バーチャル試着、FASHN AI + nano banana pro が最強説

Step 1: FASHN AI → シームレスに服を着せる
Step 2: Gemini  → 元の服の質感を転写する

pip install pillow requests google-generativeai

import base64
import time
from io import BytesIO
from PIL import Image
import requests


class FashnTryOn:
    def __init__(self, api_key: str):
        self.api_key = api_key
        self.base_url = "https://api.fashn.ai/v1"

    def try_on(self, person_image: Image.Image, garment_image: Image.Image) -> Image.Image:
        person_b64 = self._to_base64(person_image)
        garment_b64 = self._to_base64(garment_image)

        headers = {
            "Content-Type": "application/json",
            "Authorization": f"Bearer {self.api_key}",
        }

        payload = {
            "model_name": "tryon-v1.6",
            "inputs": {
                "model_image": person_b64,
                "garment_image": garment_b64,
                "category": "auto",
                "mode": "quality",
                "segmentation_free": True,
                "num_samples": 1,
                "output_format": "png",
            },
        }

        resp = requests.post(f"{self.base_url}/run", json=payload, headers=headers)
        job_id = resp.json()["id"]

        while True:
            status = requests.get(
                f"{self.base_url}/status/{job_id}",
                headers={"Authorization": f"Bearer {self.api_key}"}
            ).json()

            if status["status"] == "completed":
                img_url = status["output"][0]
                img_data = requests.get(img_url).content
                return Image.open(BytesIO(img_data))

            if status["status"] == "failed":
                raise Exception("FASHN処理失敗")

            time.sleep(3)

    def _to_base64(self, image: Image.Image) -> str:
        if image.mode == 'RGBA':
            bg = Image.new('RGB', image.size, (255, 255, 255))
            bg.paste(image, mask=image.split()[3])
            image = bg

        buffer = BytesIO()
        image.save(buffer, format='PNG')
        b64 = base64.b64encode(buffer.getvalue()).decode()
        return f"data:image/png;base64,{b64}"

import google.generativeai as genai
from io import BytesIO
from PIL import Image


class TextureEnhancer:
    def __init__(self, api_key: str):
        genai.configure(api_key=api_key)

    def enhance(self, tryon_result: Image.Image, original_garment: Image.Image) -> Image.Image:
        model = genai.GenerativeModel("gemini-3-pro-image-preview")

        tryon_result = self._resize(tryon_result, 1024)
        original_garment = self._resize(original_garment, 1024)

        prompt = """
IMAGE 1: 試着済みの画像（人物が服を着ている状態）
IMAGE 2: 元の服の画像

IMAGE 1の服の質感を、IMAGE 2に合わせて強化してください。

変えてはいけないもの：
- 人物の顔、体型、ポーズ
- 背景
- 服のシルエット

強化するもの：
- 生地のテクスチャ（織り目、糸の質感）
- 光沢感またはマット感
- シワや陰影のリアルさ
"""

        response = model.generate_content([
            tryon_result.convert('RGB'),
            original_garment.convert('RGB'),
            prompt
        ])

        for candidate in response.candidates:
            for part in candidate.content.parts:
                if hasattr(part, 'inline_data') and part.inline_data:
                    return Image.open(BytesIO(part.inline_data.data))

        return tryon_result

    def _resize(self, image: Image.Image, max_size: int) -> Image.Image:
        if max(image.size) > max_size:
            ratio = max_size / max(image.size)
            new_size = tuple(int(d * ratio) for d in image.size)
            return image.resize(new_size, Image.Resampling.LANCZOS)
        return image

def perfect_tryon(person_path: str, garment_path: str) -> Image.Image:
    person = Image.open(person_path)
    garment = Image.open(garment_path)

    print("Step 1: FASHN AI で着せ替え中...")
    fashn = FashnTryOn(api_key="YOUR_FASHN_API_KEY")
    tryon_result = fashn.try_on(person, garment)

    print("Step 2: Gemini で質感強化中...")
    enhancer = TextureEnhancer(api_key="YOUR_GEMINI_API_KEY")
    final_result = enhancer.enhance(tryon_result, garment)

    return final_result


result = perfect_tryon("person.jpg", "shirt.png")
result.save("result.png")