From 906c0899575a83ac69bb095e835fdec748891da4 Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Wed, 29 Oct 2025 16:29:01 -0700
Subject: [PATCH] Fix small performance regression with fp8 fast and scaled
 fp8. (#10537)

---
 comfy/ops.py       | 6 +++++-
 comfy/quant_ops.py | 5 +++--
 2 files changed, 8 insertions(+), 3 deletions(-)

diff --git a/comfy/ops.py b/comfy/ops.py
index 71ca7a2bd..18f6b804b 100644
--- a/comfy/ops.py
+++ b/comfy/ops.py
@@ -421,14 +421,18 @@ def fp8_linear(self, input):
 
         if scale_input is None:
             scale_input = torch.ones((), device=input.device, dtype=torch.float32)
+            input = torch.clamp(input, min=-448, max=448, out=input)
+            input = input.reshape(-1, input_shape[2]).to(dtype).contiguous()
+            layout_params_weight = {'scale': scale_input, 'orig_dtype': input_dtype}
+            quantized_input = QuantizedTensor(input.reshape(-1, input_shape[2]).to(dtype).contiguous(), TensorCoreFP8Layout, layout_params_weight)
         else:
             scale_input = scale_input.to(input.device)
+            quantized_input = QuantizedTensor.from_float(input.reshape(-1, input_shape[2]), TensorCoreFP8Layout, scale=scale_input, dtype=dtype)
 
         # Wrap weight in QuantizedTensor - this enables unified dispatch
         # Call F.linear - __torch_dispatch__ routes to fp8_linear handler in quant_ops.py!
         layout_params_weight = {'scale': scale_weight, 'orig_dtype': input_dtype}
         quantized_weight = QuantizedTensor(w, TensorCoreFP8Layout, layout_params_weight)
-        quantized_input = QuantizedTensor.from_float(input.reshape(-1, input_shape[2]), TensorCoreFP8Layout, scale=scale_input, dtype=dtype)
         o = torch.nn.functional.linear(quantized_input, quantized_weight, bias)
 
         uncast_bias_weight(self, w, bias, offload_stream)
diff --git a/comfy/quant_ops.py b/comfy/quant_ops.py
index fb35a0d40..c822fe53c 100644
--- a/comfy/quant_ops.py
+++ b/comfy/quant_ops.py
@@ -357,9 +357,10 @@ class TensorCoreFP8Layout(QuantizedLayout):
             scale = torch.tensor(scale)
         scale = scale.to(device=tensor.device, dtype=torch.float32)
 
-        lp_amax = torch.finfo(dtype).max
         tensor_scaled = tensor * (1.0 / scale).to(tensor.dtype)
-        torch.clamp(tensor_scaled, min=-lp_amax, max=lp_amax, out=tensor_scaled)
+        # TODO: uncomment this if it's actually needed because the clamp has a small performance penality'
+        # lp_amax = torch.finfo(dtype).max
+        # torch.clamp(tensor_scaled, min=-lp_amax, max=lp_amax, out=tensor_scaled)
         qdata = tensor_scaled.to(dtype, memory_format=torch.contiguous_format)
 
         layout_params = {