簡介
使用者並不總是知道如何用文字描述設計。很多時候，他們有一個參考圖像——藝術品、標誌、表情包或其他 T 卹照片——並希望 AI 從中獲得靈感來創建新設計。本文建構了模組圖像參考分析。
1. 影像參考用例
User uploads:
├── Ảnh áo thun → AI trích xuất style & tạo design tương tự
├── Logo/artwork → AI adapt thành t-shirt design
├── Meme → AI tạo meme-style design
├── Illustration → AI generate design cùng art style
└── Mood board → AI phân tích aesthetic tổng thể
一般管道
Reference Image
│
├── CLIP encode → Style embedding
│
├── Color extraction → Dominant colors
│
├── Layout detection → Bố cục (center, offset, full-print)
│
└── Style classification → Cyberpunk / Minimal / Vintage / Gaming
│
▼
IP-Adapter + ControlNet → New design variations
2. 用於風格分析的 CLIP 嵌入
import torch
from transformers import CLIPModel, CLIPProcessor
from PIL import Image
class StyleAnalyzer:
"""Phân tích style từ reference image dùng CLIP"""
def __init__(self):
self.model = CLIPModel.from_pretrained(
"openai/clip-vit-large-patch14-336"
)
self.processor = CLIPProcessor.from_pretrained(
"openai/clip-vit-large-patch14-336"
)
self.model.eval()
def get_image_embedding(self, image: Image.Image) -> torch.Tensor:
"""Trích xuất CLIP embedding từ image"""
inputs = self.processor(images=image, return_tensors="pt")
with torch.no_grad():
embedding = self.model.get_image_features(**inputs)
return embedding / embedding.norm(dim=-1, keepdim=True)
def classify_style(self, image: Image.Image) -> dict:
"""Zero-shot classification style"""
style_labels = [
"cyberpunk neon design",
"minimalist clean design",
"vintage retro design",
"gaming esports design",
"streetwear urban design",
"japanese typography design",
"graffiti art design",
"nature botanical design",
"abstract geometric design",
"pop art colorful design",
"gothic dark design",
"kawaii cute design",
]
inputs = self.processor(
text=style_labels,
images=image,
return_tensors="pt",
padding=True,
)
with torch.no_grad():
outputs = self.model(**inputs)
logits = outputs.logits_per_image
probs = logits.softmax(dim=1)
results = {}
for label, prob in zip(style_labels, probs[0]):
style_name = label.replace(" design", "")
results[style_name] = round(prob.item(), 3)
# Sort by probability
return dict(sorted(
results.items(), key=lambda x: x[1], reverse=True
))
def compute_similarity(
self, img1: Image.Image, img2: Image.Image
) -> float:
"""Tính độ tương đồng giữa 2 designs"""
emb1 = self.get_image_embedding(img1)
emb2 = self.get_image_embedding(img2)
similarity = torch.cosine_similarity(emb1, emb2)
return similarity.item()
3.調色盤提取
from sklearn.cluster import KMeans
import numpy as np
from PIL import Image
from colorsys import rgb_to_hsv
class ColorExtractor:
"""Trích xuất color palette từ reference image"""
def extract_palette(
self, image: Image.Image, n_colors: int = 5
) -> list[dict]:
# Convert to RGB array
img_array = np.array(image.convert("RGB"))
# Remove transparent pixels if RGBA
if image.mode == "RGBA":
alpha = np.array(image)[:, :, 3]
mask = alpha > 128
pixels = img_array[mask]
else:
pixels = img_array.reshape(-1, 3)
# K-means clustering
kmeans = KMeans(n_clusters=n_colors, n_init=10, random_state=42)
kmeans.fit(pixels)
# Sort by cluster size (dominant first)
colors = []
labels, counts = np.unique(kmeans.labels_, return_counts=True)
sorted_indices = np.argsort(-counts)
for idx in sorted_indices:
rgb = kmeans.cluster_centers_[idx].astype(int)
hex_color = "#{:02x}{:02x}{:02x}".format(*rgb)
percentage = counts[idx] / len(pixels) * 100
colors.append({
"rgb": tuple(rgb),
"hex": hex_color,
"percentage": round(percentage, 1),
"cmyk": self.rgb_to_cmyk(*rgb),
"name": self.closest_color_name(rgb),
})
return colors
def rgb_to_cmyk(self, r: int, g: int, b: int) -> tuple:
"""Convert RGB to CMYK cho print preview"""
r, g, b = r / 255, g / 255, b / 255
k = 1 - max(r, g, b)
if k == 1:
return (0, 0, 0, 100)
c = round((1 - r - k) / (1 - k) * 100)
m = round((1 - g - k) / (1 - k) * 100)
y = round((1 - b - k) / (1 - k) * 100)
k = round(k * 100)
return (c, m, y, k)
def check_print_compatibility(
self, colors: list[dict]
) -> list[dict]:
"""Kiểm tra màu có in được tốt không"""
warnings = []
for color in colors:
c, m, y, k = color["cmyk"]
total_ink = c + m + y + k
if total_ink > 300:
warnings.append({
"color": color["hex"],
"issue": "Tổng ink > 300% — có thể bị lem khi in",
"suggestion": "Giảm saturation hoặc thay màu gần tương tự",
})
h, s, v = rgb_to_hsv(*[x / 255 for x in color["rgb"]])
if s > 0.95 and v > 0.95:
warnings.append({
"color": color["hex"],
"issue": "Neon/highly saturated — CMYK không thể tái tạo chính xác",
"suggestion": "Giảm saturation 10-15% hoặc dùng spot color",
})
return warnings
4. IP-Adapter — 風格一致的生成
from diffusers import StableDiffusionXLPipeline
from diffusers.utils import load_image
class ImageReferenceGenerator:
"""Generate design mới dựa trên style reference"""
def __init__(self):
self.pipe = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
torch_dtype=torch.float16,
)
self.pipe.load_lora_weights("models/sdxl-fashion-lora-v2.1")
# Load IP-Adapter
self.pipe.load_ip_adapter(
"h94/IP-Adapter",
subfolder="sdxl_models",
weight_name="ip-adapter-plus_sdxl_vit-h.safetensors",
)
self.pipe.to("cuda")
def generate_from_reference(
self,
reference_image: Image.Image,
prompt: str = "",
style_strength: float = 0.6,
num_variations: int = 4,
) -> list[Image.Image]:
"""
Generate design mới giữ style reference
style_strength:
- 0.3: nhẹ, chỉ lấy cảm hứng
- 0.6: trung bình, giữ style + color
- 0.9: mạnh, rất giống reference
"""
# Set IP-Adapter scale
self.pipe.set_ip_adapter_scale(style_strength)
# Auto-enhance prompt nếu rỗng
if not prompt:
prompt = "t-shirt design, transparent background, print-ready"
images = self.pipe(
prompt=prompt,
ip_adapter_image=reference_image,
num_images_per_prompt=num_variations,
num_inference_steps=30,
guidance_scale=7.5,
).images
return images
5.佈局偵測
class LayoutDetector:
"""Phân tích bố cục design trên áo thun"""
LAYOUT_TYPES = {
"center_chest": "Design ở giữa ngực (phổ biến nhất)",
"full_front": "Design phủ toàn bộ mặt trước",
"left_chest": "Design nhỏ bên ngực trái (pocket area)",
"back_print": "Design ở lưng",
"sleeve": "Design trên tay áo",
"all_over": "Design phủ toàn bộ áo",
}
def detect_layout(self, design_image: Image.Image) -> dict:
"""Phân tích layout phù hợp cho design"""
width, height = design_image.size
aspect_ratio = width / height
# Phân tích kích thước và tỷ lệ
if aspect_ratio > 2.0:
layout = "full_front" # Wide → full front
elif aspect_ratio < 0.5:
layout = "sleeve" # Tall & narrow → sleeve
elif width < 300 and height < 300:
layout = "left_chest" # Small → pocket
else:
layout = "center_chest" # Default
# Phân tích content density
density = self._content_density(design_image)
if density > 0.7:
layout = "all_over"
return {
"recommended_layout": layout,
"description": self.LAYOUT_TYPES[layout],
"aspect_ratio": round(aspect_ratio, 2),
"content_density": round(density, 2),
"print_area_cm": self._calculate_print_area(layout),
}
def _calculate_print_area(self, layout: str) -> dict:
"""Vùng in thực tế theo layout (cm)"""
areas = {
"center_chest": {"width": 30, "height": 35},
"full_front": {"width": 38, "height": 50},
"left_chest": {"width": 10, "height": 10},
"back_print": {"width": 35, "height": 45},
"sleeve": {"width": 10, "height": 25},
"all_over": {"width": 55, "height": 75},
}
return areas.get(layout, areas["center_chest"])
總結
影像參考分析模組包括：
- CLIP風格分析 - 零樣本分類風格，計算設計之間的相似度
- 調色盤提取 — K-means聚類、CMYK相容性檢查
- IP-Adapter — 產生新設計，保持風格參考，強度可控
- 佈局偵測 — 自動推薦適當的列印區域
下一篇：多模態生成－結合文字提示+影像參考，實現更強大的設計輸出。