paintplus: vendor the app source and rename from EditmaskwithAI

Bring the full EditmaskwithAI application into the repo under paintplus/ (429 files) so the service is self-contained — the installer copies the vendored source to ~/docker/paintplus/src instead of cloning at runtime. Rename to PaintPlus (service + branding; app logic untouched): - services/editmaskwithai.sh -> services/paintplus.sh (register_service paintplus, install_paintplus, ~/docker/paintplus, Caddy paintplus:8000, Authelia option preserved) - container names -> paintplus across docker-compose*.yml; dev network -> paintplus-network - browser <title> -> "PaintPlus - AI Image Editor"; README heading -> PaintPlus with upstream provenance note - README utilities table: editmaskwithai -> paintplus Backend/frontend code (help strings referencing the old container name, the ai_photo_edit.db filename) is intentionally left as-is to avoid touching application logic. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Nb2vJ8W7bHKx1JXVvpCraH
2026-06-26 05:48:43 +00:00
parent b4e8ba2a79
commit 084922afaa
431 changed files with 87396 additions and 77 deletions
@@ -0,0 +1,100 @@
+#!/usr/bin/env python3
+"""
+GPU setup script — runs at container startup.
+Uses the same detection logic as the backend (gpu_detect.py) to show
+exactly which models will be used before the server starts.
+Non-fatal: any failure just prints a warning and startup continues.
+"""
+import os
+import sys
+
+
+def main():
+    print("Detecting GPU capabilities…")
+
+    try:
+        import torch
+    except ImportError:
+        print("⚠ PyTorch not installed — GPU detection skipped")
+        return
+
+    if torch.cuda.is_available():
+        props = torch.cuda.get_device_properties(0)
+        free_b, total_b = torch.cuda.mem_get_info(0)
+        vram_total = total_b / (1024 ** 3)
+        vram_free  = free_b  / (1024 ** 3)
+        major, minor = props.major, props.minor
+        cc = f"{major}.{minor}"
+
+        fp16 = major >= 6
+        bf16 = major >= 8
+        fp8  = major > 8 or (major == 8 and minor >= 9)
+        int8 = major >= 7
+        tc   = major >= 7
+
+        flags = []
+        if fp16: flags.append("fp16")
+        if bf16: flags.append("bf16")
+        if fp8:  flags.append("fp8")
+        if int8: flags.append("int8")
+        if tc:   flags.append("tensor-cores")
+
+        print(f"✓ GPU     : {props.name}")
+        print(f"  VRAM    : {vram_total:.1f} GB total  |  {vram_free:.1f} GB free")
+        print(f"  Compute : CC {cc}  ({', '.join(flags) or 'fp32 only'})")
+
+        if major < 6:
+            print(f"  ⚠ Pre-Pascal (CC {cc}): using fp32 — effective VRAM budget halved")
+
+    elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
+        print("✓ Apple Silicon MPS GPU detected (fp32 mode)")
+        vram_total = vram_free = 0.0
+    else:
+        print("⚠ No GPU detected — AI inference will use CPU (very slow)")
+        vram_total = vram_free = 0.0
+
+    provider = os.environ.get("AI_PROVIDER", "").lower()
+    if provider != "local_gpu":
+        print(f"  AI_PROVIDER={provider!r} — local GPU not active, skipping model selection")
+        return
+
+    # Import and run the full detection to show what was selected
+    try:
+        sys.path.insert(0, "/app")
+        from app.services.gpu_detect import detect_gpu
+        info = detect_gpu()
+
+        print(f"\n  Effective VRAM : {info.effective_vram_gb:.1f} GB  (tier: {info.tier})")
+        print("\n  Model selection:")
+        printed: set = set()
+        for op, spec in info.recommended.items():
+            if spec is None:
+                print(f"    {op:<12} → (none — will use existing upscaler)")
+            elif spec.model_id not in printed:
+                print(f"    {op:<12} → [{spec.family}] {spec.model_id}")
+                print(f"               mem_opt={spec.memory_opt}  res={spec.native_res}px  ~{spec.vram_fp16_gb}GB fp16")
+                printed.add(spec.model_id)
+            else:
+                print(f"    {op:<12} → (same as above: {spec.model_id})")
+
+        for w in info.warnings:
+            print(f"\n  ⚠ {w}")
+
+        auto_dl = os.environ.get("AUTO_DOWNLOAD_MODELS", "true").lower()
+        print()
+        if auto_dl == "true":
+            print("  AUTO_DOWNLOAD_MODELS=true")
+            print("  → Model files will download in background at startup.")
+            print("  → First request loads from local disk (20-60s, not internet).")
+            print("  → Track progress: GET /api/gpu/prefetch-status")
+        else:
+            print("  AUTO_DOWNLOAD_MODELS=false — models download on first request.")
+
+    except Exception as exc:
+        print(f"  (Could not run full detection: {exc})")
+
+    print()
+
+
+if __name__ == "__main__":
+    main()