app.py

# app.py — InstantID × Beautiful Realistic Asians v7（ZeroGPU 対応・CrucibleAI ControlNet）
# 2025-06-22

##############################################################################
# 0. 旧 API → 新 API 互換パッチ（必ず diffusers import の前に置く）
##############################################################################
from huggingface_hub import hf_hub_download
import huggingface_hub as _hf_hub

# diffusers-0.27 は cached_download() を呼び出すため、v0.28+ でも使えるよう注入
if not hasattr(_hf_hub, "cached_download"):
    _hf_hub.cached_download = hf_hub_download

##############################################################################
# 1. 標準 & 外部ライブラリ
##############################################################################
import os, io, base64, subprocess, traceback
from pathlib import Path
from typing import Optional

import numpy as np
import torch
import gradio as gr
import spaces
from fastapi import FastAPI, UploadFile, File, Form, HTTPException
from PIL import Image

from diffusers import (
    StableDiffusionControlNetPipeline,
    ControlNetModel,
    DPMSolverMultistepScheduler,
    AutoencoderKL,
)
from diffusers.loaders import AttnProcsLayers
from insightface.app import FaceAnalysis
from basicsr.utils.download_util import load_file_from_url
from realesrgan import RealESRGANer

##############################################################################
# 2. キャッシュ & パス
##############################################################################
PERSIST_BASE = Path("/data")
CACHE_ROOT = (
    PERSIST_BASE / "instantid_cache"
    if PERSIST_BASE.exists() and os.access(PERSIST_BASE, os.W_OK)
    else Path.home() / ".cache" / "instantid_cache"
)
MODELS_DIR = CACHE_ROOT / "models"
LORA_DIR = CACHE_ROOT / "lora"
UPSCALE_DIR = CACHE_ROOT / "realesrgan"
for _p in (MODELS_DIR, LORA_DIR, UPSCALE_DIR):
    _p.mkdir(parents=True, exist_ok=True)

##############################################################################
# 3. モデル URL 一覧
##############################################################################
BRA_V7_URL = (
    "https://huggingface.co/i0switch-assets/Beautiful_Realistic_Asians_v7/"
    "resolve/main/beautiful_realistic_asians_v7_fp16.safetensors"
)
IP_ADAPTER_BIN_URL = (
    "https://huggingface.co/h94/IP-Adapter/"
    "resolve/main/ip-adapter-plus-face_sd15.bin"
)
IP_ADAPTER_LORA_URL = (
    "https://huggingface.co/h94/IP-Adapter-FaceID/"
    "resolve/main/ip-adapter-faceid-plusv2_sd15_lora.safetensors"
)
REALESRGAN_URL = (
    "https://huggingface.co/aimagelab/realesrgan/"
    "resolve/main/RealESRGAN_x4plus.pth"
)

##############################################################################
# 4. ダウンローダ
##############################################################################
def download(url: str, dst: Path, attempts: int = 2):
    if dst.exists():
        return dst
    for i in range(1, attempts + 1):
        try:
            subprocess.check_call(["curl", "-L", "-o", str(dst), url])
            return dst
        except subprocess.CalledProcessError:
            print(f"[DL] Retry {i}/{attempts} failed: {url}")
    load_file_from_url(url=url, model_dir=str(dst.parent), file_name=dst.name)
    return dst

##############################################################################
# 5. グローバル変数（lazy-load）
##############################################################################
pipe: Optional[StableDiffusionControlNetPipeline] = None
face_analyser: Optional[FaceAnalysis] = None
upsampler: Optional[RealESRGANer] = None

##############################################################################
# 6. パイプライン初期化
##############################################################################
def initialize_pipelines():
    global pipe, face_analyser, upsampler
    if pipe is not None:
        return

    print("[INIT] Downloading model assets …")

    # 6-1 ベースモデル & IP-Adapter
    bra_ckpt = download(BRA_V7_URL, MODELS_DIR / "bra_v7.safetensors")
    ip_bin = download(IP_ADAPTER_BIN_URL, MODELS_DIR / "ip_adapter.bin")
    ip_lora = download(IP_ADAPTER_LORA_URL, LORA_DIR / "ip_adapter_faceid.lora")

    # 6-2 ControlNet（CrucibleAI / diffusion_sd15）
    controlnet = ControlNetModel.from_pretrained(
        "CrucibleAI/ControlNetMediaPipeFace",  # 公開リポジトリ :contentReference[oaicite:0]{index=0}
        subfolder="diffusion_sd15",            # SD-1.5 用フォルダ :contentReference[oaicite:1]{index=1}
        torch_dtype=torch.float16,
        cache_dir=str(MODELS_DIR),
    )

    # 6-3 Diffusers パイプライン
    pipe_tmp = StableDiffusionControlNetPipeline.from_pretrained(
        "runwayml/stable-diffusion-v1-5",
        controlnet=controlnet,
        vae=AutoencoderKL.from_pretrained(
            "stabilityai/sd-vae-ft-mse", torch_dtype=torch.float16
        ),
        torch_dtype=torch.float16,
        cache_dir=str(MODELS_DIR),
        safety_checker=None,
    )
    pipe_tmp.scheduler = DPMSolverMultistepScheduler.from_pretrained(
        "runwayml/stable-diffusion-v1-5",
        subfolder="scheduler",
        cache_dir=str(MODELS_DIR),
    )

    # 6-4 **IP-Adapter 読み込み（API 仕様は positional 三つ）**
    #     diffusers-0.27.2 では subfolder / weight_name が必須 :contentReference[oaicite:2]{index=2}
    ip_dir = ip_bin.parent
    pipe_tmp.load_ip_adapter(
        str(ip_dir),          # path or repo id
        "",                   # subfolder（ファイル直下なので空文字列）
        ip_bin.name           # weight_name
    )

    # IP-Adapter の追加 LoRA を合流
    AttnProcsLayers(pipe_tmp.unet.attn_processors).load_lora_weights(
        ip_lora, adapter_name="ip_faceid", safe_load=True
    )
    pipe_tmp.set_adapters(["ip_faceid"], adapter_weights=[0.6])
    pipe_tmp.to("cuda")

    pipe = pipe_tmp

    # 6-5 InsightFace
    face_analyser = FaceAnalysis(
        name="buffalo_l", root=str(MODELS_DIR), providers=["CUDAExecutionProvider"]
    )
    face_analyser.prepare(ctx_id=0, det_size=(640, 640))

    # 6-6 Real-ESRGAN
    esrgan_ckpt = download(REALESRGAN_URL, UPSCALE_DIR / "realesrgan_x4plus.pth")
    upsampler = RealESRGANer(
        scale=4,
        model_path=str(esrgan_ckpt),
        half=True,
        tile=512,
        tile_pad=10,
        pre_pad=0,
        gpu_id=0,
    )

    print("[INIT] Pipelines ready.")

##############################################################################
# 7. プロンプトテンプレ
##############################################################################
BASE_PROMPT = (
    "(masterpiece:1.2), best quality, ultra-realistic, RAW photo, 8k, "
    "cinematic lighting, textured skin, "
)
NEG_PROMPT = (
    "verybadimagenegative_v1.3, ng_deepnegative_v1_75t, "
    "(worst quality:2), (low quality:2), lowres, blurry, bad anatomy, "
    "bad hands, extra digits, watermark, signature"
)

##############################################################################
# 8. 生成関数（GPU アタッチ）
##############################################################################
@spaces.GPU(duration=60)
def generate_core(
    face_img: Image.Image,
    subject: str,
    add_prompt: str = "",
    add_neg: str = "",
    cfg: float = 7.5,
    ip_scale: float = 0.6,
    steps: int = 30,
    w: int = 768,
    h: int = 768,
    upscale: bool = False,
    up_factor: int = 4,
    progress: gr.Progress = gr.Progress(track_tqdm=True),
):
    try:
        if pipe is None:
            initialize_pipelines()

        faces = face_analyser.get(np.array(face_img))
        if len(faces) == 0:
            raise ValueError("顔が検出できませんでした。別の画像をお試しください。")

        pipe.set_adapters(["ip_faceid"], adapter_weights=[ip_scale])

        prompt = BASE_PROMPT + subject + ", " + add_prompt
        negative = NEG_PROMPT + ", " + add_neg

        result = pipe(
            prompt=prompt,
            negative_prompt=negative,
            num_inference_steps=int(steps),
            guidance_scale=float(cfg),
            image=face_img,
            control_image=None,
            width=int(w),
            height=int(h),
        ).images[0]

        if upscale and upsampler is not None:
            upsampler.scale = 4 if up_factor == 4 else 8
            result, _ = upsampler.enhance(np.array(result))
            result = Image.fromarray(result)

        return result

    except Exception as e:
        traceback.print_exc()
        raise e

##############################################################################
# 9. Gradio UI
##############################################################################
with gr.Blocks(title="InstantID × BRA v7 (ZeroGPU)") as demo:
    gr.Markdown("## InstantID × Beautiful Realistic Asians v7")
    with gr.Row():
        face_img = gr.Image(type="pil", label="Face ID", sources=["upload"])
        subject = gr.Textbox(
            label="被写体説明（例: 30代日本人女性、黒髪セミロング）", interactive=True
        )
    add_prompt = gr.Textbox(label="追加プロンプト", interactive=True)
    add_neg = gr.Textbox(label="追加ネガティブ", interactive=True)
    with gr.Row():
        cfg = gr.Slider(1, 20, value=7.5, step=0.5, label="CFG Scale")
        ip_scale = gr.Slider(0.1, 1.0, value=0.6, step=0.05, label="IP-Adapter Weight")
    with gr.Row():
        steps = gr.Slider(10, 50, value=30, step=1, label="Steps")
        w = gr.Slider(512, 1024, value=768, step=64, label="Width")
        h = gr.Slider(512, 1024, value=768, step=64, label="Height")
    with gr.Row():
        upscale = gr.Checkbox(label="Real-ESRGAN Upscale", value=False)
        up_factor = gr.Radio([4, 8], value=4, label="Upscale Factor")
    run_btn = gr.Button("Generate")
    output_img = gr.Image(type="pil", label="Result")

    run_btn.click(
        fn=generate_core,
        inputs=[
            face_img,
            subject,
            add_prompt,
            add_neg,
            cfg,
            ip_scale,
            steps,
            w,
            h,
            upscale,
            up_factor,
        ],
        outputs=output_img,
        show_progress=True,
    )

##############################################################################
# 10. FastAPI REST
##############################################################################
app = FastAPI()

@app.post("/api/generate")
async def api_generate(
    subject: str = Form(...),
    cfg: float = Form(7.5),
    steps: int = Form(30),
    ip_scale: float = Form(0.6),
    w: int = Form(768),
    h: int = Form(768),
    file: UploadFile = File(...),
):
    try:
        img_bytes = await file.read()
        pil = Image.open(io.BytesIO(img_bytes)).convert("RGB")
        res = generate_core(
            face_img=pil,
            subject=subject,
            add_prompt="",
            add_neg="",
            cfg=cfg,
            ip_scale=ip_scale,
            steps=steps,
            w=w,
            h=h,
            upscale=False,
            up_factor=4,
        )
        buf = io.BytesIO()
        res.save(buf, format="PNG")
        b64 = base64.b64encode(buf.getvalue()).decode()
        return {"image": f"data:image/png;base64,{b64}"}
    except Exception as e:
        traceback.print_exc()
        raise HTTPException(status_code=500, detail=str(e))

##############################################################################
# 11. Launch（Gradio が自動で Uvicorn を起動）
##############################################################################
demo.queue(default_concurrency_limit=2).launch(share=False)