diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py
index f116efee..e52466b3 100644
--- a/comfy/ldm/modules/attention.py
+++ b/comfy/ldm/modules/attention.py
@@ -460,6 +460,8 @@ class BasicTransformerBlock(nn.Module):
         return checkpoint(self._forward, (x, context, transformer_options), self.parameters(), self.checkpoint)
 
     def _forward(self, x, context=None, transformer_options={}):
+        if "transformer_function" in transformer_options:
+            return transformer_options["transformer_function"](self, x, context, transformer_options)
         extra_options = {}
         block = transformer_options.get("block", None)
         block_index = transformer_options.get("block_index", 0)
diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py
index cf51c4ad..21d5b1af 100644
--- a/comfy/model_patcher.py
+++ b/comfy/model_patcher.py
@@ -158,6 +158,14 @@ class ModelPatcher:
     def set_model_output_block_patch(self, patch):
         self.set_model_patch(patch, "output_block_patch")
 
+    def set_model_transformer_function(self, transformer_function):
+        self.model_options["transformer_options"]["transformer_function"] = transformer_function
+
+    def set_model_sampler_cfg_rescaler(self, cfg_rescaler_fn, disable_cfg1_optimization=False):
+        self.model_options["sampler_cfg_rescaler"] = cfg_rescaler_fn
+        if disable_cfg1_optimization:
+            self.model_options["disable_cfg1_optimization"] = True
+
     def add_object_patch(self, name, obj):
         self.object_patches[name] = obj
 
diff --git a/comfy/samplers.py b/comfy/samplers.py
index 415a35cc..bd8912bc 100644
--- a/comfy/samplers.py
+++ b/comfy/samplers.py
@@ -249,6 +249,8 @@ def cfg_function(model, cond_pred, uncond_pred, cond_scale, x, timestep, model_o
 #The main sampling function shared by all the samplers
 #Returns denoised
 def sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options={}, seed=None):
+    if "sampler_cfg_rescaler" in model_options:
+        cond_scale = model_options["sampler_cfg_rescaler"]({"cond_scale": cond_scale, "timestep": timestep})
     if math.isclose(cond_scale, 1.0) and model_options.get("disable_cfg1_optimization", False) == False:
         uncond_ = None
     else: