KSampler

The engine of generation in ComfyUI. This node takes your model, your prompt, your settings, and cranks out images by denoising a latent tensor into art. It’s where diffusion happens, and where your GPU earns its keep.

🔌 Inputs

Input	Type	Description
`MODEL`	`MODEL`	The diffusion model you're using — e.g., `dreamshaper_8.safetensors`, `revAnimated_v122.safetensors`. Required.
`CLIP`	`CLIP`	Text encoder. Usually comes from the same `CheckpointLoaderSimple`. It interprets your prompts into guidance.
`Positive Conditioning`	`CONDITIONING`	From your prompt node. Guides the image generation toward the prompt.
`Negative Conditioning`	`CONDITIONING`	Optional but highly recommended. Pushes the image away from undesirable traits (e.g., “blurry, extra limbs”).
`LATENT`	`LATENT`	The initial latent image. This can be random (for text-to-image) or derived from input for img2img, inpainting, etc.

⚙️ Parameters and Settings (Deep Dive)

🔢 `seed` (INT)

Purpose: Sets the starting noise pattern.
Same settings + same seed = same image. Crucial for reproducibility.
-1 = random seed each time.

Use Cases:

Lock for reproducibility.
Randomize when exploring.

🎛 `control_after_generate` (STRING ENUM)

Despite the name, this has nothing to do with ControlNet. This setting tells ComfyUI how to manage the seed value across batches.

Options:

fixed: Same seed for every image.
increment: Add +1 to seed for each image.
decrement: Subtract -1 per image.
randomize: Use a random seed for each.

Why it matters:

fixed = consistent image generation.
increment = ideal for controlled variations.
randomize = embrace the chaos.

🧮 `steps` (INT)

Purpose: Number of denoising iterations (the more steps, the more chances the model has to refine the image).
Typical Range: 20–50 for best balance.
Max Range: Up to 150+, but prepare to wait.

Guidance:

Too few = blurry or underdeveloped results.
Too many = diminishing returns + GPU tears.

⚖️ `cfg` (FLOAT)

(Classifier-Free Guidance Scale)

Purpose: Controls how much the output adheres to the prompt.
Typical Range: 1–20
Default Sweet Spot: 6.5–8.5

Low cfg (e.g., 2) = freedom, creativity, also prompt forgetfulness.
High cfg (e.g., 15) = "you said banana samurai, you’re getting banana samurai."

🌀 `sampler_name` (STRING ENUM)

Determines the sampling algorithm used to perform denoising.

Popular Samplers:

Euler a: Fast, chaotic, great for creativity.
DPM++ 2M Karras: Smooth, stable, photorealistic.
Heun, LMS, UniPC: All with their own quirks.

Best practice:
Try a few — results can vary dramatically by sampler.

Ready to learn more? Take a look at our deep dive on all the sampler_name options.

📆 `scheduler` (STRING ENUM)

Defines the noise schedule for denoising steps.

Options:

normal: Uniform distribution.
karras: Better for fine details (recommended).
exponential: Aggressive at early steps.

Tip: Use karras unless you're specifically told not to.

Ready to learn more? Take a look at our deep dive on all the sampler_name options.

🌫️ `denoise` (FLOAT)

Range: 0.0–1.0
1.0 = full generation from noise (text-to-image)
<1.0 = preserve structure (for img2img, inpainting, ControlNet guidance)

Example Use:

1.0 → generate from scratch
0.5 → img2img subtle change
0.1–0.3 → ControlNet with light touch

🧰 Special Requirements & Notes

Ensure your MODEL, CLIP, and VAE are compatible.
Always connect both positive and negative conditioning for best results.
Don't be afraid to play with CFG and Denoise — they're your finesse tools.
High steps + High cfg = GPU meltdown risk ⚠️

🔥 What-Not-To-Do-Unless-You-Want-a-Fire

Some mistakes are so common, so predictably catastrophic, that they deserve their own red-flagged list. If you enjoy wasting GPU hours, crashing your ComfyUI session, or summoning unholy Lovecraftian blobs, go ahead and do any of the following:

🚫 Set `steps` to 150+ with `denoise` at 1.0

Unless you’re training your patience or testing thermal limits, this is the fastest way to generate pixel soup very slowly.

Instead: Use 25–40 steps for 99% of tasks.

🚫 Use `cfg=20` because “higher must be better”

This won’t make your prompt more accurate — it’ll make your image look like a bad Photoshop job run through a paper shredder.

Instead: Stick to the 6.5–8.5 sweet spot. Go higher only if you know what you’re doing.

🚫 Forget to set `control_after_generate` when doing batch generations

You wanted 8 unique variations. You got 8 clones. Oops.

Instead: Use increment for clean batch diversity.

🚫 Use `denoise=0.1` in a text-to-image workflow

You just told the sampler to generate... almost nothing. Enjoy your blank canvas with a faint smudge of regret.

Instead: Use denoise=1.0 for full generations. Lower values are for img2img or ControlNet.

🚫 Combine `Euler a` sampler with `steps=80` thinking it'll be glorious

Nah. Euler a is a fast sampler — not meant for marathon sessions. You're not getting more detail, you're just looping futility.

Instead: Use DPM++ 2M Karras or similar for higher-step use.

🚫 Forget to connect `Negative Conditioning`

Yes, it's “optional” — just like wearing pants in public is technically optional. But without it, your output will gleefully ignore your expectations and embrace chaos (in all the wrong ways).

🚫 Use incompatible models and CLIP encoders

That “weird green mush with anime eyes and four ears”? Yeah, that’s what happens when you mix a v1.5 checkpoint with a v2 CLIP. Don’t.

🚫 Crank everything to max at once

CFG = 15, steps = 100, resolution = 2048x2048, tiling disabled, sampler = experimental beta nightly — your GPU just left the chat.

🚫 Blame the sampler when your prompt sucks

KSampler is powerful, but it’s not a miracle worker. If your prompt is “woman” and the image is cursed, well… maybe give the poor thing more to work with.

✅ Pro Tip:

When in doubt — lower your steps, simplify your prompt, lower your CFG, and only touch denoise if you know what it does. KSampler rewards precision and punishes overconfidence.

🧪 Example Workflow

[CheckpointLoaderSimple]     └─> MODEL ─────────┐                        │ [CLIPTextEncode (positive)] ──> KSampler (positive) [CLIPTextEncode (negative)] ──> KSampler (negative) [EmptyLatentImage] ───────────> KSampler (latent) CLIP ─────────────────────────> KSampler (CLIP) KSampler ─────────────────────> [Decode/Save/Display]

⚡ TL;DR Config Cheat Sheet

Setting	Default / Safe Range	Notes
`seed`	-1 (random)	Lock for repeatability
`control_after_generate`	`increment`	Varies seed across batch
`steps`	20–30	40+ for ultra detail
`cfg`	7.5	Higher = more prompt fidelity
`sampler_name`	`DPM++ 2M Karras`	Stable and detailed
`scheduler`	`karras`	Best detail control
`denoise`	1.0	Use <1.0 for image-guided workflows

📚 Additional Resources

🧠 Final Thoughts

The KSampler is where the real magic happens — the ultimate forge of diffusion sorcery. Every parameter you tweak gives you a different flavor of art, so get in there and experiment. Mastering this node means mastering your output.

Need docs for any of the surrounding nodes (EmptyLatentImage, CLIPTextEncode, etc.)? Just shout — I'll keep the documentation train rolling.

🔌 Inputs​

⚙️ Parameters and Settings (Deep Dive)​

🔢 seed (INT)​

🎛 control_after_generate (STRING ENUM)​

🧮 steps (INT)​

⚖️ cfg (FLOAT)​

🌀 sampler_name (STRING ENUM)​

📆 scheduler (STRING ENUM)​

🌫️ denoise (FLOAT)​

🧰 Special Requirements & Notes​

🔥 What-Not-To-Do-Unless-You-Want-a-Fire​

🚫 Set steps to 150+ with denoise at 1.0​

🚫 Use cfg=20 because “higher must be better”​

🚫 Forget to set control_after_generate when doing batch generations​

🚫 Use denoise=0.1 in a text-to-image workflow​

🚫 Combine Euler a sampler with steps=80 thinking it'll be glorious​

🚫 Forget to connect Negative Conditioning​

🚫 Use incompatible models and CLIP encoders​

🚫 Crank everything to max at once​

🚫 Blame the sampler when your prompt sucks​

✅ Pro Tip:​

🧪 Example Workflow​

⚡ TL;DR Config Cheat Sheet​

📚 Additional Resources​

🧠 Final Thoughts​