Added Latent Keyframe Batched Group node that supports FizzNode BatchedValueSchedule input for strengths, reorganized code
This commit is contained in:
Jedrzej Kosinski
+1
-1
@@ -1,3 +1,3 @@
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from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
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from .control.nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
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__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
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@@ -0,0 +1,70 @@
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import os
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import torch
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import numpy as np
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from PIL import Image, ImageOps
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from .logger import logger
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class LoadImagesFromDirectory:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"directory": ("STRING", {"default": ""}),
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},
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"optional": {
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"image_load_cap": ("INT", {"default": 0, "min": 0, "step": 1}),
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"start_index": ("INT", {"default": 0, "min": 0, "step": 1}),
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}
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}
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RETURN_TYPES = ("IMAGE", "MASK", "INT")
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FUNCTION = "load_images"
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CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/deprecated"
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def load_images(self, 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 (torch.cat(images, dim=0), torch.stack(masks, dim=0), image_count)
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@@ -0,0 +1,238 @@
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from typing import Union
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import numpy as np
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from collections.abc import Iterable
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from .control import LatentKeyframe, LatentKeyframeGroup
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from .logger import logger
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class LatentKeyframeNode:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"batch_index": ("INT", {"default": 0, "min": -1000, "max": 1000, "step": 1}),
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"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.00001}, ),
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},
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"optional": {
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"prev_latent_keyframe": ("LATENT_KEYFRAME", ),
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}
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}
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RETURN_TYPES = ("LATENT_KEYFRAME", )
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FUNCTION = "load_keyframe"
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CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/keyframes"
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def load_keyframe(self,
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batch_index: int,
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strength: float,
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prev_latent_keyframe: LatentKeyframeGroup=None):
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if not prev_latent_keyframe:
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prev_latent_keyframe = LatentKeyframeGroup()
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keyframe = LatentKeyframe(batch_index, strength)
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prev_latent_keyframe.add(keyframe)
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return (prev_latent_keyframe,)
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class LatentKeyframeGroupNode:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"index_strengths": ("STRING", {"multiline": True, "default": ""}),
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},
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"optional": {
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"prev_latent_keyframe": ("LATENT_KEYFRAME", ),
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"latent_optional": ("LATENT", ),
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}
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}
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RETURN_TYPES = ("LATENT_KEYFRAME", )
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FUNCTION = "load_keyframes"
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CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/keyframes"
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def validate_index(self, index: int, latent_count: int = 0, is_range: bool = False, allow_negative = False) -> int:
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# if part of range, do nothing
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if is_range:
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return index
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# otherwise, validate index
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# validate not out of range - only when latent_count is passed in
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if latent_count > 0 and index > latent_count-1:
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raise IndexError(f"Index '{index}' out of range for the total {latent_count} latents.")
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# if negative, validate not out of range
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if index < 0:
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if not allow_negative:
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raise IndexError(f"Negative indeces not allowed, but was {index}.")
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conv_index = latent_count+index
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if conv_index < 0:
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raise IndexError(f"Index '{index}', converted to '{conv_index}' out of range for the total {latent_count} latents.")
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index = conv_index
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return index
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def convert_to_index_int(self, raw_index: str, latent_count: int = 0, is_range: bool = False, allow_negative = False) -> int:
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try:
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return self.validate_index(int(raw_index), latent_count=latent_count, is_range=is_range, allow_negative=allow_negative)
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except ValueError as e:
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raise ValueError(f"index '{raw_index}' must be an integer.", e)
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def convert_to_latent_keyframes(self, latent_indeces: str, latent_count: int) -> set[LatentKeyframe]:
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if not latent_indeces:
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return set()
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all_indeces = [i for i in range(0, latent_count)]
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allow_negative = latent_count > 0
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chosen_indeces = set()
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# parse string - allow positive ints, negative ints, and ranges separated by ':'
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groups = latent_indeces.split(",")
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groups = [g.strip() for g in groups]
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for g in groups:
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# parse strengths - default to 1.0 if no strength given
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strength = 1.0
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if '=' in g:
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g, strength_str = g.split("=", 1)
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g = g.strip()
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try:
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strength = float(strength_str.strip())
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except ValueError as e:
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raise ValueError(f"strength '{strength_str}' must be a float.", e)
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if strength < 0:
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raise ValueError(f"Strength '{strength}' cannot be negative.")
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# parse range of indeces (e.g. 2:16)
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if ':' in g:
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index_range = g.split(":", 1)
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index_range = [r.strip() for r in index_range]
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start_index = self.convert_to_index_int(index_range[0], latent_count=latent_count, is_range=True, allow_negative=allow_negative)
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end_index = self.convert_to_index_int(index_range[1], latent_count=latent_count, is_range=True, allow_negative=allow_negative)
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for i in all_indeces[start_index:end_index]:
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chosen_indeces.add(LatentKeyframe(i, strength))
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# parse individual indeces
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else:
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chosen_indeces.add(LatentKeyframe(self.convert_to_index_int(g, latent_count=latent_count, allow_negative=allow_negative), strength))
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return chosen_indeces
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def load_keyframes(self,
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index_strengths: str,
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prev_latent_keyframe: LatentKeyframeGroup=None,
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latent_image_opt=None):
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if not prev_latent_keyframe:
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prev_latent_keyframe = LatentKeyframeGroup()
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curr_latent_keyframe = LatentKeyframeGroup()
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latent_count = -1
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if latent_image_opt:
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latent_count = latent_image_opt['samples'].size()[0]
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latent_keyframes = self.convert_to_latent_keyframes(index_strengths, latent_count=latent_count)
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for latent_keyframe in latent_keyframes:
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logger.info(f"keyframe {latent_keyframe.batch_index}:{latent_keyframe.strength}")
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curr_latent_keyframe.add(latent_keyframe)
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for latent_keyframe in prev_latent_keyframe.keyframes:
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curr_latent_keyframe.add(latent_keyframe)
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return (curr_latent_keyframe,)
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class LatentKeyframeInterpolationNode:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"batch_index_from": ("INT", {"default": 0, "min": -10000, "max": 10000, "step": 1}),
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"batch_index_to_excl": ("INT", {"default": 0, "min": -10000, "max": 10000, "step": 1}),
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"strength_from": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.0001}, ),
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"strength_to": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.0001}, ),
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"interpolation": (["linear", "ease-in", "ease-out", "ease-in-out"], ),
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},
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"optional": {
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"prev_latent_keyframe": ("LATENT_KEYFRAME", ),
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}
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}
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RETURN_TYPES = ("LATENT_KEYFRAME", )
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FUNCTION = "load_keyframe"
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CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/keyframes"
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def load_keyframe(self,
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batch_index_from: int,
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strength_from: float,
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batch_index_to_excl: int,
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strength_to: float,
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interpolation: str,
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prev_latent_keyframe: LatentKeyframeGroup=None):
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if (batch_index_from > batch_index_to_excl):
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raise ValueError("batch_index_from must be less than or equal to batch_index_to.")
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if (batch_index_from < 0 and batch_index_to_excl >= 0):
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raise ValueError("batch_index_from and batch_index_to must be either both positive or both negative.")
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if not prev_latent_keyframe:
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prev_latent_keyframe = LatentKeyframeGroup()
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curr_latent_keyframe = LatentKeyframeGroup()
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steps = batch_index_to_excl - batch_index_from
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diff = strength_to - strength_from
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if interpolation == "linear":
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weights = np.linspace(strength_from, strength_to, steps)
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elif interpolation == "ease-in":
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index = np.linspace(0, 1, steps)
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weights = diff * np.power(index, 2) + strength_from
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elif interpolation == "ease-out":
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index = np.linspace(0, 1, steps)
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weights = diff * (1 - np.power(1 - index, 2)) + strength_from
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elif interpolation == "ease-in-out":
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index = np.linspace(0, 1, steps)
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weights = diff * ((1 - np.cos(index * np.pi)) / 2) + strength_from
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for i in range(steps):
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keyframe = LatentKeyframe(batch_index_from + i, float(weights[i]))
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logger.info(f"keyframe {batch_index_from + i}:{weights[i]}")
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curr_latent_keyframe.add(keyframe)
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# replace values with prev_latent_keyframes
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for latent_keyframe in prev_latent_keyframe.keyframes:
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curr_latent_keyframe.add(latent_keyframe)
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return (curr_latent_keyframe,)
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class LatentKeyframeBatchedGroupNode:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"strengths": ("FLOAT", {"default": -1, "min": -1, "step": 0.0001}),
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},
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"optional": {
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"prev_latent_keyframe": ("LATENT_KEYFRAME", ),
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}
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}
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RETURN_TYPES = ("LATENT_KEYFRAME", )
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FUNCTION = "load_keyframe"
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CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/keyframes"
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def load_keyframe(self, strengths: Union[float, list[float]], prev_latent_keyframe: LatentKeyframeGroup=None):
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if not prev_latent_keyframe:
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prev_latent_keyframe = LatentKeyframeGroup()
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curr_latent_keyframe = LatentKeyframeGroup()
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# if received a normal float input, do nothing
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if type(strengths) in (float, int):
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logger.info("No batched strengths passed into Latent Keyframe Batch Group node; will not create any new keyframes.")
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# if iterable, attempt to create LatentKeyframes with chosen strengths
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elif isinstance(strengths, Iterable):
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for idx, strength in enumerate(strengths):
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keyframe = LatentKeyframe(idx, strength)
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curr_latent_keyframe.add(keyframe)
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logger.info(f"keyframe {keyframe.batch_index}:{keyframe.strength}")
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else:
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raise ValueError(f"Expected strengths to be an iterable input, but was {type(strengths).__repr__}.")
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# replace values with prev_latent_keyframes
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for latent_keyframe in prev_latent_keyframe.keyframes:
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curr_latent_keyframe.add(latent_keyframe)
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return (curr_latent_keyframe,)
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@@ -0,0 +1,194 @@
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import numpy as np
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import folder_paths
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from .control import ControlNetAdvanced, T2IAdapterAdvanced, load_controlnet, ControlNetWeightsType, T2IAdapterWeightsType,\
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LatentKeyframeGroup, TimestepKeyframe, TimestepKeyframeGroup
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from .weight_nodes import ScaledSoftControlNetWeights, SoftControlNetWeights, CustomControlNetWeights, \
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SoftT2IAdapterWeights, CustomT2IAdapterWeights
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from .latent_keyframe_nodes import LatentKeyframeGroupNode, LatentKeyframeInterpolationNode, LatentKeyframeBatchedGroupNode, LatentKeyframeNode
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from .deprecated_nodes import LoadImagesFromDirectory
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from .logger import logger
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class TimestepKeyframeNode:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}, ),
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},
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"optional": {
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"control_net_weights": ("CONTROL_NET_WEIGHTS", ),
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"t2i_adapter_weights": ("T2I_ADAPTER_WEIGHTS", ),
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"latent_keyframe": ("LATENT_KEYFRAME", ),
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"prev_timestep_keyframe": ("TIMESTEP_KEYFRAME", ),
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}
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}
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RETURN_TYPES = ("TIMESTEP_KEYFRAME", )
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FUNCTION = "load_keyframe"
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CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/keyframes"
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def load_keyframe(self,
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start_percent: float,
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control_net_weights: ControlNetWeightsType=None,
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t2i_adapter_weights: T2IAdapterWeightsType=None,
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latent_keyframe: LatentKeyframeGroup=None,
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prev_timestep_keyframe: TimestepKeyframeGroup=None):
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if not prev_timestep_keyframe:
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prev_timestep_keyframe = TimestepKeyframeGroup()
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keyframe = TimestepKeyframe(start_percent, control_net_weights, t2i_adapter_weights, latent_keyframe)
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prev_timestep_keyframe.add(keyframe)
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return (prev_timestep_keyframe,)
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class ControlNetLoaderAdvanced:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"control_net_name": (folder_paths.get_filename_list("controlnet"), ),
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},
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"optional": {
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"timestep_keyframe": ("TIMESTEP_KEYFRAME", ),
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}
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}
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RETURN_TYPES = ("CONTROL_NET", )
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FUNCTION = "load_controlnet"
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CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/loaders"
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def load_controlnet(self, control_net_name, timestep_keyframe: TimestepKeyframeGroup=None):
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controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
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controlnet = load_controlnet(controlnet_path, timestep_keyframe)
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return (controlnet,)
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class DiffControlNetLoaderAdvanced:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"model": ("MODEL",),
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"control_net_name": (folder_paths.get_filename_list("controlnet"), )
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},
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"optional": {
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"timestep_keyframe": ("TIMESTEP_KEYFRAME", ),
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}
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}
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RETURN_TYPES = ("CONTROL_NET", )
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FUNCTION = "load_controlnet"
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CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/loaders"
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def load_controlnet(self, control_net_name, timestep_keyframe: TimestepKeyframeGroup, model):
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controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
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controlnet = load_controlnet(controlnet_path, timestep_keyframe, model)
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return (controlnet,)
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class ControlNetApplyAdvanced_AdvControlNet:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"positive": ("CONDITIONING", ),
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"negative": ("CONDITIONING", ),
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"control_net": ("CONTROL_NET", ),
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"image": ("IMAGE", ),
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"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
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"start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
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"end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001})
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},
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"optional": {
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"mask_opt": ("MASK", ),
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}
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}
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||||
RETURN_TYPES = ("CONDITIONING","CONDITIONING")
|
||||
RETURN_NAMES = ("positive", "negative")
|
||||
FUNCTION = "apply_controlnet"
|
||||
|
||||
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/loaders/conditioning"
|
||||
|
||||
def apply_controlnet(self, positive, negative, control_net, image, strength, start_percent, end_percent, mask_opt=None):
|
||||
if strength == 0:
|
||||
return (positive, negative)
|
||||
|
||||
if mask_opt is not None:
|
||||
mask_hint = mask_opt.movedim(-1,1)
|
||||
control_hint = image.movedim(-1,1)
|
||||
cnets = {}
|
||||
|
||||
out = []
|
||||
for conditioning in [positive, negative]:
|
||||
c = []
|
||||
for t in conditioning:
|
||||
d = t[1].copy()
|
||||
|
||||
prev_cnet = d.get('control', None)
|
||||
if prev_cnet in cnets:
|
||||
c_net = cnets[prev_cnet]
|
||||
else:
|
||||
c_net = control_net.copy().set_cond_hint(control_hint, strength, (1.0 - start_percent, 1.0 - end_percent))
|
||||
# TODO: finish mask implemention, does nothing right now
|
||||
if mask_opt is not None:
|
||||
if isinstance(c_net, ControlNetAdvanced) or isinstance(c_net, T2IAdapterAdvanced):
|
||||
c_net.set_cond_hint_mask(mask_hint)
|
||||
else:
|
||||
logger
|
||||
c_net.set_previous_controlnet(prev_cnet)
|
||||
cnets[prev_cnet] = c_net
|
||||
|
||||
d['control'] = c_net
|
||||
d['control_apply_to_uncond'] = False
|
||||
n = [t[0], d]
|
||||
c.append(n)
|
||||
out.append(c)
|
||||
return (out[0], out[1])
|
||||
|
||||
|
||||
# NODE MAPPING
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
# Keyframes
|
||||
"TimestepKeyframe": TimestepKeyframeNode,
|
||||
"LatentKeyframe": LatentKeyframeNode,
|
||||
"LatentKeyframeGroup": LatentKeyframeGroupNode,
|
||||
"LatentKeyframeBatchedGroup": LatentKeyframeBatchedGroupNode,
|
||||
"LatentKeyframeTiming": LatentKeyframeInterpolationNode,
|
||||
# Loaders
|
||||
"ControlNetLoaderAdvanced": ControlNetLoaderAdvanced,
|
||||
"DiffControlNetLoaderAdvanced": DiffControlNetLoaderAdvanced,
|
||||
# Weights
|
||||
"ScaledSoftControlNetWeights": ScaledSoftControlNetWeights,
|
||||
"SoftControlNetWeights": SoftControlNetWeights,
|
||||
"CustomControlNetWeights": CustomControlNetWeights,
|
||||
"SoftT2IAdapterWeights": SoftT2IAdapterWeights,
|
||||
"CustomT2IAdapterWeights": CustomT2IAdapterWeights,
|
||||
# Image
|
||||
"LoadImagesFromDirectory": LoadImagesFromDirectory
|
||||
}
|
||||
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
# Keyframes
|
||||
"TimestepKeyframe": "Timestep Keyframe 🛂🅐🅒🅝",
|
||||
"LatentKeyframe": "Latent Keyframe 🛂🅐🅒🅝",
|
||||
"LatentKeyframeGroup": "Latent Keyframe Group 🛂🅐🅒🅝",
|
||||
"LatentKeyframeBatchedGroup": "Latent Keyframe Batched Group 🛂🅐🅒🅝",
|
||||
"LatentKeyframeTiming": "Latent Keyframe Interpolation 🛂🅐🅒🅝",
|
||||
# Loaders
|
||||
"ControlNetLoaderAdvanced": "Load ControlNet Model (Advanced) 🛂🅐🅒🅝",
|
||||
"DiffControlNetLoaderAdvanced": "Load ControlNet Model (diff Advanced) 🛂🅐🅒🅝",
|
||||
# Weights
|
||||
"ScaledSoftControlNetWeights": "Scaled Soft ControlNet Weights 🛂🅐🅒🅝",
|
||||
"SoftControlNetWeights": "Soft ControlNet Weights 🛂🅐🅒🅝",
|
||||
"CustomControlNetWeights": "Custom ControlNet Weights 🛂🅐🅒🅝",
|
||||
"SoftT2IAdapterWeights": "Soft T2IAdapter Weights 🛂🅐🅒🅝",
|
||||
"CustomT2IAdapterWeights": "Custom T2IAdapter Weights 🛂🅐🅒🅝",
|
||||
# Image
|
||||
"LoadImagesFromDirectory": "Load Images [DEPRECATED] 🛂🅐🅒🅝"
|
||||
}
|
||||
@@ -0,0 +1,157 @@
|
||||
from .control import TimestepKeyframe, TimestepKeyframeGroup
|
||||
from .logger import logger
|
||||
|
||||
|
||||
def get_properly_arranged_t2i_weights(initial_weights: list[float]):
|
||||
new_weights = []
|
||||
new_weights.extend([initial_weights[0]]*3)
|
||||
new_weights.extend([initial_weights[1]]*3)
|
||||
new_weights.extend([initial_weights[2]]*3)
|
||||
new_weights.extend([initial_weights[3]]*3)
|
||||
return new_weights
|
||||
|
||||
|
||||
class ScaledSoftControlNetWeights:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"base_multiplier": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"flip_weights": ([False, True], ),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
|
||||
FUNCTION = "load_weights"
|
||||
|
||||
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights"
|
||||
|
||||
def load_weights(self, base_multiplier, flip_weights):
|
||||
weights = [(base_multiplier ** float(12 - i)) for i in range(13)]
|
||||
if flip_weights:
|
||||
weights.reverse()
|
||||
return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_net_weights=weights)))
|
||||
|
||||
|
||||
class SoftControlNetWeights:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"weight_00": ("FLOAT", {"default": 0.09941396206337118, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_01": ("FLOAT", {"default": 0.12050177219802567, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_02": ("FLOAT", {"default": 0.14606275417942507, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_03": ("FLOAT", {"default": 0.17704576264172736, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_04": ("FLOAT", {"default": 0.214600924414215, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_05": ("FLOAT", {"default": 0.26012233262329093, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_06": ("FLOAT", {"default": 0.3152997971191405, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_07": ("FLOAT", {"default": 0.3821815722656249, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_08": ("FLOAT", {"default": 0.4632503906249999, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_09": ("FLOAT", {"default": 0.561515625, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_10": ("FLOAT", {"default": 0.6806249999999999, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_11": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_12": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"flip_weights": ([False, True], ),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
|
||||
FUNCTION = "load_weights"
|
||||
|
||||
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights"
|
||||
|
||||
def load_weights(self, weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06,
|
||||
weight_07, weight_08, weight_09, weight_10, weight_11, weight_12, flip_weights):
|
||||
weights = [weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06,
|
||||
weight_07, weight_08, weight_09, weight_10, weight_11, weight_12]
|
||||
if flip_weights:
|
||||
weights.reverse()
|
||||
return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_net_weights=weights)))
|
||||
|
||||
|
||||
class CustomControlNetWeights:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"weight_00": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_01": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_02": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_03": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_04": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_05": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_06": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_07": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_08": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_09": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_10": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_11": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_12": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"flip_weights": ([False, True], ),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
|
||||
FUNCTION = "load_weights"
|
||||
|
||||
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights"
|
||||
|
||||
def load_weights(self, weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06,
|
||||
weight_07, weight_08, weight_09, weight_10, weight_11, weight_12, flip_weights):
|
||||
weights = [weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06,
|
||||
weight_07, weight_08, weight_09, weight_10, weight_11, weight_12]
|
||||
if flip_weights:
|
||||
weights.reverse()
|
||||
return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_net_weights=weights)))
|
||||
|
||||
|
||||
class SoftT2IAdapterWeights:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"weight_00": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_01": ("FLOAT", {"default": 0.62, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_02": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_03": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"flip_weights": ([False, True], ),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("T2I_ADAPTER_WEIGHTS", "TIMESTEP_KEYFRAME",)
|
||||
FUNCTION = "load_weights"
|
||||
|
||||
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights"
|
||||
|
||||
def load_weights(self, weight_00, weight_01, weight_02, weight_03, flip_weights):
|
||||
weights = [weight_00, weight_01, weight_02, weight_03]
|
||||
if flip_weights:
|
||||
weights.reverse()
|
||||
weights = get_properly_arranged_t2i_weights(weights)
|
||||
return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(t2i_adapter_weights=weights)))
|
||||
|
||||
|
||||
class CustomT2IAdapterWeights:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"weight_00": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_01": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_02": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_03": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"flip_weights": ([False, True], ),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("T2I_ADAPTER_WEIGHTS", "TIMESTEP_KEYFRAME",)
|
||||
FUNCTION = "load_weights"
|
||||
|
||||
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights"
|
||||
|
||||
def load_weights(self, weight_00, weight_01, weight_02, weight_03, flip_weights):
|
||||
weights = [weight_00, weight_01, weight_02, weight_03]
|
||||
if flip_weights:
|
||||
weights.reverse()
|
||||
weights = get_properly_arranged_t2i_weights(weights)
|
||||
return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(t2i_adapter_weights=weights)))
|
||||
@@ -1,605 +0,0 @@
|
||||
import sys
|
||||
import os
|
||||
|
||||
import torch
|
||||
|
||||
import numpy as np
|
||||
from PIL import Image, ImageOps
|
||||
|
||||
import folder_paths
|
||||
|
||||
from .control import ControlNetAdvanced, T2IAdapterAdvanced, load_controlnet, ControlNetWeightsType, T2IAdapterWeightsType,\
|
||||
LatentKeyframe, LatentKeyframeGroup, TimestepKeyframe, TimestepKeyframeGroup
|
||||
from .logger import logger
|
||||
|
||||
def get_properly_arranged_t2i_weights(initial_weights: list[float]):
|
||||
new_weights = []
|
||||
new_weights.extend([initial_weights[0]]*3)
|
||||
new_weights.extend([initial_weights[1]]*3)
|
||||
new_weights.extend([initial_weights[2]]*3)
|
||||
new_weights.extend([initial_weights[3]]*3)
|
||||
return new_weights
|
||||
|
||||
|
||||
class ScaledSoftControlNetWeights:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"base_multiplier": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"flip_weights": ([False, True], ),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
|
||||
FUNCTION = "load_weights"
|
||||
|
||||
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights"
|
||||
|
||||
def load_weights(self, base_multiplier, flip_weights):
|
||||
weights = [(base_multiplier ** float(12 - i)) for i in range(13)]
|
||||
if flip_weights:
|
||||
weights.reverse()
|
||||
return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_net_weights=weights)))
|
||||
|
||||
|
||||
class SoftControlNetWeights:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"weight_00": ("FLOAT", {"default": 0.09941396206337118, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_01": ("FLOAT", {"default": 0.12050177219802567, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_02": ("FLOAT", {"default": 0.14606275417942507, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_03": ("FLOAT", {"default": 0.17704576264172736, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_04": ("FLOAT", {"default": 0.214600924414215, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_05": ("FLOAT", {"default": 0.26012233262329093, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_06": ("FLOAT", {"default": 0.3152997971191405, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_07": ("FLOAT", {"default": 0.3821815722656249, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_08": ("FLOAT", {"default": 0.4632503906249999, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_09": ("FLOAT", {"default": 0.561515625, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_10": ("FLOAT", {"default": 0.6806249999999999, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_11": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_12": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"flip_weights": ([False, True], ),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
|
||||
FUNCTION = "load_weights"
|
||||
|
||||
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights"
|
||||
|
||||
def load_weights(self, weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06,
|
||||
weight_07, weight_08, weight_09, weight_10, weight_11, weight_12, flip_weights):
|
||||
weights = [weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06,
|
||||
weight_07, weight_08, weight_09, weight_10, weight_11, weight_12]
|
||||
if flip_weights:
|
||||
weights.reverse()
|
||||
return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_net_weights=weights)))
|
||||
|
||||
|
||||
class CustomControlNetWeights:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"weight_00": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_01": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_02": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_03": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_04": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_05": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_06": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_07": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_08": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_09": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_10": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_11": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_12": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"flip_weights": ([False, True], ),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
|
||||
FUNCTION = "load_weights"
|
||||
|
||||
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights"
|
||||
|
||||
def load_weights(self, weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06,
|
||||
weight_07, weight_08, weight_09, weight_10, weight_11, weight_12, flip_weights):
|
||||
weights = [weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06,
|
||||
weight_07, weight_08, weight_09, weight_10, weight_11, weight_12]
|
||||
if flip_weights:
|
||||
weights.reverse()
|
||||
return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_net_weights=weights)))
|
||||
|
||||
|
||||
class SoftT2IAdapterWeights:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"weight_00": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_01": ("FLOAT", {"default": 0.62, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_02": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_03": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"flip_weights": ([False, True], ),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("T2I_ADAPTER_WEIGHTS", "TIMESTEP_KEYFRAME",)
|
||||
FUNCTION = "load_weights"
|
||||
|
||||
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights"
|
||||
|
||||
def load_weights(self, weight_00, weight_01, weight_02, weight_03, flip_weights):
|
||||
weights = [weight_00, weight_01, weight_02, weight_03]
|
||||
if flip_weights:
|
||||
weights.reverse()
|
||||
weights = get_properly_arranged_t2i_weights(weights)
|
||||
return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(t2i_adapter_weights=weights)))
|
||||
|
||||
|
||||
class CustomT2IAdapterWeights:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"weight_00": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_01": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_02": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"weight_03": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
|
||||
"flip_weights": ([False, True], ),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("T2I_ADAPTER_WEIGHTS", "TIMESTEP_KEYFRAME",)
|
||||
FUNCTION = "load_weights"
|
||||
|
||||
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights"
|
||||
|
||||
def load_weights(self, weight_00, weight_01, weight_02, weight_03, flip_weights):
|
||||
weights = [weight_00, weight_01, weight_02, weight_03]
|
||||
if flip_weights:
|
||||
weights.reverse()
|
||||
weights = get_properly_arranged_t2i_weights(weights)
|
||||
return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(t2i_adapter_weights=weights)))
|
||||
|
||||
|
||||
class TimestepKeyframeNode:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}, ),
|
||||
},
|
||||
"optional": {
|
||||
"control_net_weights": ("CONTROL_NET_WEIGHTS", ),
|
||||
"t2i_adapter_weights": ("T2I_ADAPTER_WEIGHTS", ),
|
||||
"latent_keyframe": ("LATENT_KEYFRAME", ),
|
||||
"prev_timestep_keyframe": ("TIMESTEP_KEYFRAME", ),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("TIMESTEP_KEYFRAME", )
|
||||
FUNCTION = "load_keyframe"
|
||||
|
||||
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/keyframes"
|
||||
|
||||
def load_keyframe(self,
|
||||
start_percent: float,
|
||||
control_net_weights: ControlNetWeightsType=None,
|
||||
t2i_adapter_weights: T2IAdapterWeightsType=None,
|
||||
latent_keyframe: LatentKeyframeGroup=None,
|
||||
prev_timestep_keyframe: TimestepKeyframeGroup=None):
|
||||
if not prev_timestep_keyframe:
|
||||
prev_timestep_keyframe = TimestepKeyframeGroup()
|
||||
keyframe = TimestepKeyframe(start_percent, control_net_weights, t2i_adapter_weights, latent_keyframe)
|
||||
prev_timestep_keyframe.add(keyframe)
|
||||
return (prev_timestep_keyframe,)
|
||||
|
||||
|
||||
class LatentKeyframeNode:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"batch_index": ("INT", {"default": 0, "min": -1000, "max": 1000, "step": 1}),
|
||||
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.00001}, ),
|
||||
},
|
||||
"optional": {
|
||||
"prev_latent_keyframe": ("LATENT_KEYFRAME", ),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("LATENT_KEYFRAME", )
|
||||
FUNCTION = "load_keyframe"
|
||||
|
||||
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/keyframes"
|
||||
|
||||
def load_keyframe(self,
|
||||
batch_index: int,
|
||||
strength: float,
|
||||
prev_latent_keyframe: LatentKeyframeGroup=None):
|
||||
if not prev_latent_keyframe:
|
||||
prev_latent_keyframe = LatentKeyframeGroup()
|
||||
keyframe = LatentKeyframe(batch_index, strength)
|
||||
prev_latent_keyframe.add(keyframe)
|
||||
return (prev_latent_keyframe,)
|
||||
|
||||
|
||||
class LatentKeyframeGroupNode:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"index_strengths": ("STRING", {"multiline": True, "default": ""}),
|
||||
},
|
||||
"optional": {
|
||||
"prev_latent_keyframe": ("LATENT_KEYFRAME", ),
|
||||
"latent_optional": ("LATENT", ),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("LATENT_KEYFRAME", )
|
||||
FUNCTION = "load_keyframes"
|
||||
|
||||
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/keyframes"
|
||||
|
||||
def validate_index(self, index: int, latent_count: int = 0, is_range: bool = False, allow_negative = False) -> int:
|
||||
# if part of range, do nothing
|
||||
if is_range:
|
||||
return index
|
||||
# otherwise, validate index
|
||||
# validate not out of range - only when latent_count is passed in
|
||||
if latent_count > 0 and index > latent_count-1:
|
||||
raise IndexError(f"Index '{index}' out of range for the total {latent_count} latents.")
|
||||
# if negative, validate not out of range
|
||||
if index < 0:
|
||||
if not allow_negative:
|
||||
raise IndexError(f"Negative indeces not allowed, but was {index}.")
|
||||
conv_index = latent_count+index
|
||||
if conv_index < 0:
|
||||
raise IndexError(f"Index '{index}', converted to '{conv_index}' out of range for the total {latent_count} latents.")
|
||||
index = conv_index
|
||||
return index
|
||||
|
||||
def convert_to_index_int(self, raw_index: str, latent_count: int = 0, is_range: bool = False, allow_negative = False) -> int:
|
||||
try:
|
||||
return self.validate_index(int(raw_index), latent_count=latent_count, is_range=is_range, allow_negative=allow_negative)
|
||||
except ValueError as e:
|
||||
raise ValueError(f"index '{raw_index}' must be an integer.", e)
|
||||
|
||||
def convert_to_latent_keyframes(self, latent_indeces: str, latent_count: int) -> set[LatentKeyframe]:
|
||||
if not latent_indeces:
|
||||
return set()
|
||||
all_indeces = [i for i in range(0, latent_count)]
|
||||
allow_negative = latent_count > 0
|
||||
chosen_indeces = set()
|
||||
# parse string - allow positive ints, negative ints, and ranges separated by ':'
|
||||
groups = latent_indeces.split(",")
|
||||
groups = [g.strip() for g in groups]
|
||||
for g in groups:
|
||||
# parse strengths - default to 1.0 if no strength given
|
||||
strength = 1.0
|
||||
if '=' in g:
|
||||
g, strength_str = g.split("=", 1)
|
||||
g = g.strip()
|
||||
try:
|
||||
strength = float(strength_str.strip())
|
||||
except ValueError as e:
|
||||
raise ValueError(f"strength '{strength_str}' must be a float.", e)
|
||||
if strength < 0:
|
||||
raise ValueError(f"Strength '{strength}' cannot be negative.")
|
||||
# parse range of indeces (e.g. 2:16)
|
||||
if ':' in g:
|
||||
index_range = g.split(":", 1)
|
||||
index_range = [r.strip() for r in index_range]
|
||||
start_index = self.convert_to_index_int(index_range[0], latent_count=latent_count, is_range=True, allow_negative=allow_negative)
|
||||
end_index = self.convert_to_index_int(index_range[1], latent_count=latent_count, is_range=True, allow_negative=allow_negative)
|
||||
for i in all_indeces[start_index:end_index]:
|
||||
chosen_indeces.add(LatentKeyframe(i, strength))
|
||||
# parse individual indeces
|
||||
else:
|
||||
chosen_indeces.add(LatentKeyframe(self.convert_to_index_int(g, latent_count=latent_count, allow_negative=allow_negative), strength))
|
||||
return chosen_indeces
|
||||
|
||||
def load_keyframes(self,
|
||||
index_strengths: str,
|
||||
prev_latent_keyframe: LatentKeyframeGroup=None,
|
||||
latent_image_opt=None):
|
||||
if not prev_latent_keyframe:
|
||||
prev_latent_keyframe = LatentKeyframeGroup()
|
||||
curr_latent_keyframe = LatentKeyframeGroup()
|
||||
|
||||
latent_count = -1
|
||||
if latent_image_opt:
|
||||
latent_count = latent_image_opt['samples'].size()[0]
|
||||
latent_keyframes = self.convert_to_latent_keyframes(index_strengths, latent_count=latent_count)
|
||||
|
||||
for latent_keyframe in latent_keyframes:
|
||||
logger.info(f"keyframe {latent_keyframe.batch_index}:{latent_keyframe.strength}")
|
||||
curr_latent_keyframe.add(latent_keyframe)
|
||||
|
||||
for latent_keyframe in prev_latent_keyframe.keyframes:
|
||||
curr_latent_keyframe.add(latent_keyframe)
|
||||
|
||||
return (curr_latent_keyframe,)
|
||||
|
||||
|
||||
class LatentKeyframeInterpolationNode:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"batch_index_from": ("INT", {"default": 0, "min": -10000, "max": 10000, "step": 1}),
|
||||
"batch_index_to_excl": ("INT", {"default": 0, "min": -10000, "max": 10000, "step": 1}),
|
||||
"strength_from": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.0001}, ),
|
||||
"strength_to": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.0001}, ),
|
||||
"interpolation": (["linear", "ease-in", "ease-out", "ease-in-out"], ),
|
||||
},
|
||||
"optional": {
|
||||
"prev_latent_keyframe": ("LATENT_KEYFRAME", ),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("LATENT_KEYFRAME", )
|
||||
FUNCTION = "load_keyframe"
|
||||
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/keyframes"
|
||||
|
||||
def load_keyframe(self,
|
||||
batch_index_from: int,
|
||||
strength_from: float,
|
||||
batch_index_to_excl: int,
|
||||
strength_to: float,
|
||||
interpolation: str,
|
||||
prev_latent_keyframe: LatentKeyframeGroup=None):
|
||||
|
||||
if (batch_index_from > batch_index_to_excl):
|
||||
raise ValueError("batch_index_from must be less than or equal to batch_index_to.")
|
||||
|
||||
if (batch_index_from < 0 and batch_index_to_excl >= 0):
|
||||
raise ValueError("batch_index_from and batch_index_to must be either both positive or both negative.")
|
||||
|
||||
if not prev_latent_keyframe:
|
||||
prev_latent_keyframe = LatentKeyframeGroup()
|
||||
curr_latent_keyframe = LatentKeyframeGroup()
|
||||
|
||||
steps = batch_index_to_excl - batch_index_from
|
||||
diff = strength_to - strength_from
|
||||
if interpolation == "linear":
|
||||
weights = np.linspace(strength_from, strength_to, steps)
|
||||
elif interpolation == "ease-in":
|
||||
index = np.linspace(0, 1, steps)
|
||||
weights = diff * np.power(index, 2) + strength_from
|
||||
elif interpolation == "ease-out":
|
||||
index = np.linspace(0, 1, steps)
|
||||
weights = diff * (1 - np.power(1 - index, 2)) + strength_from
|
||||
elif interpolation == "ease-in-out":
|
||||
index = np.linspace(0, 1, steps)
|
||||
weights = diff * ((1 - np.cos(index * np.pi)) / 2) + strength_from
|
||||
|
||||
for i in range(steps):
|
||||
keyframe = LatentKeyframe(batch_index_from + i, float(weights[i]))
|
||||
logger.info(f"keyframe {batch_index_from + i}:{weights[i]}")
|
||||
curr_latent_keyframe.add(keyframe)
|
||||
|
||||
# replace values with prev_latent_keyframes
|
||||
for latent_keyframe in prev_latent_keyframe.keyframes:
|
||||
curr_latent_keyframe.add(latent_keyframe)
|
||||
|
||||
return (curr_latent_keyframe,)
|
||||
|
||||
|
||||
class ControlNetLoaderAdvanced:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"control_net_name": (folder_paths.get_filename_list("controlnet"), ),
|
||||
},
|
||||
"optional": {
|
||||
"timestep_keyframe": ("TIMESTEP_KEYFRAME", ),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("CONTROL_NET", )
|
||||
FUNCTION = "load_controlnet"
|
||||
|
||||
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/loaders"
|
||||
|
||||
def load_controlnet(self, control_net_name, timestep_keyframe: TimestepKeyframeGroup=None):
|
||||
controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
|
||||
controlnet = load_controlnet(controlnet_path, timestep_keyframe)
|
||||
return (controlnet,)
|
||||
|
||||
|
||||
class DiffControlNetLoaderAdvanced:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"model": ("MODEL",),
|
||||
"control_net_name": (folder_paths.get_filename_list("controlnet"), )
|
||||
},
|
||||
"optional": {
|
||||
"timestep_keyframe": ("TIMESTEP_KEYFRAME", ),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("CONTROL_NET", )
|
||||
FUNCTION = "load_controlnet"
|
||||
|
||||
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/loaders"
|
||||
|
||||
def load_controlnet(self, control_net_name, timestep_keyframe: TimestepKeyframeGroup, model):
|
||||
controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
|
||||
controlnet = load_controlnet(controlnet_path, timestep_keyframe, model)
|
||||
return (controlnet,)
|
||||
|
||||
|
||||
class ControlNetApplyAdvanced_AdvControlNet:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"positive": ("CONDITIONING", ),
|
||||
"negative": ("CONDITIONING", ),
|
||||
"control_net": ("CONTROL_NET", ),
|
||||
"image": ("IMAGE", ),
|
||||
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
|
||||
"start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
|
||||
"end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001})
|
||||
},
|
||||
"optional": {
|
||||
"mask_opt": ("MASK", ),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("CONDITIONING","CONDITIONING")
|
||||
RETURN_NAMES = ("positive", "negative")
|
||||
FUNCTION = "apply_controlnet"
|
||||
|
||||
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/loaders/conditioning"
|
||||
|
||||
def apply_controlnet(self, positive, negative, control_net, image, strength, start_percent, end_percent, mask_opt=None):
|
||||
if strength == 0:
|
||||
return (positive, negative)
|
||||
|
||||
if mask_opt is not None:
|
||||
mask_hint = mask_opt.movedim(-1,1)
|
||||
control_hint = image.movedim(-1,1)
|
||||
cnets = {}
|
||||
|
||||
out = []
|
||||
for conditioning in [positive, negative]:
|
||||
c = []
|
||||
for t in conditioning:
|
||||
d = t[1].copy()
|
||||
|
||||
prev_cnet = d.get('control', None)
|
||||
if prev_cnet in cnets:
|
||||
c_net = cnets[prev_cnet]
|
||||
else:
|
||||
c_net = control_net.copy().set_cond_hint(control_hint, strength, (1.0 - start_percent, 1.0 - end_percent))
|
||||
# TODO: finish mask implemention, does nothing right now
|
||||
if mask_opt is not None:
|
||||
if isinstance(c_net, ControlNetAdvanced) or isinstance(c_net, T2IAdapterAdvanced):
|
||||
c_net.set_cond_hint_mask(mask_hint)
|
||||
else:
|
||||
logger
|
||||
c_net.set_previous_controlnet(prev_cnet)
|
||||
cnets[prev_cnet] = c_net
|
||||
|
||||
d['control'] = c_net
|
||||
d['control_apply_to_uncond'] = False
|
||||
n = [t[0], d]
|
||||
c.append(n)
|
||||
out.append(c)
|
||||
return (out[0], out[1])
|
||||
|
||||
|
||||
class LoadImagesFromDirectory:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"directory": ("STRING", {"default": ""}),
|
||||
},
|
||||
"optional": {
|
||||
"image_load_cap": ("INT", {"default": 0, "min": 0, "step": 1}),
|
||||
"start_index": ("INT", {"default": 0, "min": 0, "step": 1}),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("IMAGE", "MASK", "INT")
|
||||
FUNCTION = "load_images"
|
||||
|
||||
CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/deprecated"
|
||||
|
||||
def load_images(self, directory: str, image_load_cap: int = 0, start_index: int = 0):
|
||||
if not os.path.isdir(directory):
|
||||
raise FileNotFoundError(f"Directory '{directory} cannot be found.'")
|
||||
dir_files = os.listdir(directory)
|
||||
if len(dir_files) == 0:
|
||||
raise FileNotFoundError(f"No files in directory '{directory}'.")
|
||||
|
||||
dir_files = sorted(dir_files)
|
||||
dir_files = [os.path.join(directory, x) for x in dir_files]
|
||||
# start at start_index
|
||||
dir_files = dir_files[start_index:]
|
||||
|
||||
images = []
|
||||
masks = []
|
||||
|
||||
limit_images = False
|
||||
if image_load_cap > 0:
|
||||
limit_images = True
|
||||
image_count = 0
|
||||
|
||||
for image_path in dir_files:
|
||||
if os.path.isdir(image_path):
|
||||
continue
|
||||
if limit_images and image_count >= image_load_cap:
|
||||
break
|
||||
i = Image.open(image_path)
|
||||
i = ImageOps.exif_transpose(i)
|
||||
image = i.convert("RGB")
|
||||
image = np.array(image).astype(np.float32) / 255.0
|
||||
image = torch.from_numpy(image)[None,]
|
||||
if 'A' in i.getbands():
|
||||
mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
|
||||
mask = 1. - torch.from_numpy(mask)
|
||||
else:
|
||||
mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
|
||||
images.append(image)
|
||||
masks.append(mask)
|
||||
image_count += 1
|
||||
|
||||
if len(images) == 0:
|
||||
raise FileNotFoundError(f"No images could be loaded from directory '{directory}'.")
|
||||
|
||||
return (torch.cat(images, dim=0), torch.stack(masks, dim=0), image_count)
|
||||
|
||||
|
||||
|
||||
|
||||
# NODE MAPPING
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
# Keyframes
|
||||
"TimestepKeyframe": TimestepKeyframeNode,
|
||||
"LatentKeyframe": LatentKeyframeNode,
|
||||
"LatentKeyframeGroup": LatentKeyframeGroupNode,
|
||||
"LatentKeyframeTiming": LatentKeyframeInterpolationNode,
|
||||
# Loaders
|
||||
"ControlNetLoaderAdvanced": ControlNetLoaderAdvanced,
|
||||
"DiffControlNetLoaderAdvanced": DiffControlNetLoaderAdvanced,
|
||||
# Weights
|
||||
"ScaledSoftControlNetWeights": ScaledSoftControlNetWeights,
|
||||
"SoftControlNetWeights": SoftControlNetWeights,
|
||||
"CustomControlNetWeights": CustomControlNetWeights,
|
||||
"SoftT2IAdapterWeights": SoftT2IAdapterWeights,
|
||||
"CustomT2IAdapterWeights": CustomT2IAdapterWeights,
|
||||
# Image
|
||||
"LoadImagesFromDirectory": LoadImagesFromDirectory
|
||||
}
|
||||
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
# Keyframes
|
||||
"TimestepKeyframe": "Timestep Keyframe 🛂🅐🅒🅝",
|
||||
"LatentKeyframe": "Latent Keyframe 🛂🅐🅒🅝",
|
||||
"LatentKeyframeGroup": "Latent Keyframe Group 🛂🅐🅒🅝",
|
||||
"LatentKeyframeTiming": "Latent Keyframe Interpolation 🛂🅐🅒🅝",
|
||||
# Loaders
|
||||
"ControlNetLoaderAdvanced": "Load ControlNet Model (Advanced) 🛂🅐🅒🅝",
|
||||
"DiffControlNetLoaderAdvanced": "Load ControlNet Model (diff Advanced) 🛂🅐🅒🅝",
|
||||
# Weights
|
||||
"ScaledSoftControlNetWeights": "Scaled Soft ControlNet Weights 🛂🅐🅒🅝",
|
||||
"SoftControlNetWeights": "Soft ControlNet Weights 🛂🅐🅒🅝",
|
||||
"CustomControlNetWeights": "Custom ControlNet Weights 🛂🅐🅒🅝",
|
||||
"SoftT2IAdapterWeights": "Soft T2IAdapter Weights 🛂🅐🅒🅝",
|
||||
"CustomT2IAdapterWeights": "Custom T2IAdapter Weights 🛂🅐🅒🅝",
|
||||
# Image
|
||||
"LoadImagesFromDirectory": "Load Images [DEPRECATED] 🛂🅐🅒🅝"
|
||||
}
|
||||
Reference in New Issue
Block a user