Invert the start and end percentages in the code.

This doesn't affect how percentages behave in the frontend but breaks
things if you relied on them in the backend.

percent_to_sigma goes from 0 to 1.0 instead of 1.0 to 0 for less confusion.

Make percent 0 return an extremely large sigma and percent 1.0 return a
zero one to fix imprecision.

comfy/controlnet.py

@@ -33,7 +33,7 @@ def __init__(self, device=None):
         self.cond_hint_original = None
         self.cond_hint = None
         self.strength = 1.0
-        self.timestep_percent_range = (1.0, 0.0)
+        self.timestep_percent_range = (0.0, 1.0)
         self.timestep_range = None
 
         if device is None:
@@ -42,7 +42,7 @@ def __init__(self, device=None):
         self.previous_controlnet = None
         self.global_average_pooling = False
 
-    def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(1.0, 0.0)):
+    def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(0.0, 1.0)):
         self.cond_hint_original = cond_hint
         self.strength = strength
         self.timestep_percent_range = timestep_percent_range

comfy/model_sampling.py

@@ -76,5 +76,10 @@ def sigma(self, timestep):
         return log_sigma.exp()
 
     def percent_to_sigma(self, percent):
+        if percent <= 0.0:
+            return torch.tensor(999999999.9)
+        if percent >= 1.0:
+            return torch.tensor(0.0)
+        percent = 1.0 - percent
         return self.sigma(torch.tensor(percent * 999.0))
 

comfy/samplers.py

@@ -220,6 +220,8 @@ def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options):
                         transformer_options["patches"] = patches
 
                 transformer_options["cond_or_uncond"] = cond_or_uncond[:]
+                transformer_options["sigmas"] = timestep
+
                 c['transformer_options'] = transformer_options
 
                 if 'model_function_wrapper' in model_options:

comfy_extras/nodes_model_advanced.py

@@ -66,6 +66,11 @@ def sigma(self, timestep):
         return log_sigma.exp()
 
     def percent_to_sigma(self, percent):
+        if percent <= 0.0:
+            return torch.tensor(999999999.9)
+        if percent >= 1.0:
+            return torch.tensor(0.0)
+        percent = 1.0 - percent
         return self.sigma(torch.tensor(percent * 999.0))
 
 

nodes.py

@@ -248,8 +248,8 @@ def set_range(self, conditioning, start, end):
         c = []
         for t in conditioning:
             d = t[1].copy()
-            d['start_percent'] = 1.0 - start
-            d['end_percent'] = 1.0 - end
+            d['start_percent'] = start
+            d['end_percent'] = end
             n = [t[0], d]
             c.append(n)
         return (c, )
@@ -685,7 +685,7 @@ def apply_controlnet(self, positive, negative, control_net, image, strength, sta
                 if prev_cnet in cnets:
                     c_net = cnets[prev_cnet]
                 else:
-                    c_net = control_net.copy().set_cond_hint(control_hint, strength, (1.0 - start_percent, 1.0 - end_percent))
+                    c_net = control_net.copy().set_cond_hint(control_hint, strength, (start_percent, end_percent))
                     c_net.set_previous_controlnet(prev_cnet)
                     cnets[prev_cnet] = c_net