657 lines
27 KiB
Python
Executable File
657 lines
27 KiB
Python
Executable File
import json
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import os
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import cv2
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import numpy as np
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import torch
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import torch.nn.functional as F
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from omegaconf import OmegaConf
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from .annotator.nodes import (ImageToCanny, ImageToDepth, ImageToPose,
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VideoToCanny, VideoToDepth, VideoToPose)
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from .camera_utils import CAMERA, combine_camera_motion, get_camera_motion
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from .cogvideox_fun.nodes import (CogVideoXFunInpaintSampler,
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CogVideoXFunT2VSampler,
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CogVideoXFunV2VSampler, LoadCogVideoXFunLora,
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LoadCogVideoXFunModel)
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from .comfyui_utils import script_directory
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from .flux2.nodes import (CombineFlux2Pipeline, Flux2ControlSampler,
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Flux2T2ISampler, LoadFlux2ControlNetInModel,
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LoadFlux2ControlNetInPipeline, LoadFlux2Lora,
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LoadFlux2Model, LoadFlux2TextEncoderModel,
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LoadFlux2TransformerModel, LoadFlux2VAEModel)
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from .qwenimage.nodes import (CombineQwenImagePipeline,
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LoadQwenImageControlNetInModel,
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LoadQwenImageControlNetInPipeline,
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LoadQwenImageLora, LoadQwenImageModel,
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LoadQwenImageProcessor,
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LoadQwenImageTextEncoderModel,
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LoadQwenImageTransformerModel,
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LoadQwenImageVAEModel, QwenImageControlSampler,
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QwenImageEditPlusSampler, QwenImageEditSampler,
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QwenImageT2VSampler)
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from .wan2_1.nodes import (CombineWanPipeline, LoadWanClipEncoderModel,
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LoadWanLora, LoadWanModel, LoadWanTextEncoderModel,
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LoadWanTransformerModel, LoadWanVAEModel,
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WanI2VSampler, WanT2VSampler)
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from .wan2_1_fun.nodes import (LoadWanFunLora, LoadWanFunModel,
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WanFunInpaintSampler, WanFunT2VSampler,
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WanFunV2VSampler)
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from .wan2_2.nodes import (CombineWan2_2Pipeline, LoadWan2_2Lora,
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LoadWan2_2Model, LoadWan2_2TransformerModel,
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Wan2_2I2VSampler, Wan2_2T2VSampler)
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from .wan2_2_fun.nodes import (LoadWan2_2FunLora, LoadWan2_2FunModel,
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Wan2_2FunInpaintSampler, Wan2_2FunT2VSampler,
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Wan2_2FunV2VSampler)
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from .wan2_2_vace_fun.nodes import (CombineWan2_2VaceFunPipeline,
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LoadVaceWanTransformer3DModel,
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LoadWan2_2VaceFunModel,
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Wan2_2VaceFunSampler)
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from .z_image.nodes import (CombineZImagePipeline, LoadZImageControlNetInModel,
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LoadZImageControlNetInPipeline, LoadZImageLora,
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LoadZImageModel, LoadZImageTextEncoderModel,
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LoadZImageTransformerModel, LoadZImageVAEModel,
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ZImageControlSampler, ZImageT2ISampler)
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class FunTextBox:
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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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"prompt": ("STRING", {"multiline": True, "default": "",}),
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},
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}
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RETURN_TYPES = ("STRING_PROMPT",)
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RETURN_NAMES =("prompt",)
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FUNCTION = "process"
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CATEGORY = "CogVideoXFUNWrapper"
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def process(self, prompt):
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return (prompt, )
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class FunRiflex:
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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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"riflex_k": ("INT", {"default": 6, "min": 0, "max": 10086}),
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},
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}
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RETURN_TYPES = ("RIFLEXT_ARGS",)
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RETURN_NAMES = ("riflex_k",)
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FUNCTION = "process"
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CATEGORY = "CogVideoXFUNWrapper"
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def process(self, riflex_k):
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return (riflex_k, )
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class FunCompile:
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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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"cache_size_limit": ("INT", {"default": 64, "min": 0, "max": 10086}),
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"funmodels": ("FunModels",)
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}
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}
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RETURN_TYPES = ("FunModels",)
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RETURN_NAMES = ("funmodels",)
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FUNCTION = "compile"
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CATEGORY = "CogVideoXFUNWrapper"
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def compile(self, cache_size_limit, funmodels):
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torch._dynamo.config.cache_size_limit = cache_size_limit
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if funmodels["pipeline"].transformer.device == torch.device(type="meta"):
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if hasattr(funmodels["pipeline"].transformer, "blocks"):
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for i, block in enumerate(funmodels["pipeline"].transformer.blocks):
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if hasattr(block, "_orig_mod"):
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block = block._orig_mod
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if hasattr(funmodels["pipeline"], "transformer_2") and funmodels["pipeline"].transformer_2 is not None:
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for i, block in enumerate(funmodels["pipeline"].transformer_2.blocks):
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if hasattr(block, "_orig_mod"):
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block = block._orig_mod
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elif hasattr(funmodels["pipeline"].transformer, "transformer_blocks"):
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for i, block in enumerate(funmodels["pipeline"].transformer.transformer_blocks):
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if hasattr(block, "_orig_mod"):
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block = block._orig_mod
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if hasattr(funmodels["pipeline"], "transformer_2") and funmodels["pipeline"].transformer_2 is not None:
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for i, block in enumerate(funmodels["pipeline"].transformer_2.transformer_blocks):
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if hasattr(block, "_orig_mod"):
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block = block._orig_mod
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print("Sequential cpu offload can not work with compile. Continue")
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return (funmodels,)
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if hasattr(funmodels["pipeline"].transformer, "blocks"):
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for i, block in enumerate(funmodels["pipeline"].transformer.blocks):
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if hasattr(block, "_orig_mod"):
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block = block._orig_mod
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funmodels["pipeline"].transformer.blocks[i] = torch.compile(block)
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if hasattr(funmodels["pipeline"], "transformer_2") and funmodels["pipeline"].transformer_2 is not None:
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for i, block in enumerate(funmodels["pipeline"].transformer_2.blocks):
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if hasattr(block, "_orig_mod"):
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block = block._orig_mod
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funmodels["pipeline"].transformer_2.blocks[i] = torch.compile(block)
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elif hasattr(funmodels["pipeline"].transformer, "transformer_blocks"):
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for i, block in enumerate(funmodels["pipeline"].transformer.transformer_blocks):
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if hasattr(block, "_orig_mod"):
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block = block._orig_mod
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funmodels["pipeline"].transformer.transformer_blocks[i] = torch.compile(block)
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if hasattr(funmodels["pipeline"], "transformer_2") and funmodels["pipeline"].transformer_2 is not None:
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for i, block in enumerate(funmodels["pipeline"].transformer_2.transformer_blocks):
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if hasattr(block, "_orig_mod"):
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block = block._orig_mod
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funmodels["pipeline"].transformer_2.transformer_blocks[i] = torch.compile(block)
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else:
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funmodels["pipeline"].transformer.forward = torch.compile(funmodels["pipeline"].transformer.forward)
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if hasattr(funmodels["pipeline"], "transformer_2") and funmodels["pipeline"].transformer_2 is not None:
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funmodels["pipeline"].transformer_2.forward = torch.compile(funmodels["pipeline"].transformer_2.forward)
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print("Add Compile")
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return (funmodels,)
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class FunAttention:
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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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"attention_type": (
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["flash", "sage", "torch"],
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{"default": "flash"},
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),
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"funmodels": ("FunModels",)
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}
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}
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RETURN_TYPES = ("FunModels",)
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RETURN_NAMES = ("funmodels",)
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FUNCTION = "funattention"
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CATEGORY = "CogVideoXFUNWrapper"
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def funattention(self, attention_type, funmodels):
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os.environ['VIDEOX_ATTENTION_TYPE'] = {
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"flash": "FLASH_ATTENTION",
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"sage": "SAGE_ATTENTION",
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"torch": "TORCH_SCALED_DOT"
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}[attention_type]
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return (funmodels,)
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class LoadConfig:
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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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[
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"wan2.1/wan_civitai.yaml",
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"wan2.2/wan_civitai_t2v.yaml",
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"wan2.2/wan_civitai_i2v.yaml",
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"wan2.2/wan_civitai_5b.yaml",
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],
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{
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"default": "wan2.2/wan_civitai_i2v.yaml",
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}
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),
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}
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RETURN_TYPES = ("FunConfig",)
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RETURN_NAMES = ("config",)
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FUNCTION = "process"
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CATEGORY = "CogVideoXFUNWrapper"
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def process(self, config):
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# Load config
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config_path = f"{script_directory}/config/{config}"
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config = OmegaConf.load(config_path)
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return (config, )
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def gen_gaussian_heatmap(imgSize=200):
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circle_img = np.zeros((imgSize, imgSize,), np.float32)
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circle_mask = cv2.circle(circle_img, (imgSize//2, imgSize//2), imgSize//2 - 1, 1, -1)
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isotropicGrayscaleImage = np.zeros((imgSize, imgSize), np.float32)
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# 生成高斯图
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for i in range(imgSize):
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for j in range(imgSize):
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isotropicGrayscaleImage[i, j] = 1 / (2 * np.pi * (40 ** 2)) * np.exp(
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-1 / 2 * ((i - imgSize / 2) ** 2 / (40 ** 2) + (j - imgSize / 2) ** 2 / (40 ** 2)))
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isotropicGrayscaleImage = isotropicGrayscaleImage * circle_mask
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isotropicGrayscaleImage = (isotropicGrayscaleImage / np.max(isotropicGrayscaleImage) * 255).astype(np.uint8)
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return isotropicGrayscaleImage
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class CreateTrajectoryBasedOnKJNodes:
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# Modified from https://github.com/kijai/ComfyUI-KJNodes/blob/main/nodes/curve_nodes.py
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# Modify to meet the trajectory control requirements of EasyAnimate.
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RETURN_TYPES = ("IMAGE", )
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RETURN_NAMES = ("image", )
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FUNCTION = "createtrajectory"
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CATEGORY = "CogVideoXFUNWrapper"
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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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"coordinates": ("STRING", {"forceInput": True}),
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"masks": ("MASK", {"forceInput": True}),
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},
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}
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def createtrajectory(self, coordinates, masks):
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# Define the number of images in the batch
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if len(coordinates) < 10:
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coords_list = []
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for coords in coordinates:
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coords = json.loads(coords.replace("'", '"'))
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coords_list.append(coords)
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else:
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coords = json.loads(coordinates.replace("'", '"'))
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coords_list = [coords]
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_, frame_height, frame_width = masks.size()
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heatmap = gen_gaussian_heatmap()
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circle_size = int(50 * ((frame_height * frame_width) / (1280 * 720)) ** (1/2))
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images_list = []
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for coords in coords_list:
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_images_list = []
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for i in range(len(coords)):
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_image = np.zeros((frame_height, frame_width, 3))
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center_coordinate = [coords[i][key] for key in coords[i]]
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y1 = max(center_coordinate[1] - circle_size, 0)
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y2 = min(center_coordinate[1] + circle_size, np.shape(_image)[0] - 1)
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x1 = max(center_coordinate[0] - circle_size, 0)
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x2 = min(center_coordinate[0] + circle_size, np.shape(_image)[1] - 1)
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if x2 - x1 > 3 and y2 - y1 > 3:
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need_map = cv2.resize(heatmap, (x2 - x1, y2 - y1))[:, :, None]
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_image[y1:y2, x1:x2] = np.maximum(need_map.copy(), _image[y1:y2, x1:x2])
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_image = np.expand_dims(_image, 0) / 255
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_images_list.append(_image)
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images_list.append(np.concatenate(_images_list, axis=0))
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out_images = torch.from_numpy(np.max(np.array(images_list), axis=0))
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return (out_images, )
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class ImageMaximumNode:
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RETURN_TYPES = ("IMAGE", )
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RETURN_NAMES = ("image", )
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FUNCTION = "imagemaximum"
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CATEGORY = "CogVideoXFUNWrapper"
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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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"video_1": ("IMAGE",),
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"video_2": ("IMAGE",),
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},
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}
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def imagemaximum(self, video_1, video_2):
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length_1, h_1, w_1, c_1 = video_1.size()
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length_2, h_2, w_2, c_2 = video_2.size()
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if h_1 != h_2 or w_1 != w_2:
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video_1, video_2 = video_1.permute([0, 3, 1, 2]), video_2.permute([0, 3, 1, 2])
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video_2 = F.interpolate(video_2, video_1.size()[-2:])
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video_1, video_2 = video_1.permute([0, 2, 3, 1]), video_2.permute([0, 2, 3, 1])
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if length_1 > length_2:
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outputs = torch.maximum(video_1[:length_2], video_2)
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else:
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outputs = torch.maximum(video_1, video_2[:length_1])
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return (outputs, )
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class ImageCollectNode:
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RETURN_TYPES = ("IMAGE", )
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RETURN_NAMES = ("image", )
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FUNCTION = "imagecollect"
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CATEGORY = "CogVideoXFUNWrapper"
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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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"image_1": ("IMAGE",)
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},
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"optional": {
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"image_2": ("IMAGE",),
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}
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}
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def imagecollect(self, image_1, image_2):
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image_out = [_image_1 for _image_1 in image_1] + [_image_2 for _image_2 in image_2]
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return (image_out, )
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class CameraBasicFromChaoJie:
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# Copied from https://github.com/chaojie/ComfyUI-CameraCtrl-Wrapper/blob/main/nodes.py
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# Since ComfyUI-CameraCtrl-Wrapper requires a specific version of diffusers, which is not suitable for us.
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# The code has been copied into the current repository.
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"camera_pose":(["Static","Pan Up","Pan Down","Pan Left","Pan Right","Zoom In","Zoom Out","ACW","CW"],{"default":"Static"}),
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"speed":("FLOAT",{"default":1.0}),
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"video_length":("INT",{"default":16}),
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},
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}
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RETURN_TYPES = ("CameraPose",)
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FUNCTION = "run"
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CATEGORY = "CameraCtrl"
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def run(self,camera_pose,speed,video_length):
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camera_dict = {
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"motion":[camera_pose],
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"mode": "Basic Camera Poses", # "First A then B", "Both A and B", "Custom"
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"speed": speed,
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"complex": None
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}
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motion_list = camera_dict['motion']
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mode = camera_dict['mode']
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speed = camera_dict['speed']
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angle = np.array(CAMERA[motion_list[0]]["angle"])
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T = np.array(CAMERA[motion_list[0]]["T"])
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RT = get_camera_motion(angle, T, speed, video_length)
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return (RT,)
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class CameraCombineFromChaoJie:
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# Copied from https://github.com/chaojie/ComfyUI-CameraCtrl-Wrapper/blob/main/nodes.py
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# Since ComfyUI-CameraCtrl-Wrapper requires a specific version of diffusers, which is not suitable for us.
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# The code has been copied into the current repository.
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"camera_pose1":(["Static","Pan Up","Pan Down","Pan Left","Pan Right","Zoom In","Zoom Out","ACW","CW"],{"default":"Static"}),
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"camera_pose2":(["Static","Pan Up","Pan Down","Pan Left","Pan Right","Zoom In","Zoom Out","ACW","CW"],{"default":"Static"}),
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"camera_pose3":(["Static","Pan Up","Pan Down","Pan Left","Pan Right","Zoom In","Zoom Out","ACW","CW"],{"default":"Static"}),
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"camera_pose4":(["Static","Pan Up","Pan Down","Pan Left","Pan Right","Zoom In","Zoom Out","ACW","CW"],{"default":"Static"}),
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"speed":("FLOAT",{"default":1.0}),
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"video_length":("INT",{"default":16}),
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},
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}
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RETURN_TYPES = ("CameraPose",)
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FUNCTION = "run"
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CATEGORY = "CameraCtrl"
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def run(self,camera_pose1,camera_pose2,camera_pose3,camera_pose4,speed,video_length):
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angle = np.array(CAMERA[camera_pose1]["angle"]) + np.array(CAMERA[camera_pose2]["angle"]) + np.array(CAMERA[camera_pose3]["angle"]) + np.array(CAMERA[camera_pose4]["angle"])
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T = np.array(CAMERA[camera_pose1]["T"]) + np.array(CAMERA[camera_pose2]["T"]) + np.array(CAMERA[camera_pose3]["T"]) + np.array(CAMERA[camera_pose4]["T"])
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RT = get_camera_motion(angle, T, speed, video_length)
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return (RT,)
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class CameraJoinFromChaoJie:
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# Copied from https://github.com/chaojie/ComfyUI-CameraCtrl-Wrapper/blob/main/nodes.py
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# Since ComfyUI-CameraCtrl-Wrapper requires a specific version of diffusers, which is not suitable for us.
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# The code has been copied into the current repository.
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"camera_pose1":("CameraPose",),
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"camera_pose2":("CameraPose",),
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},
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}
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RETURN_TYPES = ("CameraPose",)
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FUNCTION = "run"
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CATEGORY = "CameraCtrl"
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def run(self,camera_pose1,camera_pose2):
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RT = combine_camera_motion(camera_pose1, camera_pose2)
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return (RT,)
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class CameraTrajectoryFromChaoJie:
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# Copied from https://github.com/chaojie/ComfyUI-CameraCtrl-Wrapper/blob/main/nodes.py
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# Since ComfyUI-CameraCtrl-Wrapper requires a specific version of diffusers, which is not suitable for us.
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# The code has been copied into the current repository.
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"camera_pose":("CameraPose",),
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"fx":("FLOAT",{"default":0.474812461, "min": 0, "max": 1, "step": 0.000000001}),
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"fy":("FLOAT",{"default":0.844111024, "min": 0, "max": 1, "step": 0.000000001}),
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"cx":("FLOAT",{"default":0.5, "min": 0, "max": 1, "step": 0.01}),
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"cy":("FLOAT",{"default":0.5, "min": 0, "max": 1, "step": 0.01}),
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},
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}
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RETURN_TYPES = ("STRING","INT",)
|
|
RETURN_NAMES = ("camera_trajectory","video_length",)
|
|
FUNCTION = "run"
|
|
CATEGORY = "CameraCtrl"
|
|
|
|
def run(self,camera_pose,fx,fy,cx,cy):
|
|
#print(camera_pose)
|
|
camera_pose_list=camera_pose.tolist()
|
|
trajs=[]
|
|
for cp in camera_pose_list:
|
|
traj=[fx,fy,cx,cy,0,0]
|
|
traj.extend(cp[0])
|
|
traj.extend(cp[1])
|
|
traj.extend(cp[2])
|
|
trajs.append(traj)
|
|
return (json.dumps(trajs),len(trajs),)
|
|
|
|
NODE_CLASS_MAPPINGS = {
|
|
"FunTextBox": FunTextBox,
|
|
"FunRiflex": FunRiflex,
|
|
"FunCompile": FunCompile,
|
|
"FunAttention": FunAttention,
|
|
|
|
"LoadCogVideoXFunModel": LoadCogVideoXFunModel,
|
|
"LoadCogVideoXFunLora": LoadCogVideoXFunLora,
|
|
"CogVideoXFunT2VSampler": CogVideoXFunT2VSampler,
|
|
"CogVideoXFunInpaintSampler": CogVideoXFunInpaintSampler,
|
|
"CogVideoXFunV2VSampler": CogVideoXFunV2VSampler,
|
|
|
|
"LoadQwenImageLora": LoadQwenImageLora,
|
|
"LoadQwenImageTextEncoderModel": LoadQwenImageTextEncoderModel,
|
|
"LoadQwenImageTransformerModel": LoadQwenImageTransformerModel,
|
|
"LoadQwenImageVAEModel": LoadQwenImageVAEModel,
|
|
"LoadQwenImageProcessor": LoadQwenImageProcessor,
|
|
"CombineQwenImagePipeline": CombineQwenImagePipeline,
|
|
"LoadQwenImageControlNetInPipeline": LoadQwenImageControlNetInPipeline,
|
|
"LoadQwenImageControlNetInModel": LoadQwenImageControlNetInModel,
|
|
|
|
"LoadQwenImageModel": LoadQwenImageModel,
|
|
"QwenImageT2VSampler": QwenImageT2VSampler,
|
|
"QwenImageEditSampler": QwenImageEditSampler,
|
|
"QwenImageEditPlusSampler": QwenImageEditPlusSampler,
|
|
"QwenImageControlSampler": QwenImageControlSampler,
|
|
|
|
"LoadFlux2Lora": LoadFlux2Lora,
|
|
"LoadFlux2TransformerModel": LoadFlux2TransformerModel,
|
|
"LoadFlux2VAEModel": LoadFlux2VAEModel,
|
|
"LoadFlux2TextEncoderModel": LoadFlux2TextEncoderModel,
|
|
"CombineFlux2Pipeline": CombineFlux2Pipeline,
|
|
"LoadFlux2ControlNetInModel": LoadFlux2ControlNetInModel,
|
|
"LoadFlux2ControlNetInPipeline": LoadFlux2ControlNetInPipeline,
|
|
|
|
"LoadFlux2Model": LoadFlux2Model,
|
|
"Flux2T2ISampler": Flux2T2ISampler,
|
|
"Flux2ControlSampler": Flux2ControlSampler,
|
|
|
|
"LoadZImageLora": LoadZImageLora,
|
|
"LoadZImageTextEncoderModel": LoadZImageTextEncoderModel,
|
|
"LoadZImageTransformerModel": LoadZImageTransformerModel,
|
|
"LoadZImageVAEModel": LoadZImageVAEModel,
|
|
"CombineZImagePipeline": CombineZImagePipeline,
|
|
"LoadZImageControlNetInPipeline": LoadZImageControlNetInPipeline,
|
|
"LoadZImageControlNetInModel": LoadZImageControlNetInModel,
|
|
|
|
"LoadZImageModel": LoadZImageModel,
|
|
"ZImageT2ISampler": ZImageT2ISampler,
|
|
"ZImageControlSampler": ZImageControlSampler,
|
|
|
|
"LoadWanClipEncoderModel": LoadWanClipEncoderModel,
|
|
"LoadWanTextEncoderModel": LoadWanTextEncoderModel,
|
|
"LoadWanTransformerModel": LoadWanTransformerModel,
|
|
"LoadWanVAEModel": LoadWanVAEModel,
|
|
"CombineWanPipeline": CombineWanPipeline,
|
|
"LoadWan2_2TransformerModel": LoadWan2_2TransformerModel,
|
|
"CombineWan2_2Pipeline": CombineWan2_2Pipeline,
|
|
|
|
"LoadWanModel": LoadWanModel,
|
|
"LoadWanLora": LoadWanLora,
|
|
"WanT2VSampler": WanT2VSampler,
|
|
"WanI2VSampler": WanI2VSampler,
|
|
|
|
"LoadWanFunModel": LoadWanFunModel,
|
|
"LoadWanFunLora": LoadWanFunLora,
|
|
"WanFunT2VSampler": WanFunT2VSampler,
|
|
"WanFunInpaintSampler": WanFunInpaintSampler,
|
|
"WanFunV2VSampler": WanFunV2VSampler,
|
|
|
|
"LoadWan2_2Model": LoadWan2_2Model,
|
|
"LoadWan2_2Lora": LoadWan2_2Lora,
|
|
"Wan2_2T2VSampler": Wan2_2T2VSampler,
|
|
"Wan2_2I2VSampler": Wan2_2I2VSampler,
|
|
|
|
"LoadWan2_2FunModel": LoadWan2_2FunModel,
|
|
"LoadWan2_2FunLora": LoadWan2_2FunLora,
|
|
"Wan2_2FunT2VSampler": Wan2_2FunT2VSampler,
|
|
"Wan2_2FunInpaintSampler": Wan2_2FunInpaintSampler,
|
|
"Wan2_2FunV2VSampler": Wan2_2FunV2VSampler,
|
|
|
|
"LoadVaceWanTransformer3DModel": LoadVaceWanTransformer3DModel,
|
|
"CombineWan2_2VaceFunPipeline": CombineWan2_2VaceFunPipeline,
|
|
|
|
"LoadWan2_2VaceFunModel": LoadWan2_2VaceFunModel,
|
|
"Wan2_2VaceFunSampler": Wan2_2VaceFunSampler,
|
|
|
|
"ImageToCanny": ImageToCanny,
|
|
"ImageToPose": ImageToPose,
|
|
"ImageToDepth": ImageToDepth,
|
|
"VideoToCanny": VideoToCanny,
|
|
"VideoToDepth": VideoToDepth,
|
|
"VideoToOpenpose": VideoToPose,
|
|
|
|
"CreateTrajectoryBasedOnKJNodes": CreateTrajectoryBasedOnKJNodes,
|
|
"CameraBasicFromChaoJie": CameraBasicFromChaoJie,
|
|
"CameraTrajectoryFromChaoJie": CameraTrajectoryFromChaoJie,
|
|
"CameraJoinFromChaoJie": CameraJoinFromChaoJie,
|
|
"CameraCombineFromChaoJie": CameraCombineFromChaoJie,
|
|
"ImageMaximumNode": ImageMaximumNode,
|
|
"ImageCollectNode": ImageCollectNode,
|
|
}
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"FunTextBox": "FunTextBox",
|
|
"FunRiflex": "FunRiflex",
|
|
"FunCompile": "FunCompile",
|
|
"FunAttention": "FunAttention",
|
|
"LoadZImageControlNetInPipeline": "LoadZImageControlNetInPipeline",
|
|
"LoadZImageControlNetInModel": "LoadZImageControlNetInModel",
|
|
|
|
"LoadCogVideoXFunModel": "Load CogVideoX-Fun Model",
|
|
"LoadCogVideoXFunLora": "Load CogVideoX-Fun Lora",
|
|
"CogVideoXFunInpaintSampler": "CogVideoX-Fun Sampler for Image to Video",
|
|
"CogVideoXFunT2VSampler": "CogVideoX-Fun Sampler for Text to Video",
|
|
"CogVideoXFunV2VSampler": "CogVideoX-Fun Sampler for Video to Video",
|
|
|
|
"LoadQwenImageLora": "Load QwenImage Lora",
|
|
"LoadQwenImageTextEncoderModel": "Load QwenImage TextEncoder Model",
|
|
"LoadQwenImageTransformerModel": "Load QwenImage Transformer Model",
|
|
"LoadQwenImageVAEModel": "Load QwenImage VAE Model",
|
|
"LoadQwenImageProcessor": "Load QwenImage Processor",
|
|
"CombineQwenImagePipeline": "Combine QwenImage Pipeline",
|
|
"LoadQwenImageControlNetInPipeline": "Load QwenImage ControlNet In Pipeline",
|
|
"LoadQwenImageControlNetInModel": "Load QwenImage ControlNet In Model",
|
|
|
|
"LoadQwenImageModel": "Load QwenImage Model",
|
|
"QwenImageT2VSampler": "QwenImage T2V Sampler",
|
|
"QwenImageEditSampler": "QwenImage Edit Sampler",
|
|
"QwenImageEditPlusSampler": "QwenImage Edit Plus Sampler",
|
|
"QwenImageControlSampler": "QwenImage Control Sampler",
|
|
|
|
"LoadFlux2Lora": "Load FLUX2 Lora",
|
|
"LoadFlux2TransformerModel": "Load FLUX2 Transformer Model",
|
|
"LoadFlux2VAEModel": "Load FLUX2 VAE Model",
|
|
"LoadFlux2TextEncoderModel": "Load FLUX2 Text Encoder Model",
|
|
"CombineFlux2Pipeline": "Combine FLUX2 Pipeline",
|
|
"LoadFlux2ControlNetInModel": "Load Flux2 ControlNet In Model",
|
|
"LoadFlux2ControlNetInPipeline": "Load Flux2 ControlNet In Pipeline",
|
|
|
|
"LoadFlux2Model": "Load FLUX2 Model",
|
|
"Flux2T2ISampler": "FLUX2 Text to Image Sampler",
|
|
"Flux2ControlSampler": "FLUX2 Control Sampler",
|
|
|
|
"LoadZImageLora": "Load ZImage Lora",
|
|
"LoadZImageTextEncoderModel": "Load ZImage TextEncoder Model",
|
|
"LoadZImageTransformerModel": "Load ZImage Transformer Model",
|
|
"LoadZImageVAEModel": "Load ZImage VAE Model",
|
|
"CombineZImagePipeline": "Combine ZImage Pipeline",
|
|
|
|
"LoadZImageModel": "Load ZImage Model",
|
|
"ZImageT2ISampler": "ZImage T2I Sampler",
|
|
"ZImageControlSampler": "ZImage Control Sampler",
|
|
|
|
"LoadWanClipEncoderModel": "Load Wan ClipEncoder Model",
|
|
"LoadWanTextEncoderModel": "Load Wan TextEncoder Model",
|
|
"LoadWanTransformerModel": "Load Wan Transformer Model",
|
|
"LoadWanVAEModel": "Load Wan VAE Model",
|
|
"CombineWanPipeline": "Combine Wan Pipeline",
|
|
"LoadWan2_2TransformerModel": "Load Wan2_2 Transformer Model",
|
|
"CombineWan2_2Pipeline": "Combine Wan2_2 Pipeline",
|
|
"LoadVaceWanTransformer3DModel": "Load Vace Wan Transformer 3DModel",
|
|
"CombineWan2_2VaceFunPipeline": "Combine Wan2_2 Vace Fun Pipeline",
|
|
|
|
"LoadWanModel": "Load Wan Model",
|
|
"LoadWanLora": "Load Wan Lora",
|
|
"WanT2VSampler": "Wan Sampler for Text to Video",
|
|
"WanI2VSampler": "Wan Sampler for Image to Video",
|
|
|
|
"LoadWanFunModel": "Load Wan Fun Model",
|
|
"LoadWanFunLora": "Load Wan Fun Lora",
|
|
"WanFunT2VSampler": "Wan Fun Sampler for Text to Video",
|
|
"WanFunInpaintSampler": "Wan Fun Sampler for Image to Video",
|
|
"WanFunV2VSampler": "Wan Fun Sampler for Video to Video",
|
|
|
|
"LoadWan2_2Model": "Load Wan 2.2 Model",
|
|
"LoadWan2_2Lora": "Load Wan 2.2 Lora",
|
|
"Wan2_2T2VSampler": "Wan 2.2 Sampler for Text to Video",
|
|
"Wan2_2I2VSampler": "Wan 2.2 Sampler for Image to Video",
|
|
|
|
"LoadWan2_2FunModel": "Load Wan 2.2 Fun Model",
|
|
"LoadWan2_2FunLora": "Load Wan 2.2 Fun Lora",
|
|
"Wan2_2FunT2VSampler": "Wan 2.2 Fun Sampler for Text to Video",
|
|
"Wan2_2FunInpaintSampler": "Wan 2.2 Fun Sampler for Image to Video",
|
|
"Wan2_2FunV2VSampler": "Wan 2.2 Fun Sampler for Video to Video",
|
|
|
|
"LoadWan2_2VaceFunModel": "Load Wan2_2 Vace Fun Model",
|
|
"Wan2_2VaceFunSampler": "Wan2_2 Vace Fun Sampler",
|
|
|
|
"ImageToCanny": "Image To Canny",
|
|
"ImageToPose": "Image To Pose",
|
|
"ImageToDepth": "Image To Depth",
|
|
"VideoToCanny": "Video To Canny",
|
|
"VideoToDepth": "Video To Depth",
|
|
"VideoToOpenpose": "Video To Pose",
|
|
|
|
"CreateTrajectoryBasedOnKJNodes": "Create Trajectory Based On KJNodes",
|
|
"CameraBasicFromChaoJie": "Camera Basic From ChaoJie",
|
|
"CameraTrajectoryFromChaoJie": "Camera Trajectory From ChaoJie",
|
|
"CameraJoinFromChaoJie": "Camera Join From ChaoJie",
|
|
"CameraCombineFromChaoJie": "Camera Combine From ChaoJie",
|
|
"ImageMaximumNode": "Image Maximum Node",
|
|
"ImageCollectNode": "Image Collect Node",
|
|
} |