diff --git a/__init__.py b/__init__.py
index c47df67..c6273d1 100644
--- a/__init__.py
+++ b/__init__.py
@@ -6,6 +6,7 @@ node_list = [ #Add list of .py files containing nodes here
     "reference_only",
     "sampler_rescalecfg",
     "sampler_tonemap",
+    "sampler_tonemap_rescalecfg",
     "sdxl_model_merging"
 ]
 
diff --git a/sampler_tonemap_rescalecfg.py b/sampler_tonemap_rescalecfg.py
new file mode 100644
index 0000000..0132330
--- /dev/null
+++ b/sampler_tonemap_rescalecfg.py
@@ -0,0 +1,55 @@
+import torch
+
+
+class TonemapNoiseWithRescaleCFG:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"model": ("MODEL",),
+                             "tonemap_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}),
+                             "rescale_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+                             }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "custom_node_experiments"
+
+    def patch(self, model, tonemap_multiplier, rescale_multiplier):
+
+        def tonemap_noise_rescale_cfg(args):
+            cond = args["cond"]
+            uncond = args["uncond"]
+            cond_scale = args["cond_scale"]
+
+            # Tonemap
+            noise_pred = (cond - uncond)
+            noise_pred_vector_magnitude = (torch.linalg.vector_norm(noise_pred, dim=(1)) + 0.0000000001)[:, None]
+            noise_pred /= noise_pred_vector_magnitude
+
+            mean = torch.mean(noise_pred_vector_magnitude, dim=(1, 2, 3), keepdim=True)
+            std = torch.std(noise_pred_vector_magnitude, dim=(1, 2, 3), keepdim=True)
+
+            top = (std * 3 + mean) * tonemap_multiplier
+
+            # Reinhard
+            noise_pred_vector_magnitude *= (1.0 / top)
+            new_magnitude = noise_pred_vector_magnitude / (noise_pred_vector_magnitude + 1.0)
+            new_magnitude *= top
+
+            # Rescale CFG
+            x_cfg = uncond + (noise_pred * new_magnitude * cond_scale)
+            ro_pos = torch.std(cond, dim=(1, 2, 3), keepdim=True)
+            ro_cfg = torch.std(x_cfg, dim=(1, 2, 3), keepdim=True)
+
+            x_rescaled = x_cfg * (ro_pos / ro_cfg)
+            x_final = rescale_multiplier * x_rescaled + (1.0 - rescale_multiplier) * x_cfg
+
+            return x_final
+
+        m = model.clone()
+        m.set_model_sampler_cfg_function(tonemap_noise_rescale_cfg)
+        return (m, )
+
+
+NODE_CLASS_MAPPINGS = {
+    "TonemapNoiseWithRescaleCFG": TonemapNoiseWithRescaleCFG,
+}