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DistanceSampler

A custom experimental sampler based on relative distances. The first few steps are slower and then the sampler accelerates (the end is made with Heun).

The idea is to get a more precise start since this is when most of the work is being done.

A more technical explaination

Pros:

  • Less body horror / merged fused people.
  • Little steps required (4-10, recommanded general use: 7 with beta or AYS)
  • Can sample simple subjects without unconditional prediction (meaning with a CFG scale at 1) with a good quality.

Cons:

  • Slow, which is also a plus. Relax, the image is generating ⛱ (but really since it requires little amounts of steps while giving a lesser percentage of horrors I personally prefer it)

Variations:

  • The variation having a "n" in the name stands for "negative" and makes use of the unconditional prediction so to determin the best output. The results may vary depending on your negative prompt. In general it seems to make less mistakes. This is what I sample with in general.

  • The "p" variation uses a comparison with each previous step so to enhance the result. In general things become smoother / less messy.

New:

Thanks to Blepping there is now an ancestral version!

More infos here

Potential compatibility issue:

If any error was to relate to tensors shape, uncomment these two lines in the file "presets_to_add.py":

extra_samplers["Distance_fast"] = distance_wrap(resample=3,resample_end=1,cfgpp=False,sharpen=False)
extra_samplers["Distance_fast_n"] = distance_wrap(resample=3,resample_end=1,cfgpp=False,sharpen=False,use_negative=True)

These are basically the same except they don't use a spherical interpolation at the end. The interpolation was made with latent spaces such as those used in Stable Diffusion in mind. These two alternatives use a weighted average instead (the difference is barely noticeable from my testing).

Comparisons

Examples below are using the beta scheduler. The amount of steps has been adjusted to match the duration has this sampler is quite slow, yet requires little amounts of steps.

left: Distance, 7 steps

right: dpmpp2m, 20 steps

combined_side_by_side

Distance, 10 steps:

distance_10_steps

Distance n, 10 steps:

distance_n_10_steps

DPM++SDE (gpu), 14 steps:

dpmppsder_14steps

Disabled Guidance (CFG=1)

CFG scale at 1 on a normal SDXL model (works for simple subjects):

ComfyUI_00645_

ComfyUI_00640_

ComfyUI_00632_

Distance p with a CFG at 1 and 6 steps:

ComfyUI_00695_

ComfyUI_00692_

About

Heuristic modification of the Heun sampler using normalized distances.

Topics

artificial-intelligencecomfyuicomfyui-nodesfluxfluxaigenerative-aipixart-sigmasd3stable-diffusion

Resources

Readme
Apache-2.0 license
Activity

Stars

44 stars

Watchers

3 watching

Forks

6 forks
Report repository

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GitHub - Extraltodeus/DistanceSampler: Heuristic modification of the Heun sampler using normalized distances. · GitHub
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DistanceSampler
A custom experimental sampler based on relative distances. The first few steps are slower and then the sampler accelerates (the end is made with Heun).
The idea is to get a more precise start since this is when most of the work is being done.
A more technical explaination
Pros:
  • Less body horror / merged fused people.
  • Little steps required (4-10, recommanded general use: 7 with beta or AYS)
  • Can sample simple subjects without unconditional prediction (meaning with a CFG scale at 1) with a good quality.
Cons:
  • Slow, which is also a plus. Relax, the image is generating ⛱ (but really since it requires little amounts of steps while giving a lesser percentage of horrors I personally prefer it)
Variations:
  • The variation having a "n" in the name stands for "negative" and makes use of the unconditional prediction so to determin the best output. The results may vary depending on your negative prompt. In general it seems to make less mistakes. This is what I sample with in general.
  • The "p" variation uses a comparison with each previous step so to enhance the result. In general things become smoother / less messy.
New:
Thanks to Blepping there is now an ancestral version!
More infos here
Potential compatibility issue:
If any error was to relate to tensors shape, uncomment these two lines in the file "presets_to_add.py":
extra_samplers["Distance_fast"] = distance_wrap(resample=3,resample_end=1,cfgpp=False,sharpen=False)
extra_samplers["Distance_fast_n"] = distance_wrap(resample=3,resample_end=1,cfgpp=False,sharpen=False,use_negative=True)

These are basically the same except they don't use a spherical interpolation at the end. The interpolation was made with latent spaces such as those used in Stable Diffusion in mind. These two alternatives use a weighted average instead (the difference is barely noticeable from my testing).
Comparisons
Examples below are using the beta scheduler. The amount of steps has been adjusted to match the duration has this sampler is quite slow, yet requires little amounts of steps.
left: Distance, 7 steps
right: dpmpp2m, 20 steps
combined_side_by_side
Distance, 10 steps:
distance_10_steps
Distance n, 10 steps:
distance_n_10_steps
DPM++SDE (gpu), 14 steps:
dpmppsder_14steps
Disabled Guidance (CFG=1)
CFG scale at 1 on a normal SDXL model (works for simple subjects):
ComfyUI_00645_
ComfyUI_00640_
ComfyUI_00632_
Distance p with a CFG at 1 and 6 steps:
ComfyUI_00695_
ComfyUI_00692_
About
Heuristic modification of the Heun sampler using normalized distances.
Topics
artificial-intelligencecomfyuicomfyui-nodesfluxfluxaigenerative-aipixart-sigmasd3stable-diffusion
Resources
Readme
Apache-2.0 license
Activity
Stars
44 stars
Watchers
3 watching
Forks
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Report repository
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GitHub - Extraltodeus/DistanceSampler: Heuristic modification of the Heun sampler using normalized distances. · GitHub
Skip to content
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Repository files navigation
DistanceSampler
A custom experimental sampler based on relative distances. The first few steps are slower and then the sampler accelerates (the end is made with Heun).
The idea is to get a more precise start since this is when most of the work is being done.
A more technical explaination
Pros:
  • Less body horror / merged fused people.
  • Little steps required (4-10, recommanded general use: 7 with beta or AYS)
  • Can sample simple subjects without unconditional prediction (meaning with a CFG scale at 1) with a good quality.
Cons:
  • Slow, which is also a plus. Relax, the image is generating ⛱ (but really since it requires little amounts of steps while giving a lesser percentage of horrors I personally prefer it)
Variations:
  • The variation having a "n" in the name stands for "negative" and makes use of the unconditional prediction so to determin the best output. The results may vary depending on your negative prompt. In general it seems to make less mistakes. This is what I sample with in general.
  • The "p" variation uses a comparison with each previous step so to enhance the result. In general things become smoother / less messy.
New:
Thanks to Blepping there is now an ancestral version!
More infos here
Potential compatibility issue:
If any error was to relate to tensors shape, uncomment these two lines in the file "presets_to_add.py":
extra_samplers["Distance_fast"] = distance_wrap(resample=3,resample_end=1,cfgpp=False,sharpen=False)
extra_samplers["Distance_fast_n"] = distance_wrap(resample=3,resample_end=1,cfgpp=False,sharpen=False,use_negative=True)

These are basically the same except they don't use a spherical interpolation at the end. The interpolation was made with latent spaces such as those used in Stable Diffusion in mind. These two alternatives use a weighted average instead (the difference is barely noticeable from my testing).
Comparisons
Examples below are using the beta scheduler. The amount of steps has been adjusted to match the duration has this sampler is quite slow, yet requires little amounts of steps.
left: Distance, 7 steps
right: dpmpp2m, 20 steps
combined_side_by_side
Distance, 10 steps:
distance_10_steps
Distance n, 10 steps:
distance_n_10_steps
DPM++SDE (gpu), 14 steps:
dpmppsder_14steps
Disabled Guidance (CFG=1)
CFG scale at 1 on a normal SDXL model (works for simple subjects):
ComfyUI_00645_
ComfyUI_00640_
ComfyUI_00632_
Distance p with a CFG at 1 and 6 steps:
ComfyUI_00695_
ComfyUI_00692_
About
Heuristic modification of the Heun sampler using normalized distances.
Topics
artificial-intelligencecomfyuicomfyui-nodesfluxfluxaigenerative-aipixart-sigmasd3stable-diffusion
Resources
Readme
Apache-2.0 license
Activity
Stars
44 stars
Watchers
3 watching
Forks
6 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

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var __re = new RegExp('^' + ".*" + '
GitHub - Extraltodeus/DistanceSampler: Heuristic modification of the Heun sampler using normalized distances. · GitHub
Skip to content
BranchesTags
Repository files navigation
DistanceSampler
A custom experimental sampler based on relative distances. The first few steps are slower and then the sampler accelerates (the end is made with Heun).
The idea is to get a more precise start since this is when most of the work is being done.
A more technical explaination
Pros:
  • Less body horror / merged fused people.
  • Little steps required (4-10, recommanded general use: 7 with beta or AYS)
  • Can sample simple subjects without unconditional prediction (meaning with a CFG scale at 1) with a good quality.
Cons:
  • Slow, which is also a plus. Relax, the image is generating ⛱ (but really since it requires little amounts of steps while giving a lesser percentage of horrors I personally prefer it)
Variations:
  • The variation having a "n" in the name stands for "negative" and makes use of the unconditional prediction so to determin the best output. The results may vary depending on your negative prompt. In general it seems to make less mistakes. This is what I sample with in general.
  • The "p" variation uses a comparison with each previous step so to enhance the result. In general things become smoother / less messy.
New:
Thanks to Blepping there is now an ancestral version!
More infos here
Potential compatibility issue:
If any error was to relate to tensors shape, uncomment these two lines in the file "presets_to_add.py":
extra_samplers["Distance_fast"] = distance_wrap(resample=3,resample_end=1,cfgpp=False,sharpen=False)
extra_samplers["Distance_fast_n"] = distance_wrap(resample=3,resample_end=1,cfgpp=False,sharpen=False,use_negative=True)

These are basically the same except they don't use a spherical interpolation at the end. The interpolation was made with latent spaces such as those used in Stable Diffusion in mind. These two alternatives use a weighted average instead (the difference is barely noticeable from my testing).
Comparisons
Examples below are using the beta scheduler. The amount of steps has been adjusted to match the duration has this sampler is quite slow, yet requires little amounts of steps.
left: Distance, 7 steps
right: dpmpp2m, 20 steps
combined_side_by_side
Distance, 10 steps:
distance_10_steps
Distance n, 10 steps:
distance_n_10_steps
DPM++SDE (gpu), 14 steps:
dpmppsder_14steps
Disabled Guidance (CFG=1)
CFG scale at 1 on a normal SDXL model (works for simple subjects):
ComfyUI_00645_
ComfyUI_00640_
ComfyUI_00632_
Distance p with a CFG at 1 and 6 steps:
ComfyUI_00695_
ComfyUI_00692_
About
Heuristic modification of the Heun sampler using normalized distances.
Topics
artificial-intelligencecomfyuicomfyui-nodesfluxfluxaigenerative-aipixart-sigmasd3stable-diffusion
Resources
Readme
Apache-2.0 license
Activity
Stars
44 stars
Watchers
3 watching
Forks
6 forks
Report repository
Releases
Packages
Used by
Contributors
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GitHub - Extraltodeus/DistanceSampler: Heuristic modification of the Heun sampler using normalized distances. · GitHub
Skip to content
BranchesTags
Repository files navigation
DistanceSampler
A custom experimental sampler based on relative distances. The first few steps are slower and then the sampler accelerates (the end is made with Heun).
The idea is to get a more precise start since this is when most of the work is being done.
A more technical explaination
Pros:
  • Less body horror / merged fused people.
  • Little steps required (4-10, recommanded general use: 7 with beta or AYS)
  • Can sample simple subjects without unconditional prediction (meaning with a CFG scale at 1) with a good quality.
Cons:
  • Slow, which is also a plus. Relax, the image is generating ⛱ (but really since it requires little amounts of steps while giving a lesser percentage of horrors I personally prefer it)
Variations:
  • The variation having a "n" in the name stands for "negative" and makes use of the unconditional prediction so to determin the best output. The results may vary depending on your negative prompt. In general it seems to make less mistakes. This is what I sample with in general.
  • The "p" variation uses a comparison with each previous step so to enhance the result. In general things become smoother / less messy.
New:
Thanks to Blepping there is now an ancestral version!
More infos here
Potential compatibility issue:
If any error was to relate to tensors shape, uncomment these two lines in the file "presets_to_add.py":
extra_samplers["Distance_fast"] = distance_wrap(resample=3,resample_end=1,cfgpp=False,sharpen=False)
extra_samplers["Distance_fast_n"] = distance_wrap(resample=3,resample_end=1,cfgpp=False,sharpen=False,use_negative=True)

These are basically the same except they don't use a spherical interpolation at the end. The interpolation was made with latent spaces such as those used in Stable Diffusion in mind. These two alternatives use a weighted average instead (the difference is barely noticeable from my testing).
Comparisons
Examples below are using the beta scheduler. The amount of steps has been adjusted to match the duration has this sampler is quite slow, yet requires little amounts of steps.
left: Distance, 7 steps
right: dpmpp2m, 20 steps
combined_side_by_side
Distance, 10 steps:
distance_10_steps
Distance n, 10 steps:
distance_n_10_steps
DPM++SDE (gpu), 14 steps:
dpmppsder_14steps
Disabled Guidance (CFG=1)
CFG scale at 1 on a normal SDXL model (works for simple subjects):
ComfyUI_00645_
ComfyUI_00640_
ComfyUI_00632_
Distance p with a CFG at 1 and 6 steps:
ComfyUI_00695_
ComfyUI_00692_
About
Heuristic modification of the Heun sampler using normalized distances.
Topics
artificial-intelligencecomfyuicomfyui-nodesfluxfluxaigenerative-aipixart-sigmasd3stable-diffusion
Resources
Readme
Apache-2.0 license
Activity
Stars
44 stars
Watchers
3 watching
Forks
6 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

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GitHub - Extraltodeus/DistanceSampler: Heuristic modification of the Heun sampler using normalized distances. · GitHub
Skip to content
BranchesTags
Repository files navigation
DistanceSampler
A custom experimental sampler based on relative distances. The first few steps are slower and then the sampler accelerates (the end is made with Heun).
The idea is to get a more precise start since this is when most of the work is being done.
A more technical explaination
Pros:
  • Less body horror / merged fused people.
  • Little steps required (4-10, recommanded general use: 7 with beta or AYS)
  • Can sample simple subjects without unconditional prediction (meaning with a CFG scale at 1) with a good quality.
Cons:
  • Slow, which is also a plus. Relax, the image is generating ⛱ (but really since it requires little amounts of steps while giving a lesser percentage of horrors I personally prefer it)
Variations:
  • The variation having a "n" in the name stands for "negative" and makes use of the unconditional prediction so to determin the best output. The results may vary depending on your negative prompt. In general it seems to make less mistakes. This is what I sample with in general.
  • The "p" variation uses a comparison with each previous step so to enhance the result. In general things become smoother / less messy.
New:
Thanks to Blepping there is now an ancestral version!
More infos here
Potential compatibility issue:
If any error was to relate to tensors shape, uncomment these two lines in the file "presets_to_add.py":
extra_samplers["Distance_fast"] = distance_wrap(resample=3,resample_end=1,cfgpp=False,sharpen=False)
extra_samplers["Distance_fast_n"] = distance_wrap(resample=3,resample_end=1,cfgpp=False,sharpen=False,use_negative=True)

These are basically the same except they don't use a spherical interpolation at the end. The interpolation was made with latent spaces such as those used in Stable Diffusion in mind. These two alternatives use a weighted average instead (the difference is barely noticeable from my testing).
Comparisons
Examples below are using the beta scheduler. The amount of steps has been adjusted to match the duration has this sampler is quite slow, yet requires little amounts of steps.
left: Distance, 7 steps
right: dpmpp2m, 20 steps
combined_side_by_side
Distance, 10 steps:
distance_10_steps
Distance n, 10 steps:
distance_n_10_steps
DPM++SDE (gpu), 14 steps:
dpmppsder_14steps
Disabled Guidance (CFG=1)
CFG scale at 1 on a normal SDXL model (works for simple subjects):
ComfyUI_00645_
ComfyUI_00640_
ComfyUI_00632_
Distance p with a CFG at 1 and 6 steps:
ComfyUI_00695_
ComfyUI_00692_
About
Heuristic modification of the Heun sampler using normalized distances.
Topics
artificial-intelligencecomfyuicomfyui-nodesfluxfluxaigenerative-aipixart-sigmasd3stable-diffusion
Resources
Readme
Apache-2.0 license
Activity
Stars
44 stars
Watchers
3 watching
Forks
6 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

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GitHub - Extraltodeus/DistanceSampler: Heuristic modification of the Heun sampler using normalized distances. · GitHub
Skip to content
BranchesTags
Repository files navigation
DistanceSampler
A custom experimental sampler based on relative distances. The first few steps are slower and then the sampler accelerates (the end is made with Heun).
The idea is to get a more precise start since this is when most of the work is being done.
A more technical explaination
Pros:
  • Less body horror / merged fused people.
  • Little steps required (4-10, recommanded general use: 7 with beta or AYS)
  • Can sample simple subjects without unconditional prediction (meaning with a CFG scale at 1) with a good quality.
Cons:
  • Slow, which is also a plus. Relax, the image is generating ⛱ (but really since it requires little amounts of steps while giving a lesser percentage of horrors I personally prefer it)
Variations:
  • The variation having a "n" in the name stands for "negative" and makes use of the unconditional prediction so to determin the best output. The results may vary depending on your negative prompt. In general it seems to make less mistakes. This is what I sample with in general.
  • The "p" variation uses a comparison with each previous step so to enhance the result. In general things become smoother / less messy.
New:
Thanks to Blepping there is now an ancestral version!
More infos here
Potential compatibility issue:
If any error was to relate to tensors shape, uncomment these two lines in the file "presets_to_add.py":
extra_samplers["Distance_fast"] = distance_wrap(resample=3,resample_end=1,cfgpp=False,sharpen=False)
extra_samplers["Distance_fast_n"] = distance_wrap(resample=3,resample_end=1,cfgpp=False,sharpen=False,use_negative=True)

These are basically the same except they don't use a spherical interpolation at the end. The interpolation was made with latent spaces such as those used in Stable Diffusion in mind. These two alternatives use a weighted average instead (the difference is barely noticeable from my testing).
Comparisons
Examples below are using the beta scheduler. The amount of steps has been adjusted to match the duration has this sampler is quite slow, yet requires little amounts of steps.
left: Distance, 7 steps
right: dpmpp2m, 20 steps
combined_side_by_side
Distance, 10 steps:
distance_10_steps
Distance n, 10 steps:
distance_n_10_steps
DPM++SDE (gpu), 14 steps:
dpmppsder_14steps
Disabled Guidance (CFG=1)
CFG scale at 1 on a normal SDXL model (works for simple subjects):
ComfyUI_00645_
ComfyUI_00640_
ComfyUI_00632_
Distance p with a CFG at 1 and 6 steps:
ComfyUI_00695_
ComfyUI_00692_
About
Heuristic modification of the Heun sampler using normalized distances.
Topics
artificial-intelligencecomfyuicomfyui-nodesfluxfluxaigenerative-aipixart-sigmasd3stable-diffusion
Resources
Readme
Apache-2.0 license
Activity
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44 stars
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3 watching
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GitHub - Extraltodeus/DistanceSampler: Heuristic modification of the Heun sampler using normalized distances. · GitHub
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DistanceSampler
A custom experimental sampler based on relative distances. The first few steps are slower and then the sampler accelerates (the end is made with Heun).
The idea is to get a more precise start since this is when most of the work is being done.
A more technical explaination
Pros:
  • Less body horror / merged fused people.
  • Little steps required (4-10, recommanded general use: 7 with beta or AYS)
  • Can sample simple subjects without unconditional prediction (meaning with a CFG scale at 1) with a good quality.
Cons:
  • Slow, which is also a plus. Relax, the image is generating ⛱ (but really since it requires little amounts of steps while giving a lesser percentage of horrors I personally prefer it)
Variations:
  • The variation having a "n" in the name stands for "negative" and makes use of the unconditional prediction so to determin the best output. The results may vary depending on your negative prompt. In general it seems to make less mistakes. This is what I sample with in general.
  • The "p" variation uses a comparison with each previous step so to enhance the result. In general things become smoother / less messy.
New:
Thanks to Blepping there is now an ancestral version!
More infos here
Potential compatibility issue:
If any error was to relate to tensors shape, uncomment these two lines in the file "presets_to_add.py":
extra_samplers["Distance_fast"] = distance_wrap(resample=3,resample_end=1,cfgpp=False,sharpen=False)
extra_samplers["Distance_fast_n"] = distance_wrap(resample=3,resample_end=1,cfgpp=False,sharpen=False,use_negative=True)

These are basically the same except they don't use a spherical interpolation at the end. The interpolation was made with latent spaces such as those used in Stable Diffusion in mind. These two alternatives use a weighted average instead (the difference is barely noticeable from my testing).
Comparisons
Examples below are using the beta scheduler. The amount of steps has been adjusted to match the duration has this sampler is quite slow, yet requires little amounts of steps.
left: Distance, 7 steps
right: dpmpp2m, 20 steps
combined_side_by_side
Distance, 10 steps:
distance_10_steps
Distance n, 10 steps:
distance_n_10_steps
DPM++SDE (gpu), 14 steps:
dpmppsder_14steps
Disabled Guidance (CFG=1)
CFG scale at 1 on a normal SDXL model (works for simple subjects):
ComfyUI_00645_
ComfyUI_00640_
ComfyUI_00632_
Distance p with a CFG at 1 and 6 steps:
ComfyUI_00695_
ComfyUI_00692_
About
Heuristic modification of the Heun sampler using normalized distances.
Topics
artificial-intelligencecomfyuicomfyui-nodesfluxfluxaigenerative-aipixart-sigmasd3stable-diffusion
Resources
Readme
Apache-2.0 license
Activity
Stars
44 stars
Watchers
3 watching
Forks
6 forks
Report repository
Releases
Packages
Used by
Contributors
Languages