395 lines
20 KiB
Python
395 lines
20 KiB
Python
from comfy_api.latest import io
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import os
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import torch
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import folder_paths
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import numpy as np
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from PIL import Image, ImageOps
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from .control import load_controlnet, is_advanced_controlnet
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from .nodes_main import AdvancedControlNetApply
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from .utils import ControlWeights, TimestepKeyframeGroup, TimestepKeyframe, get_properly_arranged_t2i_weights
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class LoadImagesFromDirectory(io.ComfyNode):
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@classmethod
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def define_schema(cls) -> io.Schema:
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return io.Schema(
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node_id='LoadImagesFromDirectory',
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display_name='🚫Load Images [DEPRECATED] 🛂🅐🅒🅝',
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category='',
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inputs=[
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io.String.Input('directory', default=''),
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io.Int.Input('image_load_cap', optional=True, default=0, max=9007199254740991, min=0, step=1),
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io.Int.Input('start_index', optional=True, default=0, max=9007199254740991, min=0, step=1)
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],
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outputs=[
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io.Image.Output('IMAGE', is_output_list=False),
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io.Mask.Output('MASK', is_output_list=False),
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io.Int.Output('INT', is_output_list=False)
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],
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is_deprecated=True
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)
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@classmethod
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def execute(cls, directory: str, image_load_cap: int = 0, start_index: int = 0):
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if not os.path.isdir(directory):
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raise FileNotFoundError(f"Directory '{directory} cannot be found.'")
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dir_files = os.listdir(directory)
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if len(dir_files) == 0:
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raise FileNotFoundError(f"No files in directory '{directory}'.")
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dir_files = sorted(dir_files)
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dir_files = [os.path.join(directory, x) for x in dir_files]
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# start at start_index
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dir_files = dir_files[start_index:]
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images = []
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masks = []
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limit_images = False
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if image_load_cap > 0:
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limit_images = True
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image_count = 0
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for image_path in dir_files:
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if os.path.isdir(image_path):
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continue
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if limit_images and image_count >= image_load_cap:
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break
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i = Image.open(image_path)
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i = ImageOps.exif_transpose(i)
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image = i.convert("RGB")
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image = np.array(image).astype(np.float32) / 255.0
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image = torch.from_numpy(image)[None,]
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if 'A' in i.getbands():
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mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
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mask = 1. - torch.from_numpy(mask)
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else:
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mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
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images.append(image)
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masks.append(mask)
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image_count += 1
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if len(images) == 0:
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raise FileNotFoundError(f"No images could be loaded from directory '{directory}'.")
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return io.NodeOutput(torch.cat(images, dim=0), torch.stack(masks, dim=0), image_count)
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class ScaledSoftUniversalWeightsDeprecated(io.ComfyNode):
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@classmethod
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def define_schema(cls) -> io.Schema:
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return io.Schema(
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node_id='ScaledSoftControlNetWeights',
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display_name='Scaled Soft Weights 🛂🅐🅒🅝',
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category='',
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inputs=[
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io.Float.Input('base_multiplier', default=0.825, max=1.0, min=0.0, step=0.001),
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io.Boolean.Input('flip_weights', default=False),
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io.Float.Input('uncond_multiplier', optional=True, default=1.0, max=1.0, min=0.0, step=0.01),
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io.Custom('CN_WEIGHTS_EXTRAS').Input('cn_extras', optional=True)
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],
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outputs=[
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io.Custom('CONTROL_NET_WEIGHTS').Output('CN_WEIGHTS', is_output_list=False),
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io.Custom('TIMESTEP_KEYFRAME').Output('TK_SHORTCUT', is_output_list=False)
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]
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)
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@classmethod
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def execute(cls, base_multiplier, flip_weights, uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
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weights = ControlWeights.universal(base_multiplier=base_multiplier, uncond_multiplier=uncond_multiplier, extras=cn_extras)
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return io.NodeOutput(weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
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class SoftControlNetWeightsDeprecated(io.ComfyNode):
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@classmethod
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def define_schema(cls) -> io.Schema:
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return io.Schema(
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node_id='SoftControlNetWeights',
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display_name='ControlNet Soft Weights 🛂🅐🅒🅝',
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category='',
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inputs=[
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io.Float.Input('weight_00', default=0.09941396206337118, max=10.0, min=0.0, step=0.001),
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io.Float.Input('weight_01', default=0.12050177219802567, max=10.0, min=0.0, step=0.001),
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io.Float.Input('weight_02', default=0.14606275417942507, max=10.0, min=0.0, step=0.001),
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io.Float.Input('weight_03', default=0.17704576264172736, max=10.0, min=0.0, step=0.001),
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io.Float.Input('weight_04', default=0.214600924414215, max=10.0, min=0.0, step=0.001),
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io.Float.Input('weight_05', default=0.26012233262329093, max=10.0, min=0.0, step=0.001),
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io.Float.Input('weight_06', default=0.3152997971191405, max=10.0, min=0.0, step=0.001),
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io.Float.Input('weight_07', default=0.3821815722656249, max=10.0, min=0.0, step=0.001),
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io.Float.Input('weight_08', default=0.4632503906249999, max=10.0, min=0.0, step=0.001),
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io.Float.Input('weight_09', default=0.561515625, max=10.0, min=0.0, step=0.001),
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io.Float.Input('weight_10', default=0.6806249999999999, max=10.0, min=0.0, step=0.001),
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io.Float.Input('weight_11', default=0.825, max=10.0, min=0.0, step=0.001),
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io.Float.Input('weight_12', default=1.0, max=10.0, min=0.0, step=0.001),
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io.Boolean.Input('flip_weights', default=False),
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io.Float.Input('uncond_multiplier', optional=True, default=1.0, max=1.0, min=0.0, step=0.01),
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io.Custom('CN_WEIGHTS_EXTRAS').Input('cn_extras', optional=True)
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],
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outputs=[
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io.Custom('CONTROL_NET_WEIGHTS').Output('CN_WEIGHTS', is_output_list=False),
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io.Custom('TIMESTEP_KEYFRAME').Output('TK_SHORTCUT', is_output_list=False)
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],
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is_deprecated=True
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)
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@classmethod
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def execute(cls, weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06,
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weight_07, weight_08, weight_09, weight_10, weight_11, weight_12, flip_weights,
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uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
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weights_output = [weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06,
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weight_07, weight_08, weight_09, weight_10, weight_11]
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weights_middle = [weight_12]
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weights = ControlWeights.controlnet(weights_output=weights_output, weights_middle=weights_middle, uncond_multiplier=uncond_multiplier, extras=cn_extras)
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return io.NodeOutput(weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
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class CustomControlNetWeightsDeprecated(io.ComfyNode):
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@classmethod
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def define_schema(cls) -> io.Schema:
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return io.Schema(
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node_id='CustomControlNetWeights',
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display_name='ControlNet Custom Weights 🛂🅐🅒🅝',
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category='',
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inputs=[
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io.Float.Input('weight_00', default=1.0, max=10.0, min=0.0, step=0.001),
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io.Float.Input('weight_01', default=1.0, max=10.0, min=0.0, step=0.001),
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io.Float.Input('weight_02', default=1.0, max=10.0, min=0.0, step=0.001),
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io.Float.Input('weight_03', default=1.0, max=10.0, min=0.0, step=0.001),
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io.Float.Input('weight_04', default=1.0, max=10.0, min=0.0, step=0.001),
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io.Float.Input('weight_05', default=1.0, max=10.0, min=0.0, step=0.001),
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io.Float.Input('weight_06', default=1.0, max=10.0, min=0.0, step=0.001),
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io.Float.Input('weight_07', default=1.0, max=10.0, min=0.0, step=0.001),
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io.Float.Input('weight_08', default=1.0, max=10.0, min=0.0, step=0.001),
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io.Float.Input('weight_09', default=1.0, max=10.0, min=0.0, step=0.001),
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io.Float.Input('weight_10', default=1.0, max=10.0, min=0.0, step=0.001),
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io.Float.Input('weight_11', default=1.0, max=10.0, min=0.0, step=0.001),
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io.Float.Input('weight_12', default=1.0, max=10.0, min=0.0, step=0.001),
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io.Boolean.Input('flip_weights', default=False),
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io.Float.Input('uncond_multiplier', optional=True, default=1.0, max=1.0, min=0.0, step=0.01),
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io.Custom('CN_WEIGHTS_EXTRAS').Input('cn_extras', optional=True)
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],
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outputs=[
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io.Custom('CONTROL_NET_WEIGHTS').Output('CN_WEIGHTS', is_output_list=False),
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io.Custom('TIMESTEP_KEYFRAME').Output('TK_SHORTCUT', is_output_list=False)
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],
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is_deprecated=True
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)
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@classmethod
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def execute(cls, weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06,
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weight_07, weight_08, weight_09, weight_10, weight_11, weight_12, flip_weights,
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uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
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weights_output = [weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06,
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weight_07, weight_08, weight_09, weight_10, weight_11]
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weights_middle = [weight_12]
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weights = ControlWeights.controlnet(weights_output=weights_output, weights_middle=weights_middle, uncond_multiplier=uncond_multiplier, extras=cn_extras)
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return io.NodeOutput(weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
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class SoftT2IAdapterWeightsDeprecated(io.ComfyNode):
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@classmethod
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def define_schema(cls) -> io.Schema:
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return io.Schema(
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node_id='SoftT2IAdapterWeights',
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display_name='T2IAdapter Soft Weights 🛂🅐🅒🅝',
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category='',
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inputs=[
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io.Float.Input('weight_00', default=0.25, max=10.0, min=0.0, step=0.001),
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io.Float.Input('weight_01', default=0.62, max=10.0, min=0.0, step=0.001),
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io.Float.Input('weight_02', default=0.825, max=10.0, min=0.0, step=0.001),
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io.Float.Input('weight_03', default=1.0, max=10.0, min=0.0, step=0.001),
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io.Boolean.Input('flip_weights', default=False),
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io.Float.Input('uncond_multiplier', optional=True, default=1.0, max=1.0, min=0.0, step=0.01),
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io.Custom('CN_WEIGHTS_EXTRAS').Input('cn_extras', optional=True)
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],
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outputs=[
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io.Custom('CONTROL_NET_WEIGHTS').Output('CN_WEIGHTS', is_output_list=False),
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io.Custom('TIMESTEP_KEYFRAME').Output('TK_SHORTCUT', is_output_list=False)
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],
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is_deprecated=True
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)
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@classmethod
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def execute(cls, weight_00, weight_01, weight_02, weight_03, flip_weights,
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uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
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weights = [weight_00, weight_01, weight_02, weight_03]
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weights = get_properly_arranged_t2i_weights(weights)
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weights = ControlWeights.t2iadapter(weights_input=weights, uncond_multiplier=uncond_multiplier, extras=cn_extras)
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return io.NodeOutput(weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
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class CustomT2IAdapterWeightsDeprecated(io.ComfyNode):
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@classmethod
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def define_schema(cls) -> io.Schema:
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return io.Schema(
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node_id='CustomT2IAdapterWeights',
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display_name='T2IAdapter Custom Weights 🛂🅐🅒🅝',
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category='',
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inputs=[
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io.Float.Input('weight_00', default=1.0, max=10.0, min=0.0, step=0.001),
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io.Float.Input('weight_01', default=1.0, max=10.0, min=0.0, step=0.001),
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io.Float.Input('weight_02', default=1.0, max=10.0, min=0.0, step=0.001),
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io.Float.Input('weight_03', default=1.0, max=10.0, min=0.0, step=0.001),
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io.Boolean.Input('flip_weights', default=False),
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io.Float.Input('uncond_multiplier', optional=True, default=1.0, max=1.0, min=0.0, step=0.01),
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io.Custom('CN_WEIGHTS_EXTRAS').Input('cn_extras', optional=True)
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],
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outputs=[
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io.Custom('CONTROL_NET_WEIGHTS').Output('CN_WEIGHTS', is_output_list=False),
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io.Custom('TIMESTEP_KEYFRAME').Output('TK_SHORTCUT', is_output_list=False)
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],
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is_deprecated=True
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)
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@classmethod
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def execute(cls, weight_00, weight_01, weight_02, weight_03, flip_weights,
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uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
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weights = [weight_00, weight_01, weight_02, weight_03]
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weights = get_properly_arranged_t2i_weights(weights)
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weights = ControlWeights.t2iadapter(weights_input=weights, uncond_multiplier=uncond_multiplier, extras=cn_extras)
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return io.NodeOutput(weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
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class AdvancedControlNetApplyDEPR(io.ComfyNode):
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@classmethod
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def define_schema(cls) -> io.Schema:
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return io.Schema(
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node_id='ACN_AdvancedControlNetApply',
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display_name='Apply Advanced ControlNet 🛂🅐🅒🅝',
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category='',
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inputs=[
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io.Conditioning.Input('positive'),
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io.Conditioning.Input('negative'),
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io.ControlNet.Input('control_net'),
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io.Image.Input('image'),
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io.Float.Input('strength', default=1.0, max=10.0, min=0.0, step=0.01),
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io.Float.Input('start_percent', default=0.0, max=1.0, min=0.0, step=0.001),
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io.Float.Input('end_percent', default=1.0, max=1.0, min=0.0, step=0.001),
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io.Mask.Input('mask_optional', optional=True),
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io.Custom('TIMESTEP_KEYFRAME').Input('timestep_kf', optional=True),
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io.Custom('LATENT_KEYFRAME').Input('latent_kf_override', optional=True),
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io.Custom('CONTROL_NET_WEIGHTS').Input('weights_override', optional=True),
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io.Model.Input('model_optional', optional=True),
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io.Vae.Input('vae_optional', optional=True)
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],
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outputs=[
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io.Conditioning.Output('positive', is_output_list=False),
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io.Conditioning.Output('negative', is_output_list=False),
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io.Model.Output('model_opt', is_output_list=False)
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],
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is_deprecated=True
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)
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@classmethod
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def execute(cls, positive, negative, control_net, image, strength, start_percent, end_percent,
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mask_optional=None, model_optional=None, vae_optional=None,
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timestep_kf: TimestepKeyframeGroup=None, latent_kf_override=None,
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weights_override: ControlWeights=None, control_apply_to_uncond=False):
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new_positive, new_negative = AdvancedControlNetApply.execute(positive=positive, negative=negative, control_net=control_net, image=image,
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strength=strength, start_percent=start_percent, end_percent=end_percent,
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mask_optional=mask_optional, vae_optional=vae_optional,
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timestep_kf=timestep_kf, latent_kf_override=latent_kf_override, weights_override=weights_override,).args
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return io.NodeOutput(new_positive, new_negative, model_optional)
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class AdvancedControlNetApplySingleDEPR(io.ComfyNode):
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@classmethod
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def define_schema(cls) -> io.Schema:
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return io.Schema(
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node_id='ACN_AdvancedControlNetApplySingle',
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display_name='Apply Advanced ControlNet(1) 🛂🅐🅒🅝',
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category='',
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inputs=[
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io.Conditioning.Input('conditioning'),
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io.ControlNet.Input('control_net'),
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io.Image.Input('image'),
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io.Float.Input('strength', default=1.0, max=10.0, min=0.0, step=0.01),
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io.Float.Input('start_percent', default=0.0, max=1.0, min=0.0, step=0.001),
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io.Float.Input('end_percent', default=1.0, max=1.0, min=0.0, step=0.001),
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io.Mask.Input('mask_optional', optional=True),
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io.Custom('TIMESTEP_KEYFRAME').Input('timestep_kf', optional=True),
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io.Custom('LATENT_KEYFRAME').Input('latent_kf_override', optional=True),
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io.Custom('CONTROL_NET_WEIGHTS').Input('weights_override', optional=True),
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io.Model.Input('model_optional', optional=True),
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io.Vae.Input('vae_optional', optional=True)
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],
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outputs=[
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io.Conditioning.Output('CONDITIONING', is_output_list=False),
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io.Model.Output('model_opt', is_output_list=False)
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],
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is_deprecated=True
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)
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@classmethod
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def execute(cls, conditioning, control_net, image, strength, start_percent, end_percent,
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mask_optional=None, model_optional=None, vae_optional=None,
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timestep_kf: TimestepKeyframeGroup=None, latent_kf_override=None,
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weights_override: ControlWeights=None):
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values = AdvancedControlNetApply.execute(positive=conditioning, negative=None, control_net=control_net, image=image,
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strength=strength, start_percent=start_percent, end_percent=end_percent,
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mask_optional=mask_optional, vae_optional=vae_optional,
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timestep_kf=timestep_kf, latent_kf_override=latent_kf_override, weights_override=weights_override,
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control_apply_to_uncond=True)
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return io.NodeOutput(values.args[0], model_optional)
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class ControlNetLoaderAdvancedDEPR(io.ComfyNode):
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@classmethod
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def define_schema(cls) -> io.Schema:
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return io.Schema(
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node_id='ControlNetLoaderAdvanced',
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display_name='Load Advanced ControlNet Model 🛂🅐🅒🅝',
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category='',
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inputs=[
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io.Combo.Input('control_net_name', options=folder_paths.get_filename_list("controlnet")),
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io.Custom('TIMESTEP_KEYFRAME').Input('tk_optional', optional=True)
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],
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outputs=[
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io.ControlNet.Output('CONTROL_NET', is_output_list=False)
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],
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is_deprecated=True
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)
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@classmethod
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def execute(cls, control_net_name,
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tk_optional: TimestepKeyframeGroup=None,
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timestep_keyframe: TimestepKeyframeGroup=None,
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):
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if timestep_keyframe is not None: # backwards compatibility
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tk_optional = timestep_keyframe
|
|
controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
|
|
controlnet = load_controlnet(controlnet_path, tk_optional)
|
|
return io.NodeOutput(controlnet,)
|
|
|
|
|
|
class DiffControlNetLoaderAdvancedDEPR(io.ComfyNode):
|
|
@classmethod
|
|
def define_schema(cls) -> io.Schema:
|
|
return io.Schema(
|
|
node_id='DiffControlNetLoaderAdvanced',
|
|
display_name='Load Advanced ControlNet Model (diff) 🛂🅐🅒🅝',
|
|
category='',
|
|
inputs=[
|
|
io.Model.Input('model'),
|
|
io.Combo.Input('control_net_name', options=folder_paths.get_filename_list("controlnet")),
|
|
io.Custom('TIMESTEP_KEYFRAME').Input('tk_optional', optional=True)
|
|
],
|
|
outputs=[
|
|
io.ControlNet.Output('CONTROL_NET', is_output_list=False)
|
|
],
|
|
is_deprecated=True
|
|
)
|
|
|
|
|
|
@classmethod
|
|
def execute(cls, control_net_name, model,
|
|
tk_optional: TimestepKeyframeGroup=None,
|
|
timestep_keyframe: TimestepKeyframeGroup=None
|
|
):
|
|
if timestep_keyframe is not None: # backwards compatibility
|
|
tk_optional = timestep_keyframe
|
|
controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
|
|
controlnet = load_controlnet(controlnet_path, tk_optional, model)
|
|
if is_advanced_controlnet(controlnet):
|
|
controlnet.verify_all_weights()
|
|
return io.NodeOutput(controlnet,)
|