docs(10_Wiki): Topic_Business/General/Graphic/Programming을 Topics/ 하위로 이동

최상위 10_Wiki/Topic_*였던 4개 카테고리 폴더를 10_Wiki/Topics/Topic_* 로 재배치. 콘텐츠 변경 없음(순수 폴더 이동) — Topics/ 하위 나머지 폴더는 이미 지난 커밋에서 전부 정리된 상태(잔존 항목은 에이전트 운영 상태 및 사용자가 보존을 요청한 업데이트0615/무제 3.canvas 뿐).
2026-07-05 00:44:01 +09:00
parent e9cbf23ab5
commit 9a135bd19d
6127 changed files with 0 additions and 0 deletions
@@ -0,0 +1,235 @@
+---
+id: wiki-2026-0508-cfg-scale-classifier-free-guidance
+title: CFG Scale (Classifier-Free Guidance)
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [CFG, classifier-free guidance, guidance scale, prompt strength, negative prompt, conditioning strength]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.93
+verification_status: applied
+tags: [diffusion, stable-diffusion, cfg, guidance, sampling, prompt-engineering, dpm-solver, conditioning]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: Python
+  framework: Diffusers / ComfyUI / Automatic1111
+---
+
+# CFG Scale (Classifier-Free Guidance)
+
+## 📌 한 줄 통찰
+> **"매 prompt 의 strict 의 dial"**. 매 diffusion 의 generation 의 매 conditioned (prompt) ↔ 매 unconditional 의 trade-off. 매 high CFG = 매 prompt 의 strict 가, 매 over-saturation. 매 sweet spot 7-9 (SDXL) / 3.5-7 (Flux).
+
+## 📖 핵심
+
+### 매 formula
+$$\epsilon_{\text{guided}} = \epsilon_{\text{uncond}} + s \cdot (\epsilon_{\text{cond}} - \epsilon_{\text{uncond}})$$
+
+- 매 s = CFG scale.
+- 매 s = 1 → 매 unconditional (prompt 무시).
+- 매 s = 7 → 매 typical.
+- 매 s > 15 → 매 over-cooked.
+- 매 negative prompt = 매 conditional 의 두 번째 (with -1 coefficient).
+
+### 매 effect
+
+| CFG | 결과 |
+|---|---|
+| < 1 | 매 random / blank |
+| 1-3 | 매 loose, 매 creative |
+| 5-7 | 매 balanced (default) |
+| 7-12 | 매 prompt-strict |
+| 13-20 | 매 over-saturated, 매 burned |
+| > 20 | 매 garbage |
+
+### 매 modern alternative
+
+#### Flux (Black Forest Labs)
+- 매 distilled CFG (CFG=1 가능).
+- 매 inference 의 fast (no double pass).
+
+#### Negative prompt
+- 매 unconditional 의 noise 의 swap.
+- 매 explicit avoidance.
+
+#### Dynamic CFG
+- 매 step 의 따라 변동.
+- 매 early high → 매 late low.
+
+#### Adaptive CFG (CFG++)
+- 매 adaptive scale.
+- 매 over-saturation 회피.
+
+### 매 sampler 와 의 interaction
+- 매 DPM++ 2M Karras: 20 step + CFG 7.
+- 매 DPM++ SDE: 30 step + CFG 5.
+- 매 Euler ancestral: 매 stochastic.
+- 매 Flux: CFG=1 + 4-step.
+
+### 매 prompt quality 와 의 관계
+- 매 좋은 prompt + CFG 7 = 매 best.
+- 매 나쁜 prompt + CFG ↑ = 매 더 나쁘게 (confident garbage).
+- 매 negative prompt 의 keyword 매 wrong → 매 오히려 push.
+
+→ **CFG ↑ ≠ 매 quality ↑**. 매 prompt quality 가 base.
+
+### 매 typical setup
+| 모델 | CFG | Steps | Sampler |
+|---|---|---|---|
+| SD 1.5 | 7-12 | 20-30 | DPM++ 2M Karras |
+| SDXL | 7-9 | 20-30 | DPM++ 2M Karras |
+| SDXL Turbo | 1 | 1-4 | Euler |
+| Flux Dev | 3.5 | 20-50 | Euler |
+| Flux Schnell | 1 | 4 | Euler |
+
+## 💻 패턴
+
+### Diffusers (basic)
+```python
+from diffusers import StableDiffusionXLPipeline
+import torch
+
+pipe = StableDiffusionXLPipeline.from_pretrained(
+    'stabilityai/stable-diffusion-xl-base-1.0',
+    torch_dtype=torch.float16,
+).to('cuda')
+
+image = pipe(
+    prompt='a cat with a hat, oil painting, vivid colors',
+    negative_prompt='blurry, low quality, watermark',
+    guidance_scale=7.0,  # 매 CFG
+    num_inference_steps=30,
+).images[0]
+```
+
+### CFG sweep (find sweet spot)
+```python
+import matplotlib.pyplot as plt
+
+cfg_values = [1, 3, 5, 7, 9, 12, 15, 20]
+fig, axes = plt.subplots(2, 4, figsize=(20, 10))
+
+for ax, cfg in zip(axes.flat, cfg_values):
+    image = pipe(prompt=prompt, guidance_scale=cfg, generator=torch.manual_seed(42)).images[0]
+    ax.imshow(image)
+    ax.set_title(f'CFG={cfg}')
+    ax.axis('off')
+plt.show()
+```
+
+### Flux (CFG=1, distilled)
+```python
+from diffusers import FluxPipeline
+
+pipe = FluxPipeline.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.bfloat16).to('cuda')
+
+image = pipe(
+    prompt='a cat with a hat',
+    guidance_scale=3.5,  # Flux dev
+    num_inference_steps=50,
+).images[0]
+
+# Schnell (4-step, distilled)
+pipe_schnell = FluxPipeline.from_pretrained('black-forest-labs/FLUX.1-schnell', torch_dtype=torch.bfloat16).to('cuda')
+image = pipe_schnell(
+    prompt=prompt,
+    guidance_scale=0,
+    num_inference_steps=4,
+).images[0]
+```
+
+### Dynamic CFG
+```python
+def dynamic_cfg_callback(pipe, step, timestep, callback_kwargs):
+    """매 early step 의 high CFG, 매 late 의 low."""
+    progress = step / pipe.num_inference_steps
+    cfg = 12 - 7 * progress  # 12 → 5
+    callback_kwargs['guidance_scale'] = cfg
+    return callback_kwargs
+
+pipe(prompt=prompt, callback_on_step_end=dynamic_cfg_callback)
+```
+
+### Custom CFG implementation
+```python
+def classifier_free_guidance(model, x_t, t, prompt_emb, neg_prompt_emb, scale):
+    # 매 batched: cond + uncond
+    emb_combined = torch.cat([neg_prompt_emb, prompt_emb])
+    x_t_combined = torch.cat([x_t, x_t])
+    
+    eps_combined = model(x_t_combined, t, emb_combined)
+    eps_uncond, eps_cond = eps_combined.chunk(2)
+    
+    # 매 guided
+    return eps_uncond + scale * (eps_cond - eps_uncond)
+```
+
+### CFG++ (adaptive)
+```python
+def cfg_pp(eps_cond, eps_uncond, scale, x_t, alpha_t):
+    """CFG++ — 매 over-saturation 회피."""
+    cfg_basic = eps_uncond + scale * (eps_cond - eps_uncond)
+    # 매 sample-space adjustment
+    delta = (cfg_basic - eps_uncond) * (1 - alpha_t)
+    return eps_uncond + scale * (eps_cond - eps_uncond) - delta
+```
+
+### Negative prompt strategy
+```python
+# 매 좋은 negative prompt
+negative_prompts = {
+    'photorealistic': 'cartoon, anime, painting, drawing',
+    'illustration': 'photo, photograph, photographic',
+    'quality': 'blurry, low quality, jpeg artifacts, watermark, signature, deformed, ugly',
+    'anatomy': 'extra limbs, deformed hands, missing fingers, distorted face',
+}
+
+prompt = 'a portrait of a woman'
+style = 'photorealistic'
+neg = negative_prompts['quality'] + ', ' + negative_prompts[style]
+
+image = pipe(prompt=prompt, negative_prompt=neg, guidance_scale=7).images[0]
+```
+
+## 🤔 결정 기준
+| 상황 | CFG |
+|---|---|
+| Photorealistic | 7-9 |
+| Stylized art | 8-12 |
+| Creative / loose | 3-5 |
+| Strict prompt | 10-15 |
+| Flux Dev | 3.5 |
+| Flux Schnell / SDXL Turbo | 1 |
+| Burning / over-saturated | < 7 |
+
+**기본값**: SDXL = 7, Flux = 3.5, Schnell = 1.
+
+## 🔗 Graph
+- 부모: [[Diffusion-Models]] · [[AI 이미지 생성 (AI Image Generation)|Image-Generation]]
+- 응용: [[Stable-Diffusion]] · [[Flux]] · [[ComfyUI]]
+- Adjacent: [[Negative Prompt]]
+
+## 🤖 LLM 활용
+**언제**: 매 image generation tuning. 매 SD / Flux pipeline. 매 quality vs prompt-fidelity trade-off.
+**언제 X**: 매 distilled model (CFG=1). 매 deterministic 매 sampler.
+
+## ❌ 안티패턴
+- **CFG 의 high 의 mean fix**: 매 over-saturation.
+- **Negative prompt 의 wrong word + high CFG**: 매 confident garbage.
+- **Same CFG 의 모든 model**: 매 distilled vs base 의 다름.
+- **Sampler 의 mismatch**: 매 sampler 별 의 sweet spot.
+- **CFG = 1 가 prompt 무시**: 매 unconditional.
+
+## 🧪 검증 / 중복
+- Verified (Ho & Salimans CFG paper, Flux docs, Diffusers).
+- 신뢰도 A.
+- Related: [[Stable-Diffusion]] · [[Flux]] · [[Negative Prompt]] · [[DPM-Solver]] · [[AI Image Generation]].
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — formula + dynamic / Flux / sweep + 매 diffusers code |