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import torch

def new_vec(mode, chunks, x):
    xs = x.clone().chunk(chunks, dim=0)
    ref_xs = torch.cat([xi[0].unsqueeze(0).expand(xi.shape[0], -1, -1).clone() for xi in xs], dim=0).clone()

    if mode == "concat":
        new_x = x.clone()
        return torch.cat([new_x, ref_xs], dim=1)
    else:

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はい、承知いたしました。llama-cpp-python の内部構造や開発に関心のある方向けに、開発者ドキュメントを作成します。

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llama-cpp-python 開発者向けドキュメント

1. 概要 (Overview)

llama-cpp-python は、C++ で実装された高性能な LLM 推論ライブラリ llama.cpp の Python バインディングです。主な目的は、llama.cpp の持つ高速な CPU/GPU 推論能力、メモリ効率（特に量子化モデル）、そして豊富な機能を、Python 開発者が容易に利用できるようにすることです。

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import gradio as gr
import pandas as pd
import random

query_general_cache = None
query_character_cache = None

df = pd.read_csv("https://huggingface.co/datasets/furusu/aesthetic_score_danbooru2024/resolve/main/part/aes6_5.csv")
#df = pd.read_csv("aes6_5.csv")
df[["tags", "characters"]] = df[["tags", "characters"]].astype(str)

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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

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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]                 --     

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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"

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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]])