sampler_cfg_function now uses a dict for the argument.

This means arguments can be added without issues.

comfy/samplers.py

@@ -273,7 +273,8 @@ def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, con
         max_total_area = model_management.maximum_batch_area()
         cond, uncond = calc_cond_uncond_batch(model_function, cond, uncond, x, timestep, max_total_area, cond_concat, model_options)
         if "sampler_cfg_function" in model_options:
-            return model_options["sampler_cfg_function"](cond, uncond, cond_scale)
+            args = {"cond": cond, "uncond": uncond, "cond_scale": cond_scale, "timestep": timestep}
+            return model_options["sampler_cfg_function"](args)
         else:
             return uncond + (cond - uncond) * cond_scale
 

comfy/sd.py

@@ -1,6 +1,7 @@
 import torch
 import contextlib
 import copy
+import inspect
 
 from . import sd1_clip
 from . import sd2_clip
@@ -313,9 +314,11 @@ class ModelPatcher:
         self.model_options["transformer_options"]["tomesd"] = {"ratio": ratio}
 
     def set_model_sampler_cfg_function(self, sampler_cfg_function):
+        if len(inspect.signature(sampler_cfg_function).parameters) == 3:
+            self.model_options["sampler_cfg_function"] = lambda args: sampler_cfg_function(args["cond"], args["uncond"], args["cond_scale"]) #Old way
+        else:
             self.model_options["sampler_cfg_function"] = sampler_cfg_function
 
-
     def set_model_patch(self, patch, name):
         to = self.model_options["transformer_options"]
         if "patches" not in to: