refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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---
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id: wiki-2026-0508-이미지-생성-최적화-image-generation-opti
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title: 이미지 생성 최적화 (Image Generation Optimization)
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Image Gen Optimization, Diffusion Inference Optimization, 이미지 생성 가속]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [ai, image-generation, optimization, inference, diffusion]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: python
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framework: diffusers-tensorrt
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---
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# 이미지 생성 최적화 (Image Generation Optimization)
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## 매 한 줄
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> **"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%.
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## 매 핵심
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### 매 optimization axes
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- **Steps**: 50 → 4 (distillation).
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- **Precision**: FP32 → FP16 → FP8 → INT4.
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- **Compilation**: eager → torch.compile → TensorRT.
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- **Caching**: KV cache, prompt embed cache, latent cache.
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- **Resolution**: 1024 → progressive (256→512→1024).
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- **Batching**: dynamic batching, continuous batching.
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### 매 distillation 기법
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- **LCM**: Latent Consistency Model, 4-step.
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- **SDXL Lightning**: 1/2/4/8-step variants.
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- **Hyper-SD**: 1-step possible.
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- **FLUX Schnell**: 4-step out-of-box.
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- **DMD2**: distribution matching, single-step quality.
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### 매 응용
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1. Realtime gen 의 sub-second UX (Krea, Magnific).
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2. On-device mobile gen (Core ML, MLC).
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3. Mass batch render 의 throughput max.
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## 💻 패턴
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### Step reduction (LCM-LoRA)
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```python
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from diffusers import StableDiffusionXLPipeline, LCMScheduler
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import torch
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pipe = StableDiffusionXLPipeline.from_pretrained(
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"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
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).to("cuda")
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pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
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pipe.load_lora_weights("latent-consistency/lcm-lora-sdxl")
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# 4-step gen
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img = pipe(prompt, num_inference_steps=4, guidance_scale=1.0).images[0]
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# 50-step (3.5s) → 4-step (0.4s) on A100
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```
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### torch.compile
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```python
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pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
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pipe.vae.decode = torch.compile(pipe.vae.decode, mode="reduce-overhead")
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# warmup
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_ = pipe("warmup", num_inference_steps=4)
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# 1.4-2x speedup after warmup
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```
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### TensorRT (production)
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```python
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# Export → TensorRT engine
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from polygraphy.backend.trt import EngineFromNetwork, NetworkFromOnnxPath, TrtRunner
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# 1. ONNX export
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torch.onnx.export(pipe.unet, dummy_inputs, "unet.onnx",
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opset_version=17, dynamic_axes={...})
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# 2. trtexec build
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# trtexec --onnx=unet.onnx --saveEngine=unet.plan --fp16 --memPoolSize=workspace:8192
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# 3. Runtime
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with TrtRunner(EngineFromNetwork(NetworkFromOnnxPath("unet.onnx"))) as r:
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out = r.infer({"sample": x, "timestep": t, "encoder_hidden_states": h})
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# 2-3x faster than torch.compile
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```
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### FP8 quantization (Hopper / Ada)
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```python
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from optimum.quanto import quantize, qfloat8, freeze
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quantize(pipe.transformer, weights=qfloat8, activations=qfloat8)
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freeze(pipe.transformer)
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# memory: 24GB → 13GB; latency: 1.3x faster on H100
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```
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### Prompt embed cache
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```python
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import hashlib, pickle
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from pathlib import Path
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class EmbedCache:
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def __init__(self, dir="./.embed_cache"):
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self.dir = Path(dir); self.dir.mkdir(exist_ok=True)
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def get_or_compute(self, prompt, encoder_fn):
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key = hashlib.sha256(prompt.encode()).hexdigest()
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p = self.dir / f"{key}.pt"
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if p.exists(): return torch.load(p)
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emb = encoder_fn(prompt)
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torch.save(emb, p)
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return emb
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cache = EmbedCache()
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emb = cache.get_or_compute(prompt, pipe.encode_prompt)
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# repeat prompt: skip text encoder entirely
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```
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### Continuous batching (server)
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```python
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# vLLM-style continuous batching for diffusion (sdxl-batched-server)
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from collections import deque
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import asyncio
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class BatchedServer:
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def __init__(self, max_batch=8, wait_ms=20):
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self.q = deque(); self.max_batch = max_batch; self.wait_ms = wait_ms
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async def submit(self, prompt):
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fut = asyncio.Future(); self.q.append((prompt, fut))
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return await fut
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async def loop(self):
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while True:
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await asyncio.sleep(self.wait_ms/1000)
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if not self.q: continue
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batch = [self.q.popleft() for _ in range(min(len(self.q), self.max_batch))]
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prompts = [p for p,_ in batch]
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imgs = pipe(prompts).images
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for (_, fut), img in zip(batch, imgs): fut.set_result(img)
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```
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### Progressive resolution
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```python
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# Cascade: 256 → 512 → 1024
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img_lo = pipe(prompt, height=256, width=256, num_inference_steps=8).images[0]
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img_md = img2img_pipe(prompt, image=img_lo, strength=0.5,
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height=512, width=512, num_inference_steps=8).images[0]
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img_hi = img2img_pipe(prompt, image=img_md, strength=0.3,
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height=1024, width=1024, num_inference_steps=8).images[0]
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# Total cost < single-pass 1024
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```
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### MLX (Apple Silicon)
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```python
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import mlx.core as mx
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from mlx_diffusion import StableDiffusion
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sd = StableDiffusion("stabilityai/sdxl-turbo", float16=True)
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img = sd.generate("a cat", n_steps=4, n_images=4)
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# M3 Max: 4-step 1024px in ~1.2s
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```
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## 매 결정 기준
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| 상황 | Approach |
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| latency critical | distill (4-step) + TensorRT |
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| memory tight | FP8/INT4 quantize |
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| Apple device | MLX |
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| repeat prompts | embed cache |
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| many concurrent | continuous batch |
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| highest quality | full 50-step + xformers |
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**기본값**: 4-step LCM/Lightning + torch.compile + FP16, escalate to TRT for >10 RPS.
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## 🔗 Graph
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- 부모: [[AI Image Generation]]
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- Adjacent: [[TensorRT]] · [[torch.compile]] · [[오픈소스 이미지 모델 미세 조정 및 배포]]
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## 🤖 LLM 활용
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**언제**: bottleneck profiling interpretation, kernel fusion plan, deploy config.
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**언제 X**: low-level CUDA kernel writing — Triton/cutlass docs 직접 참조.
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## ❌ 안티패턴
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- **Optimize before profile**: nvtx/torch profiler 없이 추측.
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- **Over-distillation**: 1-step 이라 quality cliff — perceptual eval 누락.
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- **Quantize without calib**: dynamic quant 만으로 quality 폭락.
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- **Single-process bottleneck**: GIL 무시한 sync server.
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## 🧪 검증 / 중복
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- Verified (LCM paper Luo 2023, SDXL Lightning ByteDance 2024, NVIDIA TRT-LLM docs).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — distillation + quantize + compile stack. |
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