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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Antigravity Agent
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---
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id: wiki-2026-0508-prompt-weight
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title: Prompt Weight
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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: [Prompt Weighting, Attention Weighting, Token Emphasis, Prompt Strength]
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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: [prompt-engineering, generative-ai, stable-diffusion, midjourney, image-gen]
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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 / ComfyUI / Automatic1111 / Midjourney
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---
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# Prompt Weight
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## 매 한 줄
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> **"매 emphasize / de-emphasize specific tokens in a prompt — `(word:1.3)` syntax of Stable Diffusion, `--w` of Midjourney, attention scaling under the hood"**. AUTOMATIC1111 (2022) 의 prompt-weight syntax 가 community standard 로 자리잡음. 2026 currently FLUX, SD3.5, SDXL Turbo, Midjourney v7 모두 weighting 지원; T5-encoded models 는 syntax 가 다름.
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## 매 핵심
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### 매 syntax (Stable Diffusion / A1111 / ComfyUI)
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- `(word)` — weight ×1.1.
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- `((word))` — ×1.21.
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- `(word:1.3)` — explicit weight ×1.3.
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- `[word]` — weight ÷1.1.
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- `[word:0.5]` — weight ×0.5.
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- `(red hair:1.4) (blue eyes:0.8)` — phrase-level.
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### 매 syntax (Midjourney v7)
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- `cat dog` — equal weight.
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- `cat::2 dog::1` — double-colon multi-prompt with weights.
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- `--w 0.5` — image weight (text vs reference image).
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- `--s 250` — stylize strength.
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### 매 syntax (FLUX / T5-encoded)
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- T5 understands natural language; `(word:1.3)` syntax 매 mostly ignored.
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- Use **emphasis via wording**: "very prominent X", "subtle hint of Y".
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- Some forks (forge, ComfyUI) 매 still parse weights via re-prompting.
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### 매 mechanism (under the hood)
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- CLIP/T5 text encoder → token embeddings.
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- A1111: weight w → multiply token embedding by w (post-encoding rescale).
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- Compel library: more sophisticated — interpolates between conditioning vectors.
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- Cross-attention scaling: alternative — scale K/V at attention layer.
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### 매 best practices
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- Stay between 0.5 and 1.5; 매 above 1.5 → distortion / saturation.
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- Negative prompts often more effective than `[word]` syntax.
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- Long prompts: weight the critical 3-5 tokens, leave rest at 1.0.
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- For T5 models, use natural-language emphasis instead.
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## 💻 패턴
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### diffusers + Compel (programmatic weighting)
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```python
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from diffusers import StableDiffusionXLPipeline
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from compel import Compel, ReturnedEmbeddingsType
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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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compel = Compel(
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tokenizer=[pipe.tokenizer, pipe.tokenizer_2],
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text_encoder=[pipe.text_encoder, pipe.text_encoder_2],
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returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,
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requires_pooled=[False, True],
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)
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prompt = "a (red:1.4) sports car on a (sunny:0.7) beach, cinematic"
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conditioning, pooled = compel(prompt)
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image = pipe(prompt_embeds=conditioning, pooled_prompt_embeds=pooled).images[0]
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```
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### A1111-style parsing (manual)
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```python
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import re
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def parse_weighted(prompt):
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"""Return list of (text, weight) tuples."""
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out, depth_paren, depth_brack = [], 0, 0
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# Simplified: handles (text:1.3) only
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pattern = re.compile(r"\(([^():]+):([\d.]+)\)")
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parts, last = [], 0
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for m in pattern.finditer(prompt):
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if m.start() > last:
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parts.append((prompt[last:m.start()], 1.0))
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parts.append((m.group(1), float(m.group(2))))
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last = m.end()
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if last < len(prompt):
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parts.append((prompt[last:], 1.0))
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return parts
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```
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### Cross-attention scaling (Hugging Face)
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```python
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# Scale a specific token's attention by factor
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from diffusers.models.attention_processor import AttnProcessor
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class WeightedAttn(AttnProcessor):
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def __init__(self, token_idx, scale):
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self.token_idx, self.scale = token_idx, scale
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def __call__(self, attn, hidden, encoder_hidden, attention_mask=None):
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# In encoder_hidden, multiply token_idx slot by scale before attn
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encoder_hidden = encoder_hidden.clone()
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encoder_hidden[:, self.token_idx] *= self.scale
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return super().__call__(attn, hidden, encoder_hidden, attention_mask)
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```
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### Midjourney prompt
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```
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masterpiece anime girl::3 cyberpunk city background::1 neon lights::0.5
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--ar 16:9 --s 500 --v 7
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```
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### FLUX-style natural-language emphasis (no syntax)
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```python
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# Bad (FLUX ignores): "(red hair:1.5) girl"
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# Good: "girl with strikingly vivid red hair, the red is the most prominent color in the image"
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```
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### Prompt-blending (interpolate two prompts)
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```python
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from compel import Compel
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c1 = compel("a cat in a forest")
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c2 = compel("a robot in a city")
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mixed = (c1 + c2) / 2 # Compel supports tensor arithmetic
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image = pipe(prompt_embeds=mixed).images[0]
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```
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### Step-conditional weighting (`[from:to:step]`)
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```
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[cat:dog:0.5] in a field
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# 0-50% steps: "cat", 50-100%: "dog"
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# Useful for changing subject mid-denoising
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| SDXL / SD1.5 / SD2.1 | A1111 `(word:1.3)` syntax via Compel |
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| FLUX / SD3.5 (T5) | Natural-language emphasis |
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| Midjourney v7 | `::weight` syntax |
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| Subject + style mix | Multi-prompt with `::` or compel blends |
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| Subtle adjustment | 0.8-1.2 range |
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| Strong push | 1.3-1.5; rarely above |
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| Suppress concept | Negative prompt (preferred) over `[word]` |
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**기본값**: Compel for SDXL programmatic; A1111 syntax for casual; natural language for FLUX.
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## 🔗 Graph
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- 부모: [[Prompt_Engineering|Prompt-Engineering]] · [[AI 이미지 생성 (AI Image Generation)|Image-Generation]]
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- 변형: [[Negative Prompt]]
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- 응용: [[Stable-Diffusion]] · [[FLUX]] · [[Midjourney]] · [[ComfyUI]]
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- Adjacent: [[CLIP]] · [[Diffusion-Models]] · [[ControlNet]]
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## 🤖 LLM 활용
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**언제**: image generation pipelines, fine-grained subject/style control, automated prompt synthesis.
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**언제 X**: text-only LLM prompts (GPT/Claude don't use this syntax — use emphasis words instead), T5-only models.
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## ❌ 안티패턴
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- **Weight > 2.0**: 매 saturated artifacts, deformed output.
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- **Stacking parens** `(((((word)))))`: hard to read; use explicit `(word:1.6)`.
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- **A1111 syntax on FLUX/T5**: silently ignored — switch to natural language.
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- **Weighting every token**: dilutes effect; pick 2-4 priorities.
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- **Forgetting negative prompt**: often the right tool for "not X".
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## 🧪 검증 / 중복
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- Verified (AUTOMATIC1111 wiki, Compel docs, Midjourney v7 docs 2024-2025, FLUX official guidance).
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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 — canonical prompt-weight ref + FLUX/T5 caveat |
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