laksjdjf

===========================================================================================================================================================
Layer (type (var_name))                                 Input Shape               Output Shape              Param #                   Kernel Shape
===========================================================================================================================================================
StableCascadeUnet (StableCascadeUnet)                   --                        [1, 4, 256, 256]          --                        3
├─Linear (clip_txt_pooled_mapper)                       [1, 1, 1280]              [1, 1, 5120]              6,558,720                 --
├─LayerNorm (clip_norm)                                 [1, 4, 1280]              [1, 4, 1280]              --                        --
├─Sequential (embedding)                                [1, 4, 256, 256]          [1, 320, 128, 128]        --                        --
│    └─PixelUnshuf

laksjdjf

===========================================================================================================================================================
Layer (type (var_name))                                 Input Shape               Output Shape              Param #                   Kernel Shape
===========================================================================================================================================================
StableCascadeUnet (StableCascadeUnet)                   [2, 16, 24, 24]           [2, 16, 24, 24]           8,923,136                 3
├─Linear (clip_txt_pooled_mapper)                       [2, 77, 1280]             [2, 77, 8192]             10,493,952                --
├─LayerNorm (clip_norm)                                 [2, 308, 2048]            [2, 308, 2048]            --                        --
├─Sequential (embedding)                                [2, 16, 24, 24]           [2, 2048, 24, 24]         --                        --
│    └─PixelUnshuf

laksjdjf

'''
load from sampling/custom_sampling/scheulers
input text like "999,893,...,156"
connect to SamplerCustom
'''

import torch

class TextScheduler:
    @classmethod

laksjdjf

'''
    Implementation of RCFG in https://arxiv.org/abs/2312.12491
    Node is in sampling/custom_sampling/samplers
    original_latent is OPTIONAL
    If original_latent is set, it is Self-Negative else Onetime-Negative
    cfg is recommendet near 1.0 (KSAMPLER"s cfg is ignored)
    delta is よくわかんない
'''

from comfy.samplers import KSAMPLER

laksjdjf

'''
https://arxiv.org/abs/2312.00858
1. put this file in ComfyUI/custom_nodes
2. load node from <loaders>

start_step, end_step: apply this method when the timestep is between start_step and end_step
cache_interval: interval of caching (1 means no caching)
cache_depth: depth of caching
'''

laksjdjf

import comfy
from comfy.samplers import KSAMPLER
import torch
from torchvision.transforms.functional import gaussian_blur
from comfy.k_diffusion.sampling import default_noise_sampler, get_ancestral_step, to_d, BrownianTreeNoiseSampler
from tqdm.auto import trange

def interpolate(x, size, unsharp_strength=0.0, unsharp_kernel_size=3, unsharp_sigma=0.5, unsharp=False, mode="bicubic", align_corners=False):
    x = torch.nn.functional.interpolate(x, size=size, mode=mode, align_corners=align_corners)
    if unsharp_strength > 0 and unsharp:

laksjdjf

import comfy
from comfy.samplers import KSAMPLER
import torch
from comfy.k_diffusion.sampling import default_noise_sampler, get_ancestral_step, to_d, BrownianTreeNoiseSampler
from tqdm.auto import trange

@torch.no_grad()
def sample_euler_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, upscale_ratio=2.0, start_step=5, end_step=15, upscale_n_step=3):
    """Ancestral sampling with Euler method steps."""
    extra_args = {} if extra_args is None else extra_args

laksjdjf

'''
https://gist.github.com/kohya-ss/3f774da220df102548093a7abc8538ed
1. put this file in ComfyUI/custom_nodes
2. load node from <loaders>
'''

import torch
from comfy.ldm.modules.diffusionmodules.openaimodel import forward_timestep_embed, timestep_embedding, th

def apply_control(h, control, name):

laksjdjf

================================================================================================================================================================
Layer (type (var_name))                                      Input Shape               Output Shape              Param #                   Kernel Shape
================================================================================================================================================================
Transformer2DModel (Transformer2DModel)                      [1, 4, 32, 32]            [1, 8, 32, 32]            2,304                     --
├─PatchEmbed (pos_embed)                                     [1, 4, 32, 32]            [1, 256, 1152]            --                        --
│    └─Conv2d (proj)                                         [1, 4, 32, 32]            [1, 1152, 16, 16]         19,584                    [2, 2]
├─AdaLayerNormSingle (adaln_single)                          [1]                       [1, 6912]                 --     

laksjdjf

# ref:https://github.com/v0xie/sd-webui-cads

'''
1. put this file in ComfyUI/custom_nodes
2. load node from <loader>
'''

import torch
import numpy as np
import copy