Speed: auto flash-attention/SDPA + document perf levers

transformers .generate() is the slow path; reasoning token volume and swap_eval
(2 passes) are the multipliers. Now requests attn_implementation flash_attention_2
-> sdpa -> default automatically (free speedup, flash-attn optional). README gains
a Performance section: swap_eval off (biggest free win), flash-attn, smaller model/
fewer axes, avoid nf4 for speed, and vLLM/SGLang as the real production-speed path.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

nodes/qwen_judge.py

@@ -290,14 +290,23 @@ def _load_model(model_path: str, precision: str):
     else:
         load_kwargs["dtype"] = torch.bfloat16 if precision == "bf16" else torch.float16
 
+    # Faster attention: flash_attention_2 (needs flash-attn) -> sdpa (built-in) -> default.
     model, last_err = None, None
-    for cls in candidates:
-        try:
-            model = cls.from_pretrained(model_path, **load_kwargs)
+    for attn in ("flash_attention_2", "sdpa", None):
+        kw = dict(load_kwargs)
+        if attn:
+            kw["attn_implementation"] = attn
+        for cls in candidates:
+            try:
+                model = cls.from_pretrained(model_path, **kw)
+                break
+            except Exception as e:  # wrong class OR attn impl unavailable -> try next
+                last_err = e
+                model = None
+        if model is not None:
+            if attn:
+                print(f"[QwenVLImageJudge] attention: {attn}")
             break
-        except Exception as e:  # arch not in this auto class's registry -> try the next
-            last_err = e
-            model = None
     if model is None:
         raise RuntimeError(
             f"[QwenVLImageJudge] could not load {model_path} with any of "