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Merge branch 'comfyanonymous:master' into master

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patientx 2025-07-11 20:40:01 +03:00 committed by GitHub
commit b6360d36af
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2 changed files with 15 additions and 4 deletions
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10
comfy_execution/graph.py
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@ -3,6 +3,7 @@ from typing import Type, Literal
import nodes import nodes
import asyncio import asyncio
import inspect
from comfy_execution.graph_utils import is_link from comfy_execution.graph_utils import is_link
from comfy.comfy_types.node_typing import ComfyNodeABC, InputTypeDict, InputTypeOptions from comfy.comfy_types.node_typing import ComfyNodeABC, InputTypeDict, InputTypeOptions
@ -239,8 +240,15 @@ class ExecutionList(TopologicalSort):
return True return True
return False return False
# If an available node is async, do that first.
# This will execute the asynchronous function earlier, reducing the overall time.
def is_async(node_id):
class_type = self.dynprompt.get_node(node_id)["class_type"]
class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
return inspect.iscoroutinefunction(getattr(class_def, class_def.FUNCTION))
for node_id in node_list: for node_id in node_list:
if is_output(node_id): if is_output(node_id) or is_async(node_id):
return node_id return node_id
#This should handle the VAEDecode -> preview case #This should handle the VAEDecode -> preview case

9
comfy_extras/nodes_fresca.py
View File

@ -71,8 +71,11 @@ class FreSca:
DESCRIPTION = "Applies frequency-dependent scaling to the guidance" DESCRIPTION = "Applies frequency-dependent scaling to the guidance"
def patch(self, model, scale_low, scale_high, freq_cutoff): def patch(self, model, scale_low, scale_high, freq_cutoff):
def custom_cfg_function(args): def custom_cfg_function(args):
cond = args["conds_out"][0] conds_out = args["conds_out"]
uncond = args["conds_out"][1] if len(conds_out) <= 1 or None in args["conds"][:2]:
return conds_out
cond = conds_out[0]
uncond = conds_out[1]
guidance = cond - uncond guidance = cond - uncond
filtered_guidance = Fourier_filter( filtered_guidance = Fourier_filter(
@ -83,7 +86,7 @@ class FreSca:
) )
filtered_cond = filtered_guidance + uncond filtered_cond = filtered_guidance + uncond
return [filtered_cond, uncond] return [filtered_cond, uncond] + conds_out[2:]
m = model.clone() m = model.clone()
m.set_model_sampler_pre_cfg_function(custom_cfg_function) m.set_model_sampler_pre_cfg_function(custom_cfg_function)