kohya-ss

# Claude 3 Opus とめっちゃやり取りして動くようになった
# python vae_vs_taesd_gradio.py --image_dir /path/to/image/directory

import os
import argparse
import random
from PIL import Image
import torch
from diffusers import AutoencoderKL, AutoencoderTiny
import numpy as np

kohya-ss

# Claude 3 Opus にだいたい書いてもらった
# python vae_vs_taesd.py --image_dir /path/to/image/directory

import os
import argparse
import random
from PIL import Image, ImageTk
import torch
from diffusers import AutoencoderKL, AutoencoderTiny
import tkinter as tk

kohya-ss

        each_control_net_enabled = [self.control_net_enabled] * len(self.control_nets)
        for i, t in enumerate(tqdm(timesteps)):
            # ↓ ここから
            # test: chroma key like composition
            if latents.shape[0] == 4:
                # run this script with batch size 4

                # sample prompt for ANIMAGINE XL V3.0: 2nd prompt doesn't have detailes, because it is used for making mask
                # green surface of green screen --n color, artifact, object, shadow, frame --d 1
                # 1girl, serafuku, standing, cowboy shot, green background, masterpiece, best quality --n nsfw, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name --d 1

kohya-ss

import comfy
from comfy.samplers import KSAMPLER
import torch
from torchvision.transforms.functional import gaussian_blur
from comfy.k_diffusion.sampling import default_noise_sampler, get_ancestral_step, to_d, BrownianTreeNoiseSampler
from tqdm.auto import trange


@torch.no_grad()
def sample_euler_ancestral(

kohya-ss

        # how much to increase the scale at each step: .125 seems to work well (because it's 1/8?)
        # 各ステップに拡大率をどのくらい増やすか：.125がよさそう（たぶん1/8なので）
        scale_step = 0.125

        # timesteps at which to start increasing the scale: model and prompt dependent
        # 拡大を開始するtimesteps:モデルとプロンプトによる
        start_timesteps = 800

        # how many steps to wait before increasing the scale again: smaller values lead to more artifacts, also depends on the total number of steps
        # 何ステップごとに拡大するか：総ステップ数にも関係する

kohya-ss

    def forward(self, x, timesteps=None, context=None, y=None, **kwargs):
        # broadcast timesteps to batch dimension
        timesteps = timesteps.expand(x.shape[0])

        hs = []
        t_emb = get_timestep_embedding(timesteps, self.model_channels)  # , repeat_only=False)
        t_emb = t_emb.to(x.dtype)
        emb = self.time_embed(t_emb)

        assert x.shape[0] == y.shape[0], f"batch size mismatch: {x.shape[0]} != {y.shape[0]}"