2nd/10_Wiki/Topics/AI_and_ML/이미지 생성 최적화 (Image Generation Optimization).md

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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-이미지-생성-최적화-image-generation-opti	이미지 생성 최적화 (Image Generation Optimization)	10_Wiki/Topics	verified	self	Image Gen Optimization Diffusion Inference Optimization 이미지 생성 가속	none	A	0.9	applied	ai image-generation optimization inference diffusion		2026-05-10	pending	language framework python diffusers-tensorrt

이미지 생성 최적화 (Image Generation Optimization)

매 한 줄

"매 latency × cost × quality 의 trilemma 를 step reduction, quantization, compilation 으로 동시 해결". 2026 의 production image gen 은 distillation (4-step Schnell, Lightning, LCM), quantization (FP8/INT4), graph compilation (TensorRT, torch.compile), batch fusion 을 통해 50-step 30s → 4-step 0.5s 로 압축한다. 매 quality 손실 은 perceptual eval 에서 < 5%.

매 핵심

매 optimization axes

• Steps: 50 → 4 (distillation).
• Precision: FP32 → FP16 → FP8 → INT4.
• Compilation: eager → torch.compile → TensorRT.
• Caching: KV cache, prompt embed cache, latent cache.
• Resolution: 1024 → progressive (256→512→1024).
• Batching: dynamic batching, continuous batching.

매 distillation 기법

• LCM: Latent Consistency Model, 4-step.
• SDXL Lightning: 1/2/4/8-step variants.
• Hyper-SD: 1-step possible.
• FLUX Schnell: 4-step out-of-box.
• DMD2: distribution matching, single-step quality.

매 응용

1. Realtime gen 의 sub-second UX (Krea, Magnific).
2. On-device mobile gen (Core ML, MLC).
3. Mass batch render 의 throughput max.

💻 패턴

Step reduction (LCM-LoRA)

from diffusers import StableDiffusionXLPipeline, LCMScheduler
import torch

pipe = StableDiffusionXLPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
).to("cuda")
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
pipe.load_lora_weights("latent-consistency/lcm-lora-sdxl")

# 4-step gen
img = pipe(prompt, num_inference_steps=4, guidance_scale=1.0).images[0]
# 50-step (3.5s) → 4-step (0.4s) on A100

torch.compile

pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
pipe.vae.decode = torch.compile(pipe.vae.decode, mode="reduce-overhead")

# warmup
_ = pipe("warmup", num_inference_steps=4)
# 1.4-2x speedup after warmup

TensorRT (production)

# Export → TensorRT engine
from polygraphy.backend.trt import EngineFromNetwork, NetworkFromOnnxPath, TrtRunner

# 1. ONNX export
torch.onnx.export(pipe.unet, dummy_inputs, "unet.onnx",
                  opset_version=17, dynamic_axes={...})

# 2. trtexec build
# trtexec --onnx=unet.onnx --saveEngine=unet.plan --fp16 --memPoolSize=workspace:8192

# 3. Runtime
with TrtRunner(EngineFromNetwork(NetworkFromOnnxPath("unet.onnx"))) as r:
    out = r.infer({"sample": x, "timestep": t, "encoder_hidden_states": h})
# 2-3x faster than torch.compile

FP8 quantization (Hopper / Ada)

from optimum.quanto import quantize, qfloat8, freeze

quantize(pipe.transformer, weights=qfloat8, activations=qfloat8)
freeze(pipe.transformer)
# memory: 24GB → 13GB; latency: 1.3x faster on H100

Prompt embed cache

import hashlib, pickle
from pathlib import Path

class EmbedCache:
    def __init__(self, dir="./.embed_cache"):
        self.dir = Path(dir); self.dir.mkdir(exist_ok=True)

    def get_or_compute(self, prompt, encoder_fn):
        key = hashlib.sha256(prompt.encode()).hexdigest()
        p = self.dir / f"{key}.pt"
        if p.exists(): return torch.load(p)
        emb = encoder_fn(prompt)
        torch.save(emb, p)
        return emb

cache = EmbedCache()
emb = cache.get_or_compute(prompt, pipe.encode_prompt)
# repeat prompt: skip text encoder entirely

Continuous batching (server)

# vLLM-style continuous batching for diffusion (sdxl-batched-server)
from collections import deque
import asyncio

class BatchedServer:
    def __init__(self, max_batch=8, wait_ms=20):
        self.q = deque(); self.max_batch = max_batch; self.wait_ms = wait_ms

    async def submit(self, prompt):
        fut = asyncio.Future(); self.q.append((prompt, fut))
        return await fut

    async def loop(self):
        while True:
            await asyncio.sleep(self.wait_ms/1000)
            if not self.q: continue
            batch = [self.q.popleft() for _ in range(min(len(self.q), self.max_batch))]
            prompts = [p for p,_ in batch]
            imgs = pipe(prompts).images
            for (_, fut), img in zip(batch, imgs): fut.set_result(img)

Progressive resolution

# Cascade: 256 → 512 → 1024
img_lo = pipe(prompt, height=256, width=256, num_inference_steps=8).images[0]
img_md = img2img_pipe(prompt, image=img_lo, strength=0.5,
                       height=512, width=512, num_inference_steps=8).images[0]
img_hi = img2img_pipe(prompt, image=img_md, strength=0.3,
                       height=1024, width=1024, num_inference_steps=8).images[0]
# Total cost < single-pass 1024

MLX (Apple Silicon)

import mlx.core as mx
from mlx_diffusion import StableDiffusion

sd = StableDiffusion("stabilityai/sdxl-turbo", float16=True)
img = sd.generate("a cat", n_steps=4, n_images=4)
# M3 Max: 4-step 1024px in ~1.2s

매 결정 기준

상황	Approach
latency critical	distill (4-step) + TensorRT
memory tight	FP8/INT4 quantize
Apple device	MLX
repeat prompts	embed cache
many concurrent	continuous batch
highest quality	full 50-step + xformers

기본값: 4-step LCM/Lightning + torch.compile + FP16, escalate to TRT for >10 RPS.

🔗 Graph

• 부모: AI Image Generation
• Adjacent: TensorRT · torch.compile · 오픈소스 이미지 모델 미세 조정 및 배포

🤖 LLM 활용

언제: bottleneck profiling interpretation, kernel fusion plan, deploy config. 언제 X: low-level CUDA kernel writing — Triton/cutlass docs 직접 참조.

❌ 안티패턴

• Optimize before profile: nvtx/torch profiler 없이 추측.
• Over-distillation: 1-step 이라 quality cliff — perceptual eval 누락.
• Quantize without calib: dynamic quant 만으로 quality 폭락.
• Single-process bottleneck: GIL 무시한 sync server.

🧪 검증 / 중복

• Verified (LCM paper Luo 2023, SDXL Lightning ByteDance 2024, NVIDIA TRT-LLM docs).
• 신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — distillation + quantize + compile stack.