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1girl,5534972
highres,4664611
solo,4604237
long_hair,3972398
breasts,3142271
commentary_request,3080500
looking_at_viewer,2988390
blush,2705742
smile,2598137
open_mouth,2140390

laksjdjf

import numpy as np
import matplotlib.pyplot as plt

def sigmoid(x):
    return 1 / (1 + np.exp(-x))

def inverse_sigmoid(y):
    return np.log(y / (1 - y))

# 逆シグモイド関数の微分

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================================================================================================================================================================
Layer (type (var_name))                                      Input Shape               Output Shape              Param #                   Kernel Shape
================================================================================================================================================================
SD3Transformer2DModel (SD3Transformer2DModel)                --                        [1, 16, 128, 128]         --                        --
├─PatchEmbed (pos_embed)                                     [1, 16, 128, 128]         [1, 4096, 1536]           --                        --
│    └─Conv2d (proj)                                         [1, 16, 128, 128]         [1, 1536, 64, 64]         99,840                    [2, 2]
├─CombinedTimestepTextProjEmbeddings (time_text_embed)       [1]                       [1, 1536]                 --     

laksjdjf

from PIL import Image
import hpsv2
import torch

class HPSv2:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "images": ("IMAGE", ),

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import torch
from einops import rearrange, repeat

def block_to_key(block):
    if block[0] == "input":
        return "in" + str(block[1])
    elif block[0] == "output":
        return "out" + str(block[1])
    elif block[0] == "middle":
        return "mid"

laksjdjf

import gradio as gr
import json
import requests
import argparse
from dataclasses import dataclass

############### utils ###############
BAN_TOKENS = ["<|END_OF_TURN_TOKEN|>"] # command -r 用の回避トークン

parser = argparse.ArgumentParser()

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

@torch.no_grad()
def sampler_tcd(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None, gamma=None):
    extra_args = {} if extra_args is None else extra_args
    noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
    s_in = x.new_ones([x.shape[0]])

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# https://github.com/huggingface/transformers/blob/838b87abe231fd70be5132088d0dee72a7bb8d62/src/transformers/models/opt/modeling_opt.py#L147
"""
model = AutoModelForCausalLM.from_pretrained("p1atdev/dart-v1-sft")
apply_hook(model)
"""


import torch
import torch.nn as nn
def forward_hooker(self):

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# https://huggingface.co/shadowlilac/aesthetic-shadow-v2

from transformers import pipeline
import torch
from PIL import Image
from comfy.ldm.modules.attention import optimized_attention
    
def optimized_forward(self):
    def forward(hidden_states, head_mask = None, output_attentions = False):
        query = self.query(hidden_states)

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def make_unet_conversion_map():
    unet_conversion_map_layer = []
    # unet
    # https://github.com/kohya-ss/sd-scripts/blob/2d7389185c021bc527b414563c245c5489d6328a/library/sdxl_model_util.py#L293
    for i in range(3):  # num_blocks is 3 in sdxl
        # loop over downblocks/upblocks
        for j in range(2):
            # loop over resnets/attentions for downblocks
            hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}."
            sd_down_res_prefix = f"input_blocks.{3*i + j + 1}.0."