takuma104

takuma104

import torch
import torch.nn


def test_monkey_patch():
    x = torch.randn((2, 2)).cuda()
    target = torch.nn.Linear(2, 2).cuda()
    with torch.no_grad():
        y = target(x)
        assert y.shape == (2, 2)

takuma104

takuma104

takuma104

import torch
import safetensors.torch
import sys

def dump_keys(parent, suffix=''):
    for k in sorted(parent.keys()):
        if isinstance(parent[k], torch.Tensor):
            print(f'{suffix}{k} {list(parent[k].shape)}')
        else:
            dump_keys(parent[k], f'{suffix}{k}.')

takuma104

from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
from torch
from PIL import Image

def image_grid(imgs, rows, cols):
    assert len(imgs) == rows*cols

    w, h = imgs[0].size
    grid = Image.new('RGB', size=(cols*w, rows*h))
    grid_w, grid_h = grid.size

takuma104

import torch
import sys
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline

state_dict = load_file('some_lora.safetensors')
new_state_dict = {}
for key, value in state_dict.items():
    if "lora_down" in key:
        lora_name = key.split(".")[0]

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

# 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