#### Positive Strength
+
+Dims 3:
+
+![Dims 3](../assets/example_images/noise_adv/noise_modulated_ss_dims3.png)
+
+Dims 3 (with studentt noise):
+
+![Dims 3](../assets/example_images/noise_adv/noise_modulated_ss_dims3_studentt.png)
+
+Dims 2:
+
+![Dims 2](../assets/example_images/noise_adv/noise_modulated_ss_dims2.png)
+
+Dims 1:
+
+![Dims 1](../assets/example_images/noise_adv/noise_modulated_ss_dims1.png)
+
+#### Negative Strength
+
+Dims 3:
+
+![Dims 3 Negative](../assets/example_images/noise_adv/noise_modulated_ss_neg_dims3.png)
+
+Dims 3 (with studentt noise):
+
+![Dims 3](../assets/example_images/noise_adv/noise_modulated_ss_neg_dims3_studentt.png)
+
+Dims 2:
+
+![Dims 2 Negative](../assets/example_images/noise_adv/noise_modulated_ss_neg_dims2.png)
+
+Dims 1:
+
+![Dims 1 Negative](../assets/example_images/noise_adv/noise_modulated_ss_neg_dims1.png)
+
+</details>
+
+***
+
+### `SonarRepeatedNoise`
+
+Experimental node to cache noise sampler results. Why would you want to do this? Some noise samplers are
+relatively slow (`pyramid` for example) or it may be slow to generate noise if you are mixing many types
+of noise. When `permute` is enabled, a random effect like flipping the noise or rolling it in some dimension
+will be chosen each time the noise sampler is called. I recommend leaving `permute` on. Note that repeated
+noise (especially with `permute` disabled) can be stronger than normal noise, so you may need to rescale to
+a value lower than `1.0` or decrease `s_noise` for the sampler. You may also set the maximum number of
+times noise is reused by setting `max_recycle`.
+
+<details>
+
+<summary>⭐ Expand Example Images ⭐</summary>
+
+<br/>
+
+Repeated noise is very strong (especially when permute is disabled). You generally won't get good
+results using 1.0 strength:
+
+![Normal](../assets/example_images/noise_adv/noise_repeated_normal.png)
+
+I recommend considerably decreasing the strength (example here is using 0.75 which is still a bit too much):
+
+![Adjusted](../assets/example_images/noise_adv/noise_repeated_adjusted.png)
+
+</details>
+
+***
+
+### `SonarCompositeNoise`
+
+Allows compositing noise types based on a mask. Noise is mixed based on the strength of the mask at a location.
+For example, where the mask is 1.0 (max strength) you will get 100% `noise_src` and 0% `noise_dst`. Where the
+mask is 0.75 you will get 75% `noise_src` and 25% `noise_dst`.
+
+<details>
+
+<summary>⭐ Expand Example Images ⭐</summary>
+
+<br/>
+
+These examples use a base noise type of gaussian and composite in an area with a different type
+near middle. The custom noise is also set to a higher strength than normal to highlight the effect.
+
+**No Composite (for comparison)**
+
+![No Composite](../assets/example_images/noise_base_types/noise_gaussian.png)
+
+**Brownian**
+
+![Brownian](../assets/example_images/noise_adv/noise_composite_brownian.png)
+
+**Pyramid**
+
+![Pyramid](../assets/example_images/noise_adv/noise_composite_pyramid.png)
+
+**Pyramid negative factor**
+
+![Pyramid negative](../assets/example_images/noise_adv/noise_composite_pyramid_neg.png)
+
+</details>
+
+***
+
+### `SonarScheduledNoise`
+
+Allows switching between noise types based on percentage of sampling (note: not percentage of steps).
+
+**Note**: You don't have to connect the fallback noise type but the default is to generate _no_ noise, which
+is most likely not what you want. The majority of the time, it is recommend to connect something like gaussian
+noise at 1.0 strength.
+
+All the example images here use the `SonarScheduledNoise` node so you can pick any one of them to see it
+in action!
+
+***
+
+### `SonarGuidedNoise`
+
+Works similarly as described in the [Guidance](../README.md#guidance) section of the main README, however the guidance is applied
+to the raw noise. You can use `SonarScheduledNoise` to only apply guidance at certain times. Using `euler`
+mode seems considerably stronger than `linear`. The default value should be reasonable for `euler`, may need to be
+increased somewhat for `linear`.
+
+<details>
+
+<summary>⭐ Expand Example Images ⭐</summary>
+
+<br/>
+
+#### Pattern
+
+These examples use a half circle pattern as the reference: ![pattern](../assets/example_images/noise_adv/noise_guided_ref_pattern.png)
+
+
+##### Euler
+
+Positive strength:
+
+![Positive](../assets/example_images/noise_adv/noise_guided_pattern_euler.png)
+
+Negative strength:
+
+![Negative](../assets/example_images/noise_adv/noise_guided_pattern_euler_neg.png)
+
+***
+
+##### Linear
+
+Normal positive strength:
+
+![Positive](../assets/example_images/noise_adv/noise_guided_pattern_linear.png)
+
+Normal negative strength:
+
+![Negative](../assets/example_images/noise_adv/noise_guided_pattern_linear_neg.png)
+
+Strong positive strength:
+
+![Strong Positive](../assets/example_images/noise_adv/noise_guided_pattern_linear_strong.png)
+
+Strong negative strength:
+
+![Strong Negative](../assets/example_images/noise_adv/noise_guided_pattern_linear_strong_neg.png)
+
+
+***
+
+#### Gradient
+
+These examples use a vertical gradient as the reference: ![pattern](../assets/example_images/noise_adv/noise_guided_ref_gradient.png)
+
+That is dark to light. Light to dark examples just flip the gradient vertically.
+
+##### Euler
+
+Dark to light:
+
+![Dark to light](../assets/example_images/noise_adv/noise_guided_dtol_euler.png)
+
+Light to dark:
+
+![Light to dark](../assets/example_images/noise_adv/noise_guided_ltod_euler.png)
+
+Dark to light (negative strength):
+
+![Dark to light](../assets/example_images/noise_adv/noise_guided_dtol_euler_neg.png)
+
+Light to dark (negative strength):
+
+![Light to dark](../assets/example_images/noise_adv/noise_guided_ltod_euler_neg.png)
+
+***
+
+##### Linear
+
+Dark to light:
+
+![Dark to light](../assets/example_images/noise_adv/noise_guided_dtol_linear.png)
+
+Light to dark:
+
+![Light to dark](../assets/example_images/noise_adv/noise_guided_ltod_linear.png)
+
+Dark to light (negative strength):
+
+![Dark to light](../assets/example_images/noise_adv/noise_guided_dtol_linear_neg.png)
+
+Light to dark (negative strength):
+
+![Light to dark](../assets/example_images/noise_adv/noise_guided_ltod_linear_neg.png)
+
+</details>
+
+***
+
+### `SonarRandomNoise`
+
+Randomly chooses between the noise types in the chain connected to it each time the noise sampler is called.
+You generally do not want to use `rescale` here. You can also set `mix_count` to choose and combine multiple
+types.
+
+### `SonarChannelNoise`
+
+Allows using a different noise generator per channel. The custom noise items attached to this node are treated as a list where the furthest item from the node will correspond to channel 0. For example where CN is a custom noise node and SCN is the `SonarChannelNoise` node:
+
+```plaintext
+CN (channel 0) -> CN (channel 1) -> SCN
+```
+
+Don't enable `rescale` in the custom noise nodes attached to `SonarChannelNoise`. If you want a blend of noise types for a channel, you can use something like `SonarBlendedNoise`.
+
+### `SonarBlendedNoise`
+
+Allows blending two noise generators. If [ComfyUI-bleh](https://github.com/blepping/ComfyUI-bleh) is available, you will have access to many more blending modes.
+
+### `SonarBlehOpsNoise`
+
+Only provided if [ComfyUI-bleh](https://github.com/blepping/ComfyUI-bleh) is available. Allows transforming/manipulating noise with bleh blockops expressions. For instance, you can do something like:
+
+```yaml
+- ops:
+  - [multiply, -1]
+  - [roll, -2, 0.5]
+```
+
+to flip the sign on the noise and then roll dimension -2 (height) by 50%.
@@ -0,0 +1,127 @@
+# Advanced Power Noise
+
+## `SonarPowerNoise`
+
+This node generates [fractional Brownian motion (fBm) noise](https://en.wikipedia.org/wiki/Fractional_Brownian_motion#Frequency-domain_interpretation). It offers versatility in producing various types of noise including gaussian, pink, 2D brownian noise, and all intermediates.
+
+By default, the node generates normal gaussian noise.
+
+<details>
+
+<summary>⭐⭐ Expand advanced parameter explanation ⭐⭐</summary>
+
+Here's an overview of its parameters:
+
+- `factor` and `rescale` operate similarly to `SonarCustomNoise`, enabling the addition of multiple sources of noises.
+- `time_brownian` introduces correlation across sampler timesteps for SDE solvers.
+- `alpha` is the main parameter. `alpha > 0` amplifies low frequencies; `alpha = 1` yields pink noise, and `alpha = 2` produces brownian noise. Conversely, for `alpha < 0`, it amplifies high frequencies.
+- `min_freq` and `max_freq` determine the range of frequencies allowed through. Setting `max_freq = `$\sqrt{1/2} \simeq 0.7071$ enables the passage of the highest frequencies. In cases where `alpha < 0`, setting `max_freq = 0.5` is advisable to diminish the power of diagonally oriented frequencies.
+- `stretch`, `rotate`, and `pnorm` alter the filter's shape by stretching, rotating, or cushioning the band-pass region.
+- Lowering `mix` moderates the filter's effect by blending back unfiltered gaussian noise from the same sample.
+- `common_mode` is an attempt to desaturate the latent by injecting the average across channels into every latent channel. **FIXME: it's not all channels anymore** However, this may result in a specific color due to the encoding of the unit vector by the latent space. Note that this is done _after_ the `mix`ing of unfiltered gaussian noise.
+- `channel_correlation` **FIXME**: TBD
+- Enabling `preview` provides a visual representation of the filter. `no_mix` sets `mix = 1` for the preview. The preview includes, from left to right:
+  - Fourier domain visualization: Low frequencies at the center, with black indicating filtered-out frequencies.
+  - Spatial visualization of the 2D kernel: The filtering can be interpreted as convolution with the displayed kernel.
+  - Sample: Gaussian sample with shaped frequency spectrum. A single latent channel will look like this.
+
+**Frequency-domain Interpretation**: The Fourier transform decomposes a 2D latent into sinusoids covering all spatial orientations and frequencies. For an independent and identically distributed gaussian sample, energy is evenly distributed across all frequencies and orientations. Scaling the power spectrum by $1 / f^\alpha$, where $\alpha>0$, boosts low frequencies, introducing spatial correlations.
+
+**Spatial Domain Interpretation**: A gaussian latent sample comprises independently sampled pixels, exhibiting no spatial correlations. Conversely, a requirement that each pixel value differs from its neighbors by a $\epsilon \sim \mathcal{N}(0, 1)$ results in 2D brownian noise ($\alpha=2$).
+
+**Seed Considerations**: While the node defaults to outputting gaussian noise, a given seed produce a different sample than the one produced by other gaussian noise sources. This stems from sampling the noise directly in the frequency domain to avoid the cost of a FFT. When `time_brownian = true`, noise sampling occurs in the spatial domain, ensuring that default parameters yield output equivalent to `SonarCustomNoise` set to `brownian`.
+
+</details>
+
+<br/>
+
+From a usage perspective, using positive alpha will tend to create a colorful effect, using negative alpha will create line/streak like artifacts sort of like an oil painting canvas. Start with small values at first (`-0.1`, `0.1`) and adjust as necessary. `time_brownian` makes the effect of power noise (and alpha) stronger - also note that it can only be used when sampling and not for `NoisyLatentLike`. Setting `common_mode` also generally seems to intensify these effects. Different types of models (normal EPS models, v-prediction models, SDXL) generally react differently to these exotic noise types so my advice is to experiment! Lowering `mix` uses normal gaussian noise for part of the generated noise. For example, `mix=1.0` means 100% power noise, `mix=0.5` means 50/50 power noise and normal gaussian noise. This also is about the same as setting factor to `0.5` and plugging in a `SonarCustomNoise` node with factor at `0.5` also and the type set to `guassian`.
+
+Noise from the `SonarCustomNoise` node and `SonarPowerNoise` can be freely mixed.
+
+## `SonarPowerFilterNoise`
+
+This node lets you connect a filter (see below) and a custom noise chain. It basically lets you run any type of noise through the power noise filter.
+
+New parameters:
+
+* `filter_norm_factor` controls how much normalization is applied to the filter. `1.0` means fully normalized, `0.0` means no normalization.
+* You may set the preview type to `custom` to see a color preview of the filtered noise. Note that this uses whatever preview type you have configured in ComfyUI (for example, TAESD). The preview is based on SD 1.5's interpretation of the noise.
+
+## `SonarPowerFilter`
+
+Most of the parameters here are similar to the `SonarPowerNoise` node. New parameters:
+
+* `scale` allows you to scale the filter (you could consider this to be set to `1.0` in the `SonarPowerNoise` node).
+* `compose_mode` allows you to compose multiple filters. Note that composition occurs like `current_filter OPERATION connected_filter`. So if you set `compose_mode` to `sub`, you will get `current_filter - connected_filter`. Scaling occurs before composition.
+
+## `SonarPreviewFilter`
+
+Allows you to preview a filter. It does not modify the input filter.
+
+***
+
+## Examples
+
+The example images are all workflow-included. Generated using `dpmpp_2s_ancestral`, Karras scheduler and
+starting out with gaussian noise then switching to power noise at the 35% mark. `filter_norm_factor` is set to
+1.0 in these examples.
+
+### Node Defaults
+
+This should be the same as normal gaussian noise.
+
+![PowernoiseDefault](../assets/example_images/noise_base_types/noise_powernoise_default.png)
+
+### Positive Alpha
+
+Positive alpha generally produces a colorful effect. Start with relatively low values and increase
+until you achieve the desired result. Note that these examples use _relatively_ extreme settings.
+
+With alpha 0.25:
+
+![PowernoiseAlpha_0.25](../assets/example_images/noise_base_types/noise_powernoise_alpha_0.25.png)
+
+With alpha 0.25, common mode 0.25:
+
+![PowernoiseAlpha_0.25_common_0.25](../assets/example_images/noise_base_types/noise_powernoise_alpha_0.25_common_0.25.png)
+
+With alpha 0.35:
+
+![PowernoiseAlpha_0.35](../assets/example_images/noise_base_types/noise_powernoise_alpha_0.35.png)
+
+With alpha 0.35, common mode 0.35:
+
+![PowernoiseAlpha_0.35_common_0.35](../assets/example_images/noise_base_types/noise_powernoise_alpha_0.35_common_0.35.png)
+
+With alpha 0.5:
+
+![PowernoiseAlpha_0.5](../assets/example_images/noise_base_types/noise_powernoise_alpha_0.5.png)
+
+With alpha 0.5, common mode 0.5:
+
+![PowernoiseAlpha_0.5_common_0.5](../assets/example_images/noise_base_types/noise_powernoise_alpha_0.5_common_0.5.png)
+
+### Negative Alpha
+
+With alpha -0.5:
+
+![PowernoiseAlpha-0.5](../assets/example_images/noise_base_types/noise_powernoise_alpha_-0.5.png)
+
+With alpha -1.5:
+
+![PowernoiseAlpha-1.5](../assets/example_images/noise_base_types/noise_powernoise_alpha_-1.5.png)
+
+### Time Brownian Mode
+
+![PowernoiseTb](../assets/example_images/noise_base_types/noise_powernoise_tb.png)
+
+With alpha 0.5:
+
+![PowernoiseTbAlpha_0.5](../assets/example_images/noise_base_types/noise_powernoise_tb_alpha_0.5.png)
+
+With alpha -0.5:
+
+![PowernoiseTbAlpha-0.5](../assets/example_images/noise_base_types/noise_powernoise_tb_alpha_-0.5.png)
+
+
@@ -0,0 +1,189 @@
+# Base Noise Examples
+
+The example images are all workflow-included. Generated using `dpmpp_2s_ancestral`, Karras scheduler and
+starting out with gaussian noise then switching to the custom noise type at the 35% mark.
+
+Some of these noise types are too extreme to be used for initial generations or even with pure
+noise of that type. However you can either schedule the noise type to kick in at a certain percentage
+(as in these examples) and/or mix it with something a bit more run of the mill. See
+[advanced_noise_nodes](advanced_noise_nodes.md).
+
+## Documentation TBD
+
+* `grey`
+* `onef_greenish_mix` (50/50 mix of positive/negative noise.)
+* `onef_greenish`
+* `onef_pinkish_mix` (50/50 mix of positive/negative noise.)
+* `onef_pinkish`
+* `onef_pinkishgreenish` (50/50 mix of `onef_pinkish` and `onef_greenish`.)
+* `velvet`
+* `violet`
+* `white`
+
+## Brownian
+
+This is the default noise type for SDE samplers.
+
+![Brownian](../assets/example_images/noise_base_types/noise_brownian.png)
+
+***
+## Gaussian
+
+This is the default noise type for non-SDE samplers.
+
+![Gaussian](../assets/example_images/noise_base_types/noise_gaussian.png)
+
+***
+
+## Green Test
+
+This is _probably_ not actually green noise. It produces a very colorful effect, however
+it's very strong and not really suitable for initial generation.
+
+![GreenTest](../assets/example_images/noise_base_types/noise_green_test.png)
+
+You can also use a negative multiplier to achieve a different effect:
+
+![GreenTestNeg](../assets/example_images/noise_base_types/noise_green_test_neg.png)
+
+***
+
+## Highres Pyramid
+
+![HighresPyramid](../assets/example_images/noise_base_types/noise_highres_pyramid.png)
+
+Variation using area scaling:
+
+![HighresPyramidArea](../assets/example_images/noise_base_types/noise_highres_pyramid_area.png)
+
+Variation using bislerp scaling:
+
+![HighresPyramidBislerp](../assets/example_images/noise_base_types/noise_highres_pyramid_bislerp.png)
+
+***
+
+## Laplacian
+
+![Laplacian](../assets/example_images/noise_base_types/noise_laplacian.png)
+
+***
+
+## Perlin
+
+![Perlin](../assets/example_images/noise_base_types/noise_perlin.png)
+
+***
+
+## Pink Old
+
+Previously known as `pink`. The implementation isn't correct, though in terms of results it's fine.
+
+![Pink](../assets/example_images/noise_base_types/noise_pink.png)
+
+***
+
+## Power Old
+
+Previously known as `power`. The implementation isn't correct, though in terms of results it's fine.
+
+![PowerBuiltin](../assets/example_images/noise_base_types/noise_power_builtin.png)
+
+Also see the [Advanced Power Noise](advanced_power_noise.md) examples.
+
+***
+
+## Pyramid
+
+![Pyramid](../assets/example_images/noise_base_types/noise_pyramid.png)
+
+You can also use a negative multiplier to achieve a different effect:
+
+![PyramidNeg](../assets/example_images/noise_base_types/noise_pyramid_neg.png)
+
+Variation using area scaling:
+
+![PyramidArea](../assets/example_images/noise_base_types/noise_pyramid_area.png)
+
+Variation using bislerp scaling:
+
+![PyramidBislerp](../assets/example_images/noise_base_types/noise_pyramid_bislerp.png)
+
+***
+
+## Pyramid Discount5
+
+Pyramid noise, generated with a discount of 0.5. (Generally less extreme effect.)
+
+![PyramidDiscount5](../assets/example_images/noise_base_types/noise_pyramid_discount5.png)
+
+***
+
+## Pyramid Mix
+
+Pyramid mix is a combination of positive and negative pyramid noise. The effect on
+the generation is mild compared to raw pyramid noise.
+
+![PyramidMix](../assets/example_images/noise_base_types/noise_pyramid_mix.png)
+
+You can also use a negative multiplier to achieve a different effect:
+
+![PyramidMixNeg](../assets/example_images/noise_base_types/noise_pyramid_mix_neg.png)
+
+Variation using area scaling:
+
+![PyramidMixArea](../assets/example_images/noise_base_types/noise_pyramid_mix_area.png)
+
+You can also use a negative multiplier to achieve a different effect:
+
+![PyramidMixAreaNeg](../assets/example_images/noise_base_types/noise_pyramid_mix_area_neg.png)
+
+Variation using bislerp scaling:
+
+![PyramidMixBislerp](../assets/example_images/noise_base_types/noise_pyramid_mix_bislerp.png)
+
+You can also use a negative multiplier to achieve a different effect:
+
+![PyramidMixBislerpNeg](../assets/example_images/noise_base_types/noise_pyramid_mix_bislerp_neg.png)
+
+***
+
+## Pyramid Old
+
+This may not actually be pyramid noise at all. Also note that it is quite slow to generate as it
+effectively generates noise ~60x the latent size.
+
+![PyramidOld](../assets/example_images/noise_base_types/noise_pyramid_old.png)
+
+Variation using area scaling:
+
+![PyramidOldArea](../assets/example_images/noise_base_types/noise_pyramid_old_area.png)
+
+Variation using bislerp scaling:
+
+![PyramidOldBislerp](../assets/example_images/noise_base_types/noise_pyramid_old_bislerp.png)
+
+***
+
+## Rainbow
+
+Rainbow is a mix of Perlin and Green noise types.
+
+The "mild" variation uses a relatively low proportion of green noise:
+
+![RainbowMild](../assets/example_images/noise_base_types/noise_rainbow_mild.png)
+
+The "intense" variation uses a higher proportion of green noise for a more extreme effect.
+
+![RainbowIntense](../assets/example_images/noise_base_types/noise_rainbow_intense.png)
+
+***
+
+## Studentt
+
+![Studentt](../assets/example_images/noise_base_types/noise_studentt.png)
+
+***
+
+## Uniform
+
+![Uniform](../assets/example_images/noise_base_types/noise_uniform.png)
@@ -0,0 +1,40 @@
+# FreeU Extreme
+
+I admit it's a really dumb name. This is basically a build-your-own FreeU kit.
+
+## Example Workflow
+
+Workflow image is also workflow-embedded.
+
+![FRUX Workflow](../assets/example_images/frux/fruxworkflow.png)
+
+## Nodes
+
+### `FreeUExtreme`
+
+Allows you to apply a FreeU (v1 or v2) effect to input, output or middle blocks.
+
+**Note**: ComfyUI by default does not allow patching the middle in the required way. You will need to have
+[FreeU Advanced](https://github.com/WASasquatch/FreeU_Advanced) installed and enabled, otherwise connecting
+configs to the `middle` input will have no effect.
+
+Also note that input and middle do not have a `skip` target so configs targetting that will never match.
+
+### `FreeUExtremeConfig`
+
+Better documentation coming soon hopefully. For now, see the workflow example above to get started.
+
+Also see documentation on filters [here](./advanced_power_noise.md#sonarpowerfilter).
+
+## Examples
+
+ComfyUI built-in FreeU V2 for reference:
+
+![Builtin FreeUV2](../assets/example_images/frux/freeuv2_builtin.png)
+
+FreeU Extreme example:
+
+![FreeU Extreme](../assets/example_images/frux/frux.png)
+
+Note that this is just for example purposes - no attempt was made to get a pretty generation. You
+may get better results enabling `hidden_mean` even for the skip connections.
@@ -0,0 +1,22 @@
+import contextlib
+import importlib
+
+MODULES = {}
+
+with contextlib.suppress(ImportError, NotImplementedError):
+    bleh = importlib.import_module("custom_nodes.ComfyUI-bleh")
+    bleh_version = getattr(bleh, "BLEH_VERSION", -1)
+    if bleh_version < 1:
+        raise NotImplementedError
+    MODULES["bleh"] = bleh
+
+with contextlib.suppress(ImportError, NotImplementedError):
+    import custom_nodes.ComfyUI_restart_sampling as rs
+
+    if not hasattr(rs.restart_sampling, "DEFAULT_SEGMENTS"):
+        # Dumb test but this should only exist in restart sampling versions that
+        # support plugging in custom noise.
+        raise NotImplementedError
+    MODULES["restart"] = rs
+
+__all__ = ("MODULES",)
@@ -0,0 +1,435 @@
+from __future__ import annotations
+
+import torch
+
+from .external import MODULES as EXTERNAL_MODULES
+from .powernoise import PowerFilter
+
+
+def ffilter(x, pfilter, normalization_factor=1.0, cfg_idx=None, filter_cache=None):
+    cache_key = None
+    if filter_cache is not None and cfg_idx is not None:
+        cache_key = (cfg_idx, x.shape[-2:])
+        filter_rfft = filter_cache.get(cache_key)
+    if filter_rfft is None:
+        filter_rfft = PowerFilter.normalize(
+            pfilter.build(x.shape),
+            x.shape,
+            normalization_factor=normalization_factor,
+        ).to(x.device, non_blocking=True)
+    if cache_key:
+        filter_cache[cache_key] = filter_rfft
+    x_rfft = torch.fft.rfft2(x.to(torch.float32), norm="ortho")
+    x_filt = torch.fft.irfft2(
+        x_rfft.mul_(filter_rfft),
+        s=x.shape[-2:],
+        norm="ortho",
+    )
+    return x_filt.to(x.dtype, non_blocking=True)
+
+
+BLEND_OPS = (
+    {"lerp": torch.lerp}
+    if "bleh" not in EXTERNAL_MODULES
+    else EXTERNAL_MODULES["bleh"].py.latent_utils.BLENDING_MODES
+)
+
+
+class FreeUExtremeConfigNode:
+    DESCRIPTION = "Allows setting configuration for FreeU Extreme."
+    RETURN_TYPES = ("FRUX_CONFIG",)
+    FUNCTION = "go"
+    CATEGORY = "model_patches"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "stage_1": (
+                    "BOOLEAN",
+                    {
+                        "default": True,
+                        "tooltip": "Controls whether this configuration applies to stage 1.",
+                    },
+                ),
+                "stage_2": (
+                    "BOOLEAN",
+                    {
+                        "default": False,
+                        "tooltip": "Controls whether this configuration applies to stage 2.",
+                    },
+                ),
+                "stage_3": (
+                    "BOOLEAN",
+                    {
+                        "default": False,
+                        "tooltip": "Controls whether this configuration applies to stage 3.",
+                    },
+                ),
+                "target": (
+                    ("backbone", "skip", "both"),
+                    {
+                        "tooltip": "Controls whether this filter applies to backbone or skip layers (or both).",
+                    },
+                ),
+                "start": (
+                    "FLOAT",
+                    {
+                        "default": 0.0,
+                        "min": 0.0,
+                        "max": 1.0,
+                        "step": 0.1,
+                        "round": False,
+                        "tooltip": "Start time as percentage of sampling this configuration applies to. Inclusive.",
+                    },
+                ),
+                "end": (
+                    "FLOAT",
+                    {
+                        "default": 1.0,
+                        "min": 0.0,
+                        "max": 1.0,
+                        "step": 0.1,
+                        "round": False,
+                        "tooltip": "End time as percentage of sampling this configuration applies to. Inclusive.",
+                    },
+                ),
+                "slice": (
+                    "FLOAT",
+                    {
+                        "default": 1.0,
+                        "min": 0.0,
+                        "max": 1.0,
+                        "step": 0.1,
+                        "round": False,
+                        "tooltip": "Percentage of the layer the FreeU effect is applied to.",
+                    },
+                ),
+                "slice_offset": (
+                    "FLOAT",
+                    {
+                        "default": 0.0,
+                        "min": 0.0,
+                        "max": 1.0,
+                        "step": 0.1,
+                        "round": False,
+                        "tooltip": "Offset as a percentage the layer is applied to. For example if slice is 0.25 and slice_offset is 0.25 then the filter will apply to the range 25% through 50%.",
+                    },
+                ),
+                "filter_norm": (
+                    "FLOAT",
+                    {
+                        "default": 0.0,
+                        "min": -10.0,
+                        "max": 10.0,
+                        "step": 0.1,
+                        "round": False,
+                        "tooltip": "Normalization factor applied to the filter. 1.0 means 100% normalized.",
+                    },
+                ),
+                "scale": (
+                    "FLOAT",
+                    {
+                        "default": 1,
+                        "min": -100.0,
+                        "max": 100.0,
+                        "step": 0.1,
+                        "round": False,
+                        "tooltip": "Strength of the effects applied by this configuration.",
+                    },
+                ),
+                "blend": (
+                    "FLOAT",
+                    {
+                        "default": 1.0,
+                        "min": -10.0,
+                        "max": 10.0,
+                        "step": 0.1,
+                        "round": False,
+                        "tooltip": "Blends the filtered result based on the specified strength where 1.0 means 100% filtered.",
+                    },
+                ),
+                "blend_mode": (
+                    tuple(BLEND_OPS.keys()),
+                    {
+                        "tooltip": "Mode used when blending. Generally only has an effect when blend is set to values other than 0 or 1",
+                    },
+                ),
+                "hidden_mean": (
+                    "BOOLEAN",
+                    {
+                        "default": True,
+                        "tooltip": "You can think of this as FreeU V2 mode.",
+                    },
+                ),
+                "final": (
+                    "BOOLEAN",
+                    {
+                        "default": True,
+                        "tooltip": "When enabled, other configurations won't be considered if this one matched. Otherwise, multiple configurations/filter effects can be stacked.",
+                    },
+                ),
+            },
+            "optional": {
+                "sonar_power_filter_opt": (
+                    "SONAR_POWER_FILTER",
+                    {
+                        "tooltip": "Optionally attach a Power Filter here to set filtering parameters.",
+                    },
+                ),
+                "frux_config_opt": (
+                    "FRUX_CONFIG",
+                    {
+                        "tooltip": "Optionally attach another configuration node here.",
+                    },
+                ),
+            },
+        }
+
+    @classmethod
+    def go(cls, **kwargs: dict):
+        return (FreeUExtremeConfig(**kwargs),)
+
+
+class FreeUExtremeConfig:
+    _keys = (
+        "target",
+        "stage_1",
+        "stage_2",
+        "stage_3",
+        "start",
+        "end",
+        "slice",
+        "slice_offset",
+        "filter_norm",
+        "scale",
+        "blend",
+        "blend_mode",
+        "hidden_mean",
+        "final",
+        "sonar_power_filter",
+        "frux_config",
+    )
+
+    def __init__(
+        self,
+        *,
+        target,
+        stage_1=False,
+        stage_2=False,
+        stage_3=False,
+        start=0.0,
+        end=1.0,
+        slice=1.0,  # noqa: A002
+        slice_offset=0.0,
+        filter_norm=1.0,
+        scale=1.0,
+        blend=1.0,
+        blend_mode=None,
+        hidden_mean=True,
+        final=True,
+        sonar_power_filter_opt=None,
+        frux_config_opt=None,
+    ):
+        self.target = target
+        self.stage_1 = stage_1
+        self.stage_2 = stage_2
+        self.stage_3 = stage_3
+        self.start = start
+        self.end = end
+        self.slice = slice
+        self.slice_offset = slice_offset
+        self.filter_norm = filter_norm
+        self.scale = scale
+        self.blend = blend
+        self.blend_mode = blend_mode
+        self.hidden_mean = hidden_mean
+        self.final = final
+        self.sonar_power_filter = sonar_power_filter_opt
+        self.frux_config = frux_config_opt
+
+    def get_config_list(self):
+        result = [self]
+        curr = self
+        while cfg := curr.frux_config:
+            curr = cfg
+            if (
+                cfg.start >= 1
+                or cfg.end <= 0
+                or cfg.blend == 0
+                or not (cfg.stage_1 or cfg.stage_2 or cfg.stage_3)
+            ):
+                continue
+            result.append(cfg)
+        result.reverse()
+        return result
+
+    # Hidden mean function modified from https://github.com/WASasquatch/FreeU_Advanced
+    def get_scale(self, h: torch.Tensor) -> torch.Tensor:
+        if not self.hidden_mean:
+            return self.scale
+        hmean = h.mean(1).unsqueeze(1)
+        hmax, hmin = (
+            op(hmean.view(hmean.shape[0], -1), dim=-1, keepdim=True)[0]
+            for op in (torch.max, torch.min)
+        )
+        hmean -= hmin.unsqueeze(2).unsqueeze(3)
+        hmean /= (hmax - hmin).unsqueeze(2).unsqueeze(3)
+        return 1.0 + (self.scale - 1.0) * hmean
+
+    def check_match(self, pct, stage, is_skip=False):
+        if pct < self.start or pct > self.end:
+            return False
+        if not getattr(self, f"stage_{stage}"):
+            return False
+        return not self.target not in {"skip" if is_skip else "backbone", "both"}
+
+    def apply(self, idx, x, filter_cache, cpu_fft=False):
+        _batch, features, _height, _width = x.shape
+        scale = self.get_scale(x)
+        slice_size = int(features * self.slice)
+        slice_offs = int(features * self.slice_offset)
+
+        xslice = (
+            self.apply_filter(
+                idx,
+                x[:, slice_offs : slice_offs + slice_size],
+                filter_cache,
+                cpu_fft=cpu_fft,
+            )
+            * scale
+        )
+        x[:, slice_offs : slice_offs + slice_size] = (
+            xslice
+            if self.blend == 1.0
+            else BLEND_OPS[self.blend_mode](
+                x[:, slice_offs : slice_offs + slice_size],
+                xslice,
+                self.blend,
+            )
+        )
+        return x
+
+    def apply_filter(self, idx, xslice, filter_cache, cpu_fft=False):
+        filt = self.sonar_power_filter
+        if filt is None:
+            return xslice
+        device = xslice.device
+        if cpu_fft:
+            xslice = xslice.to("cpu")
+        xslice = ffilter(
+            xslice,
+            filt,
+            normalization_factor=self.filter_norm,
+            cfg_idx=idx,
+            filter_cache=filter_cache,
+        )
+        if cpu_fft:
+            xslice = xslice.to(device)
+        return xslice
+
+    def clone(self):
+        return self.__class__(**{k: getattr(self, k) for k in self._keys})
+
+    def __repr__(self):  # noqa: D105
+        meh = {k: getattr(self, k) for k in self._keys}
+        return f"<FRUXConfig: {meh}>"
+
+
+class FreeUExtremeNode:
+    DESCRIPTION = "Main FreeU Extreme node. Allows patching a model with the FreeU (V2) effect with more control."
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "go"
+    CATEGORY = "model_patches"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "model": (
+                    "MODEL",
+                    {
+                        "tooltip": "Model to patch.",
+                    },
+                ),
+                "cpu_fft": (
+                    "BOOLEAN",
+                    {
+                        "default": False,
+                        "tooltip": "Controls whether to perform FFT calculations on the CPU. May be necessary for some GPUs that don't have native support for FFT operations at the cost of performance.",
+                    },
+                ),
+            },
+            "optional": {
+                "input_config": (
+                    "FRUX_CONFIG",
+                    {
+                        "tooltip": "Allows specifying configuration for input blocks.",
+                    },
+                ),
+                "middle_config": (
+                    "FRUX_CONFIG",
+                    {
+                        "tooltip": "Allows specifying configuration for middle blocks.",
+                    },
+                ),
+                "output_config": (
+                    "FRUX_CONFIG",
+                    {
+                        "tooltip": "Allows specifying configuration for output blocks.",
+                    },
+                ),
+            },
+        }
+
+    @classmethod
+    def go(
+        cls,
+        model,
+        cpu_fft,
+        input_config=None,
+        middle_config=None,
+        output_config=None,
+    ):
+        model_channels = model.model.model_config.unet_config["model_channels"]
+        stages = {model_channels * 4: 1, model_channels * 2: 2, model_channels: 3}
+        icfg, mcfg, ocfg = (
+            () if cfg is None else cfg.get_config_list()
+            for cfg in (input_config, middle_config, output_config)
+        )
+        m = model.clone()
+        ms = m.get_model_object("model_sampling")
+        filter_cache = {}
+
+        def handler(_typ, h_shape, cfg, x, toptions, is_skip=False):
+            stage = stages.get(h_shape[1])
+            if stage is None:
+                return x
+            sigma = toptions["sigmas"].max().detach().cpu()
+            pct = 1.0 - (ms.timestep(sigma) / 999.0)
+            for idx, ci in enumerate(cfg):
+                if not ci.check_match(pct, stage, is_skip):
+                    continue
+                x = ci.apply(idx, x, filter_cache, cpu_fft=cpu_fft)
+                if ci.final:
+                    break
+            return x
+
+        def in_patch(h, toptions):
+            return handler("input", h.shape, icfg, h, toptions)
+
+        def mid_patch(h, toptions):
+            return handler("middle", h.shape, mcfg, h, toptions)
+
+        def out_patch(h, hsp, toptions):
+            h = handler("output", h.shape, ocfg, h, toptions)
+            hsp = handler("output", h.shape, ocfg, hsp, toptions, is_skip=True)
+            return h, hsp
+
+        if icfg:
+            m.set_model_input_block_patch(in_patch)
+        if mcfg:
+            m.set_model_patch(mid_patch, "middle_block_patch")
+        if ocfg:
+            m.set_model_output_block_patch(out_patch)
+        return (m,)
@@ -0,0 +1,565 @@
+# Noise generation functions shamelessly yoinked from https://github.com/Clybius/ComfyUI-Extra-Samplers
+from __future__ import annotations
+
+import math
+from enum import Enum, auto
+from typing import Callable
+
+import torch
+from comfy.model_management import device_supports_non_blocking
+from comfy.utils import common_upscale
+from torch import FloatTensor, Generator, Tensor
+from torch.distributions import Laplace, StudentT
+
+from .external import MODULES as EXT
+
+# ruff: noqa: D412, D413, D417, D212, D407, ANN002, ANN003, FBT001, FBT002, S311
+
+
+class NoiseType(Enum):
+    BROWNIAN = auto()
+    GAUSSIAN = auto()
+    GREEN_TEST = auto()
+    GREY = auto()
+    HIGHRES_PYRAMID = auto()
+    HIGHRES_PYRAMID_AREA = auto()
+    HIGHRES_PYRAMID_BISLERP = auto()
+    LAPLACIAN = auto()
+    ONEF_GREENISH = auto()
+    ONEF_GREENISH_MIX = auto()
+    ONEF_PINKISH = auto()
+    ONEF_PINKISH_MIX = auto()
+    ONEF_PINKISHGREENISH = auto()
+    PERLIN = auto()
+    PINK_OLD = auto()
+    POWER_OLD = auto()
+    PYRAMID = auto()
+    PYRAMID_AREA = auto()
+    PYRAMID_BISLERP = auto()
+    PYRAMID_DISCOUNT5 = auto()
+    PYRAMID_MIX = auto()
+    PYRAMID_MIX_AREA = auto()
+    PYRAMID_MIX_BISLERP = auto()
+    PYRAMID_OLD = auto()
+    PYRAMID_OLD_AREA = auto()
+    PYRAMID_OLD_BISLERP = auto()
+    RAINBOW_INTENSE = auto()
+    RAINBOW_MILD = auto()
+    STUDENTT = auto()
+    UNIFORM = auto()
+    VELVET = auto()
+    VIOLET = auto()
+    WHITE = auto()
+
+    @classmethod
+    def get_names(cls, default=GAUSSIAN, skip=None):
+        if default is not None:
+            if isinstance(default, int):
+                default = cls(default)
+            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
+
+
+def scale_noise(noise, factor=1.0, *, normalized=True, threshold_std_devs=2.5):
+    if not normalized or noise.numel() == 0:
+        return noise.mul_(factor) if factor != 1 else noise
+    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
+    return noise.mul_(factor) if factor != 1 else noise
+
+
+if "bleh" in EXT:
+    scale_samples = EXT["bleh"].py.latent_utils.scale_samples
+else:
+
+    def scale_samples(
+        samples,
+        width,
+        height,
+        *,
+        mode="bicubic",
+    ):
+        return common_upscale(samples, width, height, mode, None)
+
+
+CAN_NONBLOCK = {}
+
+
+def tensor_to(tensor, dest):
+    device = dest.device if isinstance(dest, torch.Tensor) else dest
+    non_blocking = CAN_NONBLOCK.get(device)
+    if non_blocking is None:
+        non_blocking = device_supports_non_blocking(device)
+        CAN_NONBLOCK[device] = non_blocking
+    return tensor.to(dest, non_blocking=non_blocking)
+
+
+def get_positions(block_shape: tuple[int, int]) -> Tensor:
+    """
+    Generate position tensor.
+
+    Arguments:
+        block_shape -- (height, width) of position tensor
+
+    Returns:
+        position vector shaped (1, height, width, 1, 1, 2)
+    """
+    bh, bw = block_shape
+    return torch.stack(
+        torch.meshgrid(
+            [(torch.arange(b) + 0.5) / b for b in (bw, bh)],
+            indexing="xy",
+        ),
+        -1,
+    ).view(1, bh, bw, 1, 1, 2)
+
+
+def unfold_grid(vectors: Tensor) -> Tensor:
+    """
+    Unfold vector grid to batched vectors.
+
+    Arguments:
+        vectors -- grid vectors
+
+    Returns:
+        batched grid vectors
+    """
+    batch_size, _channels, gpy, gpx = vectors.shape
+    return (
+        torch.nn.functional.unfold(vectors, (2, 2))
+        .view(batch_size, 2, 4, -1)
+        .permute(0, 2, 3, 1)
+        .view(batch_size, 4, gpy - 1, gpx - 1, 2)
+    )
+
+
+def smooth_step(t: Tensor) -> Tensor:
+    """
+    Smooth step function [0, 1] -> [0, 1].
+
+    Arguments:
+        t -- input values (any shape)
+
+    Returns:
+        output values (same shape as input values)
+    """
+    return t * t * (3.0 - 2.0 * t)
+
+
+def perlin_noise_tensor(
+    vectors: Tensor,
+    positions: Tensor,
+    step: Callable | None = None,
+) -> Tensor:
+    """
+    Generate perlin noise from batched vectors and positions.
+
+    Arguments:
+        vectors -- batched grid vectors shaped (batch_size, 4, grid_height, grid_width, 2)
+        positions -- batched grid positions shaped (batch_size or 1, block_height, block_width, grid_height or 1, grid_width or 1, 2)
+
+    Keyword Arguments:
+        step -- smooth step function [0, 1] -> [0, 1] (default: `smooth_step`)
+
+    Raises:
+        NoiseError: if position and vector shapes do not match
+
+    Returns:
+        (batch_size, block_height * grid_height, block_width * grid_width)
+    """
+    if step is None:
+        step = smooth_step
+
+    batch_size = vectors.shape[0]
+    # grid height, grid width
+    gh, gw = vectors.shape[2:4]
+    # block height, block width
+    bh, bw = positions.shape[1:3]
+
+    for i in range(2):
+        if positions.shape[i + 3] not in {1, vectors.shape[i + 2]}:
+            msg = f"Blocks shapes do not match: vectors ({vectors.shape[1]}, {vectors.shape[2]}), positions {gh}, {gw})"
+            raise NoiseError(msg)
+
+    if positions.shape[0] not in {1, batch_size}:
+        msg = f"Batch sizes do not match: vectors ({vectors.shape[0]}), positions ({positions.shape[0]})"
+        raise NoiseError(msg)
+
+    vectors = vectors.view(batch_size, 4, 1, gh * gw, 2)
+    positions = positions.view(positions.shape[0], bh * bw, -1, 2)
+
+    step_x = step(positions[..., 0])
+    step_y = step(positions[..., 1])
+
+    row0 = torch.lerp(
+        (vectors[:, 0] * positions).sum(dim=-1),
+        (vectors[:, 1] * (positions - positions.new_tensor((1, 0)))).sum(dim=-1),
+        step_x,
+    )
+    row1 = torch.lerp(
+        (vectors[:, 2] * (positions - positions.new_tensor((0, 1)))).sum(dim=-1),
+        (vectors[:, 3] * (positions - positions.new_tensor((1, 1)))).sum(dim=-1),
+        step_x,
+    )
+    noise = torch.lerp(row0, row1, step_y)
+    return (
+        noise.view(
+            batch_size,
+            bh,
+            bw,
+            gh,
+            gw,
+        )
+        .permute(0, 3, 1, 4, 2)
+        .reshape(batch_size, gh * bh, gw * bw)
+    )
+
+
+def perlin_noise(
+    grid_shape: tuple[int, int],
+    out_shape: tuple[int, int],
+    batch_size: int = 1,
+    generator: Generator | None = None,
+    *args,
+    **kwargs,
+) -> Tensor:
+    """
+    Generate perlin noise with given shape. `*args` and `**kwargs` are forwarded to `Tensor` creation.
+
+    Arguments:
+        grid_shape -- Shape of grid (height, width).
+        out_shape -- Shape of output noise image (height, width).
+
+    Keyword Arguments:
+        batch_size -- (default: {1})
+        generator -- random generator used for grid vectors (default: {None})
+
+    Raises:
+        NoiseError: if grid and out shapes do not match
+
+    Returns:
+        Noise image shaped (batch_size, height, width)
+    """
+    # grid height and width
+    gh, gw = grid_shape
+    # output height and width
+    oh, ow = out_shape
+    # block height and width
+    bh, bw = oh // gh, ow // gw
+
+    if oh != bh * gh:
+        msg = f"Output height {oh} must be divisible by grid height {gh}"
+        raise NoiseError(msg)
+    if ow != bw * gw != 0:
+        msg = f"Output width {ow} must be divisible by grid width {gw}"
+        raise NoiseError(msg)
+
+    angle = torch.empty(
+        [batch_size] + [s + 1 for s in grid_shape],
+        *args,
+        **kwargs,
+    ).uniform_(to=2.0 * math.pi, generator=generator)
+    # random vectors on grid points
+    vectors = unfold_grid(torch.stack((torch.cos(angle), torch.sin(angle)), dim=1))
+    # positions inside grid cells [0, 1)
+    positions = tensor_to(get_positions((bh, bw)), vectors)
+    return perlin_noise_tensor(vectors, positions).squeeze(0)
+
+
+def rand_perlin_like(x, *, generator=None):
+    noise = (
+        torch.rand(
+            x.shape,
+            dtype=x.dtype,
+            device=x.device,
+            layout=x.layout,
+            generator=generator,
+        )
+        / 2.0
+    )
+    noise_height = noise.size(dim=2)
+    noise_width = noise.size(dim=3)
+    for _ in range(2):
+        noise += tensor_to(
+            perlin_noise(
+                (noise_height, noise_width),
+                (noise_height, noise_width),
+                batch_size=x.shape[1],  # This should be the number of channels.
+            ),
+            x.device,
+        )
+    return scale_noise(noise)
+
+
+def uniform_noise_like(x, *, generator=None):
+    return (
+        torch.rand(
+            x.shape,
+            dtype=x.dtype,
+            device=x.device,
+            layout=x.layout,
+            generator=generator,
+        ).sub_(0.5)
+    ).mul_(3.46)
+
+
+def highres_pyramid_noise_like(
+    x,
+    *,
+    discount=0.7,
+    upscale_mode="bilinear",
+    iterations=4,
+    generator=None,
+):
+    (
+        b,
+        c,
+        h,
+        w,
+    ) = x.shape  # EDIT: w and h get over-written, rename for a different variant!
+    orig_w, orig_h = w, h
+    noise = uniform_noise_like(x, generator=generator)
+    rs = torch.rand(iterations, dtype=torch.float32, generator=generator).cpu() * 2 + 2
+    for i in range(iterations):
+        r = rs[i].item()
+        h, w = min(orig_h * 15, int(h * (r**i))), min(orig_w * 15, int(w * (r**i)))
+        noise += scale_samples(
+            tensor_to(torch.randn(b, c, h, w, generator=generator), x),
+            orig_w,
+            orig_h,
+            mode=upscale_mode,
+        ).mul_(discount**i)
+        if h >= orig_h * 15 or w >= orig_w * 15:
+            break  # Lowest resolution is 1x1
+    return scale_noise(noise)
+
+
+def pyramid_old_noise_like(
+    x,
+    *,
+    generator=None,
+    device="cpu",
+    discount=0.8,
+    iterations=5,
+    upscale_mode="nearest-exact",
+):
+    size = x.shape
+    b, c, h, w = size
+    orig_h, orig_w = h, w
+    noise = torch.zeros(size=size, dtype=x.dtype, layout=x.layout, device=device)
+    r = 1
+    for i in range(iterations):
+        r *= 2
+        noise += scale_samples(
+            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,
+            ),
+            orig_w,
+            orig_h,
+            mode=upscale_mode,
+        ).mul_(discount**i)
+    return tensor_to(noise, x.device)
+
+
+# Copied from https://wandb.ai/johnowhitaker/multires_noise/reports/Multi-Resolution-Noise-for-Diffusion-Model-Training--VmlldzozNjYyOTU2
+def pyramid_noise_like(
+    x,
+    *,
+    discount=0.7,
+    upscale_mode="bilinear",
+    iterations=10,
+    generator=None,
+):
+    b, c, w, h = (
+        x.shape
+    )  # NOTE: w and h get over-written, rename for a different variant!
+    orig_w, orig_h = w, h
+    noise = torch.randn_like(x)
+    for i in range(iterations):
+        r = (
+            torch.rand(1, generator=generator).cpu().item() * 2 + 2
+        )  # Rather than always going 2x,
+        w, h = max(1, int(w / (r**i))), max(1, int(h / (r**i)))
+        noise += scale_samples(
+            tensor_to(torch.randn(b, c, w, h), x),
+            orig_h,
+            orig_w,
+            mode=upscale_mode,
+        ).mul_(
+            discount**i,
+        )
+        if w == 1 or h == 1:
+            break  # Lowest resolution is 1x1
+    return scale_noise(noise)
+
+
+def studentt_noise_like(x):
+    noise = StudentT(loc=0, scale=0.2, df=1).rsample(x.shape)
+    s: FloatTensor = torch.quantile(noise.flatten(start_dim=1).abs(), 0.75, dim=-1)
+    s = s.reshape(*s.shape, 1, 1, 1)
+    noise = noise.clamp(-s, s)
+    return torch.copysign(torch.pow(torch.abs(noise), 0.5), noise)
+
+
+def green_noise_like(x, *, generator=None):  # noqa: ARG001
+    # The comments said this didn't work and I had to learn the hard way. Turns out it's true!
+    height, width = x.shape[-2:]
+    noise = torch.randn_like(x)
+    scale = 1.0 / (width * height)
+    fy = torch.fft.fftfreq(height, device=x.device)[:, None] ** 2
+    fx = torch.fft.fftfreq(width, device=x.device) ** 2
+    f = fy + fx
+    power = torch.sqrt(f)
+    power[0, 0] = 1
+    noise = torch.fft.ifft2(torch.fft.fft2(noise) / torch.sqrt(power))
+    noise *= scale / noise.std()
+    noise = tensor_to(torch.real(noise), x.device)
+    return scale_noise(noise)
+
+
+# Completely wrong implementation here.
+def generate_1f_noise_old(tensor, alpha, k, generator=None):
+    freq = 1.0
+    spectral_density = k / freq**alpha
+    return torch.randn(tensor.shape, generator=generator) * spectral_density
+
+
+def pink_noise_old_like(x, *, generator=None):
+    return tensor_to(
+        scale_noise(generate_1f_noise_old(x, 2.0, 1.0, generator=generator)),
+        x.device,
+    )
+
+
+# Referenced from: https://github.com/WASasquatch/PowerNoiseSuite
+def generate_1f_noise(
+    tensor,
+    *,
+    alpha=-2.0,
+    k=1.0,
+    hfac=1.0,
+    wfac=1.0,
+    base_power=1.0,
+    use_sqrt=True,
+    generator=None,
+):
+    batch, _channels, height, width = tensor.shape
+    noise = torch.randn(tensor.shape, generator=generator)
+
+    freq_x = torch.fft.fftfreq(height, hfac)
+    freq_y = torch.fft.fftfreq(width, wfac)
+    fx, fy = torch.meshgrid(freq_x, freq_y, indexing="ij")
+
+    power = (fx**2 + fy**2) ** (-alpha / 2.0)
+    if k != 0:
+        power = k / power
+    power[0, 0] = base_power
+    power = power.unsqueeze(0).expand(batch, 1, height, width)
+
+    noise_fft = torch.fft.fftn(noise)
+    noise_fft /= (
+        torch.sqrt(power.to(noise_fft.dtype)) if use_sqrt else power.to(noise_fft.dtype)
+    )
+
+    return torch.fft.ifftn(noise_fft).real
+
+
+def onef_noise_like(x, *, generator=None, **kwargs):
+    return tensor_to(
+        scale_noise(generate_1f_noise(x, generator=generator, **kwargs)),
+        x.device,
+    )
+
+
+# Referenced from: https://github.com/WASasquatch/PowerNoiseSuite
+def generate_powerlaw_noise(
+    tensor: torch.Tensor,
+    *,
+    alpha=1.0,
+    div_max_dims=None,
+    use_sign=False,
+    use_div_max_abs=True,
+    generator=None,
+) -> torch.Tensor:
+    noise = torch.randn(tensor.shape, generator=generator)
+    modulation = torch.abs(noise) ** alpha
+    noise = (torch.sign(noise) if use_sign else noise).mul_(modulation)
+    if div_max_dims is not None:
+        noise /= torch.amax(
+            torch.abs(noise) if use_div_max_abs else noise,
+            keepdim=True,
+            dim=div_max_dims,
+        )
+    return noise
+
+
+def powerlaw_noise_like(x, *, generator=None, **kwargs):
+    return tensor_to(
+        scale_noise(generate_powerlaw_noise(x, generator=generator, **kwargs)),
+        x.device,
+    )
+
+
+def laplacian_noise_like(x):
+    noise = torch.randn_like(x).div_(4.0)
+    noise += tensor_to(Laplace(loc=0, scale=1.0).rsample(x.shape), noise.device)
+    return scale_noise(noise)
+
+
+def power_noise_old_like(tensor, alpha=2, k=1):  # This doesn't work properly right now
+    """Generate 1/f noise for a given tensor.
+
+    Args:
+        tensor: The tensor to add noise to.
+        alpha: The parameter that determines the slope of the spectrum.
+        k: A constant.
+
+    Returns:
+        A tensor with the same shape as `tensor` containing 1/f noise.
+    """
+    tensor = torch.randn_like(tensor)
+    fft = torch.fft.fft2(tensor)
+    freq = torch.arange(1, len(fft) + 1, dtype=torch.float).reshape(
+        (len(fft),) + (1,) * (tensor.dim() - 1),
+    )
+    spectral_density = k / freq**alpha
+    noise = tensor_to(torch.rand(tensor.shape).mul_(spectral_density), tensor.device)
+    mean = torch.mean(noise, dim=(-2, -1), keepdim=True)
+    std = torch.std(noise, dim=(-2, -1), keepdim=True)
+    return noise.sub_(mean).div_(std)
+
+
+__all__ = (
+    "NoiseError",
+    "NoiseType",
+    "green_noise_like",
+    "highres_pyramid_noise_like",
+    "laplacian_noise_like",
+    "onef_noise_like",
+    "pink_noise_old_like",
+    "power_noise_old_like",
+    "powerlaw_noise_like",
+    "pyramid_noise_like",
+    "pyramid_old_noise_like",
+    "rand_perlin_like",
+    "scale_noise",
+    "studentt_noise_like",
+    "uniform_noise_like",
+)
@@ -0,0 +1,945 @@
+# Initial implementation by https://github.com/elias-gaeros/
+# He also provided a lot of help with refactoring and other improvements. Thanks!
+# (But if anything is broken in here, I'm almost certainly the one to blame.)
+
+from __future__ import annotations
+
+import math
+import os
+import random
+
+import comfy
+import folder_paths
+import latent_preview
+import torch
+from comfy.k_diffusion.sampling import BrownianTreeNoiseSampler
+from PIL import Image
+from torch import Tensor
+
+from .nodes import (
+    WILDCARD_NOISE,
+    SonarCustomNoiseNodeBase,
+    SonarNormalizeNoiseNodeMixin,
+)
+from .noise import CustomNoiseItemBase
+from .noise_generation import scale_noise
+
+# ruff: noqa: ANN003, FBT001, FBT002
+
+PREVIEW_FORMAT = comfy.latent_formats.SD15()
+
+
+def make_preview_result(img, result, prefix="sonar_temp"):
+    output_dir = folder_paths.get_temp_directory()
+    prefix_append = f"{prefix}_" + "".join(
+        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)
+    )
+    filename = f"{filename}_{counter:05}_.png"
+    file_path = os.path.join(full_output_folder, filename)  # noqa: PTH118
+    img.save(file_path, compress_level=1)
+
+    return {
+        "ui": {
+            "images": [
+                {"filename": filename, "subfolder": subfolder, "type": "temp"},
+            ],
+        },
+        "result": result,
+    }
+
+
+class ChannelMixer:
+    def __init__(self, channel_count, common_mode, channel_correlation):
+        self.channel_count = channel_count
+        self.common_mode = common_mode
+        self.channel_correlation = channel_correlation
+        self.mixer = self.build() if common_mode is not None else None
+
+    def build(self):
+        c = self.channel_count
+        common_mode = self.common_mode
+        correlation_count = c * (c - 1) // 2
+        channel_correlation = self.channel_correlation[:correlation_count]
+        channel_correlation = torch.cat(
+            (
+                channel_correlation * common_mode,
+                torch.full(
+                    (correlation_count - channel_correlation.numel(),),
+                    common_mode,
+                ),
+            ),
+        )
+        channel_mixer = torch.eye(c).index_put_(
+            tuple(torch.tril_indices(c, c, offset=-1)),
+            channel_correlation,
+        )
+        channel_mixer += channel_mixer.tril(-1).mT
+        channel_mixer = torch.linalg.ldl_factor(channel_mixer).LD
+        dc = torch.diagonal_copy(channel_mixer)
+        torch.diagonal(channel_mixer)[:] = 1.0
+        channel_mixer *= dc.clamp_min(0).sqrt().unsqueeze(0)
+        channel_mixer /= channel_mixer.norm(dim=1, keepdim=True)
+        return channel_mixer
+
+    def to(self, *args: list, **kwargs: dict):
+        if self.mixer is not None:
+            self.mixer = self.mixer.to(*args, **kwargs)
+        return self
+
+    def apply(self, noise, shape, copy=False):
+        if self.mixer is None:
+            return noise if not copy else noise.clone()
+        b, c, h, w = shape
+        if c != self.channel_count:
+            raise ValueError("Channel count mismatch")
+        noise = self.mixer @ noise.swapaxes(0, 1).reshape(c, -1)
+        return noise.reshape(c, b, h, w).swapaxes(1, 0)
+
+    def __call__(self, *args: list, **kwargs: dict):
+        return self.apply(*args, **kwargs)
+
+
+class PowerFilter:
+    def __init__(
+        self,
+        *,
+        min_freq=0.0,
+        max_freq=0.7071,
+        stretch=1.0,
+        rotate=0.0,
+        pnorm=2.0,
+        alpha=0.0,
+        scale=1.0,
+        rel_bw=0.125,
+        oversample=4,
+        compose_with: PowerFilter | None = None,
+        compose_mode="max",
+    ):
+        self.min_freq = min_freq
+        self.max_freq = max(max_freq, min_freq)
+        self.stretch = stretch
+        self.rotate = rotate
+        self.pnorm = pnorm
+        self.alpha = alpha
+        self.scale = scale
+        self.rel_bw = rel_bw
+        self.oversample = oversample
+        self.compose_with = compose_with
+        self.compose_mode = compose_mode
+
+    def clone(self):
+        fargs = {
+            k: getattr(self, k)
+            for k in (
+                "min_freq",
+                "max_freq",
+                "stretch",
+                "rotate",
+                "pnorm",
+                "alpha",
+                "scale",
+                "rel_bw",
+                "oversample",
+                "compose_mode",
+            )
+        }
+        fargs["compose_with"] = (
+            self.compose_with.clone() if self.compose_with is not None else None
+        )
+        return self.__class__(**fargs)
+
+    @classmethod
+    def compose(cls, a, b, compose_mode="max"):
+        if a.shape != b.shape:
+            raise ValueError("Filter compose size mismatch!")
+        cf = {
+            "max": torch.max,
+            "min": torch.min,
+            "add": torch.add,
+            "sub": torch.sub,
+            "mul": torch.mul,
+        }.get(compose_mode, torch.max)
+        return cf(a, b).clamp_(min=0.0)
+
+    @classmethod
+    def normalize(cls, op, shape, mix=1.0, normalization_factor=1.0):
+        height, width = shape[-2:]
+        hfreq_bins = width // 2 + 1
+
+        # Flat unit gain frequency response
+        if mix < 1.0:
+            flat = torch.ones(1, 1, height, hfreq_bins)
+            if mix <= 0.0:
+                return flat
+        if normalization_factor != 0:
+            op *= torch.lerp(
+                torch.scalar_tensor(1.0),
+                1.0 / op.square().mean().sqrt(),
+                normalization_factor,
+            )
+        if mix < 1.0:
+            op = torch.lerp(flat, op, mix, out=op)
+        return op
+
+    def build(self, shape, override_oversample=None, composed=True):
+        """Construct a band-pass * 1/f^alpha filter in rfft space."""
+        oversample = (
+            override_oversample if override_oversample is not None else self.oversample
+        )
+        rel_bw = self.rel_bw
+        height, width = shape[-2:]
+        hfreq_bins = width // 2 + 1
+
+        # Start with an over-sampled fftshift(rfft2freq()) grid. uses complex
+        # numbers for convenient 2d rotation (unrelated to the fft complex phase
+        # space)
+        fc = torch.complex(
+            # real-fftfreq
+            torch.linspace(0, 0.5, oversample * hfreq_bins),
+            # normal fftfreq
+            torch.linspace(
+                -(height // 2) / height,
+                ((height - 1) // 2) / height,
+                oversample * height,
+            ).unsqueeze(1),
+        )
+        # Rotate, stretch and p-norm
+        if abs(self.rotate) >= 1e-3:
+            fc *= torch.exp(1.0j * torch.deg2rad(torch.scalar_tensor(self.rotate)))
+        if self.stretch > 1.0:
+            fc.real *= self.stretch
+        else:
+            fc.imag *= 1.0 / self.stretch
+        if abs(self.pnorm - 2.0) < 1e-3:
+            d = fc.abs()
+        else:
+            d = (
+                torch.view_as_real(fc)
+                .abs()
+                .pow(self.pnorm)
+                .sum(-1)
+                .pow(1.0 / self.pnorm)
+            )
+
+        # filter gain function
+        op = torch.empty_like(d)
+        m_highpass = d >= self.min_freq
+        m_lowpass = d < self.max_freq
+        m_band = m_highpass & m_lowpass
+        # 1 / f^alpha for the band-pass region
+        op[m_band] = d[m_band].pow(-self.alpha)
+        # easing gaussian (TODO: try cosine windows)
+        m_lowpass = ~m_lowpass
+        op[m_lowpass] = math.pow(self.max_freq, -self.alpha) * torch.exp(
+            -(d[m_lowpass] - self.max_freq).square() / (rel_bw * self.max_freq) ** 2,
+        )
+        if self.min_freq > 0.0:
+            m_highpass = ~m_highpass
+            op[m_highpass] = math.pow(self.min_freq, -self.alpha) * torch.exp(
+                -(d[m_highpass] - self.min_freq).square()
+                / (rel_bw * self.min_freq) ** 2,
+            )
+        op = torch.nn.functional.interpolate(
+            op[None, None, ...],
+            (height, hfreq_bins),
+            mode="bilinear",
+            align_corners=True,
+        )
+        op = op.roll(-(height // 2), -2)  # ifftshift
+        if self.alpha > 0:
+            # In general, the mean offset should be kept as is, sampled from
+            # N(0, 1 / sqrt(H*W) ). However, gain goes to inf when alpha>0.
+            op[..., 0, 0] = 0
+        if self.scale != 1.0:
+            op *= self.scale
+        if composed and self.compose_with is not None:
+            return self.compose(
+                op,
+                self.compose_with.build(shape, override_oversample=override_oversample),
+                self.compose_mode,
+            )
+        return op
+
+    def preview(
+        self,
+        size=(128, 128),
+        mix=1.0,
+        normalization_factor=1.0,
+        raw=False,
+        kernel_gain=1 / 3,
+        filter_gain=1 / 3,
+    ):
+        shape = (1, 4, *size)
+        filter_rfft = self.__class__.normalize(
+            self.build(size),
+            shape,
+            mix=mix,
+            normalization_factor=normalization_factor,
+        )
+        filter_fft = rfft2_to_fft2(filter_rfft)
+        kernel = torch.fft.irfft2(filter_rfft, s=size, norm="ortho")
+        kernel = kernel.roll((size[0] // 2, size[1] // 2), (-2, -1))
+        img = (
+            filter_fft.mul_(filter_gain).tanh_().mul_(256.0),
+            kernel.mul_(kernel_gain).tanh_().add_(1.0).mul_(128.0),
+        )
+        if raw:
+            return img
+        img = torch.cat(img, dim=-1).clamp(0, 255).to(torch.uint8)
+        return Image.fromarray(img[0, 0].numpy())
+
+
+class PowerNoiseItem(CustomNoiseItemBase):
+    def __init__(self, factor, *, channel_correlation, power_filter=None, **kwargs):
+        if isinstance(channel_correlation, str):
+            channel_correlation = torch.tensor(
+                tuple(
+                    float(val)
+                    for val in (val.strip() for val in channel_correlation.split(","))
+                    if val
+                ),
+                device="cpu",
+                dtype=torch.float,
+            )
+        if power_filter is None:
+            fargs = {
+                k: kwargs.pop(k)
+                for k in ("min_freq", "max_freq", "stretch", "rotate", "pnorm", "alpha")
+                if k in kwargs
+            }
+            power_filter = PowerFilter(**fargs)
+        super().__init__(
+            factor,
+            power_filter=power_filter,
+            channel_correlation=channel_correlation,
+            **kwargs,
+        )
+
+    def make_filter(self, shape, oversample=None):
+        return PowerFilter.normalize(
+            self.power_filter.build(shape, override_oversample=oversample),
+            shape,
+            mix=self.mix,
+            normalization_factor=getattr(self, "filter_norm_factor", 1.0),
+        )
+
+    def make_noise_sampler_internal(
+        self,
+        x: Tensor,
+        noise_sampler,
+        filter_rfft,
+        normalized=True,
+    ):
+        shape = x.shape
+        device = x.device
+        time_brownian = self.time_brownian
+
+        channel_mixer = ChannelMixer(
+            shape[1],
+            self.common_mode,
+            self.channel_correlation,
+        ).to(device, non_blocking=True)
+
+        def sampler(sigma, sigma_next):
+            noise = noise_sampler(sigma, sigma_next).to(device)
+            noise_rfft = (
+                torch.fft.rfft2(noise, norm="ortho") if time_brownian else noise
+            )
+            noise = torch.fft.irfft2(
+                noise_rfft.mul_(filter_rfft),
+                s=shape[-2:],
+                norm="ortho",
+            )
+            noise = channel_mixer(noise, shape)
+            return scale_noise(noise, self.factor, normalized=normalized)
+
+        return sampler
+
+    def make_noise_sampler(
+        self,
+        x: Tensor,
+        sigma_min: float | None,
+        sigma_max: float | None,
+        seed: int | None,
+        cpu: bool = True,
+        normalized=True,
+    ):
+        shape, device = x.shape, x.device
+        filter_rfft = self.make_filter(shape).to(device, non_blocking=True)
+        if self.time_brownian:
+            if sigma_min is None:
+                raise ValueError(
+                    "time correlated brownian mode is valid only for stochastic samplers",
+                )
+            noise_sampler = BrownianTreeNoiseSampler(
+                x,
+                sigma_min,
+                sigma_max,
+                seed=seed,
+                cpu=cpu,
+            )
+        else:
+
+            def noise_sampler(_s, _sn):
+                return torch.randn(
+                    (*shape[:-1], filter_rfft.shape[-1]),
+                    dtype=torch.complex64,
+                    device=device,
+                )
+
+        return self.make_noise_sampler_internal(
+            x,
+            noise_sampler,
+            filter_rfft,
+            normalized=normalized,
+        )
+
+    def preview(
+        self,
+        size=(128, 128),
+        noise=None,
+        kernel_gain=1 / 3,
+        filter_gain=1 / 3,
+    ):
+        filter_rfft = self.make_filter(size, oversample=1)
+        if noise is None:
+            noise = torch.fft.irfft2(
+                filter_rfft
+                * torch.randn(
+                    filter_rfft.shape,
+                    dtype=torch.complex64,
+                    generator=torch.Generator().manual_seed(0),
+                ),
+                s=size,
+                norm="ortho",
+            )
+        else:
+            noise_rfft = torch.fft.rfft2(noise, norm="ortho")
+            noise = torch.fft.irfft2(
+                noise_rfft.mul_(filter_rfft),
+                s=noise.shape[-2:],
+                norm="ortho",
+            )
+        filter_preview = self.power_filter.preview(
+            size=size,
+            normalization_factor=getattr(self, "filter_norm_factor", 1.0),
+            filter_gain=filter_gain,
+            kernel_gain=kernel_gain,
+            raw=True,
+        )
+        img = (
+            torch.cat(
+                (
+                    *filter_preview,
+                    noise.mul_(1 / 3).tanh_().add_(1.0).mul_(128.0),
+                ),
+                dim=-1,
+            )
+            .clamp(0, 255)
+            .to(torch.uint8)
+        )
+        return Image.fromarray(img[0, 0].numpy())
+
+
+def rfft2_to_fft2(x):
+    """Apply hermitian-summetry to reconstruct the second half of a fft.
+
+    Only for previews.
+    """
+    height, width = x.shape[-2:]
+    x_r = x.roll(height // 2, -2)  # torch.fft.fftshift(x, -2)
+    x_l = x_r[..., 1 : -1 if width & 1 else None]
+    x_l = torch.flip(x_l.conj(), dims=(-2, -1))
+    if height & 1 == 0:
+        x_l = x_l.roll(1, -2)
+    return torch.cat((x_l, x_r), dim=-1)
+
+
+class PowerFilterNoiseItem(PowerNoiseItem):
+    def __init__(self, factor, *, noise, normalize_noise, normalize_result, **kwargs):
+        super().__init__(
+            factor,
+            noise=noise.clone(),
+            normalize_noise=normalize_noise,
+            normalize_result=normalize_result,
+            **kwargs,
+        )
+
+    def clone_key(self, k):
+        if k == "noise":
+            return self.noise.clone()
+        return super().clone_key(k)
+
+    def make_noise_sampler(
+        self,
+        x: Tensor,
+        sigma_min: float | None,
+        sigma_max: float | None,
+        seed: int | None,
+        cpu: bool = True,
+        normalized=True,
+    ):
+        shape, device = x.shape, x.device
+        normalize_noise = self.get_normalize("normalize_noise", False)  # noqa: FBT003
+        normalize_result = self.get_normalize("normalize_result", normalized)
+        filter_rfft = self.make_filter(shape).to(device, non_blocking=True)
+        noise_sampler = self.noise.make_noise_sampler(
+            x,
+            sigma_min,
+            sigma_max,
+            seed,
+            cpu,
+            normalized=normalize_noise,
+        )
+
+        return self.make_noise_sampler_internal(
+            x,
+            noise_sampler,
+            filter_rfft,
+            normalized=normalize_result,
+        )
+
+    def preview(self, size=(128, 128)):
+        if getattr(self, "preview_type", None) != "custom":
+            return super().preview(size=size)
+        torch.manual_seed(0)
+        x = torch.randn((1, 4, *size), dtype=torch.float, device="cpu")
+        ns = self.noise.make_noise_sampler(
+            x,
+            torch.scalar_tensor(0.0),
+            torch.scalar_tensor(14.0),
+            0,
+            True,  # noqa: FBT003
+            normalized=self.normalize_noise is True,
+        )
+        filtered_ns = self.make_noise_sampler_internal(
+            x,
+            ns,
+            self.make_filter(x.shape),
+            self.normalize_result in {True, None},
+        )
+        filtered_noise = filtered_ns(
+            torch.scalar_tensor(14.0),
+            torch.scalar_tensor(10.0),
+        )
+        previewer = latent_preview.get_previewer(None, PREVIEW_FORMAT)
+        default_preview = super().preview(size=size).convert("RGB")
+        preview = previewer.decode_latent_to_preview(filtered_noise.cpu())
+        default_preview.paste(
+            preview.resize((size[-1], size[-2])),
+            box=(size[-1] * 2, 0),
+        )
+        return default_preview
+
+
+class SonarPowerNoiseNode(SonarCustomNoiseNodeBase):
+    DESCRIPTION = "Custom noise type that applies a filter to generated noise."
+
+    @classmethod
+    def INPUT_TYPES(cls, *args: list, **kwargs: dict):
+        result = super().INPUT_TYPES(*args, **kwargs)
+        result["required"] |= {
+            "time_brownian": (
+                "BOOLEAN",
+                {
+                    "default": False,
+                    "tooltip": "Controls whether brownian noise is used when mix isn't 1.0.",
+                },
+            ),
+            "alpha": (
+                "FLOAT",
+                {
+                    "default": 0.0,
+                    "min": -5.0,
+                    "max": 5.0,
+                    "step": 0.001,
+                    "round": False,
+                    "tooltip": "Values above 0 will amplify low frequencies, negative values will amplify high frequencies.",
+                },
+            ),
+            "max_freq": (
+                "FLOAT",
+                {
+                    "default": 0.7071,
+                    "min": 0.0,
+                    "max": 0.7071,
+                    "step": 0.001,
+                    "round": False,
+                    "tooltip": "Maximum frequency to pass through the filter.",
+                },
+            ),
+            "min_freq": (
+                "FLOAT",
+                {
+                    "default": 0.0,
+                    "min": 0.0,
+                    "max": 0.7071,
+                    "step": 0.001,
+                    "round": False,
+                    "tooltip": "Minimum frequency to pass through the filter.",
+                },
+            ),
+            "stretch": (
+                "FLOAT",
+                {
+                    "default": 1.0,
+                    "min": 0.01,
+                    "max": 100,
+                    "step": 0.1,
+                    "round": False,
+                    "tooltip": "Stretches the filter's shape by the specified factor.",
+                },
+            ),
+            "rotate": (
+                "FLOAT",
+                {
+                    "default": 0,
+                    "min": -90,
+                    "max": 90,
+                    "step": 5,
+                    "round": False,
+                    "tooltip": "Rotates the filter.",
+                },
+            ),
+            "pnorm": (
+                "FLOAT",
+                {
+                    "default": 2,
+                    "min": 0.125,
+                    "max": 100,
+                    "step": 0.1,
+                    "round": False,
+                    "tooltip": "Factor used for cushioning the band-pass region.",
+                },
+            ),
+            "mix": (
+                "FLOAT",
+                {
+                    "default": 1.0,
+                    "min": 0.0,
+                    "max": 1.0,
+                    "step": 0.001,
+                    "round": False,
+                    "tooltip": "Controls the ratio of filtered noise. For example, 0.75 means 75% noise with the filter effects applied, 25% raw noise.",
+                },
+            ),
+            "common_mode": (
+                "FLOAT",
+                {
+                    "default": 0.0,
+                    "min": -100.0,
+                    "max": 100.0,
+                    "step": 0.001,
+                    "round": False,
+                    "tooltip": "Attempts to desaturate thelatent by injecting the average across channels (controlled by channel_correction). Applied after mix.",
+                },
+            ),
+            "channel_correlation": (
+                "STRING",
+                {
+                    "default": "1, 1, 1, 1, 1, 1",
+                    "multiline": False,
+                    "dynamicPrompts": False,
+                    "tooltip": "Comma-separated list of channel correlation strengths.",
+                },
+            ),
+            "preview": (
+                ("none", "no_mix", "mix"),
+                {
+                    "tooltip": "When enabled, displays a preview of the filter shape and a sample of noise. Mix - previews noise after mix is applied. no_mix - only previews the filtered noise.",
+                },
+            ),
+        }
+        return result
+
+    @classmethod
+    def get_item_class(cls):
+        return PowerNoiseItem
+
+    def go(
+        self,
+        preview="none",
+        **kwargs,
+    ):
+        result = super().go(**kwargs)
+        if preview == "none":
+            return result
+        if preview == "no_mix":
+            kwargs["mix"] = 1.0
+        img = self.get_item_class()(preview_type=preview, **kwargs).preview()
+        return make_preview_result(img, result)
+
+
+class SonarPowerFilterNoiseNode(SonarPowerNoiseNode, SonarNormalizeNoiseNodeMixin):
+    DESCRIPTION = "Custom noise type that allows applying a Power Filter to another custom noise generator."
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        result = super().INPUT_TYPES(include_rescale=False, include_chain=False)
+        for k in (
+            "min_freq",
+            "max_freq",
+            "stretch",
+            "rotate",
+            "pnorm",
+            "alpha",
+            "time_brownian",
+        ):
+            del result["required"][k]
+        result["required"] |= {
+            "sonar_custom_noise": (
+                WILDCARD_NOISE,
+                {
+                    "tooltip": "Custom noise type to filter.",
+                },
+            ),
+            "sonar_power_filter": (
+                "SONAR_POWER_FILTER",
+                {
+                    "tooltip": "Filter to use.",
+                },
+            ),
+            "filter_norm_factor": (
+                "FLOAT",
+                {
+                    "default": 1.0,
+                    "min": 0.0,
+                    "max": 1.0,
+                    "step": 0.1,
+                    "round": False,
+                    "tooltip": "Normalization factor applied to the specified filter. 1.0 means 100% normalized.",
+                },
+            ),
+            "normalize_result": (
+                ("default", "forced", "disabled"),
+                {
+                    "tooltip": "Controls whether the final result is normalized to 1.0 strength.",
+                },
+            ),
+            "normalize_noise": (
+                ("default", "forced", "disabled"),
+                {
+                    "tooltip": "Controls whether the generated noise is normalized to 1.0 strength.",
+                },
+            ),
+        }
+        result["required"]["preview"] = (
+            (*result["required"]["preview"][0], "custom"),
+            {
+                "tooltip": "When enabled, displays a preview of the filter shape and a sample of noise. Mix - previews noise after mix is applied. no_mix - only previews the filtered noise. custom - Like no_mix, but will use a latent previewer to display a color preview of the generated noise. Works best when previewer is set to TAESD.",
+            },
+        )
+        return result
+
+    @classmethod
+    def get_item_class(cls):
+        return PowerFilterNoiseItem
+
+    def go(
+        self,
+        factor,
+        sonar_custom_noise,
+        sonar_power_filter,
+        filter_norm_factor,
+        normalize_noise,
+        normalize_result,
+        preview="none",
+        **kwargs: dict,
+    ):
+        return super().go(
+            factor=factor,
+            noise=sonar_custom_noise,
+            normalize_noise=self.get_normalize(normalize_noise),
+            normalize_result=self.get_normalize(normalize_result),
+            preview=preview,
+            time_brownian=True,
+            power_filter=sonar_power_filter,
+            filter_norm_factor=filter_norm_factor,
+            **kwargs,
+        )
+
+
+class SonarPowerFilterNode:
+    RETURN_TYPES = ("SONAR_POWER_FILTER",)
+    CATEGORY = "advanced/noise"
+    FUNCTION = "go"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        include_keys = {"alpha", "max_freq", "min_freq", "stretch", "rotate", "pnorm"}
+        return {
+            "required": {
+                k: v
+                for k, v in SonarPowerNoiseNode.INPUT_TYPES()["required"].items()
+                if k in include_keys
+            }
+            | {
+                "oversample": (
+                    "INT",
+                    {
+                        "default": 4,
+                        "min": 1,
+                        "max": 128,
+                        "tooltip": "Oversampling factor used for the filter size.",
+                    },
+                ),
+                "blur": (
+                    "FLOAT",
+                    {
+                        "default": 0.125,
+                        "min": -10.0,
+                        "max": 10.0,
+                        "step": 0.01,
+                        "round": False,
+                        "tooltip": "Slightly blurs the filter to reduce artifacts.",
+                    },
+                ),
+                "scale": (
+                    "FLOAT",
+                    {
+                        "default": 1,
+                        "min": -100.0,
+                        "max": 100.0,
+                        "step": 0.1,
+                        "round": False,
+                        "tooltip": "Scales the filter to the specified strength. May be negative.",
+                    },
+                ),
+                "compose_mode": (
+                    ("max", "min", "add", "sub", "mul"),
+                    {
+                        "tooltip": "Controls composition of the option attached filter. For example, when set to MUL the result will be this filter multiplied by the attached filter. No effect if the optional filter input is not attached.",
+                    },
+                ),
+            },
+            "optional": {
+                "power_filter_opt": ("SONAR_POWER_FILTER",),
+            },
+        }
+
+    @classmethod
+    def go(
+        cls,
+        min_freq=0.0,
+        max_freq=0.7071,
+        stretch=1.0,
+        rotate=0.0,
+        pnorm=2.0,
+        alpha=0.0,
+        blur=0.125,
+        oversample=4,
+        scale=1.0,
+        compose_mode="max",
+        power_filter_opt=None,
+    ):
+        return (
+            PowerFilter(
+                min_freq=min_freq,
+                max_freq=max_freq,
+                stretch=stretch,
+                rotate=rotate,
+                pnorm=pnorm,
+                alpha=alpha,
+                scale=scale,
+                rel_bw=blur,
+                oversample=oversample,
+                compose_mode=compose_mode,
+                compose_with=power_filter_opt,
+            ),
+        )
+
+
+class SonarPreviewFilterNode:
+    DESCRIPTION = "Allows previewing a Power Filter."
+    RETURN_TYPES = ("SONAR_POWER_FILTER",)
+    CATEGORY = "advanced/noise"
+    FUNCTION = "go"
+    OUTPUT_NODE = True
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "sonar_power_filter": (
+                    "SONAR_POWER_FILTER",
+                    {
+                        "tooltip": "Power Filter to preview.",
+                    },
+                ),
+                "filter_gain": (
+                    "FLOAT",
+                    {
+                        "default": 1 / 3,
+                        "min": 0.0,
+                        "max": 1000000.0,
+                        "step": 0.1,
+                        "round": False,
+                        "tooltip": "Gain factor applied to the filter part of the preview.",
+                    },
+                ),
+                "kernel_gain": (
+                    "FLOAT",
+                    {
+                        "default": 1 / 3,
+                        "min": 0.0,
+                        "max": 1000000.0,
+                        "step": 0.1,
+                        "round": False,
+                        "tooltip": "Gain factor applied to the kernel part of the preview.",
+                    },
+                ),
+                "norm_factor": (
+                    "FLOAT",
+                    {
+                        "default": 1.0,
+                        "min": 0.0,
+                        "max": 1.0,
+                        "step": 0.1,
+                        "round": False,
+                        "tooltip": "Normalization factor applied to the filter before previewing. 1.0 means 100% normalized.",
+                    },
+                ),
+                "preview_size": (
+                    (
+                        "128x128",
+                        "256x256",
+                        "384x256",
+                        "256x384",
+                        "768x512",
+                        "512x768",
+                        "768x768",
+                        "128x127",
+                        "127x128",
+                    ),
+                    {
+                        "tooltip": "Controls the size of the generated preview. Note: Sizes are in latent pixels. For most models, one latent pixel equals eight pixels",
+                    },
+                ),
+            },
+        }
+
+    @classmethod
+    def go(
+        cls,
+        sonar_power_filter,
+        filter_gain=1 / 3,
+        kernel_gain=1 / 3,
+        norm_factor=1.0,
+        preview_size="256x256",
+    ):
+        filt = sonar_power_filter.clone()
+        filt.preview_type = "custom"
+        preview_size = tuple(int(val) for val in preview_size.split("x", 1))
+        return make_preview_result(
+            filt.preview(
+                size=(preview_size[1], preview_size[0]),
+                filter_gain=filter_gain,
+                kernel_gain=kernel_gain,
+                normalization_factor=norm_factor,
+            ),
+            (filt,),
+        )
@@ -2,11 +2,14 @@

 from __future__ import annotations

+import importlib
 from enum import Enum, auto
+from sys import stderr
 from typing import Any, Callable, NamedTuple

 import torch
 from comfy.k_diffusion import sampling
+from comfy.samplers import KSampler, k_diffusion_sampling
 from torch import Tensor
 from tqdm.auto import trange

@@ -44,6 +47,8 @@ class SonarConfig(NamedTuple):


 class SonarBase:
+    DEFAULT_NOISE_TYPE = noise.NoiseType.GAUSSIAN
+
    def __init__(self, cfg: SonarConfig) -> None:
        self.history_d = None
        self.cfg = cfg
@@ -57,13 +62,13 @@ class SonarBase:
        seed: int | None = None,
    ):
        sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
-        if noise_sampler is not None and self.cfg.noise_type not in (
+        if noise_sampler is not None and self.cfg.noise_type not in {
            None,
-            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",
+            self.DEFAULT_NOISE_TYPE,
+        }:
+            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(
@@ -72,14 +77,15 @@ 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,
                seed=seed,
                cpu=True,
+                normalized=True,
            )
        self.noise_sampler = noise_sampler
        return noise_sampler
@@ -100,6 +106,7 @@ class SonarBase:
                None,
                seed=self.extra_args.get("seed"),
                cpu=True,
+                normalized=True,
            )
            self.history_d = ns(None, None)
        else:
@@ -122,7 +129,7 @@ class SonarBase:
        momentum_d = (1.0 - p) * d + p * hd

        # Euler method with momentum
-        x = x + momentum_d * dt
+        x = x + momentum_d * dt  # noqa: PLR6104

        self.update_hist(momentum_d)

@@ -150,41 +157,47 @@ class SonarGuidanceMixin:
        return ((latent - avg_s) / std_s).to(latent.dtype)

    def guidance_step(self, step_index: int, x: Tensor, denoised: Tensor):
-        if (self.guidance is None or self.guidance.factor == 0.0) or not (
-            self.guidance.start_step <= (step_index + 1) <= self.guidance.end_step
-        ):
+        if self.guidance is None or self.guidance.factor == 0.0 or not self.guidance.start_step <= step_index <= self.guidance.end_step:
            return x
        if self.ref_latent.device != x.device:
            self.ref_latent = self.ref_latent.to(device=x.device)
        if self.guidance.guidance_type == GuidanceType.LINEAR:
-            return self.guidance_linear(x)
+            return self.guidance_linear(x, self.ref_latent, self.guidance.factor)
        if self.guidance.guidance_type == GuidanceType.EULER:
-            return self.guidance_euler(step_index, x, denoised)
+            sigma, sigma_next = self.sigmas[step_index], self.sigmas[step_index + 1]
+            return self.guidance_euler(
+                sigma,
+                sigma_next,
+                x,
+                denoised,
+                self.ref_latent,
+                self.guidance.factor,
+            )
        raise ValueError("Sonar: Guidance: Unknown guidance type")

+    @staticmethod
    def guidance_euler(
-        self,
-        step_index: int,
+        sigma: Tensor,
+        sigma_next: Tensor,
        x: Tensor,
        denoised: Tensor,
-    ):
+        ref_latent: Tensor,
+        factor: float = 0.2,
+    ) -> Tensor:
        avg_t = denoised.mean(dim=[1, 2, 3], keepdim=True)
        std_t = denoised.std(dim=[1, 2, 3], keepdim=True)
-        ref_img_shift = self.ref_latent * std_t + avg_t
-        sigma, sigma_next = self.sigmas[step_index], self.sigmas[step_index + 1]
+        ref_img_shift = ref_latent * std_t + avg_t

        d = sampling.to_d(x, sigma, ref_img_shift)
-        dt = (sigma_next - sigma) * self.guidance.factor
+        dt = (sigma_next - sigma) * factor
        return x + d * dt

-    def guidance_linear(
-        self,
-        x: Tensor,
-    ):
+    @staticmethod
+    def guidance_linear(x: Tensor, ref_latent: Tensor, factor: float = 0.2) -> Tensor:
        avg_t = x.mean(dim=[1, 2, 3], keepdim=True)
        std_t = x.std(dim=[1, 2, 3], keepdim=True)
-        ref_img_shift = self.ref_latent * std_t + avg_t
-        return (1.0 - self.guidance.factor) * x + self.guidance.factor * ref_img_shift
+        ref_img_shift = ref_latent * std_t + avg_t
+        return (1.0 - factor) * x + factor * ref_img_shift


 class SonarWithGuidance(SonarBase, SonarGuidanceMixin):
@@ -250,7 +263,7 @@ class SonarEuler(SonarSampler):
                else torch.randn_like(sample)
            )
            eps = noise * self.s_noise
-            sample = sample + eps * (sigma_hat**2 - sigma**2) ** 0.5
+            sample = sample + eps * (sigma_hat**2 - sigma**2) ** 0.5  # noqa: PLR6104

        denoised = self.model(sample, sigma_hat * self.s_in, **self.extra_args)
        derivative = sampling.to_d(sample, sigma, denoised)
@@ -307,7 +320,7 @@ class SonarEuler(SonarSampler):
        )

        for i in trange(len(sigmas) - 1, disable=disable):
-            x, sigma, sigma_hat, denoised = sonar.step(
+            x, _sigma, sigma_hat, denoised = sonar.step(
                i,
                x,
            )
@@ -357,7 +370,7 @@ class SonarEulerAncestral(SonarSampler):
        result_sample = self.momentum_step(sample, derivative, dt)
        if sigma_to > 0:
            result_sample = self.guidance_step(step_index, result_sample, denoised)
-            result_sample = (
+            result_sample = (  # noqa: PLR6104
                result_sample
                + self.noise_sampler(sigma_from, sigma_to) * self.s_noise * sigma_up
            )
@@ -386,14 +399,6 @@ 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,
@@ -412,7 +417,7 @@ class SonarEulerAncestral(SonarSampler):
        )

        for i in trange(len(sigmas) - 1, disable=disable):
-            x, sigma, sigma_hat, denoised = sonar.step(
+            x, _sigma, sigma_hat, denoised = sonar.step(
                i,
                x,
            )
@@ -430,6 +435,8 @@ class SonarEulerAncestral(SonarSampler):


 class SonarDPMPPSDE(SonarSampler):
+    DEFAULT_NOISE_TYPE = noise.NoiseType.BROWNIAN
+
    def __init__(
        self,
        eta: float = 1.0,
@@ -450,7 +457,7 @@ class SonarDPMPPSDE(SonarSampler):
        return sigma.log.neg()

    # DPM++ solver algorithm copied from ComfyUI source.
-    def momentum_step(
+    def momentum_step(  # noqa: PLR0914
        self,
        step_index,
        x: Tensor,
@@ -488,7 +495,7 @@ class SonarDPMPPSDE(SonarSampler):
        self.update_hist(momentum_d)
        hd = self.history_d
        x_2 = (sigma_fn(s_) / sigma_fn(t)) * x - momentum_d
-        x_2 = x_2 + self.noise_sampler(sigma_fn(t), sigma_fn(s)) * self.s_noise * su
+        x_2 += self.noise_sampler(sigma_fn(t), sigma_fn(s)) * self.s_noise * su
        denoised_2 = self.model(x_2, sigma_fn(s) * self.s_in, **self.extra_args)

        # Step 2
@@ -520,7 +527,7 @@ class SonarDPMPPSDE(SonarSampler):
        self.init_hist_d(sample)

        sigma_from, sigma_to = self.sigmas[step_index], self.sigmas[step_index + 1]
-        sigma_down, sigma_up = sampling.get_ancestral_step(
+        sigma_down, _sigma_up = sampling.get_ancestral_step(
            sigma_from,
            sigma_to,
            eta=self.eta,
@@ -560,15 +567,6 @@ 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,
@@ -587,7 +585,7 @@ class SonarDPMPPSDE(SonarSampler):
        )

        for i in trange(len(sigmas) - 1, disable=disable):
-            x, sigma, sigma_hat, denoised = sonar.step(
+            x, _sigma, sigma_hat, denoised = sonar.step(
                i,
                x,
            )
@@ -605,10 +603,6 @@ class SonarDPMPPSDE(SonarSampler):


 def add_samplers():
-    import importlib
-
-    from comfy.samplers import KSampler, k_diffusion_sampling
-
    extra_samplers = {
        "sonar_euler": SonarEuler.sampler,
        "sonar_euler_ancestral": SonarEulerAncestral.sampler,
@@ -8,6 +8,8 @@ ignore = [
    "ANN204",
    "ANN206",
    "C901",
+    "CPY001",
+    "DOC201",
    "D100",
    "D101",
    "D102",
@@ -22,9 +24,11 @@ ignore = [
    "ERA001",
    "F403",
    "F405",
+    "FBT002",
    "PLR0912",
    "PLR0913",
    "PLR0915",
+    "PLR0917",
    "PLR2004",
    "T201",
    "TRY003",
Author	SHA1	Message	Date
blepping	6369627e99	November 2024 mega update, see changelog for details	2024-11-30 00:59:08 -07:00
blepping	4f1934345a	Fix an issue with NoisyLatentLike where it didn't correctly pass a seed to connected custom noise nodes (probably only affected Brownian) Add normalize and cpu_noise parameters to NoisyLatentLike	2024-10-10 16:09:54 -06:00
blepping	d2dc34e9cf	Merge pull request #10 from asagi4/main Don't fail in momentum samplers when self.guidance is None	2024-09-02 10:20:25 -06:00
asagi4	12ff37d8e3	Don't fail when self.guidance is None	2024-09-02 19:08:31 +03:00
blepping	55d9346713	Fix pink noise and batch sizes over 1	2024-09-01 18:11:21 -06:00
blepping	28b9d9c9c2	Add SonarAdvancedPyramidNoise node	2024-08-23 09:26:00 -06:00
blepping	1fa6b44c47	Add tooltips and descriptions for most nodes. Add repeat_batch parameter to NoisyLatentLike node. Add a node to convert SONAR_CUSTOM_NOISE to ComfyUI NOISE. Various code cleanups and lint squashing.	2024-08-23 07:28:10 -06:00
blepping	5eacd52bbf	Workaround for Python 3.10 compatibility	2024-05-21 10:35:30 -06:00
blepping	408686b9b8	Fix FreeU Extreme example images/workflow	2024-05-21 07:34:10 -06:00
blepping	4844b7109e	Mega update (#6 ) Many new features, documentation reorganized. * Add `SonarScheduledNoise`, `SonarCompositeNoise`, `SonarGuidedNoise`, `SonarRandomNoise` nodes. * Add `SonarPowerFilterNoise`, `SonarPowerFilter`, `SonarPreviewFilter` nodes. * Add `FreeUExtreme`, `FreeUExtremeConfig` nodes. * Replace `pyramid` noise type with a (hopefully) more correct implementation. You can use `pyramid_old` for the previous behavior. * Add more noise types and variations. * The `NoisyLatentLike` node now allows using brownian noise if you connect a model and sigmas. * Refactoring, cleanups, reorganization.	2024-05-21 07:06:46 -06:00
blepping	78e8451324	Feat modulated repeated noise (#5 ) * Add modulated and repeated noise nodes	2024-05-06 16:51:23 -06:00
blepping	b78cbe0b2a	Fix NoisyLatentLike to work with recent ComfyUI changes	2024-04-05 12:57:02 -06:00
blepping	24e1536cb7	Various noise improvements (#4 ) * 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. * Improve noise scaling (may change seeds). * Add `KRestartSamplerCustomNoise` if the user has a recent enough version of ComfyUI_restart_sampling installed.	2024-04-01 11:59:36 -06:00
blepping	ecadbfcd19	Improvements to NoisyLatentLike node (#3 ) * Improve NoisyLatentLike to allow calculating strength with sigmas and noise injection * Update documentation for NoisyLatentLike changes + general improvements	2024-03-20 18:37:51 -06:00
blepping	1ee8273771	Update README and changelog for SonarPowerNoise	2024-03-20 07:57:43 -06:00
elias-gaeros	52f929d54d	SonarPowerNoise (#2 ) * SonarPowerNoise: WIP * don't allow highpass > lowpass * fix filter unit gain The filter was computed as an amplitude gain map. This change computes energy gains instead. This makes easier to ensure unit-gain and makes the alpha values more in line with literature where the power spectrum is obeying the power law. new alpha = old alpha / 2 Also fixes the gain of common_mode. * allow stretch < 1.0, paramter range fixes * rename lowpass/highpass to min_freq/max_freq * remove torch.no_grad wrappers * add previews * static seed for previews * README: SonarPowerNoise documentation	2024-03-14 16:04:35 -06:00
blepping	090df2280d	Make custom noise more extensible (internal change)	2024-03-02 04:48:54 -07:00
blepping	a2fd7118b5	Noise refactor (#1 ) * Refactor noise: stage 1 * Refactor noise: stage 2 * Refactor noise: stage 3 * Refactor noise: stage 4 * Update documentation and changelog	2024-02-27 02:42:19 -07:00