feat: equal-quality speed options (TF32 + torch.compile)

Add two opt-in inference speedups to the Model Loader, validated to leave the
output perceptually identical (deviation at the fp32 rounding floor):

- tf32 (default on): TF32 matmul on Ampere+ (~1.15x).
- compile (opt-in): torch.compile the UNet (~2.1x). Stacks with TF32 to
  ~2.5x (measured 4.3s -> 1.7s on a 12s clip).

torch.compile needs a static shape (the model's adaptive-avg-pool can't trace
dynamic shapes), so the sampler pads every chunk to chunk_seconds — clips of
any length reuse one compiled graph (no per-length recompiles; verified an 8s
clip after a 12s clip ran in 0.9s with no recompile).

Researched + profiled first: CFG-batching, channel/chunk batching, and
channels_last gave ~0 gain because the GPU is already compute-bound at batch 1.

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

nodes.py

@@ -55,6 +55,17 @@ class UniverSRModelLoader:
                 }),
             },
             "optional": {
+                "tf32": ("BOOLEAN", {
+                    "default": True,
+                    "tooltip": "Enable TF32 matmul on Ampere+ GPUs (~1.15x). Perceptually lossless "
+                               "but not bit-exact; global setting. Turn off for reference fp32.",
+                }),
+                "compile": ("BOOLEAN", {
+                    "default": False,
+                    "tooltip": "torch.compile the network (~2x). First run compiles (~10-35s), then fast "
+                               "and cached. Needs CUDA. Chunks are auto-padded to a fixed size, so set the "
+                               "sampler's chunk_seconds near your typical clip length to avoid wasted compute.",
+                }),
                 "local_path": ("STRING", {
                     "default": "",
                     "tooltip": "Override: a folder with config.yaml + pytorch_model.bin, "
@@ -72,17 +83,20 @@ class UniverSRModelLoader:
     RETURN_NAMES = ("model",)
     FUNCTION = "load"
 
-    def load(self, model, device, local_path="", config_path=""):
+    def load(self, model, device, tf32=True, compile=False, local_path="", config_path=""):
         dev = _default_device() if device == "auto" else device
         if dev == "cuda" and not torch.cuda.is_available():
             print("[UniverSR] CUDA unavailable, falling back to CPU")
             dev = "cpu"
-        model_obj, cache_key = usr.load_model(model, dev, local_path=local_path, config_path=config_path)
+        model_obj, cache_key = usr.load_model(
+            model, dev, local_path=local_path, config_path=config_path,
+            tf32=tf32, compile_model=compile,
+        )
         return ({"model": model_obj, "device": dev, "cache_key": cache_key},)
 
     @classmethod
-    def IS_CHANGED(cls, model, device, local_path="", config_path=""):
-        return f"{model}:{device}:{local_path}:{config_path}"
+    def IS_CHANGED(cls, model, device, tf32=True, compile=False, local_path="", config_path=""):
+        return f"{model}:{device}:tf32={tf32}:compile={compile}:{local_path}:{config_path}"
 
 
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