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import node_helpers
import comfy.utils
import math
from typing_extensions import override
from comfy_api.latest import ComfyExtension, io
from comfy.patcher_extension import WrappersMP
import torch

class TextEncodeQwenImageEditPlusFixPixelShift(io.ComfyNode):
    @classmethod

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from typing_extensions import override
from comfy_api.latest import ComfyExtension, io
import torch

class QwenImageScaleRope(io.ComfyNode):
    @classmethod
    def define_schema(cls):
        return io.Schema(
            node_id="QwenImageScaleRope",
            category="_for_testing",

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from typing_extensions import override
from comfy_api.latest import ComfyExtension, io
import comfy

class Noise_ReferenceAlign:
    def __init__(self, seed, reference_latents, strength):
        self.seed = seed
        self.reference_latents = reference_latents
        self.strength = strength

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import numpy as np
import pandas as pd
import gradio as gr

HUNYUAN_VIDEO_DEFAULT = [
    "Hunyuan Video 544p",
    "1.0, 1.06971, 1.29073, 1.11245, 1.09596, 1.05233, 1.01415, 1.05672, 1.00848, 1.03632, 1.02974, 1.00984, 1.03028, 1.00681, 1.06614, 1.05022, 1.02592, 1.01776, 1.02985, 1.00726, 1.03727, 1.01502, 1.00992, 1.03371, 0.9976, 1.02742, 1.0093, 1.01869, 1.00815, 1.01461, 1.01152, 1.03082, 1.0061, 1.02162, 1.01999, 0.99063, 1.01186, 1.0217, 0.99947, 1.01711, 0.9904, 1.00258, 1.00878, 0.97039, 0.97686, 0.94315, 0.97728, 0.91154, 0.86139, 0.76592",
    50,
    0.24,
    -0.01,

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'''
https://github.com/Zehong-Ma/MagCache
'''

from comfy.ldm.modules.diffusionmodules.openaimodel import forward_timestep_embed, timestep_embedding, th, apply_control
import comfy.patcher_extension
import json

def linear_interpolate(data: dict, num: float, scale: float) -> float:
    if not data:

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import torch
from PIL import Image, ImageDraw, ImageFont
import numpy as np
from comfy.cli_args import args
import os

FONT_PATH = "/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf"

def flatten_dict(d):
    items = {}

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import random
import comfy.patcher_extension

kawaii_aegigoe_list = [
    "んっ…♡",
    "あぁんっ…！",
    "はぅっ…♡",
    "くぅ…ん…",
    "ひゃんっ！",
    "ふぁ…♡",

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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)