gen_img_diffusers.py

"""
VGG(
  (features): Sequential(
    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): ReLU(inplace=True)
    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (3): ReLU(inplace=True)
    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (6): ReLU(inplace=True)
    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (8): ReLU(inplace=True)
    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (11): ReLU(inplace=True)
    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (13): ReLU(inplace=True)
    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (15): ReLU(inplace=True)
    (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (18): ReLU(inplace=True)
    (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (20): ReLU(inplace=True)
    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (22): ReLU(inplace=True)
    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (25): ReLU(inplace=True)
    (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (27): ReLU(inplace=True)
    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (29): ReLU(inplace=True)
    (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))
  (classifier): Sequential(
    (0): Linear(in_features=25088, out_features=4096, bias=True)
    (1): ReLU(inplace=True)
    (2): Dropout(p=0.5, inplace=False)
    (3): Linear(in_features=4096, out_features=4096, bias=True)
    (4): ReLU(inplace=True)
    (5): Dropout(p=0.5, inplace=False)
    (6): Linear(in_features=4096, out_features=1000, bias=True)
  )
)
"""

import json
from typing import Any, List, NamedTuple, Optional, Tuple, Union, Callable
import glob
import importlib
import inspect
import time
import zipfile
from diffusers.utils import deprecate
from diffusers.configuration_utils import FrozenDict
import argparse
import math
import os
import random
import re

import diffusers
import numpy as np
import torch
import torchvision
from diffusers import (AutoencoderKL, DDPMScheduler,
                       EulerAncestralDiscreteScheduler, DPMSolverMultistepScheduler, DPMSolverSinglestepScheduler,
                       LMSDiscreteScheduler, PNDMScheduler, DDIMScheduler, EulerDiscreteScheduler, HeunDiscreteScheduler,
                       KDPM2DiscreteScheduler, KDPM2AncestralDiscreteScheduler,
                       UNet2DConditionModel, StableDiffusionPipeline)
from einops import rearrange
from torch import einsum
from tqdm import tqdm
from torchvision import transforms
from transformers import CLIPTextModel, CLIPTokenizer, CLIPModel, CLIPTextConfig
import PIL
from PIL import Image
from PIL.PngImagePlugin import PngInfo

import library.model_util as model_util
import tools.original_control_net as original_control_net
from tools.original_control_net import ControlNetInfo

# Tokenizer: checkpointから読み込むのではなくあらかじめ提供されているものを使う
TOKENIZER_PATH = "openai/clip-vit-large-patch14"
V2_STABLE_DIFFUSION_PATH = "stabilityai/stable-diffusion-2"     # ここからtokenizerだけ使う

DEFAULT_TOKEN_LENGTH = 75

# scheduler:
SCHEDULER_LINEAR_START = 0.00085
SCHEDULER_LINEAR_END = 0.0120
SCHEDULER_TIMESTEPS = 1000
SCHEDLER_SCHEDULE = 'scaled_linear'

# その他の設定
LATENT_CHANNELS = 4
DOWNSAMPLING_FACTOR = 8

# CLIP_ID_L14_336 = "openai/clip-vit-large-patch14-336"

# CLIP guided SD関連
CLIP_MODEL_PATH = "laion/CLIP-ViT-B-32-laion2B-s34B-b79K"
FEATURE_EXTRACTOR_SIZE = (224, 224)
FEATURE_EXTRACTOR_IMAGE_MEAN = [0.48145466, 0.4578275, 0.40821073]
FEATURE_EXTRACTOR_IMAGE_STD = [0.26862954, 0.26130258, 0.27577711]

VGG16_IMAGE_MEAN = [0.485, 0.456, 0.406]
VGG16_IMAGE_STD = [0.229, 0.224, 0.225]
VGG16_INPUT_RESIZE_DIV = 4

# CLIP特徴量の取得時にcutoutを使うか：使う場合にはソースを書き換えてください
NUM_CUTOUTS = 4
USE_CUTOUTS = False

# region モジュール入れ替え部
"""
高速化のためのモジュール入れ替え
"""

# FlashAttentionを使うCrossAttention
# based on https://github.com/lucidrains/memory-efficient-attention-pytorch/blob/main/memory_efficient_attention_pytorch/flash_attention.py
# LICENSE MIT https://github.com/lucidrains/memory-efficient-attention-pytorch/blob/main/LICENSE

# constants

EPSILON = 1e-6

# helper functions


def exists(val):
  return val is not None


def default(val, d):
  return val if exists(val) else d

# flash attention forwards and backwards

# https://arxiv.org/abs/2205.14135


class FlashAttentionFunction(torch.autograd.Function):
  @ staticmethod
  @ torch.no_grad()
  def forward(ctx, q, k, v, mask, causal, q_bucket_size, k_bucket_size):
    """ Algorithm 2 in the paper """

    device = q.device
    dtype = q.dtype
    max_neg_value = -torch.finfo(q.dtype).max
    qk_len_diff = max(k.shape[-2] - q.shape[-2], 0)

    o = torch.zeros_like(q)
    all_row_sums = torch.zeros((*q.shape[:-1], 1), dtype=dtype, device=device)
    all_row_maxes = torch.full((*q.shape[:-1], 1), max_neg_value, dtype=dtype, device=device)

    scale = (q.shape[-1] ** -0.5)

    if not exists(mask):
      mask = (None,) * math.ceil(q.shape[-2] / q_bucket_size)
    else:
      mask = rearrange(mask, 'b n -> b 1 1 n')
      mask = mask.split(q_bucket_size, dim=-1)

    row_splits = zip(
        q.split(q_bucket_size, dim=-2),
        o.split(q_bucket_size, dim=-2),
        mask,
        all_row_sums.split(q_bucket_size, dim=-2),
        all_row_maxes.split(q_bucket_size, dim=-2),
    )

    for ind, (qc, oc, row_mask, row_sums, row_maxes) in enumerate(row_splits):
      q_start_index = ind * q_bucket_size - qk_len_diff

      col_splits = zip(
          k.split(k_bucket_size, dim=-2),
          v.split(k_bucket_size, dim=-2),
      )

      for k_ind, (kc, vc) in enumerate(col_splits):
        k_start_index = k_ind * k_bucket_size

        attn_weights = einsum('... i d, ... j d -> ... i j', qc, kc) * scale

        if exists(row_mask):
          attn_weights.masked_fill_(~row_mask, max_neg_value)

        if causal and q_start_index < (k_start_index + k_bucket_size - 1):
          causal_mask = torch.ones((qc.shape[-2], kc.shape[-2]), dtype=torch.bool,
                                   device=device).triu(q_start_index - k_start_index + 1)
          attn_weights.masked_fill_(causal_mask, max_neg_value)

        block_row_maxes = attn_weights.amax(dim=-1, keepdims=True)
        attn_weights -= block_row_maxes
        exp_weights = torch.exp(attn_weights)

        if exists(row_mask):
          exp_weights.masked_fill_(~row_mask, 0.)

        block_row_sums = exp_weights.sum(dim=-1, keepdims=True).clamp(min=EPSILON)

        new_row_maxes = torch.maximum(block_row_maxes, row_maxes)

        exp_values = einsum('... i j, ... j d -> ... i d', exp_weights, vc)

        exp_row_max_diff = torch.exp(row_maxes - new_row_maxes)
        exp_block_row_max_diff = torch.exp(block_row_maxes - new_row_maxes)

        new_row_sums = exp_row_max_diff * row_sums + exp_block_row_max_diff * block_row_sums

        oc.mul_((row_sums / new_row_sums) * exp_row_max_diff).add_((exp_block_row_max_diff / new_row_sums) * exp_values)

        row_maxes.copy_(new_row_maxes)
        row_sums.copy_(new_row_sums)

    ctx.args = (causal, scale, mask, q_bucket_size, k_bucket_size)
    ctx.save_for_backward(q, k, v, o, all_row_sums, all_row_maxes)

    return o

  @ staticmethod
  @ torch.no_grad()
  def backward(ctx, do):
    """ Algorithm 4 in the paper """

    causal, scale, mask, q_bucket_size, k_bucket_size = ctx.args
    q, k, v, o, l, m = ctx.saved_tensors

    device = q.device

    max_neg_value = -torch.finfo(q.dtype).max
    qk_len_diff = max(k.shape[-2] - q.shape[-2], 0)

    dq = torch.zeros_like(q)
    dk = torch.zeros_like(k)
    dv = torch.zeros_like(v)

    row_splits = zip(
        q.split(q_bucket_size, dim=-2),
        o.split(q_bucket_size, dim=-2),
        do.split(q_bucket_size, dim=-2),
        mask,
        l.split(q_bucket_size, dim=-2),
        m.split(q_bucket_size, dim=-2),
        dq.split(q_bucket_size, dim=-2)
    )

    for ind, (qc, oc, doc, row_mask, lc, mc, dqc) in enumerate(row_splits):
      q_start_index = ind * q_bucket_size - qk_len_diff

      col_splits = zip(
          k.split(k_bucket_size, dim=-2),
          v.split(k_bucket_size, dim=-2),
          dk.split(k_bucket_size, dim=-2),
          dv.split(k_bucket_size, dim=-2),
      )

      for k_ind, (kc, vc, dkc, dvc) in enumerate(col_splits):
        k_start_index = k_ind * k_bucket_size

        attn_weights = einsum('... i d, ... j d -> ... i j', qc, kc) * scale

        if causal and q_start_index < (k_start_index + k_bucket_size - 1):
          causal_mask = torch.ones((qc.shape[-2], kc.shape[-2]), dtype=torch.bool,
                                   device=device).triu(q_start_index - k_start_index + 1)
          attn_weights.masked_fill_(causal_mask, max_neg_value)

        exp_attn_weights = torch.exp(attn_weights - mc)

        if exists(row_mask):
          exp_attn_weights.masked_fill_(~row_mask, 0.)

        p = exp_attn_weights / lc

        dv_chunk = einsum('... i j, ... i d -> ... j d', p, doc)
        dp = einsum('... i d, ... j d -> ... i j', doc, vc)

        D = (doc * oc).sum(dim=-1, keepdims=True)
        ds = p * scale * (dp - D)

        dq_chunk = einsum('... i j, ... j d -> ... i d', ds, kc)
        dk_chunk = einsum('... i j, ... i d -> ... j d', ds, qc)

        dqc.add_(dq_chunk)
        dkc.add_(dk_chunk)
        dvc.add_(dv_chunk)

    return dq, dk, dv, None, None, None, None


def replace_unet_modules(unet: diffusers.models.unet_2d_condition.UNet2DConditionModel, mem_eff_attn, xformers):
  if mem_eff_attn:
    replace_unet_cross_attn_to_memory_efficient()
  elif xformers:
    replace_unet_cross_attn_to_xformers()


def replace_unet_cross_attn_to_memory_efficient():
  print("Replace CrossAttention.forward to use NAI style Hypernetwork and FlashAttention")
  flash_func = FlashAttentionFunction

  def forward_flash_attn(self, x, context=None, mask=None):
    q_bucket_size = 512
    k_bucket_size = 1024

    h = self.heads
    q = self.to_q(x)

    context = context if context is not None else x
    context = context.to(x.dtype)

    if hasattr(self, 'hypernetwork') and self.hypernetwork is not None:
      context_k, context_v = self.hypernetwork.forward(x, context)
      context_k = context_k.to(x.dtype)
      context_v = context_v.to(x.dtype)
    else:
      context_k = context
      context_v = context

    k = self.to_k(context_k)
    v = self.to_v(context_v)
    del context, x

    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=h), (q, k, v))

    out = flash_func.apply(q, k, v, mask, False, q_bucket_size, k_bucket_size)

    out = rearrange(out, 'b h n d -> b n (h d)')

    # diffusers 0.7.0~
    out = self.to_out[0](out)
    out = self.to_out[1](out)
    return out

  diffusers.models.attention.CrossAttention.forward = forward_flash_attn


def replace_unet_cross_attn_to_xformers():
  print("Replace CrossAttention.forward to use NAI style Hypernetwork and xformers")
  try:
    import xformers.ops
  except ImportError:
    raise ImportError("No xformers / xformersがインストールされていないようです")

  def forward_xformers(self, x, context=None, mask=None):
    h = self.heads
    q_in = self.to_q(x)

    context = default(context, x)
    context = context.to(x.dtype)

    if hasattr(self, 'hypernetwork') and self.hypernetwork is not None:
      context_k, context_v = self.hypernetwork.forward(x, context)
      context_k = context_k.to(x.dtype)
      context_v = context_v.to(x.dtype)
    else:
      context_k = context
      context_v = context

    k_in = self.to_k(context_k)
    v_in = self.to_v(context_v)

    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b n h d', h=h), (q_in, k_in, v_in))
    del q_in, k_in, v_in

    q = q.contiguous()
    k = k.contiguous()
    v = v.contiguous()
    out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None)        # 最適なのを選んでくれる

    out = rearrange(out, 'b n h d -> b n (h d)', h=h)

    # diffusers 0.7.0~
    out = self.to_out[0](out)
    out = self.to_out[1](out)
    return out

  diffusers.models.attention.CrossAttention.forward = forward_xformers
# endregion

# region 画像生成の本体：lpw_stable_diffusion.py （ASL）からコピーして修正
# https://github.com/huggingface/diffusers/blob/main/examples/community/lpw_stable_diffusion.py
# Pipelineだけ独立して使えないのと機能追加するのとでコピーして修正


class PipelineLike():
  r"""
  Pipeline for text-to-image generation using Stable Diffusion without tokens length limit, and support parsing
  weighting in prompt.
  This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
  library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
  Args:
      vae ([`AutoencoderKL`]):
          Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
      text_encoder ([`CLIPTextModel`]):
          Frozen text-encoder. Stable Diffusion uses the text portion of
          [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
          the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
      tokenizer (`CLIPTokenizer`):
          Tokenizer of class
          [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
      unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
      scheduler ([`SchedulerMixin`]):
          A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
          [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
      safety_checker ([`StableDiffusionSafetyChecker`]):
          Classification module that estimates whether generated images could be considered offensive or harmful.
          Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
      feature_extractor ([`CLIPFeatureExtractor`]):
          Model that extracts features from generated images to be used as inputs for the `safety_checker`.
  """

  def __init__(
      self,
      device,
      vae: AutoencoderKL,
      text_encoder: CLIPTextModel,
      tokenizer: CLIPTokenizer,
      unet: UNet2DConditionModel,
      scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
      clip_skip: int,
      clip_model: CLIPModel,
      clip_guidance_scale: float,
      clip_image_guidance_scale: float,
      vgg16_model: torchvision.models.VGG,
      vgg16_guidance_scale: float,
      vgg16_layer_no: int,
      # safety_checker: StableDiffusionSafetyChecker,
      # feature_extractor: CLIPFeatureExtractor,
  ):
    super().__init__()
    self.device = device
    self.clip_skip = clip_skip

    if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
      deprecation_message = (
          f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
          f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
          "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
          " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
          " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
          " file"
      )
      deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
      new_config = dict(scheduler.config)
      new_config["steps_offset"] = 1
      scheduler._internal_dict = FrozenDict(new_config)

    if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
      deprecation_message = (
          f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
          " `clip_sample` should be set to False in the configuration file. Please make sure to update the"
          " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
          " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
          " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
      )
      deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
      new_config = dict(scheduler.config)
      new_config["clip_sample"] = False
      scheduler._internal_dict = FrozenDict(new_config)

    self.vae = vae
    self.text_encoder = text_encoder
    self.tokenizer = tokenizer
    self.unet = unet
    self.scheduler = scheduler
    self.safety_checker = None

    # Textual Inversion
    self.token_replacements = {}

    # CLIP guidance
    self.clip_guidance_scale = clip_guidance_scale
    self.clip_image_guidance_scale = clip_image_guidance_scale
    self.clip_model = clip_model
    self.normalize = transforms.Normalize(mean=FEATURE_EXTRACTOR_IMAGE_MEAN, std=FEATURE_EXTRACTOR_IMAGE_STD)
    self.make_cutouts = MakeCutouts(FEATURE_EXTRACTOR_SIZE)

    # VGG16 guidance
    self.vgg16_guidance_scale = vgg16_guidance_scale
    if self.vgg16_guidance_scale > 0.0:
      return_layers = {f'{vgg16_layer_no}': 'feat'}
      self.vgg16_feat_model = torchvision.models._utils.IntermediateLayerGetter(vgg16_model.features, return_layers=return_layers)
      self.vgg16_normalize = transforms.Normalize(mean=VGG16_IMAGE_MEAN, std=VGG16_IMAGE_STD)

    # ControlNet
    self.control_nets: List[ControlNetInfo] = []

  # Textual Inversion
  def add_token_replacement(self, target_token_id, rep_token_ids):
    self.token_replacements[target_token_id] = rep_token_ids

  def replace_token(self, tokens):
    new_tokens = []
    for token in tokens:
      if token in self.token_replacements:
        new_tokens.extend(self.token_replacements[token])
      else:
        new_tokens.append(token)
    return new_tokens

  def set_control_nets(self, ctrl_nets):
    self.control_nets = ctrl_nets

  # region xformersとか使う部分：独自に書き換えるので関係なし

  def enable_xformers_memory_efficient_attention(self):
    r"""
    Enable memory efficient attention as implemented in xformers.
    When this option is enabled, you should observe lower GPU memory usage and a potential speed up at inference
    time. Speed up at training time is not guaranteed.
    Warning: When Memory Efficient Attention and Sliced attention are both enabled, the Memory Efficient Attention
    is used.
    """
    self.unet.set_use_memory_efficient_attention_xformers(True)

  def disable_xformers_memory_efficient_attention(self):
    r"""
    Disable memory efficient attention as implemented in xformers.
    """
    self.unet.set_use_memory_efficient_attention_xformers(False)

  def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
    r"""
    Enable sliced attention computation.
    When this option is enabled, the attention module will split the input tensor in slices, to compute attention
    in several steps. This is useful to save some memory in exchange for a small speed decrease.
    Args:
        slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
            When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
            a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
            `attention_head_dim` must be a multiple of `slice_size`.
    """
    if slice_size == "auto":
      # half the attention head size is usually a good trade-off between
      # speed and memory
      slice_size = self.unet.config.attention_head_dim // 2
    self.unet.set_attention_slice(slice_size)

  def disable_attention_slicing(self):
    r"""
    Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
    back to computing attention in one step.
    """
    # set slice_size = `None` to disable `attention slicing`
    self.enable_attention_slicing(None)

  def enable_sequential_cpu_offload(self):
    r"""
    Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
    text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
    `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
    """
    # accelerateが必要になるのでとりあえず省略
    raise NotImplementedError("cpu_offload is omitted.")
    # if is_accelerate_available():
    #   from accelerate import cpu_offload
    # else:
    #   raise ImportError("Please install accelerate via `pip install accelerate`")

    # device = self.device

    # for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
    #   if cpu_offloaded_model is not None:
    #     cpu_offload(cpu_offloaded_model, device)
# endregion

  @torch.no_grad()
  def __call__(
      self,
      prompt: Union[str, List[str]],
      negative_prompt: Optional[Union[str, List[str]]] = None,
      init_image: Union[torch.FloatTensor, PIL.Image.Image, List[PIL.Image.Image]] = None,
      mask_image: Union[torch.FloatTensor, PIL.Image.Image, List[PIL.Image.Image]] = None,
      height: int = 512,
      width: int = 512,
      num_inference_steps: int = 50,
      guidance_scale: float = 7.5,
      negative_scale: float = None,
      strength: float = 0.8,
      # num_images_per_prompt: Optional[int] = 1,
      eta: float = 0.0,
      generator: Optional[torch.Generator] = None,
      latents: Optional[torch.FloatTensor] = None,
      max_embeddings_multiples: Optional[int] = 3,
      output_type: Optional[str] = "pil",
      # return_dict: bool = True,
      callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
      is_cancelled_callback: Optional[Callable[[], bool]] = None,
      callback_steps: Optional[int] = 1,
      img2img_noise=None,
      clip_prompts=None,
      clip_guide_images=None,
      **kwargs,
  ):
    r"""
    Function invoked when calling the pipeline for generation.
    Args:
        prompt (`str` or `List[str]`):
            The prompt or prompts to guide the image generation.
        negative_prompt (`str` or `List[str]`, *optional*):
            The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
            if `guidance_scale` is less than `1`).
        init_image (`torch.FloatTensor` or `PIL.Image.Image`):
            `Image`, or tensor representing an image batch, that will be used as the starting point for the
            process.
        mask_image (`torch.FloatTensor` or `PIL.Image.Image`):
            `Image`, or tensor representing an image batch, to mask `init_image`. White pixels in the mask will be
            replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a
            PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should
            contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`.
        height (`int`, *optional*, defaults to 512):
            The height in pixels of the generated image.
        width (`int`, *optional*, defaults to 512):
            The width in pixels of the generated image.
        num_inference_steps (`int`, *optional*, defaults to 50):
            The number of denoising steps. More denoising steps usually lead to a higher quality image at the
            expense of slower inference.
        guidance_scale (`float`, *optional*, defaults to 7.5):
            Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
            `guidance_scale` is defined as `w` of equation 2. of [Imagen
            Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
            1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
            usually at the expense of lower image quality.
        strength (`float`, *optional*, defaults to 0.8):
            Conceptually, indicates how much to transform the reference `init_image`. Must be between 0 and 1.
            `init_image` will be used as a starting point, adding more noise to it the larger the `strength`. The
            number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added
            noise will be maximum and the denoising process will run for the full number of iterations specified in
            `num_inference_steps`. A value of 1, therefore, essentially ignores `init_image`.
        num_images_per_prompt (`int`, *optional*, defaults to 1):
            The number of images to generate per prompt.
        eta (`float`, *optional*, defaults to 0.0):
            Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
            [`schedulers.DDIMScheduler`], will be ignored for others.
        generator (`torch.Generator`, *optional*):
            A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
            deterministic.
        latents (`torch.FloatTensor`, *optional*):
            Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
            generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
            tensor will ge generated by sampling using the supplied random `generator`.
        max_embeddings_multiples (`int`, *optional*, defaults to `3`):
            The max multiple length of prompt embeddings compared to the max output length of text encoder.
        output_type (`str`, *optional*, defaults to `"pil"`):
            The output format of the generate image. Choose between
            [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
        return_dict (`bool`, *optional*, defaults to `True`):
            Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
            plain tuple.
        callback (`Callable`, *optional*):
            A function that will be called every `callback_steps` steps during inference. The function will be
            called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
        is_cancelled_callback (`Callable`, *optional*):
            A function that will be called every `callback_steps` steps during inference. If the function returns
            `True`, the inference will be cancelled.
        callback_steps (`int`, *optional*, defaults to 1):
            The frequency at which the `callback` function will be called. If not specified, the callback will be
            called at every step.
    Returns:
        `None` if cancelled by `is_cancelled_callback`,
        [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
        [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
        When returning a tuple, the first element is a list with the generated images, and the second element is a
        list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
        (nsfw) content, according to the `safety_checker`.
    """
    num_images_per_prompt = 1              # fixed

    if isinstance(prompt, str):
      batch_size = 1
      prompt = [prompt]
    elif isinstance(prompt, list):
      batch_size = len(prompt)
    else:
      raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")

    if strength < 0 or strength > 1:
      raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")

    if height % 8 != 0 or width % 8 != 0:
      raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")

    if (callback_steps is None) or (
        callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
    ):
      raise ValueError(
          f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
          f" {type(callback_steps)}."
      )

    # get prompt text embeddings

    # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
    # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
    # corresponds to doing no classifier free guidance.
    do_classifier_free_guidance = guidance_scale > 1.0

    if not do_classifier_free_guidance and negative_scale is not None:
      print(f"negative_scale is ignored if guidance scalle <= 1.0")
      negative_scale = None

    # get unconditional embeddings for classifier free guidance
    if negative_prompt is None:
      negative_prompt = [""] * batch_size
    elif isinstance(negative_prompt, str):
      negative_prompt = [negative_prompt] * batch_size
    if batch_size != len(negative_prompt):
      raise ValueError(
          f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
          f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
          " the batch size of `prompt`."
      )

    text_embeddings, uncond_embeddings, prompt_tokens = get_weighted_text_embeddings(
        pipe=self,
        prompt=prompt,
        uncond_prompt=negative_prompt if do_classifier_free_guidance else None,
        max_embeddings_multiples=max_embeddings_multiples,
        clip_skip=self.clip_skip,
        **kwargs,
    )

    if negative_scale is not None:
      _, real_uncond_embeddings, _ = get_weighted_text_embeddings(
          pipe=self,
          prompt=prompt,                                   # こちらのトークン長に合わせてuncondを作るので75トークン超で必須
          uncond_prompt=[""]*batch_size,
          max_embeddings_multiples=max_embeddings_multiples,
          clip_skip=self.clip_skip,
          **kwargs,
      )

    if do_classifier_free_guidance:
      if negative_scale is None:
        text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
      else:
        text_embeddings = torch.cat([uncond_embeddings, text_embeddings, real_uncond_embeddings])

    # CLIP guidanceで使用するembeddingsを取得する
    if self.clip_guidance_scale > 0:
      clip_text_input = prompt_tokens
      if clip_text_input.shape[1] > self.tokenizer.model_max_length:
          # TODO 75文字を超えたら警告を出す？
        print("trim text input", clip_text_input.shape)
        clip_text_input = torch.cat([clip_text_input[:, :self.tokenizer.model_max_length-1],
                                    clip_text_input[:, -1].unsqueeze(1)], dim=1)
        print("trimmed", clip_text_input.shape)

      for i, clip_prompt in enumerate(clip_prompts):
        if clip_prompt is not None:         # clip_promptがあれば上書きする
          clip_text_input[i] = self.tokenizer(clip_prompt, padding="max_length", max_length=self.tokenizer.model_max_length,
                                              truncation=True, return_tensors="pt",).input_ids.to(self.device)

      text_embeddings_clip = self.clip_model.get_text_features(clip_text_input)
      text_embeddings_clip = text_embeddings_clip / text_embeddings_clip.norm(p=2, dim=-1, keepdim=True)      # prompt複数件でもOK

    if self.clip_image_guidance_scale > 0 or self.vgg16_guidance_scale > 0 and clip_guide_images is not None or self.control_nets:
      if isinstance(clip_guide_images, PIL.Image.Image):
        clip_guide_images = [clip_guide_images]

      if self.clip_image_guidance_scale > 0:
        clip_guide_images = [preprocess_guide_image(im) for im in clip_guide_images]
        clip_guide_images = torch.cat(clip_guide_images, dim=0)

        clip_guide_images = self.normalize(clip_guide_images).to(self.device).to(text_embeddings.dtype)
        image_embeddings_clip = self.clip_model.get_image_features(clip_guide_images)
        image_embeddings_clip = image_embeddings_clip / image_embeddings_clip.norm(p=2, dim=-1, keepdim=True)
        if len(image_embeddings_clip) == 1:
          image_embeddings_clip = image_embeddings_clip.repeat((batch_size, 1, 1, 1))
      elif self.vgg16_guidance_scale > 0:
        size = (width // VGG16_INPUT_RESIZE_DIV, height // VGG16_INPUT_RESIZE_DIV)            # とりあえず1/4に（小さいか?）
        clip_guide_images = [preprocess_vgg16_guide_image(im, size) for im in clip_guide_images]
        clip_guide_images = torch.cat(clip_guide_images, dim=0)

        clip_guide_images = self.vgg16_normalize(clip_guide_images).to(self.device).to(text_embeddings.dtype)
        image_embeddings_vgg16 = self.vgg16_feat_model(clip_guide_images)['feat']
        if len(image_embeddings_vgg16) == 1:
          image_embeddings_vgg16 = image_embeddings_vgg16.repeat((batch_size, 1, 1, 1))
      else:
        # ControlNetのhintにguide imageを流用する
        # 前処理はControlNet側で行う
        pass

    # set timesteps
    self.scheduler.set_timesteps(num_inference_steps, self.device)

    latents_dtype = text_embeddings.dtype
    init_latents_orig = None
    mask = None
    noise = None

    if init_image is None:
      # get the initial random noise unless the user supplied it

      # Unlike in other pipelines, latents need to be generated in the target device
      # for 1-to-1 results reproducibility with the CompVis implementation.
      # However this currently doesn't work in `mps`.
      latents_shape = (batch_size * num_images_per_prompt, self.unet.in_channels, height // 8, width // 8,)

      if latents is None:
        if self.device.type == "mps":
          # randn does not exist on mps
          latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype,).to(self.device)
        else:
          latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype,)
      else:
        if latents.shape != latents_shape:
          raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
        latents = latents.to(self.device)

      timesteps = self.scheduler.timesteps.to(self.device)

      # scale the initial noise by the standard deviation required by the scheduler
      latents = latents * self.scheduler.init_noise_sigma
    else:
      # image to tensor
      if isinstance(init_image, PIL.Image.Image):
        init_image = [init_image]
      if isinstance(init_image[0], PIL.Image.Image):
        init_image = [preprocess_image(im) for im in init_image]
        init_image = torch.cat(init_image)

      # mask image to tensor
      if mask_image is not None:
        if isinstance(mask_image, PIL.Image.Image):
          mask_image = [mask_image]
        if isinstance(mask_image[0], PIL.Image.Image):
          mask_image = torch.cat([preprocess_mask(im) for im in mask_image])            # H*W, 0 for repaint

      # encode the init image into latents and scale the latents
      init_image = init_image.to(device=self.device, dtype=latents_dtype)
      init_latent_dist = self.vae.encode(init_image).latent_dist
      init_latents = init_latent_dist.sample(generator=generator)
      init_latents = 0.18215 * init_latents
      if len(init_latents) == 1:
        init_latents = init_latents.repeat((batch_size, 1, 1, 1))
      init_latents_orig = init_latents

      # preprocess mask
      if mask_image is not None:
        mask = mask_image.to(device=self.device, dtype=latents_dtype)
        if len(mask) == 1:
          mask = mask.repeat((batch_size, 1, 1, 1))

        # check sizes
        if not mask.shape == init_latents.shape:
          raise ValueError("The mask and init_image should be the same size!")

      # get the original timestep using init_timestep
      offset = self.scheduler.config.get("steps_offset", 0)
      init_timestep = int(num_inference_steps * strength) + offset
      init_timestep = min(init_timestep, num_inference_steps)

      timesteps = self.scheduler.timesteps[-init_timestep]
      timesteps = torch.tensor([timesteps] * batch_size * num_images_per_prompt, device=self.device)

      # add noise to latents using the timesteps
      latents = self.scheduler.add_noise(init_latents, img2img_noise, timesteps)

      t_start = max(num_inference_steps - init_timestep + offset, 0)
      timesteps = self.scheduler.timesteps[t_start:].to(self.device)

    # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
    # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
    # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
    # and should be between [0, 1]
    accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
    extra_step_kwargs = {}
    if accepts_eta:
      extra_step_kwargs["eta"] = eta

    num_latent_input = (3 if negative_scale is not None else 2) if do_classifier_free_guidance else 1

    if self.control_nets:
      guided_hints = original_control_net.get_guided_hints(self.control_nets, num_latent_input, batch_size, clip_guide_images)

    for i, t in enumerate(tqdm(timesteps)):
      # expand the latents if we are doing classifier free guidance
      latent_model_input = latents.repeat((num_latent_input, 1, 1, 1))
      latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)

      # predict the noise residual
      if self.control_nets:
        noise_pred = original_control_net.call_unet_and_control_net(
            i, num_latent_input, self.unet, self.control_nets, guided_hints, i / len(timesteps), latent_model_input, t, text_embeddings).sample
      else:
        noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample

      # perform guidance
      if do_classifier_free_guidance:
        if negative_scale is None:
          noise_pred_uncond, noise_pred_text = noise_pred.chunk(num_latent_input)        # uncond by negative prompt
          noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
        else:
          noise_pred_negative, noise_pred_text, noise_pred_uncond = noise_pred.chunk(num_latent_input)       # uncond is real uncond
          noise_pred = noise_pred_uncond + guidance_scale * \
              (noise_pred_text - noise_pred_uncond) - negative_scale * (noise_pred_negative - noise_pred_uncond)

      # perform clip guidance
      if self.clip_guidance_scale > 0 or self.clip_image_guidance_scale > 0 or self.vgg16_guidance_scale > 0:
        text_embeddings_for_guidance = (text_embeddings.chunk(num_latent_input)[
                                        1] if do_classifier_free_guidance else text_embeddings)

        if self.clip_guidance_scale > 0:
          noise_pred, latents = self.cond_fn(latents, t, i, text_embeddings_for_guidance, noise_pred,
                                             text_embeddings_clip, self.clip_guidance_scale, NUM_CUTOUTS, USE_CUTOUTS,)
        if self.clip_image_guidance_scale > 0 and clip_guide_images is not None:
          noise_pred, latents = self.cond_fn(latents, t, i, text_embeddings_for_guidance, noise_pred,
                                             image_embeddings_clip, self.clip_image_guidance_scale, NUM_CUTOUTS, USE_CUTOUTS,)
        if self.vgg16_guidance_scale > 0 and clip_guide_images is not None:
          noise_pred, latents = self.cond_fn_vgg16(latents, t, i, text_embeddings_for_guidance, noise_pred,
                                                   image_embeddings_vgg16, self.vgg16_guidance_scale)

      # compute the previous noisy sample x_t -> x_t-1
      latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample

      if mask is not None:
        # masking
        init_latents_proper = self.scheduler.add_noise(init_latents_orig, img2img_noise, torch.tensor([t]))
        latents = (init_latents_proper * mask) + (latents * (1 - mask))

      # call the callback, if provided
      if i % callback_steps == 0:
        if callback is not None:
          callback(i, t, latents)
        if is_cancelled_callback is not None and is_cancelled_callback():
          return None

    latents = 1 / 0.18215 * latents
    image = self.vae.decode(latents).sample

    image = (image / 2 + 0.5).clamp(0, 1)

    # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
    image = image.cpu().permute(0, 2, 3, 1).float().numpy()

    if self.safety_checker is not None:
      safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(
          self.device
      )
      image, has_nsfw_concept = self.safety_checker(
          images=image,
          clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype),
      )
    else:
      has_nsfw_concept = None

    if output_type == "pil":
      # image = self.numpy_to_pil(image)
      image = (image * 255).round().astype("uint8")
      image = [Image.fromarray(im) for im in image]

    # if not return_dict:
    return (image, has_nsfw_concept)

    # return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

  def text2img(
      self,
      prompt: Union[str, List[str]],
      negative_prompt: Optional[Union[str, List[str]]] = None,
      height: int = 512,
      width: int = 512,
      num_inference_steps: int = 50,
      guidance_scale: float = 7.5,
      num_images_per_prompt: Optional[int] = 1,
      eta: float = 0.0,
      generator: Optional[torch.Generator] = None,
      latents: Optional[torch.FloatTensor] = None,
      max_embeddings_multiples: Optional[int] = 3,
      output_type: Optional[str] = "pil",
      return_dict: bool = True,
      callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
      callback_steps: Optional[int] = 1,
      **kwargs,
  ):
    r"""
    Function for text-to-image generation.
    Args:
        prompt (`str` or `List[str]`):
            The prompt or prompts to guide the image generation.
        negative_prompt (`str` or `List[str]`, *optional*):
            The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
            if `guidance_scale` is less than `1`).
        height (`int`, *optional*, defaults to 512):
            The height in pixels of the generated image.
        width (`int`, *optional*, defaults to 512):
            The width in pixels of the generated image.
        num_inference_steps (`int`, *optional*, defaults to 50):
            The number of denoising steps. More denoising steps usually lead to a higher quality image at the
            expense of slower inference.
        guidance_scale (`float`, *optional*, defaults to 7.5):
            Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
            `guidance_scale` is defined as `w` of equation 2. of [Imagen
            Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
            1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
            usually at the expense of lower image quality.
        num_images_per_prompt (`int`, *optional*, defaults to 1):
            The number of images to generate per prompt.
        eta (`float`, *optional*, defaults to 0.0):
            Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
            [`schedulers.DDIMScheduler`], will be ignored for others.
        generator (`torch.Generator`, *optional*):
            A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
            deterministic.
        latents (`torch.FloatTensor`, *optional*):
            Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
            generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
            tensor will ge generated by sampling using the supplied random `generator`.
        max_embeddings_multiples (`int`, *optional*, defaults to `3`):
            The max multiple length of prompt embeddings compared to the max output length of text encoder.
        output_type (`str`, *optional*, defaults to `"pil"`):
            The output format of the generate image. Choose between
            [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
        return_dict (`bool`, *optional*, defaults to `True`):
            Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
            plain tuple.
        callback (`Callable`, *optional*):
            A function that will be called every `callback_steps` steps during inference. The function will be
            called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
        callback_steps (`int`, *optional*, defaults to 1):
            The frequency at which the `callback` function will be called. If not specified, the callback will be
            called at every step.
    Returns:
        [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
        [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
        When returning a tuple, the first element is a list with the generated images, and the second element is a
        list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
        (nsfw) content, according to the `safety_checker`.
    """
    return self.__call__(
        prompt=prompt,
        negative_prompt=negative_prompt,
        height=height,
        width=width,
        num_inference_steps=num_inference_steps,
        guidance_scale=guidance_scale,
        num_images_per_prompt=num_images_per_prompt,
        eta=eta,
        generator=generator,
        latents=latents,
        max_embeddings_multiples=max_embeddings_multiples,
        output_type=output_type,
        return_dict=return_dict,
        callback=callback,
        callback_steps=callback_steps,
        **kwargs,
    )

  def img2img(
      self,
      init_image: Union[torch.FloatTensor, PIL.Image.Image],
      prompt: Union[str, List[str]],
      negative_prompt: Optional[Union[str, List[str]]] = None,
      strength: float = 0.8,
      num_inference_steps: Optional[int] = 50,
      guidance_scale: Optional[float] = 7.5,
      num_images_per_prompt: Optional[int] = 1,
      eta: Optional[float] = 0.0,
      generator: Optional[torch.Generator] = None,
      max_embeddings_multiples: Optional[int] = 3,
      output_type: Optional[str] = "pil",
      return_dict: bool = True,
      callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
      callback_steps: Optional[int] = 1,
      **kwargs,
  ):
    r"""
    Function for image-to-image generation.
    Args:
        init_image (`torch.FloatTensor` or `PIL.Image.Image`):
            `Image`, or tensor representing an image batch, that will be used as the starting point for the
            process.
        prompt (`str` or `List[str]`):
            The prompt or prompts to guide the image generation.
        negative_prompt (`str` or `List[str]`, *optional*):
            The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
            if `guidance_scale` is less than `1`).
        strength (`float`, *optional*, defaults to 0.8):
            Conceptually, indicates how much to transform the reference `init_image`. Must be between 0 and 1.
            `init_image` will be used as a starting point, adding more noise to it the larger the `strength`. The
            number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added
            noise will be maximum and the denoising process will run for the full number of iterations specified in
            `num_inference_steps`. A value of 1, therefore, essentially ignores `init_image`.
        num_inference_steps (`int`, *optional*, defaults to 50):
            The number of denoising steps. More denoising steps usually lead to a higher quality image at the
            expense of slower inference. This parameter will be modulated by `strength`.
        guidance_scale (`float`, *optional*, defaults to 7.5):
            Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
            `guidance_scale` is defined as `w` of equation 2. of [Imagen
            Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
            1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
            usually at the expense of lower image quality.
        num_images_per_prompt (`int`, *optional*, defaults to 1):
            The number of images to generate per prompt.
        eta (`float`, *optional*, defaults to 0.0):
            Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
            [`schedulers.DDIMScheduler`], will be ignored for others.
        generator (`torch.Generator`, *optional*):
            A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
            deterministic.
        max_embeddings_multiples (`int`, *optional*, defaults to `3`):
            The max multiple length of prompt embeddings compared to the max output length of text encoder.
        output_type (`str`, *optional*, defaults to `"pil"`):
            The output format of the generate image. Choose between
            [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
        return_dict (`bool`, *optional*, defaults to `True`):
            Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
            plain tuple.
        callback (`Callable`, *optional*):
            A function that will be called every `callback_steps` steps during inference. The function will be
            called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
        callback_steps (`int`, *optional*, defaults to 1):
            The frequency at which the `callback` function will be called. If not specified, the callback will be
            called at every step.
    Returns:
        [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
        [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
        When returning a tuple, the first element is a list with the generated images, and the second element is a
        list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
        (nsfw) content, according to the `safety_checker`.
    """
    return self.__call__(
        prompt=prompt,
        negative_prompt=negative_prompt,
        init_image=init_image,
        num_inference_steps=num_inference_steps,
        guidance_scale=guidance_scale,
        strength=strength,
        num_images_per_prompt=num_images_per_prompt,
        eta=eta,
        generator=generator,
        max_embeddings_multiples=max_embeddings_multiples,
        output_type=output_type,
        return_dict=return_dict,
        callback=callback,
        callback_steps=callback_steps,
        **kwargs,
    )

  def inpaint(
      self,
      init_image: Union[torch.FloatTensor, PIL.Image.Image],
      mask_image: Union[torch.FloatTensor, PIL.Image.Image],
      prompt: Union[str, List[str]],
      negative_prompt: Optional[Union[str, List[str]]] = None,
      strength: float = 0.8,
      num_inference_steps: Optional[int] = 50,
      guidance_scale: Optional[float] = 7.5,
      num_images_per_prompt: Optional[int] = 1,
      eta: Optional[float] = 0.0,
      generator: Optional[torch.Generator] = None,
      max_embeddings_multiples: Optional[int] = 3,
      output_type: Optional[str] = "pil",
      return_dict: bool = True,
      callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
      callback_steps: Optional[int] = 1,
      **kwargs,
  ):
    r"""
    Function for inpaint.
    Args:
        init_image (`torch.FloatTensor` or `PIL.Image.Image`):
            `Image`, or tensor representing an image batch, that will be used as the starting point for the
            process. This is the image whose masked region will be inpainted.
        mask_image (`torch.FloatTensor` or `PIL.Image.Image`):
            `Image`, or tensor representing an image batch, to mask `init_image`. White pixels in the mask will be
            replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a
            PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should
            contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`.
        prompt (`str` or `List[str]`):
            The prompt or prompts to guide the image generation.
        negative_prompt (`str` or `List[str]`, *optional*):
            The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
            if `guidance_scale` is less than `1`).
        strength (`float`, *optional*, defaults to 0.8):
            Conceptually, indicates how much to inpaint the masked area. Must be between 0 and 1. When `strength`
            is 1, the denoising process will be run on the masked area for the full number of iterations specified
            in `num_inference_steps`. `init_image` will be used as a reference for the masked area, adding more
            noise to that region the larger the `strength`. If `strength` is 0, no inpainting will occur.
        num_inference_steps (`int`, *optional*, defaults to 50):
            The reference number of denoising steps. More denoising steps usually lead to a higher quality image at
            the expense of slower inference. This parameter will be modulated by `strength`, as explained above.
        guidance_scale (`float`, *optional*, defaults to 7.5):
            Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
            `guidance_scale` is defined as `w` of equation 2. of [Imagen
            Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
            1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
            usually at the expense of lower image quality.
        num_images_per_prompt (`int`, *optional*, defaults to 1):
            The number of images to generate per prompt.
        eta (`float`, *optional*, defaults to 0.0):
            Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
            [`schedulers.DDIMScheduler`], will be ignored for others.
        generator (`torch.Generator`, *optional*):
            A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
            deterministic.
        max_embeddings_multiples (`int`, *optional*, defaults to `3`):
            The max multiple length of prompt embeddings compared to the max output length of text encoder.
        output_type (`str`, *optional*, defaults to `"pil"`):
            The output format of the generate image. Choose between
            [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
        return_dict (`bool`, *optional*, defaults to `True`):
            Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
            plain tuple.
        callback (`Callable`, *optional*):
            A function that will be called every `callback_steps` steps during inference. The function will be
            called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
        callback_steps (`int`, *optional*, defaults to 1):
            The frequency at which the `callback` function will be called. If not specified, the callback will be
            called at every step.
    Returns:
        [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
        [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
        When returning a tuple, the first element is a list with the generated images, and the second element is a
        list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
        (nsfw) content, according to the `safety_checker`.
    """
    return self.__call__(
        prompt=prompt,
        negative_prompt=negative_prompt,
        init_image=init_image,
        mask_image=mask_image,
        num_inference_steps=num_inference_steps,
        guidance_scale=guidance_scale,
        strength=strength,
        num_images_per_prompt=num_images_per_prompt,
        eta=eta,
        generator=generator,
        max_embeddings_multiples=max_embeddings_multiples,
        output_type=output_type,
        return_dict=return_dict,
        callback=callback,
        callback_steps=callback_steps,
        **kwargs,
    )

  # CLIP guidance StableDiffusion
  # copy from https://github.com/huggingface/diffusers/blob/main/examples/community/clip_guided_stable_diffusion.py

  # バッチを分解して1件ずつ処理する
  def cond_fn(self, latents, timestep, index, text_embeddings, noise_pred_original, guide_embeddings_clip, clip_guidance_scale,
              num_cutouts, use_cutouts=True, ):
    if len(latents) == 1:
      return self.cond_fn1(latents, timestep, index, text_embeddings, noise_pred_original, guide_embeddings_clip, clip_guidance_scale,
                           num_cutouts, use_cutouts)

    noise_pred = []
    cond_latents = []
    for i in range(len(latents)):
      lat1 = latents[i].unsqueeze(0)
      tem1 = text_embeddings[i].unsqueeze(0)
      npo1 = noise_pred_original[i].unsqueeze(0)
      gem1 = guide_embeddings_clip[i].unsqueeze(0)
      npr1, cla1 = self.cond_fn1(lat1, timestep, index, tem1, npo1, gem1, clip_guidance_scale, num_cutouts, use_cutouts)
      noise_pred.append(npr1)
      cond_latents.append(cla1)

    noise_pred = torch.cat(noise_pred)
    cond_latents = torch.cat(cond_latents)
    return noise_pred, cond_latents

  @torch.enable_grad()
  def cond_fn1(self, latents, timestep, index, text_embeddings, noise_pred_original, guide_embeddings_clip, clip_guidance_scale,
               num_cutouts, use_cutouts=True, ):
    latents = latents.detach().requires_grad_()

    if isinstance(self.scheduler, LMSDiscreteScheduler):
      sigma = self.scheduler.sigmas[index]
      # the model input needs to be scaled to match the continuous ODE formulation in K-LMS
      latent_model_input = latents / ((sigma**2 + 1) ** 0.5)
    else:
      latent_model_input = latents

    # predict the noise residual
    noise_pred = self.unet(latent_model_input, timestep, encoder_hidden_states=text_embeddings).sample

    if isinstance(self.scheduler, (PNDMScheduler, DDIMScheduler)):
      alpha_prod_t = self.scheduler.alphas_cumprod[timestep]
      beta_prod_t = 1 - alpha_prod_t
      # compute predicted original sample from predicted noise also called
      # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      pred_original_sample = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5)

      fac = torch.sqrt(beta_prod_t)
      sample = pred_original_sample * (fac) + latents * (1 - fac)
    elif isinstance(self.scheduler, LMSDiscreteScheduler):
      sigma = self.scheduler.sigmas[index]
      sample = latents - sigma * noise_pred
    else:
      raise ValueError(f"scheduler type {type(self.scheduler)} not supported")

    sample = 1 / 0.18215 * sample
    image = self.vae.decode(sample).sample
    image = (image / 2 + 0.5).clamp(0, 1)

    if use_cutouts:
      image = self.make_cutouts(image, num_cutouts)
    else:
      image = transforms.Resize(FEATURE_EXTRACTOR_SIZE)(image)
    image = self.normalize(image).to(latents.dtype)

    image_embeddings_clip = self.clip_model.get_image_features(image)
    image_embeddings_clip = image_embeddings_clip / image_embeddings_clip.norm(p=2, dim=-1, keepdim=True)

    if use_cutouts:
      dists = spherical_dist_loss(image_embeddings_clip, guide_embeddings_clip)
      dists = dists.view([num_cutouts, sample.shape[0], -1])
      loss = dists.sum(2).mean(0).sum() * clip_guidance_scale
    else:
      # バッチサイズが複数だと正しく動くかわからない
      loss = spherical_dist_loss(image_embeddings_clip, guide_embeddings_clip).mean() * clip_guidance_scale

    grads = -torch.autograd.grad(loss, latents)[0]

    if isinstance(self.scheduler, LMSDiscreteScheduler):
      latents = latents.detach() + grads * (sigma**2)
      noise_pred = noise_pred_original
    else:
      noise_pred = noise_pred_original - torch.sqrt(beta_prod_t) * grads
    return noise_pred, latents

  # バッチを分解して一件ずつ処理する
  def cond_fn_vgg16(self, latents, timestep, index, text_embeddings, noise_pred_original, guide_embeddings, guidance_scale):
    if len(latents) == 1:
      return self.cond_fn_vgg16_b1(latents, timestep, index, text_embeddings, noise_pred_original, guide_embeddings, guidance_scale)

    noise_pred = []
    cond_latents = []
    for i in range(len(latents)):
      lat1 = latents[i].unsqueeze(0)
      tem1 = text_embeddings[i].unsqueeze(0)
      npo1 = noise_pred_original[i].unsqueeze(0)
      gem1 = guide_embeddings[i].unsqueeze(0)
      npr1, cla1 = self.cond_fn_vgg16_b1(lat1, timestep, index, tem1, npo1, gem1, guidance_scale)
      noise_pred.append(npr1)
      cond_latents.append(cla1)

    noise_pred = torch.cat(noise_pred)
    cond_latents = torch.cat(cond_latents)
    return noise_pred, cond_latents

  # 1件だけ処理する
  @torch.enable_grad()
  def cond_fn_vgg16_b1(self, latents, timestep, index, text_embeddings, noise_pred_original, guide_embeddings, guidance_scale):
    latents = latents.detach().requires_grad_()

    if isinstance(self.scheduler, LMSDiscreteScheduler):
      sigma = self.scheduler.sigmas[index]
      # the model input needs to be scaled to match the continuous ODE formulation in K-LMS
      latent_model_input = latents / ((sigma**2 + 1) ** 0.5)
    else:
      latent_model_input = latents

    # predict the noise residual
    noise_pred = self.unet(latent_model_input, timestep, encoder_hidden_states=text_embeddings).sample

    if isinstance(self.scheduler, (PNDMScheduler, DDIMScheduler)):
      alpha_prod_t = self.scheduler.alphas_cumprod[timestep]
      beta_prod_t = 1 - alpha_prod_t
      # compute predicted original sample from predicted noise also called
      # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      pred_original_sample = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5)

      fac = torch.sqrt(beta_prod_t)
      sample = pred_original_sample * (fac) + latents * (1 - fac)
    elif isinstance(self.scheduler, LMSDiscreteScheduler):
      sigma = self.scheduler.sigmas[index]
      sample = latents - sigma * noise_pred
    else:
      raise ValueError(f"scheduler type {type(self.scheduler)} not supported")

    sample = 1 / 0.18215 * sample
    image = self.vae.decode(sample).sample
    image = (image / 2 + 0.5).clamp(0, 1)
    image = transforms.Resize((image.shape[-2] // VGG16_INPUT_RESIZE_DIV, image.shape[-1] // VGG16_INPUT_RESIZE_DIV))(image)
    image = self.vgg16_normalize(image).to(latents.dtype)

    image_embeddings = self.vgg16_feat_model(image)['feat']

    # バッチサイズが複数だと正しく動くかわからない
    loss = ((image_embeddings - guide_embeddings) ** 2).mean() * guidance_scale       # MSE style transferでコンテンツの損失はMSEなので

    grads = -torch.autograd.grad(loss, latents)[0]
    if isinstance(self.scheduler, LMSDiscreteScheduler):
      latents = latents.detach() + grads * (sigma**2)
      noise_pred = noise_pred_original
    else:
      noise_pred = noise_pred_original - torch.sqrt(beta_prod_t) * grads
    return noise_pred, latents


class MakeCutouts(torch.nn.Module):
  def __init__(self, cut_size, cut_power=1.0):
    super().__init__()

    self.cut_size = cut_size
    self.cut_power = cut_power

  def forward(self, pixel_values, num_cutouts):
    sideY, sideX = pixel_values.shape[2:4]
    max_size = min(sideX, sideY)
    min_size = min(sideX, sideY, self.cut_size)
    cutouts = []
    for _ in range(num_cutouts):
      size = int(torch.rand([]) ** self.cut_power * (max_size - min_size) + min_size)
      offsetx = torch.randint(0, sideX - size + 1, ())
      offsety = torch.randint(0, sideY - size + 1, ())
      cutout = pixel_values[:, :, offsety: offsety + size, offsetx: offsetx + size]
      cutouts.append(torch.nn.functional.adaptive_avg_pool2d(cutout, self.cut_size))
    return torch.cat(cutouts)


def spherical_dist_loss(x, y):
  x = torch.nn.functional.normalize(x, dim=-1)
  y = torch.nn.functional.normalize(y, dim=-1)
  return (x - y).norm(dim=-1).div(2).arcsin().pow(2).mul(2)


re_attention = re.compile(
    r"""
\\\(|
\\\)|
\\\[|
\\]|
\\\\|
\\|
\(|
\[|
:([+-]?[.\d]+)\)|
\)|
]|
[^\\()\[\]:]+|
:
""",
    re.X,
)


def parse_prompt_attention(text):
  """
  Parses a string with attention tokens and returns a list of pairs: text and its associated weight.
  Accepted tokens are:
    (abc) - increases attention to abc by a multiplier of 1.1
    (abc:3.12) - increases attention to abc by a multiplier of 3.12
    [abc] - decreases attention to abc by a multiplier of 1.1
    \( - literal character '('
    \[ - literal character '['
    \) - literal character ')'
    \] - literal character ']'
    \\ - literal character '\'
    anything else - just text
  >>> parse_prompt_attention('normal text')
  [['normal text', 1.0]]
  >>> parse_prompt_attention('an (important) word')
  [['an ', 1.0], ['important', 1.1], [' word', 1.0]]
  >>> parse_prompt_attention('(unbalanced')
  [['unbalanced', 1.1]]
  >>> parse_prompt_attention('\(literal\]')
  [['(literal]', 1.0]]
  >>> parse_prompt_attention('(unnecessary)(parens)')
  [['unnecessaryparens', 1.1]]
  >>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).')
  [['a ', 1.0],
   ['house', 1.5730000000000004],
   [' ', 1.1],
   ['on', 1.0],
   [' a ', 1.1],
   ['hill', 0.55],
   [', sun, ', 1.1],
   ['sky', 1.4641000000000006],
   ['.', 1.1]]
  """

  res = []
  round_brackets = []
  square_brackets = []

  round_bracket_multiplier = 1.1
  square_bracket_multiplier = 1 / 1.1

  def multiply_range(start_position, multiplier):
    for p in range(start_position, len(res)):
      res[p][1] *= multiplier

  for m in re_attention.finditer(text):
    text = m.group(0)
    weight = m.group(1)

    if text.startswith("\\"):
      res.append([text[1:], 1.0])
    elif text == "(":
      round_brackets.append(len(res))
    elif text == "[":
      square_brackets.append(len(res))
    elif weight is not None and len(round_brackets) > 0:
      multiply_range(round_brackets.pop(), float(weight))
    elif text == ")" and len(round_brackets) > 0:
      multiply_range(round_brackets.pop(), round_bracket_multiplier)
    elif text == "]" and len(square_brackets) > 0:
      multiply_range(square_brackets.pop(), square_bracket_multiplier)
    else:
      res.append([text, 1.0])

  for pos in round_brackets:
    multiply_range(pos, round_bracket_multiplier)

  for pos in square_brackets:
    multiply_range(pos, square_bracket_multiplier)

  if len(res) == 0:
    res = [["", 1.0]]

  # merge runs of identical weights
  i = 0
  while i + 1 < len(res):
    if res[i][1] == res[i + 1][1]:
      res[i][0] += res[i + 1][0]
      res.pop(i + 1)
    else:
      i += 1

  return res


def get_prompts_with_weights(pipe: PipelineLike, prompt: List[str], max_length: int):
  r"""
  Tokenize a list of prompts and return its tokens with weights of each token.
  No padding, starting or ending token is included.
  """
  tokens = []
  weights = []
  truncated = False
  for text in prompt:
    texts_and_weights = parse_prompt_attention(text)
    text_token = []
    text_weight = []
    for word, weight in texts_and_weights:
      # tokenize and discard the starting and the ending token
      token = pipe.tokenizer(word).input_ids[1:-1]

      token = pipe.replace_token(token)

      text_token += token
      # copy the weight by length of token
      text_weight += [weight] * len(token)
      # stop if the text is too long (longer than truncation limit)
      if len(text_token) > max_length:
        truncated = True
        break
    # truncate
    if len(text_token) > max_length:
      truncated = True
      text_token = text_token[:max_length]
      text_weight = text_weight[:max_length]
    tokens.append(text_token)
    weights.append(text_weight)
  if truncated:
    print("warning: Prompt was truncated. Try to shorten the prompt or increase max_embeddings_multiples")
  return tokens, weights


def pad_tokens_and_weights(tokens, weights, max_length, bos, eos, pad, no_boseos_middle=True, chunk_length=77):
  r"""
  Pad the tokens (with starting and ending tokens) and weights (with 1.0) to max_length.
  """
  max_embeddings_multiples = (max_length - 2) // (chunk_length - 2)
  weights_length = max_length if no_boseos_middle else max_embeddings_multiples * chunk_length
  for i in range(len(tokens)):
    tokens[i] = [bos] + tokens[i] + [eos] + [pad] * (max_length - 2 - len(tokens[i]))
    if no_boseos_middle:
      weights[i] = [1.0] + weights[i] + [1.0] * (max_length - 1 - len(weights[i]))
    else:
      w = []
      if len(weights[i]) == 0:
        w = [1.0] * weights_length
      else:
        for j in range(max_embeddings_multiples):
          w.append(1.0)  # weight for starting token in this chunk
          w += weights[i][j * (chunk_length - 2): min(len(weights[i]), (j + 1) * (chunk_length - 2))]
          w.append(1.0)  # weight for ending token in this chunk
        w += [1.0] * (weights_length - len(w))
      weights[i] = w[:]

  return tokens, weights


def get_unweighted_text_embeddings(
    pipe: PipelineLike,
    text_input: torch.Tensor,
    chunk_length: int,
    clip_skip: int,
    eos: int,
    pad: int,
    no_boseos_middle: Optional[bool] = True,
):
  """
  When the length of tokens is a multiple of the capacity of the text encoder,
  it should be split into chunks and sent to the text encoder individually.
  """
  max_embeddings_multiples = (text_input.shape[1] - 2) // (chunk_length - 2)
  if max_embeddings_multiples > 1:
    text_embeddings = []
    for i in range(max_embeddings_multiples):
      # extract the i-th chunk
      text_input_chunk = text_input[:, i * (chunk_length - 2): (i + 1) * (chunk_length - 2) + 2].clone()

      # cover the head and the tail by the starting and the ending tokens
      text_input_chunk[:, 0] = text_input[0, 0]
      if pad == eos:                        # v1
        text_input_chunk[:, -1] = text_input[0, -1]
      else:                                 # v2
        if text_input_chunk[:, -1] != eos and text_input_chunk[:, -1] != pad:     # 最後に普通の文字がある
          text_input_chunk[:, -1] = eos
        if text_input_chunk[:, 1] == pad:                                         # BOSだけであとはPAD
          text_input_chunk[:, 1] = eos

      if clip_skip is None or clip_skip == 1:
        text_embedding = pipe.text_encoder(text_input_chunk)[0]
      else:
        enc_out = pipe.text_encoder(text_input_chunk, output_hidden_states=True, return_dict=True)
        text_embedding = enc_out['hidden_states'][-clip_skip]
        text_embedding = pipe.text_encoder.text_model.final_layer_norm(text_embedding)

      if no_boseos_middle:
        if i == 0:
          # discard the ending token
          text_embedding = text_embedding[:, :-1]
        elif i == max_embeddings_multiples - 1:
          # discard the starting token
          text_embedding = text_embedding[:, 1:]
        else:
          # discard both starting and ending tokens
          text_embedding = text_embedding[:, 1:-1]

      text_embeddings.append(text_embedding)
    text_embeddings = torch.concat(text_embeddings, axis=1)
  else:
    if clip_skip is None or clip_skip == 1:
      text_embeddings = pipe.text_encoder(text_input)[0]
    else:
      enc_out = pipe.text_encoder(text_input, output_hidden_states=True, return_dict=True)
      text_embeddings = enc_out['hidden_states'][-clip_skip]
      text_embeddings = pipe.text_encoder.text_model.final_layer_norm(text_embeddings)
  return text_embeddings


def get_weighted_text_embeddings(
    pipe: PipelineLike,
    prompt: Union[str, List[str]],
    uncond_prompt: Optional[Union[str, List[str]]] = None,
    max_embeddings_multiples: Optional[int] = 1,
    no_boseos_middle: Optional[bool] = False,
    skip_parsing: Optional[bool] = False,
    skip_weighting: Optional[bool] = False,
    clip_skip=None,
    **kwargs,
):
  r"""
  Prompts can be assigned with local weights using brackets. For example,
  prompt 'A (very beautiful) masterpiece' highlights the words 'very beautiful',
  and the embedding tokens corresponding to the words get multiplied by a constant, 1.1.
  Also, to regularize of the embedding, the weighted embedding would be scaled to preserve the original mean.
  Args:
      pipe (`DiffusionPipeline`):
          Pipe to provide access to the tokenizer and the text encoder.
      prompt (`str` or `List[str]`):
          The prompt or prompts to guide the image generation.
      uncond_prompt (`str` or `List[str]`):
          The unconditional prompt or prompts for guide the image generation. If unconditional prompt
          is provided, the embeddings of prompt and uncond_prompt are concatenated.
      max_embeddings_multiples (`int`, *optional*, defaults to `1`):
          The max multiple length of prompt embeddings compared to the max output length of text encoder.
      no_boseos_middle (`bool`, *optional*, defaults to `False`):
          If the length of text token is multiples of the capacity of text encoder, whether reserve the starting and
          ending token in each of the chunk in the middle.
      skip_parsing (`bool`, *optional*, defaults to `False`):
          Skip the parsing of brackets.
      skip_weighting (`bool`, *optional*, defaults to `False`):
          Skip the weighting. When the parsing is skipped, it is forced True.
  """
  max_length = (pipe.tokenizer.model_max_length - 2) * max_embeddings_multiples + 2
  if isinstance(prompt, str):
    prompt = [prompt]

  if not skip_parsing:
    prompt_tokens, prompt_weights = get_prompts_with_weights(pipe, prompt, max_length - 2)
    if uncond_prompt is not None:
      if isinstance(uncond_prompt, str):
        uncond_prompt = [uncond_prompt]
      uncond_tokens, uncond_weights = get_prompts_with_weights(pipe, uncond_prompt, max_length - 2)
  else:
    prompt_tokens = [
        token[1:-1] for token in pipe.tokenizer(prompt, max_length=max_length, truncation=True).input_ids
    ]
    prompt_weights = [[1.0] * len(token) for token in prompt_tokens]
    if uncond_prompt is not None:
      if isinstance(uncond_prompt, str):
        uncond_prompt = [uncond_prompt]
      uncond_tokens = [
          token[1:-1]
          for token in pipe.tokenizer(uncond_prompt, max_length=max_length, truncation=True).input_ids
      ]
      uncond_weights = [[1.0] * len(token) for token in uncond_tokens]

  # round up the longest length of tokens to a multiple of (model_max_length - 2)
  max_length = max([len(token) for token in prompt_tokens])
  if uncond_prompt is not None:
    max_length = max(max_length, max([len(token) for token in uncond_tokens]))

  max_embeddings_multiples = min(
      max_embeddings_multiples,
      (max_length - 1) // (pipe.tokenizer.model_max_length - 2) + 1,
  )
  max_embeddings_multiples = max(1, max_embeddings_multiples)
  max_length = (pipe.tokenizer.model_max_length - 2) * max_embeddings_multiples + 2

  # pad the length of tokens and weights
  bos = pipe.tokenizer.bos_token_id
  eos = pipe.tokenizer.eos_token_id
  pad = pipe.tokenizer.pad_token_id
  prompt_tokens, prompt_weights = pad_tokens_and_weights(
      prompt_tokens,
      prompt_weights,
      max_length,
      bos,
      eos,
      pad,
      no_boseos_middle=no_boseos_middle,
      chunk_length=pipe.tokenizer.model_max_length,
  )
  prompt_tokens = torch.tensor(prompt_tokens, dtype=torch.long, device=pipe.device)
  if uncond_prompt is not None:
    uncond_tokens, uncond_weights = pad_tokens_and_weights(
        uncond_tokens,
        uncond_weights,
        max_length,
        bos,
        eos,
        pad,
        no_boseos_middle=no_boseos_middle,
        chunk_length=pipe.tokenizer.model_max_length,
    )
    uncond_tokens = torch.tensor(uncond_tokens, dtype=torch.long, device=pipe.device)

  # get the embeddings
  text_embeddings = get_unweighted_text_embeddings(
      pipe,
      prompt_tokens,
      pipe.tokenizer.model_max_length,
      clip_skip,
      eos, pad,
      no_boseos_middle=no_boseos_middle,
  )
  prompt_weights = torch.tensor(prompt_weights, dtype=text_embeddings.dtype, device=pipe.device)
  if uncond_prompt is not None:
    uncond_embeddings = get_unweighted_text_embeddings(
        pipe,
        uncond_tokens,
        pipe.tokenizer.model_max_length,
        clip_skip,
        eos, pad,
        no_boseos_middle=no_boseos_middle,
    )
    uncond_weights = torch.tensor(uncond_weights, dtype=uncond_embeddings.dtype, device=pipe.device)

  # assign weights to the prompts and normalize in the sense of mean
  # TODO: should we normalize by chunk or in a whole (current implementation)?
  # →全体でいいんじゃないかな
  if (not skip_parsing) and (not skip_weighting):
    previous_mean = text_embeddings.float().mean(axis=[-2, -1]).to(text_embeddings.dtype)
    text_embeddings *= prompt_weights.unsqueeze(-1)
    current_mean = text_embeddings.float().mean(axis=[-2, -1]).to(text_embeddings.dtype)
    text_embeddings *= (previous_mean / current_mean).unsqueeze(-1).unsqueeze(-1)
    if uncond_prompt is not None:
      previous_mean = uncond_embeddings.float().mean(axis=[-2, -1]).to(uncond_embeddings.dtype)
      uncond_embeddings *= uncond_weights.unsqueeze(-1)
      current_mean = uncond_embeddings.float().mean(axis=[-2, -1]).to(uncond_embeddings.dtype)
      uncond_embeddings *= (previous_mean / current_mean).unsqueeze(-1).unsqueeze(-1)

  if uncond_prompt is not None:
    return text_embeddings, uncond_embeddings, prompt_tokens
  return text_embeddings, None, prompt_tokens


def preprocess_guide_image(image):
  image = image.resize(FEATURE_EXTRACTOR_SIZE, resample=Image.NEAREST)        # cond_fnと合わせる
  image = np.array(image).astype(np.float32) / 255.0
  image = image[None].transpose(0, 3, 1, 2)       # nchw
  image = torch.from_numpy(image)
  return image                              # 0 to 1


# VGG16の入力は任意サイズでよいので入力画像を適宜リサイズする
def preprocess_vgg16_guide_image(image, size):
  image = image.resize(size, resample=Image.NEAREST)        # cond_fnと合わせる
  image = np.array(image).astype(np.float32) / 255.0
  image = image[None].transpose(0, 3, 1, 2)       # nchw
  image = torch.from_numpy(image)
  return image                              # 0 to 1


def preprocess_image(image):
  w, h = image.size
  w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
  image = image.resize((w, h), resample=PIL.Image.LANCZOS)
  image = np.array(image).astype(np.float32) / 255.0
  image = image[None].transpose(0, 3, 1, 2)
  image = torch.from_numpy(image)
  return 2.0 * image - 1.0


def preprocess_mask(mask):
  mask = mask.convert("L")
  w, h = mask.size
  w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
  mask = mask.resize((w // 8, h // 8), resample=PIL.Image.LANCZOS)
  mask = np.array(mask).astype(np.float32) / 255.0
  mask = np.tile(mask, (4, 1, 1))
  mask = mask[None].transpose(0, 1, 2, 3)  # what does this step do?
  mask = 1 - mask  # repaint white, keep black
  mask = torch.from_numpy(mask)
  return mask


# endregion


# def load_clip_l14_336(dtype):
#   print(f"loading CLIP: {CLIP_ID_L14_336}")
#   text_encoder = CLIPTextModel.from_pretrained(CLIP_ID_L14_336, torch_dtype=dtype)
#   return text_encoder


class BatchDataBase(NamedTuple):
  # バッチ分割が必要ないデータ
  step: int
  prompt: str
  negative_prompt: str
  seed: int
  init_image: Any
  mask_image: Any
  clip_prompt: str
  guide_image: Any


class BatchDataExt(NamedTuple):
  # バッチ分割が必要なデータ
  width: int
  height: int
  steps: int
  scale:  float
  negative_scale: float
  strength: float
  network_muls: Tuple[float]


class BatchData(NamedTuple):
  base: BatchDataBase
  ext: BatchDataExt


def main(args):
  if args.fp16:
    dtype = torch.float16
  elif args.bf16:
    dtype = torch.bfloat16
  else:
    dtype = torch.float32

  highres_fix = args.highres_fix_scale is not None
  assert not highres_fix or args.image_path is None, f"highres_fix doesn't work with img2img / highres_fixはimg2imgと同時に使えません"

  if args.v_parameterization and not args.v2:
    print("v_parameterization should be with v2 / v1でv_parameterizationを使用することは想定されていません")
  if args.v2 and args.clip_skip is not None:
    print("v2 with clip_skip will be unexpected / v2でclip_skipを使用することは想定されていません")

  # モデルを読み込む
  if not os.path.isfile(args.ckpt):             # ファイルがないならパターンで探し、一つだけ該当すればそれを使う
    files = glob.glob(args.ckpt)
    if len(files) == 1:
      args.ckpt = files[0]

  use_stable_diffusion_format = os.path.isfile(args.ckpt)
  if use_stable_diffusion_format:
    print("load StableDiffusion checkpoint")
    text_encoder, vae, unet = model_util.load_models_from_stable_diffusion_checkpoint(args.v2, args.ckpt)
  else:
    print("load Diffusers pretrained models")
    loading_pipe = StableDiffusionPipeline.from_pretrained(args.ckpt, safety_checker=None, torch_dtype=dtype)
    text_encoder = loading_pipe.text_encoder
    vae = loading_pipe.vae
    unet = loading_pipe.unet
    tokenizer = loading_pipe.tokenizer
    del loading_pipe

  # VAEを読み込む
  if args.vae is not None:
    vae = model_util.load_vae(args.vae, dtype)
    print("additional VAE loaded")

  # # 置換するCLIPを読み込む
  # if args.replace_clip_l14_336:
  #   text_encoder = load_clip_l14_336(dtype)
  #   print(f"large clip {CLIP_ID_L14_336} is loaded")

  if args.clip_guidance_scale > 0.0 or args.clip_image_guidance_scale:
    print("prepare clip model")
    clip_model = CLIPModel.from_pretrained(CLIP_MODEL_PATH, torch_dtype=dtype)
  else:
    clip_model = None

  if args.vgg16_guidance_scale > 0.0:
    print("prepare resnet model")
    vgg16_model = torchvision.models.vgg16(torchvision.models.VGG16_Weights.IMAGENET1K_V1)
  else:
    vgg16_model = None

  # xformers、Hypernetwork対応
  if not args.diffusers_xformers:
    replace_unet_modules(unet, not args.xformers, args.xformers)

  # tokenizerを読み込む
  print("loading tokenizer")
  if use_stable_diffusion_format:
    if args.v2:
      tokenizer = CLIPTokenizer.from_pretrained(V2_STABLE_DIFFUSION_PATH, subfolder="tokenizer")
    else:
      tokenizer = CLIPTokenizer.from_pretrained(TOKENIZER_PATH)  # , model_max_length=max_token_length + 2)

  # schedulerを用意する
  sched_init_args = {}
  scheduler_num_noises_per_step = 1
  if args.sampler == "ddim":
    scheduler_cls = DDIMScheduler
    scheduler_module = diffusers.schedulers.scheduling_ddim
  elif args.sampler == "ddpm":                    # ddpmはおかしくなるのでoptionから外してある
    scheduler_cls = DDPMScheduler
    scheduler_module = diffusers.schedulers.scheduling_ddpm
  elif args.sampler == "pndm":
    scheduler_cls = PNDMScheduler
    scheduler_module = diffusers.schedulers.scheduling_pndm
  elif args.sampler == 'lms' or args.sampler == 'k_lms':
    scheduler_cls = LMSDiscreteScheduler
    scheduler_module = diffusers.schedulers.scheduling_lms_discrete
  elif args.sampler == 'euler' or args.sampler == 'k_euler':
    scheduler_cls = EulerDiscreteScheduler
    scheduler_module = diffusers.schedulers.scheduling_euler_discrete
  elif args.sampler == 'euler_a' or args.sampler == 'k_euler_a':
    scheduler_cls = EulerAncestralDiscreteScheduler
    scheduler_module = diffusers.schedulers.scheduling_euler_ancestral_discrete
  elif args.sampler == "dpmsolver" or args.sampler == "dpmsolver++":
    scheduler_cls = DPMSolverMultistepScheduler
    sched_init_args['algorithm_type'] = args.sampler
    scheduler_module = diffusers.schedulers.scheduling_dpmsolver_multistep
  elif args.sampler == "dpmsingle":
    scheduler_cls = DPMSolverSinglestepScheduler
    scheduler_module = diffusers.schedulers.scheduling_dpmsolver_singlestep
  elif args.sampler == "heun":
    scheduler_cls = HeunDiscreteScheduler
    scheduler_module = diffusers.schedulers.scheduling_heun_discrete
  elif args.sampler == 'dpm_2' or args.sampler == 'k_dpm_2':
    scheduler_cls = KDPM2DiscreteScheduler
    scheduler_module = diffusers.schedulers.scheduling_k_dpm_2_discrete
  elif args.sampler == 'dpm_2_a' or args.sampler == 'k_dpm_2_a':
    scheduler_cls = KDPM2AncestralDiscreteScheduler
    scheduler_module = diffusers.schedulers.scheduling_k_dpm_2_ancestral_discrete
    scheduler_num_noises_per_step = 2

  if args.v_parameterization:
    sched_init_args['prediction_type'] = 'v_prediction'

  # samplerの乱数をあらかじめ指定するための処理

  # replace randn
  class NoiseManager:
    def __init__(self):
      self.sampler_noises = None
      self.sampler_noise_index = 0

    def reset_sampler_noises(self, noises):
      self.sampler_noise_index = 0
      self.sampler_noises = noises

    def randn(self, shape, device=None, dtype=None, layout=None, generator=None):
      # print("replacing", shape, len(self.sampler_noises), self.sampler_noise_index)
      if self.sampler_noises is not None and self.sampler_noise_index < len(self.sampler_noises):
        noise = self.sampler_noises[self.sampler_noise_index]
        if shape != noise.shape:
          noise = None
      else:
        noise = None

      if noise == None:
        print(f"unexpected noise request: {self.sampler_noise_index}, {shape}")
        noise = torch.randn(shape, dtype=dtype, device=device, generator=generator)

      self.sampler_noise_index += 1
      return noise

  class TorchRandReplacer:
    def __init__(self, noise_manager):
      self.noise_manager = noise_manager

    def __getattr__(self, item):
      if item == 'randn':
        return self.noise_manager.randn
      if hasattr(torch, item):
        return getattr(torch, item)
      raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, item))

  noise_manager = NoiseManager()
  if scheduler_module is not None:
    scheduler_module.torch = TorchRandReplacer(noise_manager)

  scheduler = scheduler_cls(num_train_timesteps=SCHEDULER_TIMESTEPS,
                            beta_start=SCHEDULER_LINEAR_START, beta_end=SCHEDULER_LINEAR_END,
                            beta_schedule=SCHEDLER_SCHEDULE, **sched_init_args)

  # clip_sample=Trueにする
  if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is False:
    print("set clip_sample to True")
    scheduler.config.clip_sample = True

  # deviceを決定する
  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")             # "mps"を考量してない

  # custom pipelineをコピったやつを生成する
  vae.to(dtype).to(device)
  text_encoder.to(dtype).to(device)
  unet.to(dtype).to(device)
  if clip_model is not None:
    clip_model.to(dtype).to(device)
  if vgg16_model is not None:
    vgg16_model.to(dtype).to(device)

  # networkを組み込む
  if args.network_module:
    networks = []
    network_default_muls = []
    for i, network_module in enumerate(args.network_module):
      print("import network module:", network_module)
      imported_module = importlib.import_module(network_module)

      network_mul = 1.0 if args.network_mul is None or len(args.network_mul) <= i else args.network_mul[i]
      network_default_muls.append(network_mul)

      net_kwargs = {}
      if args.network_args and i < len(args.network_args):
        network_args = args.network_args[i]
        # TODO escape special chars
        network_args = network_args.split(";")
        for net_arg in network_args:
          key, value = net_arg.split("=")
          net_kwargs[key] = value

      if args.network_weights and i < len(args.network_weights):
        network_weight = args.network_weights[i]
        print("load network weights from:", network_weight)

        if model_util.is_safetensors(network_weight) and args.network_show_meta:
          from safetensors.torch import safe_open
          with safe_open(network_weight, framework="pt") as f:
            metadata = f.metadata()
          if metadata is not None:
            print(f"metadata for: {network_weight}: {metadata}")

        network = imported_module.create_network_from_weights(network_mul, network_weight, vae, text_encoder, unet, **net_kwargs)
      else:
        raise ValueError("No weight. Weight is required.")
      if network is None:
        return

      network.apply_to(text_encoder, unet)

      if args.opt_channels_last:
        network.to(memory_format=torch.channels_last)
      network.to(dtype).to(device)

      networks.append(network)
  else:
    networks = []

  # ControlNetの処理
  control_nets: List[ControlNetInfo] = []
  if args.control_net_models:
    for i, model in enumerate(args.control_net_models):
      prep_type = None if not args.control_net_preps or len(args.control_net_preps) <= i else args.control_net_preps[i]
      weight = 1.0 if not args.control_net_weights or len(args.control_net_weights) <= i else args.control_net_weights[i]
      ratio = 1.0 if not args.control_net_ratios or len(args.control_net_ratios) <= i else args.control_net_ratios[i]

      ctrl_unet, ctrl_net = original_control_net.load_control_net(args.v2, unet, model)
      prep = original_control_net.load_preprocess(prep_type)
      control_nets.append(ControlNetInfo(ctrl_unet, ctrl_net, prep, weight, ratio))

  if args.opt_channels_last:
    print(f"set optimizing: channels last")
    text_encoder.to(memory_format=torch.channels_last)
    vae.to(memory_format=torch.channels_last)
    unet.to(memory_format=torch.channels_last)
    if clip_model is not None:
      clip_model.to(memory_format=torch.channels_last)
    if networks:
      for network in networks:
        network.to(memory_format=torch.channels_last)
    if vgg16_model is not None:
      vgg16_model.to(memory_format=torch.channels_last)

    for cn in control_nets:
      cn.unet.to(memory_format=torch.channels_last)
      cn.net.to(memory_format=torch.channels_last)

  pipe = PipelineLike(device, vae, text_encoder, tokenizer, unet, scheduler, args.clip_skip,
                      clip_model, args.clip_guidance_scale, args.clip_image_guidance_scale,
                      vgg16_model, args.vgg16_guidance_scale, args.vgg16_guidance_layer)
  pipe.set_control_nets(control_nets)
  print("pipeline is ready.")

  if args.diffusers_xformers:
    pipe.enable_xformers_memory_efficient_attention()

  # Textual Inversionを処理する
  if args.textual_inversion_embeddings:
    token_ids_embeds = []
    for embeds_file in args.textual_inversion_embeddings:
      if model_util.is_safetensors(embeds_file):
        from safetensors.torch import load_file
        data = load_file(embeds_file)
      else:
        data = torch.load(embeds_file, map_location="cpu")

      embeds = next(iter(data.values()))
      if type(embeds) != torch.Tensor:
        raise ValueError(f"weight file does not contains Tensor / 重みファイルのデータがTensorではありません: {embeds_file}")

      num_vectors_per_token = embeds.size()[0]
      token_string = os.path.splitext(os.path.basename(embeds_file))[0]
      token_strings = [token_string] + [f"{token_string}{i+1}" for i in range(num_vectors_per_token - 1)]

      # add new word to tokenizer, count is num_vectors_per_token
      num_added_tokens = tokenizer.add_tokens(token_strings)
      assert num_added_tokens == num_vectors_per_token, f"tokenizer has same word to token string (filename). please rename the file / 指定した名前（ファイル名）のトークンが既に存在します。ファイルをリネームしてください: {embeds_file}"

      token_ids = tokenizer.convert_tokens_to_ids(token_strings)
      print(f"Textual Inversion embeddings `{token_string}` loaded. Tokens are added: {token_ids}")
      assert min(token_ids) == token_ids[0] and token_ids[-1] == token_ids[0] + len(token_ids) - 1, f"token ids is not ordered"
      assert len(tokenizer) - 1 == token_ids[-1], f"token ids is not end of tokenize: {len(tokenizer)}"

      if num_vectors_per_token > 1:
        pipe.add_token_replacement(token_ids[0], token_ids)

      token_ids_embeds.append((token_ids, embeds))

    text_encoder.resize_token_embeddings(len(tokenizer))
    token_embeds = text_encoder.get_input_embeddings().weight.data
    for token_ids, embeds in token_ids_embeds:
      for token_id, embed in zip(token_ids, embeds):
        token_embeds[token_id] = embed

  # promptを取得する
  if args.from_file is not None:
    print(f"reading prompts from {args.from_file}")
    with open(args.from_file, "r", encoding="utf-8") as f:
      prompt_list = f.read().splitlines()
      prompt_list = [d for d in prompt_list if len(d.strip()) > 0]
  elif args.prompt is not None:
    prompt_list = [args.prompt]
  else:
    prompt_list = []

  if args.interactive:
    args.n_iter = 1

  # img2imgの前処理、画像の読み込みなど
  def load_images(path):
    if os.path.isfile(path):
      paths = [path]
    else:
      paths = glob.glob(os.path.join(path, "*.png")) + glob.glob(os.path.join(path, "*.jpg")) + \
          glob.glob(os.path.join(path, "*.jpeg")) + glob.glob(os.path.join(path, "*.webp"))
      paths.sort()

    images = []
    for p in paths:
      image = Image.open(p)
      if image.mode != "RGB":
        print(f"convert image to RGB from {image.mode}: {p}")
        image = image.convert("RGB")
      images.append(image)

    return images

  def resize_images(imgs, size):
    resized = []
    for img in imgs:
      r_img = img.resize(size, Image.Resampling.LANCZOS)
      if hasattr(img, 'filename'):                              # filename属性がない場合があるらしい
        r_img.filename = img.filename
      resized.append(r_img)
    return resized

  if args.image_path is not None:
    print(f"load image for img2img: {args.image_path}")
    init_images = load_images(args.image_path)
    assert len(init_images) > 0, f"No image / 画像がありません: {args.image_path}"
    print(f"loaded {len(init_images)} images for img2img")
  else:
    init_images = None

  if args.mask_path is not None:
    print(f"load mask for inpainting: {args.mask_path}")
    mask_images = load_images(args.mask_path)
    assert len(mask_images) > 0, f"No mask image / マスク画像がありません: {args.image_path}"
    print(f"loaded {len(mask_images)} mask images for inpainting")
  else:
    mask_images = None

  # promptがないとき、画像のPngInfoから取得する
  if init_images is not None and len(prompt_list) == 0 and not args.interactive:
    print("get prompts from images' meta data")
    for img in init_images:
      if 'prompt' in img.text:
        prompt = img.text['prompt']
        if 'negative-prompt' in img.text:
          prompt += " --n " + img.text['negative-prompt']
        prompt_list.append(prompt)

    # プロンプトと画像を一致させるため指定回数だけ繰り返す（画像を増幅する）
    l = []
    for im in init_images:
      l.extend([im] * args.images_per_prompt)
    init_images = l

    if mask_images is not None:
      l = []
      for im in mask_images:
        l.extend([im] * args.images_per_prompt)
      mask_images = l

  # 画像サイズにオプション指定があるときはリサイズする
  if init_images is not None and args.W is not None and args.H is not None:
    print(f"resize img2img source images to {args.W}*{args.H}")
    init_images = resize_images(init_images, (args.W, args.H))
    if mask_images is not None:
      print(f"resize img2img mask images to {args.W}*{args.H}")
      mask_images = resize_images(mask_images, (args.W, args.H))

  prev_image = None               # for VGG16 guided
  if args.guide_image_path is not None:
    print(f"load image for CLIP/VGG16/ControlNet guidance: {args.guide_image_path}")
    guide_images = []
    for p in args.guide_image_path:
      guide_images.extend(load_images(p))

    print(f"loaded {len(guide_images)} guide images for guidance")
    if len(guide_images) == 0:
      print(f"No guide image, use previous generated image. / ガイド画像がありません。直前に生成した画像を使います: {args.image_path}")
      guide_images = None
  else:
    guide_images = None

  # seed指定時はseedを決めておく
  if args.seed is not None:
    random.seed(args.seed)
    predefined_seeds = [random.randint(0, 0x7fffffff) for _ in range(args.n_iter * len(prompt_list) * args.images_per_prompt)]
    if len(predefined_seeds) == 1:
      predefined_seeds[0] = args.seed
  else:
    predefined_seeds = None

  # デフォルト画像サイズを設定する：img2imgではこれらの値は無視される（またはW*Hにリサイズ済み）
  if args.W is None:
    args.W = 512
  if args.H is None:
    args.H = 512

  # 画像生成のループ
  os.makedirs(args.outdir, exist_ok=True)
  max_embeddings_multiples = 1 if args.max_embeddings_multiples is None else args.max_embeddings_multiples

  for gen_iter in range(args.n_iter):
    print(f"iteration {gen_iter+1}/{args.n_iter}")
    iter_seed = random.randint(0, 0x7fffffff)

    # バッチ処理の関数
    def process_batch(batch: List[BatchData], highres_fix, highres_1st=False):
      batch_size = len(batch)

      # highres_fixの処理
      if highres_fix and not highres_1st:
        # 1st stageのバッチを作成して呼び出す：サイズを小さくして呼び出す
        print("process 1st stage1")
        batch_1st = []
        for base, ext in batch:
          width_1st = int(ext.width * args.highres_fix_scale + .5)
          height_1st = int(ext.height * args.highres_fix_scale + .5)
          width_1st = width_1st - width_1st % 32
          height_1st = height_1st - height_1st % 32

          ext_1st = BatchDataExt(width_1st, height_1st, args.highres_fix_steps, ext.scale,
                                 ext.negative_scale, ext.strength, ext.network_muls)
          batch_1st.append(BatchData(base, ext_1st))
        images_1st = process_batch(batch_1st, True, True)

        # 2nd stageのバッチを作成して以下処理する
        print("process 2nd stage1")
        batch_2nd = []
        for i, (bd, image) in enumerate(zip(batch, images_1st)):
          image = image.resize((bd.ext.width, bd.ext.height), resample=PIL.Image.LANCZOS)      # img2imgとして設定
          bd_2nd = BatchData(BatchDataBase(*bd.base[0:3], bd.base.seed+1, image, None, *bd.base[6:]), bd.ext)
          batch_2nd.append(bd_2nd)
        batch = batch_2nd

      # このバッチの情報を取り出す
      (step_first, _, _, _, init_image, mask_image, _, guide_image), \
          (width, height, steps, scale, negative_scale, strength, network_muls) = batch[0]
      noise_shape = (LATENT_CHANNELS, height // DOWNSAMPLING_FACTOR, width // DOWNSAMPLING_FACTOR)

      prompts = []
      negative_prompts = []
      start_code = torch.zeros((batch_size, *noise_shape), device=device, dtype=dtype)
      noises = [torch.zeros((batch_size, *noise_shape), device=device, dtype=dtype)
                for _ in range(steps * scheduler_num_noises_per_step)]
      seeds = []
      clip_prompts = []

      if init_image is not None:                      # img2img?
        i2i_noises = torch.zeros((batch_size, *noise_shape), device=device, dtype=dtype)
        init_images = []

        if mask_image is not None:
          mask_images = []
        else:
          mask_images = None
      else:
        i2i_noises = None
        init_images = None
        mask_images = None

      if guide_image is not None:                     # CLIP image guided?
        guide_images = []
      else:
        guide_images = None

      # バッチ内の位置に関わらず同じ乱数を使うためにここで乱数を生成しておく。あわせてimage/maskがbatch内で同一かチェックする
      all_images_are_same = True
      all_masks_are_same = True
      all_guide_images_are_same = True
      for i, ((_, prompt, negative_prompt, seed, init_image, mask_image, clip_prompt, guide_image), _) in enumerate(batch):
        prompts.append(prompt)
        negative_prompts.append(negative_prompt)
        seeds.append(seed)
        clip_prompts.append(clip_prompt)

        if init_image is not None:
          init_images.append(init_image)
          if i > 0 and all_images_are_same:
            all_images_are_same = init_images[-2] is init_image

        if mask_image is not None:
          mask_images.append(mask_image)
          if i > 0 and all_masks_are_same:
            all_masks_are_same = mask_images[-2] is mask_image

        if guide_image is not None:
          if type(guide_image) is list:
            guide_images.extend(guide_image)
            all_guide_images_are_same = False
          else:
            guide_images.append(guide_image)
            if i > 0 and all_guide_images_are_same:
              all_guide_images_are_same = guide_images[-2] is guide_image

        # make start code
        torch.manual_seed(seed)
        start_code[i] = torch.randn(noise_shape, device=device, dtype=dtype)

        # make each noises
        for j in range(steps * scheduler_num_noises_per_step):
          noises[j][i] = torch.randn(noise_shape, device=device, dtype=dtype)

        if i2i_noises is not None:                # img2img noise
          i2i_noises[i] = torch.randn(noise_shape, device=device, dtype=dtype)

      noise_manager.reset_sampler_noises(noises)

      # すべての画像が同じなら1枚だけpipeに渡すことでpipe側で処理を高速化する
      if init_images is not None and all_images_are_same:
        init_images = init_images[0]
      if mask_images is not None and all_masks_are_same:
        mask_images = mask_images[0]
      if guide_images is not None and all_guide_images_are_same:
        guide_images = guide_images[0]

      # ControlNet使用時はguide imageをリサイズする
      if control_nets:
        # TODO resampleのメソッド
        guide_images = guide_images if type(guide_images) == list else [guide_images]
        guide_images = [i.resize((width, height), resample=PIL.Image.LANCZOS) for i in guide_images]
        if len(guide_images) == 1:
          guide_images = guide_images[0]

      # generate
      if networks:
        for n, m in zip(networks, network_muls if network_muls else network_default_muls):
          n.set_multiplier(m)

      images = pipe(prompts, negative_prompts, init_images, mask_images, height, width, steps, scale, negative_scale, strength, latents=start_code,
                    output_type='pil', max_embeddings_multiples=max_embeddings_multiples, img2img_noise=i2i_noises, clip_prompts=clip_prompts, clip_guide_images=guide_images)[0]
      if highres_1st and not args.highres_fix_save_1st:
        return images

      # save image
      highres_prefix = ("0" if highres_1st else "1") if highres_fix else ""
      ts_str = time.strftime('%Y%m%d%H%M%S', time.localtime())
      for i, (image, prompt, negative_prompts, seed, clip_prompt) in enumerate(zip(images, prompts, negative_prompts, seeds, clip_prompts)):
        metadata = PngInfo()
        metadata.add_text("prompt", prompt)
        metadata.add_text("seed", str(seed))
        metadata.add_text("sampler", args.sampler)
        metadata.add_text("steps", str(steps))
        metadata.add_text("scale", str(scale))
        if negative_prompt is not None:
          metadata.add_text("negative-prompt", negative_prompt)
        if negative_scale is not None:
          metadata.add_text("negative-scale", str(negative_scale))
        if clip_prompt is not None:
          metadata.add_text("clip-prompt", clip_prompt)

        if args.use_original_file_name and init_images is not None:
          if type(init_images) is list:
            fln = os.path.splitext(os.path.basename(init_images[i % len(init_images)].filename))[0] + ".png"
          else:
            fln = os.path.splitext(os.path.basename(init_images.filename))[0] + ".png"
        elif args.sequential_file_name:
          fln = f"im_{highres_prefix}{step_first + i + 1:06d}.png"
        else:
          fln = f"im_{ts_str}_{highres_prefix}{i:03d}_{seed}.png"

        image.save(os.path.join(args.outdir, fln), pnginfo=metadata)

      if not args.no_preview and not highres_1st and args.interactive:
        try:
          import cv2
          for prompt, image in zip(prompts, images):
            cv2.imshow(prompt[:128], np.array(image)[:, :, ::-1])      # プロンプトが長いと死ぬ
            cv2.waitKey()
            cv2.destroyAllWindows()
        except ImportError:
          print("opencv-python is not installed, cannot preview / opencv-pythonがインストールされていないためプレビューできません")

      return images

    # 画像生成のプロンプトが一周するまでのループ
    prompt_index = 0
    global_step = 0
    batch_data = []
    while args.interactive or prompt_index < len(prompt_list):
      if len(prompt_list) == 0:
        # interactive
        valid = False
        while not valid:
          print("\nType prompt:")
          try:
            prompt = input()
          except EOFError:
            break

          valid = len(prompt.strip().split(' --')[0].strip()) > 0
        if not valid:                                     # EOF, end app
          break
      else:
        prompt = prompt_list[prompt_index]

      # parse prompt
      width = args.W
      height = args.H
      scale = args.scale
      negative_scale = args.negative_scale
      steps = args.steps
      seeds = None
      strength = 0.8 if args.strength is None else args.strength
      negative_prompt = ""
      clip_prompt = None
      network_muls = None

      prompt_args = prompt.strip().split(' --')
      prompt = prompt_args[0]
      print(f"prompt {prompt_index+1}/{len(prompt_list)}: {prompt}")

      for parg in prompt_args[1:]:
        try:
          m = re.match(r'w (\d+)', parg, re.IGNORECASE)
          if m:
            width = int(m.group(1))
            print(f"width: {width}")
            continue

          m = re.match(r'h (\d+)', parg, re.IGNORECASE)
          if m:
            height = int(m.group(1))
            print(f"height: {height}")
            continue

          m = re.match(r's (\d+)', parg, re.IGNORECASE)
          if m:               # steps
            steps = max(1, min(1000, int(m.group(1))))
            print(f"steps: {steps}")
            continue

          m = re.match(r'd ([\d,]+)', parg, re.IGNORECASE)
          if m:               # seed
            seeds = [int(d) for d in m.group(1).split(',')]
            print(f"seeds: {seeds}")
            continue

          m = re.match(r'l ([\d\.]+)', parg, re.IGNORECASE)
          if m:               # scale
            scale = float(m.group(1))
            print(f"scale: {scale}")
            continue

          m = re.match(r'nl ([\d\.]+|none|None)', parg, re.IGNORECASE)
          if m:               # negative scale
            if m.group(1).lower() == 'none':
              negative_scale = None
            else:
              negative_scale = float(m.group(1))
            print(f"negative scale: {negative_scale}")
            continue

          m = re.match(r't ([\d\.]+)', parg, re.IGNORECASE)
          if m:               # strength
            strength = float(m.group(1))
            print(f"strength: {strength}")
            continue

          m = re.match(r'n (.+)', parg, re.IGNORECASE)
          if m:               # negative prompt
            negative_prompt = m.group(1)
            print(f"negative prompt: {negative_prompt}")
            continue

          m = re.match(r'c (.+)', parg, re.IGNORECASE)
          if m:               # clip prompt
            clip_prompt = m.group(1)
            print(f"clip prompt: {clip_prompt}")
            continue

          m = re.match(r'am ([\d\.\-,]+)', parg, re.IGNORECASE)
          if m:               # network multiplies
            network_muls = [float(v) for v in m.group(1).split(",")]
            while len(network_muls) < len(networks):
              network_muls.append(network_muls[-1])
            print(f"network mul: {network_muls}")
            continue

        except ValueError as ex:
          print(f"Exception in parsing / 解析エラー: {parg}")
          print(ex)

      if seeds is not None:
        # 数が足りないなら繰り返す
        if len(seeds) < args.images_per_prompt:
          seeds = seeds * int(math.ceil(args.images_per_prompt / len(seeds)))
        seeds = seeds[:args.images_per_prompt]
      else:
        if predefined_seeds is not None:
          seeds = predefined_seeds[-args.images_per_prompt:]
          predefined_seeds = predefined_seeds[:-args.images_per_prompt]
        elif args.iter_same_seed:
          seeds = [iter_seed] * args.images_per_prompt
        else:
          seeds = [random.randint(0, 0x7fffffff) for _ in range(args.images_per_prompt)]
        if args.interactive:
          print(f"seed: {seeds}")

      init_image = mask_image = guide_image = None
      for seed in seeds:                  # images_per_promptの数だけ
        # 同一イメージを使うとき、本当はlatentに変換しておくと無駄がないが面倒なのでとりあえず毎回処理する
        if init_images is not None:
          init_image = init_images[global_step % len(init_images)]

          # 32単位に丸めたやつにresizeされるので踏襲する
          width, height = init_image.size
          width = width - width % 32
          height = height - height % 32
          if width != init_image.size[0] or height != init_image.size[1]:
            print(f"img2img image size is not divisible by 32 so aspect ratio is changed / img2imgの画像サイズが32で割り切れないためリサイズされます。画像が歪みます")

        if mask_images is not None:
          mask_image = mask_images[global_step % len(mask_images)]

        if guide_images is not None:
          if control_nets:                                                        # 複数件の場合あり
            c = len(control_nets)
            p = global_step % (len(guide_images) // c)
            guide_image = guide_images[p * c:p * c + c]
          else:
            guide_image = guide_images[global_step % len(guide_images)]
        elif args.clip_image_guidance_scale > 0 or args.vgg16_guidance_scale > 0:
          if prev_image is None:
            print("Generate 1st image without guide image.")
          else:
            print("Use previous image as guide image.")
            guide_image = prev_image

        b1 = BatchData(BatchDataBase(global_step, prompt, negative_prompt, seed, init_image, mask_image, clip_prompt, guide_image),
                       BatchDataExt(width, height, steps, scale, negative_scale, strength, tuple(network_muls) if network_muls else None))
        if len(batch_data) > 0 and batch_data[-1][1] != b1[1]:  # バッチ分割必要？
          process_batch(batch_data, highres_fix)
          batch_data.clear()

        batch_data.append(b1)
        if len(batch_data) == args.batch_size:
          prev_image = process_batch(batch_data, highres_fix)[0]
          batch_data.clear()

        global_step += 1

      prompt_index += 1

    if len(batch_data) > 0:
      process_batch(batch_data, highres_fix)
      batch_data.clear()

  print("done!")


if __name__ == '__main__':
  parser = argparse.ArgumentParser()

  parser.add_argument("--v2", action='store_true', help='load Stable Diffusion v2.0 model / Stable Diffusion 2.0のモデルを読み込む')
  parser.add_argument("--v_parameterization", action='store_true',
                      help='enable v-parameterization training / v-parameterization学習を有効にする')
  parser.add_argument("--prompt", type=str, default=None, help="prompt / プロンプト")
  parser.add_argument("--from_file", type=str, default=None,
                      help="if specified, load prompts from this file / 指定時はプロンプトをファイルから読み込む")
  parser.add_argument("--interactive", action='store_true', help='interactive mode (generates one image) / 対話モード（生成される画像は1枚になります）')
  parser.add_argument("--no_preview", action='store_true', help='do not show generated image in interactive mode / 対話モードで画像を表示しない')
  parser.add_argument("--image_path", type=str, default=None, help="image to inpaint or to generate from / img2imgまたはinpaintを行う元画像")
  parser.add_argument("--mask_path", type=str, default=None, help="mask in inpainting / inpaint時のマスク")
  parser.add_argument("--strength", type=float, default=None, help="img2img strength / img2img時のstrength")
  parser.add_argument("--images_per_prompt", type=int, default=1, help="number of images per prompt / プロンプトあたりの出力枚数")
  parser.add_argument("--outdir", type=str, default="outputs", help="dir to write results to / 生成画像の出力先")
  parser.add_argument("--sequential_file_name", action='store_true',  help="sequential output file name / 生成画像のファイル名を連番にする")
  parser.add_argument("--use_original_file_name", action='store_true',
                      help="prepend original file name in img2img / img2imgで元画像のファイル名を生成画像のファイル名の先頭に付ける")
  # parser.add_argument("--ddim_eta", type=float, default=0.0, help="ddim eta (eta=0.0 corresponds to deterministic sampling", )
  parser.add_argument("--n_iter", type=int, default=1, help="sample this often / 繰り返し回数")
  parser.add_argument("--H", type=int, default=None, help="image height, in pixel space / 生成画像高さ")
  parser.add_argument("--W", type=int, default=None, help="image width, in pixel space / 生成画像幅")
  parser.add_argument("--batch_size", type=int, default=1, help="batch size / バッチサイズ")
  parser.add_argument("--steps", type=int, default=50, help="number of ddim sampling steps / サンプリングステップ数")
  parser.add_argument('--sampler', type=str, default='ddim',
                      choices=['ddim', 'pndm', 'lms', 'euler', 'euler_a', 'heun', 'dpm_2', 'dpm_2_a', 'dpmsolver',
                               'dpmsolver++', 'dpmsingle',
                               'k_lms', 'k_euler', 'k_euler_a', 'k_dpm_2', 'k_dpm_2_a'],
                      help=f'sampler (scheduler) type / サンプラー（スケジューラ）の種類')
  parser.add_argument("--scale", type=float, default=7.5,
                      help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty)) / guidance scale")
  parser.add_argument("--ckpt", type=str, default=None, help="path to checkpoint of model / モデルのcheckpointファイルまたはディレクトリ")
  parser.add_argument("--vae", type=str, default=None,
                      help="path to checkpoint of vae to replace / VAEを入れ替える場合、VAEのcheckpointファイルまたはディレクトリ")
  # parser.add_argument("--replace_clip_l14_336", action='store_true',
  #                     help="Replace CLIP (Text Encoder) to l/14@336 / CLIP(Text Encoder)をl/14@336に入れ替える")
  parser.add_argument("--seed", type=int, default=None,
                      help="seed, or seed of seeds in multiple generation / 1枚生成時のseed、または複数枚生成時の乱数seedを決めるためのseed")
  parser.add_argument("--iter_same_seed", action='store_true',
                      help='use same seed for all prompts in iteration if no seed specified / 乱数seedの指定がないとき繰り返し内はすべて同じseedを使う（プロンプト間の差異の比較用）')
  parser.add_argument("--fp16", action='store_true', help='use fp16 / fp16を指定し省メモリ化する')
  parser.add_argument("--bf16", action='store_true', help='use bfloat16 / bfloat16を指定し省メモリ化する')
  parser.add_argument("--xformers", action='store_true', help='use xformers / xformersを使用し高速化する')
  parser.add_argument("--diffusers_xformers", action='store_true',
                      help='use xformers by diffusers (Hypernetworks doesn\'t work) / Diffusersでxformersを使用する（Hypernetwork利用不可）')
  parser.add_argument("--opt_channels_last", action='store_true',
                      help='set channels last option to model / モデルにchannels lastを指定し最適化する')
  parser.add_argument("--network_module", type=str, default=None, nargs='*',
                      help='additional network module to use / 追加ネットワークを使う時そのモジュール名')
  parser.add_argument("--network_weights", type=str, default=None, nargs='*',
                      help='additional network weights to load / 追加ネットワークの重み')
  parser.add_argument("--network_mul", type=float, default=None, nargs='*',
                      help='additional network multiplier / 追加ネットワークの効果の倍率')
  parser.add_argument("--network_args", type=str, default=None, nargs='*',
                      help='additional argmuments for network (key=value) / ネットワークへの追加の引数')
  parser.add_argument("--network_show_meta", action='store_true',
                      help='show metadata of network model / ネットワークモデルのメタデータを表示する')
  parser.add_argument("--textual_inversion_embeddings", type=str, default=None, nargs='*',
                      help='Embeddings files of Textual Inversion / Textual Inversionのembeddings')
  parser.add_argument("--clip_skip", type=int, default=None, help='layer number from bottom to use in CLIP / CLIPの後ろからn層目の出力を使う')
  parser.add_argument("--max_embeddings_multiples", type=int, default=None,
                      help='max embeding multiples, max token length is 75 * multiples / トークン長をデフォルトの何倍とするか 75*この値 がトークン長となる')
  parser.add_argument("--clip_guidance_scale", type=float, default=0.0,
                      help='enable CLIP guided SD, scale for guidance (DDIM, PNDM, LMS samplers only) / CLIP guided SDを有効にしてこのscaleを適用する（サンプラーはDDIM、PNDM、LMSのみ）')
  parser.add_argument("--clip_image_guidance_scale", type=float, default=0.0,
                      help='enable CLIP guided SD by image, scale for guidance / 画像によるCLIP guided SDを有効にしてこのscaleを適用する')
  parser.add_argument("--vgg16_guidance_scale", type=float, default=0.0,
                      help='enable VGG16 guided SD by image, scale for guidance / 画像によるVGG16 guided SDを有効にしてこのscaleを適用する')
  parser.add_argument("--vgg16_guidance_layer", type=int, default=20,
                      help='layer of VGG16 to calculate contents guide (1~30, 20 for conv4_2) / VGG16のcontents guideに使うレイヤー番号 (1~30、20はconv4_2)')
  parser.add_argument("--guide_image_path", type=str, default=None, nargs="*",
                      help="image to CLIP guidance / CLIP guided SDでガイドに使う画像")
  parser.add_argument("--highres_fix_scale", type=float, default=None,
                      help="enable highres fix, reso scale for 1st stage / highres fixを有効にして最初の解像度をこのscaleにする")
  parser.add_argument("--highres_fix_steps", type=int, default=28,
                      help="1st stage steps for highres fix / highres fixの最初のステージのステップ数")
  parser.add_argument("--highres_fix_save_1st", action='store_true',
                      help="save 1st stage images for highres fix / highres fixの最初のステージの画像を保存する")
  parser.add_argument("--negative_scale", type=float, default=None,
                      help="set another guidance scale for negative prompt / ネガティブプロンプトのscaleを指定する")

  parser.add_argument("--control_net_models", type=str, default=None, nargs='*',
                      help='ControlNet models to use / 使用するControlNetのモデル名')
  parser.add_argument("--control_net_preps", type=str, default=None, nargs='*',
                      help='ControlNet preprocess to use / 使用するControlNetのプリプロセス名')
  parser.add_argument("--control_net_weights", type=float, default=None, nargs='*', help='ControlNet weights / ControlNetの重み')
  parser.add_argument("--control_net_ratios", type=float, default=None, nargs='*',
                      help='ControlNet guidance ratio for steps / ControlNetでガイドするステップ比率')

  args = parser.parse_args()
  main(args)