takuma104

# Script for converting a HF Diffusers saved pipeline to a ControlNet checkpoint.
# *Only* converts the ControlNet.
# Does not convert optimizer state or any other thing.

import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file

takuma104

import math
import safetensors
import torch
from diffusers import DiffusionPipeline

"""
Kohya's LoRA format Loader for Diffusers

Usage:
```py

takuma104

Usage

from clip_text_custom_embedder import text_embeddings
from diffusers import StableDiffusionPipeline
import torch

takuma104

import torch
import json
from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
from diffusers.models.attention import Attention
from diffusers.models.attention_processor import (
    AttnProcessor2_0,
    XFormersAttnProcessor,
    LoRAAttnProcessor2_0,
    LoRAXFormersAttnProcessor,
)

takuma104

import math

import numpy as np
import safetensors
import torch
import torch.nn as nn
from PIL import Image

from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler, StableDiffusionPipeline
from diffusers.utils import _get_model_file, DIFFUSERS_CACHE

takuma104

import torch
import json
from diffusers import StableDiffusionPipeline

def print_memory_usage(width, height, batch, xformers, with_lora):
    def on_off(cond):
        return 'ON' if cond else 'OFF'
    mem_bytes = torch.cuda.max_memory_allocated()
    mem_MB = int(mem_bytes/(10**6))
    dict = {'width':width, 'height':height, 'batch':batch, 'xformers':on_off(xformers), 

takuma104

import torch
from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
import matplotlib.pyplot as plt
from PIL import Image
import numpy as np

plt.rcParams["figure.figsize"] = (10,5)
plt.rcParams['figure.facecolor'] = 'white'

def generate_tuxedo_cat_picture(fn_prefix, seed=0):

takuma104

import torch
import sys
from safetensors.torch import load_file

if __name__ == "__main__":
    filename = sys.argv[1]
    state_dict = load_file(filename)
    for key, value in state_dict.items():
        if "lora_down" in key:
            lora_name = key.split(".")[0]

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takuma104

import json
import struct
import sys
from pprint import pprint

def parse_safetensors_header(file_path):
    # modified from https://huggingface.co/docs/safetensors/metadata_parsing
    with open(file_path, 'rb') as f:
        header_bytes = f.read(8)
        # Interpret the bytes as a little-endian unsigned 64-bit integer