nodes/Kosinkadink-ComfyUI-Advanced-ControlNet
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Merge pull request #215 from Kosinkadink/develop

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Fixed backwards compatibility for new KF scheduling behavior, changes…
This commit is contained in:
Jedrzej Kosinski
2025-01-05 15:38:32 -06:00
committed by GitHub
parent c4d9455052 ab6526fa35
commit f7a42fe6dc
2 changed files with 3 additions and 2 deletions
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adv_control/utils.py
+2 -1
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@@ -18,6 +18,7 @@ from .logger import logger
BIGMIN = -(2**53-1) BIGMIN = -(2**53-1)
BIGMAX = (2**53-1) BIGMAX = (2**53-1)
BIGMAX_TENSOR = torch.tensor(BIGMAX)
ORIG_PREVIOUS_CONTROLNET = "_orig_previous_controlnet" ORIG_PREVIOUS_CONTROLNET = "_orig_previous_controlnet"
CONTROL_INIT_BY_ACN = "_control_init_by_ACN" CONTROL_INIT_BY_ACN = "_control_init_by_ACN"
@@ -562,7 +563,7 @@ class AdvancedControlBase:
# get current step percent # get current step percent
curr_t: float = self.t curr_t: float = self.t
prev_index = self._current_timestep_index prev_index = self._current_timestep_index
max_sigma = torch.max(transformer_options.get("sigmas", BIGMAX)) max_sigma = torch.max(transformer_options.get("sample_sigmas", BIGMAX_TENSOR))
# if met guaranteed steps (or no current keyframe), look for next keyframe in case need to switch # if met guaranteed steps (or no current keyframe), look for next keyframe in case need to switch
if self._current_timestep_keyframe is None or self._current_used_steps >= self._current_timestep_keyframe.get_effective_guarantee_steps(max_sigma): if self._current_timestep_keyframe is None or self._current_used_steps >= self._current_timestep_keyframe.get_effective_guarantee_steps(max_sigma):
# if has next index, loop through and see if need to switch # if has next index, loop through and see if need to switch
pyproject.toml
+1 -1
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@@ -1,7 +1,7 @@
[project] [project]
name = "comfyui-advanced-controlnet" name = "comfyui-advanced-controlnet"
description = "Nodes for scheduling ControlNet strength across timesteps and batched latents, as well as applying custom weights and attention masks." description = "Nodes for scheduling ControlNet strength across timesteps and batched latents, as well as applying custom weights and attention masks."
version = "1.5.0" version = "1.5.1"
license = { file = "LICENSE" } license = { file = "LICENSE" }
dependencies = [] dependencies = []