From 1664f191a588a9f334d534d7b54241739a574ea9 Mon Sep 17 00:00:00 2001
From: Koratahiu~ <haidermontage191@gmail.com>
Date: Wed, 1 Oct 2025 06:19:43 +0300
Subject: [PATCH] Epsilon Scaling method

---
 comfy_extras/nodes_eps.py | 66 +++++++++++++++++++++++++++++++++++++++
 nodes.py                  |  1 +
 2 files changed, 67 insertions(+)
 create mode 100644 comfy_extras/nodes_eps.py

diff --git a/comfy_extras/nodes_eps.py b/comfy_extras/nodes_eps.py
new file mode 100644
index 000000000..c3d980833
--- /dev/null
+++ b/comfy_extras/nodes_eps.py
@@ -0,0 +1,66 @@
+import torch
+
+class EpsilonScaling:
+    """
+    Implements the Epsilon Scaling method from 'Elucidating the Exposure Bias in Diffusion Models' 
+    (https://arxiv.org/abs/2308.15321v6).
+
+    This method mitigates exposure bias by scaling the predicted noise during sampling, 
+    which can significantly improve sample quality. This implementation uses the "uniform schedule"
+    recommended by the paper for its practicality and effectiveness.
+    """
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "scaling_factor": ("FLOAT", {
+                    "default": 1.005,
+                    "min": 0.5,
+                    "max": 1.5,
+                    "step": 0.001,
+                    "display": "number"
+                }),
+            }
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "model_patches/unet"
+
+    def patch(self, model, scaling_factor):
+        # Prevent division by zero, though the UI's min value should prevent this.
+        if scaling_factor == 0:
+            scaling_factor = 1e-9
+
+        def epsilon_scaling_function(args):
+            """
+            This function is applied after the CFG guidance has been calculated.
+            It recalculates the denoised latent by scaling the predicted noise.
+            """
+            denoised = args["denoised"]
+            x = args["input"]
+
+            noise_pred = x - denoised
+
+            scaled_noise_pred = noise_pred / scaling_factor
+
+            new_denoised = x - scaled_noise_pred
+
+            return new_denoised
+
+        # Clone the model patcher to avoid modifying the original model in place
+        model_clone = model.clone()
+
+        # Apply the patch using set_model_sampler_post_cfg_function.
+        # disable_cfg1_optimization=True is crucial. This patch needs the outputs of both the
+        # conditional and unconditional models to correctly calculate the guided noise, even when 
+        # the CFG scale is 1.0. Disabling the optimization ensures both are always computed.
+        model_clone.set_model_sampler_post_cfg_function(epsilon_scaling_function, disable_cfg1_optimization=True)
+
+        return (model_clone,)
+
+NODE_CLASS_MAPPINGS = {
+    "Epsilon Scaling": EpsilonScaling
+}
\ No newline at end of file
diff --git a/nodes.py b/nodes.py
index 1a6784b68..88d712993 100644
--- a/nodes.py
+++ b/nodes.py
@@ -2297,6 +2297,7 @@ async def init_builtin_extra_nodes():
         "nodes_gits.py",
         "nodes_controlnet.py",
         "nodes_hunyuan.py",
+        "nodes_eps.py",
         "nodes_flux.py",
         "nodes_lora_extract.py",
         "nodes_torch_compile.py",