2nd/10_Wiki/Topics/AI_and_ML/샘플링 스텝 (Sampling Steps).md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-샘플링-스텝-sampling-steps	샘플링 스텝 (Sampling Steps)	10_Wiki/Topics	verified	self	Sampling Steps Diffusion Steps Inference Steps num_inference_steps	none	A	0.9	applied	diffusion sampling sdxl flux inference image-generation		2026-05-10	pending	language framework python diffusers/ComfyUI

샘플링 스텝 (Sampling Steps)

매 한 줄

"매 step 은 noise → image 의 길 위 의 한 발자국". 매 diffusion model 에서 num_inference_steps 는 매 reverse-diffusion ODE 의 discretization count — 매 적으면 빠르지만 muddy, 매 많으면 sharp 지만 expensive 하고 어느 임계점 이상 은 의미 X. 매 2026 의 modern sampler (DPM++ 2M Karras, FLUX 의 Euler) + Lightning/Turbo distillation 으로 매 sweet-spot 이 12–30 steps 로 안정.

매 핵심

매 정의

• Sampling step: 매 reverse diffusion 의 한 iteration. matrix t = T → 0 의 discretization.
• Sampler / scheduler: 매 step 사이 noise 의 schedule + 알고리즘 (Euler, DPM, UniPC, LMS, DDIM, DPM++ SDE).
• CFG (guidance scale): 매 step 마다 conditional vs unconditional 의 weighting.
• Sigma schedule: 매 noise level 의 t-vs-sigma curve (linear, karras, exponential).

매 sampler family (2026 state)

• Euler / Euler a: 매 simple, fast, good baseline (15–25).
• DPM++ 2M Karras: 매 SDXL community default (20–30). 매 quality leader 의 하나.
• DPM++ 3M SDE: 매 detail 강함 (28–40).
• UniPC: 매 빠른 convergence (10–20).
• DDIM: 매 deterministic, ControlNet 호환.
• LCM / Turbo / Lightning: 매 distilled 1–8 steps.
• FLUX 의 Euler / fm_euler: 매 flow-matching 형식. 매 25–30 steps default.

매 step 수 의 trade-off

• 매 5–10: muddy, oversmooth (distilled 모델 의 경우 ok).
• 매 15–20: 매 production sweet spot (대부분 SDXL / FLUX dev).
• 매 25–30: 매 detail-critical scene.
• 매 40+: 매 returns diminishing. 매 거의 무의미.

매 응용 의 결정 요인

1. Latency budget (real-time vs batch).
2. Sampler 의 스타일 (Karras vs exponential).
3. Distillation 의 사용 여부.
4. ControlNet / IP-Adapter 의 noise floor.

💻 패턴

Pattern 1 — diffusers basic

from diffusers import StableDiffusionXLPipeline, DPMSolverMultistepScheduler
import torch

pipe = StableDiffusionXLPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
).to("cuda")
pipe.scheduler = DPMSolverMultistepScheduler.from_config(
    pipe.scheduler.config, algorithm_type="dpmsolver++", use_karras_sigmas=True
)

img = pipe(prompt="cinematic portrait, dramatic light",
           num_inference_steps=25, guidance_scale=6.5).images[0]

Pattern 2 — Turbo / Lightning (low-step)

from diffusers import StableDiffusionXLPipeline, EulerDiscreteScheduler
pipe = StableDiffusionXLPipeline.from_pretrained(
    "stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16"
).to("cuda")
pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")

img = pipe(prompt="a wizard casting a fireball",
           num_inference_steps=4, guidance_scale=0.0).images[0]

Pattern 3 — FLUX (flow-matching)

from diffusers import FluxPipeline
pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16
).to("cuda")
img = pipe(prompt="forest stream at golden hour",
           num_inference_steps=28, guidance_scale=3.5,
           max_sequence_length=512).images[0]

Pattern 4 — UniPC (fast convergence)

from diffusers import UniPCMultistepScheduler
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
img = pipe(prompt="..", num_inference_steps=12, guidance_scale=6.0).images[0]

Pattern 5 — sweep test

import os, time
prompts = ["a stoic samurai standing in falling snow"]
for steps in (8, 12, 16, 20, 25, 30, 40):
    t0 = time.time()
    out = pipe(prompt=prompts[0], num_inference_steps=steps,
               guidance_scale=6.5, generator=torch.Generator("cuda").manual_seed(42)).images[0]
    out.save(f"sweep_{steps:02d}.png")
    print(f"steps={steps} time={time.time()-t0:.2f}s")

Pattern 6 — Karras sigmas custom

import torch
def karras_sigmas(n, sigma_min=0.029, sigma_max=14.6, rho=7.0):
    ramp = torch.linspace(0, 1, n)
    min_inv = sigma_min ** (1/rho); max_inv = sigma_max ** (1/rho)
    return (max_inv + ramp * (min_inv - max_inv)) ** rho
sigmas = karras_sigmas(20).to("cuda")
pipe.scheduler.set_timesteps(sigmas=sigmas)

Pattern 7 — ControlNet 의 step 협응

# ControlNet 의 conditioning step 시작/종료 비율
img = pipe(prompt=p, image=control_img, num_inference_steps=25,
           controlnet_conditioning_scale=0.9,
           control_guidance_start=0.0,   # 첫 step 부터
           control_guidance_end=0.7      # 70% 지점 에서 중단 → free 마지막 30%
           ).images[0]

Pattern 8 — early-exit (latent preview)

# Inspect mid-process latent
def callback(pipe, step, timestep, callback_kwargs):
    if step == 5:
        latents = callback_kwargs["latents"]
        # decode preview, show, etc.
    return callback_kwargs

pipe(prompt=p, num_inference_steps=20,
     callback_on_step_end=callback,
     callback_on_step_end_tensor_inputs=["latents"])

매 결정 기준

상황	Steps	Sampler
SDXL turbo / lightning	1–4	Euler
LCM	4–8	LCM
SDXL 일반	20–28	DPM++ 2M Karras
SDXL detail-critical	28–35	DPM++ 3M SDE
FLUX dev	25–30	flow-match Euler
FLUX schnell (distilled)	4	Euler
ControlNet inpaint	25–30	DPM++ / UniPC

기본값: 매 SDXL → 25 steps DPM++ 2M Karras CFG 6.5. 매 FLUX dev → 28 steps CFG 3.5.

🔗 Graph

• 부모: Stable Diffusion · Diffusion Models
• 변형: CFG 스케일(Classifier-Free Guidance Scale)
• 응용: AI 이미지 생성 (AI Image Generation) · 사후 편집 (Post-editing)
• Adjacent: FLUX · ComfyUI

🤖 LLM 활용

언제: 매 sweep config 의 generation, 매 sampler comparison table 의 작성. 언제 X: 매 visual quality 의 final judge — 매 image grid 의 inspection 이 필요.

❌ 안티패턴

• Steps 100: 매 cost 4× 의 quality gain 거의 0.
• Distilled model + high steps: 매 SDXL Turbo 의 30 step → 매 over-burn.
• Sampler 의 random pick: 매 prompt-sampler interaction 의 무시.
• CFG + steps 의 단독 변경: 매 둘은 결합 — 매 high CFG → 매 더 많은 step 필요.

🧪 검증 / 중복

• Verified (k-diffusion repo, diffusers schedulers docs, ComfyUI manager).
• 신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — sampler family + step decision matrix