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README.md

@@ -462,10 +462,10 @@ class ControlNetExampleForge(scripts.Script):
 
         # The advanced_frame_weighting is a weight applied to each image in a batch.
         # The length of this list must be same with batch size
-        # For example, if batch size is 5, the below list is [0, 0.25, 0.5, 0.75, 1.0]
+        # For example, if batch size is 5, the below list is [0.2, 0.4, 0.6, 0.8, 1.0]
         # If you view the 5 images as 5 frames in a video, this will lead to
         # progressively stronger control over time.
-        advanced_frame_weighting = [float(i) / float(batch_size - 1) for i in range(batch_size)]
+        advanced_frame_weighting = [float(i + 1) / float(batch_size) for i in range(batch_size)]
 
         # The advanced_sigma_weighting allows you to dynamically compute control
         # weights given diffusion timestep (sigma).

extensions-builtin/sd_forge_controlnet_example/scripts/sd_forge_controlnet_example.py

@@ -112,10 +112,10 @@ class ControlNetExampleForge(scripts.Script):
 
         # The advanced_frame_weighting is a weight applied to each image in a batch.
         # The length of this list must be same with batch size
-        # For example, if batch size is 5, the below list is [0, 0.25, 0.5, 0.75, 1.0]
+        # For example, if batch size is 5, the below list is [0.2, 0.4, 0.6, 0.8, 1.0]
         # If you view the 5 images as 5 frames in a video, this will lead to
         # progressively stronger control over time.
-        advanced_frame_weighting = [float(i) / float(batch_size - 1) for i in range(batch_size)]
+        advanced_frame_weighting = [float(i + 1) / float(batch_size) for i in range(batch_size)]
 
         # The advanced_sigma_weighting allows you to dynamically compute control
         # weights given diffusion timestep (sigma).
@@ -125,10 +125,10 @@ class ControlNetExampleForge(scripts.Script):
         advanced_sigma_weighting = lambda s: (s - sigma_min) / (sigma_max - sigma_min)
 
         # But in this simple example we do not use them
-        positive_advanced_weighting = None
-        negative_advanced_weighting = None
-        advanced_frame_weighting = None
-        advanced_sigma_weighting = None
+        # positive_advanced_weighting = None
+        # negative_advanced_weighting = None
+        # advanced_frame_weighting = None
+        # advanced_sigma_weighting = None
 
         unet = apply_controlnet_advanced(unet=unet, controlnet=self.model, image_bhwc=control_image,
                                          strength=0.6, start_percent=0.0, end_percent=0.8,

modules_forge/controlnet.py

@@ -57,3 +57,14 @@ def apply_controlnet_advanced(
     m = unet.clone()
     m.add_patched_controlnet(cnet)
     return m
+
+
+def compute_controlnet_weighting(
+        control,
+        positive_advanced_weighting,
+        negative_advanced_weighting,
+        advanced_frame_weighting,
+        advanced_sigma_weighting,
+        transformer_options
+):
+    return control

modules_forge/patch_basic.py

@@ -4,6 +4,7 @@ import ldm_patched.modules.samplers
 from ldm_patched.modules.controlnet import ControlBase
 from ldm_patched.modules.samplers import get_area_and_mult, can_concat_cond, cond_cat
 from ldm_patched.modules import model_management
+from modules_forge.controlnet import compute_controlnet_weighting
 
 
 def patched_control_merge(self, control_input, control_output, control_prev, output_dtype):
@@ -38,11 +39,14 @@ def patched_control_merge(self, control_input, control_output, control_prev, out
 
             out[key].append(x)
 
-    if self.positive_advanced_weighting is not None or self.negative_advanced_weighting:
-        # TODO: Implement here
-        cond_or_uncond = self.current_cond_or_uncond
-        a = 0
-        pass
+    out = compute_controlnet_weighting(
+        out,
+        positive_advanced_weighting=self.positive_advanced_weighting,
+        negative_advanced_weighting=self.negative_advanced_weighting,
+        advanced_frame_weighting=self.advanced_frame_weighting,
+        advanced_sigma_weighting=self.advanced_sigma_weighting,
+        transformer_options=self.transformer_options
+    )
 
     if control_prev is not None:
         for x in ['input', 'middle', 'output']:
@@ -129,9 +133,6 @@ def patched_calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_op
         c = cond_cat(c)
         timestep_ = torch.cat([timestep] * batch_chunks)
 
-        if control is not None:
-            c['control'] = control.get_control(input_x, timestep_, c, len(cond_or_uncond))
-
         transformer_options = {}
         if 'transformer_options' in model_options:
             transformer_options = model_options['transformer_options'].copy()
@@ -154,6 +155,7 @@ def patched_calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_op
 
         if control is not None:
             control.transformer_options = transformer_options
+            c['control'] = control.get_control(input_x, timestep_, c, len(cond_or_uncond))
 
         if 'model_function_wrapper' in model_options:
             output = model_options['model_function_wrapper'](model.apply_model, {"input": input_x, "timestep": timestep_, "c": c, "cond_or_uncond": cond_or_uncond}).chunk(batch_chunks)