mirror of
https://github.com/kohya-ss/sd-scripts.git
synced 2026-04-14 16:22:28 +00:00
3414 lines
144 KiB
Python
3414 lines
144 KiB
Python
import itertools
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import json
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from typing import Any, List, NamedTuple, Optional, Tuple, Union, Callable
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import glob
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import importlib
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import importlib.util
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import sys
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import inspect
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import time
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import zipfile
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from diffusers.utils import deprecate
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from diffusers.configuration_utils import FrozenDict
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import argparse
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import math
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import os
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import random
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import re
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import diffusers
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import numpy as np
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import torch
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from library.device_utils import init_ipex, clean_memory, get_preferred_device
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init_ipex()
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import torchvision
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from diffusers import (
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AutoencoderKL,
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DDPMScheduler,
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EulerAncestralDiscreteScheduler,
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DPMSolverMultistepScheduler,
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DPMSolverSinglestepScheduler,
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LMSDiscreteScheduler,
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PNDMScheduler,
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DDIMScheduler,
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EulerDiscreteScheduler,
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HeunDiscreteScheduler,
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KDPM2DiscreteScheduler,
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KDPM2AncestralDiscreteScheduler,
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# UNet2DConditionModel,
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StableDiffusionPipeline,
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)
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from einops import rearrange
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from tqdm import tqdm
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from torchvision import transforms
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from transformers import CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection, CLIPImageProcessor
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import PIL
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from PIL import Image
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from PIL.PngImagePlugin import PngInfo
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import library.model_util as model_util
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import library.train_util as train_util
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import library.sdxl_model_util as sdxl_model_util
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import library.sdxl_train_util as sdxl_train_util
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from networks.lora import LoRANetwork
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import tools.original_control_net as original_control_net
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from tools.original_control_net import ControlNetInfo
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from library.original_unet import UNet2DConditionModel, InferUNet2DConditionModel
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from library.sdxl_original_unet import InferSdxlUNet2DConditionModel
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from library.original_unet import FlashAttentionFunction
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from networks.control_net_lllite import ControlNetLLLite
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from library.utils import GradualLatent, EulerAncestralDiscreteSchedulerGL
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from library.utils import setup_logging, add_logging_arguments
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setup_logging()
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import logging
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logger = logging.getLogger(__name__)
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# scheduler:
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SCHEDULER_LINEAR_START = 0.00085
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SCHEDULER_LINEAR_END = 0.0120
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SCHEDULER_TIMESTEPS = 1000
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SCHEDLER_SCHEDULE = "scaled_linear"
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# その他の設定
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LATENT_CHANNELS = 4
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DOWNSAMPLING_FACTOR = 8
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CLIP_VISION_MODEL = "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k"
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# region モジュール入れ替え部
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"""
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高速化のためのモジュール入れ替え
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"""
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# def replace_unet_modules(unet: diffusers.models.unets.unet_2d_condition.UNet2DConditionModel, mem_eff_attn, xformers, sdpa):
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def replace_unet_modules(unet, mem_eff_attn, xformers, sdpa):
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if mem_eff_attn:
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logger.info("Enable memory efficient attention for U-Net")
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# これはDiffusersのU-Netではなく自前のU-Netなので置き換えなくても良い
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unet.set_use_memory_efficient_attention(False, True)
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elif xformers:
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logger.info("Enable xformers for U-Net")
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try:
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import xformers.ops
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except ImportError:
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raise ImportError("No xformers / xformersがインストールされていないようです")
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unet.set_use_memory_efficient_attention(True, False)
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elif sdpa:
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logger.info("Enable SDPA for U-Net")
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unet.set_use_memory_efficient_attention(False, False)
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unet.set_use_sdpa(True)
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# TODO common train_util.py
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def replace_vae_modules(vae: diffusers.models.AutoencoderKL, mem_eff_attn, xformers, sdpa):
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if mem_eff_attn:
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replace_vae_attn_to_memory_efficient()
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elif xformers:
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# replace_vae_attn_to_xformers() # 解像度によってxformersがエラーを出す？
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vae.set_use_memory_efficient_attention_xformers(True) # とりあえずこっちを使う
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elif sdpa:
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replace_vae_attn_to_sdpa()
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def replace_vae_attn_to_memory_efficient():
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logger.info("VAE Attention.forward has been replaced to FlashAttention (not xformers)")
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flash_func = FlashAttentionFunction
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def forward_flash_attn(self, hidden_states, **kwargs):
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q_bucket_size = 512
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k_bucket_size = 1024
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residual = hidden_states
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batch, channel, height, width = hidden_states.shape
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# norm
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hidden_states = self.group_norm(hidden_states)
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hidden_states = hidden_states.view(batch, channel, height * width).transpose(1, 2)
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# proj to q, k, v
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query_proj = self.to_q(hidden_states)
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key_proj = self.to_k(hidden_states)
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value_proj = self.to_v(hidden_states)
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query_proj, key_proj, value_proj = map(
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lambda t: rearrange(t, "b n (h d) -> b h n d", h=self.heads), (query_proj, key_proj, value_proj)
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)
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out = flash_func.apply(query_proj, key_proj, value_proj, None, False, q_bucket_size, k_bucket_size)
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out = rearrange(out, "b h n d -> b n (h d)")
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# compute next hidden_states
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# linear proj
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hidden_states = self.to_out[0](hidden_states)
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# dropout
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hidden_states = self.to_out[1](hidden_states)
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hidden_states = hidden_states.transpose(-1, -2).reshape(batch, channel, height, width)
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# res connect and rescale
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hidden_states = (hidden_states + residual) / self.rescale_output_factor
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return hidden_states
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def forward_flash_attn_0_14(self, hidden_states, **kwargs):
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if not hasattr(self, "to_q"):
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self.to_q = self.query
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self.to_k = self.key
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self.to_v = self.value
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self.to_out = [self.proj_attn, torch.nn.Identity()]
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self.heads = self.num_heads
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return forward_flash_attn(self, hidden_states, **kwargs)
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if diffusers.__version__ < "0.15.0":
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diffusers.models.attention.AttentionBlock.forward = forward_flash_attn_0_14
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else:
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diffusers.models.attention_processor.Attention.forward = forward_flash_attn
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def replace_vae_attn_to_xformers():
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logger.info("VAE: Attention.forward has been replaced to xformers")
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import xformers.ops
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def forward_xformers(self, hidden_states, **kwargs):
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residual = hidden_states
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batch, channel, height, width = hidden_states.shape
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# norm
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hidden_states = self.group_norm(hidden_states)
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hidden_states = hidden_states.view(batch, channel, height * width).transpose(1, 2)
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# proj to q, k, v
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query_proj = self.to_q(hidden_states)
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key_proj = self.to_k(hidden_states)
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value_proj = self.to_v(hidden_states)
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query_proj, key_proj, value_proj = map(
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lambda t: rearrange(t, "b n (h d) -> b h n d", h=self.heads), (query_proj, key_proj, value_proj)
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)
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query_proj = query_proj.contiguous()
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key_proj = key_proj.contiguous()
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value_proj = value_proj.contiguous()
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out = xformers.ops.memory_efficient_attention(query_proj, key_proj, value_proj, attn_bias=None)
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out = rearrange(out, "b h n d -> b n (h d)")
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# compute next hidden_states
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# linear proj
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hidden_states = self.to_out[0](hidden_states)
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# dropout
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hidden_states = self.to_out[1](hidden_states)
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hidden_states = hidden_states.transpose(-1, -2).reshape(batch, channel, height, width)
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# res connect and rescale
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hidden_states = (hidden_states + residual) / self.rescale_output_factor
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return hidden_states
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def forward_xformers_0_14(self, hidden_states, **kwargs):
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if not hasattr(self, "to_q"):
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self.to_q = self.query
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self.to_k = self.key
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self.to_v = self.value
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self.to_out = [self.proj_attn, torch.nn.Identity()]
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self.heads = self.num_heads
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return forward_xformers(self, hidden_states, **kwargs)
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if diffusers.__version__ < "0.15.0":
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diffusers.models.attention.AttentionBlock.forward = forward_xformers_0_14
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else:
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diffusers.models.attention_processor.Attention.forward = forward_xformers
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def replace_vae_attn_to_sdpa():
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logger.info("VAE: Attention.forward has been replaced to sdpa")
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def forward_sdpa(self, hidden_states, **kwargs):
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residual = hidden_states
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batch, channel, height, width = hidden_states.shape
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# norm
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hidden_states = self.group_norm(hidden_states)
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hidden_states = hidden_states.view(batch, channel, height * width).transpose(1, 2)
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# proj to q, k, v
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query_proj = self.to_q(hidden_states)
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key_proj = self.to_k(hidden_states)
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value_proj = self.to_v(hidden_states)
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query_proj, key_proj, value_proj = map(
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lambda t: rearrange(t, "b n (h d) -> b n h d", h=self.heads), (query_proj, key_proj, value_proj)
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)
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out = torch.nn.functional.scaled_dot_product_attention(
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query_proj, key_proj, value_proj, attn_mask=None, dropout_p=0.0, is_causal=False
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)
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out = rearrange(out, "b n h d -> b n (h d)")
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# compute next hidden_states
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# linear proj
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hidden_states = self.to_out[0](hidden_states)
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# dropout
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hidden_states = self.to_out[1](hidden_states)
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hidden_states = hidden_states.transpose(-1, -2).reshape(batch, channel, height, width)
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# res connect and rescale
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hidden_states = (hidden_states + residual) / self.rescale_output_factor
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return hidden_states
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def forward_sdpa_0_14(self, hidden_states, **kwargs):
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if not hasattr(self, "to_q"):
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self.to_q = self.query
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self.to_k = self.key
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self.to_v = self.value
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self.to_out = [self.proj_attn, torch.nn.Identity()]
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self.heads = self.num_heads
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return forward_sdpa(self, hidden_states, **kwargs)
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if diffusers.__version__ < "0.15.0":
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diffusers.models.attention.AttentionBlock.forward = forward_sdpa_0_14
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else:
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diffusers.models.attention_processor.Attention.forward = forward_sdpa
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# endregion
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# region 画像生成の本体：lpw_stable_diffusion.py （ASL）からコピーして修正
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# https://github.com/huggingface/diffusers/blob/main/examples/community/lpw_stable_diffusion.py
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# 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
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class PipelineLike:
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def __init__(
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self,
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is_sdxl,
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device,
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vae: AutoencoderKL,
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text_encoders: List[CLIPTextModel],
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tokenizers: List[CLIPTokenizer],
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unet: InferSdxlUNet2DConditionModel,
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scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
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clip_skip: int,
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):
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super().__init__()
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self.is_sdxl = is_sdxl
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self.device = device
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self.clip_skip = clip_skip
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if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
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deprecation_message = (
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f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
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f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
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"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
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" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
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" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
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" file"
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)
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deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
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new_config = dict(scheduler.config)
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new_config["steps_offset"] = 1
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scheduler._internal_dict = FrozenDict(new_config)
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if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
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deprecation_message = (
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f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
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" `clip_sample` should be set to False in the configuration file. Please make sure to update the"
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" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
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" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
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" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
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)
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deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
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new_config = dict(scheduler.config)
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new_config["clip_sample"] = False
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scheduler._internal_dict = FrozenDict(new_config)
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self.vae = vae
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self.text_encoders = text_encoders
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self.tokenizers = tokenizers
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self.unet: Union[InferUNet2DConditionModel, InferSdxlUNet2DConditionModel] = unet
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self.scheduler = scheduler
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self.safety_checker = None
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self.clip_vision_model: CLIPVisionModelWithProjection = None
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self.clip_vision_processor: CLIPImageProcessor = None
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self.clip_vision_strength = 0.0
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# Textual Inversion
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self.token_replacements_list = []
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for _ in range(len(self.text_encoders)):
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self.token_replacements_list.append({})
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# ControlNet
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self.control_nets: List[ControlNetInfo] = [] # only for SD 1.5
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self.control_net_lllites: List[ControlNetLLLite] = []
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self.control_net_enabled = True # control_netsが空ならTrueでもFalseでもControlNetは動作しない
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self.gradual_latent: GradualLatent = None
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# Textual Inversion
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def add_token_replacement(self, text_encoder_index, target_token_id, rep_token_ids):
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self.token_replacements_list[text_encoder_index][target_token_id] = rep_token_ids
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def set_enable_control_net(self, en: bool):
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self.control_net_enabled = en
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def get_token_replacer(self, tokenizer):
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tokenizer_index = self.tokenizers.index(tokenizer)
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token_replacements = self.token_replacements_list[tokenizer_index]
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def replace_tokens(tokens):
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# print("replace_tokens", tokens, "=>", token_replacements)
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if isinstance(tokens, torch.Tensor):
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tokens = tokens.tolist()
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new_tokens = []
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for token in tokens:
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if token in token_replacements:
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replacement = token_replacements[token]
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new_tokens.extend(replacement)
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else:
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new_tokens.append(token)
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return new_tokens
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return replace_tokens
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def set_control_nets(self, ctrl_nets):
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self.control_nets = ctrl_nets
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def set_control_net_lllites(self, ctrl_net_lllites):
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self.control_net_lllites = ctrl_net_lllites
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def set_gradual_latent(self, gradual_latent):
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if gradual_latent is None:
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logger.info("gradual_latent is disabled")
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self.gradual_latent = None
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else:
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logger.info(f"gradual_latent is enabled: {gradual_latent}")
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self.gradual_latent = gradual_latent # (ds_ratio, start_timesteps, every_n_steps, ratio_step)
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@torch.no_grad()
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def __call__(
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self,
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prompt: Union[str, List[str]],
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negative_prompt: Optional[Union[str, List[str]]] = None,
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init_image: Union[torch.FloatTensor, PIL.Image.Image, List[PIL.Image.Image]] = None,
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mask_image: Union[torch.FloatTensor, PIL.Image.Image, List[PIL.Image.Image]] = None,
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height: int = 1024,
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width: int = 1024,
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original_height: int = None,
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original_width: int = None,
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original_height_negative: int = None,
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original_width_negative: int = None,
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crop_top: int = 0,
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crop_left: int = 0,
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num_inference_steps: int = 50,
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guidance_scale: float = 7.5,
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negative_scale: float = None,
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strength: float = 0.8,
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# num_images_per_prompt: Optional[int] = 1,
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eta: float = 0.0,
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generator: Optional[torch.Generator] = None,
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||
latents: Optional[torch.FloatTensor] = None,
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||
max_embeddings_multiples: Optional[int] = 3,
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output_type: Optional[str] = "pil",
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||
vae_batch_size: float = None,
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return_latents: bool = False,
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||
# return_dict: bool = True,
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callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
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is_cancelled_callback: Optional[Callable[[], bool]] = None,
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callback_steps: Optional[int] = 1,
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img2img_noise=None,
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clip_guide_images=None,
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||
emb_normalize_mode: str = "original",
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**kwargs,
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||
):
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# TODO support secondary prompt
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||
num_images_per_prompt = 1 # fixed because already prompt is repeated
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if isinstance(prompt, str):
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batch_size = 1
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prompt = [prompt]
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elif isinstance(prompt, list):
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||
batch_size = len(prompt)
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else:
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raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
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||
regional_network = " AND " in prompt[0]
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vae_batch_size = (
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batch_size
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||
if vae_batch_size is None
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||
else (int(vae_batch_size) if vae_batch_size >= 1 else max(1, int(batch_size * vae_batch_size)))
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||
)
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if strength < 0 or strength > 1:
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||
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:
|
||
logger.warning(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`."
|
||
)
|
||
|
||
tes_text_embs = []
|
||
tes_uncond_embs = []
|
||
tes_real_uncond_embs = []
|
||
|
||
for tokenizer, text_encoder in zip(self.tokenizers, self.text_encoders):
|
||
token_replacer = self.get_token_replacer(tokenizer)
|
||
|
||
# use last text_pool, because it is from text encoder 2
|
||
text_embeddings, text_pool, uncond_embeddings, uncond_pool, _ = get_weighted_text_embeddings(
|
||
self.is_sdxl,
|
||
tokenizer,
|
||
text_encoder,
|
||
prompt=prompt,
|
||
uncond_prompt=negative_prompt if do_classifier_free_guidance else None,
|
||
max_embeddings_multiples=max_embeddings_multiples,
|
||
clip_skip=self.clip_skip,
|
||
token_replacer=token_replacer,
|
||
device=self.device,
|
||
emb_normalize_mode=emb_normalize_mode,
|
||
**kwargs,
|
||
)
|
||
tes_text_embs.append(text_embeddings)
|
||
tes_uncond_embs.append(uncond_embeddings)
|
||
|
||
if negative_scale is not None:
|
||
_, real_uncond_embeddings, _ = get_weighted_text_embeddings(
|
||
self.is_sdxl,
|
||
token_replacer,
|
||
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,
|
||
token_replacer=token_replacer,
|
||
device=self.device,
|
||
emb_normalize_mode=emb_normalize_mode,
|
||
**kwargs,
|
||
)
|
||
tes_real_uncond_embs.append(real_uncond_embeddings)
|
||
|
||
# concat text encoder outputs
|
||
text_embeddings = tes_text_embs[0]
|
||
uncond_embeddings = tes_uncond_embs[0]
|
||
for i in range(1, len(tes_text_embs)):
|
||
text_embeddings = torch.cat([text_embeddings, tes_text_embs[i]], dim=2) # n,77,2048
|
||
if do_classifier_free_guidance:
|
||
uncond_embeddings = torch.cat([uncond_embeddings, tes_uncond_embs[i]], dim=2) # n,77,2048
|
||
|
||
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])
|
||
|
||
if self.control_net_lllites:
|
||
# ControlNetのhintにguide imageを流用する。ControlNetの場合はControlNet側で行う
|
||
if isinstance(clip_guide_images, PIL.Image.Image):
|
||
clip_guide_images = [clip_guide_images]
|
||
if isinstance(clip_guide_images[0], PIL.Image.Image):
|
||
clip_guide_images = [preprocess_image(im) for im in clip_guide_images]
|
||
clip_guide_images = torch.cat(clip_guide_images)
|
||
if isinstance(clip_guide_images, list):
|
||
clip_guide_images = torch.stack(clip_guide_images)
|
||
|
||
clip_guide_images = clip_guide_images.to(self.device, dtype=text_embeddings.dtype)
|
||
|
||
# create size embs
|
||
if original_height is None:
|
||
original_height = height
|
||
if original_width is None:
|
||
original_width = width
|
||
if original_height_negative is None:
|
||
original_height_negative = original_height
|
||
if original_width_negative is None:
|
||
original_width_negative = original_width
|
||
if crop_top is None:
|
||
crop_top = 0
|
||
if crop_left is None:
|
||
crop_left = 0
|
||
if self.is_sdxl:
|
||
emb1 = sdxl_train_util.get_timestep_embedding(torch.FloatTensor([original_height, original_width]).unsqueeze(0), 256)
|
||
uc_emb1 = sdxl_train_util.get_timestep_embedding(
|
||
torch.FloatTensor([original_height_negative, original_width_negative]).unsqueeze(0), 256
|
||
)
|
||
emb2 = sdxl_train_util.get_timestep_embedding(torch.FloatTensor([crop_top, crop_left]).unsqueeze(0), 256)
|
||
emb3 = sdxl_train_util.get_timestep_embedding(torch.FloatTensor([height, width]).unsqueeze(0), 256)
|
||
c_vector = torch.cat([emb1, emb2, emb3], dim=1).to(self.device, dtype=text_embeddings.dtype).repeat(batch_size, 1)
|
||
uc_vector = torch.cat([uc_emb1, emb2, emb3], dim=1).to(self.device, dtype=text_embeddings.dtype).repeat(batch_size, 1)
|
||
|
||
if regional_network:
|
||
# use last pool for conditioning
|
||
num_sub_prompts = len(text_pool) // batch_size
|
||
text_pool = text_pool[num_sub_prompts - 1 :: num_sub_prompts] # last subprompt
|
||
|
||
if init_image is not None and self.clip_vision_model is not None:
|
||
logger.info(f"encode by clip_vision_model and apply clip_vision_strength={self.clip_vision_strength}")
|
||
vision_input = self.clip_vision_processor(init_image, return_tensors="pt", device=self.device)
|
||
pixel_values = vision_input["pixel_values"].to(self.device, dtype=text_embeddings.dtype)
|
||
|
||
clip_vision_embeddings = self.clip_vision_model(
|
||
pixel_values=pixel_values, output_hidden_states=True, return_dict=True
|
||
)
|
||
clip_vision_embeddings = clip_vision_embeddings.image_embeds
|
||
|
||
if len(clip_vision_embeddings) == 1 and batch_size > 1:
|
||
clip_vision_embeddings = clip_vision_embeddings.repeat((batch_size, 1))
|
||
|
||
clip_vision_embeddings = clip_vision_embeddings * self.clip_vision_strength
|
||
assert clip_vision_embeddings.shape == text_pool.shape, f"{clip_vision_embeddings.shape} != {text_pool.shape}"
|
||
text_pool = clip_vision_embeddings # replace: same as ComfyUI (?)
|
||
|
||
c_vector = torch.cat([text_pool, c_vector], dim=1)
|
||
if do_classifier_free_guidance:
|
||
uc_vector = torch.cat([uncond_pool, uc_vector], dim=1)
|
||
vector_embeddings = torch.cat([uc_vector, c_vector])
|
||
else:
|
||
vector_embeddings = c_vector
|
||
|
||
# set timesteps
|
||
self.scheduler.set_timesteps(num_inference_steps, self.device)
|
||
|
||
latents_dtype = text_embeddings.dtype
|
||
init_latents_orig = None
|
||
mask = 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)
|
||
if isinstance(init_image, list):
|
||
init_image = torch.stack(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)
|
||
if init_image.size()[-2:] == (height // 8, width // 8):
|
||
init_latents = init_image
|
||
else:
|
||
if vae_batch_size >= batch_size:
|
||
init_latent_dist = self.vae.encode(init_image.to(self.vae.dtype)).latent_dist
|
||
init_latents = init_latent_dist.sample(generator=generator)
|
||
else:
|
||
if torch.cuda.is_available():
|
||
torch.cuda.empty_cache()
|
||
init_latents = []
|
||
for i in tqdm(range(0, min(batch_size, len(init_image)), vae_batch_size)):
|
||
init_latent_dist = self.vae.encode(
|
||
(init_image[i : i + vae_batch_size] if vae_batch_size > 1 else init_image[i].unsqueeze(0)).to(
|
||
self.vae.dtype
|
||
)
|
||
).latent_dist
|
||
init_latents.append(init_latent_dist.sample(generator=generator))
|
||
init_latents = torch.cat(init_latents)
|
||
|
||
init_latents = (sdxl_model_util.VAE_SCALE_FACTOR if self.is_sdxl else 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)
|
||
each_control_net_enabled = [self.control_net_enabled] * len(self.control_nets)
|
||
|
||
if self.control_net_lllites:
|
||
# guided_hints = original_control_net.get_guided_hints(self.control_nets, num_latent_input, batch_size, clip_guide_images)
|
||
if self.control_net_enabled:
|
||
for control_net, _ in self.control_net_lllites:
|
||
with torch.no_grad():
|
||
control_net.set_cond_image(clip_guide_images)
|
||
else:
|
||
for control_net, _ in self.control_net_lllites:
|
||
control_net.set_cond_image(None)
|
||
|
||
each_control_net_enabled = [self.control_net_enabled] * len(self.control_net_lllites)
|
||
|
||
enable_gradual_latent = False
|
||
if self.gradual_latent:
|
||
if not hasattr(self.scheduler, "set_gradual_latent_params"):
|
||
logger.warning("gradual_latent is not supported for this scheduler. Ignoring.")
|
||
logger.warning(f"{self.scheduler.__class__.__name__}")
|
||
else:
|
||
enable_gradual_latent = True
|
||
step_elapsed = 1000
|
||
current_ratio = self.gradual_latent.ratio
|
||
|
||
# first, we downscale the latents to the specified ratio / 最初に指定された比率にlatentsをダウンスケールする
|
||
height, width = latents.shape[-2:]
|
||
org_dtype = latents.dtype
|
||
if org_dtype == torch.bfloat16:
|
||
latents = latents.float()
|
||
latents = torch.nn.functional.interpolate(
|
||
latents, scale_factor=current_ratio, mode="bicubic", align_corners=False
|
||
).to(org_dtype)
|
||
|
||
# apply unsharp mask / アンシャープマスクを適用する
|
||
if self.gradual_latent.gaussian_blur_ksize:
|
||
latents = self.gradual_latent.apply_unshark_mask(latents)
|
||
|
||
for i, t in enumerate(tqdm(timesteps)):
|
||
resized_size = None
|
||
if enable_gradual_latent:
|
||
# gradually upscale the latents / latentsを徐々にアップスケールする
|
||
if (
|
||
t < self.gradual_latent.start_timesteps
|
||
and current_ratio < 1.0
|
||
and step_elapsed >= self.gradual_latent.every_n_steps
|
||
):
|
||
current_ratio = min(current_ratio + self.gradual_latent.ratio_step, 1.0)
|
||
# make divisible by 8 because size of latents must be divisible at bottom of UNet
|
||
h = int(height * current_ratio) // 8 * 8
|
||
w = int(width * current_ratio) // 8 * 8
|
||
resized_size = (h, w)
|
||
self.scheduler.set_gradual_latent_params(resized_size, self.gradual_latent)
|
||
step_elapsed = 0
|
||
else:
|
||
self.scheduler.set_gradual_latent_params(None, None)
|
||
step_elapsed += 1
|
||
|
||
# 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)
|
||
|
||
# disable ControlNet-LLLite if ratio is set. ControlNet is disabled in ControlNetInfo
|
||
if self.control_net_lllites:
|
||
for j, ((control_net, ratio), enabled) in enumerate(zip(self.control_net_lllites, each_control_net_enabled)):
|
||
if not enabled or ratio >= 1.0:
|
||
continue
|
||
if ratio < i / len(timesteps):
|
||
logger.info(f"ControlNetLLLite {j} is disabled (ratio={ratio} at {i} / {len(timesteps)})")
|
||
control_net.set_cond_image(None)
|
||
each_control_net_enabled[j] = False
|
||
|
||
# predict the noise residual
|
||
if self.control_nets and self.control_net_enabled:
|
||
if regional_network:
|
||
num_sub_and_neg_prompts = len(text_embeddings) // batch_size
|
||
text_emb_last = text_embeddings[num_sub_and_neg_prompts - 2 :: num_sub_and_neg_prompts] # last subprompt
|
||
else:
|
||
text_emb_last = text_embeddings
|
||
|
||
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,
|
||
text_emb_last,
|
||
).sample
|
||
elif self.is_sdxl:
|
||
noise_pred = self.unet(latent_model_input, t, text_embeddings, vector_embeddings)
|
||
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)
|
||
)
|
||
|
||
# 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
|
||
|
||
if return_latents:
|
||
return latents
|
||
|
||
latents = 1 / (sdxl_model_util.VAE_SCALE_FACTOR if self.is_sdxl else 0.18215) * latents
|
||
if vae_batch_size >= batch_size:
|
||
image = self.vae.decode(latents.to(self.vae.dtype)).sample
|
||
else:
|
||
if torch.cuda.is_available():
|
||
torch.cuda.empty_cache()
|
||
images = []
|
||
for i in tqdm(range(0, batch_size, vae_batch_size)):
|
||
images.append(
|
||
self.vae.decode(
|
||
(latents[i : i + vae_batch_size] if vae_batch_size > 1 else latents[i].unsqueeze(0)).to(self.vae.dtype)
|
||
).sample
|
||
)
|
||
image = torch.cat(images)
|
||
|
||
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 torch.cuda.is_available():
|
||
torch.cuda.empty_cache()
|
||
|
||
if output_type == "pil":
|
||
# image = self.numpy_to_pil(image)
|
||
image = (image * 255).round().astype("uint8")
|
||
image = [Image.fromarray(im) for im in image]
|
||
|
||
return image
|
||
|
||
# return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
|
||
|
||
|
||
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
|
||
|
||
# keep break as separate token
|
||
text = text.replace("BREAK", "\\BREAK\\")
|
||
|
||
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] and res[i][0].strip() != "BREAK" and res[i + 1][0].strip() != "BREAK":
|
||
res[i][0] += res[i + 1][0]
|
||
res.pop(i + 1)
|
||
else:
|
||
i += 1
|
||
|
||
return res
|
||
|
||
|
||
def get_prompts_with_weights(tokenizer: CLIPTokenizer, token_replacer, 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:
|
||
if word.strip() == "BREAK":
|
||
# pad until next multiple of tokenizer's max token length
|
||
pad_len = tokenizer.model_max_length - (len(text_token) % tokenizer.model_max_length)
|
||
logger.info(f"BREAK pad_len: {pad_len}")
|
||
for i in range(pad_len):
|
||
# v2のときEOSをつけるべきかどうかわからないぜ
|
||
# if i == 0:
|
||
# text_token.append(tokenizer.eos_token_id)
|
||
# else:
|
||
text_token.append(tokenizer.pad_token_id)
|
||
text_weight.append(1.0)
|
||
continue
|
||
|
||
# tokenize and discard the starting and the ending token
|
||
token = tokenizer(word).input_ids[1:-1]
|
||
|
||
token = token_replacer(token) # for Textual Inversion
|
||
|
||
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:
|
||
logger.warning("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(
|
||
is_sdxl: bool,
|
||
text_encoder: CLIPTextModel,
|
||
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 = []
|
||
pool = None
|
||
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
|
||
for j in range(len(text_input_chunk)):
|
||
if text_input_chunk[j, -1] != eos and text_input_chunk[j, -1] != pad: # 最後に普通の文字がある
|
||
text_input_chunk[j, -1] = eos
|
||
if text_input_chunk[j, 1] == pad: # BOSだけであとはPAD
|
||
text_input_chunk[j, 1] = eos
|
||
|
||
# in sdxl, value of clip_skip is same for Text Encoder 1 and 2
|
||
enc_out = text_encoder(text_input_chunk, output_hidden_states=True, return_dict=True)
|
||
text_embedding = enc_out["hidden_states"][-clip_skip]
|
||
if not is_sdxl: # SD 1.5 requires final_layer_norm
|
||
text_embedding = text_encoder.text_model.final_layer_norm(text_embedding)
|
||
if pool is None:
|
||
pool = enc_out.get("text_embeds", None) # use 1st chunk, if provided
|
||
if pool is not None:
|
||
pool = train_util.pool_workaround(text_encoder, enc_out["last_hidden_state"], text_input_chunk, eos)
|
||
|
||
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:
|
||
enc_out = text_encoder(text_input, output_hidden_states=True, return_dict=True)
|
||
text_embeddings = enc_out["hidden_states"][-clip_skip]
|
||
if not is_sdxl: # SD 1.5 requires final_layer_norm
|
||
text_embeddings = text_encoder.text_model.final_layer_norm(text_embeddings)
|
||
pool = enc_out.get("text_embeds", None) # text encoder 1 doesn't return this
|
||
if pool is not None:
|
||
pool = train_util.pool_workaround(text_encoder, enc_out["last_hidden_state"], text_input, eos)
|
||
return text_embeddings, pool
|
||
|
||
|
||
def get_weighted_text_embeddings(
|
||
is_sdxl: bool,
|
||
tokenizer: CLIPTokenizer,
|
||
text_encoder: CLIPTextModel,
|
||
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: int = 1,
|
||
token_replacer=None,
|
||
device=None,
|
||
emb_normalize_mode: Optional[str] = "original", # "original", "abs", "none"
|
||
**kwargs,
|
||
):
|
||
max_length = (tokenizer.model_max_length - 2) * max_embeddings_multiples + 2
|
||
if isinstance(prompt, str):
|
||
prompt = [prompt]
|
||
|
||
# split the prompts with "AND". each prompt must have the same number of splits
|
||
new_prompts = []
|
||
for p in prompt:
|
||
new_prompts.extend(p.split(" AND "))
|
||
prompt = new_prompts
|
||
|
||
if not skip_parsing:
|
||
prompt_tokens, prompt_weights = get_prompts_with_weights(tokenizer, token_replacer, 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(tokenizer, token_replacer, uncond_prompt, max_length - 2)
|
||
else:
|
||
prompt_tokens = [token[1:-1] for token in 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 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) // (tokenizer.model_max_length - 2) + 1,
|
||
)
|
||
max_embeddings_multiples = max(1, max_embeddings_multiples)
|
||
max_length = (tokenizer.model_max_length - 2) * max_embeddings_multiples + 2
|
||
|
||
# pad the length of tokens and weights
|
||
bos = tokenizer.bos_token_id
|
||
eos = tokenizer.eos_token_id
|
||
pad = 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=tokenizer.model_max_length,
|
||
)
|
||
prompt_tokens = torch.tensor(prompt_tokens, dtype=torch.long, device=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=tokenizer.model_max_length,
|
||
)
|
||
uncond_tokens = torch.tensor(uncond_tokens, dtype=torch.long, device=device)
|
||
|
||
# get the embeddings
|
||
text_embeddings, text_pool = get_unweighted_text_embeddings(
|
||
is_sdxl,
|
||
text_encoder,
|
||
prompt_tokens,
|
||
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=device)
|
||
if uncond_prompt is not None:
|
||
uncond_embeddings, uncond_pool = get_unweighted_text_embeddings(
|
||
is_sdxl,
|
||
text_encoder,
|
||
uncond_tokens,
|
||
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=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):
|
||
if emb_normalize_mode == "abs":
|
||
previous_mean = text_embeddings.float().abs().mean(axis=[-2, -1]).to(text_embeddings.dtype)
|
||
text_embeddings *= prompt_weights.unsqueeze(-1)
|
||
current_mean = text_embeddings.float().abs().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().abs().mean(axis=[-2, -1]).to(uncond_embeddings.dtype)
|
||
uncond_embeddings *= uncond_weights.unsqueeze(-1)
|
||
current_mean = uncond_embeddings.float().abs().mean(axis=[-2, -1]).to(uncond_embeddings.dtype)
|
||
uncond_embeddings *= (previous_mean / current_mean).unsqueeze(-1).unsqueeze(-1)
|
||
|
||
elif emb_normalize_mode == "none":
|
||
text_embeddings *= prompt_weights.unsqueeze(-1)
|
||
if uncond_prompt is not None:
|
||
uncond_embeddings *= uncond_weights.unsqueeze(-1)
|
||
|
||
else: # "original"
|
||
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, text_pool, uncond_embeddings, uncond_pool, prompt_tokens
|
||
return text_embeddings, text_pool, None, None, prompt_tokens
|
||
|
||
|
||
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.BILINEAR) # 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
|
||
|
||
|
||
# regular expression for dynamic prompt:
|
||
# starts and ends with "{" and "}"
|
||
# contains at least one variant divided by "|"
|
||
# optional framgments divided by "$$" at start
|
||
# if the first fragment is "E" or "e", enumerate all variants
|
||
# if the second fragment is a number or two numbers, repeat the variants in the range
|
||
# if the third fragment is a string, use it as a separator
|
||
|
||
RE_DYNAMIC_PROMPT = re.compile(r"\{((e|E)\$\$)?(([\d\-]+)\$\$)?(([^\|\}]+?)\$\$)?(.+?((\|).+?)*?)\}")
|
||
|
||
|
||
def handle_dynamic_prompt_variants(prompt, repeat_count):
|
||
founds = list(RE_DYNAMIC_PROMPT.finditer(prompt))
|
||
if not founds:
|
||
return [prompt]
|
||
|
||
# make each replacement for each variant
|
||
enumerating = False
|
||
replacers = []
|
||
for found in founds:
|
||
# if "e$$" is found, enumerate all variants
|
||
found_enumerating = found.group(2) is not None
|
||
enumerating = enumerating or found_enumerating
|
||
|
||
separator = ", " if found.group(6) is None else found.group(6)
|
||
variants = found.group(7).split("|")
|
||
|
||
# parse count range
|
||
count_range = found.group(4)
|
||
if count_range is None:
|
||
count_range = [1, 1]
|
||
else:
|
||
count_range = count_range.split("-")
|
||
if len(count_range) == 1:
|
||
count_range = [int(count_range[0]), int(count_range[0])]
|
||
elif len(count_range) == 2:
|
||
count_range = [int(count_range[0]), int(count_range[1])]
|
||
else:
|
||
logger.warning(f"invalid count range: {count_range}")
|
||
count_range = [1, 1]
|
||
if count_range[0] > count_range[1]:
|
||
count_range = [count_range[1], count_range[0]]
|
||
if count_range[0] < 0:
|
||
count_range[0] = 0
|
||
if count_range[1] > len(variants):
|
||
count_range[1] = len(variants)
|
||
|
||
if found_enumerating:
|
||
# make function to enumerate all combinations
|
||
def make_replacer_enum(vari, cr, sep):
|
||
def replacer():
|
||
values = []
|
||
for count in range(cr[0], cr[1] + 1):
|
||
for comb in itertools.combinations(vari, count):
|
||
values.append(sep.join(comb))
|
||
return values
|
||
|
||
return replacer
|
||
|
||
replacers.append(make_replacer_enum(variants, count_range, separator))
|
||
else:
|
||
# make function to choose random combinations
|
||
def make_replacer_single(vari, cr, sep):
|
||
def replacer():
|
||
count = random.randint(cr[0], cr[1])
|
||
comb = random.sample(vari, count)
|
||
return [sep.join(comb)]
|
||
|
||
return replacer
|
||
|
||
replacers.append(make_replacer_single(variants, count_range, separator))
|
||
|
||
# make each prompt
|
||
if not enumerating:
|
||
# if not enumerating, repeat the prompt, replace each variant randomly
|
||
prompts = []
|
||
for _ in range(repeat_count):
|
||
current = prompt
|
||
for found, replacer in zip(founds, replacers):
|
||
current = current.replace(found.group(0), replacer()[0], 1)
|
||
prompts.append(current)
|
||
else:
|
||
# if enumerating, iterate all combinations for previous prompts
|
||
prompts = [prompt]
|
||
|
||
for found, replacer in zip(founds, replacers):
|
||
if found.group(2) is not None:
|
||
# make all combinations for existing prompts
|
||
new_prompts = []
|
||
for current in prompts:
|
||
replecements = replacer()
|
||
for replecement in replecements:
|
||
new_prompts.append(current.replace(found.group(0), replecement, 1))
|
||
prompts = new_prompts
|
||
|
||
for found, replacer in zip(founds, replacers):
|
||
# make random selection for existing prompts
|
||
if found.group(2) is None:
|
||
for i in range(len(prompts)):
|
||
prompts[i] = prompts[i].replace(found.group(0), replacer()[0], 1)
|
||
|
||
return prompts
|
||
|
||
|
||
# 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
|
||
raw_prompt: str
|
||
file_name: Optional[str]
|
||
|
||
|
||
class BatchDataExt(NamedTuple):
|
||
# バッチ分割が必要なデータ
|
||
width: int
|
||
height: int
|
||
original_width: int
|
||
original_height: int
|
||
original_width_negative: int
|
||
original_height_negative: int
|
||
crop_left: int
|
||
crop_top: int
|
||
steps: int
|
||
scale: float
|
||
negative_scale: float
|
||
strength: float
|
||
network_muls: Tuple[float]
|
||
num_sub_prompts: int
|
||
|
||
|
||
class BatchData(NamedTuple):
|
||
return_latents: bool
|
||
base: BatchDataBase
|
||
ext: BatchDataExt
|
||
|
||
|
||
class ListPrompter:
|
||
def __init__(self, prompts: List[str]):
|
||
self.prompts = prompts
|
||
self.index = 0
|
||
|
||
def shuffle(self):
|
||
random.shuffle(self.prompts)
|
||
|
||
def __len__(self):
|
||
return len(self.prompts)
|
||
|
||
def __call__(self, *args, **kwargs):
|
||
if self.index >= len(self.prompts):
|
||
self.index = 0 # reset
|
||
return None
|
||
|
||
prompt = self.prompts[self.index]
|
||
self.index += 1
|
||
return prompt
|
||
|
||
|
||
def main(args):
|
||
|
||
if args.fp16:
|
||
dtype = torch.float16
|
||
elif args.bf16:
|
||
dtype = torch.bfloat16
|
||
else:
|
||
dtype = torch.float32
|
||
|
||
device = get_preferred_device()
|
||
|
||
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.v2 and args.clip_skip is not None:
|
||
logger.warning("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.exists(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]
|
||
|
||
name_or_path = os.readlink(args.ckpt) if os.path.islink(args.ckpt) else args.ckpt
|
||
use_stable_diffusion_format = os.path.isfile(name_or_path) # determine SD or Diffusers
|
||
|
||
# SDXLかどうかを判定する
|
||
is_sdxl = args.sdxl
|
||
if not is_sdxl and not args.v1 and not args.v2: # どれも指定されていない場合は自動で判定する
|
||
if use_stable_diffusion_format:
|
||
# if file size > 5.5GB, sdxl
|
||
is_sdxl = os.path.getsize(name_or_path) > 5.5 * 1024**3
|
||
else:
|
||
# if `text_encoder_2` subdirectory exists, sdxl
|
||
is_sdxl = os.path.isdir(os.path.join(name_or_path, "text_encoder_2"))
|
||
logger.info(f"SDXL: {is_sdxl}")
|
||
|
||
if is_sdxl:
|
||
if args.clip_skip is None:
|
||
args.clip_skip = 2
|
||
|
||
model_dtype = sdxl_train_util.match_mixed_precision(args, dtype)
|
||
(_, text_encoder1, text_encoder2, vae, unet, _, _) = sdxl_train_util._load_target_model(
|
||
args.ckpt, args.vae, sdxl_model_util.MODEL_VERSION_SDXL_BASE_V1_0, dtype, device, model_dtype
|
||
)
|
||
unet: InferSdxlUNet2DConditionModel = InferSdxlUNet2DConditionModel(unet)
|
||
text_encoders = [text_encoder1, text_encoder2]
|
||
else:
|
||
if args.clip_skip is None:
|
||
args.clip_skip = 2 if args.v2 else 1
|
||
|
||
if use_stable_diffusion_format:
|
||
logger.info("load StableDiffusion checkpoint")
|
||
text_encoder, vae, unet = model_util.load_models_from_stable_diffusion_checkpoint(args.v2, args.ckpt)
|
||
else:
|
||
logger.info("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
|
||
|
||
# Diffusers U-Net to original U-Net
|
||
original_unet = UNet2DConditionModel(
|
||
unet.config.sample_size,
|
||
unet.config.attention_head_dim,
|
||
unet.config.cross_attention_dim,
|
||
unet.config.use_linear_projection,
|
||
unet.config.upcast_attention,
|
||
)
|
||
original_unet.load_state_dict(unet.state_dict())
|
||
unet = original_unet
|
||
unet: InferUNet2DConditionModel = InferUNet2DConditionModel(unet)
|
||
text_encoders = [text_encoder]
|
||
|
||
# VAEを読み込む
|
||
if args.vae is not None:
|
||
vae = model_util.load_vae(args.vae, dtype)
|
||
logger.info("additional VAE loaded")
|
||
|
||
# xformers、Hypernetwork対応
|
||
if not args.diffusers_xformers:
|
||
mem_eff = not (args.xformers or args.sdpa)
|
||
replace_unet_modules(unet, mem_eff, args.xformers, args.sdpa)
|
||
replace_vae_modules(vae, mem_eff, args.xformers, args.sdpa)
|
||
|
||
# tokenizerを読み込む
|
||
logger.info("loading tokenizer")
|
||
if is_sdxl:
|
||
tokenizer1, tokenizer2 = sdxl_train_util.load_tokenizers(args)
|
||
tokenizers = [tokenizer1, tokenizer2]
|
||
else:
|
||
if use_stable_diffusion_format:
|
||
tokenizer = train_util.load_tokenizer(args)
|
||
tokenizers = [tokenizer]
|
||
|
||
# schedulerを用意する
|
||
sched_init_args = {}
|
||
has_steps_offset = True
|
||
has_clip_sample = True
|
||
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
|
||
has_clip_sample = False
|
||
elif args.sampler == "lms" or args.sampler == "k_lms":
|
||
scheduler_cls = LMSDiscreteScheduler
|
||
scheduler_module = diffusers.schedulers.scheduling_lms_discrete
|
||
has_clip_sample = False
|
||
elif args.sampler == "euler" or args.sampler == "k_euler":
|
||
scheduler_cls = EulerDiscreteScheduler
|
||
scheduler_module = diffusers.schedulers.scheduling_euler_discrete
|
||
has_clip_sample = False
|
||
elif args.sampler == "euler_a" or args.sampler == "k_euler_a":
|
||
scheduler_cls = EulerAncestralDiscreteSchedulerGL
|
||
scheduler_module = diffusers.schedulers.scheduling_euler_ancestral_discrete
|
||
has_clip_sample = False
|
||
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
|
||
has_clip_sample = False
|
||
elif args.sampler == "dpmsingle":
|
||
scheduler_cls = DPMSolverSinglestepScheduler
|
||
scheduler_module = diffusers.schedulers.scheduling_dpmsolver_singlestep
|
||
has_clip_sample = False
|
||
has_steps_offset = False
|
||
elif args.sampler == "heun":
|
||
scheduler_cls = HeunDiscreteScheduler
|
||
scheduler_module = diffusers.schedulers.scheduling_heun_discrete
|
||
has_clip_sample = False
|
||
elif args.sampler == "dpm_2" or args.sampler == "k_dpm_2":
|
||
scheduler_cls = KDPM2DiscreteScheduler
|
||
scheduler_module = diffusers.schedulers.scheduling_k_dpm_2_discrete
|
||
has_clip_sample = False
|
||
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
|
||
has_clip_sample = False
|
||
|
||
if args.v_parameterization:
|
||
sched_init_args["prediction_type"] = "v_prediction"
|
||
|
||
# 警告を出さないようにする
|
||
if has_steps_offset:
|
||
sched_init_args["steps_offset"] = 1
|
||
if has_clip_sample:
|
||
sched_init_args["clip_sample"] = False
|
||
|
||
# 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:
|
||
logger.warning(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,
|
||
)
|
||
|
||
# ↓以下は結局PipeでFalseに設定されるので意味がなかった
|
||
# # 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
|
||
if args.vae_slices:
|
||
from library.slicing_vae import SlicingAutoencoderKL
|
||
|
||
sli_vae = SlicingAutoencoderKL(
|
||
act_fn="silu",
|
||
block_out_channels=(128, 256, 512, 512),
|
||
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D"],
|
||
in_channels=3,
|
||
latent_channels=4,
|
||
layers_per_block=2,
|
||
norm_num_groups=32,
|
||
out_channels=3,
|
||
sample_size=512,
|
||
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
|
||
num_slices=args.vae_slices,
|
||
)
|
||
sli_vae.load_state_dict(vae.state_dict()) # vaeのパラメータをコピーする
|
||
vae = sli_vae
|
||
del sli_vae
|
||
|
||
vae_dtype = dtype
|
||
if args.no_half_vae:
|
||
logger.info("set vae_dtype to float32")
|
||
vae_dtype = torch.float32
|
||
vae.to(vae_dtype).to(device)
|
||
vae.eval()
|
||
|
||
for text_encoder in text_encoders:
|
||
text_encoder.to(dtype).to(device)
|
||
text_encoder.eval()
|
||
unet.to(dtype).to(device)
|
||
unet.eval()
|
||
|
||
# networkを組み込む
|
||
if args.network_module:
|
||
networks = []
|
||
network_default_muls = []
|
||
network_pre_calc = args.network_pre_calc
|
||
|
||
# merge関連の引数を統合する
|
||
if args.network_merge:
|
||
network_merge = len(args.network_module) # all networks are merged
|
||
elif args.network_merge_n_models:
|
||
network_merge = args.network_merge_n_models
|
||
else:
|
||
network_merge = 0
|
||
logger.info(f"network_merge: {network_merge}")
|
||
|
||
for i, network_module in enumerate(args.network_module):
|
||
logger.info(f"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]
|
||
|
||
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 is None or len(args.network_weights) <= i:
|
||
raise ValueError("No weight. Weight is required.")
|
||
|
||
network_weight = args.network_weights[i]
|
||
logger.info(f"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:
|
||
logger.info(f"metadata for: {network_weight}: {metadata}")
|
||
|
||
network, weights_sd = imported_module.create_network_from_weights(
|
||
network_mul, network_weight, vae, text_encoders, unet, for_inference=True, **net_kwargs
|
||
)
|
||
if network is None:
|
||
return
|
||
|
||
mergeable = network.is_mergeable()
|
||
if network_merge and not mergeable:
|
||
logger.warning("network is not mergiable. ignore merge option.")
|
||
|
||
if not mergeable or i >= network_merge:
|
||
# not merging
|
||
network.apply_to(text_encoders, unet)
|
||
info = network.load_state_dict(weights_sd, False) # network.load_weightsを使うようにするとよい
|
||
logger.info(f"weights are loaded: {info}")
|
||
|
||
if args.opt_channels_last:
|
||
network.to(memory_format=torch.channels_last)
|
||
network.to(dtype).to(device)
|
||
|
||
if network_pre_calc:
|
||
logger.info("backup original weights")
|
||
network.backup_weights()
|
||
|
||
networks.append(network)
|
||
network_default_muls.append(network_mul)
|
||
else:
|
||
network.merge_to(text_encoders, unet, weights_sd, dtype, device)
|
||
|
||
else:
|
||
networks = []
|
||
|
||
# upscalerの指定があれば取得する
|
||
upscaler = None
|
||
if args.highres_fix_upscaler:
|
||
logger.info("import upscaler module: {args.highres_fix_upscaler}")
|
||
imported_module = importlib.import_module(args.highres_fix_upscaler)
|
||
|
||
us_kwargs = {}
|
||
if args.highres_fix_upscaler_args:
|
||
for net_arg in args.highres_fix_upscaler_args.split(";"):
|
||
key, value = net_arg.split("=")
|
||
us_kwargs[key] = value
|
||
|
||
logger.info("create upscaler")
|
||
upscaler = imported_module.create_upscaler(**us_kwargs)
|
||
upscaler.to(dtype).to(device)
|
||
|
||
# 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))
|
||
|
||
control_net_lllites: List[Tuple[ControlNetLLLite, float]] = []
|
||
if args.control_net_lllite_models:
|
||
for i, model_file in enumerate(args.control_net_lllite_models):
|
||
logger.info(f"loading ControlNet-LLLite: {model_file}")
|
||
|
||
from safetensors.torch import load_file
|
||
|
||
state_dict = load_file(model_file)
|
||
mlp_dim = None
|
||
cond_emb_dim = None
|
||
for key, value in state_dict.items():
|
||
if mlp_dim is None and "down.0.weight" in key:
|
||
mlp_dim = value.shape[0]
|
||
elif cond_emb_dim is None and "conditioning1.0" in key:
|
||
cond_emb_dim = value.shape[0] * 2
|
||
if mlp_dim is not None and cond_emb_dim is not None:
|
||
break
|
||
assert mlp_dim is not None and cond_emb_dim is not None, f"invalid control net: {model_file}"
|
||
|
||
multiplier = (
|
||
1.0
|
||
if not args.control_net_multipliers or len(args.control_net_multipliers) <= i
|
||
else args.control_net_multipliers[i]
|
||
)
|
||
ratio = 1.0 if not args.control_net_ratios or len(args.control_net_ratios) <= i else args.control_net_ratios[i]
|
||
|
||
control_net_lllite = ControlNetLLLite(unet, cond_emb_dim, mlp_dim, multiplier=multiplier)
|
||
control_net_lllite.apply_to()
|
||
control_net_lllite.load_state_dict(state_dict)
|
||
control_net_lllite.to(dtype).to(device)
|
||
control_net_lllite.set_batch_cond_only(False, False)
|
||
control_net_lllites.append((control_net_lllite, ratio))
|
||
assert (
|
||
len(control_nets) == 0 or len(control_net_lllites) == 0
|
||
), "ControlNet and ControlNet-LLLite cannot be used at the same time"
|
||
|
||
if args.opt_channels_last:
|
||
logger.info(f"set optimizing: channels last")
|
||
for text_encoder in text_encoders:
|
||
text_encoder.to(memory_format=torch.channels_last)
|
||
vae.to(memory_format=torch.channels_last)
|
||
unet.to(memory_format=torch.channels_last)
|
||
if networks:
|
||
for network in networks:
|
||
network.to(memory_format=torch.channels_last)
|
||
|
||
for cn in control_nets:
|
||
cn.to(memory_format=torch.channels_last)
|
||
|
||
for cn in control_net_lllites:
|
||
cn.to(memory_format=torch.channels_last)
|
||
|
||
pipe = PipelineLike(
|
||
is_sdxl,
|
||
device,
|
||
vae,
|
||
text_encoders,
|
||
tokenizers,
|
||
unet,
|
||
scheduler,
|
||
args.clip_skip,
|
||
)
|
||
pipe.set_control_nets(control_nets)
|
||
pipe.set_control_net_lllites(control_net_lllites)
|
||
logger.info("pipeline is ready.")
|
||
|
||
if args.diffusers_xformers:
|
||
pipe.enable_xformers_memory_efficient_attention()
|
||
|
||
# Deep Shrink
|
||
if args.ds_depth_1 is not None:
|
||
unet.set_deep_shrink(args.ds_depth_1, args.ds_timesteps_1, args.ds_depth_2, args.ds_timesteps_2, args.ds_ratio)
|
||
|
||
# Gradual Latent
|
||
if args.gradual_latent_timesteps is not None:
|
||
if args.gradual_latent_unsharp_params:
|
||
us_params = args.gradual_latent_unsharp_params.split(",")
|
||
us_ksize, us_sigma, us_strength = [float(v) for v in us_params[:3]]
|
||
us_target_x = True if len(us_params) <= 3 else bool(int(us_params[3]))
|
||
us_ksize = int(us_ksize)
|
||
else:
|
||
us_ksize, us_sigma, us_strength, us_target_x = None, None, None, None
|
||
|
||
gradual_latent = GradualLatent(
|
||
args.gradual_latent_ratio,
|
||
args.gradual_latent_timesteps,
|
||
args.gradual_latent_every_n_steps,
|
||
args.gradual_latent_ratio_step,
|
||
args.gradual_latent_s_noise,
|
||
us_ksize,
|
||
us_sigma,
|
||
us_strength,
|
||
us_target_x,
|
||
)
|
||
pipe.set_gradual_latent(gradual_latent)
|
||
|
||
# Textual Inversionを処理する
|
||
if args.textual_inversion_embeddings:
|
||
token_ids_embeds1 = []
|
||
token_ids_embeds2 = []
|
||
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")
|
||
|
||
if "string_to_param" in data:
|
||
data = data["string_to_param"]
|
||
if is_sdxl:
|
||
|
||
embeds1 = data["clip_l"] # text encoder 1
|
||
embeds2 = data["clip_g"] # text encoder 2
|
||
else:
|
||
embeds1 = next(iter(data.values()))
|
||
embeds2 = None
|
||
|
||
num_vectors_per_token = embeds1.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_tokens1 = tokenizers[0].add_tokens(token_strings)
|
||
num_added_tokens2 = tokenizers[1].add_tokens(token_strings) if is_sdxl else 0
|
||
assert num_added_tokens1 == num_vectors_per_token and (
|
||
num_added_tokens2 == 0 or num_added_tokens2 == num_vectors_per_token
|
||
), (
|
||
f"tokenizer has same word to token string (filename): {embeds_file}"
|
||
+ f" / 指定した名前（ファイル名）のトークンが既に存在します: {embeds_file}"
|
||
)
|
||
|
||
token_ids1 = tokenizers[0].convert_tokens_to_ids(token_strings)
|
||
token_ids2 = tokenizers[1].convert_tokens_to_ids(token_strings) if is_sdxl else None
|
||
logger.info(f"Textual Inversion embeddings `{token_string}` loaded. Tokens are added: {token_ids1} and {token_ids2}")
|
||
assert (
|
||
min(token_ids1) == token_ids1[0] and token_ids1[-1] == token_ids1[0] + len(token_ids1) - 1
|
||
), f"token ids1 is not ordered"
|
||
assert not is_sdxl or (
|
||
min(token_ids2) == token_ids2[0] and token_ids2[-1] == token_ids2[0] + len(token_ids2) - 1
|
||
), f"token ids2 is not ordered"
|
||
assert len(tokenizers[0]) - 1 == token_ids1[-1], f"token ids 1 is not end of tokenize: {len(tokenizers[0])}"
|
||
assert (
|
||
not is_sdxl or len(tokenizers[1]) - 1 == token_ids2[-1]
|
||
), f"token ids 2 is not end of tokenize: {len(tokenizers[1])}"
|
||
|
||
if num_vectors_per_token > 1:
|
||
pipe.add_token_replacement(0, token_ids1[0], token_ids1) # hoge -> hoge, hogea, hogeb, ...
|
||
if is_sdxl:
|
||
pipe.add_token_replacement(1, token_ids2[0], token_ids2)
|
||
|
||
token_ids_embeds1.append((token_ids1, embeds1))
|
||
if is_sdxl:
|
||
token_ids_embeds2.append((token_ids2, embeds2))
|
||
|
||
text_encoders[0].resize_token_embeddings(len(tokenizers[0]))
|
||
token_embeds1 = text_encoders[0].get_input_embeddings().weight.data
|
||
for token_ids, embeds in token_ids_embeds1:
|
||
for token_id, embed in zip(token_ids, embeds):
|
||
token_embeds1[token_id] = embed
|
||
|
||
if is_sdxl:
|
||
text_encoders[1].resize_token_embeddings(len(tokenizers[1]))
|
||
token_embeds2 = text_encoders[1].get_input_embeddings().weight.data
|
||
for token_ids, embeds in token_ids_embeds2:
|
||
for token_id, embed in zip(token_ids, embeds):
|
||
token_embeds2[token_id] = embed
|
||
|
||
# promptを取得する
|
||
prompt_list = None
|
||
if args.from_file is not None:
|
||
logger.info(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 and d[0] != "#"]
|
||
prompter = ListPrompter(prompt_list)
|
||
|
||
elif args.from_module is not None:
|
||
|
||
def load_module_from_path(module_name, file_path):
|
||
spec = importlib.util.spec_from_file_location(module_name, file_path)
|
||
if spec is None:
|
||
raise ImportError(f"Module '{module_name}' cannot be loaded from '{file_path}'")
|
||
module = importlib.util.module_from_spec(spec)
|
||
sys.modules[module_name] = module
|
||
spec.loader.exec_module(module)
|
||
return module
|
||
|
||
logger.info(f"reading prompts from module: {args.from_module}")
|
||
prompt_module = load_module_from_path("prompt_module", args.from_module)
|
||
|
||
prompter = prompt_module.get_prompter(args, pipe, networks)
|
||
|
||
elif args.prompt is not None:
|
||
prompter = ListPrompter([args.prompt])
|
||
|
||
else:
|
||
prompter = None # interactive mode
|
||
|
||
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":
|
||
logger.info(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:
|
||
logger.info(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}"
|
||
logger.info(f"loaded {len(init_images)} images for img2img")
|
||
|
||
# CLIP Vision
|
||
if args.clip_vision_strength is not None:
|
||
logger.info(f"load CLIP Vision model: {CLIP_VISION_MODEL}")
|
||
vision_model = CLIPVisionModelWithProjection.from_pretrained(CLIP_VISION_MODEL, projection_dim=1280)
|
||
vision_model.to(device, dtype)
|
||
processor = CLIPImageProcessor.from_pretrained(CLIP_VISION_MODEL)
|
||
|
||
pipe.clip_vision_model = vision_model
|
||
pipe.clip_vision_processor = processor
|
||
pipe.clip_vision_strength = args.clip_vision_strength
|
||
logger.info(f"CLIP Vision model loaded.")
|
||
|
||
else:
|
||
init_images = None
|
||
|
||
if args.mask_path is not None:
|
||
logger.info(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}"
|
||
logger.info(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 prompter is None and not args.interactive:
|
||
logger.info("get prompts from images' metadata")
|
||
prompt_list = []
|
||
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)
|
||
prompter = ListPrompter(prompt_list)
|
||
|
||
# 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 args.W is not None and args.H is not None:
|
||
# highres fix を考慮に入れる
|
||
w, h = args.W, args.H
|
||
if highres_fix:
|
||
w = int(w * args.highres_fix_scale + 0.5)
|
||
h = int(h * args.highres_fix_scale + 0.5)
|
||
|
||
if init_images is not None:
|
||
logger.info(f"resize img2img source images to {w}*{h}")
|
||
init_images = resize_images(init_images, (w, h))
|
||
if mask_images is not None:
|
||
logger.info(f"resize img2img mask images to {w}*{h}")
|
||
mask_images = resize_images(mask_images, (w, h))
|
||
|
||
regional_network = False
|
||
if networks and mask_images:
|
||
# mask を領域情報として流用する、現在は一回のコマンド呼び出しで1枚だけ対応
|
||
regional_network = True
|
||
logger.info("use mask as region")
|
||
|
||
size = None
|
||
for i, network in enumerate(networks):
|
||
if (i < 3 and args.network_regional_mask_max_color_codes is None) or i < args.network_regional_mask_max_color_codes:
|
||
np_mask = np.array(mask_images[0])
|
||
|
||
if args.network_regional_mask_max_color_codes:
|
||
# カラーコードでマスクを指定する
|
||
ch0 = (i + 1) & 1
|
||
ch1 = ((i + 1) >> 1) & 1
|
||
ch2 = ((i + 1) >> 2) & 1
|
||
np_mask = np.all(np_mask == np.array([ch0, ch1, ch2]) * 255, axis=2)
|
||
np_mask = np_mask.astype(np.uint8) * 255
|
||
else:
|
||
np_mask = np_mask[:, :, i]
|
||
size = np_mask.shape
|
||
else:
|
||
np_mask = np.full(size, 255, dtype=np.uint8)
|
||
mask = torch.from_numpy(np_mask.astype(np.float32) / 255.0)
|
||
network.set_region(i, i == len(networks) - 1, mask)
|
||
mask_images = None
|
||
|
||
prev_image = None # for VGG16 guided
|
||
if args.guide_image_path is not None:
|
||
logger.info(f"load image for ControlNet guidance: {args.guide_image_path}")
|
||
guide_images = []
|
||
for p in args.guide_image_path:
|
||
guide_images.extend(load_images(p))
|
||
|
||
logger.info(f"loaded {len(guide_images)} guide images for guidance")
|
||
if len(guide_images) == 0:
|
||
logger.warning(
|
||
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
|
||
|
||
# 新しい乱数生成器を作成する
|
||
if args.seed is not None:
|
||
if prompt_list and len(prompt_list) == 1 and args.images_per_prompt == 1:
|
||
# 引数のseedをそのまま使う
|
||
def fixed_seed(*args, **kwargs):
|
||
return args.seed
|
||
|
||
seed_random = SimpleNamespace(randint=fixed_seed)
|
||
else:
|
||
seed_random = random.Random(args.seed)
|
||
else:
|
||
seed_random = random.Random()
|
||
|
||
# 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 = 1024 if is_sdxl else 512
|
||
if args.H is None:
|
||
args.H = 1024 if is_sdxl else 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):
|
||
logger.info(f"iteration {gen_iter+1}/{args.n_iter}")
|
||
if args.iter_same_seed:
|
||
iter_seed = seed_random.randint(0, 2**32 - 1)
|
||
else:
|
||
iter_seed = None
|
||
|
||
# shuffle prompt list
|
||
if args.shuffle_prompts:
|
||
prompter.shuffle()
|
||
|
||
# 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
|
||
is_1st_latent = upscaler.support_latents() if upscaler else args.highres_fix_latents_upscaling
|
||
|
||
logger.info("process 1st stage")
|
||
batch_1st = []
|
||
for _, base, ext in batch:
|
||
|
||
def scale_and_round(x):
|
||
if x is None:
|
||
return None
|
||
return int(x * args.highres_fix_scale + 0.5)
|
||
|
||
width_1st = scale_and_round(ext.width)
|
||
height_1st = scale_and_round(ext.height)
|
||
width_1st = width_1st - width_1st % 32
|
||
height_1st = height_1st - height_1st % 32
|
||
|
||
original_width_1st = scale_and_round(ext.original_width)
|
||
original_height_1st = scale_and_round(ext.original_height)
|
||
original_width_negative_1st = scale_and_round(ext.original_width_negative)
|
||
original_height_negative_1st = scale_and_round(ext.original_height_negative)
|
||
crop_left_1st = scale_and_round(ext.crop_left)
|
||
crop_top_1st = scale_and_round(ext.crop_top)
|
||
|
||
strength_1st = ext.strength if args.highres_fix_strength is None else args.highres_fix_strength
|
||
|
||
ext_1st = BatchDataExt(
|
||
width_1st,
|
||
height_1st,
|
||
original_width_1st,
|
||
original_height_1st,
|
||
original_width_negative_1st,
|
||
original_height_negative_1st,
|
||
crop_left_1st,
|
||
crop_top_1st,
|
||
args.highres_fix_steps,
|
||
ext.scale,
|
||
ext.negative_scale,
|
||
strength_1st,
|
||
ext.network_muls,
|
||
ext.num_sub_prompts,
|
||
)
|
||
batch_1st.append(BatchData(is_1st_latent, base, ext_1st))
|
||
|
||
pipe.set_enable_control_net(True) # 1st stageではControlNetを有効にする
|
||
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
|
||
logger.info("process 2nd stage")
|
||
width_2nd, height_2nd = batch[0].ext.width, batch[0].ext.height
|
||
|
||
if upscaler:
|
||
# upscalerを使って画像を拡大する
|
||
lowreso_imgs = None if is_1st_latent else images_1st
|
||
lowreso_latents = None if not is_1st_latent else images_1st
|
||
|
||
# 戻り値はPIL.Image.Imageかtorch.Tensorのlatents
|
||
batch_size = len(images_1st)
|
||
vae_batch_size = (
|
||
batch_size
|
||
if args.vae_batch_size is None
|
||
else (max(1, int(batch_size * args.vae_batch_size)) if args.vae_batch_size < 1 else args.vae_batch_size)
|
||
)
|
||
vae_batch_size = int(vae_batch_size)
|
||
images_1st = upscaler.upscale(
|
||
vae, lowreso_imgs, lowreso_latents, dtype, width_2nd, height_2nd, batch_size, vae_batch_size
|
||
)
|
||
|
||
elif args.highres_fix_latents_upscaling:
|
||
# latentを拡大する
|
||
org_dtype = images_1st.dtype
|
||
if images_1st.dtype == torch.bfloat16:
|
||
images_1st = images_1st.to(torch.float) # interpolateがbf16をサポートしていない
|
||
images_1st = torch.nn.functional.interpolate(
|
||
images_1st, (batch[0].ext.height // 8, batch[0].ext.width // 8), mode="bilinear"
|
||
) # , antialias=True)
|
||
images_1st = images_1st.to(org_dtype)
|
||
|
||
else:
|
||
# 画像をLANCZOSで拡大する
|
||
images_1st = [image.resize((width_2nd, height_2nd), resample=PIL.Image.LANCZOS) for image in images_1st]
|
||
|
||
batch_2nd = []
|
||
for i, (bd, image) in enumerate(zip(batch, images_1st)):
|
||
bd_2nd = BatchData(False, BatchDataBase(*bd.base[0:3], bd.base.seed + 1, image, None, *bd.base[6:]), bd.ext)
|
||
batch_2nd.append(bd_2nd)
|
||
batch = batch_2nd
|
||
|
||
if args.highres_fix_disable_control_net:
|
||
pipe.set_enable_control_net(False) # オプション指定時、2nd stageではControlNetを無効にする
|
||
|
||
# このバッチの情報を取り出す
|
||
(
|
||
return_latents,
|
||
(step_first, _, _, _, init_image, mask_image, _, guide_image, _, _),
|
||
(
|
||
width,
|
||
height,
|
||
original_width,
|
||
original_height,
|
||
original_width_negative,
|
||
original_height_negative,
|
||
crop_left,
|
||
crop_top,
|
||
steps,
|
||
scale,
|
||
negative_scale,
|
||
strength,
|
||
network_muls,
|
||
num_sub_prompts,
|
||
),
|
||
) = batch[0]
|
||
noise_shape = (LATENT_CHANNELS, height // DOWNSAMPLING_FACTOR, width // DOWNSAMPLING_FACTOR)
|
||
|
||
prompts = []
|
||
negative_prompts = []
|
||
raw_prompts = []
|
||
filenames = []
|
||
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, raw_prompt, filename),
|
||
_,
|
||
) in enumerate(batch):
|
||
prompts.append(prompt)
|
||
negative_prompts.append(negative_prompt)
|
||
seeds.append(seed)
|
||
clip_prompts.append(clip_prompt)
|
||
raw_prompts.append(raw_prompt)
|
||
filenames.append(filename)
|
||
|
||
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 or control_net_lllites:
|
||
# 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:
|
||
# 追加ネットワークの処理
|
||
shared = {}
|
||
for n, m in zip(networks, network_muls if network_muls else network_default_muls):
|
||
n.set_multiplier(m)
|
||
if regional_network:
|
||
# TODO バッチから ds_ratio を取り出すべき
|
||
n.set_current_generation(batch_size, num_sub_prompts, width, height, shared, unet.ds_ratio)
|
||
|
||
if not regional_network and network_pre_calc:
|
||
for n in networks:
|
||
n.restore_weights()
|
||
for n in networks:
|
||
n.pre_calculation()
|
||
logger.info("pre-calculation... done")
|
||
|
||
images = pipe(
|
||
prompts,
|
||
negative_prompts,
|
||
init_images,
|
||
mask_images,
|
||
height,
|
||
width,
|
||
original_height,
|
||
original_width,
|
||
original_height_negative,
|
||
original_width_negative,
|
||
crop_top,
|
||
crop_left,
|
||
steps,
|
||
scale,
|
||
negative_scale,
|
||
strength,
|
||
latents=start_code,
|
||
output_type="pil",
|
||
max_embeddings_multiples=max_embeddings_multiples,
|
||
img2img_noise=i2i_noises,
|
||
vae_batch_size=args.vae_batch_size,
|
||
return_latents=return_latents,
|
||
clip_prompts=clip_prompts,
|
||
clip_guide_images=guide_images,
|
||
emb_normalize_mode=args.emb_normalize_mode,
|
||
)
|
||
if highres_1st and not args.highres_fix_save_1st: # return images or latents
|
||
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, raw_prompt, filename) in enumerate(
|
||
zip(images, prompts, negative_prompts, seeds, clip_prompts, raw_prompts, filenames)
|
||
):
|
||
if highres_fix:
|
||
seed -= 1 # record original seed
|
||
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 raw_prompt is not None:
|
||
metadata.add_text("raw-prompt", raw_prompt)
|
||
if is_sdxl:
|
||
metadata.add_text("original-height", str(original_height))
|
||
metadata.add_text("original-width", str(original_width))
|
||
metadata.add_text("original-height-negative", str(original_height_negative))
|
||
metadata.add_text("original-width-negative", str(original_width_negative))
|
||
metadata.add_text("crop-top", str(crop_top))
|
||
metadata.add_text("crop-left", str(crop_left))
|
||
|
||
if filename is not None:
|
||
fln = first_available_filename(args.outdir, filename) #Checks to make sure is not existing file, else returns first available sequential filename
|
||
else:
|
||
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"
|
||
|
||
if fln.endswith(".webp"):
|
||
image.save(os.path.join(args.outdir, fln), pnginfo=metadata, quality=100) # lossy
|
||
else:
|
||
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:
|
||
logger.warning(
|
||
"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 True:
|
||
if args.interactive:
|
||
# interactive
|
||
valid = False
|
||
while not valid:
|
||
logger.info("\nType prompt:")
|
||
try:
|
||
raw_prompt = input()
|
||
except EOFError:
|
||
break
|
||
|
||
valid = len(raw_prompt.strip().split(" --")[0].strip()) > 0
|
||
if not valid: # EOF, end app
|
||
break
|
||
else:
|
||
raw_prompt = prompter(args, pipe, seed_random, iter_seed, prompt_index, global_step)
|
||
if raw_prompt is None:
|
||
break
|
||
|
||
# sd-dynamic-prompts like variants:
|
||
# count is 1 (not dynamic) or images_per_prompt (no enumeration) or arbitrary (enumeration)
|
||
raw_prompts = handle_dynamic_prompt_variants(raw_prompt, args.images_per_prompt)
|
||
|
||
# repeat prompt
|
||
for pi in range(args.images_per_prompt if len(raw_prompts) == 1 else len(raw_prompts)):
|
||
raw_prompt = raw_prompts[pi] if len(raw_prompts) > 1 else raw_prompts[0]
|
||
filename = None
|
||
|
||
if pi == 0 or len(raw_prompts) > 1:
|
||
# parse prompt: if prompt is not changed, skip parsing
|
||
width = args.W
|
||
height = args.H
|
||
original_width = args.original_width
|
||
original_height = args.original_height
|
||
original_width_negative = args.original_width_negative
|
||
original_height_negative = args.original_height_negative
|
||
crop_top = args.crop_top
|
||
crop_left = args.crop_left
|
||
scale = args.scale
|
||
negative_scale = args.negative_scale
|
||
steps = args.steps
|
||
seed = None
|
||
if pi == 0:
|
||
seeds = None
|
||
strength = 0.8 if args.strength is None else args.strength
|
||
negative_prompt = ""
|
||
clip_prompt = None
|
||
network_muls = None
|
||
|
||
# Deep Shrink
|
||
ds_depth_1 = None # means no override
|
||
ds_timesteps_1 = args.ds_timesteps_1
|
||
ds_depth_2 = args.ds_depth_2
|
||
ds_timesteps_2 = args.ds_timesteps_2
|
||
ds_ratio = args.ds_ratio
|
||
|
||
# Gradual Latent
|
||
gl_timesteps = None # means no override
|
||
gl_ratio = args.gradual_latent_ratio
|
||
gl_every_n_steps = args.gradual_latent_every_n_steps
|
||
gl_ratio_step = args.gradual_latent_ratio_step
|
||
gl_s_noise = args.gradual_latent_s_noise
|
||
gl_unsharp_params = args.gradual_latent_unsharp_params
|
||
|
||
prompt_args = raw_prompt.strip().split(" --")
|
||
prompt = prompt_args[0]
|
||
length = len(prompter) if hasattr(prompter, "__len__") else 0
|
||
logger.info(f"prompt {prompt_index+1}/{length}: {prompt}")
|
||
|
||
for parg in prompt_args[1:]:
|
||
try:
|
||
m = re.match(r"w (\d+)", parg, re.IGNORECASE)
|
||
if m:
|
||
width = int(m.group(1))
|
||
logger.info(f"width: {width}")
|
||
continue
|
||
|
||
m = re.match(r"h (\d+)", parg, re.IGNORECASE)
|
||
if m:
|
||
height = int(m.group(1))
|
||
logger.info(f"height: {height}")
|
||
continue
|
||
|
||
m = re.match(r"ow (\d+)", parg, re.IGNORECASE)
|
||
if m:
|
||
original_width = int(m.group(1))
|
||
logger.info(f"original width: {original_width}")
|
||
continue
|
||
|
||
m = re.match(r"oh (\d+)", parg, re.IGNORECASE)
|
||
if m:
|
||
original_height = int(m.group(1))
|
||
logger.info(f"original height: {original_height}")
|
||
continue
|
||
|
||
m = re.match(r"nw (\d+)", parg, re.IGNORECASE)
|
||
if m:
|
||
original_width_negative = int(m.group(1))
|
||
logger.info(f"original width negative: {original_width_negative}")
|
||
continue
|
||
|
||
m = re.match(r"nh (\d+)", parg, re.IGNORECASE)
|
||
if m:
|
||
original_height_negative = int(m.group(1))
|
||
logger.info(f"original height negative: {original_height_negative}")
|
||
continue
|
||
|
||
m = re.match(r"ct (\d+)", parg, re.IGNORECASE)
|
||
if m:
|
||
crop_top = int(m.group(1))
|
||
logger.info(f"crop top: {crop_top}")
|
||
continue
|
||
|
||
m = re.match(r"cl (\d+)", parg, re.IGNORECASE)
|
||
if m:
|
||
crop_left = int(m.group(1))
|
||
logger.info(f"crop left: {crop_left}")
|
||
continue
|
||
|
||
m = re.match(r"s (\d+)", parg, re.IGNORECASE)
|
||
if m: # steps
|
||
steps = max(1, min(1000, int(m.group(1))))
|
||
logger.info(f"steps: {steps}")
|
||
continue
|
||
|
||
m = re.match(r"d ([\d,]+)", parg, re.IGNORECASE)
|
||
if m: # seed
|
||
if pi > 0 and len(raw_prompts) > 1: #Bypass od 2nd loop for dynamic prompts
|
||
continue
|
||
logger.info(f"{m}")
|
||
seeds = m.group(1).split(",")
|
||
seeds = [int(d.strip()) for d in seeds]
|
||
logger.info(f"seeds: {seeds}")
|
||
continue
|
||
|
||
m = re.match(r"l ([\d\.]+)", parg, re.IGNORECASE)
|
||
if m: # scale
|
||
scale = float(m.group(1))
|
||
logger.info(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))
|
||
logger.info(f"negative scale: {negative_scale}")
|
||
continue
|
||
|
||
m = re.match(r"t ([\d\.]+)", parg, re.IGNORECASE)
|
||
if m: # strength
|
||
strength = float(m.group(1))
|
||
logger.info(f"strength: {strength}")
|
||
continue
|
||
|
||
m = re.match(r"n (.+)", parg, re.IGNORECASE)
|
||
if m: # negative prompt
|
||
negative_prompt = m.group(1)
|
||
logger.info(f"negative prompt: {negative_prompt}")
|
||
continue
|
||
|
||
m = re.match(r"c (.+)", parg, re.IGNORECASE)
|
||
if m: # clip prompt
|
||
clip_prompt = m.group(1)
|
||
logger.info(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])
|
||
logger.info(f"network mul: {network_muls}")
|
||
continue
|
||
|
||
# Deep Shrink
|
||
m = re.match(r"dsd1 ([\d\.]+)", parg, re.IGNORECASE)
|
||
if m: # deep shrink depth 1
|
||
ds_depth_1 = int(m.group(1))
|
||
logger.info(f"deep shrink depth 1: {ds_depth_1}")
|
||
continue
|
||
|
||
m = re.match(r"dst1 ([\d\.]+)", parg, re.IGNORECASE)
|
||
if m: # deep shrink timesteps 1
|
||
ds_timesteps_1 = int(m.group(1))
|
||
ds_depth_1 = ds_depth_1 if ds_depth_1 is not None else -1 # -1 means override
|
||
logger.info(f"deep shrink timesteps 1: {ds_timesteps_1}")
|
||
continue
|
||
|
||
m = re.match(r"dsd2 ([\d\.]+)", parg, re.IGNORECASE)
|
||
if m: # deep shrink depth 2
|
||
ds_depth_2 = int(m.group(1))
|
||
ds_depth_1 = ds_depth_1 if ds_depth_1 is not None else -1 # -1 means override
|
||
logger.info(f"deep shrink depth 2: {ds_depth_2}")
|
||
continue
|
||
|
||
m = re.match(r"dst2 ([\d\.]+)", parg, re.IGNORECASE)
|
||
if m: # deep shrink timesteps 2
|
||
ds_timesteps_2 = int(m.group(1))
|
||
ds_depth_1 = ds_depth_1 if ds_depth_1 is not None else -1 # -1 means override
|
||
logger.info(f"deep shrink timesteps 2: {ds_timesteps_2}")
|
||
continue
|
||
|
||
m = re.match(r"dsr ([\d\.]+)", parg, re.IGNORECASE)
|
||
if m: # deep shrink ratio
|
||
ds_ratio = float(m.group(1))
|
||
ds_depth_1 = ds_depth_1 if ds_depth_1 is not None else -1 # -1 means override
|
||
logger.info(f"deep shrink ratio: {ds_ratio}")
|
||
continue
|
||
|
||
# Gradual Latent
|
||
m = re.match(r"glt ([\d\.]+)", parg, re.IGNORECASE)
|
||
if m: # gradual latent timesteps
|
||
gl_timesteps = int(m.group(1))
|
||
logger.info(f"gradual latent timesteps: {gl_timesteps}")
|
||
continue
|
||
|
||
m = re.match(r"glr ([\d\.]+)", parg, re.IGNORECASE)
|
||
if m: # gradual latent ratio
|
||
gl_ratio = float(m.group(1))
|
||
gl_timesteps = gl_timesteps if gl_timesteps is not None else -1 # -1 means override
|
||
logger.info(f"gradual latent ratio: {ds_ratio}")
|
||
continue
|
||
|
||
m = re.match(r"gle ([\d\.]+)", parg, re.IGNORECASE)
|
||
if m: # gradual latent every n steps
|
||
gl_every_n_steps = int(m.group(1))
|
||
gl_timesteps = gl_timesteps if gl_timesteps is not None else -1 # -1 means override
|
||
logger.info(f"gradual latent every n steps: {gl_every_n_steps}")
|
||
continue
|
||
|
||
m = re.match(r"gls ([\d\.]+)", parg, re.IGNORECASE)
|
||
if m: # gradual latent ratio step
|
||
gl_ratio_step = float(m.group(1))
|
||
gl_timesteps = gl_timesteps if gl_timesteps is not None else -1 # -1 means override
|
||
logger.info(f"gradual latent ratio step: {gl_ratio_step}")
|
||
continue
|
||
|
||
m = re.match(r"glsn ([\d\.]+)", parg, re.IGNORECASE)
|
||
if m: # gradual latent s noise
|
||
gl_s_noise = float(m.group(1))
|
||
gl_timesteps = gl_timesteps if gl_timesteps is not None else -1 # -1 means override
|
||
logger.info(f"gradual latent s noise: {gl_s_noise}")
|
||
continue
|
||
|
||
m = re.match(r"glus ([\d\.\-,]+)", parg, re.IGNORECASE)
|
||
if m: # gradual latent unsharp params
|
||
gl_unsharp_params = m.group(1)
|
||
gl_timesteps = gl_timesteps if gl_timesteps is not None else -1 # -1 means override
|
||
logger.info(f"gradual latent unsharp params: {gl_unsharp_params}")
|
||
continue
|
||
|
||
m = re.match(r"f (.+)", parg, re.IGNORECASE)
|
||
if m: # filename
|
||
filename = m.group(1)
|
||
continue
|
||
|
||
except ValueError as ex:
|
||
logger.error(f"Exception in parsing / 解析エラー: {parg}")
|
||
logger.error(f"{ex}")
|
||
|
||
# override filename to add index number if more than one image per prompt
|
||
if filename is not None and (args.images_per_prompt > 1 or len(raw_prompts) > 1):
|
||
fileext = os.path.splitext(filename)
|
||
filename = fileext[0] + "_%s" % pi + fileext[1]
|
||
logger.info(f"filename: {filename}")
|
||
|
||
# override Deep Shrink
|
||
if ds_depth_1 is not None:
|
||
if ds_depth_1 < 0:
|
||
ds_depth_1 = args.ds_depth_1 or 3
|
||
unet.set_deep_shrink(ds_depth_1, ds_timesteps_1, ds_depth_2, ds_timesteps_2, ds_ratio)
|
||
|
||
# override Gradual Latent
|
||
if gl_timesteps is not None:
|
||
if gl_timesteps < 0:
|
||
gl_timesteps = args.gradual_latent_timesteps or 650
|
||
if gl_unsharp_params is not None:
|
||
unsharp_params = gl_unsharp_params.split(",")
|
||
us_ksize, us_sigma, us_strength = [float(v) for v in unsharp_params[:3]]
|
||
us_target_x = True if len(unsharp_params) < 4 else bool(int(unsharp_params[3]))
|
||
us_ksize = int(us_ksize)
|
||
else:
|
||
us_ksize, us_sigma, us_strength, us_target_x = None, None, None, None
|
||
gradual_latent = GradualLatent(
|
||
gl_ratio,
|
||
gl_timesteps,
|
||
gl_every_n_steps,
|
||
gl_ratio_step,
|
||
gl_s_noise,
|
||
us_ksize,
|
||
us_sigma,
|
||
us_strength,
|
||
us_target_x,
|
||
)
|
||
pipe.set_gradual_latent(gradual_latent)
|
||
|
||
# prepare seed
|
||
if seeds is not None: # given in prompt
|
||
# num_images_per_promptが多い場合は足りなくなるので、足りない分は前のを使う
|
||
# Previous implementation may result in unexpected behaviour when number of seeds is lesss than number of repeats. Last seed is taken for rest of repeated prompts. Add condition if last element is -1, to start randomizing seed.
|
||
if len(seeds) > 1:
|
||
seed = seeds.pop(0)
|
||
elif len(seeds) == 1:
|
||
if seeds[0] == -1:
|
||
seeds = None
|
||
else:
|
||
seed = seeds.pop(0)
|
||
|
||
|
||
else:
|
||
if args.iter_same_seed:
|
||
seed = iter_seed
|
||
else:
|
||
seed = None # 前のを消す
|
||
|
||
if seed is None:
|
||
seed = seed_random.randint(0, 2**32 - 1)
|
||
if args.interactive:
|
||
logger.info(f"seed: {seed}")
|
||
# logger.info(f"seed: {seed}") #debugging logger. Uncomment to verify if expected seed is added correctly.
|
||
|
||
# prepare init image, guide image and mask
|
||
init_image = mask_image = guide_image = None
|
||
|
||
# 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)]
|
||
|
||
# img2imgの場合は、基本的に元画像のサイズで生成する。highres fixの場合はargs.W, args.Hとscaleに従いリサイズ済みなので無視する
|
||
# 32単位に丸めたやつにresizeされるので踏襲する
|
||
if not highres_fix:
|
||
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]:
|
||
logger.warning(
|
||
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 or control_net_lllites: # 複数件の場合あり
|
||
c = max(len(control_nets), len(control_net_lllites))
|
||
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)]
|
||
|
||
if regional_network:
|
||
num_sub_prompts = len(prompt.split(" AND "))
|
||
assert (
|
||
len(networks) <= num_sub_prompts
|
||
), "Number of networks must be less than or equal to number of sub prompts."
|
||
else:
|
||
num_sub_prompts = None
|
||
|
||
b1 = BatchData(
|
||
False,
|
||
BatchDataBase(
|
||
global_step,
|
||
prompt,
|
||
negative_prompt,
|
||
seed,
|
||
init_image,
|
||
mask_image,
|
||
clip_prompt,
|
||
guide_image,
|
||
raw_prompt,
|
||
filename,
|
||
),
|
||
BatchDataExt(
|
||
width,
|
||
height,
|
||
original_width,
|
||
original_height,
|
||
original_width_negative,
|
||
original_height_negative,
|
||
crop_left,
|
||
crop_top,
|
||
steps,
|
||
scale,
|
||
negative_scale,
|
||
strength,
|
||
tuple(network_muls) if network_muls else None,
|
||
num_sub_prompts,
|
||
),
|
||
)
|
||
if len(batch_data) > 0 and batch_data[-1].ext != b1.ext: # 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()
|
||
|
||
logger.info("done!")
|
||
|
||
def first_available_filename(path, filename):
|
||
"""
|
||
Checks if filename is in use.
|
||
if filename is in use, appends a running number
|
||
e.g. filename = 'file.png':
|
||
|
||
file.png
|
||
file_1.png
|
||
file_2.png
|
||
|
||
Runs in log(n) time where n is the number of existing files in sequence
|
||
"""
|
||
i = 1
|
||
if not os.path.exists(os.path.join(path, filename)):
|
||
return filename
|
||
fileext = os.path.splitext(filename)
|
||
filename = fileext[0] + "_%s" + fileext[1]
|
||
# First do an exponential search
|
||
while os.path.exists(os.path.join(path,filename % i)):
|
||
i = i * 2
|
||
|
||
# Result lies somewhere in the interval (i/2..i]
|
||
# We call this interval (a..b] and narrow it down until a + 1 = b
|
||
a, b = (i // 2, i)
|
||
while a + 1 < b:
|
||
c = (a + b) // 2 # interval midpoint
|
||
a, b = (c, b) if os.path.exists(os.path.join(path,filename % c)) else (a, c)
|
||
|
||
return filename % b
|
||
def setup_parser() -> argparse.ArgumentParser:
|
||
parser = argparse.ArgumentParser()
|
||
|
||
add_logging_arguments(parser)
|
||
|
||
parser.add_argument(
|
||
"--sdxl", action="store_true", help="load Stable Diffusion XL model / Stable Diffusion XLのモデルを読み込む"
|
||
)
|
||
parser.add_argument(
|
||
"--v1", action="store_true", help="load Stable Diffusion v1.x model / Stable Diffusion 1.xのモデルを読み込む"
|
||
)
|
||
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(
|
||
"--from_module",
|
||
type=str,
|
||
default=None,
|
||
help="if specified, load prompts from this module / 指定時はプロンプトをモジュールから読み込む",
|
||
)
|
||
parser.add_argument(
|
||
"--prompter_module_args", type=str, default=None, help="args for prompter module / prompterモジュールの引数"
|
||
)
|
||
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(
|
||
"--original_height",
|
||
type=int,
|
||
default=None,
|
||
help="original height for SDXL conditioning / SDXLの条件付けに用いるoriginal heightの値",
|
||
)
|
||
parser.add_argument(
|
||
"--original_width",
|
||
type=int,
|
||
default=None,
|
||
help="original width for SDXL conditioning / SDXLの条件付けに用いるoriginal widthの値",
|
||
)
|
||
parser.add_argument(
|
||
"--original_height_negative",
|
||
type=int,
|
||
default=None,
|
||
help="original height for SDXL unconditioning / SDXLのネガティブ条件付けに用いるoriginal heightの値",
|
||
)
|
||
parser.add_argument(
|
||
"--original_width_negative",
|
||
type=int,
|
||
default=None,
|
||
help="original width for SDXL unconditioning / SDXLのネガティブ条件付けに用いるoriginal widthの値",
|
||
)
|
||
parser.add_argument(
|
||
"--crop_top", type=int, default=None, help="crop top for SDXL conditioning / SDXLの条件付けに用いるcrop topの値"
|
||
)
|
||
parser.add_argument(
|
||
"--crop_left", type=int, default=None, help="crop left for SDXL conditioning / SDXLの条件付けに用いるcrop leftの値"
|
||
)
|
||
parser.add_argument("--batch_size", type=int, default=1, help="batch size / バッチサイズ")
|
||
parser.add_argument(
|
||
"--vae_batch_size",
|
||
type=float,
|
||
default=None,
|
||
help="batch size for VAE, < 1.0 for ratio / VAE処理時のバッチサイズ、1未満の値の場合は通常バッチサイズの比率",
|
||
)
|
||
parser.add_argument(
|
||
"--vae_slices",
|
||
type=int,
|
||
default=None,
|
||
help="number of slices to split image into for VAE to reduce VRAM usage, None for no splitting (default), slower if specified. 16 or 32 recommended / VAE処理時にVRAM使用量削減のため画像を分割するスライス数、Noneの場合は分割しない（デフォルト）、指定すると遅くなる。16か32程度を推奨",
|
||
)
|
||
parser.add_argument(
|
||
"--no_half_vae", action="store_true", help="do not use fp16/bf16 precision for VAE / VAE処理時にfp16/bf16を使わない"
|
||
)
|
||
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(
|
||
"--tokenizer_cache_dir",
|
||
type=str,
|
||
default=None,
|
||
help="directory for caching Tokenizer (for offline training) / Tokenizerをキャッシュするディレクトリ（ネット接続なしでの学習のため）",
|
||
)
|
||
# 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(
|
||
"--shuffle_prompts",
|
||
action="store_true",
|
||
help="shuffle prompts in iteration / 繰り返し内のプロンプトをシャッフルする",
|
||
)
|
||
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("--sdpa", action="store_true", help="use sdpa in PyTorch 2 / sdpa")
|
||
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 arguments 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(
|
||
"--network_merge_n_models",
|
||
type=int,
|
||
default=None,
|
||
help="merge this number of networks / この数だけネットワークをマージする",
|
||
)
|
||
parser.add_argument(
|
||
"--network_merge", action="store_true", help="merge network weights to original model / ネットワークの重みをマージする"
|
||
)
|
||
parser.add_argument(
|
||
"--network_pre_calc",
|
||
action="store_true",
|
||
help="pre-calculate network for generation / ネットワークのあらかじめ計算して生成する",
|
||
)
|
||
parser.add_argument(
|
||
"--network_regional_mask_max_color_codes",
|
||
type=int,
|
||
default=None,
|
||
help="max color codes for regional mask (default is None, mask by channel) / regional maskの最大色数（デフォルトはNoneでチャンネルごとのマスク）",
|
||
)
|
||
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, default is 1 for SD1/2, 2 for SDXL "
|
||
+ "/ CLIPの後ろからn層目の出力を使う（デフォルトはSD1/2の場合1、SDXLの場合2）",
|
||
)
|
||
parser.add_argument(
|
||
"--max_embeddings_multiples",
|
||
type=int,
|
||
default=None,
|
||
help="max embedding 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(
|
||
"--emb_normalize_mode",
|
||
type=str,
|
||
default="original",
|
||
choices=["original", "none", "abs"],
|
||
help="embedding normalization mode / embeddingの正規化モード",
|
||
)
|
||
parser.add_argument(
|
||
"--guide_image_path", type=str, default=None, nargs="*", help="image to ControlNet / ControlNet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_strength",
|
||
type=float,
|
||
default=None,
|
||
help="1st stage img2img strength for highres fix / highres fixの最初のステージのimg2img時のstrength、省略時はstrengthと同じ",
|
||
)
|
||
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(
|
||
"--highres_fix_latents_upscaling",
|
||
action="store_true",
|
||
help="use latents upscaling for highres fix / highres fixでlatentで拡大する",
|
||
)
|
||
parser.add_argument(
|
||
"--highres_fix_upscaler",
|
||
type=str,
|
||
default=None,
|
||
help="upscaler module for highres fix / highres fixで使うupscalerのモジュール名",
|
||
)
|
||
parser.add_argument(
|
||
"--highres_fix_upscaler_args",
|
||
type=str,
|
||
default=None,
|
||
help="additional arguments for upscaler (key=value) / upscalerへの追加の引数",
|
||
)
|
||
parser.add_argument(
|
||
"--highres_fix_disable_control_net",
|
||
action="store_true",
|
||
help="disable ControlNet for highres fix / highres fixでControlNetを使わない",
|
||
)
|
||
|
||
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_lllite_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_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_multipliers", type=float, default=None, nargs="*", help="ControlNet multiplier / ControlNetの適用率"
|
||
)
|
||
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",
|
||
)
|
||
parser.add_argument(
|
||
"--clip_vision_strength",
|
||
type=float,
|
||
default=None,
|
||
help="enable CLIP Vision Conditioning for img2img with this strength / img2imgでCLIP Vision Conditioningを有効にしてこのstrengthで処理する",
|
||
)
|
||
|
||
# Deep Shrink
|
||
parser.add_argument(
|
||
"--ds_depth_1",
|
||
type=int,
|
||
default=None,
|
||
help="Enable Deep Shrink with this depth 1, valid values are 0 to 8 / Deep Shrinkをこのdepthで有効にする",
|
||
)
|
||
parser.add_argument(
|
||
"--ds_timesteps_1",
|
||
type=int,
|
||
default=650,
|
||
help="Apply Deep Shrink depth 1 until this timesteps / Deep Shrink depth 1を適用するtimesteps",
|
||
)
|
||
parser.add_argument("--ds_depth_2", type=int, default=None, help="Deep Shrink depth 2 / Deep Shrinkのdepth 2")
|
||
parser.add_argument(
|
||
"--ds_timesteps_2",
|
||
type=int,
|
||
default=650,
|
||
help="Apply Deep Shrink depth 2 until this timesteps / Deep Shrink depth 2を適用するtimesteps",
|
||
)
|
||
parser.add_argument(
|
||
"--ds_ratio", type=float, default=0.5, help="Deep Shrink ratio for downsampling / Deep Shrinkのdownsampling比率"
|
||
)
|
||
|
||
# gradual latent
|
||
parser.add_argument(
|
||
"--gradual_latent_timesteps",
|
||
type=int,
|
||
default=None,
|
||
help="enable Gradual Latent hires fix and apply upscaling from this timesteps / Gradual Latent hires fixをこのtimestepsで有効にし、このtimestepsからアップスケーリングを適用する",
|
||
)
|
||
parser.add_argument(
|
||
"--gradual_latent_ratio",
|
||
type=float,
|
||
default=0.5,
|
||
help=" this size ratio, 0.5 means 1/2 / Gradual Latent hires fixをこのサイズ比率で有効にする、0.5は1/2を意味する",
|
||
)
|
||
parser.add_argument(
|
||
"--gradual_latent_ratio_step",
|
||
type=float,
|
||
default=0.125,
|
||
help="step to increase ratio for Gradual Latent / Gradual Latentのratioをどのくらいずつ上げるか",
|
||
)
|
||
parser.add_argument(
|
||
"--gradual_latent_every_n_steps",
|
||
type=int,
|
||
default=3,
|
||
help="steps to increase size of latents every this steps for Gradual Latent / Gradual Latentでlatentsのサイズをこのステップごとに上げる",
|
||
)
|
||
parser.add_argument(
|
||
"--gradual_latent_s_noise",
|
||
type=float,
|
||
default=1.0,
|
||
help="s_noise for Gradual Latent / Gradual Latentのs_noise",
|
||
)
|
||
parser.add_argument(
|
||
"--gradual_latent_unsharp_params",
|
||
type=str,
|
||
default=None,
|
||
help="unsharp mask parameters for Gradual Latent: ksize, sigma, strength, target-x (1 means True). `3,0.5,0.5,1` or `3,1.0,1.0,0` is recommended /"
|
||
+ " Gradual Latentのunsharp maskのパラメータ: ksize, sigma, strength, target-x. `3,0.5,0.5,1` または `3,1.0,1.0,0` が推奨",
|
||
)
|
||
parser.add_argument("--full_fp16", action="store_true", help="Loading model in fp16")
|
||
parser.add_argument(
|
||
"--full_bf16", action="store_true", help="Loading model in bf16"
|
||
)
|
||
|
||
# # parser.add_argument(
|
||
# "--control_net_image_path", type=str, default=None, nargs="*", help="image for ControlNet guidance / ControlNetでガイドに使う画像"
|
||
# )
|
||
|
||
return parser
|
||
|
||
|
||
if __name__ == "__main__":
|
||
parser = setup_parser()
|
||
|
||
args = parser.parse_args()
|
||
main(args)
|