Create oldapp.py

oldapp.py

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+# app.py — InstantID × Beautiful Realistic Asians v7 (ZeroGPU-friendly, persistent cache)
+"""Persistent-cache backend for InstantID portrait generation.
+   * 依存モデルは /data が書込可ならそこへ、それ以外は ~/.cache に保存
+   * wget を使った簡易リトライ DL
+"""
+# --- ★ Monkey-Patch: torchvision 0.17+ で消えた functional_tensor を補完 ---
+import types, sys
+from torchvision.transforms import functional as F
+
+mod = types.ModuleType("torchvision.transforms.functional_tensor")
+# 必要なのは rgb_to_grayscale だけなのでこれだけエイリアス
+mod.rgb_to_grayscale = F.rgb_to_grayscale
+sys.modules["torchvision.transforms.functional_tensor"] = mod
+# ---------------------------------------------------------------------------
+
+import os, subprocess, cv2, torch, spaces, gradio as gr, numpy as np
+from pathlib import Path
+from PIL import Image
+from diffusers import (
+    StableDiffusionPipeline, ControlNetModel,
+    DPMSolverMultistepScheduler, AutoencoderKL,
+)
+from compel import Compel
+from insightface.app import FaceAnalysis
+
+##############################################################################
+# 0. キャッシュ用ディレクトリ
+##############################################################################
+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"
+)
+print("cache →", CACHE_ROOT)
+
+MODELS_DIR  = CACHE_ROOT / "models"
+LORA_DIR    = MODELS_DIR / "Lora"            # FaceID LoRA などを置く
+EMB_DIR     = CACHE_ROOT / "embeddings"
+UPSCALE_DIR = CACHE_ROOT / "realesrgan"
+for p in (MODELS_DIR, LORA_DIR, EMB_DIR, UPSCALE_DIR):
+    p.mkdir(parents=True, exist_ok=True)
+
+def dl(url: str, dst: Path, attempts: int = 2):
+    """wget + リトライの簡易ダウンローダ"""
+    if dst.exists():
+        print("✓", dst.relative_to(CACHE_ROOT)); return
+    for i in range(1, attempts + 1):
+        print(f"⬇ {dst.name} (try {i}/{attempts})")
+        if subprocess.call(["wget", "-q", "-O", str(dst), url]) == 0:
+            return
+    raise RuntimeError(f"download failed → {url}")
+
+##############################################################################
+# 1. 必要アセットのダウンロード
+##############################################################################
+print("— asset check —")
+
+# 1-A. ベース checkpoint
+BASE_CKPT = MODELS_DIR / "beautiful_realistic_asians_v7_fp16.safetensors"
+dl(
+    "https://civitai.com/api/download/models/177164?type=Model&format=SafeTensor&size=pruned&fp=fp16",
+    BASE_CKPT,
+)
+
+# 1-B. FaceID LoRA（Δのみ）
+LORA_FILE = LORA_DIR / "ip-adapter-faceid-plusv2_sd15_lora.safetensors"
+dl(
+    "https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid-plusv2_sd15_lora.safetensors",
+    LORA_FILE,
+)
+
+# 1-C. textual inversion Embeddings
+EMB_URLS = {
+    "ng_deepnegative_v1_75t.pt": [
+        "https://huggingface.co/datasets/gsdf/EasyNegative/resolve/main/ng_deepnegative_v1_75t.pt",
+        "https://huggingface.co/mrpxl2/animetarotV51.safetensors/raw/cc3008c0148061896549a995cc297aef0af4ef1b/ng_deepnegative_v1_75t.pt",
+    ],
+    "badhandv4.pt": [
+        "https://huggingface.co/datasets/gsdf/ConceptLab/resolve/main/badhandv4.pt",
+        "https://huggingface.co/nolanaatama/embeddings/raw/main/badhandv4.pt",
+    ],
+    "CyberRealistic_Negative-neg.pt": [
+        "https://huggingface.co/datasets/gsdf/ConceptLab/resolve/main/CyberRealistic_Negative-neg.pt",
+        "https://huggingface.co/wsj1995/embeddings/raw/main/CyberRealistic_Negative-neg.civitai.info",
+    ],
+    "UnrealisticDream.pt": [
+        "https://huggingface.co/datasets/gsdf/ConceptLab/resolve/main/UnrealisticDream.pt",
+        "https://huggingface.co/imagepipeline/UnrealisticDream/raw/main/f84133b4-aad8-44be-b9ce-7e7e3a8c111f.pt",
+    ],
+}
+for fname, urls in EMB_URLS.items():
+    dst = EMB_DIR / fname
+    for idx, u in enumerate(urls, 1):
+        try:
+            dl(u, dst); break
+        except RuntimeError:
+            if idx == len(urls): raise
+            print("    ↳ fallback URL …")
+
+# 1-D. Real-ESRGAN weights (×8)
+RRG_WEIGHTS = UPSCALE_DIR / "RealESRGAN_x8plus.pth"
+RRG_URLS = [
+    "https://huggingface.co/NoCrypt/Superscale_RealESRGAN/resolve/main/RealESRGAN_x8plus.pth",
+    "https://huggingface.co/ai-forever/Real-ESRGAN/raw/main/RealESRGAN_x8.pth",
+    "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/8x_NMKD-Superscale_100k.pth",
+]
+for idx, link in enumerate(RRG_URLS, 1):
+    try:
+        dl(link, RRG_WEIGHTS); break
+    except RuntimeError:
+        if idx == len(RRG_URLS): raise
+        print("    ↳ fallback URL …")
+
+##############################################################################
+# 2. ランタイム初期化
+##############################################################################
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+dtype  = torch.float16 if torch.cuda.is_available() else torch.float32
+print("device:", device, "| dtype:", dtype)
+
+providers = (
+    ["CUDAExecutionProvider", "CPUExecutionProvider"]
+    if torch.cuda.is_available()
+    else ["CPUExecutionProvider"]
+)
+face_app = FaceAnalysis(name="buffalo_l", root=str(CACHE_ROOT), providers=providers)
+face_app.prepare(ctx_id=(0 if torch.cuda.is_available() else -1), det_size=(640, 640))
+
+# ControlNet + SD パイプライン
+#controlnet = ControlNetModel.from_pretrained(
+#    "InstantX/InstantID", subfolder="ControlNetModel", torch_dtype=dtype
+#)
+pipe = StableDiffusionPipeline.from_single_file(
+    BASE_CKPT, torch_dtype=dtype, safety_checker=None, use_safetensors=True, clip_skip=2
+)
+pipe.vae = AutoencoderKL.from_pretrained(
+    "stabilityai/sd-vae-ft-mse", torch_dtype=dtype
+).to(device)
+#pipe.controlnet = controlnet
+pipe.scheduler = DPMSolverMultistepScheduler.from_config(
+    pipe.scheduler.config, use_karras_sigmas=True, algorithm_type="sde-dpmsolver++"
+)
+
+# --- ここが核心：画像エンコーダ込みで公式レポから直接ロード ------------------
+pipe.load_ip_adapter(
+    "h94/IP-Adapter",               # Hugging Face Hub ID
+    subfolder="models",             # ip-adapter-plus-face_sd15.bin が入っているフォルダ
+    weight_name="ip-adapter-plus-face_sd15.bin",
+)
+# ---------------------------------------------------------------------------
+
+# FaceID LoRA（差分 LoRA のみ）
+pipe.load_lora_weights(str(LORA_DIR), weight_name=LORA_FILE.name)
+pipe.set_ip_adapter_scale(0.65)
+
+# textual inversion 読み込み
+for emb in EMB_DIR.glob("*.*"):
+    try:
+        pipe.load_textual_inversion(emb, token=emb.stem)
+        print("emb loaded →", emb.stem)
+    except Exception:
+        print("emb skip →", emb.name)
+pipe.to(device)
+
+# compel プロセッサを初期化
+compel_proc = Compel(
+    tokenizer=pipe.tokenizer,
+    text_encoder=pipe.text_encoder,
+    truncate_long_prompts=False  # 長いプロンプトを切り捨てない
+)
+print("pipeline ready ✔")
+
+##############################################################################
+# 3. アップスケーラ
+##############################################################################
+try:
+    from basicsr.archs.rrdb_arch import RRDBNet
+    try:
+        from realesrgan import RealESRGAN
+    except ImportError:
+        from realesrgan import RealESRGANer as RealESRGAN
+    rrdb = RRDBNet(3, 3, 64, 23, 32, scale=8)
+    upsampler = RealESRGAN(device, rrdb, scale=8)
+    upsampler.load_weights(str(RRG_WEIGHTS))
+    UPSCALE_OK = True
+except Exception as e:
+    print("Real-ESRGAN disabled →", e)
+    UPSCALE_OK = False
+
+##############################################################################
+# 4. プロンプト & 生成関数
+##############################################################################
+BASE_PROMPT = (
+    "Cinematic photo, (best quality:1.1), ultra-realistic, photorealistic of {subject}, "
+    "natural skin texture, bokeh, standing, front view, full body shot, thighs, "
+    "Canon EOS R5, 85 mm, f/1.4, ISO 200, 1/160 s, RAW"
+)
+NEG_PROMPT = (
+    "ng_deepnegative_v1_75t, BadDream:0.6, UnrealisticDream:0.8, badhandv4:0.9, "
+    "(worst quality:2), (low quality:1.8), lowres, blurry, jpeg artifacts, "
+    "painting, sketch, illustration, cartoon, anime, cgi, render, 3d, "
+    "monochrome, grayscale, text, logo, watermark, signature, username, "
+    "bad anatomy, malformed, deformed, extra limbs, fused fingers, missing fingers, "
+    "missing arms, missing legs, skin blemishes, acne, age spot"
+)
+
+@spaces.GPU(duration=60)
+def generate(
+    face_np, subject, add_prompt, add_neg, cfg, ip_scale, steps, w, h, upscale, up_factor,
+    progress=gr.Progress(track_tqdm=True),
+):
+    if face_np is None or face_np.size == 0:
+        raise gr.Error("顔画像をアップロードしてください。")
+
+    prompt = BASE_PROMPT.format(subject=(subject.strip() or "a beautiful 20yo woman"))
+    if add_prompt:
+        prompt += ", " + add_prompt
+    neg = NEG_PROMPT + (", " + add_neg if add_neg else "")
+
+    pipe.set_ip_adapter_scale(ip_scale)
+    img_in = Image.fromarray(face_np)
+
+    # compelで長さを揃え、.unsqueeze(0)でバッチ次元を追加する
+    prompt_embeds, negative_prompt_embeds = compel_proc([prompt, neg])
+    prompt_embeds = prompt_embeds.unsqueeze(0)
+    negative_prompt_embeds = negative_prompt_embeds.unsqueeze(0)
+
+    result = pipe(
+        prompt_embeds=prompt_embeds,
+        negative_prompt_embeds=negative_prompt_embeds,
+        ip_adapter_image=img_in,
+        #image=img_in,
+        #controlnet_conditioning_scale=0.9,
+        num_inference_steps=int(steps) + 5,
+        guidance_scale=cfg,
+        width=int(w),
+        height=int(h),
+    ).images[0]
+
+    if upscale:
+        if UPSCALE_OK:
+            up, _ = upsampler.enhance(
+                cv2.cvtColor(np.array(result), cv2.COLOR_RGB2BGR), outscale=up_factor
+            )
+            result = Image.fromarray(cv2.cvtColor(up, cv2.COLOR_BGR2RGB))
+        else:
+            result = result.resize(
+                (int(result.width * up_factor), int(result.height * up_factor)),
+                Image.LANCZOS,
+            )
+    return result
+
+##############################################################################
+# 5. Gradio UI
+##############################################################################
+with gr.Blocks() as demo:
+    gr.Markdown("# InstantID – Beautiful Realistic Asians v7")
+    with gr.Row():
+        with gr.Column():
+            face_in   = gr.Image(label="顔写真", type="numpy")
+            subj_in   = gr.Textbox(label="被写体説明", placeholder="e.g. woman in black suit, smiling")
+            add_in    = gr.Textbox(label="追加プロンプト")
+            addneg_in = gr.Textbox(label="追加ネガティブ")
+            ip_sld    = gr.Slider(0, 1.5, 0.65, step=0.05, label="IP-Adapter scale")
+            cfg_sld   = gr.Slider(1, 15, 6, step=0.5, label="CFG")
+            step_sld  = gr.Slider(10, 50, 20, step=1, label="Steps")
+            w_sld     = gr.Slider(512, 1024, 512, step=64, label="幅")
+            h_sld     = gr.Slider(512, 1024, 768, step=64, label="高さ")
+            up_ck     = gr.Checkbox(label="アップスケール", value=True)
+            up_fac    = gr.Slider(1, 8, 2, step=1, label="倍率")
+            btn       = gr.Button("生成", variant="primary")
+        with gr.Column():
+            out_img = gr.Image(label="結果")
+
+    btn.click(
+        generate,
+        [face_in, subj_in, add_in, addneg_in, cfg_sld, ip_sld, step_sld, w_sld, h_sld, up_ck, up_fac],
+        out_img,
+        api_name="predict",
+    )
+
+print("launching …")
+demo.queue().launch(show_error=True)