From a14468cb5480d0fa7013a79f8f2d10c321c01db1 Mon Sep 17 00:00:00 2001 From: kohya-ss <52813779+kohya-ss@users.noreply.github.com> Date: Thu, 24 Sep 2026 18:58:33 +0900 Subject: [PATCH 1/2] Remove gen_img_diffusers.py, the old SD1.x / SD2.x image generation script It has not worked since the refactoring removed `train_util.load_tokenizer` (the only thing it needed to run again), and gen_img.py supports everything it did except the experimental CLIP / VGG16 guidance. No user has reported the breakage, so rather than keeping 4,000 lines of duplicated code alive, remove it. The file remains available in the previous releases. The remaining references in the docs (train_network_README-ja / -zh and train_ti_README-ja) now point to gen_img.py. Co-Authored-By: Claude Fable 5.1 --- README-ja.md | 1 + README.md | 1 + docs/train_network_README-ja.md | 2 +- docs/train_network_README-zh.md | 2 +- docs/train_ti_README-ja.md | 2 +- gen_img_diffusers.py | 4027 ------------------------------- library/sampling.py | 2 +- 7 files changed, 6 insertions(+), 4031 deletions(-) delete mode 100644 gen_img_diffusers.py diff --git a/README-ja.md b/README-ja.md index 7b2d6477e1..b1c7284ca6 100644 --- a/README-ja.md +++ b/README-ja.md @@ -58,6 +58,7 @@ Stable Diffusion等の画像生成モデルの学習、モデルによる画像 - `transformers` 5.x の `CLIPTokenizer` は、オリジナルの CLIP トークナイザが行っていた `ftfy` によるテキスト正規化（曲がった引用符の直線化、全角文字の半角化など）を行わなくなりました。sd-scripts 側で同じ正規化を行うようにしたため、トークナイズ結果は従来と変わりません。 - `tests/local` のローカル回帰テストにより、Text Encoder の出力、VAE の出力、ノイズスケジューラが従来のバージョンと同一であることを確認しています。 - `diffusers` 0.40 は `.to(dtype)` のたびに "There are modules in AutoencoderKL that should be kept in float32: [] ..." という誤った警告を出します（リストが空でも警告する diffusers 側のバグ）。sd-scripts ではリストが空の場合にこの警告を抑制しています。 + - SD1.x / SD2.x 用の古い画像生成スクリプト `gen_img_diffusers.py` を削除しました。しばらく前から動作しておらず（リファクタリングで削除された関数に依存していました）、実験的な CLIP / VGG16 guidance を除くすべての機能は `gen_img.py` でサポートされています。代わりに `gen_img.py` をお使いください（[gen_img_README-ja.md](./docs/gen_img_README-ja.md) をご覧ください）。ファイルは以前のリリースから取得できます。[PR #XXXX](https://github.com/kohya-ss/sd-scripts/pull/XXXX) - 学習中のサンプル画像生成の `--sample_sampler`、および `gen_img.py` / `sdxl_gen_img.py` の `--sampler` で、`dpmsolver` と `dpmsingle` を指定すると最近のバージョンの `diffusers` でエラーになる問題を修正しました。[PR #2438](https://github.com/kohya-ss/sd-scripts/pull/2438) - `lms` / `k_lms` サンプラーには `requirements.txt` に含まれない `scipy` パッケージが必要です。`scipy` が未インストールの場合、（最初のサンプル生成時ではなく）起動時に分かりやすいエラーを表示するようにしました。使用する場合は `pip install scipy` を実行してください。 - Windows on ARM64（NVIDIA RTX Spark PC など）に対応しました。[PR #2430](https://github.com/kohya-ss/sd-scripts/pull/2430)、[PR #2431](https://github.com/kohya-ss/sd-scripts/pull/2431)、[PR #2433](https://github.com/kohya-ss/sd-scripts/pull/2433) diff --git a/README.md b/README.md index e188a88361..f189c44de4 100644 --- a/README.md +++ b/README.md @@ -58,6 +58,7 @@ If you find this project helpful, please consider supporting its development via - `transformers` 5.6 changed the internal structure of `CLIPTextModel` (the `text_model` submodule was removed). sd-scripts now wraps the model so that the checkpoint keys, the LoRA weight names of the text encoders (`lora_te_text_model_...`) and `text_encoder.text_model.*` access stay the same as before. Nothing changes for `transformers` < 5.6, where the wrapper is not applied. - `transformers` 5.6 also switched the attention implementation of T5 (T5-XXL of FLUX.1 / SD3, byT5 of HunyuanImage) to SDPA, which should be faster and use less memory. The bf16/fp16 outputs of T5 differ very slightly from previous versions (cosine similarity ≈ 0.998 for T5-XXL, the accuracy against fp32 is the same). This may change generated images or trained weights in minor details, and cached text encoder outputs from previous versions are still usable. The outputs of the other text encoders are identical. - Please run `pip install --upgrade -r requirements.txt` after updating. + - Removed `gen_img_diffusers.py`, the old image generation script for SD1.x / SD2.x. It had not worked for a while (it depended on a function removed by the refactoring) and `gen_img.py` supports everything it did except the experimental CLIP / VGG16 guidance. Please use `gen_img.py` instead (see [gen_img_README.md](./docs/gen_img_README.md)). The file is still available in the previous releases. [PR #XXXX](https://github.com/kohya-ss/sd-scripts/pull/XXXX) - Fixed the `dpmsolver` and `dpmsingle` samplers of `--sample_sampler` (sample image generation during training) and `--sampler` of `gen_img.py` / `sdxl_gen_img.py`, which failed with an error on recent versions of `diffusers`. [PR #2438](https://github.com/kohya-ss/sd-scripts/pull/2438) - The `lms` / `k_lms` samplers require the `scipy` package, which is not included in `requirements.txt`. A clear error message is now shown at startup (instead of at the first sample generation) when `scipy` is missing. Please run `pip install scipy` to use them. - Added support for Windows on ARM64 (e.g. NVIDIA RTX Spark PCs). [PR #2430](https://github.com/kohya-ss/sd-scripts/pull/2430), [PR #2431](https://github.com/kohya-ss/sd-scripts/pull/2431), [PR #2433](https://github.com/kohya-ss/sd-scripts/pull/2433) diff --git a/docs/train_network_README-ja.md b/docs/train_network_README-ja.md index 55c80c4b0a..32a56bd198 100644 --- a/docs/train_network_README-ja.md +++ b/docs/train_network_README-ja.md @@ -339,7 +339,7 @@ python networks\svd_merge_lora.py ## 当リポジトリ内の画像生成スクリプトで生成する -gen_img_diffusers.pyに、--network_module、--network_weightsの各オプションを追加してください。意味は学習時と同様です。 +gen_img.pyに、--network_module、--network_weightsの各オプションを追加してください。意味は学習時と同様です。詳細は[画像生成スクリプトのドキュメント](gen_img_README-ja.md)を参照してください。 --network_mulオプションで0~1.0の数値を指定すると、LoRAの適用率を変えられます。 diff --git a/docs/train_network_README-zh.md b/docs/train_network_README-zh.md index 830014f72d..db7cae84c4 100644 --- a/docs/train_network_README-zh.md +++ b/docs/train_network_README-zh.md @@ -317,7 +317,7 @@ python networks\svd_merge_lora.py ## 在此存储库中生成图像的脚本中 -请在`gen_img_diffusers.py`中添加`--network_module`和`--network_weights`选项。其含义与训练时相同。 +请在`gen_img.py`中添加`--network_module`和`--network_weights`选项。其含义与训练时相同。详情请参考[图像生成脚本的文档](gen_img_README.md)。 通过`--network_mul`选项，可以指定0~1.0的数字来改变LoRA的应用率。 diff --git a/docs/train_ti_README-ja.md b/docs/train_ti_README-ja.md index 86f45a5dcf..7558201108 100644 --- a/docs/train_ti_README-ja.md +++ b/docs/train_ti_README-ja.md @@ -101,5 +101,5 @@ tokenizerがすでに持っている単語（一般的な単語）は使用で ## 当リポジトリ内の画像生成スクリプトで生成する -gen_img_diffusers.pyに、``--textual_inversion_embeddings`` オプションで学習したembeddingsファイルを指定してください（複数可）。プロンプトでembeddingsファイルのファイル名（拡張子を除く）を使うと、そのembeddingsが適用されます。 +gen_img.pyに、``--textual_inversion_embeddings`` オプションで学習したembeddingsファイルを指定してください（複数可）。プロンプトでembeddingsファイルのファイル名（拡張子を除く）を使うと、そのembeddingsが適用されます。 diff --git a/gen_img_diffusers.py b/gen_img_diffusers.py deleted file mode 100644 index 4258463a6b..0000000000 --- a/gen_img_diffusers.py +++ /dev/null @@ -1,4027 +0,0 @@ -""" -VGG( - (features): Sequential( - (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) - (1): ReLU(inplace=True) - (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) - (3): ReLU(inplace=True) - (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) - (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) - (6): ReLU(inplace=True) - (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) - (8): ReLU(inplace=True) - (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) - (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) - (11): ReLU(inplace=True) - (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) - (13): ReLU(inplace=True) - (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) - (15): ReLU(inplace=True) - (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) - (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) - (18): ReLU(inplace=True) - (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) - (20): ReLU(inplace=True) - (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) - (22): ReLU(inplace=True) - (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) - (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) - (25): ReLU(inplace=True) - (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) - (27): ReLU(inplace=True) - (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) - (29): ReLU(inplace=True) - (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) - ) - (avgpool): AdaptiveAvgPool2d(output_size=(7, 7)) - (classifier): Sequential( - (0): Linear(in_features=25088, out_features=4096, bias=True) - (1): ReLU(inplace=True) - (2): Dropout(p=0.5, inplace=False) - (3): Linear(in_features=4096, out_features=4096, bias=True) - (4): ReLU(inplace=True) - (5): Dropout(p=0.5, inplace=False) - (6): Linear(in_features=4096, out_features=1000, bias=True) - ) -) -""" - -import itertools -import json -from typing import Any, Dict, List, NamedTuple, Optional, Tuple, Union, Callable -import glob -import importlib -import inspect -import time -import zipfile -from diffusers.utils import deprecate -from diffusers.configuration_utils import FrozenDict -import argparse -import math -import os -import random -import re - -import diffusers -import numpy as np - -import torch -from library.device_utils import init_ipex, clean_memory, get_preferred_device -init_ipex() - -import torchvision -from diffusers import ( - AutoencoderKL, - DDPMScheduler, - EulerAncestralDiscreteScheduler, - DPMSolverMultistepScheduler, - DPMSolverSinglestepScheduler, - LMSDiscreteScheduler, - PNDMScheduler, - DDIMScheduler, - EulerDiscreteScheduler, - HeunDiscreteScheduler, - KDPM2DiscreteScheduler, - KDPM2AncestralDiscreteScheduler, - # UNet2DConditionModel, - StableDiffusionPipeline, -) -from einops import rearrange -from tqdm import tqdm -from torchvision import transforms -from transformers import CLIPTextModel, CLIPTokenizer, CLIPModel, CLIPTextConfig -import PIL -from PIL import Image -from PIL.PngImagePlugin import PngInfo - -import library.model_util as model_util -import library.train_util as train_util -from networks.lora import LoRANetwork -import tools.original_control_net as original_control_net -from tools.original_control_net import ControlNetInfo -from library.original_unet import UNet2DConditionModel, InferUNet2DConditionModel, SampleOutput -from library.original_unet import FlashAttentionFunction -from library.utils import GradualLatent, EulerAncestralDiscreteSchedulerGL -from library.utils import setup_logging, add_logging_arguments - -setup_logging() -import logging - -logger = logging.getLogger(__name__) - - -# XTI hijack: monkey-patches for UNet2DConditionModel / CrossAttnDownBlock2D / CrossAttnUpBlock2D -# used when --XTI_embeddings is specified. -def unet_forward_XTI( - self, - sample: torch.FloatTensor, - timestep: Union[torch.Tensor, float, int], - encoder_hidden_states: torch.Tensor, - class_labels: Optional[torch.Tensor] = None, - return_dict: bool = True, -) -> Union[Dict, Tuple]: - default_overall_up_factor = 2**self.num_upsamplers - - forward_upsample_size = False - upsample_size = None - - if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]): - forward_upsample_size = True - - # 1. time - timesteps = timestep - timesteps = self.handle_unusual_timesteps(sample, timesteps) - - t_emb = self.time_proj(timesteps) - t_emb = t_emb.to(dtype=self.dtype) - emb = self.time_embedding(t_emb) - - # 2. pre-process - sample = self.conv_in(sample) - - # 3. down - down_block_res_samples = (sample,) - down_i = 0 - for downsample_block in self.down_blocks: - if downsample_block.has_cross_attention: - sample, res_samples = downsample_block( - hidden_states=sample, - temb=emb, - encoder_hidden_states=encoder_hidden_states[down_i : down_i + 2], - ) - down_i += 2 - else: - sample, res_samples = downsample_block(hidden_states=sample, temb=emb) - - down_block_res_samples += res_samples - - # 4. mid - sample = self.mid_block(sample, emb, encoder_hidden_states=encoder_hidden_states[6]) - - # 5. up - up_i = 7 - for i, upsample_block in enumerate(self.up_blocks): - is_final_block = i == len(self.up_blocks) - 1 - - res_samples = down_block_res_samples[-len(upsample_block.resnets) :] - down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)] - - if not is_final_block and forward_upsample_size: - upsample_size = down_block_res_samples[-1].shape[2:] - - if upsample_block.has_cross_attention: - sample = upsample_block( - hidden_states=sample, - temb=emb, - res_hidden_states_tuple=res_samples, - encoder_hidden_states=encoder_hidden_states[up_i : up_i + 3], - upsample_size=upsample_size, - ) - up_i += 3 - else: - sample = upsample_block( - hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size - ) - - # 6. post-process - sample = self.conv_norm_out(sample) - sample = self.conv_act(sample) - sample = self.conv_out(sample) - - if not return_dict: - return (sample,) - - return SampleOutput(sample=sample) - - -def downblock_forward_XTI( - self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None, cross_attention_kwargs=None -): - output_states = () - i = 0 - - for resnet, attn in zip(self.resnets, self.attentions): - if self.training and self.gradient_checkpointing: - - def create_custom_forward(module, return_dict=None): - def custom_forward(*inputs): - if return_dict is not None: - return module(*inputs, return_dict=return_dict) - else: - return module(*inputs) - - return custom_forward - - hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb) - hidden_states = torch.utils.checkpoint.checkpoint( - create_custom_forward(attn, return_dict=False), hidden_states, encoder_hidden_states[i] - )[0] - else: - hidden_states = resnet(hidden_states, temb) - hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states[i]).sample - - output_states += (hidden_states,) - i += 1 - - if self.downsamplers is not None: - for downsampler in self.downsamplers: - hidden_states = downsampler(hidden_states) - - output_states += (hidden_states,) - - return hidden_states, output_states - - -def upblock_forward_XTI( - self, - hidden_states, - res_hidden_states_tuple, - temb=None, - encoder_hidden_states=None, - upsample_size=None, -): - i = 0 - for resnet, attn in zip(self.resnets, self.attentions): - res_hidden_states = res_hidden_states_tuple[-1] - res_hidden_states_tuple = res_hidden_states_tuple[:-1] - hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) - - if self.training and self.gradient_checkpointing: - - def create_custom_forward(module, return_dict=None): - def custom_forward(*inputs): - if return_dict is not None: - return module(*inputs, return_dict=return_dict) - else: - return module(*inputs) - - return custom_forward - - hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb) - hidden_states = torch.utils.checkpoint.checkpoint( - create_custom_forward(attn, return_dict=False), hidden_states, encoder_hidden_states[i] - )[0] - else: - hidden_states = resnet(hidden_states, temb) - hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states[i]).sample - - i += 1 - - if self.upsamplers is not None: - for upsampler in self.upsamplers: - hidden_states = upsampler(hidden_states, upsample_size) - - return hidden_states - - -# scheduler: -SCHEDULER_LINEAR_START = 0.00085 -SCHEDULER_LINEAR_END = 0.0120 -SCHEDULER_TIMESTEPS = 1000 -SCHEDLER_SCHEDULE = "scaled_linear" - -# その他の設定 -LATENT_CHANNELS = 4 -DOWNSAMPLING_FACTOR = 8 - -# CLIP_ID_L14_336 = "openai/clip-vit-large-patch14-336" - -# CLIP guided SD関連 -CLIP_MODEL_PATH = "laion/CLIP-ViT-B-32-laion2B-s34B-b79K" -FEATURE_EXTRACTOR_SIZE = (224, 224) -FEATURE_EXTRACTOR_IMAGE_MEAN = [0.48145466, 0.4578275, 0.40821073] -FEATURE_EXTRACTOR_IMAGE_STD = [0.26862954, 0.26130258, 0.27577711] - -VGG16_IMAGE_MEAN = [0.485, 0.456, 0.406] -VGG16_IMAGE_STD = [0.229, 0.224, 0.225] -VGG16_INPUT_RESIZE_DIV = 4 - -# CLIP特徴量の取得時にcutoutを使うか：使う場合にはソースを書き換えてください -NUM_CUTOUTS = 4 -USE_CUTOUTS = False - -# region モジュール入れ替え部 -""" -高速化のためのモジュール入れ替え -""" - - -def replace_unet_modules(unet: diffusers.models.UNet2DConditionModel, mem_eff_attn, xformers, sdpa): - if mem_eff_attn: - logger.info("Enable memory efficient attention for U-Net") - - # 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 - unet.set_use_memory_efficient_attention(False, True) - elif xformers: - logger.info("Enable xformers for U-Net") - try: - import xformers.ops - except ImportError: - raise ImportError("No xformers / xformersがインストールされていないようです") - - unet.set_use_memory_efficient_attention(True, False) - elif sdpa: - logger.info("Enable SDPA for U-Net") - unet.set_use_memory_efficient_attention(False, False) - unet.set_use_sdpa(True) - - -# TODO common train_util.py -def replace_vae_modules(vae: diffusers.models.AutoencoderKL, mem_eff_attn, xformers, sdpa): - if mem_eff_attn: - replace_vae_attn_to_memory_efficient() - elif xformers: - replace_vae_attn_to_xformers() - elif sdpa: - replace_vae_attn_to_sdpa() - - -def replace_vae_attn_to_memory_efficient(): - logger.info("VAE Attention.forward has been replaced to FlashAttention (not xformers)") - flash_func = FlashAttentionFunction - - def forward_flash_attn(self, hidden_states, **kwargs): - q_bucket_size = 512 - k_bucket_size = 1024 - - residual = hidden_states - batch, channel, height, width = hidden_states.shape - - # norm - hidden_states = self.group_norm(hidden_states) - - hidden_states = hidden_states.view(batch, channel, height * width).transpose(1, 2) - - # proj to q, k, v - query_proj = self.to_q(hidden_states) - key_proj = self.to_k(hidden_states) - value_proj = self.to_v(hidden_states) - - query_proj, key_proj, value_proj = map( - lambda t: rearrange(t, "b n (h d) -> b h n d", h=self.heads), (query_proj, key_proj, value_proj) - ) - - out = flash_func.apply(query_proj, key_proj, value_proj, None, False, q_bucket_size, k_bucket_size) - - out = rearrange(out, "b h n d -> b n (h d)") - - # compute next hidden_states - # linear proj - hidden_states = self.to_out[0](hidden_states) - # dropout - hidden_states = self.to_out[1](hidden_states) - - hidden_states = hidden_states.transpose(-1, -2).reshape(batch, channel, height, width) - - # res connect and rescale - hidden_states = (hidden_states + residual) / self.rescale_output_factor - return hidden_states - - def forward_flash_attn_0_14(self, hidden_states, **kwargs): - if not hasattr(self, "to_q"): - self.to_q = self.query - self.to_k = self.key - self.to_v = self.value - self.to_out = [self.proj_attn, torch.nn.Identity()] - self.heads = self.num_heads - return forward_flash_attn(self, hidden_states, **kwargs) - - if diffusers.__version__ < "0.15.0": - diffusers.models.attention.AttentionBlock.forward = forward_flash_attn_0_14 - else: - diffusers.models.attention_processor.Attention.forward = forward_flash_attn - - -def replace_vae_attn_to_xformers(): - logger.info("VAE: Attention.forward has been replaced to xformers") - import xformers.ops - - def forward_xformers(self, hidden_states, **kwargs): - residual = hidden_states - batch, channel, height, width = hidden_states.shape - - # norm - hidden_states = self.group_norm(hidden_states) - - hidden_states = hidden_states.view(batch, channel, height * width).transpose(1, 2) - - # proj to q, k, v - query_proj = self.to_q(hidden_states) - key_proj = self.to_k(hidden_states) - value_proj = self.to_v(hidden_states) - - query_proj, key_proj, value_proj = map( - lambda t: rearrange(t, "b n (h d) -> b h n d", h=self.heads), (query_proj, key_proj, value_proj) - ) - - query_proj = query_proj.contiguous() - key_proj = key_proj.contiguous() - value_proj = value_proj.contiguous() - out = xformers.ops.memory_efficient_attention(query_proj, key_proj, value_proj, attn_bias=None) - - out = rearrange(out, "b h n d -> b n (h d)") - - # compute next hidden_states - # linear proj - hidden_states = self.to_out[0](hidden_states) - # dropout - hidden_states = self.to_out[1](hidden_states) - - hidden_states = hidden_states.transpose(-1, -2).reshape(batch, channel, height, width) - - # res connect and rescale - hidden_states = (hidden_states + residual) / self.rescale_output_factor - return hidden_states - - def forward_xformers_0_14(self, hidden_states, **kwargs): - if not hasattr(self, "to_q"): - self.to_q = self.query - self.to_k = self.key - self.to_v = self.value - self.to_out = [self.proj_attn, torch.nn.Identity()] - self.heads = self.num_heads - return forward_xformers(self, hidden_states, **kwargs) - - if diffusers.__version__ < "0.15.0": - diffusers.models.attention.AttentionBlock.forward = forward_xformers_0_14 - else: - diffusers.models.attention_processor.Attention.forward = forward_xformers - - -def replace_vae_attn_to_sdpa(): - logger.info("VAE: Attention.forward has been replaced to sdpa") - - def forward_sdpa(self, hidden_states, **kwargs): - residual = hidden_states - batch, channel, height, width = hidden_states.shape - - # norm - hidden_states = self.group_norm(hidden_states) - - hidden_states = hidden_states.view(batch, channel, height * width).transpose(1, 2) - - # proj to q, k, v - query_proj = self.to_q(hidden_states) - key_proj = self.to_k(hidden_states) - value_proj = self.to_v(hidden_states) - - query_proj, key_proj, value_proj = map( - lambda t: rearrange(t, "b n (h d) -> b n h d", h=self.heads), (query_proj, key_proj, value_proj) - ) - - out = torch.nn.functional.scaled_dot_product_attention( - query_proj, key_proj, value_proj, attn_mask=None, dropout_p=0.0, is_causal=False - ) - - out = rearrange(out, "b n h d -> b n (h d)") - - # compute next hidden_states - # linear proj - hidden_states = self.to_out[0](hidden_states) - # dropout - hidden_states = self.to_out[1](hidden_states) - - hidden_states = hidden_states.transpose(-1, -2).reshape(batch, channel, height, width) - - # res connect and rescale - hidden_states = (hidden_states + residual) / self.rescale_output_factor - return hidden_states - - def forward_sdpa_0_14(self, hidden_states, **kwargs): - if not hasattr(self, "to_q"): - self.to_q = self.query - self.to_k = self.key - self.to_v = self.value - self.to_out = [self.proj_attn, torch.nn.Identity()] - self.heads = self.num_heads - return forward_sdpa(self, hidden_states, **kwargs) - - if diffusers.__version__ < "0.15.0": - diffusers.models.attention.AttentionBlock.forward = forward_sdpa_0_14 - else: - diffusers.models.attention_processor.Attention.forward = forward_sdpa - - -# endregion - -# region 画像生成の本体：lpw_stable_diffusion.py （ASL）からコピーして修正 -# https://github.com/huggingface/diffusers/blob/main/examples/community/lpw_stable_diffusion.py -# Pipelineだけ独立して使えないのと機能追加するのとでコピーして修正 - - -class PipelineLike: - r""" - Pipeline for text-to-image generation using Stable Diffusion without tokens length limit, and support parsing - weighting in prompt. - This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the - library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) - Args: - vae ([`AutoencoderKL`]): - Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. - text_encoder ([`CLIPTextModel`]): - Frozen text-encoder. Stable Diffusion uses the text portion of - [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically - the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. - tokenizer (`CLIPTokenizer`): - Tokenizer of class - [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). - unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. - scheduler ([`SchedulerMixin`]): - A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of - [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. - safety_checker ([`StableDiffusionSafetyChecker`]): - Classification module that estimates whether generated images could be considered offensive or harmful. - Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details. - feature_extractor ([`CLIPFeatureExtractor`]): - Model that extracts features from generated images to be used as inputs for the `safety_checker`. - """ - - def __init__( - self, - device, - vae: AutoencoderKL, - text_encoder: CLIPTextModel, - tokenizer: CLIPTokenizer, - unet: InferUNet2DConditionModel, - scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler], - clip_skip: int, - clip_model: CLIPModel, - clip_guidance_scale: float, - clip_image_guidance_scale: float, - vgg16_model: torchvision.models.VGG, - vgg16_guidance_scale: float, - vgg16_layer_no: int, - # safety_checker: StableDiffusionSafetyChecker, - # feature_extractor: CLIPFeatureExtractor, - ): - super().__init__() - self.device = device - self.clip_skip = clip_skip - - if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1: - deprecation_message = ( - f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`" - f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure " - "to update the config accordingly as leaving `steps_offset` might led to incorrect results" - " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub," - " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`" - " file" - ) - deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False) - new_config = dict(scheduler.config) - new_config["steps_offset"] = 1 - scheduler._internal_dict = FrozenDict(new_config) - - if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True: - deprecation_message = ( - f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`." - " `clip_sample` should be set to False in the configuration file. Please make sure to update the" - " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in" - " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very" - " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file" - ) - deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False) - new_config = dict(scheduler.config) - new_config["clip_sample"] = False - scheduler._internal_dict = FrozenDict(new_config) - - self.vae = vae - self.text_encoder = text_encoder - self.tokenizer = tokenizer - self.unet = unet - self.scheduler = scheduler - self.safety_checker = None - - # Textual Inversion - self.token_replacements = {} - - # XTI - self.token_replacements_XTI = {} - - # CLIP guidance - self.clip_guidance_scale = clip_guidance_scale - self.clip_image_guidance_scale = clip_image_guidance_scale - self.clip_model = clip_model - self.normalize = transforms.Normalize(mean=FEATURE_EXTRACTOR_IMAGE_MEAN, std=FEATURE_EXTRACTOR_IMAGE_STD) - self.make_cutouts = MakeCutouts(FEATURE_EXTRACTOR_SIZE) - - # VGG16 guidance - self.vgg16_guidance_scale = vgg16_guidance_scale - if self.vgg16_guidance_scale > 0.0: - return_layers = {f"{vgg16_layer_no}": "feat"} - self.vgg16_feat_model = torchvision.models._utils.IntermediateLayerGetter( - vgg16_model.features, return_layers=return_layers - ) - self.vgg16_normalize = transforms.Normalize(mean=VGG16_IMAGE_MEAN, std=VGG16_IMAGE_STD) - - # ControlNet - self.control_nets: List[ControlNetInfo] = [] - self.control_net_enabled = True # control_netsが空ならTrueでもFalseでもControlNetは動作しない - - self.gradual_latent: GradualLatent = None - - # Textual Inversion - def add_token_replacement(self, target_token_id, rep_token_ids): - self.token_replacements[target_token_id] = rep_token_ids - - def set_enable_control_net(self, en: bool): - self.control_net_enabled = en - - def replace_token(self, tokens, layer=None): - new_tokens = [] - for token in tokens: - if token in self.token_replacements: - replacer_ = self.token_replacements[token] - if layer: - replacer = [] - for r in replacer_: - if r in self.token_replacements_XTI: - replacer.append(self.token_replacements_XTI[r][layer]) - else: - replacer = replacer_ - new_tokens.extend(replacer) - else: - new_tokens.append(token) - return new_tokens - - def add_token_replacement_XTI(self, target_token_id, rep_token_ids): - self.token_replacements_XTI[target_token_id] = rep_token_ids - - def set_control_nets(self, ctrl_nets): - self.control_nets = ctrl_nets - - def set_gradual_latent(self, gradual_latent): - if gradual_latent is None: - logger.info("gradual_latent is disabled") - self.gradual_latent = None - else: - logger.info(f"gradual_latent is enabled: {gradual_latent}") - self.gradual_latent = gradual_latent # (ds_ratio, start_timesteps, every_n_steps, ratio_step) - - # region xformersとか使う部分：独自に書き換えるので関係なし - - def enable_xformers_memory_efficient_attention(self): - r""" - Enable memory efficient attention as implemented in xformers. - When this option is enabled, you should observe lower GPU memory usage and a potential speed up at inference - time. Speed up at training time is not guaranteed. - Warning: When Memory Efficient Attention and Sliced attention are both enabled, the Memory Efficient Attention - is used. - """ - self.unet.set_use_memory_efficient_attention_xformers(True) - - def disable_xformers_memory_efficient_attention(self): - r""" - Disable memory efficient attention as implemented in xformers. - """ - self.unet.set_use_memory_efficient_attention_xformers(False) - - def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"): - r""" - Enable sliced attention computation. - When this option is enabled, the attention module will split the input tensor in slices, to compute attention - in several steps. This is useful to save some memory in exchange for a small speed decrease. - Args: - slice_size (`str` or `int`, *optional*, defaults to `"auto"`): - When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If - a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case, - `attention_head_dim` must be a multiple of `slice_size`. - """ - if slice_size == "auto": - # half the attention head size is usually a good trade-off between - # speed and memory - slice_size = self.unet.config.attention_head_dim // 2 - self.unet.set_attention_slice(slice_size) - - def disable_attention_slicing(self): - r""" - Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go - back to computing attention in one step. - """ - # set slice_size = `None` to disable `attention slicing` - self.enable_attention_slicing(None) - - def enable_sequential_cpu_offload(self): - r""" - Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, - text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a - `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. - """ - # accelerateが必要になるのでとりあえず省略 - raise NotImplementedError("cpu_offload is omitted.") - # if is_accelerate_available(): - # from accelerate import cpu_offload - # else: - # raise ImportError("Please install accelerate via `pip install accelerate`") - - # device = self.device - - # for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]: - # if cpu_offloaded_model is not None: - # cpu_offload(cpu_offloaded_model, device) - - # endregion - - @torch.no_grad() - def __call__( - self, - prompt: Union[str, List[str]], - negative_prompt: Optional[Union[str, List[str]]] = None, - init_image: Union[torch.FloatTensor, PIL.Image.Image, List[PIL.Image.Image]] = None, - mask_image: Union[torch.FloatTensor, PIL.Image.Image, List[PIL.Image.Image]] = None, - height: int = 512, - width: int = 512, - num_inference_steps: int = 50, - guidance_scale: float = 7.5, - negative_scale: float = None, - strength: float = 0.8, - # num_images_per_prompt: Optional[int] = 1, - eta: float = 0.0, - generator: Optional[torch.Generator] = None, - latents: Optional[torch.FloatTensor] = None, - max_embeddings_multiples: Optional[int] = 3, - output_type: Optional[str] = "pil", - vae_batch_size: float = None, - return_latents: bool = False, - # return_dict: bool = True, - callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, - is_cancelled_callback: Optional[Callable[[], bool]] = None, - callback_steps: Optional[int] = 1, - img2img_noise=None, - clip_prompts=None, - clip_guide_images=None, - networks: Optional[List[LoRANetwork]] = None, - **kwargs, - ): - r""" - Function invoked when calling the pipeline for generation. - Args: - prompt (`str` or `List[str]`): - The prompt or prompts to guide the image generation. - negative_prompt (`str` or `List[str]`, *optional*): - The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored - if `guidance_scale` is less than `1`). - init_image (`torch.FloatTensor` or `PIL.Image.Image`): - `Image`, or tensor representing an image batch, that will be used as the starting point for the - process. - mask_image (`torch.FloatTensor` or `PIL.Image.Image`): - `Image`, or tensor representing an image batch, to mask `init_image`. White pixels in the mask will be - replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a - PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should - contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`. - height (`int`, *optional*, defaults to 512): - The height in pixels of the generated image. - width (`int`, *optional*, defaults to 512): - The width in pixels of the generated image. - num_inference_steps (`int`, *optional*, defaults to 50): - The number of denoising steps. More denoising steps usually lead to a higher quality image at the - expense of slower inference. - guidance_scale (`float`, *optional*, defaults to 7.5): - Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). - `guidance_scale` is defined as `w` of equation 2. of [Imagen - Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > - 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, - usually at the expense of lower image quality. - strength (`float`, *optional*, defaults to 0.8): - Conceptually, indicates how much to transform the reference `init_image`. Must be between 0 and 1. - `init_image` will be used as a starting point, adding more noise to it the larger the `strength`. The - number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added - noise will be maximum and the denoising process will run for the full number of iterations specified in - `num_inference_steps`. A value of 1, therefore, essentially ignores `init_image`. - num_images_per_prompt (`int`, *optional*, defaults to 1): - The number of images to generate per prompt. - eta (`float`, *optional*, defaults to 0.0): - Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to - [`schedulers.DDIMScheduler`], will be ignored for others. - generator (`torch.Generator`, *optional*): - A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation - deterministic. - latents (`torch.FloatTensor`, *optional*): - Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image - generation. Can be used to tweak the same generation with different prompts. If not provided, a latents - tensor will ge generated by sampling using the supplied random `generator`. - max_embeddings_multiples (`int`, *optional*, defaults to `3`): - The max multiple length of prompt embeddings compared to the max output length of text encoder. - output_type (`str`, *optional*, defaults to `"pil"`): - The output format of the generate image. Choose between - [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. - return_dict (`bool`, *optional*, defaults to `True`): - Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a - plain tuple. - callback (`Callable`, *optional*): - A function that will be called every `callback_steps` steps during inference. The function will be - called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. - is_cancelled_callback (`Callable`, *optional*): - A function that will be called every `callback_steps` steps during inference. If the function returns - `True`, the inference will be cancelled. - callback_steps (`int`, *optional*, defaults to 1): - The frequency at which the `callback` function will be called. If not specified, the callback will be - called at every step. - Returns: - `None` if cancelled by `is_cancelled_callback`, - [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: - [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. - When returning a tuple, the first element is a list with the generated images, and the second element is a - list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" - (nsfw) content, according to the `safety_checker`. - """ - num_images_per_prompt = 1 # fixed - - if isinstance(prompt, str): - batch_size = 1 - prompt = [prompt] - elif isinstance(prompt, list): - batch_size = len(prompt) - else: - raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") - reginonal_network = " AND " in prompt[0] - - vae_batch_size = ( - batch_size - if vae_batch_size is None - else (int(vae_batch_size) if vae_batch_size >= 1 else max(1, int(batch_size * vae_batch_size))) - ) - - if strength < 0 or strength > 1: - raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}") - - if height % 8 != 0 or width % 8 != 0: - raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") - - if (callback_steps is None) or ( - callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) - ): - raise ValueError( - f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." - ) - - # get prompt text embeddings - - # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) - # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` - # corresponds to doing no classifier free guidance. - do_classifier_free_guidance = guidance_scale > 1.0 - - if not do_classifier_free_guidance and negative_scale is not None: - 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`." - ) - - if not self.token_replacements_XTI: - text_embeddings, uncond_embeddings, prompt_tokens = get_weighted_text_embeddings( - pipe=self, - prompt=prompt, - uncond_prompt=negative_prompt if do_classifier_free_guidance else None, - max_embeddings_multiples=max_embeddings_multiples, - clip_skip=self.clip_skip, - **kwargs, - ) - - if negative_scale is not None: - _, real_uncond_embeddings, _ = get_weighted_text_embeddings( - pipe=self, - prompt=prompt, # こちらのトークン長に合わせてuncondを作るので75トークン超で必須 - uncond_prompt=[""] * batch_size, - max_embeddings_multiples=max_embeddings_multiples, - clip_skip=self.clip_skip, - **kwargs, - ) - - if self.token_replacements_XTI: - text_embeddings_concat = [] - for layer in [ - "IN01", - "IN02", - "IN04", - "IN05", - "IN07", - "IN08", - "MID", - "OUT03", - "OUT04", - "OUT05", - "OUT06", - "OUT07", - "OUT08", - "OUT09", - "OUT10", - "OUT11", - ]: - text_embeddings, uncond_embeddings, prompt_tokens = get_weighted_text_embeddings( - pipe=self, - prompt=prompt, - uncond_prompt=negative_prompt if do_classifier_free_guidance else None, - max_embeddings_multiples=max_embeddings_multiples, - clip_skip=self.clip_skip, - layer=layer, - **kwargs, - ) - if do_classifier_free_guidance: - if negative_scale is None: - text_embeddings_concat.append(torch.cat([uncond_embeddings, text_embeddings])) - else: - text_embeddings_concat.append(torch.cat([uncond_embeddings, text_embeddings, real_uncond_embeddings])) - text_embeddings = torch.stack(text_embeddings_concat) - else: - if do_classifier_free_guidance: - if negative_scale is None: - text_embeddings = torch.cat([uncond_embeddings, text_embeddings]) - else: - text_embeddings = torch.cat([uncond_embeddings, text_embeddings, real_uncond_embeddings]) - - # CLIP guidanceで使用するembeddingsを取得する - if self.clip_guidance_scale > 0: - clip_text_input = prompt_tokens - if clip_text_input.shape[1] > self.tokenizer.model_max_length: - # TODO 75文字を超えたら警告を出す？ - logger.info(f"trim text input {clip_text_input.shape}") - clip_text_input = torch.cat( - [clip_text_input[:, : self.tokenizer.model_max_length - 1], clip_text_input[:, -1].unsqueeze(1)], dim=1 - ) - logger.info(f"trimmed {clip_text_input.shape}") - - for i, clip_prompt in enumerate(clip_prompts): - if clip_prompt is not None: # clip_promptがあれば上書きする - clip_text_input[i] = self.tokenizer( - clip_prompt, - padding="max_length", - max_length=self.tokenizer.model_max_length, - truncation=True, - return_tensors="pt", - ).input_ids.to(self.device) - - text_embeddings_clip = self.clip_model.get_text_features(clip_text_input) - text_embeddings_clip = text_embeddings_clip / text_embeddings_clip.norm(p=2, dim=-1, keepdim=True) # prompt複数件でもOK - - if ( - self.clip_image_guidance_scale > 0 - or self.vgg16_guidance_scale > 0 - and clip_guide_images is not None - or self.control_nets - ): - if isinstance(clip_guide_images, PIL.Image.Image): - clip_guide_images = [clip_guide_images] - - if self.clip_image_guidance_scale > 0: - clip_guide_images = [preprocess_guide_image(im) for im in clip_guide_images] - clip_guide_images = torch.cat(clip_guide_images, dim=0) - - clip_guide_images = self.normalize(clip_guide_images).to(self.device).to(text_embeddings.dtype) - image_embeddings_clip = self.clip_model.get_image_features(clip_guide_images) - image_embeddings_clip = image_embeddings_clip / image_embeddings_clip.norm(p=2, dim=-1, keepdim=True) - if len(image_embeddings_clip) == 1: - image_embeddings_clip = image_embeddings_clip.repeat((batch_size, 1, 1, 1)) - elif self.vgg16_guidance_scale > 0: - size = (width // VGG16_INPUT_RESIZE_DIV, height // VGG16_INPUT_RESIZE_DIV) # とりあえず1/4に（小さいか?） - clip_guide_images = [preprocess_vgg16_guide_image(im, size) for im in clip_guide_images] - clip_guide_images = torch.cat(clip_guide_images, dim=0) - - clip_guide_images = self.vgg16_normalize(clip_guide_images).to(self.device).to(text_embeddings.dtype) - image_embeddings_vgg16 = self.vgg16_feat_model(clip_guide_images)["feat"] - if len(image_embeddings_vgg16) == 1: - image_embeddings_vgg16 = image_embeddings_vgg16.repeat((batch_size, 1, 1, 1)) - else: - # ControlNetのhintにguide imageを流用する - # 前処理はControlNet側で行う - pass - - # set timesteps - self.scheduler.set_timesteps(num_inference_steps, self.device) - - latents_dtype = text_embeddings.dtype - init_latents_orig = None - mask = None - - 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).latent_dist - init_latents = init_latent_dist.sample(generator=generator) - else: - clean_memory() - 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) - ).latent_dist - init_latents.append(init_latent_dist.sample(generator=generator)) - init_latents = torch.cat(init_latents) - - init_latents = 0.18215 * init_latents - - if len(init_latents) == 1: - init_latents = init_latents.repeat((batch_size, 1, 1, 1)) - init_latents_orig = init_latents - - # preprocess mask - if mask_image is not None: - mask = mask_image.to(device=self.device, dtype=latents_dtype) - if len(mask) == 1: - mask = mask.repeat((batch_size, 1, 1, 1)) - - # check sizes - if not mask.shape == init_latents.shape: - raise ValueError("The mask and init_image should be the same size!") - - # get the original timestep using init_timestep - offset = self.scheduler.config.get("steps_offset", 0) - init_timestep = int(num_inference_steps * strength) + offset - init_timestep = min(init_timestep, num_inference_steps) - - timesteps = self.scheduler.timesteps[-init_timestep] - timesteps = torch.tensor([timesteps] * batch_size * num_images_per_prompt, device=self.device) - - # add noise to latents using the timesteps - latents = self.scheduler.add_noise(init_latents, img2img_noise, timesteps) - - t_start = max(num_inference_steps - init_timestep + offset, 0) - timesteps = self.scheduler.timesteps[t_start:].to(self.device) - - # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature - # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. - # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 - # and should be between [0, 1] - accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) - extra_step_kwargs = {} - if accepts_eta: - extra_step_kwargs["eta"] = eta - - num_latent_input = (3 if negative_scale is not None else 2) if do_classifier_free_guidance else 1 - - if self.control_nets: - guided_hints = original_control_net.get_guided_hints(self.control_nets, num_latent_input, batch_size, clip_guide_images) - - if reginonal_network: - num_sub_and_neg_prompts = len(text_embeddings) // batch_size - # last subprompt and negative prompt - text_emb_last = [] - for j in range(batch_size): - text_emb_last.append(text_embeddings[(j + 1) * num_sub_and_neg_prompts - 2]) - text_emb_last.append(text_embeddings[(j + 1) * num_sub_and_neg_prompts - 1]) - text_emb_last = torch.stack(text_emb_last) - else: - text_emb_last = text_embeddings - - enable_gradual_latent = False - if self.gradual_latent: - if not hasattr(self.scheduler, "set_gradual_latent_params"): - logger.info("gradual_latent is not supported for this scheduler. Ignoring.") - logger.info(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) - - # predict the noise residual - if self.control_nets and self.control_net_enabled: - 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 - else: - noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample - - # perform guidance - if do_classifier_free_guidance: - if negative_scale is None: - noise_pred_uncond, noise_pred_text = noise_pred.chunk(num_latent_input) # uncond by negative prompt - noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) - else: - noise_pred_negative, noise_pred_text, noise_pred_uncond = noise_pred.chunk( - num_latent_input - ) # uncond is real uncond - noise_pred = ( - noise_pred_uncond - + guidance_scale * (noise_pred_text - noise_pred_uncond) - - negative_scale * (noise_pred_negative - noise_pred_uncond) - ) - - # perform clip guidance - if self.clip_guidance_scale > 0 or self.clip_image_guidance_scale > 0 or self.vgg16_guidance_scale > 0: - text_embeddings_for_guidance = ( - text_embeddings.chunk(num_latent_input)[1] if do_classifier_free_guidance else text_embeddings - ) - - if self.clip_guidance_scale > 0: - noise_pred, latents = self.cond_fn( - latents, - t, - i, - text_embeddings_for_guidance, - noise_pred, - text_embeddings_clip, - self.clip_guidance_scale, - NUM_CUTOUTS, - USE_CUTOUTS, - ) - if self.clip_image_guidance_scale > 0 and clip_guide_images is not None: - noise_pred, latents = self.cond_fn( - latents, - t, - i, - text_embeddings_for_guidance, - noise_pred, - image_embeddings_clip, - self.clip_image_guidance_scale, - NUM_CUTOUTS, - USE_CUTOUTS, - ) - if self.vgg16_guidance_scale > 0 and clip_guide_images is not None: - noise_pred, latents = self.cond_fn_vgg16( - latents, t, i, text_embeddings_for_guidance, noise_pred, image_embeddings_vgg16, self.vgg16_guidance_scale - ) - - # compute the previous noisy sample x_t -> x_t-1 - latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample - - if mask is not None: - # masking - init_latents_proper = self.scheduler.add_noise(init_latents_orig, img2img_noise, torch.tensor([t])) - latents = (init_latents_proper * mask) + (latents * (1 - mask)) - - # call the callback, if provided - if i % callback_steps == 0: - if callback is not None: - callback(i, t, latents) - if is_cancelled_callback is not None and is_cancelled_callback(): - return None - - if return_latents: - return (latents, False) - - latents = 1 / 0.18215 * latents - if vae_batch_size >= batch_size: - image = self.vae.decode(latents).sample - else: - clean_memory() - 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)).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 self.safety_checker is not None: - safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(self.device) - image, has_nsfw_concept = self.safety_checker( - images=image, - clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype), - ) - else: - has_nsfw_concept = None - - if output_type == "pil": - # image = self.numpy_to_pil(image) - image = (image * 255).round().astype("uint8") - image = [Image.fromarray(im) for im in image] - - # if not return_dict: - return (image, has_nsfw_concept) - - # return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) - - def text2img( - self, - prompt: Union[str, List[str]], - negative_prompt: Optional[Union[str, List[str]]] = None, - height: int = 512, - width: int = 512, - num_inference_steps: int = 50, - guidance_scale: float = 7.5, - num_images_per_prompt: Optional[int] = 1, - eta: float = 0.0, - generator: Optional[torch.Generator] = None, - latents: Optional[torch.FloatTensor] = None, - max_embeddings_multiples: Optional[int] = 3, - output_type: Optional[str] = "pil", - return_dict: bool = True, - callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, - callback_steps: Optional[int] = 1, - **kwargs, - ): - r""" - Function for text-to-image generation. - Args: - prompt (`str` or `List[str]`): - The prompt or prompts to guide the image generation. - negative_prompt (`str` or `List[str]`, *optional*): - The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored - if `guidance_scale` is less than `1`). - height (`int`, *optional*, defaults to 512): - The height in pixels of the generated image. - width (`int`, *optional*, defaults to 512): - The width in pixels of the generated image. - num_inference_steps (`int`, *optional*, defaults to 50): - The number of denoising steps. More denoising steps usually lead to a higher quality image at the - expense of slower inference. - guidance_scale (`float`, *optional*, defaults to 7.5): - Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). - `guidance_scale` is defined as `w` of equation 2. of [Imagen - Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > - 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, - usually at the expense of lower image quality. - num_images_per_prompt (`int`, *optional*, defaults to 1): - The number of images to generate per prompt. - eta (`float`, *optional*, defaults to 0.0): - Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to - [`schedulers.DDIMScheduler`], will be ignored for others. - generator (`torch.Generator`, *optional*): - A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation - deterministic. - latents (`torch.FloatTensor`, *optional*): - Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image - generation. Can be used to tweak the same generation with different prompts. If not provided, a latents - tensor will ge generated by sampling using the supplied random `generator`. - max_embeddings_multiples (`int`, *optional*, defaults to `3`): - The max multiple length of prompt embeddings compared to the max output length of text encoder. - output_type (`str`, *optional*, defaults to `"pil"`): - The output format of the generate image. Choose between - [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. - return_dict (`bool`, *optional*, defaults to `True`): - Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a - plain tuple. - callback (`Callable`, *optional*): - A function that will be called every `callback_steps` steps during inference. The function will be - called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. - callback_steps (`int`, *optional*, defaults to 1): - The frequency at which the `callback` function will be called. If not specified, the callback will be - called at every step. - Returns: - [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: - [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. - When returning a tuple, the first element is a list with the generated images, and the second element is a - list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" - (nsfw) content, according to the `safety_checker`. - """ - return self.__call__( - prompt=prompt, - negative_prompt=negative_prompt, - height=height, - width=width, - num_inference_steps=num_inference_steps, - guidance_scale=guidance_scale, - num_images_per_prompt=num_images_per_prompt, - eta=eta, - generator=generator, - latents=latents, - max_embeddings_multiples=max_embeddings_multiples, - output_type=output_type, - return_dict=return_dict, - callback=callback, - callback_steps=callback_steps, - **kwargs, - ) - - def img2img( - self, - init_image: Union[torch.FloatTensor, PIL.Image.Image], - prompt: Union[str, List[str]], - negative_prompt: Optional[Union[str, List[str]]] = None, - strength: float = 0.8, - num_inference_steps: Optional[int] = 50, - guidance_scale: Optional[float] = 7.5, - num_images_per_prompt: Optional[int] = 1, - eta: Optional[float] = 0.0, - generator: Optional[torch.Generator] = None, - max_embeddings_multiples: Optional[int] = 3, - output_type: Optional[str] = "pil", - return_dict: bool = True, - callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, - callback_steps: Optional[int] = 1, - **kwargs, - ): - r""" - Function for image-to-image generation. - Args: - init_image (`torch.FloatTensor` or `PIL.Image.Image`): - `Image`, or tensor representing an image batch, that will be used as the starting point for the - process. - prompt (`str` or `List[str]`): - The prompt or prompts to guide the image generation. - negative_prompt (`str` or `List[str]`, *optional*): - The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored - if `guidance_scale` is less than `1`). - strength (`float`, *optional*, defaults to 0.8): - Conceptually, indicates how much to transform the reference `init_image`. Must be between 0 and 1. - `init_image` will be used as a starting point, adding more noise to it the larger the `strength`. The - number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added - noise will be maximum and the denoising process will run for the full number of iterations specified in - `num_inference_steps`. A value of 1, therefore, essentially ignores `init_image`. - num_inference_steps (`int`, *optional*, defaults to 50): - The number of denoising steps. More denoising steps usually lead to a higher quality image at the - expense of slower inference. This parameter will be modulated by `strength`. - guidance_scale (`float`, *optional*, defaults to 7.5): - Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). - `guidance_scale` is defined as `w` of equation 2. of [Imagen - Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > - 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, - usually at the expense of lower image quality. - num_images_per_prompt (`int`, *optional*, defaults to 1): - The number of images to generate per prompt. - eta (`float`, *optional*, defaults to 0.0): - Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to - [`schedulers.DDIMScheduler`], will be ignored for others. - generator (`torch.Generator`, *optional*): - A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation - deterministic. - max_embeddings_multiples (`int`, *optional*, defaults to `3`): - The max multiple length of prompt embeddings compared to the max output length of text encoder. - output_type (`str`, *optional*, defaults to `"pil"`): - The output format of the generate image. Choose between - [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. - return_dict (`bool`, *optional*, defaults to `True`): - Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a - plain tuple. - callback (`Callable`, *optional*): - A function that will be called every `callback_steps` steps during inference. The function will be - called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. - callback_steps (`int`, *optional*, defaults to 1): - The frequency at which the `callback` function will be called. If not specified, the callback will be - called at every step. - Returns: - [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: - [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. - When returning a tuple, the first element is a list with the generated images, and the second element is a - list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" - (nsfw) content, according to the `safety_checker`. - """ - return self.__call__( - prompt=prompt, - negative_prompt=negative_prompt, - init_image=init_image, - num_inference_steps=num_inference_steps, - guidance_scale=guidance_scale, - strength=strength, - num_images_per_prompt=num_images_per_prompt, - eta=eta, - generator=generator, - max_embeddings_multiples=max_embeddings_multiples, - output_type=output_type, - return_dict=return_dict, - callback=callback, - callback_steps=callback_steps, - **kwargs, - ) - - def inpaint( - self, - init_image: Union[torch.FloatTensor, PIL.Image.Image], - mask_image: Union[torch.FloatTensor, PIL.Image.Image], - prompt: Union[str, List[str]], - negative_prompt: Optional[Union[str, List[str]]] = None, - strength: float = 0.8, - num_inference_steps: Optional[int] = 50, - guidance_scale: Optional[float] = 7.5, - num_images_per_prompt: Optional[int] = 1, - eta: Optional[float] = 0.0, - generator: Optional[torch.Generator] = None, - max_embeddings_multiples: Optional[int] = 3, - output_type: Optional[str] = "pil", - return_dict: bool = True, - callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, - callback_steps: Optional[int] = 1, - **kwargs, - ): - r""" - Function for inpaint. - Args: - init_image (`torch.FloatTensor` or `PIL.Image.Image`): - `Image`, or tensor representing an image batch, that will be used as the starting point for the - process. This is the image whose masked region will be inpainted. - mask_image (`torch.FloatTensor` or `PIL.Image.Image`): - `Image`, or tensor representing an image batch, to mask `init_image`. White pixels in the mask will be - replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a - PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should - contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`. - prompt (`str` or `List[str]`): - The prompt or prompts to guide the image generation. - negative_prompt (`str` or `List[str]`, *optional*): - The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored - if `guidance_scale` is less than `1`). - strength (`float`, *optional*, defaults to 0.8): - Conceptually, indicates how much to inpaint the masked area. Must be between 0 and 1. When `strength` - is 1, the denoising process will be run on the masked area for the full number of iterations specified - in `num_inference_steps`. `init_image` will be used as a reference for the masked area, adding more - noise to that region the larger the `strength`. If `strength` is 0, no inpainting will occur. - num_inference_steps (`int`, *optional*, defaults to 50): - The reference number of denoising steps. More denoising steps usually lead to a higher quality image at - the expense of slower inference. This parameter will be modulated by `strength`, as explained above. - guidance_scale (`float`, *optional*, defaults to 7.5): - Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). - `guidance_scale` is defined as `w` of equation 2. of [Imagen - Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > - 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, - usually at the expense of lower image quality. - num_images_per_prompt (`int`, *optional*, defaults to 1): - The number of images to generate per prompt. - eta (`float`, *optional*, defaults to 0.0): - Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to - [`schedulers.DDIMScheduler`], will be ignored for others. - generator (`torch.Generator`, *optional*): - A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation - deterministic. - max_embeddings_multiples (`int`, *optional*, defaults to `3`): - The max multiple length of prompt embeddings compared to the max output length of text encoder. - output_type (`str`, *optional*, defaults to `"pil"`): - The output format of the generate image. Choose between - [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. - return_dict (`bool`, *optional*, defaults to `True`): - Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a - plain tuple. - callback (`Callable`, *optional*): - A function that will be called every `callback_steps` steps during inference. The function will be - called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. - callback_steps (`int`, *optional*, defaults to 1): - The frequency at which the `callback` function will be called. If not specified, the callback will be - called at every step. - Returns: - [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: - [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. - When returning a tuple, the first element is a list with the generated images, and the second element is a - list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" - (nsfw) content, according to the `safety_checker`. - """ - return self.__call__( - prompt=prompt, - negative_prompt=negative_prompt, - init_image=init_image, - mask_image=mask_image, - num_inference_steps=num_inference_steps, - guidance_scale=guidance_scale, - strength=strength, - num_images_per_prompt=num_images_per_prompt, - eta=eta, - generator=generator, - max_embeddings_multiples=max_embeddings_multiples, - output_type=output_type, - return_dict=return_dict, - callback=callback, - callback_steps=callback_steps, - **kwargs, - ) - - # CLIP guidance StableDiffusion - # copy from https://github.com/huggingface/diffusers/blob/main/examples/community/clip_guided_stable_diffusion.py - - # バッチを分解して1件ずつ処理する - def cond_fn( - self, - latents, - timestep, - index, - text_embeddings, - noise_pred_original, - guide_embeddings_clip, - clip_guidance_scale, - num_cutouts, - use_cutouts=True, - ): - if len(latents) == 1: - return self.cond_fn1( - latents, - timestep, - index, - text_embeddings, - noise_pred_original, - guide_embeddings_clip, - clip_guidance_scale, - num_cutouts, - use_cutouts, - ) - - noise_pred = [] - cond_latents = [] - for i in range(len(latents)): - lat1 = latents[i].unsqueeze(0) - tem1 = text_embeddings[i].unsqueeze(0) - npo1 = noise_pred_original[i].unsqueeze(0) - gem1 = guide_embeddings_clip[i].unsqueeze(0) - npr1, cla1 = self.cond_fn1(lat1, timestep, index, tem1, npo1, gem1, clip_guidance_scale, num_cutouts, use_cutouts) - noise_pred.append(npr1) - cond_latents.append(cla1) - - noise_pred = torch.cat(noise_pred) - cond_latents = torch.cat(cond_latents) - return noise_pred, cond_latents - - @torch.enable_grad() - def cond_fn1( - self, - latents, - timestep, - index, - text_embeddings, - noise_pred_original, - guide_embeddings_clip, - clip_guidance_scale, - num_cutouts, - use_cutouts=True, - ): - latents = latents.detach().requires_grad_() - - if isinstance(self.scheduler, LMSDiscreteScheduler): - sigma = self.scheduler.sigmas[index] - # the model input needs to be scaled to match the continuous ODE formulation in K-LMS - latent_model_input = latents / ((sigma**2 + 1) ** 0.5) - else: - latent_model_input = latents - - # predict the noise residual - noise_pred = self.unet(latent_model_input, timestep, encoder_hidden_states=text_embeddings).sample - - if isinstance(self.scheduler, (PNDMScheduler, DDIMScheduler)): - alpha_prod_t = self.scheduler.alphas_cumprod[timestep] - beta_prod_t = 1 - alpha_prod_t - # compute predicted original sample from predicted noise also called - # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf - pred_original_sample = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5) - - fac = torch.sqrt(beta_prod_t) - sample = pred_original_sample * (fac) + latents * (1 - fac) - elif isinstance(self.scheduler, LMSDiscreteScheduler): - sigma = self.scheduler.sigmas[index] - sample = latents - sigma * noise_pred - else: - raise ValueError(f"scheduler type {type(self.scheduler)} not supported") - - sample = 1 / 0.18215 * sample - image = self.vae.decode(sample).sample - image = (image / 2 + 0.5).clamp(0, 1) - - if use_cutouts: - image = self.make_cutouts(image, num_cutouts) - else: - image = transforms.Resize(FEATURE_EXTRACTOR_SIZE)(image) - image = self.normalize(image).to(latents.dtype) - - image_embeddings_clip = self.clip_model.get_image_features(image) - image_embeddings_clip = image_embeddings_clip / image_embeddings_clip.norm(p=2, dim=-1, keepdim=True) - - if use_cutouts: - dists = spherical_dist_loss(image_embeddings_clip, guide_embeddings_clip) - dists = dists.view([num_cutouts, sample.shape[0], -1]) - loss = dists.sum(2).mean(0).sum() * clip_guidance_scale - else: - # バッチサイズが複数だと正しく動くかわからない - loss = spherical_dist_loss(image_embeddings_clip, guide_embeddings_clip).mean() * clip_guidance_scale - - grads = -torch.autograd.grad(loss, latents)[0] - - if isinstance(self.scheduler, LMSDiscreteScheduler): - latents = latents.detach() + grads * (sigma**2) - noise_pred = noise_pred_original - else: - noise_pred = noise_pred_original - torch.sqrt(beta_prod_t) * grads - return noise_pred, latents - - # バッチを分解して一件ずつ処理する - def cond_fn_vgg16(self, latents, timestep, index, text_embeddings, noise_pred_original, guide_embeddings, guidance_scale): - if len(latents) == 1: - return self.cond_fn_vgg16_b1( - latents, timestep, index, text_embeddings, noise_pred_original, guide_embeddings, guidance_scale - ) - - noise_pred = [] - cond_latents = [] - for i in range(len(latents)): - lat1 = latents[i].unsqueeze(0) - tem1 = text_embeddings[i].unsqueeze(0) - npo1 = noise_pred_original[i].unsqueeze(0) - gem1 = guide_embeddings[i].unsqueeze(0) - npr1, cla1 = self.cond_fn_vgg16_b1(lat1, timestep, index, tem1, npo1, gem1, guidance_scale) - noise_pred.append(npr1) - cond_latents.append(cla1) - - noise_pred = torch.cat(noise_pred) - cond_latents = torch.cat(cond_latents) - return noise_pred, cond_latents - - # 1件だけ処理する - @torch.enable_grad() - def cond_fn_vgg16_b1(self, latents, timestep, index, text_embeddings, noise_pred_original, guide_embeddings, guidance_scale): - latents = latents.detach().requires_grad_() - - if isinstance(self.scheduler, LMSDiscreteScheduler): - sigma = self.scheduler.sigmas[index] - # the model input needs to be scaled to match the continuous ODE formulation in K-LMS - latent_model_input = latents / ((sigma**2 + 1) ** 0.5) - else: - latent_model_input = latents - - # predict the noise residual - noise_pred = self.unet(latent_model_input, timestep, encoder_hidden_states=text_embeddings).sample - - if isinstance(self.scheduler, (PNDMScheduler, DDIMScheduler)): - alpha_prod_t = self.scheduler.alphas_cumprod[timestep] - beta_prod_t = 1 - alpha_prod_t - # compute predicted original sample from predicted noise also called - # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf - pred_original_sample = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5) - - fac = torch.sqrt(beta_prod_t) - sample = pred_original_sample * (fac) + latents * (1 - fac) - elif isinstance(self.scheduler, LMSDiscreteScheduler): - sigma = self.scheduler.sigmas[index] - sample = latents - sigma * noise_pred - else: - raise ValueError(f"scheduler type {type(self.scheduler)} not supported") - - sample = 1 / 0.18215 * sample - image = self.vae.decode(sample).sample - image = (image / 2 + 0.5).clamp(0, 1) - image = transforms.Resize((image.shape[-2] // VGG16_INPUT_RESIZE_DIV, image.shape[-1] // VGG16_INPUT_RESIZE_DIV))(image) - image = self.vgg16_normalize(image).to(latents.dtype) - - image_embeddings = self.vgg16_feat_model(image)["feat"] - - # バッチサイズが複数だと正しく動くかわからない - loss = ( - (image_embeddings - guide_embeddings) ** 2 - ).mean() * guidance_scale # MSE style transferでコンテンツの損失はMSEなので - - grads = -torch.autograd.grad(loss, latents)[0] - if isinstance(self.scheduler, LMSDiscreteScheduler): - latents = latents.detach() + grads * (sigma**2) - noise_pred = noise_pred_original - else: - noise_pred = noise_pred_original - torch.sqrt(beta_prod_t) * grads - return noise_pred, latents - - -class MakeCutouts(torch.nn.Module): - def __init__(self, cut_size, cut_power=1.0): - super().__init__() - - self.cut_size = cut_size - self.cut_power = cut_power - - def forward(self, pixel_values, num_cutouts): - sideY, sideX = pixel_values.shape[2:4] - max_size = min(sideX, sideY) - min_size = min(sideX, sideY, self.cut_size) - cutouts = [] - for _ in range(num_cutouts): - size = int(torch.rand([]) ** self.cut_power * (max_size - min_size) + min_size) - offsetx = torch.randint(0, sideX - size + 1, ()) - offsety = torch.randint(0, sideY - size + 1, ()) - cutout = pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size] - cutouts.append(torch.nn.functional.adaptive_avg_pool2d(cutout, self.cut_size)) - return torch.cat(cutouts) - - -def spherical_dist_loss(x, y): - x = torch.nn.functional.normalize(x, dim=-1) - y = torch.nn.functional.normalize(y, dim=-1) - return (x - y).norm(dim=-1).div(2).arcsin().pow(2).mul(2) - - -re_attention = re.compile( - r""" -\\\(| -\\\)| -\\\[| -\\]| -\\\\| -\\| -\(| -\[| -:([+-]?[.\d]+)\)| -\)| -]| -[^\\()\[\]:]+| -: -""", - re.X, -) - - -def parse_prompt_attention(text): - """ - Parses a string with attention tokens and returns a list of pairs: text and its associated weight. - Accepted tokens are: - (abc) - increases attention to abc by a multiplier of 1.1 - (abc:3.12) - increases attention to abc by a multiplier of 3.12 - [abc] - decreases attention to abc by a multiplier of 1.1 - \( - literal character '(' - \[ - literal character '[' - \) - literal character ')' - \] - literal character ']' - \\ - literal character '\' - anything else - just text - >>> parse_prompt_attention('normal text') - [['normal text', 1.0]] - >>> parse_prompt_attention('an (important) word') - [['an ', 1.0], ['important', 1.1], [' word', 1.0]] - >>> parse_prompt_attention('(unbalanced') - [['unbalanced', 1.1]] - >>> parse_prompt_attention('\(literal\]') - [['(literal]', 1.0]] - >>> parse_prompt_attention('(unnecessary)(parens)') - [['unnecessaryparens', 1.1]] - >>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).') - [['a ', 1.0], - ['house', 1.5730000000000004], - [' ', 1.1], - ['on', 1.0], - [' a ', 1.1], - ['hill', 0.55], - [', sun, ', 1.1], - ['sky', 1.4641000000000006], - ['.', 1.1]] - """ - - res = [] - round_brackets = [] - square_brackets = [] - - round_bracket_multiplier = 1.1 - square_bracket_multiplier = 1 / 1.1 - - def multiply_range(start_position, multiplier): - for p in range(start_position, len(res)): - res[p][1] *= multiplier - - # 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(pipe: PipelineLike, prompt: List[str], max_length: int, layer=None): - 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 = pipe.tokenizer.model_max_length - (len(text_token) % pipe.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(pipe.tokenizer.eos_token_id) - # else: - text_token.append(pipe.tokenizer.pad_token_id) - text_weight.append(1.0) - continue - - # tokenize and discard the starting and the ending token - token = pipe.tokenizer(word).input_ids[1:-1] - - token = pipe.replace_token(token, layer=layer) - - 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("Prompt was truncated. Try to shorten the prompt or increase max_embeddings_multiples") - return tokens, weights - - -def pad_tokens_and_weights(tokens, weights, max_length, bos, eos, pad, no_boseos_middle=True, chunk_length=77): - r""" - Pad the tokens (with starting and ending tokens) and weights (with 1.0) to max_length. - """ - max_embeddings_multiples = (max_length - 2) // (chunk_length - 2) - weights_length = max_length if no_boseos_middle else max_embeddings_multiples * chunk_length - for i in range(len(tokens)): - tokens[i] = [bos] + tokens[i] + [eos] + [pad] * (max_length - 2 - len(tokens[i])) - if no_boseos_middle: - weights[i] = [1.0] + weights[i] + [1.0] * (max_length - 1 - len(weights[i])) - else: - w = [] - if len(weights[i]) == 0: - w = [1.0] * weights_length - else: - for j in range(max_embeddings_multiples): - w.append(1.0) # weight for starting token in this chunk - w += weights[i][j * (chunk_length - 2) : min(len(weights[i]), (j + 1) * (chunk_length - 2))] - w.append(1.0) # weight for ending token in this chunk - w += [1.0] * (weights_length - len(w)) - weights[i] = w[:] - - return tokens, weights - - -def get_unweighted_text_embeddings( - pipe: PipelineLike, - text_input: torch.Tensor, - chunk_length: int, - clip_skip: int, - eos: int, - pad: int, - no_boseos_middle: Optional[bool] = True, -): - """ - When the length of tokens is a multiple of the capacity of the text encoder, - it should be split into chunks and sent to the text encoder individually. - """ - max_embeddings_multiples = (text_input.shape[1] - 2) // (chunk_length - 2) - if max_embeddings_multiples > 1: - text_embeddings = [] - for i in range(max_embeddings_multiples): - # extract the i-th chunk - text_input_chunk = text_input[:, i * (chunk_length - 2) : (i + 1) * (chunk_length - 2) + 2].clone() - - # cover the head and the tail by the starting and the ending tokens - text_input_chunk[:, 0] = text_input[0, 0] - if pad == eos: # v1 - text_input_chunk[:, -1] = text_input[0, -1] - else: # v2 - 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 - - if clip_skip is None or clip_skip == 1: - text_embedding = pipe.text_encoder(text_input_chunk)[0] - else: - enc_out = pipe.text_encoder(text_input_chunk, output_hidden_states=True, return_dict=True) - text_embedding = enc_out["hidden_states"][-clip_skip] - text_embedding = pipe.text_encoder.text_model.final_layer_norm(text_embedding) - - if no_boseos_middle: - if i == 0: - # discard the ending token - text_embedding = text_embedding[:, :-1] - elif i == max_embeddings_multiples - 1: - # discard the starting token - text_embedding = text_embedding[:, 1:] - else: - # discard both starting and ending tokens - text_embedding = text_embedding[:, 1:-1] - - text_embeddings.append(text_embedding) - text_embeddings = torch.concat(text_embeddings, axis=1) - else: - if clip_skip is None or clip_skip == 1: - text_embeddings = pipe.text_encoder(text_input)[0] - else: - enc_out = pipe.text_encoder(text_input, output_hidden_states=True, return_dict=True) - text_embeddings = enc_out["hidden_states"][-clip_skip] - text_embeddings = pipe.text_encoder.text_model.final_layer_norm(text_embeddings) - return text_embeddings - - -def get_weighted_text_embeddings( - pipe: PipelineLike, - prompt: Union[str, List[str]], - uncond_prompt: Optional[Union[str, List[str]]] = None, - max_embeddings_multiples: Optional[int] = 1, - no_boseos_middle: Optional[bool] = False, - skip_parsing: Optional[bool] = False, - skip_weighting: Optional[bool] = False, - clip_skip=None, - layer=None, - **kwargs, -): - r""" - Prompts can be assigned with local weights using brackets. For example, - prompt 'A (very beautiful) masterpiece' highlights the words 'very beautiful', - and the embedding tokens corresponding to the words get multiplied by a constant, 1.1. - Also, to regularize of the embedding, the weighted embedding would be scaled to preserve the original mean. - Args: - pipe (`DiffusionPipeline`): - Pipe to provide access to the tokenizer and the text encoder. - prompt (`str` or `List[str]`): - The prompt or prompts to guide the image generation. - uncond_prompt (`str` or `List[str]`): - The unconditional prompt or prompts for guide the image generation. If unconditional prompt - is provided, the embeddings of prompt and uncond_prompt are concatenated. - max_embeddings_multiples (`int`, *optional*, defaults to `1`): - The max multiple length of prompt embeddings compared to the max output length of text encoder. - no_boseos_middle (`bool`, *optional*, defaults to `False`): - If the length of text token is multiples of the capacity of text encoder, whether reserve the starting and - ending token in each of the chunk in the middle. - skip_parsing (`bool`, *optional*, defaults to `False`): - Skip the parsing of brackets. - skip_weighting (`bool`, *optional*, defaults to `False`): - Skip the weighting. When the parsing is skipped, it is forced True. - """ - max_length = (pipe.tokenizer.model_max_length - 2) * max_embeddings_multiples + 2 - if isinstance(prompt, str): - prompt = [prompt] - - # 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(pipe, prompt, max_length - 2, layer=layer) - if uncond_prompt is not None: - if isinstance(uncond_prompt, str): - uncond_prompt = [uncond_prompt] - uncond_tokens, uncond_weights = get_prompts_with_weights(pipe, uncond_prompt, max_length - 2, layer=layer) - else: - prompt_tokens = [token[1:-1] for token in pipe.tokenizer(prompt, max_length=max_length, truncation=True).input_ids] - prompt_weights = [[1.0] * len(token) for token in prompt_tokens] - if uncond_prompt is not None: - if isinstance(uncond_prompt, str): - uncond_prompt = [uncond_prompt] - uncond_tokens = [ - token[1:-1] for token in pipe.tokenizer(uncond_prompt, max_length=max_length, truncation=True).input_ids - ] - uncond_weights = [[1.0] * len(token) for token in uncond_tokens] - - # round up the longest length of tokens to a multiple of (model_max_length - 2) - max_length = max([len(token) for token in prompt_tokens]) - if uncond_prompt is not None: - max_length = max(max_length, max([len(token) for token in uncond_tokens])) - - max_embeddings_multiples = min( - max_embeddings_multiples, - (max_length - 1) // (pipe.tokenizer.model_max_length - 2) + 1, - ) - max_embeddings_multiples = max(1, max_embeddings_multiples) - max_length = (pipe.tokenizer.model_max_length - 2) * max_embeddings_multiples + 2 - - # pad the length of tokens and weights - bos = pipe.tokenizer.bos_token_id - eos = pipe.tokenizer.eos_token_id - pad = pipe.tokenizer.pad_token_id - prompt_tokens, prompt_weights = pad_tokens_and_weights( - prompt_tokens, - prompt_weights, - max_length, - bos, - eos, - pad, - no_boseos_middle=no_boseos_middle, - chunk_length=pipe.tokenizer.model_max_length, - ) - prompt_tokens = torch.tensor(prompt_tokens, dtype=torch.long, device=pipe.device) - if uncond_prompt is not None: - uncond_tokens, uncond_weights = pad_tokens_and_weights( - uncond_tokens, - uncond_weights, - max_length, - bos, - eos, - pad, - no_boseos_middle=no_boseos_middle, - chunk_length=pipe.tokenizer.model_max_length, - ) - uncond_tokens = torch.tensor(uncond_tokens, dtype=torch.long, device=pipe.device) - - # get the embeddings - text_embeddings = get_unweighted_text_embeddings( - pipe, - prompt_tokens, - pipe.tokenizer.model_max_length, - clip_skip, - eos, - pad, - no_boseos_middle=no_boseos_middle, - ) - prompt_weights = torch.tensor(prompt_weights, dtype=text_embeddings.dtype, device=pipe.device) - if uncond_prompt is not None: - uncond_embeddings = get_unweighted_text_embeddings( - pipe, - uncond_tokens, - pipe.tokenizer.model_max_length, - clip_skip, - eos, - pad, - no_boseos_middle=no_boseos_middle, - ) - uncond_weights = torch.tensor(uncond_weights, dtype=uncond_embeddings.dtype, device=pipe.device) - - # assign weights to the prompts and normalize in the sense of mean - # TODO: should we normalize by chunk or in a whole (current implementation)? - # →全体でいいんじゃないかな - if (not skip_parsing) and (not skip_weighting): - previous_mean = text_embeddings.float().mean(axis=[-2, -1]).to(text_embeddings.dtype) - text_embeddings *= prompt_weights.unsqueeze(-1) - current_mean = text_embeddings.float().mean(axis=[-2, -1]).to(text_embeddings.dtype) - text_embeddings *= (previous_mean / current_mean).unsqueeze(-1).unsqueeze(-1) - if uncond_prompt is not None: - previous_mean = uncond_embeddings.float().mean(axis=[-2, -1]).to(uncond_embeddings.dtype) - uncond_embeddings *= uncond_weights.unsqueeze(-1) - current_mean = uncond_embeddings.float().mean(axis=[-2, -1]).to(uncond_embeddings.dtype) - uncond_embeddings *= (previous_mean / current_mean).unsqueeze(-1).unsqueeze(-1) - - if uncond_prompt is not None: - return text_embeddings, uncond_embeddings, prompt_tokens - return text_embeddings, None, prompt_tokens - - -def preprocess_guide_image(image): - image = image.resize(FEATURE_EXTRACTOR_SIZE, resample=Image.NEAREST) # cond_fnと合わせる - image = np.array(image).astype(np.float32) / 255.0 - image = image[None].transpose(0, 3, 1, 2) # nchw - image = torch.from_numpy(image) - return image # 0 to 1 - - -# VGG16の入力は任意サイズでよいので入力画像を適宜リサイズする -def preprocess_vgg16_guide_image(image, size): - image = image.resize(size, resample=Image.NEAREST) # cond_fnと合わせる - image = np.array(image).astype(np.float32) / 255.0 - image = image[None].transpose(0, 3, 1, 2) # nchw - image = torch.from_numpy(image) - return image # 0 to 1 - - -def preprocess_image(image): - w, h = image.size - w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32 - image = image.resize((w, h), resample=PIL.Image.LANCZOS) - image = np.array(image).astype(np.float32) / 255.0 - image = image[None].transpose(0, 3, 1, 2) - image = torch.from_numpy(image) - return 2.0 * image - 1.0 - - -def preprocess_mask(mask): - mask = mask.convert("L") - w, h = mask.size - w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32 - mask = mask.resize((w // 8, h // 8), resample=PIL.Image.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): -# logger.info(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 - - -class BatchDataExt(NamedTuple): - # バッチ分割が必要なデータ - width: int - height: int - steps: int - scale: float - negative_scale: float - strength: float - network_muls: Tuple[float] - num_sub_prompts: int - - -class BatchData(NamedTuple): - return_latents: bool - base: BatchDataBase - ext: BatchDataExt - - -def main(args): - if args.fp16: - dtype = torch.float16 - elif args.bf16: - dtype = torch.bfloat16 - else: - dtype = torch.float32 - - highres_fix = args.highres_fix_scale is not None - # assert not highres_fix or args.image_path is None, f"highres_fix doesn't work with img2img / highres_fixはimg2imgと同時に使えません" - - if args.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.isfile(args.ckpt): # ファイルがないならパターンで探し、一つだけ該当すればそれを使う - files = glob.glob(args.ckpt) - if len(files) == 1: - args.ckpt = files[0] - - use_stable_diffusion_format = os.path.isfile(args.ckpt) - if use_stable_diffusion_format: - 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) - - # 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") - - # # 置換するCLIPを読み込む - # if args.replace_clip_l14_336: - # text_encoder = load_clip_l14_336(dtype) - # logger.info(f"large clip {CLIP_ID_L14_336} is loaded") - - if args.clip_guidance_scale > 0.0 or args.clip_image_guidance_scale: - logger.info("prepare clip model") - clip_model = CLIPModel.from_pretrained(CLIP_MODEL_PATH, torch_dtype=dtype) - else: - clip_model = None - - if args.vgg16_guidance_scale > 0.0: - logger.info("prepare resnet model") - vgg16_model = torchvision.models.vgg16(torchvision.models.VGG16_Weights.IMAGENET1K_V1) - else: - vgg16_model = None - - # xformers、Hypernetwork対応 - if not args.diffusers_xformers: - 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 use_stable_diffusion_format: - tokenizer = train_util.load_tokenizer(args) - - # schedulerを用意する - sched_init_args = {} - scheduler_num_noises_per_step = 1 - if args.sampler == "ddim": - scheduler_cls = DDIMScheduler - scheduler_module = diffusers.schedulers.scheduling_ddim - elif args.sampler == "ddpm": # ddpmはおかしくなるのでoptionから外してある - scheduler_cls = DDPMScheduler - scheduler_module = diffusers.schedulers.scheduling_ddpm - elif args.sampler == "pndm": - scheduler_cls = PNDMScheduler - scheduler_module = diffusers.schedulers.scheduling_pndm - elif args.sampler == "lms" or args.sampler == "k_lms": - scheduler_cls = LMSDiscreteScheduler - scheduler_module = diffusers.schedulers.scheduling_lms_discrete - elif args.sampler == "euler" or args.sampler == "k_euler": - scheduler_cls = EulerDiscreteScheduler - scheduler_module = diffusers.schedulers.scheduling_euler_discrete - elif args.sampler == "euler_a" or args.sampler == "k_euler_a": - scheduler_cls = EulerAncestralDiscreteSchedulerGL - scheduler_module = diffusers.schedulers.scheduling_euler_ancestral_discrete - elif args.sampler == "dpmsolver" or args.sampler == "dpmsolver++": - scheduler_cls = DPMSolverMultistepScheduler - sched_init_args["algorithm_type"] = args.sampler - scheduler_module = diffusers.schedulers.scheduling_dpmsolver_multistep - elif args.sampler == "dpmsingle": - scheduler_cls = DPMSolverSinglestepScheduler - scheduler_module = diffusers.schedulers.scheduling_dpmsolver_singlestep - elif args.sampler == "heun": - scheduler_cls = HeunDiscreteScheduler - scheduler_module = diffusers.schedulers.scheduling_heun_discrete - elif args.sampler == "dpm_2" or args.sampler == "k_dpm_2": - scheduler_cls = KDPM2DiscreteScheduler - scheduler_module = diffusers.schedulers.scheduling_k_dpm_2_discrete - elif args.sampler == "dpm_2_a" or args.sampler == "k_dpm_2_a": - scheduler_cls = KDPM2AncestralDiscreteScheduler - scheduler_module = diffusers.schedulers.scheduling_k_dpm_2_ancestral_discrete - scheduler_num_noises_per_step = 2 - - if args.v_parameterization: - sched_init_args["prediction_type"] = "v_prediction" - - # samplerの乱数をあらかじめ指定するための処理 - - # replace randn - class NoiseManager: - def __init__(self): - self.sampler_noises = None - self.sampler_noise_index = 0 - - def reset_sampler_noises(self, noises): - self.sampler_noise_index = 0 - self.sampler_noises = noises - - def randn(self, shape, device=None, dtype=None, layout=None, generator=None): - # logger.info(f"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, - ) - - # clip_sample=Trueにする - if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is False: - logger.info("set clip_sample to True") - scheduler.config.clip_sample = True - - # deviceを決定する - device = get_preferred_device() - - # 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.to(dtype).to(device) - vae.eval() - - text_encoder.to(dtype).to(device) - unet.to(dtype).to(device) - - text_encoder.eval() - unet.eval() - - if clip_model is not None: - clip_model.to(dtype).to(device) - clip_model.eval() - if vgg16_model is not None: - vgg16_model.to(dtype).to(device) - vgg16_model.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 - - 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_encoder, 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_encoder, 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_encoder, 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(f"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)) - - if args.opt_channels_last: - logger.info(f"set optimizing: channels last") - text_encoder.to(memory_format=torch.channels_last) - vae.to(memory_format=torch.channels_last) - unet.to(memory_format=torch.channels_last) - if clip_model is not None: - clip_model.to(memory_format=torch.channels_last) - if networks: - for network in networks: - network.to(memory_format=torch.channels_last) - if vgg16_model is not None: - vgg16_model.to(memory_format=torch.channels_last) - - for cn in control_nets: - cn.unet.to(memory_format=torch.channels_last) - cn.net.to(memory_format=torch.channels_last) - - pipe = PipelineLike( - device, - vae, - text_encoder, - tokenizer, - unet, - scheduler, - args.clip_skip, - clip_model, - args.clip_guidance_scale, - args.clip_image_guidance_scale, - vgg16_model, - args.vgg16_guidance_scale, - args.vgg16_guidance_layer, - ) - pipe.set_control_nets(control_nets) - 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) - - # Extended Textual Inversion および Textual Inversionを処理する - if args.XTI_embeddings: - diffusers.models.UNet2DConditionModel.forward = unet_forward_XTI - diffusers.models.unets.unet_2d_blocks.CrossAttnDownBlock2D.forward = downblock_forward_XTI - diffusers.models.unets.unet_2d_blocks.CrossAttnUpBlock2D.forward = upblock_forward_XTI - - if args.textual_inversion_embeddings: - token_ids_embeds = [] - for embeds_file in args.textual_inversion_embeddings: - if model_util.is_safetensors(embeds_file): - from safetensors.torch import load_file - - data = load_file(embeds_file) - else: - data = torch.load(embeds_file, map_location="cpu") - - if "string_to_param" in data: - data = data["string_to_param"] - embeds = next(iter(data.values())) - - if type(embeds) != torch.Tensor: - raise ValueError( - f"weight file does not contains Tensor / 重みファイルのデータがTensorではありません: {embeds_file}" - ) - - num_vectors_per_token = embeds.size()[0] - token_string = os.path.splitext(os.path.basename(embeds_file))[0] - token_strings = [token_string] + [f"{token_string}{i+1}" for i in range(num_vectors_per_token - 1)] - - # add new word to tokenizer, count is num_vectors_per_token - num_added_tokens = tokenizer.add_tokens(token_strings) - assert ( - num_added_tokens == num_vectors_per_token - ), f"tokenizer has same word to token string (filename). please rename the file / 指定した名前（ファイル名）のトークンが既に存在します。ファイルをリネームしてください: {embeds_file}" - - token_ids = tokenizer.convert_tokens_to_ids(token_strings) - logger.info(f"Textual Inversion embeddings `{token_string}` loaded. Tokens are added: {token_ids}") - assert ( - min(token_ids) == token_ids[0] and token_ids[-1] == token_ids[0] + len(token_ids) - 1 - ), f"token ids is not ordered" - assert len(tokenizer) - 1 == token_ids[-1], f"token ids is not end of tokenize: {len(tokenizer)}" - - if num_vectors_per_token > 1: - pipe.add_token_replacement(token_ids[0], token_ids) - - token_ids_embeds.append((token_ids, embeds)) - - text_encoder.resize_token_embeddings(len(tokenizer)) - token_embeds = text_encoder.get_input_embeddings().weight.data - for token_ids, embeds in token_ids_embeds: - for token_id, embed in zip(token_ids, embeds): - token_embeds[token_id] = embed - - if args.XTI_embeddings: - XTI_layers = [ - "IN01", - "IN02", - "IN04", - "IN05", - "IN07", - "IN08", - "MID", - "OUT03", - "OUT04", - "OUT05", - "OUT06", - "OUT07", - "OUT08", - "OUT09", - "OUT10", - "OUT11", - ] - token_ids_embeds_XTI = [] - for embeds_file in args.XTI_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 set(data.keys()) != set(XTI_layers): - raise ValueError("NOT XTI") - embeds = torch.concat(list(data.values())) - num_vectors_per_token = data["MID"].size()[0] - - token_string = os.path.splitext(os.path.basename(embeds_file))[0] - token_strings = [token_string] + [f"{token_string}{i+1}" for i in range(num_vectors_per_token - 1)] - - # add new word to tokenizer, count is num_vectors_per_token - num_added_tokens = tokenizer.add_tokens(token_strings) - assert ( - num_added_tokens == num_vectors_per_token - ), f"tokenizer has same word to token string (filename). please rename the file / 指定した名前（ファイル名）のトークンが既に存在します。ファイルをリネームしてください: {embeds_file}" - - token_ids = tokenizer.convert_tokens_to_ids(token_strings) - logger.info(f"XTI embeddings `{token_string}` loaded. Tokens are added: {token_ids}") - - # if num_vectors_per_token > 1: - pipe.add_token_replacement(token_ids[0], token_ids) - - token_strings_XTI = [] - for layer_name in XTI_layers: - token_strings_XTI += [f"{t}_{layer_name}" for t in token_strings] - tokenizer.add_tokens(token_strings_XTI) - token_ids_XTI = tokenizer.convert_tokens_to_ids(token_strings_XTI) - token_ids_embeds_XTI.append((token_ids_XTI, embeds)) - for t in token_ids: - t_XTI_dic = {} - for i, layer_name in enumerate(XTI_layers): - t_XTI_dic[layer_name] = t + (i + 1) * num_added_tokens - pipe.add_token_replacement_XTI(t, t_XTI_dic) - - text_encoder.resize_token_embeddings(len(tokenizer)) - token_embeds = text_encoder.get_input_embeddings().weight.data - for token_ids, embeds in token_ids_embeds_XTI: - for token_id, embed in zip(token_ids, embeds): - token_embeds[token_id] = embed - - # promptを取得する - if args.from_file is not None: - 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] != "#"] - elif args.prompt is not None: - prompt_list = [args.prompt] - else: - prompt_list = [] - - if args.interactive: - args.n_iter = 1 - - # img2imgの前処理、画像の読み込みなど - def load_images(path): - if os.path.isfile(path): - paths = [path] - else: - paths = ( - glob.glob(os.path.join(path, "*.png")) - + glob.glob(os.path.join(path, "*.jpg")) - + glob.glob(os.path.join(path, "*.jpeg")) - + glob.glob(os.path.join(path, "*.webp")) - ) - paths.sort() - - images = [] - for p in paths: - image = Image.open(p) - if image.mode != "RGB": - 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") - 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 len(prompt_list) == 0 and not args.interactive: - logger.info("get prompts from images' meta data") - for img in init_images: - if "prompt" in img.text: - prompt = img.text["prompt"] - if "negative-prompt" in img.text: - prompt += " --n " + img.text["negative-prompt"] - prompt_list.append(prompt) - - # プロンプトと画像を一致させるため指定回数だけ繰り返す（画像を増幅する） - l = [] - for im in init_images: - l.extend([im] * args.images_per_prompt) - init_images = l - - if mask_images is not None: - l = [] - for im in mask_images: - l.extend([im] * args.images_per_prompt) - mask_images = l - - # 画像サイズにオプション指定があるときはリサイズする - if 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 CLIP/VGG16/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.info( - f"No guide image, use previous generated image. / ガイド画像がありません。直前に生成した画像を使います: {args.image_path}" - ) - guide_images = None - else: - guide_images = None - - # seed指定時はseedを決めておく - if args.seed is not None: - # dynamic promptを使うと足りなくなる→images_per_promptを適当に大きくしておいてもらう - random.seed(args.seed) - predefined_seeds = [random.randint(0, 0x7FFFFFFF) for _ in range(args.n_iter * len(prompt_list) * args.images_per_prompt)] - if len(predefined_seeds) == 1: - predefined_seeds[0] = args.seed - else: - predefined_seeds = None - - # デフォルト画像サイズを設定する：img2imgではこれらの値は無視される（またはW*Hにリサイズ済み） - if args.W is None: - args.W = 512 - if args.H is None: - args.H = 512 - - # 画像生成のループ - os.makedirs(args.outdir, exist_ok=True) - max_embeddings_multiples = 1 if args.max_embeddings_multiples is None else args.max_embeddings_multiples - - for gen_iter in range(args.n_iter): - logger.info(f"iteration {gen_iter+1}/{args.n_iter}") - iter_seed = random.randint(0, 0x7FFFFFFF) - - # shuffle prompt list - if args.shuffle_prompts: - random.shuffle(prompt_list) - - # 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: - width_1st = int(ext.width * args.highres_fix_scale + 0.5) - height_1st = int(ext.height * args.highres_fix_scale + 0.5) - width_1st = width_1st - width_1st % 32 - height_1st = height_1st - height_1st % 32 - - strength_1st = ext.strength if args.highres_fix_strength is None else args.highres_fix_strength - - ext_1st = BatchDataExt( - width_1st, - height_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, 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 = [] - 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), - _, - ) in enumerate(batch): - prompts.append(prompt) - negative_prompts.append(negative_prompt) - seeds.append(seed) - clip_prompts.append(clip_prompt) - raw_prompts.append(raw_prompt) - - if init_image is not None: - init_images.append(init_image) - if i > 0 and all_images_are_same: - all_images_are_same = init_images[-2] is init_image - - if mask_image is not None: - mask_images.append(mask_image) - if i > 0 and all_masks_are_same: - all_masks_are_same = mask_images[-2] is mask_image - - if guide_image is not None: - if type(guide_image) is list: - guide_images.extend(guide_image) - all_guide_images_are_same = False - else: - guide_images.append(guide_image) - if i > 0 and all_guide_images_are_same: - all_guide_images_are_same = guide_images[-2] is guide_image - - # make start code - torch.manual_seed(seed) - start_code[i] = torch.randn(noise_shape, device=device, dtype=dtype) - - # make each noises - for j in range(steps * scheduler_num_noises_per_step): - noises[j][i] = torch.randn(noise_shape, device=device, dtype=dtype) - - if i2i_noises is not None: # img2img noise - i2i_noises[i] = torch.randn(noise_shape, device=device, dtype=dtype) - - noise_manager.reset_sampler_noises(noises) - - # すべての画像が同じなら1枚だけpipeに渡すことでpipe側で処理を高速化する - if init_images is not None and all_images_are_same: - init_images = init_images[0] - if mask_images is not None and all_masks_are_same: - mask_images = mask_images[0] - if guide_images is not None and all_guide_images_are_same: - guide_images = guide_images[0] - - # ControlNet使用時はguide imageをリサイズする - if control_nets: - # TODO resampleのメソッド - guide_images = guide_images if type(guide_images) == list else [guide_images] - guide_images = [i.resize((width, height), resample=PIL.Image.LANCZOS) for i in guide_images] - if len(guide_images) == 1: - guide_images = guide_images[0] - - # generate - if networks: - # 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: - n.set_current_generation(batch_size, num_sub_prompts, width, height, shared) - - 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, - 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, - )[0] - 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) in enumerate( - zip(images, prompts, negative_prompts, seeds, clip_prompts, raw_prompts) - ): - 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 args.use_original_file_name and init_images is not None: - if type(init_images) is list: - fln = os.path.splitext(os.path.basename(init_images[i % len(init_images)].filename))[0] + ".png" - else: - fln = os.path.splitext(os.path.basename(init_images.filename))[0] + ".png" - elif args.sequential_file_name: - fln = f"im_{highres_prefix}{step_first + i + 1:06d}.png" - else: - fln = f"im_{ts_str}_{highres_prefix}{i:03d}_{seed}.png" - - image.save(os.path.join(args.outdir, fln), pnginfo=metadata) - - if not args.no_preview and not highres_1st and args.interactive: - try: - import cv2 - - for prompt, image in zip(prompts, images): - cv2.imshow(prompt[:128], np.array(image)[:, :, ::-1]) # プロンプトが長いと死ぬ - cv2.waitKey() - cv2.destroyAllWindows() - except ImportError: - logger.info( - "opencv-python is not installed, cannot preview / opencv-pythonがインストールされていないためプレビューできません" - ) - - return images - - # 画像生成のプロンプトが一周するまでのループ - prompt_index = 0 - global_step = 0 - batch_data = [] - while args.interactive or prompt_index < len(prompt_list): - if len(prompt_list) == 0: - # interactive - valid = False - while not valid: - logger.info("") - logger.info("Type 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 = prompt_list[prompt_index] - - # 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] - - if pi == 0 or len(raw_prompts) > 1: - # parse prompt: if prompt is not changed, skip parsing - width = args.W - height = args.H - scale = args.scale - negative_scale = args.negative_scale - steps = args.steps - seed = None - 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] - logger.info(f"prompt {prompt_index+1}/{len(prompt_list)}: {prompt}") - - for parg in prompt_args[1:]: - try: - m = re.match(r"w (\d+)", parg, re.IGNORECASE) - if m: - width = int(m.group(1)) - 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"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 - seeds = [int(d) for d in m.group(1).split(",")] - 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 - - except ValueError as ex: - logger.info(f"Exception in parsing / 解析エラー: {parg}") - logger.info(ex) - - # 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]] - logger.info(f'{unsharp_params}') - 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 - # 数が足りないなら前のをそのまま使う - if len(seeds) > 0: - seed = seeds.pop(0) - else: - if predefined_seeds is not None: - if len(predefined_seeds) > 0: - seed = predefined_seeds.pop(0) - else: - logger.info("predefined seeds are exhausted") - seed = None - elif args.iter_same_seed: - seed = iter_seed - else: - seed = None # 前のを消す - - if seed is None: - seed = random.randint(0, 0x7FFFFFFF) - if args.interactive: - logger.info(f"seed: {seed}") - - # 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.info( - f"img2img image size is not divisible by 32 so aspect ratio is changed / img2imgの画像サイズが32で割り切れないためリサイズされます。画像が歪みます" - ) - - if mask_images is not None: - mask_image = mask_images[global_step % len(mask_images)] - - if guide_images is not None: - if control_nets: # 複数件の場合あり - c = len(control_nets) - p = global_step % (len(guide_images) // c) - guide_image = guide_images[p * c : p * c + c] - else: - guide_image = guide_images[global_step % len(guide_images)] - elif args.clip_image_guidance_scale > 0 or args.vgg16_guidance_scale > 0: - if prev_image is None: - logger.info("Generate 1st image without guide image.") - else: - logger.info("Use previous image as guide image.") - guide_image = prev_image - - 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 - ), - BatchDataExt( - width, - height, - 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 setup_parser() -> argparse.ArgumentParser: - parser = argparse.ArgumentParser() - - add_logging_arguments(parser) - - parser.add_argument( - "--v2", action="store_true", help="load Stable Diffusion v2.0 model / Stable Diffusion 2.0のモデルを読み込む" - ) - parser.add_argument( - "--v_parameterization", action="store_true", help="enable v-parameterization training / v-parameterization学習を有効にする" - ) - parser.add_argument("--prompt", type=str, default=None, help="prompt / プロンプト") - parser.add_argument( - "--from_file", - type=str, - default=None, - help="if specified, load prompts from this file / 指定時はプロンプトをファイルから読み込む", - ) - parser.add_argument( - "--interactive", - action="store_true", - help="interactive mode (generates one image) / 対話モード（生成される画像は1枚になります）", - ) - parser.add_argument( - "--no_preview", action="store_true", help="do not show generated image in interactive mode / 対話モードで画像を表示しない" - ) - parser.add_argument( - "--image_path", type=str, default=None, help="image to inpaint or to generate from / img2imgまたはinpaintを行う元画像" - ) - parser.add_argument("--mask_path", type=str, default=None, help="mask in inpainting / inpaint時のマスク") - parser.add_argument("--strength", type=float, default=None, help="img2img strength / img2img時のstrength") - parser.add_argument("--images_per_prompt", type=int, default=1, help="number of images per prompt / プロンプトあたりの出力枚数") - parser.add_argument("--outdir", type=str, default="outputs", help="dir to write results to / 生成画像の出力先") - parser.add_argument( - "--sequential_file_name", action="store_true", help="sequential output file name / 生成画像のファイル名を連番にする" - ) - parser.add_argument( - "--use_original_file_name", - action="store_true", - help="prepend original file name in img2img / img2imgで元画像のファイル名を生成画像のファイル名の先頭に付ける", - ) - # parser.add_argument("--ddim_eta", type=float, default=0.0, help="ddim eta (eta=0.0 corresponds to deterministic sampling", ) - parser.add_argument("--n_iter", type=int, default=1, help="sample this often / 繰り返し回数") - parser.add_argument("--H", type=int, default=None, help="image height, in pixel space / 生成画像高さ") - parser.add_argument("--W", type=int, default=None, help="image width, in pixel space / 生成画像幅") - parser.add_argument("--batch_size", type=int, default=1, help="batch size / バッチサイズ") - parser.add_argument( - "--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("--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( - "--XTI_embeddings", - type=str, - default=None, - nargs="*", - help="Embeddings files of Extended Textual Inversion / Extended Textual Inversionのembeddings", - ) - parser.add_argument( - "--clip_skip", type=int, default=None, help="layer number from bottom to use in CLIP / CLIPの後ろからn層目の出力を使う" - ) - parser.add_argument( - "--max_embeddings_multiples", - type=int, - default=None, - help="max 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( - "--clip_guidance_scale", - type=float, - default=0.0, - help="enable CLIP guided SD, scale for guidance (DDIM, PNDM, LMS samplers only) / CLIP guided SDを有効にしてこのscaleを適用する（サンプラーはDDIM、PNDM、LMSのみ）", - ) - parser.add_argument( - "--clip_image_guidance_scale", - type=float, - default=0.0, - help="enable CLIP guided SD by image, scale for guidance / 画像によるCLIP guided SDを有効にしてこのscaleを適用する", - ) - parser.add_argument( - "--vgg16_guidance_scale", - type=float, - default=0.0, - help="enable VGG16 guided SD by image, scale for guidance / 画像によるVGG16 guided SDを有効にしてこのscaleを適用する", - ) - parser.add_argument( - "--vgg16_guidance_layer", - type=int, - default=20, - help="layer of VGG16 to calculate contents guide (1~30, 20 for conv4_2) / VGG16のcontents guideに使うレイヤー番号 (1~30、20はconv4_2)", - ) - parser.add_argument( - "--guide_image_path", type=str, default=None, nargs="*", help="image to CLIP guidance / CLIP guided SDでガイドに使う画像" - ) - parser.add_argument( - "--highres_fix_scale", - type=float, - default=None, - help="enable highres fix, reso scale for 1st stage / highres fixを有効にして最初の解像度をこのscaleにする", - ) - parser.add_argument( - "--highres_fix_steps", - type=int, - default=28, - help="1st stage steps for highres fix / highres fixの最初のステージのステップ数", - ) - parser.add_argument( - "--highres_fix_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_models", type=str, default=None, nargs="*", help="ControlNet models to use / 使用するControlNetのモデル名" - ) - parser.add_argument( - "--control_net_preps", - type=str, - default=None, - nargs="*", - help="ControlNet preprocess to use / 使用するControlNetのプリプロセス名", - ) - parser.add_argument("--control_net_weights", type=float, default=None, nargs="*", help="ControlNet weights / ControlNetの重み") - parser.add_argument( - "--control_net_ratios", - type=float, - default=None, - nargs="*", - help="ControlNet guidance ratio for steps / ControlNetでガイドするステップ比率", - ) - # 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" - # ) - - # 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 3 / 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", - ) - - return parser - - -if __name__ == "__main__": - parser = setup_parser() - - args = parser.parse_args() - setup_logging(args, reset=True) - main(args) diff --git a/library/sampling.py b/library/sampling.py index 2bc5c5c4cc..50de030c08 100644 --- a/library/sampling.py +++ b/library/sampling.py @@ -151,7 +151,7 @@ def sample_images(*args, **kwargs): def line_to_prompt_dict(line: str) -> dict: - # subset of gen_img_diffusers + # subset of gen_img.py prompt_args = line.split(" --") prompt_dict = {} prompt_dict["prompt"] = prompt_args[0] From 7c33fd41f3a65bb0d2c88e2c0ae5457264ee67db Mon Sep 17 00:00:00 2001 From: kohya-ss <52813779+kohya-ss@users.noreply.github.com> Date: Thu, 24 Sep 2026 18:58:43 +0900 Subject: [PATCH 2/2] Fill in the PR number in the README change history Co-Authored-By: Claude Fable 5.1 --- README-ja.md | 2 +- README.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/README-ja.md b/README-ja.md index b1c7284ca6..b51c6bdbc0 100644 --- a/README-ja.md +++ b/README-ja.md @@ -58,7 +58,7 @@ Stable Diffusion等の画像生成モデルの学習、モデルによる画像 - `transformers` 5.x の `CLIPTokenizer` は、オリジナルの CLIP トークナイザが行っていた `ftfy` によるテキスト正規化（曲がった引用符の直線化、全角文字の半角化など）を行わなくなりました。sd-scripts 側で同じ正規化を行うようにしたため、トークナイズ結果は従来と変わりません。 - `tests/local` のローカル回帰テストにより、Text Encoder の出力、VAE の出力、ノイズスケジューラが従来のバージョンと同一であることを確認しています。 - `diffusers` 0.40 は `.to(dtype)` のたびに "There are modules in AutoencoderKL that should be kept in float32: [] ..." という誤った警告を出します（リストが空でも警告する diffusers 側のバグ）。sd-scripts ではリストが空の場合にこの警告を抑制しています。 - - SD1.x / SD2.x 用の古い画像生成スクリプト `gen_img_diffusers.py` を削除しました。しばらく前から動作しておらず（リファクタリングで削除された関数に依存していました）、実験的な CLIP / VGG16 guidance を除くすべての機能は `gen_img.py` でサポートされています。代わりに `gen_img.py` をお使いください（[gen_img_README-ja.md](./docs/gen_img_README-ja.md) をご覧ください）。ファイルは以前のリリースから取得できます。[PR #XXXX](https://github.com/kohya-ss/sd-scripts/pull/XXXX) + - SD1.x / SD2.x 用の古い画像生成スクリプト `gen_img_diffusers.py` を削除しました。しばらく前から動作しておらず（リファクタリングで削除された関数に依存していました）、実験的な CLIP / VGG16 guidance を除くすべての機能は `gen_img.py` でサポートされています。代わりに `gen_img.py` をお使いください（[gen_img_README-ja.md](./docs/gen_img_README-ja.md) をご覧ください）。ファイルは以前のリリースから取得できます。[PR #2439](https://github.com/kohya-ss/sd-scripts/pull/2439) - 学習中のサンプル画像生成の `--sample_sampler`、および `gen_img.py` / `sdxl_gen_img.py` の `--sampler` で、`dpmsolver` と `dpmsingle` を指定すると最近のバージョンの `diffusers` でエラーになる問題を修正しました。[PR #2438](https://github.com/kohya-ss/sd-scripts/pull/2438) - `lms` / `k_lms` サンプラーには `requirements.txt` に含まれない `scipy` パッケージが必要です。`scipy` が未インストールの場合、（最初のサンプル生成時ではなく）起動時に分かりやすいエラーを表示するようにしました。使用する場合は `pip install scipy` を実行してください。 - Windows on ARM64（NVIDIA RTX Spark PC など）に対応しました。[PR #2430](https://github.com/kohya-ss/sd-scripts/pull/2430)、[PR #2431](https://github.com/kohya-ss/sd-scripts/pull/2431)、[PR #2433](https://github.com/kohya-ss/sd-scripts/pull/2433) diff --git a/README.md b/README.md index f189c44de4..709048a445 100644 --- a/README.md +++ b/README.md @@ -58,7 +58,7 @@ If you find this project helpful, please consider supporting its development via - `transformers` 5.6 changed the internal structure of `CLIPTextModel` (the `text_model` submodule was removed). sd-scripts now wraps the model so that the checkpoint keys, the LoRA weight names of the text encoders (`lora_te_text_model_...`) and `text_encoder.text_model.*` access stay the same as before. Nothing changes for `transformers` < 5.6, where the wrapper is not applied. - `transformers` 5.6 also switched the attention implementation of T5 (T5-XXL of FLUX.1 / SD3, byT5 of HunyuanImage) to SDPA, which should be faster and use less memory. The bf16/fp16 outputs of T5 differ very slightly from previous versions (cosine similarity ≈ 0.998 for T5-XXL, the accuracy against fp32 is the same). This may change generated images or trained weights in minor details, and cached text encoder outputs from previous versions are still usable. The outputs of the other text encoders are identical. - Please run `pip install --upgrade -r requirements.txt` after updating. - - Removed `gen_img_diffusers.py`, the old image generation script for SD1.x / SD2.x. It had not worked for a while (it depended on a function removed by the refactoring) and `gen_img.py` supports everything it did except the experimental CLIP / VGG16 guidance. Please use `gen_img.py` instead (see [gen_img_README.md](./docs/gen_img_README.md)). The file is still available in the previous releases. [PR #XXXX](https://github.com/kohya-ss/sd-scripts/pull/XXXX) + - Removed `gen_img_diffusers.py`, the old image generation script for SD1.x / SD2.x. It had not worked for a while (it depended on a function removed by the refactoring) and `gen_img.py` supports everything it did except the experimental CLIP / VGG16 guidance. Please use `gen_img.py` instead (see [gen_img_README.md](./docs/gen_img_README.md)). The file is still available in the previous releases. [PR #2439](https://github.com/kohya-ss/sd-scripts/pull/2439) - Fixed the `dpmsolver` and `dpmsingle` samplers of `--sample_sampler` (sample image generation during training) and `--sampler` of `gen_img.py` / `sdxl_gen_img.py`, which failed with an error on recent versions of `diffusers`. [PR #2438](https://github.com/kohya-ss/sd-scripts/pull/2438) - The `lms` / `k_lms` samplers require the `scipy` package, which is not included in `requirements.txt`. A clear error message is now shown at startup (instead of at the first sample generation) when `scipy` is missing. Please run `pip install scipy` to use them. - Added support for Windows on ARM64 (e.g. NVIDIA RTX Spark PCs). [PR #2430](https://github.com/kohya-ss/sd-scripts/pull/2430), [PR #2431](https://github.com/kohya-ss/sd-scripts/pull/2431), [PR #2433](https://github.com/kohya-ss/sd-scripts/pull/2433)