From a125cd84b054a57729b5eecab930ca9408719832 Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Sat, 11 Oct 2025 21:28:01 -0700
Subject: [PATCH] Improve AMD performance. (#10302)

I honestly have no idea why this improves things but it does.
---
 comfy/model_management.py | 7 ++-----
 1 file changed, 2 insertions(+), 5 deletions(-)

diff --git a/comfy/model_management.py b/comfy/model_management.py
index c5b817b62..146c00925 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -332,6 +332,7 @@ except:
 SUPPORT_FP8_OPS = args.supports_fp8_compute
 try:
     if is_amd():
+        torch.backends.cudnn.enabled = False  # Seems to improve things a lot on AMD
         try:
             rocm_version = tuple(map(int, str(torch.version.hip).split(".")[:2]))
         except:
@@ -925,11 +926,7 @@ def vae_dtype(device=None, allowed_dtypes=[]):
         if d == torch.float16 and should_use_fp16(device):
             return d
 
-        # NOTE: bfloat16 seems to work on AMD for the VAE but is extremely slow in some cases compared to fp32
-        # slowness still a problem on pytorch nightly 2.9.0.dev20250720+rocm6.4 tested on RDNA3
-        # also a problem on RDNA4 except fp32 is also slow there.
-        # This is due to large bf16 convolutions being extremely slow.
-        if d == torch.bfloat16 and ((not is_amd()) or amd_min_version(device, min_rdna_version=4)) and should_use_bf16(device):
+        if d == torch.bfloat16 and should_use_bf16(device):
             return d
 
     return torch.float32