Log HF cache state before from_pretrained to diagnose hang

Prints whether the transformer and pipeline model dirs exist in
/data/hf_cache/hub before loading, so we can confirm if the V23
weights are missing from the dataset mount.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

app.py

@@ -45,6 +45,15 @@ print("[startup] all imports done", flush=True)
 
 dtype = torch.bfloat16
 
+_hf_cache = os.path.join(os.environ.get("HF_HOME", "/data/hf_cache"), "hub")
+_transformer_cache = os.path.join(_hf_cache, "models--prithivMLmods--Qwen-Image-Edit-Rapid-AIO-V23")
+_pipeline_cache  = os.path.join(_hf_cache, "models--FireRedTeam--FireRed-Image-Edit-1.1")
+print(f"[startup] HF cache dir : {_hf_cache}", flush=True)
+print(f"[startup] transformer cache exists: {os.path.isdir(_transformer_cache)}", flush=True)
+print(f"[startup] pipeline cache exists   : {os.path.isdir(_pipeline_cache)}", flush=True)
+if os.path.isdir(_hf_cache):
+    print(f"[startup] /hub contents: {os.listdir(_hf_cache)}", flush=True)
+
 print("[startup] loading transformer from_pretrained (prithivMLmods/Qwen-Image-Edit-Rapid-AIO-V23)...", flush=True)
 _transformer = QwenImageTransformer2DModel.from_pretrained(
     "prithivMLmods/Qwen-Image-Edit-Rapid-AIO-V23",