nodes.py

import torch

import os
import sys
import json
import hashlib
import traceback
import math
import time
import random
import logging

from PIL import Image, ImageOps, ImageSequence
from PIL.PngImagePlugin import PngInfo
import numpy as np
import safetensors.torch

sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "quasar"))


import quasar.diffusers_load
import quasar.samplers
import quasar.sample
import quasar.sd
import quasar.utils
import quasar.controlnet

import quasar.clip_vision

import quasar.model_management
from quasar.cli_args import args

import importlib

import folder_paths
import latent_preview
import node_helpers

def before_node_execution():
    quasar.model_management.throw_exception_if_processing_interrupted()

def interrupt_processing(value=True):
    quasar.model_management.interrupt_current_processing(value)

MAX_RESOLUTION=16384

class CLIPTextEncode:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {"text": ("STRING", {"multiline": True}), "clip": ("CLIP", )}}
    RETURN_TYPES = ("CONDITIONING",)
    FUNCTION = "encode"

    CATEGORY = "conditioning"

    def encode(self, clip, text):
        tokens = clip.tokenize(text)
        cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
        return ([[cond, {"pooled_output": pooled}]], )

class ConditioningCombine:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {"conditioning_1": ("CONDITIONING", ), "conditioning_2": ("CONDITIONING", )}}
    RETURN_TYPES = ("CONDITIONING",)
    FUNCTION = "combine"

    CATEGORY = "conditioning"

    def combine(self, conditioning_1, conditioning_2):
        return (conditioning_1 + conditioning_2, )

class ConditioningAverage :
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {"conditioning_to": ("CONDITIONING", ), "conditioning_from": ("CONDITIONING", ),
                              "conditioning_to_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
                             }}
    RETURN_TYPES = ("CONDITIONING",)
    FUNCTION = "addWeighted"

    CATEGORY = "conditioning"

    def addWeighted(self, conditioning_to, conditioning_from, conditioning_to_strength):
        out = []

        if len(conditioning_from) > 1:
            logging.warning("Warning: ConditioningAverage conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to.")

        cond_from = conditioning_from[0][0]
        pooled_output_from = conditioning_from[0][1].get("pooled_output", None)

        for i in range(len(conditioning_to)):
            t1 = conditioning_to[i][0]
            pooled_output_to = conditioning_to[i][1].get("pooled_output", pooled_output_from)
            t0 = cond_from[:,:t1.shape[1]]
            if t0.shape[1] < t1.shape[1]:
                t0 = torch.cat([t0] + [torch.zeros((1, (t1.shape[1] - t0.shape[1]), t1.shape[2]))], dim=1)

            tw = torch.mul(t1, conditioning_to_strength) + torch.mul(t0, (1.0 - conditioning_to_strength))
            t_to = conditioning_to[i][1].copy()
            if pooled_output_from is not None and pooled_output_to is not None:
                t_to["pooled_output"] = torch.mul(pooled_output_to, conditioning_to_strength) + torch.mul(pooled_output_from, (1.0 - conditioning_to_strength))
            elif pooled_output_from is not None:
                t_to["pooled_output"] = pooled_output_from

            n = [tw, t_to]
            out.append(n)
        return (out, )

class ConditioningConcat:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {
            "conditioning_to": ("CONDITIONING",),
            "conditioning_from": ("CONDITIONING",),
            }}
    RETURN_TYPES = ("CONDITIONING",)
    FUNCTION = "concat"

    CATEGORY = "conditioning"

    def concat(self, conditioning_to, conditioning_from):
        out = []

        if len(conditioning_from) > 1:
            logging.warning("Warning: ConditioningConcat conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to.")

        cond_from = conditioning_from[0][0]

        for i in range(len(conditioning_to)):
            t1 = conditioning_to[i][0]
            tw = torch.cat((t1, cond_from),1)
            n = [tw, conditioning_to[i][1].copy()]
            out.append(n)

        return (out, )

class ConditioningSetArea:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {"conditioning": ("CONDITIONING", ),
                              "width": ("INT", {"default": 64, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
                              "height": ("INT", {"default": 64, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
                              "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
                              "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
                              "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
                             }}
    RETURN_TYPES = ("CONDITIONING",)
    FUNCTION = "append"

    CATEGORY = "conditioning"

    def append(self, conditioning, width, height, x, y, strength):
        c = node_helpers.conditioning_set_values(conditioning, {"area": (height // 8, width // 8, y // 8, x // 8),
                                                                "strength": strength,
                                                                "set_area_to_bounds": False})
        return (c, )

class ConditioningSetAreaPercentage:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {"conditioning": ("CONDITIONING", ),
                              "width": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}),
                              "height": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}),
                              "x": ("FLOAT", {"default": 0, "min": 0, "max": 1.0, "step": 0.01}),
                              "y": ("FLOAT", {"default": 0, "min": 0, "max": 1.0, "step": 0.01}),
                              "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
                             }}
    RETURN_TYPES = ("CONDITIONING",)
    FUNCTION = "append"

    CATEGORY = "conditioning"

    def append(self, conditioning, width, height, x, y, strength):
        c = node_helpers.conditioning_set_values(conditioning, {"area": ("percentage", height, width, y, x),
                                                                "strength": strength,
                                                                "set_area_to_bounds": False})
        return (c, )

class ConditioningSetAreaStrength:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {"conditioning": ("CONDITIONING", ),
                              "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
                             }}
    RETURN_TYPES = ("CONDITIONING",)
    FUNCTION = "append"

    CATEGORY = "conditioning"

    def append(self, conditioning, strength):
        c = node_helpers.conditioning_set_values(conditioning, {"strength": strength})
        return (c, )


class ConditioningSetMask:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {"conditioning": ("CONDITIONING", ),
                              "mask": ("MASK", ),
                              "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
                              "set_cond_area": (["default", "mask bounds"],),
                             }}
    RETURN_TYPES = ("CONDITIONING",)
    FUNCTION = "append"

    CATEGORY = "conditioning"

    def append(self, conditioning, mask, set_cond_area, strength):
        set_area_to_bounds = False
        if set_cond_area != "default":
            set_area_to_bounds = True
        if len(mask.shape) < 3:
            mask = mask.unsqueeze(0)

        c = node_helpers.conditioning_set_values(conditioning, {"mask": mask,
                                                                "set_area_to_bounds": set_area_to_bounds,
                                                                "mask_strength": strength})
        return (c, )

class ConditioningZeroOut:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {"conditioning": ("CONDITIONING", )}}
    RETURN_TYPES = ("CONDITIONING",)
    FUNCTION = "zero_out"

    CATEGORY = "advanced/conditioning"

    def zero_out(self, conditioning):
        c = []
        for t in conditioning:
            d = t[1].copy()
            if "pooled_output" in d:
                d["pooled_output"] = torch.zeros_like(d["pooled_output"])
            n = [torch.zeros_like(t[0]), d]
            c.append(n)
        return (c, )

class ConditioningSetTimestepRange:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {"conditioning": ("CONDITIONING", ),
                             "start": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
                             "end": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001})
                             }}
    RETURN_TYPES = ("CONDITIONING",)
    FUNCTION = "set_range"

    CATEGORY = "advanced/conditioning"

    def set_range(self, conditioning, start, end):
        c = node_helpers.conditioning_set_values(conditioning, {"start_percent": start,
                                                                "end_percent": end})
        return (c, )

class VAEDecode:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "samples": ("LATENT", ), "vae": ("VAE", )}}
    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "decode"

    CATEGORY = "latent"

    def decode(self, vae, samples):
        return (vae.decode(samples["samples"]), )

class VAEDecodeTiled:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {"samples": ("LATENT", ), "vae": ("VAE", ),
                             "tile_size": ("INT", {"default": 512, "min": 320, "max": 4096, "step": 64})
                            }}
    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "decode"

    CATEGORY = "_for_testing"

    def decode(self, vae, samples, tile_size):
        return (vae.decode_tiled(samples["samples"], tile_x=tile_size // 8, tile_y=tile_size // 8, ), )

class VAEEncode:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "pixels": ("IMAGE", ), "vae": ("VAE", )}}
    RETURN_TYPES = ("LATENT",)
    FUNCTION = "encode"

    CATEGORY = "latent"

    def encode(self, vae, pixels):
        t = vae.encode(pixels[:,:,:,:3])
        return ({"samples":t}, )

class VAEEncodeTiled:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {"pixels": ("IMAGE", ), "vae": ("VAE", ),
                             "tile_size": ("INT", {"default": 512, "min": 320, "max": 4096, "step": 64})
                            }}
    RETURN_TYPES = ("LATENT",)
    FUNCTION = "encode"

    CATEGORY = "_for_testing"

    def encode(self, vae, pixels, tile_size):
        t = vae.encode_tiled(pixels[:,:,:,:3], tile_x=tile_size, tile_y=tile_size, )
        return ({"samples":t}, )

class VAEEncodeForInpaint:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "pixels": ("IMAGE", ), "vae": ("VAE", ), "mask": ("MASK", ), "grow_mask_by": ("INT", {"default": 6, "min": 0, "max": 64, "step": 1}),}}
    RETURN_TYPES = ("LATENT",)
    FUNCTION = "encode"

    CATEGORY = "latent/inpaint"

    def encode(self, vae, pixels, mask, grow_mask_by=6):
        x = (pixels.shape[1] // vae.downscale_ratio) * vae.downscale_ratio
        y = (pixels.shape[2] // vae.downscale_ratio) * vae.downscale_ratio
        mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(pixels.shape[1], pixels.shape[2]), mode="bilinear")

        pixels = pixels.clone()
        if pixels.shape[1] != x or pixels.shape[2] != y:
            x_offset = (pixels.shape[1] % vae.downscale_ratio) // 2
            y_offset = (pixels.shape[2] % vae.downscale_ratio) // 2
            pixels = pixels[:,x_offset:x + x_offset, y_offset:y + y_offset,:]
            mask = mask[:,:,x_offset:x + x_offset, y_offset:y + y_offset]

        #grow mask by a few pixels to keep things seamless in latent space
        if grow_mask_by == 0:
            mask_erosion = mask
        else:
            kernel_tensor = torch.ones((1, 1, grow_mask_by, grow_mask_by))
            padding = math.ceil((grow_mask_by - 1) / 2)

            mask_erosion = torch.clamp(torch.nn.functional.conv2d(mask.round(), kernel_tensor, padding=padding), 0, 1)

        m = (1.0 - mask.round()).squeeze(1)
        for i in range(3):
            pixels[:,:,:,i] -= 0.5
            pixels[:,:,:,i] *= m
            pixels[:,:,:,i] += 0.5
        t = vae.encode(pixels)

        return ({"samples":t, "noise_mask": (mask_erosion[:,:,:x,:y].round())}, )


class InpaintModelConditioning:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {"positive": ("CONDITIONING", ),
                             "negative": ("CONDITIONING", ),
                             "vae": ("VAE", ),
                             "pixels": ("IMAGE", ),
                             "mask": ("MASK", ),
                             }}

    RETURN_TYPES = ("CONDITIONING","CONDITIONING","LATENT")
    RETURN_NAMES = ("positive", "negative", "latent")
    FUNCTION = "encode"

    CATEGORY = "conditioning/inpaint"

    def encode(self, positive, negative, pixels, vae, mask):
        x = (pixels.shape[1] // 8) * 8
        y = (pixels.shape[2] // 8) * 8
        mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(pixels.shape[1], pixels.shape[2]), mode="bilinear")

        orig_pixels = pixels
        pixels = orig_pixels.clone()
        if pixels.shape[1] != x or pixels.shape[2] != y:
            x_offset = (pixels.shape[1] % 8) // 2
            y_offset = (pixels.shape[2] % 8) // 2
            pixels = pixels[:,x_offset:x + x_offset, y_offset:y + y_offset,:]
            mask = mask[:,:,x_offset:x + x_offset, y_offset:y + y_offset]

        m = (1.0 - mask.round()).squeeze(1)
        for i in range(3):
            pixels[:,:,:,i] -= 0.5
            pixels[:,:,:,i] *= m
            pixels[:,:,:,i] += 0.5
        concat_latent = vae.encode(pixels)
        orig_latent = vae.encode(orig_pixels)

        out_latent = {}

        out_latent["samples"] = orig_latent
        out_latent["noise_mask"] = mask

        out = []
        for conditioning in [positive, negative]:
            c = node_helpers.conditioning_set_values(conditioning, {"concat_latent_image": concat_latent,
                                                                    "concat_mask": mask})
            out.append(c)
        return (out[0], out[1], out_latent)


class SaveLatent:
    def __init__(self):
        self.output_dir = folder_paths.get_output_directory()

    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "samples": ("LATENT", ),
                              "filename_prefix": ("STRING", {"default": "latents/QuasarUI"})},
                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
                }
    RETURN_TYPES = ()
    FUNCTION = "save"

    OUTPUT_NODE = True

    CATEGORY = "_for_testing"

    def save(self, samples, filename_prefix="QuasarUI", prompt=None, extra_pnginfo=None):
        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)

        # support save metadata for latent sharing
        prompt_info = ""
        if prompt is not None:
            prompt_info = json.dumps(prompt)

        metadata = None
        if not args.disable_metadata:
            metadata = {"prompt": prompt_info}
            if extra_pnginfo is not None:
                for x in extra_pnginfo:
                    metadata[x] = json.dumps(extra_pnginfo[x])

        file = f"{filename}_{counter:05}_.latent"

        results = list()
        results.append({
            "filename": file,
            "subfolder": subfolder,
            "type": "output"
        })

        file = os.path.join(full_output_folder, file)

        output = {}
        output["latent_tensor"] = samples["samples"]
        output["latent_format_version_0"] = torch.tensor([])

        quasar.utils.save_torch_file(output, file, metadata=metadata)
        return { "ui": { "latents": results } }


class LoadLatent:
    @classmethod
    def INPUT_TYPES(s):
        input_dir = folder_paths.get_input_directory()
        files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f)) and f.endswith(".latent")]
        return {"required": {"latent": [sorted(files), ]}, }

    CATEGORY = "_for_testing"

    RETURN_TYPES = ("LATENT", )
    FUNCTION = "load"

    def load(self, latent):
        latent_path = folder_paths.get_annotated_filepath(latent)
        latent = safetensors.torch.load_file(latent_path, device="cpu")
        multiplier = 1.0
        if "latent_format_version_0" not in latent:
            multiplier = 1.0 / 0.18215
        samples = {"samples": latent["latent_tensor"].float() * multiplier}
        return (samples, )

    @classmethod
    def IS_CHANGED(s, latent):
        image_path = folder_paths.get_annotated_filepath(latent)
        m = hashlib.sha256()
        with open(image_path, 'rb') as f:
            m.update(f.read())
        return m.digest().hex()

    @classmethod
    def VALIDATE_INPUTS(s, latent):
        if not folder_paths.exists_annotated_filepath(latent):
            return "Invalid latent file: {}".format(latent)
        return True


class CheckpointLoader:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "config_name": (folder_paths.get_filename_list("configs"), ),
                              "ckpt_name": (folder_paths.get_filename_list("checkpoints"), )}}
    RETURN_TYPES = ("MODEL", "CLIP", "VAE")
    FUNCTION = "load_checkpoint"

    CATEGORY = "advanced/loaders"

    def load_checkpoint(self, config_name, ckpt_name, output_vae=True, output_clip=True):
        config_path = folder_paths.get_full_path("configs", config_name)
        ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
        return quasar.sd.load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))

class CheckpointLoaderSimple:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ),
                             }}
    RETURN_TYPES = ("MODEL", "CLIP", "VAE")
    FUNCTION = "load_checkpoint"

    CATEGORY = "loaders"

    def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True):
        ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
        out = quasar.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
        return out[:3]

class DiffusersLoader:
    @classmethod
    def INPUT_TYPES(cls):
        paths = []
        for search_path in folder_paths.get_folder_paths("diffusers"):
            if os.path.exists(search_path):
                for root, subdir, files in os.walk(search_path, followlinks=True):
                    if "model_index.json" in files:
                        paths.append(os.path.relpath(root, start=search_path))

        return {"required": {"model_path": (paths,), }}
    RETURN_TYPES = ("MODEL", "CLIP", "VAE")
    FUNCTION = "load_checkpoint"

    CATEGORY = "advanced/loaders/deprecated"

    def load_checkpoint(self, model_path, output_vae=True, output_clip=True):
        for search_path in folder_paths.get_folder_paths("diffusers"):
            if os.path.exists(search_path):
                path = os.path.join(search_path, model_path)
                if os.path.exists(path):
                    model_path = path
                    break

        return quasar.diffusers_load.load_diffusers(model_path, output_vae=output_vae, output_clip=output_clip, embedding_directory=folder_paths.get_folder_paths("embeddings"))


class unCLIPCheckpointLoader:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ),
                             }}
    RETURN_TYPES = ("MODEL", "CLIP", "VAE", "CLIP_VISION")
    FUNCTION = "load_checkpoint"

    CATEGORY = "loaders"

    def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True):
        ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
        out = quasar.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
        return out

class CLIPSetLastLayer:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "clip": ("CLIP", ),
                              "stop_at_clip_layer": ("INT", {"default": -1, "min": -24, "max": -1, "step": 1}),
                              }}
    RETURN_TYPES = ("CLIP",)
    FUNCTION = "set_last_layer"

    CATEGORY = "conditioning"

    def set_last_layer(self, clip, stop_at_clip_layer):
        clip = clip.clone()
        clip.clip_layer(stop_at_clip_layer)
        return (clip,)

class LoraLoader:
    def __init__(self):
        self.loaded_lora = None

    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "model": ("MODEL",),
                              "clip": ("CLIP", ),
                              "lora_name": (folder_paths.get_filename_list("loras"), ),
                              "strength_model": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}),
                              "strength_clip": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}),
                              }}
    RETURN_TYPES = ("MODEL", "CLIP")
    FUNCTION = "load_lora"

    CATEGORY = "loaders"

    def load_lora(self, model, clip, lora_name, strength_model, strength_clip):
        if strength_model == 0 and strength_clip == 0:
            return (model, clip)

        lora_path = folder_paths.get_full_path("loras", lora_name)
        lora = None
        if self.loaded_lora is not None:
            if self.loaded_lora[0] == lora_path:
                lora = self.loaded_lora[1]
            else:
                temp = self.loaded_lora
                self.loaded_lora = None
                del temp

        if lora is None:
            lora = quasar.utils.load_torch_file(lora_path, safe_load=True)
            self.loaded_lora = (lora_path, lora)

        model_lora, clip_lora = quasar.sd.load_lora_for_models(model, clip, lora, strength_model, strength_clip)
        return (model_lora, clip_lora)

class LoraLoaderModelOnly(LoraLoader):
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "model": ("MODEL",),
                              "lora_name": (folder_paths.get_filename_list("loras"), ),
                              "strength_model": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}),
                              }}
    RETURN_TYPES = ("MODEL",)
    FUNCTION = "load_lora_model_only"

    def load_lora_model_only(self, model, lora_name, strength_model):
        return (self.load_lora(model, None, lora_name, strength_model, 0)[0],)

class VAELoader:
    @staticmethod
    def vae_list():
        vaes = folder_paths.get_filename_list("vae")
        approx_vaes = folder_paths.get_filename_list("vae_approx")
        sdxl_taesd_enc = False
        sdxl_taesd_dec = False
        sd1_taesd_enc = False
        sd1_taesd_dec = False

        for v in approx_vaes:
            if v.startswith("taesd_decoder."):
                sd1_taesd_dec = True
            elif v.startswith("taesd_encoder."):
                sd1_taesd_enc = True
            elif v.startswith("taesdxl_decoder."):
                sdxl_taesd_dec = True
            elif v.startswith("taesdxl_encoder."):
                sdxl_taesd_enc = True
        if sd1_taesd_dec and sd1_taesd_enc:
            vaes.append("taesd")
        if sdxl_taesd_dec and sdxl_taesd_enc:
            vaes.append("taesdxl")
        return vaes

    @staticmethod
    def load_taesd(name):
        sd = {}
        approx_vaes = folder_paths.get_filename_list("vae_approx")

        encoder = next(filter(lambda a: a.startswith("{}_encoder.".format(name)), approx_vaes))
        decoder = next(filter(lambda a: a.startswith("{}_decoder.".format(name)), approx_vaes))

        enc = quasar.utils.load_torch_file(folder_paths.get_full_path("vae_approx", encoder))
        for k in enc:
            sd["taesd_encoder.{}".format(k)] = enc[k]

        dec = quasar.utils.load_torch_file(folder_paths.get_full_path("vae_approx", decoder))
        for k in dec:
            sd["taesd_decoder.{}".format(k)] = dec[k]

        if name == "taesd":
            sd["vae_scale"] = torch.tensor(0.18215)
        elif name == "taesdxl":
            sd["vae_scale"] = torch.tensor(0.13025)
        return sd

    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "vae_name": (s.vae_list(), )}}
    RETURN_TYPES = ("VAE",)
    FUNCTION = "load_vae"

    CATEGORY = "loaders"

    #TODO: scale factor?
    def load_vae(self, vae_name):
        if vae_name in ["taesd", "taesdxl"]:
            sd = self.load_taesd(vae_name)
        else:
            vae_path = folder_paths.get_full_path("vae", vae_name)
            sd = quasar.utils.load_torch_file(vae_path)
        vae = quasar.sd.VAE(sd=sd)
        return (vae,)

class ControlNetLoader:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "control_net_name": (folder_paths.get_filename_list("controlnet"), )}}

    RETURN_TYPES = ("CONTROL_NET",)
    FUNCTION = "load_controlnet"

    CATEGORY = "loaders"

    def load_controlnet(self, control_net_name):
        controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
        controlnet = quasar.controlnet.load_controlnet(controlnet_path)
        return (controlnet,)

class DiffControlNetLoader:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "model": ("MODEL",),
                              "control_net_name": (folder_paths.get_filename_list("controlnet"), )}}

    RETURN_TYPES = ("CONTROL_NET",)
    FUNCTION = "load_controlnet"

    CATEGORY = "loaders"

    def load_controlnet(self, model, control_net_name):
        controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
        controlnet = quasar.controlnet.load_controlnet(controlnet_path, model)
        return (controlnet,)


class ControlNetApply:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {"conditioning": ("CONDITIONING", ),
                             "control_net": ("CONTROL_NET", ),
                             "image": ("IMAGE", ),
                             "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01})
                             }}
    RETURN_TYPES = ("CONDITIONING",)
    FUNCTION = "apply_controlnet"

    CATEGORY = "conditioning"

    def apply_controlnet(self, conditioning, control_net, image, strength):
        if strength == 0:
            return (conditioning, )

        c = []
        control_hint = image.movedim(-1,1)
        for t in conditioning:
            n = [t[0], t[1].copy()]
            c_net = control_net.copy().set_cond_hint(control_hint, strength)
            if 'control' in t[1]:
                c_net.set_previous_controlnet(t[1]['control'])
            n[1]['control'] = c_net
            n[1]['control_apply_to_uncond'] = True
            c.append(n)
        return (c, )


class ControlNetApplyAdvanced:
    @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})
                             }}

    RETURN_TYPES = ("CONDITIONING","CONDITIONING")
    RETURN_NAMES = ("positive", "negative")
    FUNCTION = "apply_controlnet"

    CATEGORY = "conditioning"

    def apply_controlnet(self, positive, negative, control_net, image, strength, start_percent, end_percent):
        if strength == 0:
            return (positive, negative)

        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, (start_percent, end_percent))
                    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 UNETLoader:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "unet_name": (folder_paths.get_filename_list("unet"), ),
                             }}
    RETURN_TYPES = ("MODEL",)
    FUNCTION = "load_unet"

    CATEGORY = "advanced/loaders"

    def load_unet(self, unet_name):
        unet_path = folder_paths.get_full_path("unet", unet_name)
        model = quasar.sd.load_unet(unet_path)
        return (model,)

class CLIPLoader:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "clip_name": (folder_paths.get_filename_list("clip"), ),
                              "type": (["stable_diffusion", "stable_cascade"], ),
                             }}
    RETURN_TYPES = ("CLIP",)
    FUNCTION = "load_clip"

    CATEGORY = "advanced/loaders"

    def load_clip(self, clip_name, type="stable_diffusion"):
        clip_type = quasar.sd.CLIPType.STABLE_DIFFUSION
        if type == "stable_cascade":
            clip_type = quasar.sd.CLIPType.STABLE_CASCADE

        clip_path = folder_paths.get_full_path("clip", clip_name)
        clip = quasar.sd.load_clip(ckpt_paths=[clip_path], embedding_directory=folder_paths.get_folder_paths("embeddings"), clip_type=clip_type)
        return (clip,)

class DualCLIPLoader:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "clip_name1": (folder_paths.get_filename_list("clip"), ), "clip_name2": (folder_paths.get_filename_list("clip"), ),
                             }}
    RETURN_TYPES = ("CLIP",)
    FUNCTION = "load_clip"

    CATEGORY = "advanced/loaders"

    def load_clip(self, clip_name1, clip_name2):
        clip_path1 = folder_paths.get_full_path("clip", clip_name1)
        clip_path2 = folder_paths.get_full_path("clip", clip_name2)
        clip = quasar.sd.load_clip(ckpt_paths=[clip_path1, clip_path2], embedding_directory=folder_paths.get_folder_paths("embeddings"))
        return (clip,)

class CLIPVisionLoader:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "clip_name": (folder_paths.get_filename_list("clip_vision"), ),
                             }}
    RETURN_TYPES = ("CLIP_VISION",)
    FUNCTION = "load_clip"

    CATEGORY = "loaders"

    def load_clip(self, clip_name):
        clip_path = folder_paths.get_full_path("clip_vision", clip_name)
        clip_vision = quasar.clip_vision.load(clip_path)
        return (clip_vision,)

class CLIPVisionEncode:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "clip_vision": ("CLIP_VISION",),
                              "image": ("IMAGE",)
                             }}
    RETURN_TYPES = ("CLIP_VISION_OUTPUT",)
    FUNCTION = "encode"

    CATEGORY = "conditioning"

    def encode(self, clip_vision, image):
        output = clip_vision.encode_image(image)
        return (output,)

class StyleModelLoader:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "style_model_name": (folder_paths.get_filename_list("style_models"), )}}

    RETURN_TYPES = ("STYLE_MODEL",)
    FUNCTION = "load_style_model"

    CATEGORY = "loaders"

    def load_style_model(self, style_model_name):
        style_model_path = folder_paths.get_full_path("style_models", style_model_name)
        style_model = quasar.sd.load_style_model(style_model_path)
        return (style_model,)


class StyleModelApply:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {"conditioning": ("CONDITIONING", ),
                             "style_model": ("STYLE_MODEL", ),
                             "clip_vision_output": ("CLIP_VISION_OUTPUT", ),
                             }}
    RETURN_TYPES = ("CONDITIONING",)
    FUNCTION = "apply_stylemodel"

    CATEGORY = "conditioning/style_model"

    def apply_stylemodel(self, clip_vision_output, style_model, conditioning):
        cond = style_model.get_cond(clip_vision_output).flatten(start_dim=0, end_dim=1).unsqueeze(dim=0)
        c = []
        for t in conditioning:
            n = [torch.cat((t[0], cond), dim=1), t[1].copy()]
            c.append(n)
        return (c, )

class unCLIPConditioning:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {"conditioning": ("CONDITIONING", ),
                             "clip_vision_output": ("CLIP_VISION_OUTPUT", ),
                             "strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
                             "noise_augmentation": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
                             }}
    RETURN_TYPES = ("CONDITIONING",)
    FUNCTION = "apply_adm"

    CATEGORY = "conditioning"

    def apply_adm(self, conditioning, clip_vision_output, strength, noise_augmentation):
        if strength == 0:
            return (conditioning, )

        c = []
        for t in conditioning:
            o = t[1].copy()
            x = {"clip_vision_output": clip_vision_output, "strength": strength, "noise_augmentation": noise_augmentation}
            if "unclip_conditioning" in o:
                o["unclip_conditioning"] = o["unclip_conditioning"][:] + [x]
            else:
                o["unclip_conditioning"] = [x]
            n = [t[0], o]
            c.append(n)
        return (c, )

class GLIGENLoader:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "gligen_name": (folder_paths.get_filename_list("gligen"), )}}

    RETURN_TYPES = ("GLIGEN",)
    FUNCTION = "load_gligen"

    CATEGORY = "loaders"

    def load_gligen(self, gligen_name):
        gligen_path = folder_paths.get_full_path("gligen", gligen_name)
        gligen = quasar.sd.load_gligen(gligen_path)
        return (gligen,)

class GLIGENTextBoxApply:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {"conditioning_to": ("CONDITIONING", ),
                              "clip": ("CLIP", ),
                              "gligen_textbox_model": ("GLIGEN", ),
                              "text": ("STRING", {"multiline": True}),
                              "width": ("INT", {"default": 64, "min": 8, "max": MAX_RESOLUTION, "step": 8}),
                              "height": ("INT", {"default": 64, "min": 8, "max": MAX_RESOLUTION, "step": 8}),
                              "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
                              "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
                             }}
    RETURN_TYPES = ("CONDITIONING",)
    FUNCTION = "append"

    CATEGORY = "conditioning/gligen"

    def append(self, conditioning_to, clip, gligen_textbox_model, text, width, height, x, y):
        c = []
        cond, cond_pooled = clip.encode_from_tokens(clip.tokenize(text), return_pooled="unprojected")
        for t in conditioning_to:
            n = [t[0], t[1].copy()]
            position_params = [(cond_pooled, height // 8, width // 8, y // 8, x // 8)]
            prev = []
            if "gligen" in n[1]:
                prev = n[1]['gligen'][2]

            n[1]['gligen'] = ("position", gligen_textbox_model, prev + position_params)
            c.append(n)
        return (c, )

class EmptyLatentImage:
    def __init__(self):
        self.device = quasar.model_management.intermediate_device()

    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "width": ("INT", {"default": 512, "min": 16, "max": MAX_RESOLUTION, "step": 8}),
                              "height": ("INT", {"default": 512, "min": 16, "max": MAX_RESOLUTION, "step": 8}),
                              "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}}
    RETURN_TYPES = ("LATENT",)
    FUNCTION = "generate"

    CATEGORY = "latent"

    def generate(self, width, height, batch_size=1):
        latent = torch.zeros([batch_size, 4, height // 8, width // 8], device=self.device)
        return ({"samples":latent}, )


class LatentFromBatch:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "samples": ("LATENT",),
                              "batch_index": ("INT", {"default": 0, "min": 0, "max": 63}),
                              "length": ("INT", {"default": 1, "min": 1, "max": 64}),
                              }}
    RETURN_TYPES = ("LATENT",)
    FUNCTION = "frombatch"

    CATEGORY = "latent/batch"

    def frombatch(self, samples, batch_index, length):
        s = samples.copy()
        s_in = samples["samples"]
        batch_index = min(s_in.shape[0] - 1, batch_index)
        length = min(s_in.shape[0] - batch_index, length)
        s["samples"] = s_in[batch_index:batch_index + length].clone()
        if "noise_mask" in samples:
            masks = samples["noise_mask"]
            if masks.shape[0] == 1:
                s["noise_mask"] = masks.clone()
            else:
                if masks.shape[0] < s_in.shape[0]:
                    masks = masks.repeat(math.ceil(s_in.shape[0] / masks.shape[0]), 1, 1, 1)[:s_in.shape[0]]
                s["noise_mask"] = masks[batch_index:batch_index + length].clone()
        if "batch_index" not in s:
            s["batch_index"] = [x for x in range(batch_index, batch_index+length)]
        else:
            s["batch_index"] = samples["batch_index"][batch_index:batch_index + length]
        return (s,)
    
class RepeatLatentBatch:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "samples": ("LATENT",),
                              "amount": ("INT", {"default": 1, "min": 1, "max": 64}),
                              }}
    RETURN_TYPES = ("LATENT",)
    FUNCTION = "repeat"

    CATEGORY = "latent/batch"

    def repeat(self, samples, amount):
        s = samples.copy()
        s_in = samples["samples"]
        
        s["samples"] = s_in.repeat((amount, 1,1,1))
        if "noise_mask" in samples and samples["noise_mask"].shape[0] > 1:
            masks = samples["noise_mask"]
            if masks.shape[0] < s_in.shape[0]:
                masks = masks.repeat(math.ceil(s_in.shape[0] / masks.shape[0]), 1, 1, 1)[:s_in.shape[0]]
            s["noise_mask"] = samples["noise_mask"].repeat((amount, 1,1,1))
        if "batch_index" in s:
            offset = max(s["batch_index"]) - min(s["batch_index"]) + 1
            s["batch_index"] = s["batch_index"] + [x + (i * offset) for i in range(1, amount) for x in s["batch_index"]]
        return (s,)

class LatentUpscale:
    upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "bislerp"]
    crop_methods = ["disabled", "center"]

    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "samples": ("LATENT",), "upscale_method": (s.upscale_methods,),
                              "width": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
                              "height": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
                              "crop": (s.crop_methods,)}}
    RETURN_TYPES = ("LATENT",)
    FUNCTION = "upscale"

    CATEGORY = "latent"

    def upscale(self, samples, upscale_method, width, height, crop):
        if width == 0 and height == 0:
            s = samples
        else:
            s = samples.copy()

            if width == 0:
                height = max(64, height)
                width = max(64, round(samples["samples"].shape[3] * height / samples["samples"].shape[2]))
            elif height == 0:
                width = max(64, width)
                height = max(64, round(samples["samples"].shape[2] * width / samples["samples"].shape[3]))
            else:
                width = max(64, width)
                height = max(64, height)

            s["samples"] = quasar.utils.common_upscale(samples["samples"], width // 8, height // 8, upscale_method, crop)
        return (s,)

class LatentUpscaleBy:
    upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "bislerp"]

    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "samples": ("LATENT",), "upscale_method": (s.upscale_methods,),
                              "scale_by": ("FLOAT", {"default": 1.5, "min": 0.01, "max": 8.0, "step": 0.01}),}}
    RETURN_TYPES = ("LATENT",)
    FUNCTION = "upscale"

    CATEGORY = "latent"

    def upscale(self, samples, upscale_method, scale_by):
        s = samples.copy()
        width = round(samples["samples"].shape[3] * scale_by)
        height = round(samples["samples"].shape[2] * scale_by)
        s["samples"] = quasar.utils.common_upscale(samples["samples"], width, height, upscale_method, "disabled")
        return (s,)

class LatentRotate:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "samples": ("LATENT",),
                              "rotation": (["none", "90 degrees", "180 degrees", "270 degrees"],),
                              }}
    RETURN_TYPES = ("LATENT",)
    FUNCTION = "rotate"

    CATEGORY = "latent/transform"

    def rotate(self, samples, rotation):
        s = samples.copy()
        rotate_by = 0
        if rotation.startswith("90"):
            rotate_by = 1
        elif rotation.startswith("180"):
            rotate_by = 2
        elif rotation.startswith("270"):
            rotate_by = 3

        s["samples"] = torch.rot90(samples["samples"], k=rotate_by, dims=[3, 2])
        return (s,)

class LatentFlip:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "samples": ("LATENT",),
                              "flip_method": (["x-axis: vertically", "y-axis: horizontally"],),
                              }}
    RETURN_TYPES = ("LATENT",)
    FUNCTION = "flip"

    CATEGORY = "latent/transform"

    def flip(self, samples, flip_method):
        s = samples.copy()
        if flip_method.startswith("x"):
            s["samples"] = torch.flip(samples["samples"], dims=[2])
        elif flip_method.startswith("y"):
            s["samples"] = torch.flip(samples["samples"], dims=[3])

        return (s,)

class LatentComposite:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "samples_to": ("LATENT",),
                              "samples_from": ("LATENT",),
                              "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
                              "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
                              "feather": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
                              }}
    RETURN_TYPES = ("LATENT",)
    FUNCTION = "composite"

    CATEGORY = "latent"

    def composite(self, samples_to, samples_from, x, y, composite_method="normal", feather=0):
        x =  x // 8
        y = y // 8
        feather = feather // 8
        samples_out = samples_to.copy()
        s = samples_to["samples"].clone()
        samples_to = samples_to["samples"]
        samples_from = samples_from["samples"]
        if feather == 0:
            s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x]
        else:
            samples_from = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x]
            mask = torch.ones_like(samples_from)
            for t in range(feather):
                if y != 0:
                    mask[:,:,t:1+t,:] *= ((1.0/feather) * (t + 1))

                if y + samples_from.shape[2] < samples_to.shape[2]:
                    mask[:,:,mask.shape[2] -1 -t: mask.shape[2]-t,:] *= ((1.0/feather) * (t + 1))
                if x != 0:
                    mask[:,:,:,t:1+t] *= ((1.0/feather) * (t + 1))
                if x + samples_from.shape[3] < samples_to.shape[3]:
                    mask[:,:,:,mask.shape[3]- 1 - t: mask.shape[3]- t] *= ((1.0/feather) * (t + 1))
            rev_mask = torch.ones_like(mask) - mask
            s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x] * mask + s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] * rev_mask
        samples_out["samples"] = s
        return (samples_out,)

class LatentBlend:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {
            "samples1": ("LATENT",),
            "samples2": ("LATENT",),
            "blend_factor": ("FLOAT", {
                "default": 0.5,
                "min": 0,
                "max": 1,
                "step": 0.01
            }),
        }}

    RETURN_TYPES = ("LATENT",)
    FUNCTION = "blend"

    CATEGORY = "_for_testing"

    def blend(self, samples1, samples2, blend_factor:float, blend_mode: str="normal"):

        samples_out = samples1.copy()
        samples1 = samples1["samples"]
        samples2 = samples2["samples"]

        if samples1.shape != samples2.shape:
            samples2.permute(0, 3, 1, 2)
            samples2 = quasar.utils.common_upscale(samples2, samples1.shape[3], samples1.shape[2], 'bicubic', crop='center')
            samples2.permute(0, 2, 3, 1)

        samples_blended = self.blend_mode(samples1, samples2, blend_mode)
        samples_blended = samples1 * blend_factor + samples_blended * (1 - blend_factor)
        samples_out["samples"] = samples_blended
        return (samples_out,)

    def blend_mode(self, img1, img2, mode):
        if mode == "normal":
            return img2
        else:
            raise ValueError(f"Unsupported blend mode: {mode}")

class LatentCrop:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "samples": ("LATENT",),
                              "width": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
                              "height": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
                              "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
                              "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
                              }}
    RETURN_TYPES = ("LATENT",)
    FUNCTION = "crop"

    CATEGORY = "latent/transform"

    def crop(self, samples, width, height, x, y):
        s = samples.copy()
        samples = samples['samples']
        x =  x // 8
        y = y // 8

        #enfonce minimum size of 64
        if x > (samples.shape[3] - 8):
            x = samples.shape[3] - 8
        if y > (samples.shape[2] - 8):
            y = samples.shape[2] - 8

        new_height = height // 8
        new_width = width // 8
        to_x = new_width + x
        to_y = new_height + y
        s['samples'] = samples[:,:,y:to_y, x:to_x]
        return (s,)

class SetLatentNoiseMask:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "samples": ("LATENT",),
                              "mask": ("MASK",),
                              }}
    RETURN_TYPES = ("LATENT",)
    FUNCTION = "set_mask"

    CATEGORY = "latent/inpaint"

    def set_mask(self, samples, mask):
        s = samples.copy()
        s["noise_mask"] = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1]))
        return (s,)

def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False):
    latent_image = latent["samples"]
    if disable_noise:
        noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
    else:
        batch_inds = latent["batch_index"] if "batch_index" in latent else None
        noise = quasar.sample.prepare_noise(latent_image, seed, batch_inds)

    noise_mask = None
    if "noise_mask" in latent:
        noise_mask = latent["noise_mask"]

    callback = latent_preview.prepare_callback(model, steps)
    disable_pbar = not quasar.utils.PROGRESS_BAR_ENABLED
    samples = quasar.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
                                  denoise=denoise, disable_noise=disable_noise, start_step=start_step, last_step=last_step,
                                  force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
    out = latent.copy()
    out["samples"] = samples
    return (out, )

class KSampler:
    @classmethod
    def INPUT_TYPES(s):
        return {"required":
                    {"model": ("MODEL",),
                    "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
                    "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
                    "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}),
                    "sampler_name": (quasar.samplers.KSampler.SAMPLERS, ),
                    "scheduler": (quasar.samplers.KSampler.SCHEDULERS, ),
                    "positive": ("CONDITIONING", ),
                    "negative": ("CONDITIONING", ),
                    "latent_image": ("LATENT", ),
                    "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
                     }
                }

    RETURN_TYPES = ("LATENT",)
    FUNCTION = "sample"

    CATEGORY = "sampling"

    def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0):
        return common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise)

class KSamplerAdvanced:
    @classmethod
    def INPUT_TYPES(s):
        return {"required":
                    {"model": ("MODEL",),
                    "add_noise": (["enable", "disable"], ),
                    "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
                    "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
                    "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}),
                    "sampler_name": (quasar.samplers.KSampler.SAMPLERS, ),
                    "scheduler": (quasar.samplers.KSampler.SCHEDULERS, ),
                    "positive": ("CONDITIONING", ),
                    "negative": ("CONDITIONING", ),
                    "latent_image": ("LATENT", ),
                    "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}),
                    "end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}),
                    "return_with_leftover_noise": (["disable", "enable"], ),
                     }
                }

    RETURN_TYPES = ("LATENT",)
    FUNCTION = "sample"

    CATEGORY = "sampling"

    def sample(self, model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, return_with_leftover_noise, denoise=1.0):
        force_full_denoise = True
        if return_with_leftover_noise == "enable":
            force_full_denoise = False
        disable_noise = False
        if add_noise == "disable":
            disable_noise = True
        return common_ksampler(model, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise, disable_noise=disable_noise, start_step=start_at_step, last_step=end_at_step, force_full_denoise=force_full_denoise)

class SaveImage:
    def __init__(self):
        self.output_dir = folder_paths.get_output_directory()
        self.type = "output"
        self.prefix_append = ""
        self.compress_level = 4

    @classmethod
    def INPUT_TYPES(s):
        return {"required": 
                    {"images": ("IMAGE", ),
                     "filename_prefix": ("STRING", {"default": "QuasarUI"})},
                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
                }

    RETURN_TYPES = ()
    FUNCTION = "save_images"

    OUTPUT_NODE = True

    CATEGORY = "image"

    def save_images(self, images, filename_prefix="QuasarUI", prompt=None, extra_pnginfo=None):
        filename_prefix += self.prefix_append
        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0])
        results = list()
        for (batch_number, image) in enumerate(images):
            i = 255. * image.cpu().numpy()
            img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
            metadata = None
            if not args.disable_metadata:
                metadata = PngInfo()
                if prompt is not None:
                    metadata.add_text("prompt", json.dumps(prompt))
                if extra_pnginfo is not None:
                    for x in extra_pnginfo:
                        metadata.add_text(x, json.dumps(extra_pnginfo[x]))

            filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
            file = f"{filename_with_batch_num}_{counter:05}_.png"
            img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=self.compress_level)
            results.append({
                "filename": file,
                "subfolder": subfolder,
                "type": self.type
            })
            counter += 1

        return { "ui": { "images": results } }

class PreviewImage(SaveImage):
    def __init__(self):
        self.output_dir = folder_paths.get_temp_directory()
        self.type = "temp"
        self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5))
        self.compress_level = 1

    @classmethod
    def INPUT_TYPES(s):
        return {"required":
                    {"images": ("IMAGE", ), },
                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
                }

class LoadImage:
    @classmethod
    def INPUT_TYPES(s):
        input_dir = folder_paths.get_input_directory()
        files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
        return {"required":
                    {"image": (sorted(files), {"image_upload": True})},
                }

    CATEGORY = "image"

    RETURN_TYPES = ("IMAGE", "MASK")
    FUNCTION = "load_image"
    def load_image(self, image):
        image_path = folder_paths.get_annotated_filepath(image)
        img = Image.open(image_path)
        output_images = []
        output_masks = []
        for i in ImageSequence.Iterator(img):
            i = ImageOps.exif_transpose(i)
            if i.mode == 'I':
                i = i.point(lambda i: i * (1 / 255))
            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")
            output_images.append(image)
            output_masks.append(mask.unsqueeze(0))

        if len(output_images) > 1:
            output_image = torch.cat(output_images, dim=0)
            output_mask = torch.cat(output_masks, dim=0)
        else:
            output_image = output_images[0]
            output_mask = output_masks[0]

        return (output_image, output_mask)

    @classmethod
    def IS_CHANGED(s, image):
        image_path = folder_paths.get_annotated_filepath(image)
        m = hashlib.sha256()
        with open(image_path, 'rb') as f:
            m.update(f.read())
        return m.digest().hex()

    @classmethod
    def VALIDATE_INPUTS(s, image):
        if not folder_paths.exists_annotated_filepath(image):
            return "Invalid image file: {}".format(image)

        return True

class LoadImageMask:
    _color_channels = ["alpha", "red", "green", "blue"]
    @classmethod
    def INPUT_TYPES(s):
        input_dir = folder_paths.get_input_directory()
        files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
        return {"required":
                    {"image": (sorted(files), {"image_upload": True}),
                     "channel": (s._color_channels, ), }
                }

    CATEGORY = "mask"

    RETURN_TYPES = ("MASK",)
    FUNCTION = "load_image"
    def load_image(self, image, channel):
        image_path = folder_paths.get_annotated_filepath(image)
        i = Image.open(image_path)
        i = ImageOps.exif_transpose(i)
        if i.getbands() != ("R", "G", "B", "A"):
            if i.mode == 'I':
                i = i.point(lambda i: i * (1 / 255))
            i = i.convert("RGBA")
        mask = None
        c = channel[0].upper()
        if c in i.getbands():
            mask = np.array(i.getchannel(c)).astype(np.float32) / 255.0
            mask = torch.from_numpy(mask)
            if c == 'A':
                mask = 1. - mask
        else:
            mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
        return (mask.unsqueeze(0),)

    @classmethod
    def IS_CHANGED(s, image, channel):
        image_path = folder_paths.get_annotated_filepath(image)
        m = hashlib.sha256()
        with open(image_path, 'rb') as f:
            m.update(f.read())
        return m.digest().hex()

    @classmethod
    def VALIDATE_INPUTS(s, image):
        if not folder_paths.exists_annotated_filepath(image):
            return "Invalid image file: {}".format(image)

        return True

class ImageScale:
    upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"]
    crop_methods = ["disabled", "center"]

    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "image": ("IMAGE",), "upscale_method": (s.upscale_methods,),
                              "width": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
                              "height": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
                              "crop": (s.crop_methods,)}}
    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "upscale"

    CATEGORY = "image/upscaling"

    def upscale(self, image, upscale_method, width, height, crop):
        if width == 0 and height == 0:
            s = image
        else:
            samples = image.movedim(-1,1)

            if width == 0:
                width = max(1, round(samples.shape[3] * height / samples.shape[2]))
            elif height == 0:
                height = max(1, round(samples.shape[2] * width / samples.shape[3]))

            s = quasar.utils.common_upscale(samples, width, height, upscale_method, crop)
            s = s.movedim(1,-1)
        return (s,)

class ImageScaleBy:
    upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"]

    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "image": ("IMAGE",), "upscale_method": (s.upscale_methods,),
                              "scale_by": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 8.0, "step": 0.01}),}}
    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "upscale"

    CATEGORY = "image/upscaling"

    def upscale(self, image, upscale_method, scale_by):
        samples = image.movedim(-1,1)
        width = round(samples.shape[3] * scale_by)
        height = round(samples.shape[2] * scale_by)
        s = quasar.utils.common_upscale(samples, width, height, upscale_method, "disabled")
        s = s.movedim(1,-1)
        return (s,)

class ImageInvert:

    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "image": ("IMAGE",)}}

    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "invert"

    CATEGORY = "image"

    def invert(self, image):
        s = 1.0 - image
        return (s,)

class ImageBatch:

    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "image1": ("IMAGE",), "image2": ("IMAGE",)}}

    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "batch"

    CATEGORY = "image"

    def batch(self, image1, image2):
        if image1.shape[1:] != image2.shape[1:]:
            image2 = quasar.utils.common_upscale(image2.movedim(-1,1), image1.shape[2], image1.shape[1], "bilinear", "center").movedim(1,-1)
        s = torch.cat((image1, image2), dim=0)
        return (s,)

class EmptyImage:
    def __init__(self, device="cpu"):
        self.device = device

    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
                              "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
                              "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
                              "color": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFF, "step": 1, "display": "color"}),
                              }}
    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "generate"

    CATEGORY = "image"

    def generate(self, width, height, batch_size=1, color=0):
        r = torch.full([batch_size, height, width, 1], ((color >> 16) & 0xFF) / 0xFF)
        g = torch.full([batch_size, height, width, 1], ((color >> 8) & 0xFF) / 0xFF)
        b = torch.full([batch_size, height, width, 1], ((color) & 0xFF) / 0xFF)
        return (torch.cat((r, g, b), dim=-1), )

class ImagePadForOutpaint:

    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "image": ("IMAGE",),
                "left": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
                "top": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
                "right": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
                "bottom": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
                "feathering": ("INT", {"default": 40, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
            }
        }

    RETURN_TYPES = ("IMAGE", "MASK")
    FUNCTION = "expand_image"

    CATEGORY = "image"

    def expand_image(self, image, left, top, right, bottom, feathering):
        d1, d2, d3, d4 = image.size()

        new_image = torch.ones(
            (d1, d2 + top + bottom, d3 + left + right, d4),
            dtype=torch.float32,
        ) * 0.5

        new_image[:, top:top + d2, left:left + d3, :] = image

        mask = torch.ones(
            (d2 + top + bottom, d3 + left + right),
            dtype=torch.float32,
        )

        t = torch.zeros(
            (d2, d3),
            dtype=torch.float32
        )

        if feathering > 0 and feathering * 2 < d2 and feathering * 2 < d3:

            for i in range(d2):
                for j in range(d3):
                    dt = i if top != 0 else d2
                    db = d2 - i if bottom != 0 else d2

                    dl = j if left != 0 else d3
                    dr = d3 - j if right != 0 else d3

                    d = min(dt, db, dl, dr)

                    if d >= feathering:
                        continue

                    v = (feathering - d) / feathering

                    t[i, j] = v * v

        mask[top:top + d2, left:left + d3] = t

        return (new_image, mask)


NODE_CLASS_MAPPINGS = {
    "KSampler": KSampler,
    "CheckpointLoaderSimple": CheckpointLoaderSimple,
    "CLIPTextEncode": CLIPTextEncode,
    "CLIPSetLastLayer": CLIPSetLastLayer,
    "VAEDecode": VAEDecode,
    "VAEEncode": VAEEncode,
    "VAEEncodeForInpaint": VAEEncodeForInpaint,
    "VAELoader": VAELoader,
    "EmptyLatentImage": EmptyLatentImage,
    "LatentUpscale": LatentUpscale,
    "LatentUpscaleBy": LatentUpscaleBy,
    "LatentFromBatch": LatentFromBatch,
    "RepeatLatentBatch": RepeatLatentBatch,
    "SaveImage": SaveImage,
    "PreviewImage": PreviewImage,
    "LoadImage": LoadImage,
    "LoadImageMask": LoadImageMask,
    "ImageScale": ImageScale,
    "ImageScaleBy": ImageScaleBy,
    "ImageInvert": ImageInvert,
    "ImageBatch": ImageBatch,
    "ImagePadForOutpaint": ImagePadForOutpaint,
    "EmptyImage": EmptyImage,
    "ConditioningAverage": ConditioningAverage ,
    "ConditioningCombine": ConditioningCombine,
    "ConditioningConcat": ConditioningConcat,
    "ConditioningSetArea": ConditioningSetArea,
    "ConditioningSetAreaPercentage": ConditioningSetAreaPercentage,
    "ConditioningSetAreaStrength": ConditioningSetAreaStrength,
    "ConditioningSetMask": ConditioningSetMask,
    "KSamplerAdvanced": KSamplerAdvanced,
    "SetLatentNoiseMask": SetLatentNoiseMask,
    "LatentComposite": LatentComposite,
    "LatentBlend": LatentBlend,
    "LatentRotate": LatentRotate,
    "LatentFlip": LatentFlip,
    "LatentCrop": LatentCrop,
    "LoraLoader": LoraLoader,
    "CLIPLoader": CLIPLoader,
    "UNETLoader": UNETLoader,
    "DualCLIPLoader": DualCLIPLoader,
    "CLIPVisionEncode": CLIPVisionEncode,
    "StyleModelApply": StyleModelApply,
    "unCLIPConditioning": unCLIPConditioning,
    "ControlNetApply": ControlNetApply,
    "ControlNetApplyAdvanced": ControlNetApplyAdvanced,
    "ControlNetLoader": ControlNetLoader,
    "DiffControlNetLoader": DiffControlNetLoader,
    "StyleModelLoader": StyleModelLoader,
    "CLIPVisionLoader": CLIPVisionLoader,
    "VAEDecodeTiled": VAEDecodeTiled,
    "VAEEncodeTiled": VAEEncodeTiled,
    "unCLIPCheckpointLoader": unCLIPCheckpointLoader,
    "GLIGENLoader": GLIGENLoader,
    "GLIGENTextBoxApply": GLIGENTextBoxApply,
    "InpaintModelConditioning": InpaintModelConditioning,

    "CheckpointLoader": CheckpointLoader,
    "DiffusersLoader": DiffusersLoader,

    "LoadLatent": LoadLatent,
    "SaveLatent": SaveLatent,

    "ConditioningZeroOut": ConditioningZeroOut,
    "ConditioningSetTimestepRange": ConditioningSetTimestepRange,
    "LoraLoaderModelOnly": LoraLoaderModelOnly,
}

NODE_DISPLAY_NAME_MAPPINGS = {
    # Sampling
    "KSampler": "KSampler",
    "KSamplerAdvanced": "KSampler (Advanced)",
    # Loaders
    "CheckpointLoader": "Load Checkpoint With Config (DEPRECATED)",
    "CheckpointLoaderSimple": "Load Checkpoint",
    "VAELoader": "Load VAE",
    "LoraLoader": "Load LoRA",
    "CLIPLoader": "Load CLIP",
    "ControlNetLoader": "Load ControlNet Model",
    "DiffControlNetLoader": "Load ControlNet Model (diff)",
    "StyleModelLoader": "Load Style Model",
    "CLIPVisionLoader": "Load CLIP Vision",
    "UpscaleModelLoader": "Load Upscale Model",
    # Conditioning
    "CLIPVisionEncode": "CLIP Vision Encode",
    "StyleModelApply": "Apply Style Model",
    "CLIPTextEncode": "CLIP Text Encode (Prompt)",
    "CLIPSetLastLayer": "CLIP Set Last Layer",
    "ConditioningCombine": "Conditioning (Combine)",
    "ConditioningAverage ": "Conditioning (Average)",
    "ConditioningConcat": "Conditioning (Concat)",
    "ConditioningSetArea": "Conditioning (Set Area)",
    "ConditioningSetAreaPercentage": "Conditioning (Set Area with Percentage)",
    "ConditioningSetMask": "Conditioning (Set Mask)",
    "ControlNetApply": "Apply ControlNet",
    "ControlNetApplyAdvanced": "Apply ControlNet (Advanced)",
    # Latent
    "VAEEncodeForInpaint": "VAE Encode (for Inpainting)",
    "SetLatentNoiseMask": "Set Latent Noise Mask",
    "VAEDecode": "VAE Decode",
    "VAEEncode": "VAE Encode",
    "LatentRotate": "Rotate Latent",
    "LatentFlip": "Flip Latent",
    "LatentCrop": "Crop Latent",
    "EmptyLatentImage": "Empty Latent Image",
    "LatentUpscale": "Upscale Latent",
    "LatentUpscaleBy": "Upscale Latent By",
    "LatentComposite": "Latent Composite",
    "LatentBlend": "Latent Blend",
    "LatentFromBatch" : "Latent From Batch",
    "RepeatLatentBatch": "Repeat Latent Batch",
    # Image
    "SaveImage": "Save Image",
    "PreviewImage": "Preview Image",
    "LoadImage": "Load Image",
    "LoadImageMask": "Load Image (as Mask)",
    "ImageScale": "Upscale Image",
    "ImageScaleBy": "Upscale Image By",
    "ImageUpscaleWithModel": "Upscale Image (using Model)",
    "ImageInvert": "Invert Image",
    "ImagePadForOutpaint": "Pad Image for Outpainting",
    "ImageBatch": "Batch Images",
    # _for_testing
    "VAEDecodeTiled": "VAE Decode (Tiled)",
    "VAEEncodeTiled": "VAE Encode (Tiled)",
}

EXTENSION_WEB_DIRS = {}

def load_custom_node(module_path, ignore=set()):
    module_name = os.path.basename(module_path)
    if os.path.isfile(module_path):
        sp = os.path.splitext(module_path)
        module_name = sp[0]
    try:
        logging.debug("Trying to load custom node {}".format(module_path))
        if os.path.isfile(module_path):
            module_spec = importlib.util.spec_from_file_location(module_name, module_path)
            module_dir = os.path.split(module_path)[0]
        else:
            module_spec = importlib.util.spec_from_file_location(module_name, os.path.join(module_path, "__init__.py"))
            module_dir = module_path

        module = importlib.util.module_from_spec(module_spec)
        sys.modules[module_name] = module
        module_spec.loader.exec_module(module)

        if hasattr(module, "WEB_DIRECTORY") and getattr(module, "WEB_DIRECTORY") is not None:
            web_dir = os.path.abspath(os.path.join(module_dir, getattr(module, "WEB_DIRECTORY")))
            if os.path.isdir(web_dir):
                EXTENSION_WEB_DIRS[module_name] = web_dir

        if hasattr(module, "NODE_CLASS_MAPPINGS") and getattr(module, "NODE_CLASS_MAPPINGS") is not None:
            for name in module.NODE_CLASS_MAPPINGS:
                if name not in ignore:
                    NODE_CLASS_MAPPINGS[name] = module.NODE_CLASS_MAPPINGS[name]
            if hasattr(module, "NODE_DISPLAY_NAME_MAPPINGS") and getattr(module, "NODE_DISPLAY_NAME_MAPPINGS") is not None:
                NODE_DISPLAY_NAME_MAPPINGS.update(module.NODE_DISPLAY_NAME_MAPPINGS)
            return True
        else:
            logging.warning(f"Skip {module_path} module for custom nodes due to the lack of NODE_CLASS_MAPPINGS.")
            return False
    except Exception as e:
        logging.warning(traceback.format_exc())
        logging.warning(f"Cannot import {module_path} module for custom nodes: {e}")
        return False

def load_custom_nodes():
    base_node_names = set(NODE_CLASS_MAPPINGS.keys())
    node_paths = folder_paths.get_folder_paths("custom_nodes")
    node_import_times = []
    for custom_node_path in node_paths:
        possible_modules = os.listdir(os.path.realpath(custom_node_path))
        if "__pycache__" in possible_modules:
            possible_modules.remove("__pycache__")

        for possible_module in possible_modules:
            module_path = os.path.join(custom_node_path, possible_module)
            if os.path.isfile(module_path) and os.path.splitext(module_path)[1] != ".py": continue
            if module_path.endswith(".disabled"): continue
            time_before = time.perf_counter()
            success = load_custom_node(module_path, base_node_names)
            node_import_times.append((time.perf_counter() - time_before, module_path, success))

    if len(node_import_times) > 0:
        logging.info("\nImport times for custom nodes:")
        for n in sorted(node_import_times):
            if n[2]:
                import_message = ""
            else:
                import_message = " (IMPORT FAILED)"
            logging.info("{:6.1f} seconds{}: {}".format(n[0], import_message, n[1]))
        logging.info("")

def init_custom_nodes():
    extras_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "quasar_extras")
    extras_files = [
        "nodes_latent.py",
        "nodes_hypernetwork.py",
        "nodes_upscale_model.py",
        "nodes_post_processing.py",
        "nodes_mask.py",
        "nodes_compositing.py",
        "nodes_rebatch.py",
        "nodes_model_merging.py",
        "nodes_tomesd.py",
        "nodes_clip_sdxl.py",
        "nodes_canny.py",
        "nodes_freelunch.py",
        "nodes_custom_sampler.py",
        "nodes_hypertile.py",
        "nodes_model_advanced.py",
        "nodes_model_downscale.py",
        "nodes_images.py",
        "nodes_video_model.py",
        "nodes_sag.py",
        "nodes_perpneg.py",
        "nodes_stable3d.py",
        "nodes_sdupscale.py",
        "nodes_photomaker.py",
        "nodes_cond.py",
        "nodes_morphology.py",
        "nodes_stable_cascade.py",
        "nodes_differential_diffusion.py",
        "nodes_ip2p.py",
        "nodes_model_merging_model_specific.py",
    ]

    import_failed = []
    for node_file in extras_files:
        if not load_custom_node(os.path.join(extras_dir, node_file)):
            import_failed.append(node_file)

    load_custom_nodes()

    if len(import_failed) > 0:
        logging.warning("WARNING: some quasar_extras/ nodes did not import correctly. This may be because they are missing some dependencies.\n")
        for node in import_failed:
            logging.warning("IMPORT FAILED: {}".format(node))
        logging.warning("\nThis issue might be caused by new missing dependencies added the last time you updated QuasarUI.")
        if args.windows_standalone_build:
            logging.warning("Please run the update script: update/update_quasarui.bat")
        else:
            logging.warning("Please do a: pip install -r requirements.txt")
        logging.warning("")