Cleanup SkipLayerGuidanceSD3 node.

comfy_extras/nodes_sd3.py

@@ -104,7 +104,7 @@ class SkipLayerGuidanceSD3:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {"model": ("MODEL", ),
-                             "layers": ("STRING", {"default": "7,8,9", "multiline": False}),
+                             "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})
@@ -119,11 +119,12 @@ def skip_guidance(self, model, layers, scale, start_percent, end_percent):
         if layers == "" or layers == None:
             return (model, )
         # check if layer is comma separated integers
-        assert layers.replace(",", "").isdigit(), "Layers must be comma separated integers"
         def skip(args, extra_args):
             return args
 
         model_sampling = model.get_model_object("model_sampling")
+        sigma_start = model_sampling.percent_to_sigma(start_percent)
+        sigma_end = model_sampling.percent_to_sigma(end_percent)
 
         def post_cfg_function(args):
             model = args["model"]
@@ -137,10 +138,9 @@ def post_cfg_function(args):
             for layer in layers:
                 model_options = comfy.model_patcher.set_model_options_patch_replace(model_options, skip, "dit", "double_block", layer)
             model_sampling.percent_to_sigma(start_percent)
-            sigma_start = model_sampling.percent_to_sigma(start_percent)
-            sigma_end = model_sampling.percent_to_sigma(end_percent)
+
             sigma_ = sigma[0].item()
-            if scale > 0 and sigma_ > sigma_end and sigma_ < sigma_start:
+            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
             return cfg_result