Deploy NFA Track R FLUX.2 Fun CN ZeroGPU (real depth CN)

README.md

@@ -24,6 +24,7 @@ Soft Flux2 `image=depth` is **banned forever** on this path.
 | Base | `black-forest-labs/FLUX.2-dev` |
 | GPU | `@spaces.GPU(duration=300, size="large")` = **48GB** @ **1×** Pro minutes |
 | Memory mode | VideoX-Fun **`model_cpu_offload_and_qfloat8`** (official low-VRAM Fun CN path) |
+| Weights | **HF Mount volumes** (not ephemeral download): `/data/FLUX.2-dev` + `/data/Fun-CN` |
 | Escalation | If OOM → redeploy with `size="xlarge"` + `model_cpu_offload` (2× quota) |
 
 Requires Space secret **`HF_TOKEN`** (gated FLUX.2-dev license accepted on the account).

app.py

@@ -59,11 +59,15 @@ MEM_MODE = (
 
 APP_DIR = Path(__file__).resolve().parent
 CONFIG_PATH = APP_DIR / "config" / "flux2_control.yaml"
+# Prefer HF Mount volumes (no 178GB ephemeral download). Fallback: cache download.
+MODEL_DIR = Path(
+    os.environ.get("NFA_FLUX2_MOUNT") or "/data/FLUX.2-dev"
+)
+CN_MOUNT_DIR = Path(os.environ.get("NFA_FUN_CN_MOUNT") or "/data/Fun-CN")
 CACHE_ROOT = Path(
     os.environ.get("NFA_FUN_CN_CACHE")
     or (Path.home() / ".cache" / "nfa_fun_cn")
 )
-MODEL_DIR = CACHE_ROOT / "FLUX.2-dev"
 
 _PIPE = None
 _CN_FILE_PATH: Path | None = None
@@ -74,46 +78,67 @@ def _resolve_cn_path() -> Path:
     global _CN_FILE_PATH
     if _CN_FILE_PATH is not None and _CN_FILE_PATH.is_file():
         return _CN_FILE_PATH
-    direct = CACHE_ROOT / CN_FILE
-    if direct.is_file():
-        _CN_FILE_PATH = direct
-        return direct
-    nested = list(CACHE_ROOT.rglob(CN_FILE))
-    if nested:
-        _CN_FILE_PATH = nested[0]
-        return nested[0]
-    raise FileNotFoundError(f"Fun CN weights missing: {CN_FILE}")
+    candidates = [
+        CN_MOUNT_DIR / CN_FILE,
+        CACHE_ROOT / CN_FILE,
+    ]
+    candidates.extend(CN_MOUNT_DIR.rglob(CN_FILE) if CN_MOUNT_DIR.is_dir() else [])
+    candidates.extend(CACHE_ROOT.rglob(CN_FILE) if CACHE_ROOT.is_dir() else [])
+    for c in candidates:
+        if c.is_file():
+            _CN_FILE_PATH = c
+            return c
+    raise FileNotFoundError(
+        f"Fun CN weights missing: {CN_FILE}. "
+        "Mount alibaba-pai/FLUX.2-dev-Fun-Controlnet-Union at /data/Fun-CN "
+        "or download into cache."
+    )
 
 
 def _ensure_weights() -> None:
-    """Download on CPU (must run outside @spaces.GPU so quota is not burned)."""
-    global _WEIGHTS_READY, _CN_FILE_PATH
-    if _WEIGHTS_READY and MODEL_DIR.is_dir():
+    """Resolve mounted Hub volumes, or (last resort) selective download."""
+    global _WEIGHTS_READY, MODEL_DIR
+    if MODEL_DIR.is_dir() and (MODEL_DIR / "model_index.json").is_file():
         try:
             _resolve_cn_path()
+            _WEIGHTS_READY = True
+            print(f"[nfa-fun-cn] using mounts model={MODEL_DIR} cn={_CN_FILE_PATH}", flush=True)
             return
         except FileNotFoundError:
             pass
+
+    # Fallback: selective download (may OOM disk on ZeroGPU — mounts preferred)
+    print(
+        "[nfa-fun-cn] WARN mounts missing; selective download fallback "
+        "(transformer+vae+tokenizer+text_encoder+scheduler only)",
+        flush=True,
+    )
+    cache_model = CACHE_ROOT / "FLUX.2-dev"
     CACHE_ROOT.mkdir(parents=True, exist_ok=True)
     token = HF_TOKEN or None
-    print(f"[nfa-fun-cn] snapshot {BASE_MODEL} -> {MODEL_DIR}", flush=True)
     snapshot_download(
         repo_id=BASE_MODEL,
-        local_dir=str(MODEL_DIR),
-        local_dir_use_symlinks=False,
+        local_dir=str(cache_model),
         token=token,
+        allow_patterns=[
+            "model_index.json",
+            "transformer/*",
+            "vae/*",
+            "tokenizer/*",
+            "text_encoder/*",
+            "scheduler/*",
+        ],
     )
-    print(f"[nfa-fun-cn] download {CN_REPO}/{CN_FILE}", flush=True)
     path = hf_hub_download(
         repo_id=CN_REPO,
         filename=CN_FILE,
         local_dir=str(CACHE_ROOT),
-        local_dir_use_symlinks=False,
         token=token,
     )
+    MODEL_DIR = cache_model
     _CN_FILE_PATH = Path(path)
     _WEIGHTS_READY = True
-    print(f"[nfa-fun-cn] weights ready cn={_CN_FILE_PATH}", flush=True)
+    print(f"[nfa-fun-cn] weights ready model={MODEL_DIR} cn={_CN_FILE_PATH}", flush=True)
 
 
 def _prep_depth(depth_image: Image.Image, width: int, height: int) -> Image.Image: