Nodes/comfyui-AGSoft/🧩AGSoft KSampler
ComfyUI Node

🧩AGSoft KSampler

A KSampler that hands back the receipt

By Art-xmaster·Created about a year ago·Updated a day ago· 29
🧩AGSoft KSampler
  • model
  • positive
  • latent_image
  • negative
  • sigmas
  • vae
  • audio_vae
  • options
  • latent
  • images
  • audio
  • options
seed0
steps20
cfg1.0
sampler_name
scheduler
denoise1.00

ComfyUI's stock KSampler is fine until you want to know what you actually ran. Seed, steps, CFG, sampler, scheduler, denoise - they're all sitting in widgets, but if you want them in a log, a filename, or a workflow summary, you have to wire each one out by hand. AGSoft KSampler is a drop-in KSampler that also hands you every parameter as strings and JSON. If you've ever stared at a folder of outputs wondering which settings made which image, this is the node that answers.

What you get

The sampling side is stock behavior: model, positive/negative conditioning, latent_image, seed (with control-after-generate), steps, cfg, sampler_name, scheduler, denoise - all standard, and it uses ComfyUI's own common_ksampler under the hood, so results match what you'd get from a normal sampler. The deltas are the outputs.

  • latent - the denoised latent, same as stock.
  • image - a decoded image, if you connect the optional vae. That's a genuinely useful convenience: sampler + VAE decode in one node, no separate VAE Decode needed.
  • Then the receipt: seed_str, steps_str, cfg_str, sampler_str, scheduler_str, denoise_str - every parameter as a string you can splice into a filename, a text label, or a Save Image caption.
  • params_json - the whole lot as a JSON document, ready for structured logging or auto-generated workflow notes.

The design intent is obvious from the output list: this is a node for people who treat generation as an experiment and want provenance attached. It's the difference between "I ran something" and "I ran euler_ancestral, seed 42, CFG 7.5, 28 steps."

One honest caveat

If you don't connect the VAE, the image output is a 1×1 dummy tensor - the node deliberately avoids crashing and just hands back a placeholder. So if you wire image somewhere without a VAE connected, you'll get a black pixel, not a preview. Use the latent output plus a real VAE Decode in that case, or connect the VAE.

Installing it

Pack standard: ComfyUI Manager → search comfyui-AGSoft → install, restart. Or:

cd ComfyUI/custom_nodes
git clone https://github.com/Art-xmaster/comfyui-AGSoft.git

No models, no dependencies beyond ComfyUI's core - it's a thin wrapper over the built-in sampler.

Common issues

The VAE dummy-tensor behavior above is the one that'll catch you. Other than that, it behaves exactly like a stock KSampler, so any sampler troubleshooting you already know applies. If you're comparing results against another KSampler in the same graph, remember seed still respects control-after-generate, so both nodes will change seed together when you hit queue. And for style-transfer or img2img use, denoise below 1.0 works as expected - the logging just records what you set, which is the point.

CategoryAGSoft/🧩KSampler

Inputs (14)

NameTypeDefaultDescription
modelMODELThe model used for denoising the input latent. Модель для денойзинга входного латента.
positiveCONDITIONINGThe conditioning describing the attributes you want to include. Условие с атрибутами, которые нужно включить.
latent_imageLATENTThe latent image to denoise. Латентное изображение для денойзинга.
seedINT00–18446744073709550000The random seed used for creating the noise. Сид для генерации шума.
stepsINT201–10000Steps count, unless sigmas/options provide their own. Шаги, если сигмы/options не задали свои.
cfgFLOAT1.00–100CFG scale. Usually 1.0 for H3. Масштаб CFG. Обычно 1.0 для H3.
sampler_nameCOMBOThe algorithm used when sampling. Алгоритм сэмплирования.
schedulerCOMBOThe scheduler controls how noise is gradually removed. Планировщик определяет, как удаляется шум.
negativeoptCONDITIONINGOptional negative conditioning. Empty conditioning is created if not connected. Опциональный негатив. Если не подключён — пустой конд.
sigmasoptSIGMASOptional custom sigmas. If connected, steps/scheduler are ignored. Опциональные сигмы. Если подключены, steps/scheduler игнорируются.
denoiseoptFLOAT1.000–1Denoise amount (used only when sigmas are NOT connected). Степень денойзинга (только если сигмы НЕ подключены).
vaeoptVAEOptional VAE for decoding into images/video. Опциональный VAE для декодирования в изображения/видео.
audio_vaeoptVAEOptional Audio VAE for decoding the audio latent. Опциональный Audio VAE для декодирования аудио-латента.
optionsoptKSAMPLER_OPTIONSOverrides from options_single/options_dual/options_lora. Dict = single override; LIST = whole series in ONE queue run (images batched). LoRA keys (lora_name, strength_model, strength_clip) patch a model clone per series item. Переопределения от options_single/options_dual/options_lora. Словарь = одно переопределение; СПИСОК = вся серия за один прогон (картинки батчем). LoRA-ключи (lora_name, strength_model, strength_clip) патчат клон модели на каждый элемент серии.

Outputs (4)

NameTypeDescription
latentLATENTThe denoised latent (last item of the series). Деноизированный латент (последний элемент серии).
imagesIMAGEDecoded images/video; batched when a series runs. Декодированные изображения/видео; батчем при серии.
audioAUDIOThe decoded audio (last item of the series). Декодированное аудио (последний элемент серии).
optionsKSAMPLER_OPTIONSActual sampling parameters of the last run. Фактические параметры последнего прогона.