簡介
這是該平台最重要的人工智慧模組——文字到設計引擎。使用者只需用文字描述，AI就會創造出可列印的T卹設計。本文將引導您專門針對時尚領域使用 LoRA 微調 SDXL。
1. 為什麼需要Fine-tune？
SDXL 基礎 vs 微調
| 方面 | SDXL 底座 | SDXL 專為時尚而微調 |
|---|---|---|
| 輸出 | 一般藝術照 | 生產就緒的 T 卹設計 |
| 背景 | 風景、質感 | 透明/純色背景 |
| 風格 | 款式多 | 針對街頭服飾、簡約、遊戲進行了最佳化... |
| 印刷區 | 不明白 | 了解前/後/袖子的位置 |
| 顏色 | 通用RGB | 針對 CMYK 列印進行了最佳化 |
建立 T 卹設計時的 SDXL Base 問題
Prompt: "cyberpunk smiley face t-shirt design"
SDXL Base output:
❌ Render cả người mặc áo (không phải chỉ design)
❌ Background phức tạp (không thể tách)
❌ Design bị cắt ở viền
❌ Không phù hợp print area
SDXL Fine-tuned output:
✅ Chỉ design, transparent background
✅ Tỷ lệ phù hợp cho front chest print
✅ Màu sắc tối ưu cho in vải
✅ Clean edges, vector-like quality
2. 準備資料集
資料來源
Dataset cho fine-tuning SDXL:
1. T-shirt design marketplaces
- Scrape (có license) từ các POD platforms
- Creative Commons designs
2. Custom generated
- Dùng SDXL base + img2img + manual cleanup
- Midjourney / DALL-E generated + cleanup
3. Real product photos
- Chụp áo thun thật → extract design
- Background removal
Target: 5,000 – 10,000 design images
資料管道
from PIL import Image
from pathlib import Path
import json
class FashionDatasetPipeline:
"""Pipeline chuẩn bị dataset cho fine-tuning"""
def process_image(self, img_path: str) -> dict:
img = Image.open(img_path)
# 1. Resize to training resolution
img = self.resize_and_pad(img, target_size=1024)
# 2. Background removal (transparent)
img = self.remove_background(img)
# 3. Center the design
img = self.center_design(img)
# 4. Quality check
if not self.quality_check(img):
return None
return {
"image": img,
"metadata": self.extract_metadata(img),
}
def generate_caption(self, img: Image, metadata: dict) -> str:
"""Auto-generate caption cho training"""
# Dùng CLIP + LLM để tạo caption
clip_tags = self.clip_classify(img)
style = self.detect_style(img) # cyberpunk, minimal, etc.
colors = self.extract_colors(img)
caption = (
f"a {style} t-shirt design, "
f"{', '.join(clip_tags)}, "
f"color palette: {', '.join(colors)}, "
f"isolated on transparent background, "
f"high resolution, print-ready"
)
return caption
def create_training_dataset(
self, input_dir: str, output_dir: str
):
"""Tạo dataset format cho Diffusers training"""
metadata = []
for img_path in Path(input_dir).glob("*.png"):
result = self.process_image(str(img_path))
if result is None:
continue
caption = self.generate_caption(
result["image"], result["metadata"]
)
# Save processed image
out_path = Path(output_dir) / img_path.name
result["image"].save(out_path)
metadata.append({
"file_name": img_path.name,
"text": caption,
})
# Save metadata.jsonl
with open(Path(output_dir) / "metadata.jsonl", "w") as f:
for item in metadata:
f.write(json.dumps(item) + "\n")
3. LoRA 微調
訓練配置
from diffusers import StableDiffusionXLPipeline
from peft import LoraConfig
import torch
# LoRA config cho fashion
lora_config = LoraConfig(
r=32, # Rank
lora_alpha=32, # Alpha
target_modules=[
"to_q", "to_v", "to_k", "to_out.0", # Attention
"proj_in", "proj_out", # Projections
],
lora_dropout=0.05,
)
# Training arguments
training_args = {
"pretrained_model": "stabilityai/stable-diffusion-xl-base-1.0",
"dataset": "./dataset/tshirt_designs",
"output_dir": "./models/sdxl-fashion-lora",
# Training params
"learning_rate": 1e-4,
"train_batch_size": 4,
"gradient_accumulation_steps": 4,
"max_train_steps": 5000,
"lr_scheduler": "cosine",
"lr_warmup_steps": 500,
# Resolution
"resolution": 1024,
"center_crop": True,
"random_flip": True,
# Optimization
"mixed_precision": "bf16",
"use_8bit_adam": True,
"gradient_checkpointing": True,
"enable_xformers": True,
# Validation
"validation_prompt": "minimal geometric t-shirt design, clean lines, transparent background",
"validation_epochs": 1,
"num_validation_images": 4,
}
訓練腳本
# Launch training
accelerate launch train_dreambooth_lora_sdxl.py \
--pretrained_model_name_or_path="stabilityai/stable-diffusion-xl-base-1.0" \
--dataset_name="./dataset/tshirt_designs" \
--output_dir="./models/sdxl-fashion-lora-v1" \
--resolution=1024 \
--train_batch_size=4 \
--gradient_accumulation_steps=4 \
--learning_rate=1e-4 \
--lr_scheduler="cosine" \
--lr_warmup_steps=500 \
--max_train_steps=5000 \
--rank=32 \
--mixed_precision="bf16" \
--validation_prompt="cyberpunk neon t-shirt design, transparent background" \
--validation_epochs=1 \
--seed=42
評價
def evaluate_fashion_lora(model_path: str, test_prompts: list[str]):
"""Đánh giá chất lượng LoRA cho fashion"""
pipe = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
torch_dtype=torch.float16,
)
pipe.load_lora_weights(model_path)
pipe.to("cuda")
metrics = {
"clip_score": [], # Text-image alignment
"aesthetic_score": [], # Chất lượng thẩm mỹ
"transparency_rate": [], # % có transparent background
"print_ready_rate": [], # % phù hợp cho in
}
for prompt in test_prompts:
images = pipe(
prompt=prompt,
num_images_per_prompt=4,
num_inference_steps=30,
).images
for img in images:
metrics["clip_score"].append(
calculate_clip_score(img, prompt)
)
metrics["aesthetic_score"].append(
calculate_aesthetic_score(img)
)
metrics["transparency_rate"].append(
has_transparent_background(img)
)
metrics["print_ready_rate"].append(
is_print_ready(img)
)
return {k: sum(v) / len(v) for k, v in metrics.items()}
4. 推理管道
產生服務
class DesignGenerationService:
"""Service tạo design từ text prompt"""
def __init__(self):
self.pipe = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
torch_dtype=torch.float16,
variant="fp16",
)
self.pipe.load_lora_weights("models/sdxl-fashion-lora-v2.1")
self.pipe.to("cuda")
# Enable optimizations
self.pipe.enable_xformers_memory_efficient_attention()
def generate(
self,
prompt: str,
negative_prompt: str | None = None,
num_variations: int = 4,
seed: int | None = None,
) -> list[Image.Image]:
# Default negative prompt cho fashion
if negative_prompt is None:
negative_prompt = (
"blurry, low quality, watermark, text overlay, "
"person wearing shirt, full body, mannequin, "
"wrinkled fabric, photographic background, "
"distorted, deformed"
)
# Auto-enhance prompt
enhanced_prompt = self.enhance_prompt(prompt)
generator = torch.Generator("cuda")
if seed:
generator.manual_seed(seed)
images = self.pipe(
prompt=enhanced_prompt,
negative_prompt=negative_prompt,
num_images_per_prompt=num_variations,
num_inference_steps=30,
guidance_scale=7.5,
generator=generator,
).images
# Post-processing
processed = []
for img in images:
img = self.ensure_transparent_bg(img)
img = self.center_and_crop(img)
img = self.upscale_for_print(img)
processed.append(img)
return processed
def enhance_prompt(self, user_prompt: str) -> str:
"""Tự động cải thiện prompt cho fashion domain"""
suffix = (
", t-shirt design, isolated design element, "
"transparent background, high resolution, "
"clean edges, vector art style, print-ready, "
"professional quality"
)
return user_prompt.strip() + suffix
5. 雙語支援（EN/VI）
class BilingualPromptHandler:
"""Xử lý prompt tiếng Anh và tiếng Việt"""
def __init__(self):
# Sử dụng LLM để dịch và enhance prompt
self.llm_client = openai.Client()
async def process_prompt(self, prompt: str) -> str:
# Detect language
lang = self.detect_language(prompt)
if lang == "vi":
# Dịch sang tiếng Anh + enhance
enhanced = await self.translate_and_enhance(prompt)
else:
enhanced = await self.enhance_english(prompt)
return enhanced
async def translate_and_enhance(self, vi_prompt: str) -> str:
response = await self.llm_client.chat.completions.create(
model="gpt-4o-mini",
messages=[{
"role": "system",
"content": (
"Translate this Vietnamese t-shirt design prompt "
"to English. Keep the creative intent. "
"Add fashion design keywords."
)
}, {
"role": "user",
"content": vi_prompt
}],
temperature=0.3,
)
return response.choices[0].message.content
6. 設計變化策略
class VariationGenerator:
"""Tạo nhiều variations từ 1 prompt"""
def generate_variations(
self, prompt: str, num_variations: int = 4
) -> list[Image.Image]:
variations = []
# Strategy 1: Seed variation (cùng prompt, khác seed)
seeds = self._generate_diverse_seeds(num_variations)
for seed in seeds:
img = self.pipe(prompt=prompt, seed=seed)
variations.append(img)
return variations
def _generate_diverse_seeds(self, n: int) -> list[int]:
"""Tạo seeds cho output đa dạng"""
import random
base = random.randint(0, 2**32)
# Spread seeds để output khác nhau
return [base + i * 1000 for i in range(n)]
總結
第 4 課涵蓋整個 文本到設計 流程：
- 資料集準備－時尚資料的收集、處理、自動字幕
- LoRA微調－專門針對T卹設計訓練SDXL LoRA
- 評估 — CLIP 分數、美觀分數、列印就緒率
- 推理管道 — 自動增強提示、生成、後處理
- 雙語支援 — 處理越南語/英語提示
- 變化策略 — 建立 2-4 種不同的設計變化
下一篇文章：影像參考分析 — 使用 CLIP 和 IP-Adapter 分析參考影像。