aredden

import torch

torch.set_printoptions(precision=4, sci_mode=False)
import triton
import triton.language as tl
from torch import Tensor


def quanitze_fp8_tensorwise(x: torch.Tensor, dtype=torch.float8_e4m3fn):
    scale = x.abs().max() / torch.finfo(dtype).max

aredden

from typing import List, Any
import enum
from cuda import cudart


CUDART_VERSION = 12020

CUDA_EGL_MAX_PLANES = 3

CUDA_IPC_HANDLE_SIZE = 64

aredden

import numpy as np
import torch
from torchvision.transforms import functional as F
from PIL import Image

image_path = "./117.webp"
num_images = 32
total_rot = 360
rot_step = total_rot / num_images

aredden

from functools import wraps
from typing import Any, Dict, Iterable, Optional, Tuple, TypeVar
import torch
T = TypeVar("T", bound=callable)

ref_map = {
    torch.float64: [torch.float16,torch.float32,torch.bfloat16,torch.half],
    torch.float32: [torch.float16,torch.bfloat16,torch.half],
    torch.float16: [],
    torch.bfloat16: [],

aredden

import torch
from einops import rearrange

_magma_table = [
    (0.001462, 0.000466, 0.013866),
    (0.002258, 0.001295, 0.018331),
    (0.003279, 0.002305, 0.023708),
    (0.004512, 0.003490, 0.029965),
    (0.005950, 0.004843, 0.037130),
    (0.007588, 0.006356, 0.044973),

aredden

from pathlib import Path
from argparse import ArgumentParser
from typing import Optional
from pydantic import BaseModel

class Args(BaseModel):
    input_path: Path
    recursive: bool = False
    output_file: Optional[Path]
    extensions: list[str] = ['jpeg','jpg','png','webp']

aredden

import argparse
from typing import List, Union
import cv2, os, pathlib
from PIL import Image
from loguru import logger
from more_itertools import flatten
from mediapipe.python.solutions.face_detection import FaceDetection
import numpy as np
from traceback import format_exception

aredden

from typing import AsyncGenerator, List, Coroutine, Any, Callable, Union
import asyncio
from loguru import logger


async def async_limit_iterator(
    limit=8,
    jobs: List[Any] = [],
    coroutine_fn: Callable[[Any], Coroutine[Any, None, None]] = None,
    throw_exceptions: bool = False,

aredden

aredden

const { ExifTool, Tags } = require('exiftool-vendored');
/**
 * Function to write makernote and exif data to a PNG file.
 * @param {string} filePath - Path to the PNG file.
 * @param {string} makerNote - Makernote exif key data to write.
 * @param {string} comment - Comment exif key data to write.
 * @return {Promise<boolean>} - True if successful, false otherwise.
 */
export const writeMetadata = async (filePath, makerNote, comment) => {
	const tool = new ExifTool();