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 +1,96 @@
+"""
+GPU status and model management endpoints.
+All under /api/gpu prefix.
+"""
+from fastapi import APIRouter
+from pydantic import BaseModel
+from typing import Optional, List
+import asyncio
+
+router = APIRouter(prefix="/api/gpu", tags=["gpu"])
+
+
+@router.get("/status")
+async def gpu_status():
+    """
+    Full GPU capability report: hardware, feature flags, VRAM budget,
+    and which model was selected for each operation.
+    Frontend polls this to show GPU badge and tool availability.
+    """
+    from app.services.gpu_detect import get_cached_gpu_info
+    from app.services.local_diffusion import get_all_model_states
+
+    info = get_cached_gpu_info()
+
+    return {
+        # Hardware
+        "backend":             info.backend,
+        "device_name":         info.device_name,
+        "vram_total_gb":       info.vram_total_gb,
+        "vram_free_gb":        info.vram_free_gb,
+        "compute_capability":  info.compute_capability,
+        # Feature flags
+        "fp16":          info.fp16,
+        "bf16":          info.bf16,
+        "fp8":           info.fp8,
+        "int8":          info.int8,
+        "tensor_cores":  info.tensor_cores,
+        "xformers":      info.xformers,
+        # Derived
+        "effective_vram_gb": info.effective_vram_gb,
+        "tier":              info.tier,
+        # Selected models per operation
+        "recommended": {
+            op: (
+                {
+                    "model_id":   spec.model_id,
+                    "family":     spec.family,
+                    "memory_opt": spec.memory_opt,
+                    "native_res": spec.native_res,
+                    "vram_fp16_gb": spec.vram_fp16_gb,
+                }
+                if spec else None
+            )
+            for op, spec in info.recommended.items()
+        },
+        "pipeline_states": get_all_model_states(),
+        "warnings":    info.warnings,
+        "capabilities": info.capabilities,
+    }
+
+
+class PrefetchRequest(BaseModel):
+    operations: Optional[List[str]] = None
+
+
+@router.post("/prefetch")
+async def prefetch_models(req: PrefetchRequest = PrefetchRequest()):
+    """
+    Eagerly load pipelines into GPU memory for the requested operations.
+    Returns immediately; poll /api/gpu/prefetch-status for progress.
+    Default: inpaint, txt2img, img2img.
+    """
+    ops = req.operations or ["inpaint", "txt2img", "img2img"]
+    valid = {"inpaint", "txt2img", "img2img", "outpaint", "upscale"}
+    ops = [op for op in ops if op in valid]
+
+    from app.services.local_diffusion import get_local_diffusion_provider
+    provider = get_local_diffusion_provider()
+
+    async def _prefetch():
+        for op in ops:
+            try:
+                await provider._get_pipeline(op)
+                print(f"[gpu] Prefetch complete: {op}")
+            except Exception as exc:
+                print(f"[gpu] Prefetch failed for {op}: {exc}")
+
+    asyncio.create_task(_prefetch())
+    return {"status": "prefetch_started", "operations": ops}
+
+
+@router.get("/prefetch-status")
+async def prefetch_status():
+    """Poll model download / load progress."""
+    from app.services.local_diffusion import get_all_model_states
+    return {"models": get_all_model_states()}