簡介
版式是 T 卹設計的重要組成部分——引言、迷因文字、品牌名稱、風格化字體。人工智慧不僅可以創建圖像，還必須產生具有正確字體樣式的漂亮文本，並將其放置在襯衫上的正確位置。
1. 人工智慧中的版面挑戰
為什麼文字產生很困難？
Stable Diffusion (SDXL) + text:
❌ Chữ bị sai spelling ("COFEE" thay vì "COFFEE")
❌ Chữ bị méo, khó đọc
❌ Font không consistent
❌ Chữ bị mirror/reverse
Giải pháp: KHÔNG dùng Diffusion model để render text trực tiếp.
→ Dùng text rendering engine riêng + composite lên design.
2. 文字產生管道
User Input
├── "Tạo áo với quote motivational"
│
├── LLM generate quote text
│ └── "HUSTLE HARDER"
│
├── Font recommendation engine
│ └── Bold sans-serif, uppercase
│
├── Text rendering (Pillow/Cairo)
│ └── High-res text image (transparent)
│
├── Style transfer (optional)
│ └── Neon glow, shadow, gradient
│
└── Composite onto design
└── Auto-placement + position
3. AI文字內容產生器
class TextContentGenerator:
"""AI generate text content cho áo thun"""
async def generate_text(
self,
category: str,
style: str = "default",
language: str = "en",
) -> list[str]:
prompt = f"""
Generate 5 short text/quotes for a {style} style t-shirt.
Category: {category}
Language: {language}
Rules:
- Maximum 5 words per line
- Maximum 2 lines
- ALL CAPS preferred for impact
- Catchy, memorable, trendy
- No offensive content
Return as JSON array of strings.
"""
response = await self.llm.chat.completions.create(
model="gpt-4o-mini",
messages=[{"role": "user", "content": prompt}],
response_format={"type": "json_object"},
)
return json.loads(
response.choices[0].message.content
)["texts"]
4.字體推薦引擎
class FontRecommender:
"""Gợi ý font phù hợp theo style design"""
FONT_STYLES = {
"cyberpunk": [
{"name": "Orbitron", "weight": "Bold", "case": "upper"},
{"name": "Rajdhani", "weight": "SemiBold", "case": "upper"},
],
"minimal": [
{"name": "Montserrat", "weight": "Light", "case": "upper"},
{"name": "Futura", "weight": "Medium", "case": "mixed"},
],
"streetwear": [
{"name": "Impact", "weight": "Regular", "case": "upper"},
{"name": "Bebas Neue", "weight": "Regular", "case": "upper"},
],
"vintage": [
{"name": "Playfair Display", "weight": "Bold", "case": "mixed"},
{"name": "Lora", "weight": "Regular", "case": "mixed"},
],
"gaming": [
{"name": "Press Start 2P", "weight": "Regular", "case": "upper"},
{"name": "Audiowide", "weight": "Regular", "case": "upper"},
],
"japanese": [
{"name": "Noto Sans JP", "weight": "Black", "case": "mixed"},
{"name": "M PLUS 1p", "weight": "Bold", "case": "mixed"},
],
}
def recommend(self, design_style: str) -> list[dict]:
return self.FONT_STYLES.get(
design_style, self.FONT_STYLES["minimal"]
)
5. 文字渲染引擎
from PIL import Image, ImageDraw, ImageFont
class TextRenderer:
"""Render text thành image cho composite"""
def render(
self,
text: str,
font_name: str,
font_size: int,
color: str = "#FFFFFF",
effects: list[str] | None = None,
) -> Image.Image:
# Load font
font = ImageFont.truetype(
f"fonts/{font_name}.ttf", font_size
)
# Calculate text size
bbox = font.getbbox(text)
text_width = bbox[2] - bbox[0]
text_height = bbox[3] - bbox[1]
# Create canvas with padding
padding = font_size // 4
canvas = Image.new(
"RGBA",
(text_width + padding * 2, text_height + padding * 2),
(0, 0, 0, 0)
)
draw = ImageDraw.Draw(canvas)
draw.text(
(padding, padding),
text, font=font,
fill=color,
)
# Apply effects
if effects:
for effect in effects:
canvas = self._apply_effect(canvas, effect)
return canvas
def _apply_effect(
self, img: Image.Image, effect: str
) -> Image.Image:
if effect == "neon_glow":
return self._neon_glow(img)
elif effect == "drop_shadow":
return self._drop_shadow(img)
elif effect == "outline":
return self._outline(img)
elif effect == "gradient":
return self._gradient_fill(img)
return img
def _neon_glow(self, img: Image.Image) -> Image.Image:
"""Hiệu ứng neon glow cho text"""
from PIL import ImageFilter
import numpy as np
# Create glow layer
glow = img.filter(ImageFilter.GaussianBlur(radius=10))
glow = glow.filter(ImageFilter.GaussianBlur(radius=5))
# Brighten glow
glow_array = np.array(glow)
glow_array[:, :, :3] = np.clip(
glow_array[:, :, :3] * 1.5, 0, 255
).astype(np.uint8)
glow = Image.fromarray(glow_array)
# Composite: glow behind text
result = Image.alpha_composite(glow, img)
return result
6. 自動放置
class TextPlacer:
"""Tự động đặt text vào vị trí phù hợp trên design"""
def auto_place(
self,
design: Image.Image,
text_image: Image.Image,
position: str = "auto",
) -> Image.Image:
if position == "auto":
position = self._find_best_position(
design, text_image
)
positions = {
"top_center": self._place_top_center,
"bottom_center": self._place_bottom_center,
"center": self._place_center,
"arc_top": self._place_arc_top,
"arc_bottom": self._place_arc_bottom,
}
placer = positions.get(position, self._place_center)
return placer(design, text_image)
def _find_best_position(
self,
design: Image.Image,
text_image: Image.Image,
) -> str:
"""AI-detect vùng trống tốt nhất cho text"""
# Phân tích content density theo vùng
regions = self._analyze_regions(design)
# Text đặt ở vùng ít content nhất
least_dense = min(regions, key=lambda r: r["density"])
return least_dense["position"]
總結
人工智慧排版系統：
- 文字產生 — LLM 建立引言、迷因文字、口號
- 字體推薦－根據設計風格的字體建議
- 文本渲染 — Pillow/Cairo 渲染高品質文本
- 效果 — 霓虹燈發光、陰影、輪廓、漸變
- 自動放置 — 在設計上找到合適的空白區域
下一篇文章開始第 4 部分：AI 個人化——AI 系統學習每個使用者的美感趣味。