Add rescaling_scale from STG to SkipLayerGuidanceDiT.

Author: comfyanonymous

comfy_extras/nodes_slg.py

@@ -16,7 +16,8 @@ def INPUT_TYPES(s):
                              "single_layers": ("STRING", {"default": "7, 8, 9", "multiline": False}),
                              "scale": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 10.0, "step": 0.1}),
                              "start_percent": ("FLOAT", {"default": 0.01, "min": 0.0, "max": 1.0, "step": 0.001}),
-                             "end_percent": ("FLOAT", {"default": 0.15, "min": 0.0, "max": 1.0, "step": 0.001})
+                             "end_percent": ("FLOAT", {"default": 0.15, "min": 0.0, "max": 1.0, "step": 0.001}),
+                             "rescaling_scale": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.01}),
                                 }}
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "skip_guidance"
@@ -26,7 +27,7 @@ def INPUT_TYPES(s):
 
     CATEGORY = "advanced/guidance"
 
-    def skip_guidance(self, model, scale, start_percent, end_percent, double_layers="", single_layers=""):
+    def skip_guidance(self, model, scale, start_percent, end_percent, double_layers="", single_layers="", rescaling_scale=0):
         # check if layer is comma separated integers
         def skip(args, extra_args):
             return args
@@ -65,6 +66,11 @@ def post_cfg_function(args):
             if scale > 0 and sigma_ >= sigma_end and sigma_ <= sigma_start:
                 (slg,) = comfy.samplers.calc_cond_batch(model, [cond], x, sigma, model_options)
                 cfg_result = cfg_result + (cond_pred - slg) * scale
+                if rescaling_scale != 0:
+                    factor = cond_pred.std() / cfg_result.std()
+                    factor = rescaling_scale * factor + (1 - rescaling_scale)
+                    cfg_result *= factor
+
             return cfg_result
 
         m = model.clone()