Claude
ed2a0d7f0c
- upscale.py: add InstallStatus dataclass + ensure_ncnn_installed() async function that downloads and extracts the NCNN binary for the current platform (Linux/macOS/Windows), tracks progress (0-100%), and busts the caps cache when done - main.py: trigger ensure_ncnn_installed() as a background task on app startup when no AI upscaler is detected - print_tools.py: /upscale/available triggers install task when no AI upscaler found; new GET /upscale/install-status endpoint for polling - upscale.js: if no AI upscaler on open, poll install-status showing a progress bar notification, then refresh caps and proceed when done https://claude.ai/code/session_01B58MaJCU1R6KwBDJCp8AfN
422 lines
15 KiB
Python
422 lines
15 KiB
Python
"""
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Upscale service — auto-detects best available method and runs it.
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Auto-installs Real-ESRGAN NCNN Vulkan binary on first use if no AI upscaler found.
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Priority (auto mode):
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1. Real-ESRGAN PyTorch + CUDA GPU — fastest, best quality
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2. Real-ESRGAN PyTorch + Apple MPS — fast on Apple Silicon
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3. Real-ESRGAN NCNN Vulkan binary — fast on any GPU (Intel/AMD/integrated)
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4. Real-ESRGAN PyTorch CPU — works, slow (warn user)
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5. Lanczos — always available, instant
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Capability probe is run once at first call and cached.
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NCNN binary is auto-downloaded if no AI upscaler is found.
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"""
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import asyncio
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import os
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import platform
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import shutil
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import stat
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import subprocess
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import sys
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import tempfile
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import urllib.request
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import zipfile
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from dataclasses import dataclass, field
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from enum import Enum
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from io import BytesIO
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from pathlib import Path
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from typing import Optional
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from PIL import Image
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# ── NCNN auto-install ─────────────────────────────────────────────────────────
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NCNN_DEST_DIR = Path("/app/data/models/realesrgan")
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NCNN_VERSION = "v0.2.5.0"
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NCNN_BASE_URL = f"https://github.com/xinntao/Real-ESRGAN/releases/download/{NCNN_VERSION}"
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_PLATFORM_ZIP = {
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"linux": f"realesrgan-ncnn-vulkan-{NCNN_VERSION}-ubuntu.zip",
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"darwin": f"realesrgan-ncnn-vulkan-{NCNN_VERSION}-macos.zip",
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"win32": f"realesrgan-ncnn-vulkan-{NCNN_VERSION}-windows.zip",
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"windows": f"realesrgan-ncnn-vulkan-{NCNN_VERSION}-windows.zip",
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}
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class InstallState(str, Enum):
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idle = "idle"
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downloading = "downloading"
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extracting = "extracting"
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done = "done"
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failed = "failed"
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@dataclass
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class InstallStatus:
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state: InstallState = InstallState.idle
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progress: int = 0 # 0-100
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message: str = ""
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error: str = ""
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_install_status = InstallStatus()
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_install_lock = asyncio.Lock()
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def get_install_status() -> dict:
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s = _install_status
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return {
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"state": s.state.value,
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"progress": s.progress,
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"message": s.message,
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"error": s.error,
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}
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def _ncnn_binary_name() -> str:
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plat = sys.platform.lower()
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return "realesrgan-ncnn-vulkan.exe" if "win" in plat else "realesrgan-ncnn-vulkan"
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async def ensure_ncnn_installed() -> Optional[Path]:
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"""
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Check if NCNN binary is present; if not, download and install it.
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Returns the binary Path on success, None on failure.
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Serialised via _install_lock so concurrent callers wait for a single install.
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"""
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global _install_status
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binary_path = NCNN_DEST_DIR / _ncnn_binary_name()
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if binary_path.exists() and os.access(binary_path, os.X_OK):
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_install_status = InstallStatus(state=InstallState.done, progress=100,
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message="Already installed.")
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return binary_path
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async with _install_lock:
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# Re-check after acquiring lock (another coroutine may have just finished)
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if binary_path.exists() and os.access(binary_path, os.X_OK):
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_install_status = InstallStatus(state=InstallState.done, progress=100,
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message="Already installed.")
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return binary_path
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if _install_status.state == InstallState.downloading:
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return None # install already in progress
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plat = sys.platform.lower()
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zip_name = _PLATFORM_ZIP.get(plat)
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if not zip_name:
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_install_status = InstallStatus(
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state=InstallState.failed,
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error=f"Unsupported platform: {plat}",
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)
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return None
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url = f"{NCNN_BASE_URL}/{zip_name}"
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try:
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NCNN_DEST_DIR.mkdir(parents=True, exist_ok=True)
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zip_path = NCNN_DEST_DIR / zip_name
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# Download
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_install_status = InstallStatus(
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state=InstallState.downloading, progress=0,
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message=f"Downloading Real-ESRGAN NCNN {NCNN_VERSION}…",
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)
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def _do_download():
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def _progress(count, block, total):
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if total > 0:
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pct = min(90, int(count * block * 90 / total))
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_install_status.progress = pct
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urllib.request.urlretrieve(url, zip_path, _progress)
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loop = asyncio.get_event_loop()
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await loop.run_in_executor(None, _do_download)
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# Extract
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_install_status.state = InstallState.extracting
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_install_status.progress = 92
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_install_status.message = "Extracting…"
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def _do_extract():
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with zipfile.ZipFile(zip_path, "r") as zf:
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zf.extractall(NCNN_DEST_DIR)
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# Find binary (may be in a subdirectory)
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found = list(NCNN_DEST_DIR.rglob(_ncnn_binary_name()))
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if not found:
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raise FileNotFoundError(f"Binary not found after extract: {_ncnn_binary_name()}")
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extracted = found[0]
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if extracted != binary_path:
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extracted.rename(binary_path)
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# Make executable
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if "win" not in sys.platform.lower():
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binary_path.chmod(
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binary_path.stat().st_mode | stat.S_IEXEC | stat.S_IXGRP | stat.S_IXOTH
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)
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zip_path.unlink(missing_ok=True)
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await loop.run_in_executor(None, _do_extract)
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_install_status = InstallStatus(
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state=InstallState.done, progress=100,
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message=f"Installed: {binary_path}",
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)
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# Bust caps cache so probe picks up new binary
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invalidate_caps_cache()
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return binary_path
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except Exception as exc:
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_install_status = InstallStatus(
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state=InstallState.failed,
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error=str(exc),
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message="Installation failed.",
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)
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print(f"[upscale] NCNN auto-install failed: {exc}")
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return None
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# ── Capability detection ──────────────────────────────────────────────────────
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_caps: Optional[dict] = None
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def probe_upscale_capabilities() -> dict:
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"""
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Detect what upscaling hardware and software is available.
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Result is cached after first call.
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"""
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global _caps
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if _caps is not None:
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return _caps
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caps = {
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"lanczos": True,
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"realesrgan_pytorch": False,
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"realesrgan_pytorch_device": None,
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"realesrgan_ncnn": False,
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"realesrgan_ncnn_path": None,
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"recommended": "lanczos",
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"recommended_label": "Lanczos (no AI upscaler found)",
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"methods": ["lanczos"],
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"ncnn_install_status": get_install_status(),
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}
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# ── PyTorch path ──────────────────────────────────────────────────────────
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pytorch_device = None
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try:
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import torch
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if torch.cuda.is_available():
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pytorch_device = "cuda"
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elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
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pytorch_device = "mps"
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else:
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pytorch_device = "cpu"
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except ImportError:
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pass
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if pytorch_device:
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try:
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import realesrgan # noqa: F401
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from basicsr.archs.rrdbnet_arch import RRDBNet # noqa: F401
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caps["realesrgan_pytorch"] = True
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caps["realesrgan_pytorch_device"] = pytorch_device
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caps["methods"].append("realesrgan_pytorch")
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except ImportError:
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pass
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# ── NCNN Vulkan binary ────────────────────────────────────────────────────
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ncnn_path = _find_ncnn_binary()
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if ncnn_path:
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caps["realesrgan_ncnn"] = True
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caps["realesrgan_ncnn_path"] = str(ncnn_path)
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caps["methods"].append("realesrgan_ncnn")
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# ── Pick recommended ──────────────────────────────────────────────────────
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if caps["realesrgan_pytorch"] and pytorch_device in ("cuda", "mps"):
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device_label = "CUDA GPU" if pytorch_device == "cuda" else "Apple Silicon"
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caps["recommended"] = "realesrgan_pytorch"
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caps["recommended_label"] = f"Real-ESRGAN ({device_label})"
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elif caps["realesrgan_ncnn"]:
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caps["recommended"] = "realesrgan_ncnn"
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caps["recommended_label"] = "Real-ESRGAN NCNN (Vulkan)"
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elif caps["realesrgan_pytorch"] and pytorch_device == "cpu":
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caps["recommended"] = "realesrgan_pytorch"
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caps["recommended_label"] = "Real-ESRGAN (CPU — may be slow)"
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else:
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caps["recommended"] = "lanczos"
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caps["recommended_label"] = "Lanczos (installing Real-ESRGAN…)"
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_caps = caps
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return caps
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def _find_ncnn_binary() -> Optional[Path]:
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"""Find realesrgan-ncnn-vulkan binary on the system."""
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found = shutil.which("realesrgan-ncnn-vulkan")
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if found:
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return Path(found)
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candidates = [
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NCNN_DEST_DIR / _ncnn_binary_name(),
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Path("/usr/local/bin/realesrgan-ncnn-vulkan"),
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Path.home() / ".local/bin/realesrgan-ncnn-vulkan",
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Path(r"C:/realesrgan-ncnn-vulkan/realesrgan-ncnn-vulkan.exe"),
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Path("/opt/homebrew/bin/realesrgan-ncnn-vulkan"),
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]
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for p in candidates:
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if p.exists() and os.access(p, os.X_OK):
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return p
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return None
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def invalidate_caps_cache():
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"""Call after installing new software so next probe picks it up."""
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global _caps
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_caps = None
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# ── Upscale implementations ───────────────────────────────────────────────────
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def _to_png_bytes(img: Image.Image) -> bytes:
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buf = BytesIO()
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img.save(buf, format="PNG")
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return buf.getvalue()
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def upscale_lanczos(image: Image.Image, scale: float) -> tuple[bytes, str]:
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"""Pure Pillow Lanczos — instant, always available."""
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new_w = round(image.width * scale)
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new_h = round(image.height * scale)
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result = image.resize((new_w, new_h), Image.Resampling.LANCZOS)
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return _to_png_bytes(result), "lanczos"
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def upscale_realesrgan_pytorch(image: Image.Image, scale: float) -> tuple[bytes, str]:
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"""
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Real-ESRGAN via PyTorch.
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Uses CUDA > MPS > CPU automatically based on what's available.
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"""
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import torch
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from basicsr.archs.rrdbnet_arch import RRDBNet
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from realesrgan import RealESRGANer
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caps = probe_upscale_capabilities()
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device = caps.get("realesrgan_pytorch_device", "cpu")
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model_scale = 2 if scale <= 2.5 else 4
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model = RRDBNet(
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num_in_ch=3, num_out_ch=3, num_feat=64,
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num_block=23, num_grow_ch=32, scale=model_scale
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)
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model_dir = Path("/app/data/models/realesrgan")
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model_dir.mkdir(parents=True, exist_ok=True)
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model_name = f"RealESRGAN_x{model_scale}plus.pth"
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model_path = model_dir / model_name
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if not model_path.exists():
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model_path = None
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upsampler = RealESRGANer(
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scale=model_scale,
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model_path=str(model_path) if model_path else None,
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model=model,
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tile=512,
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tile_pad=10,
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pre_pad=0,
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half=(device == "cuda"),
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device=torch.device(device),
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)
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import numpy as np
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img_bgr = np.array(image)[:, :, ::-1].copy()
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enhanced, _ = upsampler.enhance(img_bgr, outscale=scale)
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result = Image.fromarray(enhanced[:, :, ::-1])
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label = f"realesrgan_pytorch_{device}"
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return _to_png_bytes(result), label
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def upscale_realesrgan_ncnn(image: Image.Image, scale: float) -> tuple[bytes, str]:
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"""
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Real-ESRGAN via NCNN Vulkan binary — works on any GPU.
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Runs as subprocess with temp file I/O.
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"""
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caps = probe_upscale_capabilities()
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binary = caps.get("realesrgan_ncnn_path")
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if not binary:
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raise RuntimeError("realesrgan-ncnn-vulkan binary not found")
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model_scale = 4 if scale > 2.5 else 2
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target_w = round(image.width * scale)
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target_h = round(image.height * scale)
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with tempfile.TemporaryDirectory() as tmpdir:
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in_path = Path(tmpdir) / "input.png"
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out_path = Path(tmpdir) / "output.png"
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image.save(in_path, format="PNG")
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model_name = f"realesrgan-x{model_scale}plus"
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cmd = [
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binary, "-i", str(in_path), "-o", str(out_path),
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"-s", str(model_scale), "-n", model_name, "-f", "png",
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]
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result_proc = subprocess.run(cmd, capture_output=True, timeout=300)
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if result_proc.returncode != 0:
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raise RuntimeError(
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f"realesrgan-ncnn-vulkan failed: {result_proc.stderr.decode()}"
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)
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result = Image.open(out_path).convert("RGB")
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if result.width != target_w or result.height != target_h:
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result = result.resize((target_w, target_h), Image.Resampling.LANCZOS)
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return _to_png_bytes(result), "realesrgan_ncnn"
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# ── Public entry point ────────────────────────────────────────────────────────
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def upscale_sync(image: Image.Image, scale: float, method: str = "auto") -> tuple[bytes, str]:
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"""Upscale image synchronously. Returns (png_bytes, method_used_label)."""
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caps = probe_upscale_capabilities()
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if method == "auto":
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method = caps["recommended"]
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if method == "realesrgan_pytorch":
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if caps["realesrgan_pytorch"]:
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try:
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return upscale_realesrgan_pytorch(image, scale)
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except Exception as e:
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print(f"Real-ESRGAN PyTorch failed, falling back: {e}")
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if caps["realesrgan_ncnn"]:
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try:
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return upscale_realesrgan_ncnn(image, scale)
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except Exception as e:
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print(f"Real-ESRGAN NCNN fallback failed: {e}")
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return upscale_lanczos(image, scale)
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if method == "realesrgan_ncnn":
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if caps["realesrgan_ncnn"]:
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try:
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return upscale_realesrgan_ncnn(image, scale)
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except Exception as e:
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print(f"Real-ESRGAN NCNN failed, falling back: {e}")
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if caps["realesrgan_pytorch"]:
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try:
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return upscale_realesrgan_pytorch(image, scale)
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except Exception as e:
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print(f"Real-ESRGAN PyTorch fallback failed: {e}")
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return upscale_lanczos(image, scale)
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return upscale_lanczos(image, scale)
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async def upscale_image(image: Image.Image, scale: float, method: str = "auto") -> tuple[bytes, str]:
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"""Async wrapper — runs upscale in thread pool to avoid blocking the event loop."""
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loop = asyncio.get_event_loop()
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return await loop.run_in_executor(None, upscale_sync, image, scale, method)
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