nodes/robertvoy-ComfyUI-Flux-Continuum
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity
Files
5b53103ab6bbb5d484cf95bbdb4cbe33dbb605ae
robertvoy-ComfyUI-Flux-Cont…/misc.py
T
robertvoy 2944095277 Update 1.7.0
2025-06-19 13:39:06 +10:00

939 lines
34 KiB
Python
Raw Blame History

import nodes
from server import PromptServer
import torch
import comfy.samplers
import os
import time
from PIL import Image, ImageDraw, ImageFont, ImageColor, ImageFilter
import torchvision.transforms.v2 as T
import numpy as np
import folder_paths
import numpy as np
import json
from typing import Any, Mapping, Tuple
class DenoiseSlider:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"value": ("FLOAT", { "display": "slider", "default": 0.5, "min": 0.0, "max": 1.0, "step": 0.001 }),
},
}
RETURN_TYPES = ("FLOAT", )
FUNCTION = "execute"
CATEGORY = "Flux-Continuum/Sliders"
DESCRIPTION = """Control the **denoising strength** for img2img operations, including: inpainting, ultimate upscaler and detailer.
- A value of **1.0** means a completely new image.
- A value of **0.0** means no change to the latent image."""
def execute(self, value):
return (value, )
class StepSlider:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"value": ("FLOAT", { "display": "slider", "default": 25.0, "min": 0.0, "max": 50.0, "step": 1.0 }),
},
}
RETURN_TYPES = ("INT", )
FUNCTION = "execute"
CATEGORY = "Flux-Continuum/Sliders"
DESCRIPTION = "Set the number of **sampling steps**. Higher values can increase detail but take longer to process."
def execute(self, value):
# Use round() instead of int() to ensure proper integer conversion
return (int(round(value)), )
class BatchSlider:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"value": ("FLOAT", { "display": "slider", "default": 1.0, "min": 1.0, "max": 10.0, "step": 1.0 }),
},
}
RETURN_TYPES = ("INT", )
FUNCTION = "execute"
CATEGORY = "Flux-Continuum/Sliders"
DESCRIPTION = "Provides a slider for controlling batch size with range 1-10"
def execute(self, value):
# Use round() instead of int() to ensure proper integer conversion
return (int(round(value)), )
class ResolutionMultiplySlider:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"value": ("FLOAT", { "display": "slider", "default": 1.0, "min": 1.0, "max": 10.0, "step": 0.1 }),
},
}
RETURN_TYPES = ("FLOAT", )
FUNCTION = "execute"
CATEGORY = "Flux-Continuum/Sliders"
DESCRIPTION = "Provides a slider for controlling resolution multiplication for upscaling, with range 1-10"
def execute(self, value):
return (value, )
class GPUSlider:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"value": ("FLOAT", { "display": "slider", "default": 1.0, "min": 1.0, "max": 4.0, "step": 1.0 }),
},
}
RETURN_TYPES = ("INT", )
FUNCTION = "execute"
CATEGORY = "Flux-Continuum/Sliders"
DESCRIPTION = "Provides a slider for selecting number of GPUs with range 1-4"
def execute(self, value):
# Use round() instead of int() to ensure proper integer conversion
return (int(round(value)), )
class SelectFromBatch:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"value": ("FLOAT", { "display": "slider", "default": 0.0, "min": 0.0, "max": 24.0, "step": 1.0 }),
},
}
RETURN_TYPES = ("INT", )
FUNCTION = "execute"
CATEGORY = "Flux-Continuum/Sliders"
DESCRIPTION = "Provides a slider for selecting specific images from a batch with range 0-24"
def execute(self, value):
# Use round() instead of int() to ensure proper integer conversion
return (int(round(value)), )
class GuidanceSlider:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"value": ("FLOAT", { "display": "slider", "default": 2.5, "min": -1.0, "max": 30.0, "step": 0.1 }),
},
}
RETURN_TYPES = ("FLOAT", )
FUNCTION = "execute"
CATEGORY = "Flux-Continuum/Sliders"
DESCRIPTION = "Higher values make the output adhere more strictly to the prompt. Select between different presets for convenience. NOTE: FLUX Continuum workflow automatically sets your guidance to 30 when you're doing inpainting, outpainting, canny, or depth operations."
def execute(self, value):
# Return the float value directly
return (value, )
class MaxShiftSlider:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"value": ("FLOAT", { "display": "slider", "default": 1.15, "min": 0.0, "max": 4.0, "step": 0.05 }),
},
}
RETURN_TYPES = ("FLOAT", )
FUNCTION = "execute"
CATEGORY = "Flux-Continuum/Sliders"
DESCRIPTION = "Control the **maximum pixel shift**, often used to introduce variation."
def execute(self, value):
# Return the float value directly
return (value, )
class ControlNetSlider:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"Strength": ("FLOAT", { "display": "slider", "default": 1, "min": 0.0, "max": 1.0, "step": 0.05 }),
"Start": ("FLOAT", { "display": "slider", "default": 0, "min": 0.0, "max": 1.0, "step": 0.05 }),
"End": ("FLOAT", { "display": "slider", "default": 1, "min": 0.0, "max": 1.0, "step": 0.05 }),
},
}
RETURN_TYPES = ("VEC3", )
FUNCTION = "execute"
CATEGORY = "Flux-Continuum/Sliders"
DESCRIPTION = """- **Strength**: The overall influence of the ControlNet.
- **Start**: The step at which the ControlNet begins to apply (as a percentage).
- **End**: The step at which the ControlNet stops applying (as a percentage)."""
def execute(self, Strength, Start, End):
# Return the three values as a VEC3
return ((Strength, Start, End), )
class CannySlider:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"Low_Threshold": ("FLOAT", { "display": "slider", "default": 0.40, "min": 0.1, "max": 0.99, "step": 0.01 }),
"High_Threshold": ("FLOAT", { "display": "slider", "default": 0.80, "min": 0.1, "max": 0.99, "step": 0.01 })
},
}
RETURN_TYPES = ("VEC2", )
FUNCTION = "execute"
CATEGORY = "Flux-Continuum/Sliders"
DESCRIPTION = "Provides two sliders for canny preprocessor parameters"
def execute(self, Low_Threshold, High_Threshold):
# Return the two values as a VEC2
return ((Low_Threshold, High_Threshold), )
class IPAdapterSlider:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"IP1": ("FLOAT", { "display": "slider", "default": 0, "min": 0.0, "max": 1.0, "step": 0.05 }),
"IP2": ("FLOAT", { "display": "slider", "default": 0, "min": 0.0, "max": 1.0, "step": 0.05 }),
"IP3": ("FLOAT", { "display": "slider", "default": 0, "min": 0.0, "max": 1.0, "step": 0.05 }),
},
}
RETURN_TYPES = ("VEC3",)
FUNCTION = "execute"
CATEGORY = "Flux-Continuum/Sliders"
DESCRIPTION = "Control the strength of up to three different Redux inputs simultaneously."
def execute(self, IP1, IP2, IP3):
# Return the three values as a VEC3
return ((IP1, IP2, IP3),)
class SEGSPass:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"SEGS": ("SEGS",),
},
}
RETURN_TYPES = ("SEGS", )
FUNCTION = "execute"
CATEGORY = "Flux-Continuum/Utilities"
def execute(self, SEGS):
# Return the integer value directly
return (SEGS, )
class PipePass:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"PIPE_LINE": ("PIPE_LINE",),
},
}
RETURN_TYPES = ("PIPE_LINE", )
FUNCTION = "execute"
CATEGORY = "Flux-Continuum/Utilities"
def execute(self, PIPE_LINE):
return (PIPE_LINE, )
class LatentPass:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"latent": ("LATENT",),
},
}
RETURN_TYPES = ("LATENT", )
FUNCTION = "execute"
CATEGORY = "Flux-Continuum/Utilities"
def execute(self, latent):
# Simply pass through the latent data
return (latent, )
class IntPass:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"INT": ("INT",),
},
}
RETURN_TYPES = ("INT", )
FUNCTION = "execute"
CATEGORY = "Flux-Continuum/Utilities"
def execute(self, INT):
# Simply pass through an integer
return (INT, )
class ResolutionPicker:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"resolution": (["704x1408 (0.5)","704x1344 (0.52)","768x1344 (0.57)","768x1280 (0.6)","832x1216 (0.68)","832x1152 (0.72)","896x1152 (0.78)","896x1088 (0.82)","960x1088 (0.88)","960x1024 (0.94)","1024x1024 (1.0)","1024x960 (1.07)","1088x960 (1.13)","1088x896 (1.21)","1152x896 (1.29)","1152x832 (1.38)","1216x832 (1.46)","1280x768 (1.67)","1344x768 (1.75)","1344x704 (1.91)","1408x704 (2.0)","1472x704 (2.09)","1536x640 (2.4)","1600x640 (2.5)","1664x576 (2.89)","1728x576 (3.0)",], {"default": "1024x1024 (1.0)"}),
}}
RETURN_TYPES = (["704x1408 (0.5)","704x1344 (0.52)","768x1344 (0.57)","768x1280 (0.6)","832x1216 (0.68)","832x1152 (0.72)","896x1152 (0.78)","896x1088 (0.82)","960x1088 (0.88)","960x1024 (0.94)","1024x1024 (1.0)","1024x960 (1.07)","1088x960 (1.13)","1088x896 (1.21)","1152x896 (1.29)","1152x832 (1.38)","1216x832 (1.46)","1280x768 (1.67)","1344x768 (1.75)","1344x704 (1.91)","1408x704 (2.0)","1472x704 (2.09)","1536x640 (2.4)","1600x640 (2.5)","1664x576 (2.89)","1728x576 (3.0)",],)
RETURN_NAMES = ("resolution",)
FUNCTION = "execute"
CATEGORY = "Flux-Continuum/Utilities"
DESCRIPTION = "Provides a convenient dropdown menu to select from a list of common, pre-calculated image **resolutions** and their aspect ratios. Perfect for FLUX."
def execute(self, resolution):
return (resolution,)
class SamplerParameterPacker:
CATEGORY = 'Flux-Continuum/Utilities'
RETURN_TYPES = ("SAMPLER_PARAMS",)
RETURN_NAMES = ("sampler_params",)
FUNCTION = "pack_parameters"
DESCRIPTION = "Packs sampler and scheduler selections into a single parameter object for efficient passing"
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"sampler": (comfy.samplers.KSampler.SAMPLERS,),
"scheduler": (comfy.samplers.KSampler.SCHEDULERS,),
}}
def pack_parameters(self, sampler, scheduler):
return ((sampler, str(sampler), scheduler, str(scheduler)),)
class SamplerParameterUnpacker:
CATEGORY = 'Flux-Continuum/Utilities'
RETURN_TYPES = (comfy.samplers.KSampler.SAMPLERS, "STRING", comfy.samplers.KSampler.SCHEDULERS, "STRING",)
RETURN_NAMES = ("sampler", "sampler_name", "scheduler", "scheduler_name",)
FUNCTION = "unpack_parameters"
DESCRIPTION = "Unpacks previously packed sampler parameters back into individual components"
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"sampler_params": ("SAMPLER_PARAMS",),
}}
def unpack_parameters(self, sampler_params):
sampler, sampler_name, scheduler, scheduler_name = sampler_params
return (sampler, sampler_name, scheduler, scheduler_name,)
class TextVersions:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"text": ("STRING", {"default": "", "multiline": True, "dynamicPrompts": True}),
},
}
RETURN_TYPES = ("STRING",)
RETURN_NAMES = ("text",)
FUNCTION = "process_text"
CATEGORY = "Flux-Continuum/Utilities"
DESCRIPTION = "Provides a multi-tab interface for managing different versions of text input"
def __init__(self):
self.order = 0
def process_text(self, text):
return (text,)
def workflow_to_map(workflow):
nodes_map = {}
links = {}
# Create a lookup table for links and nodes
for links_data in workflow['links']:
links[links_data[0]] = links_data[1:]
for node_data in workflow['nodes']:
nodes_map[str(node_data['id'])] = node_data
return nodes_map, links
def is_execution_model_version_supported():
try:
import comfy_execution
return True
except:
return False
class AnyType(str):
def __ne__(self, __value: object) -> bool:
return False
any_typ = AnyType("*")
class ImpactControlBridgeFix:
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"value": (any_typ,),
"mode": ("BOOLEAN", {"default": True, "label_on": "Active", "label_off": "Stop/Mute/Bypass"}),
"behavior": (["Stop", "Mute", "Bypass"], ),
},
"hidden": {"unique_id": "UNIQUE_ID", "prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}
}
FUNCTION = "doit"
CATEGORY = "Flux-Continuum/Utilities"
RETURN_TYPES = (any_typ,)
RETURN_NAMES = ("value",)
OUTPUT_NODE = True
DESCRIPTION = ("When behavior is Stop and mode is active, the input value is passed directly to the output.\n"
"When behavior is Mute/Bypass and mode is active, the node connected to the output is changed to active state.\n"
"When behavior is Stop and mode is Stop/Mute/Bypass, the workflow execution of the current node is halted.\n"
"When behavior is Mute/Bypass and mode is Stop/Mute/Bypass, the node connected to the output is changed to Mute/Bypass state.")
@classmethod
def IS_CHANGED(self, value, mode, behavior="Stop", unique_id=None, prompt=None, extra_pnginfo=None):
if behavior == "Stop":
return value, mode, behavior
try:
if prompt and 'extra_data' in prompt and 'extra_pnginfo' in prompt['extra_data']:
workflow = prompt['extra_data']['extra_pnginfo'].get('workflow')
if workflow:
nodes_map, links = workflow_to_map(workflow)
next_nodes = []
for link in nodes_map[unique_id]['outputs'][0]['links']:
node_id = str(links[link][2])
if node_id in nodes_map:
next_nodes.append(node_id)
return next_nodes
except:
pass
return 0
def doit(self, value, mode, behavior="Stop", unique_id=None, prompt=None, extra_pnginfo=None):
# Check for execution model support
if is_execution_model_version_supported():
from comfy_execution.graph import ExecutionBlocker
else:
print("[Impact Pack] ImpactControlBridge: ComfyUI is outdated. The 'Stop' behavior cannot function properly.")
# Handle Stop behavior
if behavior == "Stop":
if mode:
return (value, )
else:
return (ExecutionBlocker(None), )
# Handle other behaviors
try:
# Validate extra_pnginfo
if not extra_pnginfo or not isinstance(extra_pnginfo, dict) or 'workflow' not in extra_pnginfo:
return (value, )
workflow_nodes, links = workflow_to_map(extra_pnginfo['workflow'])
# Initialize node lists
active_nodes = []
mute_nodes = []
bypass_nodes = []
node_outputs = workflow_nodes.get(unique_id, {}).get('outputs', [])
if not node_outputs:
return (value, )
output_links = node_outputs[0].get('links', [])
for link in output_links:
try:
node_id = str(links[link][2])
next_nodes = []
if node_id in workflow_nodes:
next_nodes.append(node_id)
for next_node_id in next_nodes:
node_mode = workflow_nodes[next_node_id].get('mode', 0)
if node_mode == 0:
active_nodes.append(next_node_id)
elif node_mode == 2:
mute_nodes.append(next_node_id)
elif node_mode == 4:
bypass_nodes.append(next_node_id)
except:
continue
# Handle mode-specific behavior
if mode:
# active
should_be_active_nodes = mute_nodes + bypass_nodes
if should_be_active_nodes:
PromptServer.instance.send_sync("impact-bridge-continue",
{"node_id": unique_id,
'actives': list(should_be_active_nodes)})
nodes.interrupt_processing()
elif behavior == "Mute" or behavior == True:
# mute
should_be_mute_nodes = active_nodes + bypass_nodes
if should_be_mute_nodes:
PromptServer.instance.send_sync("impact-bridge-continue",
{"node_id": unique_id,
'mutes': list(should_be_mute_nodes)})
nodes.interrupt_processing()
else:
# bypass
should_be_bypass_nodes = active_nodes + mute_nodes
if should_be_bypass_nodes:
PromptServer.instance.send_sync("impact-bridge-continue",
{"node_id": unique_id,
'bypasses': list(should_be_bypass_nodes)})
nodes.interrupt_processing()
except Exception as e:
print(f"[Impact Pack] Error in ImpactControlBridge: {str(e)}")
return (value, )
class BooleanToEnabled:
"""Convert boolean value to enabled string format"""
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"BOOLEAN": ("BOOLEAN",),
},
}
RETURN_TYPES = (["true", "false", "remote"],) # Match the exact format from RemoteQueueWorker
RETURN_NAMES = ("enabled",)
FUNCTION = "convert"
CATEGORY = "Flux-Continuum/Utilities"
TITLE = "Boolean to Enabled"
DESCRIPTION = "Converts boolean values to 'true'/'false'/'remote' strings for ComfyUI_NetDist"
def convert(self, BOOLEAN):
# Convert boolean to appropriate string value
return ("true" if BOOLEAN else "false",)
class OutputGetString:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
},
"hidden": {
"unique_id": "UNIQUE_ID",
"prompt": "PROMPT",
"title": ("STRING", {"default": ""})
}
}
RETURN_TYPES = ("STRING",)
RETURN_NAMES = ("STRING",)
FUNCTION = "process"
CATEGORY = "Flux-Continuum/Utilities"
OUTPUT_NODE = True
def process(self, title, unique_id, prompt):
title = title[len("Output - "):]
return (title,)
# Type definition for Vec3
Vec3 = Tuple[float, float, float]
Vec2 = Tuple[float, float]
# Zero vector constant
VEC3_ZERO = (0.0, 0.0, 0.0)
VEC2_ZERO = (0.0, 0.0)
class SplitVec3:
@classmethod
def INPUT_TYPES(cls) -> Mapping[str, Any]:
return {"required": {"a": ("VEC3", {"default": VEC3_ZERO})}}
RETURN_TYPES = ("FLOAT", "FLOAT", "FLOAT")
FUNCTION = "op"
CATEGORY = "Flux-Continuum/Utilities"
DESCRIPTION = "Splits a vector3 input into its three individual float components"
def op(self, a: Vec3) -> tuple[float, float, float]:
return (a[0], a[1], a[2])
class SplitVec2:
@classmethod
def INPUT_TYPES(cls) -> Mapping[str, Any]:
return {"required": {"a": ("VEC2", {"default": (0.0, 0.0)})}}
RETURN_TYPES = ("FLOAT", "FLOAT")
FUNCTION = "op"
CATEGORY = "Flux-Continuum/Utilities"
DESCRIPTION = "Splits a vector2 input into its two individual float components"
def op(self, a) -> tuple[float, float]:
return (a[0], a[1])
class SimpleTextTruncate:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"text": ("STRING", {"forceInput": True}),
"word_count": ("INT", {"default": 10, "min": 0, "max": 99999999, "step": 1}),
}
}
RETURN_TYPES = ("STRING",)
RETURN_NAMES = ("TEXT",)
FUNCTION = "truncate_words"
CATEGORY = "Text Operations"
DESCRIPTION = "Truncates input text to a specified number of words"
def truncate_words(self, text, word_count):
if text is None:
return ("",) # Return as a tuple
words = str(text).split()
result = ' '.join(words[:word_count])
# Return as a tuple since RETURN_TYPES is defined as a tuple
return (result,)
class FluxContinuumModelRouter:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"condition": ("STRING", {"default": ""})
},
"optional": {
"flux_fill": ("MODEL", {"lazy": True}), # Lazy load for inpainting/outpainting
"flux_depth": ("MODEL", {"lazy": True}), # Lazy load for depth
"flux_canny": ("MODEL", {"lazy": True}), # Lazy load for canny
"flux_dev": ("MODEL", {"lazy": True}), # Lazy load for default case
}
}
RETURN_TYPES = ("MODEL",)
FUNCTION = "route_model"
CATEGORY = "Flux-Continuum/Utilities"
DESCRIPTION = "For Flux Continuum workflow only. Routes model selection based on conditional input for different tasks (fill, depth, canny, dev)"
def check_lazy_status(self, condition, flux_fill=None, flux_depth=None, flux_canny=None, flux_dev=None):
condition = condition.lower().strip()
needed = []
# Only request the model we actually need based on the condition
if condition in ["inpainting", "outpainting"]:
if flux_fill is None:
needed.append("flux_fill")
elif condition == "depth":
if flux_depth is None:
needed.append("flux_depth")
elif condition == "canny":
if flux_canny is None:
needed.append("flux_canny")
else:
if flux_dev is None:
needed.append("flux_dev")
return needed
def route_model(self, condition, flux_fill=None, flux_depth=None, flux_canny=None, flux_dev=None):
condition = condition.lower().strip()
if condition in ["inpainting", "outpainting"]:
print(f"ModelRouter: Condition '{condition}' matched - Selected flux_fill model")
return (flux_fill,)
elif condition == "depth":
print(f"ModelRouter: Condition '{condition}' matched - Selected flux_depth model")
return (flux_depth,)
elif condition == "canny":
print(f"ModelRouter: Condition '{condition}' matched - Selected flux_canny model")
return (flux_canny,)
else:
return (flux_dev,)
class ConfigurableModelRouter:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
# This will be a text box widget on the node for manual input
"condition": ("STRING", {"multiline": False, "default": "default"}),
# The JSON config is also a widget on the node
"routing_config": ("STRING", {
"multiline": True,
"default": '{\n "default": 1,\n "inpainting": 2,\n "depth": 3,\n "canny": 4\n}'
}),
},
"optional": {
"model_1": ("MODEL", {"lazy": True}),
"model_2": ("MODEL", {"lazy": True}),
"model_3": ("MODEL", {"lazy": True}),
"model_4": ("MODEL", {"lazy": True}),
"model_5": ("MODEL", {"lazy": True}),
}
}
RETURN_TYPES = ("MODEL",)
FUNCTION = "route_model"
CATEGORY = "Flux-Continuum/Utilities"
DESCRIPTION = """
A dynamic model router that selects one of its inputs based on a configurable JSON mapping.
How to Use:
1. **Configure Logic:** Edit the `routing_config` JSON to map condition strings (e.g., `"inpainting"`) to an input index (e.g., `2`).
2. The `"default"` key is used if no other condition matches.
"""
# It's an instance method, so it can correctly read the widget values.
def check_lazy_status(self, condition, routing_config, **kwargs):
needed = []
try:
config = json.loads(routing_config)
# Use the values from the widgets to find the target index
target_index = config.get(condition.strip().lower(), config.get("default", 1))
# Construct the name of the model input we need to load
model_key = f"model_{target_index}"
# If the required model hasn't been loaded yet, request it by name
if kwargs.get(model_key) is None:
needed.append(model_key)
except:
# If the JSON is invalid, do nothing.
pass
print(f"[Model Router Check] Condition: '{condition}', Needing to load: {needed}")
return needed
def route_model(self, condition, routing_config, **kwargs):
# This logic runs after the needed model has been loaded.
config = json.loads(routing_config)
target_index = config.get(condition.strip().lower(), config.get("default", 1))
model_key = f"model_{target_index}"
# Check that the model exists and is connected
if model_key not in kwargs or kwargs.get(model_key) is None:
raise ValueError(f"Input '{model_key}' is required for condition '{condition}' but is not connected or loaded.")
print(f"Model Router: Successfully routed to '{model_key}'")
return (kwargs[model_key],)
class ImageBatchBoolean:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image1": ("IMAGE",),
"image2": ("IMAGE", {"lazy": True}), # Make image2 lazy
"batch_enabled": ("BOOLEAN", {"default": True}),
}
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "batch"
CATEGORY = "Flux-Continuum/Utilities"
def check_lazy_status(self, image1, image2, batch_enabled):
needed = []
# Only need image2 if batching is enabled
if image2 is None and batch_enabled:
needed.append("image2")
return needed
def batch(self, image1, image2, batch_enabled):
# If batching is disabled, just return the first image
if not batch_enabled:
return (image1,)
# If batching is enabled, perform the normal batch operation
if image1.shape[1:] != image2.shape[1:]:
image2 = comfy.utils.common_upscale(
image2.movedim(-1,1),
image1.shape[2],
image1.shape[1],
"bilinear",
"center"
).movedim(1,-1)
s = torch.cat((image1, image2), dim=0)
return (s,)
# based on ComfyUI Essentials: github.com/cubiq/ComfyUI_essentials
MAX_RESOLUTION = 2048
FONTS_DIR = os.path.join(os.path.dirname(os.path.realpath(__file__)), "fonts")
def hex_to_rgba(hex_color):
hex_color = hex_color.lstrip('#')
if len(hex_color) == 6:
r, g, b = tuple(int(hex_color[i:i+2], 16) for i in (0, 2, 4))
return (r, g, b, 255)
elif len(hex_color) == 8:
r, g, b, a = tuple(int(hex_color[i:i+2], 16) for i in (0, 2, 4, 6))
return (r, g, b, a)
else:
raise ValueError("Invalid hex color format")
class DrawTextConfig:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"font": (sorted([f for f in os.listdir(FONTS_DIR) if f.endswith('.ttf') or f.endswith('.otf')]), ),
"size": ("INT", { "default": 56, "min": 1, "max": 9999, "step": 1 }),
"color": ("STRING", { "multiline": False, "default": "#FFFFFF" }),
"background_color": ("STRING", { "multiline": False, "default": "#00000000" }),
"padding": ("INT", { "default": 20, "min": 0, "max": 500, "step": 1 }),
"shadow_distance": ("INT", { "default": 0, "min": 0, "max": 100, "step": 1 }),
"shadow_blur": ("INT", { "default": 0, "min": 0, "max": 100, "step": 1 }),
"shadow_color": ("STRING", { "multiline": False, "default": "#000000" }),
"horizontal_align": (["left", "center", "right"],),
"vertical_align": (["top", "center", "bottom"],),
"offset_x": ("INT", { "default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION, "step": 1 }),
"offset_y": ("INT", { "default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION, "step": 1 }),
"direction": (["ltr", "rtl"],),
}}
RETURN_TYPES = ("TEXT_STYLE",)
FUNCTION = "configure"
CATEGORY = "text"
DESCRIPTION = "Configures text rendering parameters including font, size, color, alignment, and effects"
def configure(self, font, size, color, background_color, padding, shadow_distance, shadow_blur,
shadow_color, horizontal_align, vertical_align, offset_x, offset_y, direction):
return ({
"font": font,
"size": size,
"color": color,
"background_color": background_color,
"padding": padding,
"shadow_distance": shadow_distance,
"shadow_blur": shadow_blur,
"shadow_color": shadow_color,
"horizontal_align": horizontal_align,
"vertical_align": vertical_align,
"offset_x": offset_x,
"offset_y": offset_y,
"direction": direction
},)
class ConfigurableDrawText:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"TEXT": ("STRING", {"multiline": True}),
"TEXT_STYLE": ("TEXT_STYLE",),
"IMAGE": ("IMAGE",),
}}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "draw"
CATEGORY = "text"
DESCRIPTION = "Renders text onto images using previously configured text style parameters"
def draw(self, TEXT, TEXT_STYLE, IMAGE):
font = ImageFont.truetype(os.path.join(FONTS_DIR, TEXT_STYLE["font"]), TEXT_STYLE["size"])
lines = TEXT.split("\n")
if TEXT_STYLE["direction"] == "rtl":
lines = [line[::-1] for line in lines]
ascent, descent = font.getmetrics()
line_spacing = ascent + descent
text_width = max(font.getbbox(line)[2] - font.getbbox(line)[0] for line in lines)
text_height = line_spacing * (len(lines) - 1) + ascent + descent
IMAGE = T.ToPILImage()(IMAGE.permute([0,3,1,2])[0]).convert('RGBA')
width = IMAGE.width
height = IMAGE.height
image = Image.new('RGBA', (width, height), (0,0,0,0))
box_width = text_width + (TEXT_STYLE["padding"] * 2)
box_height = text_height + (TEXT_STYLE["padding"] * 2)
if TEXT_STYLE["horizontal_align"] == "left":
box_x = TEXT_STYLE["offset_x"]
elif TEXT_STYLE["horizontal_align"] == "center":
box_x = (width - box_width) // 2 + TEXT_STYLE["offset_x"]
else: # right
box_x = width - box_width + TEXT_STYLE["offset_x"]
if TEXT_STYLE["vertical_align"] == "top":
box_y = TEXT_STYLE["offset_y"]
elif TEXT_STYLE["vertical_align"] == "center":
box_y = (height - box_height) // 2 + TEXT_STYLE["offset_y"]
else: # bottom
box_y = height - box_height + TEXT_STYLE["offset_y"]
x = box_x + TEXT_STYLE["padding"]
y = box_y + TEXT_STYLE["padding"]
draw = ImageDraw.Draw(image)
draw.rectangle([box_x, box_y, box_x + box_width, box_y + box_height],
fill=hex_to_rgba(TEXT_STYLE["background_color"]))
image_shadow = None
if TEXT_STYLE["shadow_distance"] > 0:
image_shadow = image.copy()
for i, line in enumerate(lines):
current_y = y + (i * line_spacing)
draw = ImageDraw.Draw(image)
draw.text((x, current_y), line, font=font, fill=hex_to_rgba(TEXT_STYLE["color"]))
if image_shadow is not None:
draw = ImageDraw.Draw(image_shadow)
draw.text((x + TEXT_STYLE["shadow_distance"], current_y + TEXT_STYLE["shadow_distance"]),
line, font=font, fill=hex_to_rgba(TEXT_STYLE["shadow_color"]))
if image_shadow is not None:
image_shadow = image_shadow.filter(ImageFilter.GaussianBlur(TEXT_STYLE["shadow_blur"]))
image = Image.alpha_composite(image_shadow, image)
image = Image.alpha_composite(IMAGE, image)
image = T.ToTensor()(image).unsqueeze(0).permute([0,2,3,1])
return (image[:, :, :, :3],)
MISC_CLASS_MAPPINGS = {
"DenoiseSlider": DenoiseSlider,
"StepSlider": StepSlider,
"GuidanceSlider": GuidanceSlider,
"BatchSlider": BatchSlider,
"MaxShiftSlider": MaxShiftSlider,
"ControlNetSlider": ControlNetSlider,
"IPAdapterSlider": IPAdapterSlider,
"CannySlider": CannySlider,
"SelectFromBatch": SelectFromBatch,
"GPUSlider": GPUSlider,
"SEGSPass": SEGSPass,
"IntPass": IntPass,
"PipePass": PipePass,
"LatentPass": LatentPass,
"ResolutionPicker": ResolutionPicker,
"ResolutionMultiplySlider": ResolutionMultiplySlider,
"SamplerParameterPacker": SamplerParameterPacker,
"SamplerParameterUnpacker": SamplerParameterUnpacker,
"TextVersions": TextVersions,
"ImpactControlBridgeFix": ImpactControlBridgeFix,
"BooleanToEnabled": BooleanToEnabled,
"OutputGetString": OutputGetString,
"SplitVec2": SplitVec2,
"SplitVec3": SplitVec3,
"SimpleTextTruncate": SimpleTextTruncate,
"FluxContinuumModelRouter": FluxContinuumModelRouter,
"ConfigurableModelRouter": ConfigurableModelRouter,
"ImageBatchBoolean": ImageBatchBoolean,
"DrawTextConfig": DrawTextConfig,
"ConfigurableDrawText": ConfigurableDrawText
}
Reference in New Issue View Git Blame Copy Permalink