diff --git a/horde_worker_regen/amd_go_fast/amd_go_fast.py b/horde_worker_regen/amd_go_fast/amd_go_fast.py
index c81d3c16..1d2404d7 100644
--- a/horde_worker_regen/amd_go_fast/amd_go_fast.py
+++ b/horde_worker_regen/amd_go_fast/amd_go_fast.py
@@ -1,40 +1,54 @@
 import torch
+from torch import Tensor
+from typing import Callable
 from loguru import logger
 
-if "AMD" in torch.cuda.get_device_name() or "Radeon" in torch.cuda.get_device_name():
-    try:  # this import is handled via  script, skipping it in mypy. If this fails somehow the module will simply not run.
-        from flash_attn import flash_attn_func  # type: ignore
-
-        sdpa = torch.nn.functional.scaled_dot_product_attention
-
-        def sdpa_hijack(
-            query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False, scale=None, enable_gqa=False
-        ):
-            if query.shape[3] <= 256 and attn_mask is None and query.dtype != torch.float32:
-                hidden_states = flash_attn_func(
-                    q=query.transpose(1, 2),
-                    k=key.transpose(1, 2),
-                    v=value.transpose(1, 2),
-                    dropout_p=dropout_p,
-                    causal=is_causal,
-                    softmax_scale=scale,
-                ).transpose(1, 2)
-            else:
-                hidden_states = sdpa(
-                    query=query,
-                    key=key,
-                    value=value,
-                    attn_mask=attn_mask,
-                    dropout_p=dropout_p,
-                    is_causal=is_causal,
-                    scale=scale,
-                    enable_gqa=enable_gqa,
-                )
-            return hidden_states
-
-        torch.nn.functional.scaled_dot_product_attention = sdpa_hijack
-        logger.debug("# # # AMD GO FAST # # #")
-    except ImportError as e:
-        logger.debug(f"# # # AMD GO SLOW {e} # # #")
-else:
-    logger.debug(f"# # # AMD GO SLOW Could not detect AMD GPU from: {torch.cuda.get_device_name()} # # #")
+
+def _patch_sdpa(
+    patch_func: Callable[[Tensor, Tensor, Tensor, Tensor | None, float, bool, float | None], Tensor],
+):
+    """(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False, scale=None)"""
+
+    torch_sdpa = torch.nn.functional.scaled_dot_product_attention
+
+    def sdpa_hijack_flash(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False, scale=None):
+        try:
+            return patch_func(query, key, value, attn_mask, dropout_p, is_causal, scale)
+        except Exception:
+            hidden_states = torch_sdpa(
+                query=query,
+                key=key,
+                value=value,
+                attn_mask=attn_mask,
+                dropout_p=dropout_p,
+                is_causal=is_causal,
+                scale=scale,
+            )
+        return hidden_states
+
+    torch.nn.functional.scaled_dot_product_attention = sdpa_hijack_flash
+
+
+try:
+    from flash_attn import flash_attn_func
+
+    def sdpa_hijack_flash(q, k, v, m, p, c, s):
+        assert m is None
+        result = flash_attn_func(
+            q=q.transpose(1, 2),
+            k=k.transpose(1, 2),
+            v=v.transpose(1, 2),
+            dropout_p=p,
+            softmax_scale=s if s else q.shape[-1] ** (-0.5),
+            causal=c,
+        )
+        assert isinstance(result, Tensor)
+        return result.transpose(1, 2)
+
+    _patch_sdpa(sdpa_hijack_flash)
+    logger.debug("# # # Patched SDPA with Flash Attention # # #")
+except ImportError as e:
+    logger.debug(f"# # # Could not load Flash Attention for hijack: {e} # # #")
+
+NODE_CLASS_MAPPINGS = {}
+NODE_DISPLAY_NAME_MAPPINGS = {}