BLEH

A ComfyUI nodes collection of utility and model patching functions. Also includes improved previewer that allows previewing batches during generation.

Features

1. Better TAESD previews (see below).
2. Allow setting seed, timestep range and step interval for HyperTile (look for the BlehHyperTile node).
3. Allow applying Kohya Deep Shrink to multiple blocks, also allow gradually fading out the downscale factor (look for the BlehDeepShrink node).
4. Allow discarding penultimate sigma (look for the BlehDiscardPenultimateSigma node). This can be useful if you find certain samplers are ruining your image by spewing a bunch of noise into it at the very end (usually only an issue with dpm2 a or SDE samplers).
5. Allow more conveniently switching between samplers during sampling (look for the BlehInsaneChainSampler node).
6. Apply arbitrary model patches at an interval and/or for a percentage of sampling (look for the BlehModelPatchConditional node).
7. Ensure a seed is set even when add_noise is turned off in a sampler. Yes, that's right: if you don't have add_noise enabled no seed gets set for samplers like euler_a and it's not possible to reproduce generations. (look for the BlehForceSeedSampler node). For SamplerCustomAdvanced you can use BlehDisableNoise to accomplish the same thing.
8. Allows swapping to a refiner model at a predefined time (look for the BlehRefinerAfter node).
9. Allow defining arbitrary model patches (look for the BlehBlockOps node).
10. Experimental blockwise CFG type effect (look for the BlehBlockCFG node).

Configuration

Copy either blehconfig.yaml.example or blehconfig.json.example to blehconfig.yaml or blehconfig.json respectively and edit the copy. When loading configuration, the YAML file will be prioritized if it exists and Python has YAML support.

Restart ComfyUI to apply any new changes.

Better TAESD previews

• Supports setting max preview size (ComfyUI default is hardcoded to 512 max).
• Supports showing previews for more than the first latent in the batch.
• Supports throttling previews. Do you really need your expensive TAESD preview to get updated 3 times a second?

Current defaults:

Key	Default	Description
enabled	true	Toggles whether enhanced TAESD previews are enabled
max_size	768	Max width or height for previews. Note this does not affect TAESD decoding, just the preview image
max_width	max_size	Same as max_size except allows setting the width independently. Previews may not work well with non-square max dimensions.
max_height	max_size	Same as max_size except allows setting the height independently. Previews may not work well with non-square max dimensions.
max_batch	4	Max number of latents in a batch to preview
max_batch_cols	2	Max number of columns to use when previewing batches
throttle_secs	2	Max frequency to decode the latents for previewing. 0.25 would be every quarter second, 2 would be once every two seconds
maxed_batch_step_mode	false	When false, you will see the first max_batch previews, when true you will see previews spread across the batch
preview_device	null	null (use the default device) or a string with a PyTorch device name like "cpu", "cuda:0", etc. Can be used to run TAESD previews on CPU or other available devices. Not recommended to change this unless you really need to, using the CPU device may prevent out of memory errors but will likely significantly slow down generation.
skip_upscale_layers	0	The TAESD model has three upscale layers, each doubles the size of the result. Skipping some of them will significantly speed up TAESD previews at the cost of smaller preview image results. You can set this to -1 to automatically pop layers until at least one dimension is within the max width/height or -2 to aggressively pop until both dimensions are within the limit.

These defaults are conservative. I would recommend setting throttle_secs to something relatively high (like 5-10) especially if you are generating batches at high resolution.

Slightly more detailed explanation for maxed_batch_step_mode: If max previews is set to 3 and the batch size is 15 you will see previews for indexes 0, 5, 10. Or to put it a different way, it steps through the batch by batch_size / max_previews rounded up. This behavior may be useful for previewing generations with a high batch count like when using AnimateDiff.

More detailed explanation for skipping upscale layers: Latents (the thing you're running the TAESD preview on) are 8 times smaller than the image you get decoding by normal VAE or TAESD. The TAESD decoder has three upscale layers, each doubling the size: 1 * 2 * 2 * 2 = 8. So for example if normal decoding would get you a 1280x1280 image, skipping one TAESD upscale layer will get you a 640x640 result, skipping two will get you 320x320 and so on. I did some testing running TAESD decode on CPU for a 1280x1280 image: the base speed is about 1.95 sec base, 1.15 sec with one upscale layer skipped, 0.44 sec with two upscale layers skipped and 0.16 sec with all three upscale layers popped (of course you only get a 160x160 preview at that point). The upshot is if you are using TAESD to preview large images or batches or you want to run TAESD on CPU (normally pretty slow) you would probably benefit from setting skip_upscale_layers to 1 or 2. Also if your max preview size is 768 and you are decoding a 1280x1280 image, it's just going to get scaled down to 768x768 anyway.

BlehModelPatchConditional

Note: Very experimental.

This node takes a default model and a matched model. When the interval or start/end percentage match, the matched model will apply, otherwise the default one will. This can be used to apply something like HyperTile, Self Attention Guidance or other arbitrary model patches conditionally.

The first sampling step that matches the timestep range always applies matched, after that the following behavior applies: If the interval is positive then you just get matched every interval steps. It is also possible to set interval to a negative value, for example -3 would mean out of every three steps, the first two use matched and the third doesn't.

Notes and limitations: Not all types of model modifications/patches can be intercepted with a node like this. You also almost certainly can't use this to mix different models: both inputs should be instances of the same loaded model. It's also probably a bad idea to apply further patches on top of the BlehModelPatchConditional node output: it should most likely be the last thing before a sampler or something that actually uses the model.

BlehHyperTile

Adds the ability to set a seed and timestep range that HyperTile gets applied for. Not well tested, and I just assumed the Inspire version works which may or may not be the case.

It is also possible to set an interval for HyperTile steps, this time it is just normal sampling steps that match the timestep range. The first sampling step that matches the timestep range always applies HyperTile, after that the following behavior applies: If the interval is positive then you just get HyperTile every interval steps. It is also possible to set interval to a negative value, for example -3 would mean out of every three steps, the first two have HyperTile and the third doesn't.

Note: Timesteps start from 999 and count down to 0 and also are not necessarily linear. Figuring out exactly which sampling step a timestep applies to is left as an exercise for you, dear node user. As an example, Karras and exponential samplers essentially rush to low timesteps and spend quite a bit of time there.

HyperTile credits:

The node was originally taken by Comfy from taken from: https://github.com/tfernd/HyperTile/

Then the Inspire node pack took it from the base ComfyUI node: https://github.com/ltdrdata/ComfyUI-Inspire-Pack

Then I took it from the Inspire node pack. The original license was MIT so I assume yoinking it into this repo is probably okay.

BlehDeepShrink

AKA PatchModelAddDownScale AKA Kohya Deep Shrink. Compared to the built-in Deep Shrink node this version has the following differences:

1. Instead of choosing a block to apply the downscale effect to, you can enter a comma-separated list of blocks. This may or not actually be useful but it seems like you can get interesting effects applying it to multiple blocks. Try 2,3 or 1,2,3.
2. Adds a start_fadeout_percent input. When this is less than end_percent the downscale will be scaled to end at end_percent. For example, if downscale_factor=2.0, start_percent=0.0, end_percent=0.5 and start_fadeout_percent=0.0 then at 25% you could expect downscale_factor to be around 1.5. This is because we are deep shrinking between 0 and 50% and we are halfway through the effect range. (downscale_factor=1.0 would of course be a no-op and values below 1 don't seem to work.)
3. Expands the options for upscale and downscale types, you can also turn on antialiasing for bicubic and bilinear modes.

Notes: It seems like when shrinking multiple blocks, blocks downstream are also affected. So if you do x2 downscaling on 3 blocks, you are going to be applying x2 * 3 downscaling to the lowest block (and maybe downstream ones?). I am not 100% sure how it works, but the takeway is you want to reduce the downscale amount when you are downscaling multiple blocks. For example, using blocks 2,3,4 and a downscale factor of 2.0 or 2.5 generating at 3072x3072 seems to work pretty well. Another note is schedulers that move at a steady pace seem to produce better results when fading out the deep shrink effect. In other words, exponential or Karras schedulers don't work well (and may produce complete nonsense). ddim_uniform and sgm_uniform seem to work pretty well and normal appears to be decent.

Deep Shrink credits:

Adapted from the ComfyUI source which I presume was adapted from the version Kohya initially published.

BlehInsaneChainSampler

Note: I'd recommend using my Overly Complicated Sampling node pack over this. It generally has better tools for scheduling samplers.

A picture is worth a thousand words, so:

This will use heunpp2 for the first five steps, euler_ancestral for the next five, and dpmpp_2m for however many remain.

This is basically the same as chaining a bunch of samplers together and manually setting the start/end steps.

Note: Even though the dpmpp_2m insane chain sampler node has steps=1 it will run for five steps. This is because the requirement of fifteen total steps must be fulfilled and... you can't really sample stuff without a sampler. Also note progress might be a little weird splitting sampling up like this.

BlehForceSeedSampler

Currently, the way ComfyUI's advanced and custom samplers work is if you turn off add_noise no global RNG seed gets set. Samplers like euler_a use this (SDE samplers use a different RNG method and aren't subject to this issue). Anyway, the upshot is you will get a different generation every time regardless of what the seed is set to. This node simply wraps another sampler and ensures that the seed gets set.

BlehDisableNoise

Basically the same idea as BlehForceSeedSampler, however it is usable with SamplerCustomAdvanced.

BlehPlug

You can connect this node to any input and it will be the same as if the input had no connection. Why is this useful? It's mainly for Use Everywhere — sometimes it's desirable to leave an input unconnected, but if you have Use Everywhere broadcasting an output it can be inconvenient. Just shove a plug in those inputs.

BlehSetSamplerPreset

Allows associating a SAMPLER with a name in list of samplers (bleh_preset_0, etc) so you can use a custom sampler in places that do not allow custom sampling - FaceDetailer for example. You can adjust the number of presets by setting the environment variable COMFYUI_BLEH_SAMPLER_PRESET_COUNT - it defaults to 1 if unset. If set to 0, no sampler presets will be added to the list.

This node needs to run before sampling with the preset begins - it takes a wildcard input with can be used to pass through something like the model or latent to make sure the node runs before sampling. Note: Since the input and outputs are wildcards, ComfyUI's normal type checking does not apply here - be sure you connect the output to something that supports the input type. For example, if you connect a MODEL to any_input, ComfyUI will let you connect that to something expecting LATENT which won't work very well.

It's also possible to override the sigmas used for sampling - possibly to do something like Restart sampling in nodes that don't currently allow you to pass in sigmas. This is an advanced option, if you don't know that you need it then I suggest not connecting anything here. Note: If the sampler is adding noise then you likely will get unexpected results if the two sets of sigmas start at different values. (This could also be considered a feature since it effectively lets you apply a multiplier to the initial noise.)

The dummy_opt input can be attached to anything and isn't used by the node. However, you can connect something like a string or integer and change it to ensure the node runs again and sets your preset. See the note below.

Note: One thing to be aware of is that this node assigns the preset within the ComfyUI server when it runs, so if you are changing and using the same preset ID between samplers, you need to make sure the BlehSetSamplerPreset node runs before the corresponding sampler. For example, suppose you have a workflow that looks like Set Preset 0 -> KSampler1 (with preset 0) -> Set Preset 0 -> KSampler2 (with preset 0). The Set Preset nodes will run before each KSampler as expected the first time you execute the workflow. However, if you go back and change a setting in KSampler1 and run the workflow, this won't cause the first Set Preset node to run again so you'll be sampling with whatever got assigned to the preset with the second Set Preset node. You can change a value connected to the dummy_opt input to force the node to run again.

BlehRefinerAfter

Allows switching to a refiner model at a predefined time. There are three time modes:

• timestep: Note that this is not a sampling step but a value between 0 and 999 where 999 is the beginning of sampling and 0 is the end. It is basically equivalent to the percentage of sampling remaining - 999 = ~99.9% sampling remaining.
• percent: Value between 0.0 and 1.0 where 0.0 is the start of sampling and 1.0 is the end. Note that this is not based on sampling steps.
• sigma: Advanced option. If you don't know what this is you probably don't need to worry about it.

Note: This only patches the unet apply function, most other stuff including conditioning comes from the base model so you likely can only use this to swap between models that are closely related. For example, switching from SD 1.5 to SDXL is not going to work at all.

BlehBlockCFG

Experimental model patch that attempts to guide either cond (positive prompt) or uncond (negative prompt) away from its opposite. In other words, when applied to cond it will try to push it further away from what uncond is doing and vice versa. Stronger effect when applied to cond or output blocks. The defaults are reasonable for SD 1.5 (or as reasonable as weird stuff like this can be).

Enter comma separated blocks numbers starting with one of Iinput, Output or Middle like i4,m0,o4. You may also use * rather than a block number to select all blocks in the category, for example i*, o* matches all input and all output blocks.

The patch can be applied to the same model multiple times.

Is it good, or even doing what I think? Who knows! Both positive and negative scales seem to have positive effect on the generation. Low negative scales applied to cond seem to make the generation bright and colorful.

Note: Probably only works with SD 1.x and SDXL. Middle block patching will probably only work if you have FreeU_Advanced installed.

Note: Doesn't work correctly with Tiled Diffusion when using tile batch sizes over 1.

BlehBlockOps

Very experimental advanced node that allows defining model patches using YAML. This node is still under development and may be changed.

The top level YAML should consist of a list of objects with a condition if, a list of ops that run if the condition succeeds. Objects then and else also take the same form as the top level object and apply when the if condition matches (or not in the case of else).

All object fields (if, then, else, ops) are optional. An empty object is valid, it just doesn't do anything.

- if:
    cond1: [value1, value2]
    cond2: value # Values may be specified as a list or single item.
  ops: [[opname1, oparg1, oparg2], [opname2, oparg1, oparg2]]
  then:
    if: [[opname1, oparg1, oparg2]] # Conditions may also be specified as a list.
    ops: [] # and so on
    else:
      ops: []
    # then and else may also be nested to an arbitrary depth.

Note: Blocks match by default, conditions restrict them. So a block with no if matches everything.

Expand to see full node documentation

Conditions

type: One of input, input_after_skip, middle, output (preceding are block patches), latent, post_cfg. Note: ComfyUI doesn't allow patching the middle blocks by default, this feature is only available if you have FreeU Advanced installed and enabled. (It patches ComfyUI to support patching the middle blocks.)

block: The block number. Only applies when type is input, input_after_skip, middle or output.

stage: The model stage. Applies to the same types as block. You can think of this in terms of FreeU's b1, b2 - the number is the stage.

percent: Percentage of sampling completed as a number between 0.0 and 1.0. Note that this is sampling percentage, not percentage of steps. Does not apply to type latent.

from_percent: Matches when sampling is greater or equal to the percent. Same restrictions as percent.

to_percent: Matches when sampling is less or equal to the percent. Same restrictions as from_percent.

step: Only applies when sigmas are connected to the BlehBlockOps node. A step will be determined as the index of the closest matching sigma. In other words, if you don't connect sigmas that exactly match the sigmas used for sampling you won't get accurate steps. Does not apply to type latent.

step_exact: Same restrictions as step, however will only be set if the current sigma exactly matches a step. Otherwise the value will be -1.

from_step: As above, but matches when the step is greater or equal to the value.

from_step: As above, but matches when the step is less or equal to the value.

step_interval: Same restrictions as the other step condition types. Matches when the step modulus interval is 0. In other words, every other step starting from the first step you'd use an interval of 2 and the then branch (since 1 % 2 == 1 which is not 0).

cond: Generic condition, has two forms:

Comparison: Takes three arguments: comparison type (eq, ne, gt, lt, ge, le), a condition type with a numeric value (block, stage, percent, step, step_exact) and a value or list of values to compare with.

Example:

- if: [cond, [lt, percent, 0.35]]

Logic: Takes a logic operation type (not, and, or) and a list of condition blocks. Note: The logic operation is applied to the result of the condition block and not the fields within it.

Example:

- if:
    cond: [not,
      [cond, [or,
        [cond, [lt, step, 1]],
        [cond, [gt, step, 5]],
      ]]
    ] # A verbose way of expressing step >= 1 and step <= 5
- if:
    - [cond, [ge, step, 1]]
    - [cond, [le, step, 5]] # Same as above
- if: [[from_step, 1], [to_step, 5]] # Also same as above

Operations

Operations mostly modify a target which can be h or hsp. hsp is only a valid target when type is output. I think it has something to do with skip connections but I don't know the specifics. It's important for FreeU.

Default values are show in parenthesis next to the operation argument name. You may supply an incomplete argument list, in which case default values will be used for the remaining arguments. Ex: [flip] is the same as [flip, h]. You may also specify the arguments as a map, keys that aren't included will use the default values. Ex: [flip, {direction: h}]

slice: Applies a filtering operation on a slice of the target.

1. scale(1.0): Slice scale, 1.0 would mean apply to 100% of the target, 0.5 would mean 50% of it.
2. strength(1.0): Scales the target. 1.0 would mean 100%.
3. blend(1.0): Ratio of the transformed value to blend in. 1.0 means replace it with no blending.
4. blend_mode(bislerp): See the blend mode section.
5. use_hidden_mean(true): No idea what this does really, but FreeU V2 uses it when slicing and V1 doesn't.

ffilter: Applies a Fourier filter operation to the target.

1. scale(1.0): Scales the target. 1.0 would mean 100%.
2. filter(none): May be a string with a predefined filter name or a list of lists defining filters. See the filter section.
3. filter_strength(0.5): Strength of the filter. 1.0 would mean to apply it at 100%.
4. threshold(1): Threshold for the Fourier filter. This generally should be 1.

scale_torch: Scales the target up or down, using PyTorch's interpolate function.

1. type(bicubic): One of bicubic, nearest, bilinear or area.
2. scale_width(1.0): Ratio to scale the width. 2.0 would mean double it, 0.5 would mean half of it.
3. scale_height(1.0): As above.
4. antialias(false): true to apply antialiasing after scaling or false.

unscale_torch: Scale the target to be the same size as hsp. Only can be used when the target isn't hsp and condition type is output. Can be used to reverse a scale or scale_torch operation without having to worry about calculating the ratios to get the original size back.

1. type(bicubic): Same as scale_torch.
2. antialias(false): Same as scale_torch.

scale: Scales the target up or down using various functions. See the scaling functions section.

1. type_width(bicubic): Scaling function to use for width. Note if the type is one of the ones from scale_torch it cannot be combined with other scaling functions.
2. type_height(bicubic): As above.
3. scale_width(1.0): Ratio to scale the width. 2.0 would mean double it, 0.5 would mean half of it.
4. scale_height(1.0): As above.
5. antialias_size(0): Size of the antialias kernel. Between 1 and 7 inclusive. Higher numbers seem to increase blurriness.

unscale: Like unscale_torch except it supports more scale functions and can specify width/height scale function independently. Same restriction as scale.

1. type_width(bicubic): Scaling function to use for width. Note if the type is one of the ones from scale_torch it cannot be combined with other scaling functions.
2. type_height(bicubic): As above.
3. antialias_size(0): Size of the antialias kernel. Between 1 and 7 inclusive. Higher numbers seem to increase blurriness.

flip: Flips the target.

1. direction(h): h for horizontal flip, v for vertical. Note that latents generally don't tolerate being flipped very well.

rot90: Does a 90 degree rotation of the target.

1. count(1): Number of times to rotate (can also be negative). Note that if you rotate in a way that makes the tensors not match then stuff will probably break. also as with flip it generally is pretty destructive to latents.

roll: Rotates the values in a dimension of the target.

1. direction(c): horizontal, vertical, channels. Note that when type is input, input_after_skip, middle or output you aren't actually dealing with a latent. The second dimension ("channels") is actually the features in the layer. Rotating them can produce some pretty weird effects.
2. amount(1): If it's a number greater than -1.0 and less than 1.0 this will rotate forward or backward by a percentage of the size. Otherwise it is interpreted as the number of items to rotate forward or backward.

roll_channels: Same as roll but you only specify the count, it always targets channels and you can't use percentages.

1. count(1): Number of channels to rotate. May be negative.

target_skip: Changes the target.

1. active(true): If true will target hsp, otherwise will target h. Targeting hsp is only allowed when type is output, no effect otherwise.

multiply: Multiply the target by the value.

1. factor(1.0): Multiplier. 2.0 would double all values in the target.

antialias: Applies an antialias effect to the target. Works the same ase with scale.

1. size(7): The antialias kernel size as a number between 1 and 7.

noise: Adds noise to the target. Can only be used when sigmas are connected. Noise will be scaled by sigma - sigma_next.

1. scale(0.5): Additionally scale the noise by the supplied factor. 1.0 would mean no scaling, 2.0 would double it, etc.
2. type(gaussian): Only gaussian unless ComfyUI-sonar is installed and active, otherwise you may use the additional noise types Sonar provides.
3. scale_mode(sigdiff): sigdiff scales the noise by the current sigma minus the next (requires sigmas connected), sigma scales by the current sigma, none or an invalid type uses no scaling (you get exactly noise * scale).

debug: Outputs some debug information about the state.

blend_op: Allows applying a blend function to the result of another operation.

1. blend(1.0): Ratio of the transformed value to blend in.
2. blend_mode(bislerp): See the blend mode section.
3. ops(empty): The operation as a list, with the name first. i.e. [blend_op, 0.5, inject, [multiply, 0.5]]. May also be a list of operations.

pad: Pads the target.

1. mode(reflect): One of constant, reflect, replicate, circular - see https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html#torch.nn.functional.pad
2. top(0): Amount of top padding. If this is a floating point value, it will be treated as a percentage of the dimension.
3. bottom(0): " " "
4. left(0): " " "
5. right(0): " " "
6. constant(0): Constant value to use, only applies when mode is constant.

Note: If you pad input (rather than input_after_skip) then you will need to crop the corresponding block in output for both h and hsp (i.e. with target_skip).

crop: Crops the target.

1. top(0): Items to crop from the top. If this is a floating point value, it will be treated as a percentage of the dimension.
2. bottom(0): " " "
3. left(0): " " "
4. right(0): " " "

mask_example_op: Applies providing a mask by example and masks the result of an operation or list of operations.

1. scale_mode(bicubic) type: Same as with scale.
2. antialias(7) size: Same as with scale.
3. mask(mask targeting corners): A two dimensional list of mask values. See below.
4. ops(empty): Same as with blend_op.

Simple example of a mask:

[ [1.0, 0.0, 0.0, 1.0],
  [0.0, 0.0, 0.0, 0.0],
  [1.0, 0.0, 0.0, 1.0],
]

With this mask, the result of the mask ops will be applied at full strength to the corners. The mask is scaled up to the size of the target tensor, so with this example the masked corners will be proportionately quite large if the latent or tensor is much bigger than the mask. There are two convenience tricks for defining larger masks without having to specify each value:

• If the first element in a row is "rep" then the second element is interpreted as a row repeat count and the rest of the items in the row constitute the row. Ex: ["rep", 2, 1, 0, 1] expands to two rows of 1, 0, 1.
• If a column item is a list, the first element is interpreted as the repeat count and the remaining items are repeated however many times. Ex: [2, 1.2, 0.5] as a column would expand to 1.2, 0.5, 1.2, 0.5.

These two shortcuts can be combined. A mask of [["rep", 2, 1, [3, 0], 2]] expands to:

[
  [1, 0, 0, 0, 2],
  [1, 0, 0, 0, 2],
]

apply_enhancement: Applies an enhancement to the target.

1. scale: 1.0
2. type: korniabilateralblur

Blend Modes

1. bislerp: Interpolates between tensors a and b using normalized linear interpolation.
2. colorize: Supposedly transfers color. May or may not work that way.
3. cosinterp: Cosine interpolation.
4. cuberp
5. hslerp: Hybrid Spherical Linear Interporation, supposedly smooths transitions between orientations and colors.
6. inject: Inject just adds the value scaled by the ratio, so if ratio is 1.0 this simply adds it.
7. lerp: Linear interpolation.
8. lineardodge: Supposedly simulates a brightning effect.

Filters

1. none
2. bandpass
3. lowpass: Allows low frequencies and suppresses high frequencies.
4. highpass: Allows high frequencies and suppresses low frequencies.
5. passthrough: Maybe doesn't do anything?
6. gaussianblur: Blur.
7. edge: Edge enhance.
8. sharpen: Sharpens the target.
9. multilowpass: The multi versions apply to multiple bands.
10. multihighpass
11. multipassthrough
12. multigaussianblur
13. multiedge
14. multisharpen

Custom filters may also be defined. For example, gaussianblur in the YAML filter definition would be [[10,0.5]], sharpen would be [[10, 1.5]].

Scaling Functions

See Scaling Types below.

Examples

FreeU V2

# FreeU V2 b1=1.1, b2=1.2, s1=0.9, s2=0.2
- if:
    type: output
    stage: 1
  ops:
    - [slice, 0.75, 1.1, 1, null, true]
    - [target_skip, true]
    - [ffilter, 0.9, none, 1.0, 1]
- if:
    type: output
    stage: 2
  ops:
    - [slice, 0.75, 1.2, 1, null, true]
    - [target_skip, true]
    - [ffilter, 0.2, none, 1.0, 1]

Kohya Deep Shrink

# Deep Shrink, downscale 2, apply up to 35%.
- if:
    type: input_after_skip
    block: 3
    to_percent: 0.35
  ops: [[scale, bicubic, bicubic, 0.5, 0.5, 0]]
- if:
    type: output
  ops: [[unscale, bicubic, bicubic, 0]]

BlehLatentOps

Basically the same as BlehBlockOps, except the condition type will be latent. Obviously stuff involving steps, percentages, etc do not apply. This node allows you to apply the blending/filtering/scaling operations to a latent.

BlehLatentScaleBy

Like the builtin LatentScaleBy node, however it allows setting the horizontal and vertical scaling types and scales independently as well as allowing providing an extended list of scaling options. Can also be useful for testing what different types of scaling or enhancement effects look like.

Scaling Types

• bicubic: Generally the safe option.
• bilinear: Like bicubic but slightly not as good?
• nearest-exact
• area
• bislerp: Interpolates between tensors a and b using normalized linear interpolation.
• colorize: Supposedly transfers color. May or may not work that way.
• hslerp: Hybrid Spherical Linear Interporation, supposedly smooths transitions between orientations and colors.
• bibislerp: Uses bislerp as the slerp function in bislerp. When slerping once just isn't enough.
• cosinterp: Cosine interpolation.
• cuberp: Cubic interpolation.
• inject: Adds the value scaled by the ratio. Probably not the best for scaling.
• lineardodge: Supposedly simulates a brightning effect.
• random: Chooses a random relatively normal scaling function each time. My thought is this will avoid artifacts from a specific scaling type from getting reinforced each step. Generally only useful for Deep Shrink or jankhdiffusion.
• randomaa: Like random, however it will also choose a random antialias size.

Scaling types like bicubic+something will apply the something enhancement after scaling. See below.

Scaling types that start with rev like revinject reverse the arguments to the scaling function. For example, inject does a + b * scale, revinject does b + a * scale. When is this desirable? I really don't know! Just stuff to experiment with. It may or may not be useful. (revcosinterp looks better than cosinterp though.)

Note: Scaling types like random are very experimental and may be modified or removed.

Enhancement Types

• randmultihighlowpass: Randomly uses multihighpass or multilowpass filter. Effect is generally quite strong.
• randhilowpass: Randomly uses a highpass or lowpass filter. When you filter both high and low frequencies you are left with... nothing! The effect is very strong. May not be useful.
• randlowbandpass: Randomly uses a bandpass or lowpass filter.
• randhibandpass: Randomly uses a bandpass or highpass filter.
• renoise1: Adds some gaussian noise. Starts off relatively weak and increases based on sigma.
• renoise2: Adds some guassian noise. Starts relatively strong and decreases based on sigma.
• korniabilateralblur: Applies a bilateral (edge preserving) blur effect.
• korniagaussianblur: Applies a guassian blur effect.
• korniasharpen: Applies a sharpen effect.
• korniaedge: Applies an edge enhancement effect.
• korniarevedge: Applies an edge softening effect - may not work correctly.
• korniarandblursharp: Randomly chooses between blurring and sharpening.

Also may be an item from Filters.

Note: These enhancements are very experimental and may be modified or removed.

Credits

Latent blending and scaling and filter functions based on implementation from https://github.com/WASasquatch/FreeU_Advanced - thanks!