├── .github └── FUNDING.yml ├── .gitignore ├── LICENSE.Apache-2.0 ├── LICENSE.GAPLV3 ├── LICENSE.MIT ├── README.md ├── StableTuner.cmd ├── configs ├── 4090_SD2_512_dreambooth.json ├── 4090_SD2_512_finetune.json └── test.json ├── environment.yaml ├── install_stabletuner.bat ├── requirements.txt ├── resources ├── accelerate_windows │ └── accelerate_default_config.yaml ├── bitsandbytes_windows │ ├── cextension.py │ ├── libbitsandbytes_cpu.dll │ ├── libbitsandbytes_cuda116.dll │ └── main.py ├── stableTuner_icon.png ├── stableTuner_logo.png └── stableTuner_notebook.ipynb └── scripts ├── __init__.py ├── captionBuddy.py ├── clip_segmentation.py ├── configuration_gui.py ├── convert_diffusers_to_sd_cli.py ├── converters.py ├── dataloaders_util.py ├── lion_pytorch ├── __init__.py ├── lion_pytorch.py └── triton.py ├── lora_utils.py ├── model_util.py ├── trainer.py ├── trainer_util.py └── windows_install.py /.github/FUNDING.yml: -------------------------------------------------------------------------------- 1 | # These are supported funding model platforms 2 | 3 | github: # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2] 4 | patreon: # Replace with a single Patreon username 5 | open_collective: # Replace with a single Open Collective username 6 | ko_fi: # stabletuner 7 | tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel 8 | community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry 9 | liberapay: # Replace with a single Liberapay username 10 | issuehunt: # Replace with a single IssueHunt username 11 | otechie: # Replace with a single Otechie username 12 | lfx_crowdfunding: # Replace with a single LFX Crowdfunding project-name e.g., cloud-foundry 13 | custom: # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2'] 14 | -------------------------------------------------------------------------------- /LICENSE.Apache-2.0: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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3 | 4 |
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6 | 7 | # Stable Tuner, Fine-tune your SD 8 | 9 | Buy Me a Coffee at ko-fi.com Join the discord :) 10 | 11 | [![IMAGE ALT TEXT HERE](https://img.youtube.com/vi/36Z4ETFZx94/0.jpg)](https://www.youtube.com/watch?v=36Z4ETFZx94) 12 | 13 | ### Join the Discord for training and chill ;) 14 | 15 | Stable Tuner wants to be the easiest and most complete Stable Diffusion tuner :) 16 | 17 | Features 18 | 19 | - **For End Users** - ST was made to provide a solution that is convenient but powerful on windows, if you wanted to try finetuning, there's no better option, for Linux folks, a bash script will be added at a later date if there's enough interest. 20 | - **More models, more fun** - ST Now supports Depth2Img and Inpainting models training! 21 | - **Your Training, Everywhere** - ST Now supports cloud training as well!, package up your data and start training on Runpod/Colab etc with a few clicks! 22 | - **Easy Installation** - ST makes installing convenient, using a bat file, ST will setup an environment ready for work and will install all the necessary components to get your training started fast! 23 | - **Friendly GUI** - ST features a full GUI to configure training runs, import and export settings, view tool tips for options, test your new model in the playground, convert the model to CKPT and more! 24 | - **Better Performance** - Using Diffusers, Xformers, CUDNN 1.8 and Bitsandbytes along with Latent caching allows for higher batch sizes and faster speeds, higher batch sizes = better quality model!. 25 | - **A Toolbox** - Use Caption Buddy to quickly generate and edit captions for your dataset in one streamlined tool, ST is building a toolbox for the must-have tools if you're training models. 26 | - **Fine Tuning Mindset** - ST is built to fine-tune, unlike Dreambooth, ST is meant to fine-tune a model, providing tools and settings to make most of your 3090/4090s, Dreambooth is still an option. 27 | - **Filename/Caption/Token based learning** - You can train using the individual file names as caption, use a caption txt file or a single token DB style, for finetuning file name and captions are best. 28 | - **Aspect Ratio Bucketing** - Using Aspect Ratio bucketing you can use any aspect ratio or resolution for your training images, images will get shuffled into buckets and resized to your chosen resolution target!, supports up to 1024 resolution!. 29 | - **Remote monitoring using Telegram** - Want to keep tabs on your training? set a bot up in Telegram and receive samples and notifications as you train, 30 | - **Better Sampling controls** - To gauge how your model is doing sampling is important, to that effect ST gives you the option to add sample prompts as you see fit, set the number of images to produce per prompt, send a controlled seed prompt (to gauge how a seed changes) or even use random aspect ratios to see how buckets are changing your generations!. 31 | - **Better Dataset Handling** - Use Dataset balancing to even out multiple concepts so they don't over-power each other, add class images to dataset to train them directly, override per dataset if necessary. 32 | - **Quality of life** - Many options to tune the experience to your liking, use save latent caching to avoid regenerating them at every run, use high batch-sizes to maximize training speed and performance, use epochs instead of steps to gauge progress better!. 33 | - **Built for Diffusers** - ST uses HF's Diffusers library to allow the best and fastest implementations going forward, as of now, training 1.4,1.5,2 and 2-768 work great. 34 | 35 | ## Installation 36 | 37 | Download and install Anaconda or miniconda and clone this repo, run the install_stabletuner.bat, when finished start the app with the StableTuner.cmd file. 38 | 39 | Note: If your anaconda is installed in a directory different from the standard installation directory, please create a text file called custom_conda_path.txt and put the path to your anaconda installation inside before running the install_stabletuner.bat file. 40 | 41 | ## CUDNN 8.6 42 | 43 | **NOTICE - As of this writing this step is no longer necessary and the installer will download CUDNN by itself, keeping it here for now** 44 | 45 | Due to the filesize I can't host the DLLs needed for CUDNN 8.6 on Github, I **strongly** advise you download them for a speed boost in sample generation (almost **50%** on 4090) you can download them from here: CUDNN 8.6 46 | 47 | To install simply unzip the directory and place in the same directory as StableTuner.cmd, run install_stabletuner.bat and you're good to go! 48 | 49 | ## Usage 50 | 51 | Refer to the tool tips in the GUI for more information, if you have any questions feel free to ask in the Discord 52 | 53 | ## Kudos 54 | 55 | - Shivam - For the original code and inspiration - A2 License 56 | - Diffusers - For the latest and greatest implementations - A2 License 57 | - Everydream - For the Aspect Ratio bucketing - MIT License 58 | - Sygil.dev - For the environment setup - GAPLV3 License 59 | - sd_dreambooth_extension - for the bitsandbytes files and install script 60 | - StabilityAI - For the latest and greatest models 61 | - The whole SD community - For making this possible 62 | 63 | ## What's next? 64 | 65 | - Linux support 66 | - More models 67 | - Advanced model mixing 68 | - And more! :D 69 | - Support me on Ko-Fi and come hang out in Discord to help me decide what's next :) 70 | -------------------------------------------------------------------------------- /StableTuner.cmd: -------------------------------------------------------------------------------- 1 | @echo off 2 | :: This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/). 3 | :: 4 | :: Copyright 2022 Sygil-Dev team. 5 | :: This program is free software: you can redistribute it and/or modify 6 | :: it under the terms of the GNU Affero General Public License as published by 7 | :: the Free Software Foundation, either version 3 of the License, or 8 | :: (at your option) any later version. 9 | :: 10 | :: This program is distributed in the hope that it will be useful, 11 | :: but WITHOUT ANY WARRANTY; without even the implied warranty of 12 | :: MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 13 | :: GNU Affero General Public License for more details. 14 | :: 15 | :: You should have received a copy of the GNU Affero General Public License 16 | :: along with this program. If not, see . 17 | :: Run all commands using this script's directory as the working directory 18 | cd %~dp0 19 | 20 | :: copy over the first line from environment.yaml, e.g. name: ldm, and take the second word after splitting by ":" delimiter 21 | set v_conda_env_name="ST" 22 | 23 | 24 | echo Environment name is set as %v_conda_env_name% as per environment.yaml 25 | 26 | :: Put the path to conda directory in a file called "custom_conda_path.txt" if it's installed at non-standard path 27 | IF EXIST custom_conda_path.txt ( 28 | FOR /F %%i IN (custom_conda_path.txt) DO set v_custom_path=%%i 29 | ) 30 | 31 | set INSTALL_ENV_DIR=%cd%\installer_files\env 32 | set PATH=%INSTALL_ENV_DIR%;%INSTALL_ENV_DIR%\Library\bin;%INSTALL_ENV_DIR%\Scripts;%INSTALL_ENV_DIR%\Library\usr\bin;%PATH% 33 | 34 | set v_paths=%INSTALL_ENV_DIR% 35 | set v_paths=%v_paths%;%ProgramData%\miniconda3 36 | set v_paths=%v_paths%;%USERPROFILE%\miniconda3 37 | set v_paths=%v_paths%;%ProgramData%\anaconda3 38 | set v_paths=%v_paths%;%USERPROFILE%\anaconda3 39 | 40 | for %%a in (%v_paths%) do ( 41 | IF NOT "%v_custom_path%"=="" ( 42 | set v_paths=%v_custom_path%;%v_paths% 43 | ) 44 | ) 45 | 46 | for %%a in (%v_paths%) do ( 47 | if EXIST "%%a\Scripts\activate.bat" ( 48 | SET v_conda_path=%%a 49 | echo anaconda3/miniconda3 detected in %%a 50 | goto :CONDA_FOUND 51 | ) 52 | ) 53 | 54 | IF "%v_conda_path%"=="" ( 55 | echo anaconda3/miniconda3 not found. Install from here https://docs.conda.io/en/latest/miniconda.html 56 | pause 57 | exit /b 1 58 | ) 59 | 60 | :CONDA_FOUND 61 | echo Starting conda environment %v_conda_env_name% from %v_conda_path% 62 | 63 | call "%v_conda_path%\Scripts\activate.bat" "%v_conda_env_name%" 64 | 65 | ::call git pull | findstr /r /c:"changed" && set "HasChanges=1" 66 | ::IF "%HasChanges%" == "0" GOTO START_GUI 67 | ::echo StableTuner updated, running installer! 68 | ::call conda env create --name "%v_conda_env_name%" -f environment.yaml 69 | ::call conda env update --name "%v_conda_env_name%" -f environment.yaml 70 | ::python windows_install.py 71 | :START_GUI 72 | ::set HasChanges=0 73 | python scripts/configuration_gui.py 74 | 75 | ::cmd /k 76 | -------------------------------------------------------------------------------- /configs/4090_SD2_512_dreambooth.json: -------------------------------------------------------------------------------- 1 | { 2 | "concepts": [ 3 | { 4 | "instance_prompt": "token", 5 | "class_prompt": "class token", 6 | "instance_data_dir": "data path", 7 | "class_data_dir": "class data path", 8 | "do_not_balance": 0 9 | } 10 | ], 11 | "sample_prompts": [], 12 | "add_controlled_seed_to_sample": [ 13 | "3434554" 14 | ], 15 | "model_path": "stabilityai/stable-diffusion-2-base", 16 | "vae_path": "stabilityai/sd-vae-ft-mse", 17 | "output_path": "models/art_test", 18 | "send_telegram_updates": 0, 19 | "telegram_token": "", 20 | "telegram_chat_id": "", 21 | "resolution": "512", 22 | "batch_size": "16", 23 | "train_epocs": "30", 24 | "mixed_precision": "fp16", 25 | "use_8bit_adam": 1, 26 | "use_gradient_checkpointing": 1, 27 | "accumulation_steps": "1", 28 | "learning_rate": "5e-6", 29 | "warmup_steps": "0", 30 | "learning_rate_scheduler": "constant", 31 | "use_latent_cache": 1, 32 | "save_latent_cache": 1, 33 | "regenerate_latent_cache": 0, 34 | "train_text_encoder": 1, 35 | "with_prior_loss_preservation": 1, 36 | "prior_loss_preservation_weight": "1.0", 37 | "use_image_names_as_captions": 1, 38 | "auto_balance_concept_datasets": 1, 39 | "add_class_images_to_dataset": 0, 40 | "number_of_class_images": "200", 41 | "save_every_n_epochs": "5", 42 | "number_of_samples_to_generate": "1", 43 | "sample_height": "512", 44 | "sample_width": "512", 45 | "sample_random_aspect_ratio": 0, 46 | "save_on_training_start": 0, 47 | "aspect_ratio_bucketing": 1, 48 | "seed": "12354" 49 | } -------------------------------------------------------------------------------- /configs/4090_SD2_512_finetune.json: -------------------------------------------------------------------------------- 1 | { 2 | "concepts": [ 3 | { 4 | "instance_prompt": "token", 5 | "class_prompt": "class token", 6 | "instance_data_dir": "data path", 7 | "class_data_dir": "class data path", 8 | "do_not_balance": 0 9 | } 10 | ], 11 | "sample_prompts": [], 12 | "add_controlled_seed_to_sample": [ 13 | "3434554" 14 | ], 15 | "model_path": "stabilityai/stable-diffusion-2-base", 16 | "vae_path": "stabilityai/sd-vae-ft-mse", 17 | "output_path": "models/art_test", 18 | "send_telegram_updates": 0, 19 | "telegram_token": "", 20 | "telegram_chat_id": "", 21 | "resolution": "512", 22 | "batch_size": "24", 23 | "train_epocs": "200", 24 | "mixed_precision": "fp16", 25 | "use_8bit_adam": 1, 26 | "use_gradient_checkpointing": 1, 27 | "accumulation_steps": "1", 28 | "learning_rate": "1e-6", 29 | "warmup_steps": "0", 30 | "learning_rate_scheduler": "constant", 31 | "use_latent_cache": 1, 32 | "save_latent_cache": 1, 33 | "regenerate_latent_cache": 0, 34 | "train_text_encoder": 1, 35 | "with_prior_loss_preservation": 0, 36 | "prior_loss_preservation_weight": "1.0", 37 | "use_image_names_as_captions": 1, 38 | "auto_balance_concept_datasets": 1, 39 | "add_class_images_to_dataset": 0, 40 | "number_of_class_images": "200", 41 | "save_every_n_epochs": "15", 42 | "number_of_samples_to_generate": "1", 43 | "sample_height": "512", 44 | "sample_width": "512", 45 | "sample_random_aspect_ratio": 0, 46 | "save_on_training_start": 0, 47 | "aspect_ratio_bucketing": 1, 48 | "seed": "12354" 49 | } -------------------------------------------------------------------------------- /configs/test.json: -------------------------------------------------------------------------------- 1 | { 2 | "concepts": [], 3 | "sample_prompts": [], 4 | "add_controlled_seed_to_sample": [ 5 | "3434554" 6 | ], 7 | "model_path": "stabilityai/stable-diffusion-2", 8 | "vae_path": "stabilityai/sd-vae-ft-mse", 9 | "output_path": "models/art_test", 10 | "send_telegram_updates": 1, 11 | "telegram_token": "5587505616:AAGsjLRzjx5YQaVNwc88mAfLyPZRPXO505k", 12 | "telegram_chat_id": "-819756645", 13 | "resolution": "768", 14 | "batch_size": "16", 15 | "train_epocs": "200", 16 | "mixed_precision": "fp16", 17 | "use_8bit_adam": 1, 18 | "use_gradient_checkpointing": 1, 19 | "accumulation_steps": "1", 20 | "learning_rate": "1e-6", 21 | "warmup_steps": "0", 22 | "learning_rate_scheduler": "constant", 23 | "use_latent_cache": 1, 24 | "save_latent_cache": 1, 25 | "regenerate_latent_cache": 1, 26 | "train_text_encoder": 1, 27 | "with_prior_loss_preservation": 0, 28 | "prior_loss_preservation_weight": "1.0", 29 | "use_image_names_as_captions": 1, 30 | "auto_balance_concept_datasets": 1, 31 | "add_class_images_to_dataset": 0, 32 | "number_of_class_images": "200", 33 | "save_every_n_epochs": "15", 34 | "number_of_samples_to_generate": "1", 35 | "sample_height": "768", 36 | "sample_width": "768", 37 | "sample_random_aspect_ratio": 0, 38 | "save_on_training_start": 0, 39 | "aspect_ratio_bucketing": 0, 40 | "seed": "12354" 41 | } -------------------------------------------------------------------------------- /environment.yaml: -------------------------------------------------------------------------------- 1 | name: ST 2 | # This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/). 3 | 4 | # Copyright 2022 Sygil-Dev team. 5 | # This program is free software: you can redistribute it and/or modify 6 | # it under the terms of the GNU Affero General Public License as published by 7 | # the Free Software Foundation, either version 3 of the License, or 8 | # (at your option) any later version. 9 | 10 | # This program is distributed in the hope that it will be useful, 11 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 12 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 13 | # GNU Affero General Public License for more details. 14 | 15 | # You should have received a copy of the GNU Affero General Public License 16 | # along with this program. If not, see . 17 | channels: 18 | - conda-forge 19 | - defaults 20 | - nvidia 21 | # Psst. If you change a dependency, make sure it's mirrored in the docker requirement 22 | # files as well. 23 | dependencies: 24 | - nodejs 25 | - yarn 26 | - cudatoolkit 27 | - git 28 | - numpy 29 | - pip 30 | - python=3.10.0 31 | - scikit-image 32 | - pip: 33 | - -r requirements.txt -------------------------------------------------------------------------------- /install_stabletuner.bat: -------------------------------------------------------------------------------- 1 | @echo off 2 | :: This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/). 3 | :: 4 | :: Copyright 2022 Sygil-Dev team. 5 | :: This program is free software: you can redistribute it and/or modify 6 | :: it under the terms of the GNU Affero General Public License as published by 7 | :: the Free Software Foundation, either version 3 of the License, or 8 | :: (at your option) any later version. 9 | :: 10 | :: This program is distributed in the hope that it will be useful, 11 | :: but WITHOUT ANY WARRANTY; without even the implied warranty of 12 | :: MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 13 | :: GNU Affero General Public License for more details. 14 | :: 15 | :: You should have received a copy of the GNU Affero General Public License 16 | :: along with this program. If not, see . 17 | :: Run all commands using this script's directory as the working directory 18 | cd %~dp0 19 | 20 | :: copy over the first line from environment.yaml, e.g. name: ldm, and take the second word after splitting by ":" delimiter 21 | for /F "tokens=2 delims=: " %%i in (environment.yaml) DO ( 22 | set v_conda_env_name=%%i 23 | goto EOL 24 | ) 25 | :EOL 26 | 27 | echo Environment name is set as %v_conda_env_name% as per environment.yaml 28 | 29 | :: Put the path to conda directory in a file called "custom_conda_path.txt" if it's installed at non-standard path 30 | IF EXIST custom_conda_path.txt ( 31 | FOR /F %%i IN (custom_conda_path.txt) DO set v_custom_path=%%i 32 | ) 33 | 34 | set INSTALL_ENV_DIR=%cd%\installer_files\env 35 | set PATH=%INSTALL_ENV_DIR%;%INSTALL_ENV_DIR%\Library\bin;%INSTALL_ENV_DIR%\Scripts;%INSTALL_ENV_DIR%\Library\usr\bin;%PATH% 36 | 37 | set v_paths=%INSTALL_ENV_DIR% 38 | set v_paths=%v_paths%;%ProgramData%\miniconda3 39 | set v_paths=%v_paths%;%USERPROFILE%\miniconda3 40 | set v_paths=%v_paths%;%ProgramData%\anaconda3 41 | set v_paths=%v_paths%;%USERPROFILE%\anaconda3 42 | 43 | for %%a in (%v_paths%) do ( 44 | IF NOT "%v_custom_path%"=="" ( 45 | set v_paths=%v_custom_path%;%v_paths% 46 | ) 47 | ) 48 | 49 | for %%a in (%v_paths%) do ( 50 | if EXIST "%%a\Scripts\activate.bat" ( 51 | SET v_conda_path=%%a 52 | echo anaconda3/miniconda3 detected in %%a 53 | goto :CONDA_FOUND 54 | ) 55 | ) 56 | 57 | IF "%v_conda_path%"=="" ( 58 | echo anaconda3/miniconda3 not found. Install from here https://docs.conda.io/en/latest/miniconda.html 59 | pause 60 | exit /b 1 61 | ) 62 | 63 | :CONDA_FOUND 64 | echo Found Anaconda 65 | 66 | :SKIP_RESTORE 67 | call "%v_conda_path%\Scripts\activate.bat" 68 | 69 | 70 | call conda env create --name "%v_conda_env_name%" -f environment.yaml 71 | 72 | 73 | 74 | call "%v_conda_path%\Scripts\activate.bat" "%v_conda_env_name%" 75 | 76 | :PROMPT 77 | python scripts/windows_install.py 78 | pause -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | accelerate==0.15.0 2 | transformers>=4.25.1 3 | ftfy==6.1.1 4 | albumentations==1.3.0 5 | opencv-python==4.6.0.66 6 | einops==0.6.0 7 | pytorch_lightning==1.8.5.post0 8 | bitsandbytes==0.35.0 9 | tensorboard==2.11.0 10 | gitpython==3.1.29 11 | fairscale==0.4.13 12 | timm==0.6.12 13 | OmegaConf==2.3.0 14 | safetensors==0.2.6 15 | customtkinter==5.0.3 16 | tokenizers==0.13.2 17 | pyperclip==1.8.2 18 | gradio 19 | keyboard 20 | huggingface -------------------------------------------------------------------------------- /resources/accelerate_windows/accelerate_default_config.yaml: -------------------------------------------------------------------------------- 1 | command_file: null 2 | commands: null 3 | compute_environment: LOCAL_MACHINE 4 | deepspeed_config: {} 5 | distributed_type: 'NO' 6 | downcast_bf16: 'no' 7 | dynamo_backend: 'NO' 8 | fsdp_config: {} 9 | gpu_ids: all 10 | machine_rank: 0 11 | main_process_ip: null 12 | main_process_port: null 13 | main_training_function: main 14 | megatron_lm_config: {} 15 | mixed_precision: fp16 16 | num_machines: 1 17 | num_processes: 1 18 | rdzv_backend: static 19 | same_network: true 20 | tpu_name: null 21 | tpu_zone: null 22 | use_cpu: false 23 | -------------------------------------------------------------------------------- /resources/bitsandbytes_windows/cextension.py: -------------------------------------------------------------------------------- 1 | import ctypes as ct 2 | from pathlib import Path 3 | from warnings import warn 4 | 5 | from .cuda_setup.main import evaluate_cuda_setup 6 | 7 | 8 | class CUDALibrary_Singleton(object): 9 | _instance = None 10 | 11 | def __init__(self): 12 | raise RuntimeError("Call get_instance() instead") 13 | 14 | def initialize(self): 15 | binary_name = evaluate_cuda_setup() 16 | package_dir = Path(__file__).parent 17 | binary_path = package_dir / binary_name 18 | 19 | if not binary_path.exists(): 20 | print(f"CUDA SETUP: TODO: compile library for specific version: {binary_name}") 21 | legacy_binary_name = "libbitsandbytes.so" 22 | print(f"CUDA SETUP: Defaulting to {legacy_binary_name}...") 23 | binary_path = package_dir / legacy_binary_name 24 | if not binary_path.exists(): 25 | print('CUDA SETUP: CUDA detection failed. Either CUDA driver not installed, CUDA not installed, or you have multiple conflicting CUDA libraries!') 26 | print('CUDA SETUP: If you compiled from source, try again with `make CUDA_VERSION=DETECTED_CUDA_VERSION` for example, `make CUDA_VERSION=113`.') 27 | raise Exception('CUDA SETUP: Setup Failed!') 28 | # self.lib = ct.cdll.LoadLibrary(binary_path) 29 | self.lib = ct.cdll.LoadLibrary(str(binary_path)) # $$$ 30 | else: 31 | print(f"CUDA SETUP: Loading binary {binary_path}...") 32 | # self.lib = ct.cdll.LoadLibrary(binary_path) 33 | self.lib = ct.cdll.LoadLibrary(str(binary_path)) # $$$ 34 | 35 | @classmethod 36 | def get_instance(cls): 37 | if cls._instance is None: 38 | cls._instance = cls.__new__(cls) 39 | cls._instance.initialize() 40 | return cls._instance 41 | 42 | 43 | lib = CUDALibrary_Singleton.get_instance().lib 44 | try: 45 | lib.cadam32bit_g32 46 | lib.get_context.restype = ct.c_void_p 47 | lib.get_cusparse.restype = ct.c_void_p 48 | COMPILED_WITH_CUDA = True 49 | except AttributeError: 50 | warn( 51 | "The installed version of bitsandbytes was compiled without GPU support. " 52 | "8-bit optimizers and GPU quantization are unavailable." 53 | ) 54 | COMPILED_WITH_CUDA = False 55 | -------------------------------------------------------------------------------- /resources/bitsandbytes_windows/libbitsandbytes_cpu.dll: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/devilismyfriend/StableTuner/4c63853399590289ea88b6fcd6565f1a407c916c/resources/bitsandbytes_windows/libbitsandbytes_cpu.dll -------------------------------------------------------------------------------- /resources/bitsandbytes_windows/libbitsandbytes_cuda116.dll: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/devilismyfriend/StableTuner/4c63853399590289ea88b6fcd6565f1a407c916c/resources/bitsandbytes_windows/libbitsandbytes_cuda116.dll -------------------------------------------------------------------------------- /resources/bitsandbytes_windows/main.py: -------------------------------------------------------------------------------- 1 | """ 2 | extract factors the build is dependent on: 3 | [X] compute capability 4 | [ ] TODO: Q - What if we have multiple GPUs of different makes? 5 | - CUDA version 6 | - Software: 7 | - CPU-only: only CPU quantization functions (no optimizer, no matrix multipl) 8 | - CuBLAS-LT: full-build 8-bit optimizer 9 | - no CuBLAS-LT: no 8-bit matrix multiplication (`nomatmul`) 10 | 11 | evaluation: 12 | - if paths faulty, return meaningful error 13 | - else: 14 | - determine CUDA version 15 | - determine capabilities 16 | - based on that set the default path 17 | """ 18 | 19 | import ctypes 20 | 21 | from .paths import determine_cuda_runtime_lib_path 22 | 23 | 24 | def check_cuda_result(cuda, result_val): 25 | # 3. Check for CUDA errors 26 | if result_val != 0: 27 | error_str = ctypes.c_char_p() 28 | cuda.cuGetErrorString(result_val, ctypes.byref(error_str)) 29 | print(f"CUDA exception! Error code: {error_str.value.decode()}") 30 | 31 | def get_cuda_version(cuda, cudart_path): 32 | # https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART____VERSION.html#group__CUDART____VERSION 33 | try: 34 | cudart = ctypes.CDLL(cudart_path) 35 | except OSError: 36 | # TODO: shouldn't we error or at least warn here? 37 | print(f'ERROR: libcudart.so could not be read from path: {cudart_path}!') 38 | return None 39 | 40 | version = ctypes.c_int() 41 | check_cuda_result(cuda, cudart.cudaRuntimeGetVersion(ctypes.byref(version))) 42 | version = int(version.value) 43 | major = version//1000 44 | minor = (version-(major*1000))//10 45 | 46 | if major < 11: 47 | print('CUDA SETUP: CUDA version lower than 11 are currenlty not supported for LLM.int8(). You will be only to use 8-bit optimizers and quantization routines!!') 48 | 49 | return f'{major}{minor}' 50 | 51 | 52 | def get_cuda_lib_handle(): 53 | # 1. find libcuda.so library (GPU driver) (/usr/lib) 54 | try: 55 | cuda = ctypes.CDLL("libcuda.so") 56 | except OSError: 57 | # TODO: shouldn't we error or at least warn here? 58 | print('CUDA SETUP: WARNING! libcuda.so not found! Do you have a CUDA driver installed? If you are on a cluster, make sure you are on a CUDA machine!') 59 | return None 60 | check_cuda_result(cuda, cuda.cuInit(0)) 61 | 62 | return cuda 63 | 64 | 65 | def get_compute_capabilities(cuda): 66 | """ 67 | 1. find libcuda.so library (GPU driver) (/usr/lib) 68 | init_device -> init variables -> call function by reference 69 | 2. call extern C function to determine CC 70 | (https://docs.nvidia.com/cuda/cuda-driver-api/group__CUDA__DEVICE__DEPRECATED.html) 71 | 3. Check for CUDA errors 72 | https://stackoverflow.com/questions/14038589/what-is-the-canonical-way-to-check-for-errors-using-the-cuda-runtime-api 73 | # bits taken from https://gist.github.com/f0k/63a664160d016a491b2cbea15913d549 74 | """ 75 | 76 | 77 | nGpus = ctypes.c_int() 78 | cc_major = ctypes.c_int() 79 | cc_minor = ctypes.c_int() 80 | 81 | device = ctypes.c_int() 82 | 83 | check_cuda_result(cuda, cuda.cuDeviceGetCount(ctypes.byref(nGpus))) 84 | ccs = [] 85 | for i in range(nGpus.value): 86 | check_cuda_result(cuda, cuda.cuDeviceGet(ctypes.byref(device), i)) 87 | ref_major = ctypes.byref(cc_major) 88 | ref_minor = ctypes.byref(cc_minor) 89 | # 2. call extern C function to determine CC 90 | check_cuda_result( 91 | cuda, cuda.cuDeviceComputeCapability(ref_major, ref_minor, device) 92 | ) 93 | ccs.append(f"{cc_major.value}.{cc_minor.value}") 94 | 95 | return ccs 96 | 97 | 98 | # def get_compute_capability()-> Union[List[str, ...], None]: # FIXME: error 99 | def get_compute_capability(cuda): 100 | """ 101 | Extracts the highest compute capbility from all available GPUs, as compute 102 | capabilities are downwards compatible. If no GPUs are detected, it returns 103 | None. 104 | """ 105 | ccs = get_compute_capabilities(cuda) 106 | if ccs is not None: 107 | # TODO: handle different compute capabilities; for now, take the max 108 | return ccs[-1] 109 | return None 110 | 111 | 112 | def evaluate_cuda_setup(): 113 | print('') 114 | print('='*35 + 'BUG REPORT' + '='*35) 115 | print('Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues') 116 | print('For effortless bug reporting copy-paste your error into this form: https://docs.google.com/forms/d/e/1FAIpQLScPB8emS3Thkp66nvqwmjTEgxp8Y9ufuWTzFyr9kJ5AoI47dQ/viewform?usp=sf_link') 117 | print('='*80) 118 | return "libbitsandbytes_cuda116.dll" # $$$ 119 | 120 | binary_name = "libbitsandbytes_cpu.so" 121 | #if not torch.cuda.is_available(): 122 | #print('No GPU detected. Loading CPU library...') 123 | #return binary_name 124 | 125 | cudart_path = determine_cuda_runtime_lib_path() 126 | if cudart_path is None: 127 | print( 128 | "WARNING: No libcudart.so found! Install CUDA or the cudatoolkit package (anaconda)!" 129 | ) 130 | return binary_name 131 | 132 | print(f"CUDA SETUP: CUDA runtime path found: {cudart_path}") 133 | cuda = get_cuda_lib_handle() 134 | cc = get_compute_capability(cuda) 135 | print(f"CUDA SETUP: Highest compute capability among GPUs detected: {cc}") 136 | cuda_version_string = get_cuda_version(cuda, cudart_path) 137 | 138 | 139 | if cc == '': 140 | print( 141 | "WARNING: No GPU detected! Check your CUDA paths. Processing to load CPU-only library..." 142 | ) 143 | return binary_name 144 | 145 | # 7.5 is the minimum CC vor cublaslt 146 | has_cublaslt = cc in ["7.5", "8.0", "8.6"] 147 | 148 | # TODO: 149 | # (1) CUDA missing cases (no CUDA installed by CUDA driver (nvidia-smi accessible) 150 | # (2) Multiple CUDA versions installed 151 | 152 | # we use ls -l instead of nvcc to determine the cuda version 153 | # since most installations will have the libcudart.so installed, but not the compiler 154 | print(f'CUDA SETUP: Detected CUDA version {cuda_version_string}') 155 | 156 | def get_binary_name(): 157 | "if not has_cublaslt (CC < 7.5), then we have to choose _nocublaslt.so" 158 | bin_base_name = "libbitsandbytes_cuda" 159 | if has_cublaslt: 160 | return f"{bin_base_name}{cuda_version_string}.so" 161 | else: 162 | return f"{bin_base_name}{cuda_version_string}_nocublaslt.so" 163 | 164 | binary_name = get_binary_name() 165 | 166 | return binary_name 167 | -------------------------------------------------------------------------------- /resources/stableTuner_icon.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/devilismyfriend/StableTuner/4c63853399590289ea88b6fcd6565f1a407c916c/resources/stableTuner_icon.png -------------------------------------------------------------------------------- /resources/stableTuner_logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/devilismyfriend/StableTuner/4c63853399590289ea88b6fcd6565f1a407c916c/resources/stableTuner_logo.png -------------------------------------------------------------------------------- /resources/stableTuner_notebook.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "attachments": {}, 5 | "cell_type": "markdown", 6 | "metadata": {}, 7 | "source": [ 8 | "\n", 9 | "# Welcome to StableTuner, Let's get started!\n", 10 | "#### This notebook will guide you through the setup process.\n", 11 | "\n", 12 | "\n", 13 | "__[Join the ST Discord for support, chat and fun times :)](https://discord.gg/DahNECrBUZ)__" 14 | ] 15 | }, 16 | { 17 | "attachments": {}, 18 | "cell_type": "markdown", 19 | "metadata": {}, 20 | "source": [ 21 | "#### Start by uploading your payload.zip file (just drag and drop it to the file area) and run this cell as it gets uploaded." 22 | ] 23 | }, 24 | { 25 | "cell_type": "code", 26 | "execution_count": null, 27 | "metadata": {}, 28 | "outputs": [], 29 | "source": [ 30 | "#Much thanks to IndustriaDitat and entmike for helping making ST linux compatible!\n", 31 | "from IPython.display import clear_output\n", 32 | "from subprocess import getoutput\n", 33 | "installed_xformers = False\n", 34 | "GPU_CardName = getoutput('nvidia-smi --query-gpu=name --format=csv,noheader')\n", 35 | "\n", 36 | "%pip install torch==1.12.1+cu116 torchvision==0.13.1+cu116 --extra-index-url \"https://download.pytorch.org/whl/cu116\"\n", 37 | "%pip install -U --pre triton\n", 38 | "%pip install ninja bitsandbytes\n", 39 | "if '4090' in GPU_CardName:\n", 40 | " %pip install https://huggingface.co/industriaditat/xformers_precompiles/resolve/main/RTX4090-xf14-cu116-py38/xformers-0.0.14.dev0-cp38-cp38-linux_x86_64.whl\n", 41 | " installed_xformers = True\n", 42 | "if '3090' in GPU_CardName:\n", 43 | " %pip install https://huggingface.co/industriaditat/xformers_precompiles/resolve/main/RTX3090-xf14-cu116-py38/xformers-0.0.14.dev0-cp38-cp38-linux_x86_64.whl\n", 44 | " installed_xformers = True\n", 45 | "if 'A5000' in GPU_CardName:\n", 46 | " %pip install https://huggingface.co/industriaditat/xformers_precompiles/resolve/main/A5000-xf14-cu116-py38/xformers-0.0.14.dev0-cp38-cp38-linux_x86_64.whl\n", 47 | " installed_xformers = True\n", 48 | "if 'T4' in GPU_CardName:\n", 49 | " %pip install https://huggingface.co/r4ziel/xformers_pre_built/resolve/main/xformers-0.0.14.dev0-cp38-cp38-linux_x86_64_t4.whl\n", 50 | " installed_xformers = True\n", 51 | "if 'A100' in GPU_CardName:\n", 52 | " %pip install https://huggingface.co/industriaditat/xformers_precompiles/resolve/main/A100_13dev/xformers-0.0.13.dev0-py3-none-any.whl\n", 53 | " installed_xformers = True\n", 54 | "if 'V100' in GPU_CardName:\n", 55 | " %pip install https://huggingface.co/industriaditat/xformers_precompiles/resolve/main/V100_13dev/xformers-0.0.13.dev0-py3-none-any.whl\n", 56 | " installed_xformers = True\n", 57 | "if installed_xformers == False:\n", 58 | " clear_output()\n", 59 | " print(\"No precompiled xformers found for your GPU. Please wait while we compile xformers for your GPU, this might take 20-40 minutes.\")\n", 60 | " %pip install git+https://github.com/facebookresearch/xformers@1d31a3a#egg=xformers\n", 61 | "%pip install git+https://github.com/huggingface/diffusers.git@0ca1724#egg=diffusers --force-reinstall\n", 62 | "clear_output()\n", 63 | "print(\"Done!\")" 64 | ] 65 | }, 66 | { 67 | "attachments": {}, 68 | "cell_type": "markdown", 69 | "metadata": {}, 70 | "source": [ 71 | "#### Upload finished?, time to run this next cell!" 72 | ] 73 | }, 74 | { 75 | "cell_type": "code", 76 | "execution_count": null, 77 | "metadata": {}, 78 | "outputs": [], 79 | "source": [ 80 | "!unzip -o -q payload.zip\n", 81 | "%pip install -r requirements.txt\n", 82 | "clear_output()\n", 83 | "print(\"Done!\")" 84 | ] 85 | }, 86 | { 87 | "attachments": {}, 88 | "cell_type": "markdown", 89 | "metadata": {}, 90 | "source": [ 91 | "#### Looks like you're done installing, let's get training!\n", 92 | "\n" 93 | ] 94 | }, 95 | { 96 | "cell_type": "code", 97 | "execution_count": null, 98 | "metadata": {}, 99 | "outputs": [], 100 | "source": [ 101 | "changeMe" 102 | ] 103 | }, 104 | { 105 | "attachments": {}, 106 | "cell_type": "markdown", 107 | "metadata": {}, 108 | "source": [ 109 | "## Model Playground\n", 110 | "\n", 111 | "#### This is where you can test your model and package it up." 112 | ] 113 | }, 114 | { 115 | "attachments": {}, 116 | "cell_type": "markdown", 117 | "metadata": {}, 118 | "source": [ 119 | "Run this cell and select your output model, you can upload the model to HuggingFace or run the next cell to use the Web UI and play around with your model." 120 | ] 121 | }, 122 | { 123 | "cell_type": "code", 124 | "execution_count": null, 125 | "metadata": {}, 126 | "outputs": [], 127 | "source": [ 128 | "from ipywidgets import widgets\n", 129 | "import os\n", 130 | "import glob\n", 131 | "from IPython.display import clear_output\n", 132 | "import torch\n", 133 | "from torch import autocast\n", 134 | "from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler\n", 135 | "from IPython.display import display\n", 136 | "import random\n", 137 | "import gradio as gr\n", 138 | "from requests import HTTPError\n", 139 | "from huggingface_hub import create_repo\n", 140 | "from huggingface_hub import HfApi\n", 141 | "from huggingface_hub import login\n", 142 | "from huggingface_hub import logging\n", 143 | "from huggingface_hub.utils import hf_raise_for_status, HfHubHTTPError\n", 144 | "def upload_hf(token,repo_name,model_path):\n", 145 | " if token == '':\n", 146 | " print(\"Please enter your HuggingFace token.\")\n", 147 | " return\n", 148 | " if repo_name == '':\n", 149 | " print(\"Please enter your model name.\")\n", 150 | " return\n", 151 | " if model_path == '':\n", 152 | " print(\"Couldn't find a valid model\")\n", 153 | " return\n", 154 | " api = login(token=token)\n", 155 | " api = HfApi()\n", 156 | " hf_username = HfApi().whoami(token)['name']\n", 157 | " project_repo = repo_name\n", 158 | " try:\n", 159 | " create_repo(f'{hf_username}/{project_repo}', repo_type='model',token=token)\n", 160 | " except HTTPError as http_e:\n", 161 | " if http_e.response.status_code == 409:\n", 162 | " print('The repo already exists')\n", 163 | " pass\n", 164 | " else:\n", 165 | " print(f'An error occurred: {http_e.reason}')\n", 166 | " except HfHubHTTPError as hfhub_e:\n", 167 | " if hfhub_e.response.status_code == 409:\n", 168 | " pass\n", 169 | " else:\n", 170 | " print(f'An error occurred: {hfhub_e.message}')\n", 171 | " try:\n", 172 | " print('Uploading...')\n", 173 | " api.upload_folder(\n", 174 | " folder_path=model_path,\n", 175 | " path_in_repo='',\n", 176 | " repo_id=f'{hf_username}/{project_repo}',\n", 177 | " repo_type=\"model\",\n", 178 | " ignore_patterns=\"**/logs/\",\n", 179 | " )\n", 180 | " print('Done!')\n", 181 | " print(f'Model is at https://huggingface.co/{hf_username}/{project_repo}')\n", 182 | " except Exception as general_e:\n", 183 | " print(f'Exception occurred: {general_e}')\n", 184 | "if 'output' not in os.listdir():\n", 185 | " print(\"No output folder found. Please run the training cell first.\")\n", 186 | "models = []\n", 187 | "model_dir = os.listdir('output')[0]\n", 188 | "output_sort = sorted(glob.iglob('output' + os.sep + model_dir + os.sep+ '*'), key=os.path.getctime, reverse=True)\n", 189 | "if len(output_sort) == 0:\n", 190 | " print(\"No models found in output folder. Please run the training cell first.\")\n", 191 | "for model in output_sort:\n", 192 | " required_folders = [\"vae\", \"unet\", \"tokenizer\", \"text_encoder\"]\n", 193 | " if all(x in os.listdir(model) for x in required_folders):\n", 194 | " models.append(model)\n", 195 | "model_selection = widgets.Dropdown(\n", 196 | " layout={'width': 'initial'},\n", 197 | " style={'description_width': 'initial'},\n", 198 | " options=models,\n", 199 | " value=models[0],\n", 200 | " # rows=10,\n", 201 | " description='Select Checkpoint:',\n", 202 | " disabled=False\n", 203 | ")\n", 204 | "upload_btn = widgets.Button(\n", 205 | " description='Upload to HuggingFace Hub',\n", 206 | " style={'description_width': 'initial'},\n", 207 | " layout={'width': 'initial'},\n", 208 | " disabled=False,\n", 209 | " button_style='', # 'success', 'info', 'warning', 'danger' or ''\n", 210 | " tooltip='Press to start upload',\n", 211 | " icon='check' # (FontAwesome names without the `fa-` prefix)\n", 212 | ")\n", 213 | "token_txt = widgets.Text(\n", 214 | " value='',\n", 215 | " style={'description_width': 'initial'},\n", 216 | " layout={'width': 'initial'},\n", 217 | " placeholder='HF Token',\n", 218 | " description='Hugging Face Token:',\n", 219 | " disabled=False\n", 220 | ")\n", 221 | "repo_txt = widgets.Text(\n", 222 | " value=model_dir,\n", 223 | " style={'description_width': 'initial'},\n", 224 | " placeholder='Give your model a name',\n", 225 | " description='Model Name:',\n", 226 | " disabled=False\n", 227 | ")\n", 228 | "upload_btn.on_click(lambda x: upload_hf(token_txt.value,repo_txt.value,model_selection.value))\n", 229 | "clear_output()\n", 230 | "display(model_selection)\n", 231 | "display(token_txt)\n", 232 | "display(repo_txt)\n", 233 | "display(upload_btn)\n", 234 | "print('You can input your HuggingFace token and repo to upload your model to the HuggingFace Hub, make sure to use a write API token!')\n", 235 | "print('Alternatively, you can run the next cell to open up a UI where you can generate and zip up the model.')\n" 236 | ] 237 | }, 238 | { 239 | "attachments": {}, 240 | "cell_type": "markdown", 241 | "metadata": {}, 242 | "source": [ 243 | "All ready?, lets play around with it ;), this next cell will load a small UI for you to generate images and zip it up for download, re-run this cell if you selected a new model!" 244 | ] 245 | }, 246 | { 247 | "cell_type": "code", 248 | "execution_count": null, 249 | "metadata": {}, 250 | "outputs": [], 251 | "source": [ 252 | "model_path = model_selection.value\n", 253 | "pipe = StableDiffusionPipeline.from_pretrained(model_path,safety_checker=None, torch_dtype=torch.float16).to(\"cuda\")\n", 254 | "scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)\n", 255 | "pipe.scheduler = scheduler\n", 256 | "print('Loaded checkpoint')\n", 257 | "def inference(prompt, negative_prompt, num_samples, height=512, width=512, num_inference_steps=50,seed=-1,guidance_scale=7.5):\n", 258 | " with torch.autocast(\"cuda\"), torch.inference_mode():\n", 259 | " if seed != -1:\n", 260 | " g_cuda = torch.Generator(device='cuda')\n", 261 | " g_cuda.manual_seed(int(seed))\n", 262 | " else:\n", 263 | " seed = random.randint(0, 100000)\n", 264 | " g_cuda = torch.Generator(device='cuda')\n", 265 | " g_cuda.manual_seed(seed)\n", 266 | " return pipe(\n", 267 | " prompt, height=int(height), width=int(width),\n", 268 | " negative_prompt=negative_prompt,\n", 269 | " num_images_per_prompt=int(num_samples),\n", 270 | " num_inference_steps=int(num_inference_steps), guidance_scale=guidance_scale,\n", 271 | " generator=g_cuda\n", 272 | " ).images, seed\n", 273 | "def zip_model():\n", 274 | " import shutil\n", 275 | " print('Zipping Model!, Please wait until you see a done message, this can take a few minutes, you can keep generating while you wait!')\n", 276 | " curLocation = os.getcwd()\n", 277 | " model_name = os.path.dirname(model_path)\n", 278 | " shutil.make_archive(model_name,'zip',model_path)\n", 279 | " os.chdir(curLocation)\n", 280 | " print('Done!')\n", 281 | "with gr.Blocks() as demo:\n", 282 | " with gr.Row():\n", 283 | " with gr.Column():\n", 284 | " prompt = gr.Textbox(label=\"Prompt\", value=\"photo of zwx dog in a bucket\")\n", 285 | " negative_prompt = gr.Textbox(label=\"Negative Prompt\", value=\"\")\n", 286 | " with gr.Row():\n", 287 | " run = gr.Button(value=\"Generate\")\n", 288 | " zip = gr.Button(value=\"Zip Model For Download\")\n", 289 | " with gr.Row():\n", 290 | " num_samples = gr.Number(label=\"Number of Samples\", value=4)\n", 291 | " guidance_scale = gr.Number(label=\"Guidance Scale\", value=7.5)\n", 292 | " with gr.Row():\n", 293 | " height = gr.Number(label=\"Height\", value=512)\n", 294 | " width = gr.Number(label=\"Width\", value=512)\n", 295 | " with gr.Row():\n", 296 | " num_inference_steps = gr.Slider(label=\"Steps\", value=25)\n", 297 | " seed = gr.Number(label=\"Seed\", value=-1)\n", 298 | " with gr.Column():\n", 299 | " gallery = gr.Gallery()\n", 300 | " seedDisplay = gr.Number(label=\"Used Seed:\", value=0)\n", 301 | "\n", 302 | " run.click(inference, inputs=[prompt, negative_prompt, num_samples, height, width, num_inference_steps,seed, guidance_scale], outputs=[gallery,seedDisplay])\n", 303 | " zip.click(zip_model)\n", 304 | "demo.launch(debug=True,share=True)" 305 | ] 306 | }, 307 | { 308 | "attachments": {}, 309 | "cell_type": "markdown", 310 | "metadata": {}, 311 | "source": [ 312 | "### This is the end, for now :) ,you can convert your model to CKPT back in StableTuner!." 313 | ] 314 | } 315 | ], 316 | "metadata": { 317 | "kernelspec": { 318 | "display_name": "Python 3", 319 | "language": "python", 320 | "name": "python3" 321 | }, 322 | "language_info": { 323 | "codemirror_mode": { 324 | "name": "ipython", 325 | "version": 3 326 | }, 327 | "file_extension": ".py", 328 | "mimetype": "text/x-python", 329 | "name": "python", 330 | "nbconvert_exporter": "python", 331 | "pygments_lexer": "ipython3", 332 | "version": "3.10.9 (tags/v3.10.9:1dd9be6, Dec 6 2022, 20:01:21) [MSC v.1934 64 bit (AMD64)]" 333 | }, 334 | "orig_nbformat": 4, 335 | "vscode": { 336 | "interpreter": { 337 | "hash": "886cb931ea414ad2a87adcccbb1ce9166879eb6056301acd331591c6290ceca8" 338 | } 339 | } 340 | }, 341 | "nbformat": 4, 342 | "nbformat_minor": 2 343 | } 344 | -------------------------------------------------------------------------------- /scripts/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/devilismyfriend/StableTuner/4c63853399590289ea88b6fcd6565f1a407c916c/scripts/__init__.py -------------------------------------------------------------------------------- /scripts/captionBuddy.py: -------------------------------------------------------------------------------- 1 | import tkinter as tk 2 | from tkinter import ttk, Menu 3 | import os 4 | import subprocess 5 | from PIL import Image, ImageTk, ImageDraw 6 | import tkinter.filedialog as fd 7 | import json 8 | import sys 9 | import os 10 | import sys 11 | from torchvision import transforms 12 | from torchvision.transforms.functional import InterpolationMode 13 | import torch 14 | import subprocess 15 | import numpy as np 16 | import requests 17 | import random 18 | import customtkinter as ctk 19 | from customtkinter import ThemeManager 20 | 21 | from clip_segmentation import ClipSeg 22 | 23 | #main class 24 | ctk.set_appearance_mode("dark") 25 | ctk.set_default_color_theme("blue") 26 | 27 | class BatchMaskWindow(ctk.CTkToplevel): 28 | def __init__(self, parent, path, *args, **kwargs): 29 | ctk.CTkToplevel.__init__(self, parent, *args, **kwargs) 30 | self.parent = parent 31 | 32 | self.title("Batch process masks") 33 | self.geometry("320x310") 34 | self.resizable(False, False) 35 | self.wait_visibility() 36 | self.grab_set() 37 | self.focus_set() 38 | 39 | self.mode_var = tk.StringVar(self, "Create if absent") 40 | self.modes = ["Replace all masks", "Create if absent", "Add to existing", "Subtract from existing"] 41 | 42 | self.frame = ctk.CTkFrame(self, width=600, height=300) 43 | self.frame.grid(row=0, column=0, sticky="nsew", padx=10, pady=10) 44 | 45 | self.path_label = ctk.CTkLabel(self.frame, text="Folder", width=100) 46 | self.path_label.grid(row=0, column=0, sticky="w",padx=5, pady=5) 47 | self.path_entry = ctk.CTkEntry(self.frame, width=150) 48 | self.path_entry.insert(0, path) 49 | self.path_entry.grid(row=0, column=1, sticky="w", padx=5, pady=5) 50 | self.path_button = ctk.CTkButton(self.frame, width=30, text="...", command=lambda: self.browse_for_path(self.path_entry)) 51 | self.path_button.grid(row=0, column=1, sticky="e", padx=5, pady=5) 52 | 53 | self.prompt_label = ctk.CTkLabel(self.frame, text="Prompt", width=100) 54 | self.prompt_label.grid(row=1, column=0, sticky="w",padx=5, pady=5) 55 | self.prompt_entry = ctk.CTkEntry(self.frame, width=200) 56 | self.prompt_entry.grid(row=1, column=1, sticky="w", padx=5, pady=5) 57 | 58 | self.mode_label = ctk.CTkLabel(self.frame, text="Mode", width=100) 59 | self.mode_label.grid(row=2, column=0, sticky="w", padx=5, pady=5) 60 | self.mode_dropdown = ctk.CTkOptionMenu(self.frame, variable=self.mode_var, values=self.modes, dynamic_resizing=False, width=200) 61 | self.mode_dropdown.grid(row=2, column=1, sticky="w", padx=5, pady=5) 62 | 63 | self.threshold_label = ctk.CTkLabel(self.frame, text="Threshold", width=100) 64 | self.threshold_label.grid(row=3, column=0, sticky="w", padx=5, pady=5) 65 | self.threshold_entry = ctk.CTkEntry(self.frame, width=200, placeholder_text="0.0 - 1.0") 66 | self.threshold_entry.insert(0, "0.3") 67 | self.threshold_entry.grid(row=3, column=1, sticky="w", padx=5, pady=5) 68 | 69 | self.smooth_label = ctk.CTkLabel(self.frame, text="Smooth", width=100) 70 | self.smooth_label.grid(row=4, column=0, sticky="w", padx=5, pady=5) 71 | self.smooth_entry = ctk.CTkEntry(self.frame, width=200, placeholder_text="5") 72 | self.smooth_entry.insert(0, 5) 73 | self.smooth_entry.grid(row=4, column=1, sticky="w", padx=5, pady=5) 74 | 75 | self.expand_label = ctk.CTkLabel(self.frame, text="Expand", width=100) 76 | self.expand_label.grid(row=5, column=0, sticky="w", padx=5, pady=5) 77 | self.expand_entry = ctk.CTkEntry(self.frame, width=200, placeholder_text="10") 78 | self.expand_entry.insert(0, 10) 79 | self.expand_entry.grid(row=5, column=1, sticky="w", padx=5, pady=5) 80 | 81 | self.progress_label = ctk.CTkLabel(self.frame, text="Progress: 0/0", width=100) 82 | self.progress_label.grid(row=6, column=0, sticky="w", padx=5, pady=5) 83 | self.progress = ctk.CTkProgressBar(self.frame, orientation="horizontal", mode="determinate", width=200) 84 | self.progress.grid(row=6, column=1, sticky="w", padx=5, pady=5) 85 | 86 | self.create_masks_button = ctk.CTkButton(self.frame, text="Create Masks", width=310, command=self.create_masks) 87 | self.create_masks_button.grid(row=7, column=0, columnspan=2, sticky="w", padx=5, pady=5) 88 | 89 | self.frame.pack(fill="both", expand=True) 90 | 91 | def browse_for_path(self, entry_box): 92 | # get the path from the user 93 | path = fd.askdirectory() 94 | # set the path to the entry box 95 | # delete entry box text 96 | entry_box.focus_set() 97 | entry_box.delete(0, tk.END) 98 | entry_box.insert(0, path) 99 | self.focus_set() 100 | 101 | def set_progress(self, value, max_value): 102 | progress = value / max_value 103 | self.progress.set(progress) 104 | self.progress_label.configure(text="{0}/{1}".format(value, max_value)) 105 | self.progress.update() 106 | 107 | def create_masks(self): 108 | self.parent.load_clip_seg_model() 109 | 110 | mode = { 111 | "Replace all masks": "replace", 112 | "Create if absent": "fill", 113 | "Add to existing": "add", 114 | "Subtract from existing": "subtract" 115 | }[self.mode_var.get()] 116 | 117 | self.parent.clip_seg.mask_folder( 118 | sample_dir=self.path_entry.get(), 119 | prompts=[self.prompt_entry.get()], 120 | mode=mode, 121 | threshold=float(self.threshold_entry.get()), 122 | smooth_pixels=int(self.smooth_entry.get()), 123 | expand_pixels=int(self.expand_entry.get()), 124 | progress_callback=self.set_progress, 125 | ) 126 | self.parent.load_image() 127 | 128 | 129 | def _check_file_type(f: str) -> bool: 130 | return f.lower().endswith(('.jpg', '.jpeg', '.png', '.webp', ".bmp", ".tiff")) 131 | 132 | 133 | class ImageBrowser(ctk.CTkToplevel): 134 | def __init__(self,mainProcess=None): 135 | super().__init__() 136 | if not os.path.exists("scripts/BLIP"): 137 | print("Getting BLIP from GitHub.") 138 | subprocess.run(["git", "clone", "https://github.com/salesforce/BLIP", "scripts/BLIP"]) 139 | #if not os.path.exists("scripts/CLIP"): 140 | # print("Getting CLIP from GitHub.") 141 | # subprocess.run(["git", "clone", "https://github.com/pharmapsychotic/clip-interrogator.git', 'scripts/CLIP"]) 142 | blip_path = "scripts/BLIP" 143 | sys.path.append(blip_path) 144 | #clip_path = "scripts/CLIP" 145 | #sys.path.append(clip_path) 146 | self.mainProcess = mainProcess 147 | self.captioner_folder = os.path.dirname(os.path.realpath(__file__)) 148 | self.clip_seg = None 149 | self.PILimage = None 150 | self.PILmask = None 151 | self.mask_draw_x = 0 152 | self.mask_draw_y = 0 153 | self.mask_draw_radius = 20 154 | #self = master 155 | #self.overrideredirect(True) 156 | #self.title_bar = TitleBar(self) 157 | #self.title_bar.pack(side="top", fill="x") 158 | #make not user resizable 159 | self.title("Caption Buddy") 160 | #self.resizable(False, False) 161 | self.geometry("720x820") 162 | self.top_frame = ctk.CTkFrame(self,fg_color='transparent') 163 | self.top_frame.pack(side="top", fill="x",expand=False) 164 | self.top_subframe = ctk.CTkFrame(self.top_frame,fg_color='transparent') 165 | self.top_subframe.pack(side="bottom", fill="x",pady=10) 166 | self.top_subframe.grid_columnconfigure(0, weight=1) 167 | self.top_subframe.grid_columnconfigure(1, weight=1) 168 | self.tip_frame = ctk.CTkFrame(self,fg_color='transparent') 169 | self.tip_frame.pack(side="top") 170 | self.dark_mode_var = "#202020" 171 | #self.dark_purple_mode_var = "#1B0F1B" 172 | self.dark_mode_title_var = "#286aff" 173 | self.dark_mode_button_pressed_var = "#BB91B6" 174 | self.dark_mode_button_var = "#8ea0e1" 175 | self.dark_mode_text_var = "#c6c7c8" 176 | #self.configure(bg_color=self.dark_mode_var) 177 | self.canvas = ctk.CTkLabel(self,text='', width=600, height=600) 178 | #self.canvas.configure(bg_color=self.dark_mode_var) 179 | #create temporary image for canvas 180 | self.canvas.pack() 181 | self.cur_img_index = 0 182 | self.image_count = 0 183 | #make a frame with a grid under the canvas 184 | self.frame = ctk.CTkFrame(self) 185 | #grid 186 | self.frame.grid_columnconfigure(0, weight=1) 187 | self.frame.grid_columnconfigure(1, weight=100) 188 | self.frame.grid_columnconfigure(2, weight=1) 189 | self.frame.grid_rowconfigure(0, weight=1) 190 | 191 | #show the frame 192 | self.frame.pack(side="bottom", fill="x") 193 | #bottom frame 194 | self.bottom_frame = ctk.CTkFrame(self) 195 | #make grid 196 | self.bottom_frame.grid_columnconfigure(0, weight=0) 197 | self.bottom_frame.grid_columnconfigure(1, weight=2) 198 | self.bottom_frame.grid_columnconfigure(2, weight=0) 199 | self.bottom_frame.grid_columnconfigure(3, weight=2) 200 | self.bottom_frame.grid_columnconfigure(4, weight=0) 201 | self.bottom_frame.grid_columnconfigure(5, weight=2) 202 | self.bottom_frame.grid_rowconfigure(0, weight=1) 203 | #show the frame 204 | self.bottom_frame.pack(side="bottom", fill="x") 205 | 206 | self.image_index = 0 207 | self.image_list = [] 208 | self.caption = '' 209 | self.caption_file = '' 210 | self.caption_file_path = '' 211 | self.caption_file_name = '' 212 | self.caption_file_ext = '' 213 | self.caption_file_name_no_ext = '' 214 | self.output_format='text' 215 | #check if bad_files.txt exists 216 | if os.path.exists("bad_files.txt"): 217 | #delete it 218 | os.remove("bad_files.txt") 219 | self.use_blip = True 220 | self.debug = False 221 | self.create_widgets() 222 | self.load_blip_model() 223 | self.load_options() 224 | #self.open_folder() 225 | 226 | self.canvas.focus_force() 227 | self.canvas.bind("", self.next_image) 228 | self.canvas.bind("", self.prev_image) 229 | #on close window 230 | self.protocol("WM_DELETE_WINDOW", self.on_closing) 231 | def on_closing(self): 232 | #self.save_options() 233 | self.mainProcess.deiconify() 234 | self.destroy() 235 | def create_widgets(self): 236 | self.output_folder = '' 237 | 238 | # add a checkbox to toggle auto generate caption 239 | self.auto_generate_caption = tk.BooleanVar(self.top_subframe) 240 | self.auto_generate_caption.set(True) 241 | self.auto_generate_caption_checkbox = ctk.CTkCheckBox(self.top_subframe, text="Auto Generate Caption", variable=self.auto_generate_caption,width=50) 242 | self.auto_generate_caption_checkbox.pack(side="left", fill="x", expand=True, padx=10) 243 | 244 | # add a checkbox to skip auto generating captions if they already exist 245 | self.auto_generate_caption_text_override = tk.BooleanVar(self.top_subframe) 246 | self.auto_generate_caption_text_override.set(False) 247 | self.auto_generate_caption_checkbox_text_override = ctk.CTkCheckBox(self.top_subframe, text="Skip Auto Generate If Text Caption Exists", variable=self.auto_generate_caption_text_override,width=50) 248 | self.auto_generate_caption_checkbox_text_override.pack(side="left", fill="x", expand=True, padx=10) 249 | 250 | # add a checkbox to enable mask editing 251 | self.enable_mask_editing = tk.BooleanVar(self.top_subframe) 252 | self.enable_mask_editing.set(False) 253 | self.enable_mask_editing_checkbox = ctk.CTkCheckBox(self.top_subframe, text="Enable Mask Editing", variable=self.enable_mask_editing, width=50) 254 | self.enable_mask_editing_checkbox.pack(side="left", fill="x", expand=True, padx=10) 255 | 256 | self.open_button = ctk.CTkButton(self.top_frame,text="Load Folder",fg_color=("gray75", "gray25"), command=self.open_folder,width=50) 257 | #self.open_button.grid(row=0, column=1) 258 | self.open_button.pack(side="left", fill="x",expand=True,padx=10) 259 | #add a batch folder button 260 | self.batch_folder_caption_button = ctk.CTkButton(self.top_frame, text="Batch Folder Caption", fg_color=("gray75", "gray25"), command=self.batch_folder_caption, width=50) 261 | self.batch_folder_caption_button.pack(side="left", fill="x", expand=True, padx=10) 262 | self.batch_folder_mask_button = ctk.CTkButton(self.top_frame, text="Batch Folder Mask", fg_color=("gray75", "gray25"), command=self.batch_folder_mask, width=50) 263 | self.batch_folder_mask_button.pack(side="left", fill="x", expand=True, padx=10) 264 | 265 | #add an options button to the same row as the open button 266 | self.options_button = ctk.CTkButton(self.top_frame, text="Options",fg_color=("gray75", "gray25"), command=self.open_options,width=50) 267 | self.options_button.pack(side="left", fill="x",expand=True,padx=10) 268 | #add generate caption button 269 | self.generate_caption_button = ctk.CTkButton(self.top_frame, text="Generate Caption",fg_color=("gray75", "gray25"), command=self.generate_caption,width=50) 270 | self.generate_caption_button.pack(side="left", fill="x",expand=True,padx=10) 271 | 272 | #add a label for tips under the buttons 273 | self.tips_label = ctk.CTkLabel(self.tip_frame, text="Use Alt with left and right arrow keys to navigate images, enter to save the caption.") 274 | self.tips_label.pack(side="top") 275 | #add image count label 276 | self.image_count_label = ctk.CTkLabel(self.tip_frame, text=f"Image {self.cur_img_index} of {self.image_count}") 277 | self.image_count_label.pack(side="top") 278 | 279 | self.image_label = ctk.CTkLabel(self.canvas,text='',width=100,height=100) 280 | self.image_label.grid(row=0, column=0, sticky="nsew") 281 | #self.image_label.bind("", self.click_canvas) 282 | self.image_label.bind("", self.draw_mask) 283 | self.image_label.bind("", self.draw_mask) 284 | self.image_label.bind("", self.draw_mask) 285 | self.image_label.bind("", self.draw_mask_radius) 286 | #self.image_label.pack(side="top") 287 | #previous button 288 | self.prev_button = ctk.CTkButton(self.frame,text="Previous", command= lambda event=None: self.prev_image(event),width=50) 289 | #grid 290 | self.prev_button.grid(row=1, column=0, sticky="w",padx=5,pady=10) 291 | #self.prev_button.pack(side="left") 292 | #self.prev_button.bind("", self.prev_image) 293 | self.caption_entry = ctk.CTkEntry(self.frame) 294 | #grid 295 | self.caption_entry.grid(row=1, column=1, rowspan=3, sticky="nsew",pady=10) 296 | #bind to enter key 297 | self.caption_entry.bind("", self.save) 298 | self.canvas.bind("", self.save) 299 | self.caption_entry.bind("", self.next_image) 300 | self.caption_entry.bind("", self.prev_image) 301 | self.caption_entry.bind("", self.delete_word) 302 | #next button 303 | 304 | self.next_button = ctk.CTkButton(self.frame,text='Next', command= lambda event=None: self.next_image(event),width=50) 305 | #self.next_button["text"] = "Next" 306 | #grid 307 | self.next_button.grid(row=1, column=2, sticky="e",padx=5,pady=10) 308 | #add two entry boxes and labels in the style of :replace _ with _ 309 | #create replace string variable 310 | self.replace_label = ctk.CTkLabel(self.bottom_frame, text="Replace:") 311 | self.replace_label.grid(row=0, column=0, sticky="w",padx=5) 312 | self.replace_entry = ctk.CTkEntry(self.bottom_frame, ) 313 | self.replace_entry.grid(row=0, column=1, sticky="nsew",padx=5) 314 | self.replace_entry.bind("", self.save) 315 | #self.replace_entry.bind("", self.replace) 316 | #with label 317 | #create with string variable 318 | self.with_label = ctk.CTkLabel(self.bottom_frame, text="With:") 319 | self.with_label.grid(row=0, column=2, sticky="w",padx=5) 320 | self.with_entry = ctk.CTkEntry(self.bottom_frame, ) 321 | self.with_entry.grid(row=0, column=3, sticky="nswe",padx=5) 322 | self.with_entry.bind("", self.save) 323 | #add another entry with label, add suffix 324 | 325 | #create prefix string var 326 | self.prefix_label = ctk.CTkLabel(self.bottom_frame, text="Add to start:") 327 | self.prefix_label.grid(row=0, column=4, sticky="w",padx=5) 328 | self.prefix_entry = ctk.CTkEntry(self.bottom_frame, ) 329 | self.prefix_entry.grid(row=0, column=5, sticky="nsew",padx=5) 330 | self.prefix_entry.bind("", self.save) 331 | 332 | #create suffix string var 333 | self.suffix_label = ctk.CTkLabel(self.bottom_frame, text="Add to end:") 334 | self.suffix_label.grid(row=0, column=6, sticky="w",padx=5) 335 | self.suffix_entry = ctk.CTkEntry(self.bottom_frame, ) 336 | self.suffix_entry.grid(row=0, column=7, sticky="nsew",padx=5) 337 | self.suffix_entry.bind("", self.save) 338 | self.all_entries = [self.replace_entry, self.with_entry, self.suffix_entry, self.caption_entry, self.prefix_entry] 339 | #bind right click menu to all entries 340 | for entry in self.all_entries: 341 | entry.bind("", self.create_right_click_menu) 342 | def batch_folder_caption(self): 343 | #show imgs in folder askdirectory 344 | #ask user if to batch current folder or select folder 345 | #if bad_files.txt exists, delete it 346 | self.bad_files = [] 347 | if os.path.exists('bad_files.txt'): 348 | os.remove('bad_files.txt') 349 | try: 350 | #check if self.folder is set 351 | self.folder 352 | except AttributeError: 353 | self.folder = '' 354 | if self.folder == '': 355 | self.folder = fd.askdirectory(title="Select Folder to Batch Process", initialdir=os.getcwd()) 356 | batch_input_dir = self.folder 357 | else: 358 | ask = tk.messagebox.askquestion("Batch Folder", "Batch current folder?") 359 | if ask == 'yes': 360 | batch_input_dir = self.folder 361 | else: 362 | batch_input_dir = fd.askdirectory(title="Select Folder to Batch Process", initialdir=os.getcwd()) 363 | ask2 = tk.messagebox.askquestion("Batch Folder", "Save output to same directory?") 364 | if ask2 == 'yes': 365 | batch_output_dir = batch_input_dir 366 | else: 367 | batch_output_dir = fd.askdirectory(title="Select Folder to Save Batch Processed Images", initialdir=os.getcwd()) 368 | if batch_input_dir == '': 369 | return 370 | if batch_output_dir == '': 371 | batch_output_dir = batch_input_dir 372 | 373 | self.caption_file_name = os.path.basename(batch_input_dir) 374 | self.image_list = [] 375 | for file in os.listdir(batch_input_dir): 376 | if _check_file_type(file) and not file.endswith('-masklabel.png'): 377 | self.image_list.append(os.path.join(batch_input_dir, file)) 378 | self.image_index = 0 379 | #use progress bar class 380 | #pba = tk.Tk() 381 | #pba.title("Batch Processing") 382 | #remove icon 383 | #pba.wm_attributes('-toolwindow','True') 384 | pb = ProgressbarWithCancel(max=len(self.image_list)) 385 | #pb.set_max(len(self.image_list)) 386 | pb.set_progress(0) 387 | 388 | #if batch_output_dir doesn't exist, create it 389 | if not os.path.exists(batch_output_dir): 390 | os.makedirs(batch_output_dir) 391 | for i in range(len(self.image_list)): 392 | radnom_chance = random.randint(0,25) 393 | if radnom_chance == 0: 394 | pb.set_random_label() 395 | if pb.is_cancelled(): 396 | pb.destroy() 397 | return 398 | self.image_index = i 399 | #get float value of progress between 0 and 1 according to the image index and the total number of images 400 | progress = i / len(self.image_list) 401 | pb.set_progress(progress) 402 | self.update() 403 | try: 404 | img = Image.open(self.image_list[i]).convert("RGB") 405 | except: 406 | self.bad_files.append(self.image_list[i]) 407 | #skip file 408 | continue 409 | tensor = transforms.Compose([ 410 | transforms.Resize((self.blipSize, self.blipSize), interpolation=InterpolationMode.BICUBIC), 411 | transforms.ToTensor(), 412 | transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)) 413 | ]) 414 | torch_image = tensor(img).unsqueeze(0).to(torch.device("cuda")) 415 | if self.nucleus_sampling: 416 | captions = self.blip_decoder.generate(torch_image, sample=True, top_p=self.q_factor) 417 | else: 418 | captions = self.blip_decoder.generate(torch_image, sample=False, num_beams=16, min_length=self.min_length, \ 419 | max_length=48, repetition_penalty=self.q_factor) 420 | caption = captions[0] 421 | self.replace = self.replace_entry.get() 422 | self.replace_with = self.with_entry.get() 423 | self.suffix_var = self.suffix_entry.get() 424 | self.prefix = self.prefix_entry.get() 425 | #prepare the caption 426 | if self.suffix_var.startswith(',') or self.suffix_var.startswith(' '): 427 | self.suffix_var = self.suffix_var 428 | else: 429 | self.suffix_var = ' ' + self.suffix_var 430 | caption = caption.replace(self.replace, self.replace_with) 431 | if self.prefix != '': 432 | if self.prefix.endswith(' '): 433 | self.prefix = self.prefix[:-1] 434 | if not self.prefix.endswith(','): 435 | self.prefix = self.prefix+',' 436 | caption = self.prefix + ' ' + caption 437 | if caption.endswith(',') or caption.endswith('.'): 438 | caption = caption[:-1] 439 | caption = caption +', ' + self.suffix_var 440 | else: 441 | caption = caption + self.suffix_var 442 | #saving the captioned image 443 | if self.output_format == 'text': 444 | #text file with same name as image 445 | imgName = os.path.basename(self.image_list[self.image_index]) 446 | imgName = imgName[:imgName.rfind('.')] 447 | caption_file = os.path.join(batch_output_dir, imgName + '.txt') 448 | with open(caption_file, 'w') as f: 449 | f.write(caption) 450 | elif self.output_format == 'filename': 451 | #duplicate image with caption as file name 452 | img.save(os.path.join(batch_output_dir, caption+'.png')) 453 | progress = i + 1 / len(self.image_list) 454 | pb.set_progress(progress) 455 | #show message box when done 456 | pb.destroy() 457 | donemsg = tk.messagebox.showinfo("Batch Folder", "Batching complete!",parent=self.master) 458 | if len(self.bad_files) > 0: 459 | bad_files_msg = tk.messagebox.showinfo("Bad Files", "Couldn't process " + str(len(self.bad_files)) + "files,\nFor a list of problematic files see bad_files.txt",parent=self.master) 460 | with open('bad_files.txt', 'w') as f: 461 | for item in self.bad_files: 462 | f.write(item + '\n') 463 | 464 | #ask user if we should load the batch output folder 465 | ask3 = tk.messagebox.askquestion("Batch Folder", "Load batch output folder?") 466 | if ask3 == 'yes': 467 | self.image_index = 0 468 | self.open_folder(folder=batch_output_dir) 469 | #focus on donemsg 470 | #donemsg.focus_force() 471 | def generate_caption(self): 472 | #get the image 473 | tensor = transforms.Compose([ 474 | #transforms.CenterCrop(SIZE), 475 | transforms.Resize((self.blipSize, self.blipSize), interpolation=InterpolationMode.BICUBIC), 476 | transforms.ToTensor(), 477 | transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)) 478 | ]) 479 | torch_image = tensor(self.PILimage).unsqueeze(0).to(torch.device("cuda")) 480 | if self.nucleus_sampling: 481 | captions = self.blip_decoder.generate(torch_image, sample=True, top_p=self.q_factor) 482 | else: 483 | captions = self.blip_decoder.generate(torch_image, sample=False, num_beams=16, min_length=self.min_length, \ 484 | max_length=48, repetition_penalty=self.q_factor) 485 | self.caption = captions[0] 486 | self.caption_entry.delete(0, tk.END) 487 | self.caption_entry.insert(0, self.caption) 488 | #change the caption entry color to red 489 | self.caption_entry.configure(fg_color='red') 490 | def load_blip_model(self): 491 | self.blipSize = 384 492 | blip_model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth' 493 | #check if options file exists 494 | if os.path.exists(os.path.join(self.captioner_folder, 'options.json')): 495 | with open(os.path.join(self.captioner_folder, 'options.json'), 'r') as f: 496 | self.nucleus_sampling = json.load(f)['nucleus_sampling'] 497 | self.q_factor = json.load(f)['q_factor'] 498 | self.min_length = json.load(f)['min_length'] 499 | else: 500 | self.nucleus_sampling = False 501 | self.q_factor = 1.0 502 | self.min_length = 22 503 | config_path = os.path.join(self.captioner_folder, "BLIP/configs/med_config.json") 504 | cache_folder = os.path.join(self.captioner_folder, "BLIP/cache") 505 | model_path = os.path.join(self.captioner_folder, "BLIP/models/model_base_caption_capfilt_large.pth") 506 | if not os.path.exists(cache_folder): 507 | os.makedirs(cache_folder) 508 | 509 | if not os.path.exists(model_path): 510 | print(f"Downloading BLIP to {cache_folder}") 511 | with requests.get(blip_model_url, stream=True) as session: 512 | session.raise_for_status() 513 | with open(model_path, 'wb') as f: 514 | for chunk in session.iter_content(chunk_size=1024): 515 | f.write(chunk) 516 | print('Download complete') 517 | else: 518 | print(f"Found BLIP model") 519 | import models.blip 520 | blip_decoder = models.blip.blip_decoder(pretrained=model_path, image_size=self.blipSize, vit='base', med_config=config_path) 521 | blip_decoder.eval() 522 | self.blip_decoder = blip_decoder.to(torch.device("cuda")) 523 | 524 | def batch_folder_mask(self): 525 | folder = '' 526 | try: 527 | # check if self.folder is set 528 | folder = self.folder 529 | except: 530 | pass 531 | 532 | dialog = BatchMaskWindow(self, folder) 533 | dialog.mainloop() 534 | 535 | def load_clip_seg_model(self): 536 | if self.clip_seg is None: 537 | self.clip_seg = ClipSeg() 538 | 539 | def open_folder(self,folder=None): 540 | if folder is None: 541 | self.folder = fd.askdirectory() 542 | else: 543 | self.folder = folder 544 | if self.folder == '': 545 | return 546 | self.output_folder = self.folder 547 | self.image_list = [os.path.join(self.folder, f) for f in os.listdir(self.folder) if _check_file_type(f) and not f.endswith('-masklabel.png') and not f.endswith('-depth.png')] 548 | #self.image_list.sort() 549 | #sort the image list alphabetically so that the images are in the same order every time 550 | self.image_list.sort(key=lambda x: x.lower()) 551 | 552 | self.image_count = len(self.image_list) 553 | if self.image_count == 0: 554 | tk.messagebox.showinfo("No Images", "No images found in the selected folder") 555 | return 556 | #update the image count label 557 | 558 | self.image_index = 0 559 | self.image_count_label.configure(text=f'Image {self.image_index+1} of {self.image_count}') 560 | self.output_folder = self.folder 561 | self.load_image() 562 | self.caption_entry.focus_set() 563 | 564 | def draw_mask(self, event): 565 | if not self.enable_mask_editing.get(): 566 | return 567 | 568 | if event.widget != self.image_label.children["!label"]: 569 | return 570 | 571 | start_x = int(event.x / self.image_size[0] * self.PILimage.width) 572 | start_y = int(event.y / self.image_size[1] * self.PILimage.height) 573 | end_x = int(self.mask_draw_x / self.image_size[0] * self.PILimage.width) 574 | end_y = int(self.mask_draw_y / self.image_size[1] * self.PILimage.height) 575 | 576 | self.mask_draw_x = event.x 577 | self.mask_draw_y = event.y 578 | 579 | color = None 580 | 581 | if event.state & 0x0100 or event.num == 1: # left mouse button 582 | color = (255, 255, 255) 583 | elif event.state & 0x0400 or event.num == 3: # right mouse button 584 | color = (0, 0, 0) 585 | 586 | if color is not None: 587 | if self.PILmask is None: 588 | self.PILmask = Image.new('RGB', size=self.PILimage.size, color=(0, 0, 0)) 589 | 590 | draw = ImageDraw.Draw(self.PILmask) 591 | draw.line((start_x, start_y, end_x, end_y), fill=color, width=self.mask_draw_radius + self.mask_draw_radius + 1) 592 | draw.ellipse((start_x - self.mask_draw_radius, start_y - self.mask_draw_radius, start_x + self.mask_draw_radius, start_y + self.mask_draw_radius), fill=color, outline=None) 593 | draw.ellipse((end_x - self.mask_draw_radius, end_y - self.mask_draw_radius, end_x + self.mask_draw_radius, end_y + self.mask_draw_radius), fill=color, outline=None) 594 | 595 | self.compose_masked_image() 596 | self.display_image() 597 | 598 | def draw_mask_radius(self, event): 599 | if event.widget != self.image_label.children["!label"]: 600 | return 601 | 602 | delta = -np.sign(event.delta) * 5 603 | self.mask_draw_radius += delta 604 | 605 | def compose_masked_image(self): 606 | np_image = np.array(self.PILimage).astype(np.float32) / 255.0 607 | np_mask = np.array(self.PILmask).astype(np.float32) / 255.0 608 | np_mask = np.clip(np_mask, 0.4, 1.0) 609 | np_masked_image = (np_image * np_mask * 255.0).astype(np.uint8) 610 | self.image = Image.fromarray(np_masked_image, mode='RGB') 611 | 612 | def display_image(self): 613 | #resize to fit 600x600 while maintaining aspect ratio 614 | width, height = self.image.size 615 | if width > height: 616 | new_width = 600 617 | new_height = int(600 * height / width) 618 | else: 619 | new_height = 600 620 | new_width = int(600 * width / height) 621 | self.image_size = (new_width, new_height) 622 | self.image = self.image.resize(self.image_size, Image.Resampling.LANCZOS) 623 | self.image = ctk.CTkImage(self.image, size=self.image_size) 624 | self.image_label.configure(image=self.image) 625 | 626 | def load_image(self): 627 | try: 628 | self.PILimage = Image.open(self.image_list[self.image_index]).convert('RGB') 629 | except: 630 | print(f'Error opening image {self.image_list[self.image_index]}') 631 | print('Logged path to bad_files.txt') 632 | #if bad_files.txt doesn't exist, create it 633 | if not os.path.exists('bad_files.txt'): 634 | with open('bad_files.txt', 'w') as f: 635 | f.write(self.image_list[self.image_index]+'\n') 636 | else: 637 | with open('bad_files.txt', 'a') as f: 638 | f.write(self.image_list[self.image_index]+'\n') 639 | return 640 | 641 | self.image = self.PILimage.copy() 642 | 643 | try: 644 | self.PILmask = None 645 | mask_filename = os.path.splitext(self.image_list[self.image_index])[0] + '-masklabel.png' 646 | if os.path.exists(mask_filename): 647 | self.PILmask = Image.open(mask_filename).convert('RGB') 648 | self.compose_masked_image() 649 | except Exception as e: 650 | print(f'Error opening mask for {self.image_list[self.image_index]}') 651 | print('Logged path to bad_files.txt') 652 | #if bad_files.txt doesn't exist, create it 653 | if not os.path.exists('bad_files.txt'): 654 | with open('bad_files.txt', 'w') as f: 655 | f.write(self.image_list[self.image_index]+'\n') 656 | else: 657 | with open('bad_files.txt', 'a') as f: 658 | f.write(self.image_list[self.image_index]+'\n') 659 | return 660 | 661 | self.display_image() 662 | 663 | self.caption_file_path = self.image_list[self.image_index] 664 | self.caption_file_name = os.path.basename(self.caption_file_path) 665 | self.caption_file_ext = os.path.splitext(self.caption_file_name)[1] 666 | self.caption_file_name_no_ext = os.path.splitext(self.caption_file_name)[0] 667 | self.caption_file = os.path.join(self.folder, self.caption_file_name_no_ext + '.txt') 668 | if os.path.isfile(self.caption_file) and self.auto_generate_caption.get() == False or os.path.isfile(self.caption_file) and self.auto_generate_caption.get() == True and self.auto_generate_caption_text_override.get() == True: 669 | with open(self.caption_file, 'r') as f: 670 | self.caption = f.read() 671 | self.caption_entry.delete(0, tk.END) 672 | self.caption_entry.insert(0, self.caption) 673 | self.caption_entry.configure(fg_color=ThemeManager.theme["CTkEntry"]["fg_color"]) 674 | self.use_blip = False 675 | elif os.path.isfile(self.caption_file) and self.auto_generate_caption.get() == True and self.auto_generate_caption_text_override.get() == False or os.path.isfile(self.caption_file)==False and self.auto_generate_caption.get() == True and self.auto_generate_caption_text_override.get() == True: 676 | self.use_blip = True 677 | self.caption_entry.delete(0, tk.END) 678 | elif os.path.isfile(self.caption_file) == False and self.auto_generate_caption.get() == False: 679 | self.caption_entry.delete(0, tk.END) 680 | return 681 | if self.use_blip and self.debug==False: 682 | tensor = transforms.Compose([ 683 | transforms.Resize((self.blipSize, self.blipSize), interpolation=InterpolationMode.BICUBIC), 684 | transforms.ToTensor(), 685 | transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)) 686 | ]) 687 | torch_image = tensor(self.PILimage).unsqueeze(0).to(torch.device("cuda")) 688 | if self.nucleus_sampling: 689 | captions = self.blip_decoder.generate(torch_image, sample=True, top_p=self.q_factor) 690 | else: 691 | captions = self.blip_decoder.generate(torch_image, sample=False, num_beams=16, min_length=self.min_length, \ 692 | max_length=48, repetition_penalty=self.q_factor) 693 | self.caption = captions[0] 694 | self.caption_entry.delete(0, tk.END) 695 | self.caption_entry.insert(0, self.caption) 696 | #change the caption entry color to red 697 | self.caption_entry.configure(fg_color='red') 698 | 699 | def save(self, event): 700 | self.save_caption() 701 | 702 | if self.enable_mask_editing.get(): 703 | self.save_mask() 704 | 705 | def save_mask(self): 706 | mask_filename = os.path.splitext(self.image_list[self.image_index])[0] + '-masklabel.png' 707 | if self.PILmask is not None: 708 | self.PILmask.save(mask_filename) 709 | 710 | def save_caption(self): 711 | self.caption = self.caption_entry.get() 712 | self.replace = self.replace_entry.get() 713 | self.replace_with = self.with_entry.get() 714 | self.suffix_var = self.suffix_entry.get() 715 | self.prefix = self.prefix_entry.get() 716 | #prepare the caption 717 | self.caption = self.caption.replace(self.replace, self.replace_with) 718 | if self.suffix_var.startswith(',') or self.suffix_var.startswith(' '): 719 | self.suffix_var = self.suffix_var 720 | else: 721 | self.suffix_var = ' ' + self.suffix_var 722 | if self.prefix != '': 723 | if self.prefix.endswith(' '): 724 | self.prefix = self.prefix[:-1] 725 | if not self.prefix.endswith(','): 726 | self.prefix = self.prefix+',' 727 | self.caption = self.prefix + ' ' + self.caption 728 | if self.caption.endswith(',') or self.caption.endswith('.'): 729 | self.caption = self.caption[:-1] 730 | self.caption = self.caption +', ' + self.suffix_var 731 | else: 732 | self.caption = self.caption + self.suffix_var 733 | self.caption = self.caption.strip() 734 | if self.output_folder != self.folder: 735 | outputFolder = self.output_folder 736 | else: 737 | outputFolder = self.folder 738 | if self.output_format == 'text': 739 | #text file with same name as image 740 | #image name 741 | #print('test') 742 | imgName = os.path.basename(self.image_list[self.image_index]) 743 | imgName = imgName[:imgName.rfind('.')] 744 | self.caption_file = os.path.join(outputFolder, imgName + '.txt') 745 | with open(self.caption_file, 'w') as f: 746 | f.write(self.caption) 747 | elif self.output_format == 'filename': 748 | #duplicate image with caption as file name 749 | #make sure self.caption doesn't contain any illegal characters 750 | illegal_chars = ['/', '\\', ':', '*', '?', '"', "'",'<', '>', '|', '.'] 751 | for char in illegal_chars: 752 | self.caption = self.caption.replace(char, '') 753 | self.PILimage.save(os.path.join(outputFolder, self.caption+'.png')) 754 | self.caption_entry.delete(0, tk.END) 755 | self.caption_entry.insert(0, self.caption) 756 | self.caption_entry.configure(fg_color='green') 757 | 758 | self.caption_entry.focus_force() 759 | def delete_word(self,event): 760 | ent = event.widget 761 | end_idx = ent.index(tk.INSERT) 762 | start_idx = ent.get().rfind(" ", None, end_idx) 763 | ent.selection_range(start_idx, end_idx) 764 | def prev_image(self, event): 765 | if self.image_index > 0: 766 | self.image_index -= 1 767 | self.image_count_label.configure(text=f'Image {self.image_index+1} of {self.image_count}') 768 | self.load_image() 769 | self.caption_entry.focus_set() 770 | self.caption_entry.focus_force() 771 | def next_image(self, event): 772 | if self.image_index < len(self.image_list) - 1: 773 | self.image_index += 1 774 | self.image_count_label.configure(text=f'Image {self.image_index+1} of {self.image_count}') 775 | self.load_image() 776 | self.caption_entry.focus_set() 777 | self.caption_entry.focus_force() 778 | def open_options(self): 779 | self.options_window = ctk.CTkToplevel(self) 780 | self.options_window.title("Options") 781 | self.options_window.geometry("320x550") 782 | #disable reszie 783 | self.options_window.resizable(False, False) 784 | self.options_window.focus_force() 785 | self.options_window.grab_set() 786 | self.options_window.transient(self) 787 | self.options_window.protocol("WM_DELETE_WINDOW", self.close_options) 788 | #add title label 789 | self.options_title_label = ctk.CTkLabel(self.options_window, text="Options",font=ctk.CTkFont(size=20, weight="bold")) 790 | self.options_title_label.pack(side="top", pady=5) 791 | #add an entry with a button to select a folder as output folder 792 | self.output_folder_label = ctk.CTkLabel(self.options_window, text="Output Folder") 793 | self.output_folder_label.pack(side="top", pady=5) 794 | self.output_folder_entry = ctk.CTkEntry(self.options_window) 795 | self.output_folder_entry.pack(side="top", fill="x", expand=False,padx=15, pady=5) 796 | self.output_folder_entry.insert(0, self.output_folder) 797 | self.output_folder_button = ctk.CTkButton(self.options_window, text="Select Folder", command=self.select_output_folder,fg_color=("gray75", "gray25")) 798 | self.output_folder_button.pack(side="top", pady=5) 799 | #add radio buttons to select the output format between text and filename 800 | self.output_format_label = ctk.CTkLabel(self.options_window, text="Output Format") 801 | self.output_format_label.pack(side="top", pady=5) 802 | self.output_format_var = tk.StringVar(self.options_window) 803 | self.output_format_var.set(self.output_format) 804 | self.output_format_text = ctk.CTkRadioButton(self.options_window, text="Text File", variable=self.output_format_var, value="text") 805 | self.output_format_text.pack(side="top", pady=5) 806 | self.output_format_filename = ctk.CTkRadioButton(self.options_window, text="File name", variable=self.output_format_var, value="filename") 807 | self.output_format_filename.pack(side="top", pady=5) 808 | #add BLIP settings section 809 | self.blip_settings_label = ctk.CTkLabel(self.options_window, text="BLIP Settings",font=ctk.CTkFont(size=20, weight="bold")) 810 | self.blip_settings_label.pack(side="top", pady=10) 811 | #add a checkbox to use nucleas sampling or not 812 | self.nucleus_sampling_var = tk.IntVar(self.options_window) 813 | self.nucleus_sampling_checkbox = ctk.CTkCheckBox(self.options_window, text="Use nucleus sampling", variable=self.nucleus_sampling_var) 814 | self.nucleus_sampling_checkbox.pack(side="top", pady=5) 815 | if self.debug: 816 | self.nucleus_sampling = 0 817 | self.q_factor = 0.5 818 | self.min_length = 10 819 | self.nucleus_sampling_var.set(self.nucleus_sampling) 820 | #add a float entry to set the q factor 821 | self.q_factor_label = ctk.CTkLabel(self.options_window, text="Q Factor") 822 | self.q_factor_label.pack(side="top", pady=5) 823 | self.q_factor_entry = ctk.CTkEntry(self.options_window) 824 | self.q_factor_entry.insert(0, self.q_factor) 825 | self.q_factor_entry.pack(side="top", pady=5) 826 | #add a int entry to set the number minimum length 827 | self.min_length_label = ctk.CTkLabel(self.options_window, text="Minimum Length") 828 | self.min_length_label.pack(side="top", pady=5) 829 | self.min_length_entry = ctk.CTkEntry(self.options_window) 830 | self.min_length_entry.insert(0, self.min_length) 831 | self.min_length_entry.pack(side="top", pady=5) 832 | #add a horozontal radio button to select between None, ViT-L-14/openai, ViT-H-14/laion2b_s32b_b79k 833 | #self.model_label = ctk.CTkLabel(self.options_window, text="CLIP Interrogation") 834 | #self.model_label.pack(side="top") 835 | #self.model_var = tk.StringVar(self.options_window) 836 | #self.model_var.set(self.model) 837 | #self.model_none = tk.Radiobutton(self.options_window, text="None", variable=self.model_var, value="None") 838 | #self.model_none.pack(side="top") 839 | #self.model_vit_l_14 = tk.Radiobutton(self.options_window, text="ViT-L-14/openai", variable=self.model_var, value="ViT-L-14/openai") 840 | #self.model_vit_l_14.pack(side="top") 841 | #self.model_vit_h_14 = tk.Radiobutton(self.options_window, text="ViT-H-14/laion2b_s32b_b79k", variable=self.model_var, value="ViT-H-14/laion2b_s32b_b79k") 842 | #self.model_vit_h_14.pack(side="top") 843 | 844 | #add a save button 845 | self.save_button = ctk.CTkButton(self.options_window, text="Save", command=self.save_options, fg_color=("gray75", "gray25")) 846 | self.save_button.pack(side="top",fill='x',pady=10,padx=10) 847 | #all entries list 848 | entries = [self.output_folder_entry, self.q_factor_entry, self.min_length_entry] 849 | #bind the right click to all entries 850 | for entry in entries: 851 | entry.bind("", self.create_right_click_menu) 852 | self.options_file = os.path.join(self.captioner_folder, 'captioner_options.json') 853 | if os.path.isfile(self.options_file): 854 | with open(self.options_file, 'r') as f: 855 | self.options = json.load(f) 856 | self.output_folder_entry.delete(0, tk.END) 857 | self.output_folder_entry.insert(0, self.output_folder) 858 | self.output_format_var.set(self.options['output_format']) 859 | self.nucleus_sampling_var.set(self.options['nucleus_sampling']) 860 | self.q_factor_entry.delete(0, tk.END) 861 | self.q_factor_entry.insert(0, self.options['q_factor']) 862 | self.min_length_entry.delete(0, tk.END) 863 | self.min_length_entry.insert(0, self.options['min_length']) 864 | def load_options(self): 865 | self.options_file = os.path.join(self.captioner_folder, 'captioner_options.json') 866 | if os.path.isfile(self.options_file): 867 | with open(self.options_file, 'r') as f: 868 | self.options = json.load(f) 869 | #self.output_folder = self.folder 870 | #self.output_folder = self.options['output_folder'] 871 | if 'folder' in self.__dict__: 872 | self.output_folder = self.folder 873 | else: 874 | self.output_folder = '' 875 | self.output_format = self.options['output_format'] 876 | self.nucleus_sampling = self.options['nucleus_sampling'] 877 | self.q_factor = self.options['q_factor'] 878 | self.min_length = self.options['min_length'] 879 | else: 880 | #if self has folder, use it, otherwise use the current folder 881 | if 'folder' in self.__dict__ : 882 | self.output_folder = self.folder 883 | else: 884 | self.output_folder = '' 885 | self.output_format = "text" 886 | self.nucleus_sampling = False 887 | self.q_factor = 0.9 888 | self.min_length =22 889 | def save_options(self): 890 | self.output_folder = self.output_folder_entry.get() 891 | self.output_format = self.output_format_var.get() 892 | self.nucleus_sampling = self.nucleus_sampling_var.get() 893 | self.q_factor = float(self.q_factor_entry.get()) 894 | self.min_length = int(self.min_length_entry.get()) 895 | #save options to a file 896 | self.options_file = os.path.join(self.captioner_folder, 'captioner_options.json') 897 | with open(self.options_file, 'w') as f: 898 | json.dump({'output_folder': self.output_folder, 'output_format': self.output_format, 'nucleus_sampling': self.nucleus_sampling, 'q_factor': self.q_factor, 'min_length': self.min_length}, f) 899 | self.close_options() 900 | 901 | def select_output_folder(self): 902 | self.output_folder = fd.askdirectory() 903 | self.output_folder_entry.delete(0, tk.END) 904 | self.output_folder_entry.insert(0, self.output_folder) 905 | def close_options(self): 906 | self.options_window.destroy() 907 | self.caption_entry.focus_force() 908 | def create_right_click_menu(self, event): 909 | #create a menu 910 | self.menu = Menu(self, tearoff=0) 911 | #add commands to the menu 912 | self.menu.add_command(label="Cut", command=lambda: self.focus_get().event_generate("<>")) 913 | self.menu.add_command(label="Copy", command=lambda: self.focus_get().event_generate("<>")) 914 | self.menu.add_command(label="Paste", command=lambda: self.focus_get().event_generate("<>")) 915 | self.menu.add_command(label="Select All", command=lambda: self.focus_get().event_generate("<>")) 916 | #display the menu 917 | try: 918 | self.menu.tk_popup(event.x_root, event.y_root) 919 | finally: 920 | #make sure to release the grab (Tk 8.0a1 only) 921 | self.menu.grab_release() 922 | 923 | 924 | #progress bar class with cancel button 925 | class ProgressbarWithCancel(ctk.CTkToplevel): 926 | def __init__(self,max=None, **kw): 927 | super().__init__(**kw) 928 | self.title("Batching...") 929 | self.max = max 930 | self.possibleLabels = ['Searching for answers...',"I'm working, I promise.",'ARE THOSE TENTACLES?!','Weird data man...','Another one bites the dust' ,"I think it's a cat?" ,'Looking for the meaning of life', 'Dreaming of captions'] 931 | 932 | self.label = ctk.CTkLabel(self, text="Searching for answers...") 933 | self.label.pack(side="top", fill="x", expand=True,padx=10,pady=10) 934 | self.progress = ctk.CTkProgressBar(self, orientation="horizontal", mode="determinate") 935 | self.progress.pack(side="left", fill="x", expand=True,padx=10,pady=10) 936 | self.cancel_button = ctk.CTkButton(self, text="Cancel", command=self.cancel) 937 | self.cancel_button.pack(side="right",padx=10,pady=10) 938 | self.cancelled = False 939 | self.count_label = ctk.CTkLabel(self, text="0/{0}".format(self.max)) 940 | self.count_label.pack(side="right",padx=10,pady=10) 941 | def set_random_label(self): 942 | import random 943 | self.label["text"] = random.choice(self.possibleLabels) 944 | #pop from list 945 | #self.possibleLabels.remove(self.label["text"]) 946 | def cancel(self): 947 | self.cancelled = True 948 | def set_progress(self, value): 949 | self.progress.set(value) 950 | self.count_label.configure(text="{0}/{1}".format(int(value * self.max), self.max)) 951 | def get_progress(self): 952 | return self.progress.get 953 | def set_max(self, value): 954 | return value 955 | def get_max(self): 956 | return self.progress["maximum"] 957 | def is_cancelled(self): 958 | return self.cancelled 959 | #quit the progress bar window 960 | 961 | 962 | #run when imported as a module 963 | if __name__ == "__main__": 964 | 965 | #root = tk.Tk() 966 | app = ImageBrowser() 967 | app.mainloop() 968 | -------------------------------------------------------------------------------- /scripts/clip_segmentation.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import os 3 | from typing import Optional, Callable 4 | 5 | import torch 6 | from PIL import Image 7 | from torch import Tensor, nn 8 | from torchvision.transforms import transforms, functional 9 | from tqdm.auto import tqdm 10 | from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation 11 | 12 | DEVICE = "cuda" 13 | 14 | 15 | def parse_args(): 16 | parser = argparse.ArgumentParser(description="ClipSeg script.") 17 | parser.add_argument( 18 | "--sample_dir", 19 | type=str, 20 | required=True, 21 | help="directory where samples are located", 22 | ) 23 | parser.add_argument( 24 | "--add_prompt", 25 | type=str, 26 | required=True, 27 | action="append", 28 | help="a prompt used to create a mask", 29 | dest="prompts", 30 | ) 31 | parser.add_argument( 32 | "--mode", 33 | type=str, 34 | default='fill', 35 | required=False, 36 | help="Either replace, fill, add or subtract", 37 | ) 38 | parser.add_argument( 39 | "--threshold", 40 | type=float, 41 | default='0.3', 42 | required=False, 43 | help="threshold for including pixels in the mask", 44 | ) 45 | parser.add_argument( 46 | "--smooth_pixels", 47 | type=int, 48 | default=5, 49 | required=False, 50 | help="radius of a smoothing operation applied to the generated mask", 51 | ) 52 | parser.add_argument( 53 | "--expand_pixels", 54 | type=int, 55 | default=10, 56 | required=False, 57 | help="amount of expansion of the generated mask in all directions", 58 | ) 59 | 60 | args = parser.parse_args() 61 | return args 62 | 63 | 64 | class MaskSample: 65 | def __init__(self, filename: str): 66 | self.image_filename = filename 67 | self.mask_filename = os.path.splitext(filename)[0] + "-masklabel.png" 68 | 69 | self.image = None 70 | self.mask_tensor = None 71 | 72 | self.height = 0 73 | self.width = 0 74 | 75 | self.image2Tensor = transforms.Compose([ 76 | transforms.ToTensor(), 77 | ]) 78 | 79 | self.tensor2Image = transforms.Compose([ 80 | transforms.ToPILImage(), 81 | ]) 82 | 83 | def get_image(self) -> Image: 84 | if self.image is None: 85 | self.image = Image.open(self.image_filename).convert('RGB') 86 | self.height = self.image.height 87 | self.width = self.image.width 88 | 89 | return self.image 90 | 91 | def get_mask_tensor(self) -> Tensor: 92 | if self.mask_tensor is None and os.path.exists(self.mask_filename): 93 | mask = Image.open(self.mask_filename).convert('L') 94 | mask = self.image2Tensor(mask) 95 | mask = mask.to(DEVICE) 96 | self.mask_tensor = mask.unsqueeze(0) 97 | 98 | return self.mask_tensor 99 | 100 | def set_mask_tensor(self, mask_tensor: Tensor): 101 | self.mask_tensor = mask_tensor 102 | 103 | def add_mask_tensor(self, mask_tensor: Tensor): 104 | mask = self.get_mask_tensor() 105 | if mask is None: 106 | mask = mask_tensor 107 | else: 108 | mask += mask_tensor 109 | mask = torch.clamp(mask, 0, 1) 110 | 111 | self.mask_tensor = mask 112 | 113 | def subtract_mask_tensor(self, mask_tensor: Tensor): 114 | mask = self.get_mask_tensor() 115 | if mask is None: 116 | mask = mask_tensor 117 | else: 118 | mask -= mask_tensor 119 | mask = torch.clamp(mask, 0, 1) 120 | 121 | self.mask_tensor = mask 122 | 123 | def save_mask(self): 124 | if self.mask_tensor is not None: 125 | mask = self.mask_tensor.cpu().squeeze() 126 | mask = self.tensor2Image(mask).convert('RGB') 127 | mask.save(self.mask_filename) 128 | 129 | 130 | class ClipSeg: 131 | def __init__(self): 132 | self.processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined") 133 | 134 | self.model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined") 135 | self.model.eval() 136 | self.model.to(DEVICE) 137 | 138 | self.smoothing_kernel_radius = None 139 | self.smoothing_kernel = self.__create_average_kernel(self.smoothing_kernel_radius) 140 | 141 | self.expand_kernel_radius = None 142 | self.expand_kernel = self.__create_average_kernel(self.expand_kernel_radius) 143 | 144 | @staticmethod 145 | def __create_average_kernel(kernel_radius: Optional[int]): 146 | if kernel_radius is None: 147 | return None 148 | 149 | kernel_size = kernel_radius * 2 + 1 150 | kernel_weights = torch.ones(1, 1, kernel_size, kernel_size) / (kernel_size * kernel_size) 151 | kernel = nn.Conv2d(in_channels=1, out_channels=1, kernel_size=kernel_size, bias=False, padding_mode='replicate', padding=kernel_radius) 152 | kernel.weight.data = kernel_weights 153 | kernel.requires_grad_(False) 154 | kernel.to(DEVICE) 155 | return kernel 156 | 157 | @staticmethod 158 | def __get_sample_filenames(sample_dir: str) -> [str]: 159 | filenames = [] 160 | for filename in os.listdir(sample_dir): 161 | ext = os.path.splitext(filename)[1].lower() 162 | if ext in ['.jpg', '.jpeg', '.png', '.bmp', '.webp'] and '-masklabel.png' not in filename: 163 | filenames.append(os.path.join(sample_dir, filename)) 164 | 165 | return filenames 166 | 167 | def __process_mask(self, mask: Tensor, target_height: int, target_width: int, threshold: float) -> Tensor: 168 | while len(mask.shape) < 4: 169 | mask = mask.unsqueeze(0) 170 | 171 | mask = torch.sigmoid(mask) 172 | mask = mask.sum(1).unsqueeze(1) 173 | if self.smoothing_kernel is not None: 174 | mask = self.smoothing_kernel(mask) 175 | mask = functional.resize(mask, [target_height, target_width]) 176 | mask = (mask > threshold).float() 177 | if self.expand_kernel is not None: 178 | mask = self.expand_kernel(mask) 179 | mask = (mask > 0).float() 180 | 181 | return mask 182 | 183 | def mask_image(self, filename: str, prompts: [str], mode: str = 'fill', threshold: float = 0.3, smooth_pixels: int = 5, expand_pixels: int = 10): 184 | """ 185 | Masks a sample 186 | 187 | Parameters: 188 | filename (`str`): a sample filename 189 | prompts (`[str]`): a list of prompts used to create a mask 190 | mode (`str`): can be one of 191 | - replace: creates new masks for all samples, even if a mask already exists 192 | - fill: creates new masks for all samples without a mask 193 | - add: adds the new region to existing masks 194 | - subtract: subtracts the new region from existing masks 195 | threshold (`float`): threshold for including pixels in the mask 196 | smooth_pixels (`int`): radius of a smoothing operation applied to the generated mask 197 | expand_pixels (`int`): amount of expansion of the generated mask in all directions 198 | """ 199 | 200 | mask_sample = MaskSample(filename) 201 | 202 | if mode == 'fill' and mask_sample.get_mask_tensor() is not None: 203 | return 204 | 205 | if self.smoothing_kernel_radius != smooth_pixels: 206 | self.smoothing_kernel = self.__create_average_kernel(smooth_pixels) 207 | self.smoothing_kernel_radius = smooth_pixels 208 | 209 | if self.expand_kernel_radius != expand_pixels: 210 | self.expand_kernel = self.__create_average_kernel(expand_pixels) 211 | self.expand_kernel_radius = expand_pixels 212 | 213 | inputs = self.processor(text=prompts, images=[mask_sample.get_image()] * len(prompts), padding="max_length", return_tensors="pt") 214 | inputs.to(DEVICE) 215 | with torch.no_grad(): 216 | outputs = self.model(**inputs) 217 | predicted_mask = self.__process_mask(outputs.logits, mask_sample.height, mask_sample.width, threshold) 218 | 219 | if mode == 'replace' or mode == 'fill': 220 | mask_sample.set_mask_tensor(predicted_mask) 221 | elif mode == 'add': 222 | mask_sample.add_mask_tensor(predicted_mask) 223 | elif mode == 'subtract': 224 | mask_sample.subtract_mask_tensor(predicted_mask) 225 | 226 | mask_sample.save_mask() 227 | 228 | def mask_folder( 229 | self, 230 | sample_dir: str, 231 | prompts: [str], 232 | mode: str = 'fill', 233 | threshold: float = 0.3, 234 | smooth_pixels: int = 5, 235 | expand_pixels: int = 10, 236 | progress_callback: Callable[[int, int], None] = None, 237 | error_callback: Callable[[str], None] = None, 238 | ): 239 | """ 240 | Masks all samples in a folder 241 | 242 | Parameters: 243 | sample_dir (`str`): directory where samples are located 244 | prompts (`[str]`): a list of prompts used to create a mask 245 | mode (`str`): can be one of 246 | - replace: creates new masks for all samples, even if a mask already exists 247 | - fill: creates new masks for all samples without a mask 248 | - add: adds the new region to existing masks 249 | - subtract: subtracts the new region from existing masks 250 | threshold (`float`): threshold for including pixels in the mask 251 | smooth_pixels (`int`): radius of a smoothing operation applied to the generated mask 252 | expand_pixels (`int`): amount of expansion of the generated mask in all directions 253 | progress_callback (`Callable[[int, int], None]`): called after every processed image 254 | error_callback (`Callable[[str], None]`): called for every exception 255 | """ 256 | 257 | filenames = self.__get_sample_filenames(sample_dir) 258 | self.mask_images( 259 | filenames=filenames, 260 | prompts=prompts, 261 | mode=mode, 262 | threshold=threshold, 263 | smooth_pixels=smooth_pixels, 264 | expand_pixels=expand_pixels, 265 | progress_callback=progress_callback, 266 | error_callback=error_callback, 267 | ) 268 | 269 | def mask_images( 270 | self, 271 | filenames: [str], 272 | prompts: [str], 273 | mode: str = 'fill', 274 | threshold: float = 0.3, 275 | smooth_pixels: int = 5, 276 | expand_pixels: int = 10, 277 | progress_callback: Callable[[int, int], None] = None, 278 | error_callback: Callable[[str], None] = None, 279 | ): 280 | """ 281 | Masks all samples in a list 282 | 283 | Parameters: 284 | filenames (`[str]`): a list of sample filenames 285 | prompts (`[str]`): a list of prompts used to create a mask 286 | mode (`str`): can be one of 287 | - replace: creates new masks for all samples, even if a mask already exists 288 | - fill: creates new masks for all samples without a mask 289 | - add: adds the new region to existing masks 290 | - subtract: subtracts the new region from existing masks 291 | threshold (`float`): threshold for including pixels in the mask 292 | smooth_pixels (`int`): radius of a smoothing operation applied to the generated mask 293 | expand_pixels (`int`): amount of expansion of the generated mask in all directions 294 | progress_callback (`Callable[[int, int], None]`): called after every processed image 295 | error_callback (`Callable[[str], None]`): called for every exception 296 | """ 297 | 298 | if progress_callback is not None: 299 | progress_callback(0, len(filenames)) 300 | for i, filename in enumerate(tqdm(filenames)): 301 | try: 302 | self.mask_image(filename, prompts, mode, threshold, smooth_pixels, expand_pixels) 303 | except Exception as e: 304 | if error_callback is not None: 305 | error_callback(filename) 306 | if progress_callback is not None: 307 | progress_callback(i + 1, len(filenames)) 308 | 309 | 310 | def main(): 311 | args = parse_args() 312 | clip_seg = ClipSeg() 313 | clip_seg.mask_folder( 314 | sample_dir=args.sample_dir, 315 | prompts=args.prompts, 316 | mode=args.mode, 317 | threshold=args.threshold, 318 | smooth_pixels=args.smooth_pixels, 319 | expand_pixels=args.expand_pixels, 320 | error_callback=lambda filename: print("Error while processing image " + filename) 321 | ) 322 | 323 | 324 | if __name__ == "__main__": 325 | main() 326 | -------------------------------------------------------------------------------- /scripts/convert_diffusers_to_sd_cli.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import os 3 | try: 4 | import converters 5 | except ImportError: 6 | 7 | #if there's a scripts folder where the script is, add it to the path 8 | if 'scripts' in os.listdir(os.path.dirname(os.path.abspath(__file__))): 9 | sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '\\scripts') 10 | else: 11 | print('Could not find scripts folder. Please add it to the path manually or place this file in it.') 12 | import converters 13 | 14 | 15 | if __name__ == '__main__': 16 | args = sys.argv[1:] 17 | if len(args) != 2: 18 | print('Usage: python3 convert_diffusers_to_sd.py ') 19 | sys.exit(1) 20 | model_path = args[0] 21 | output_path = args[1] 22 | converters.Convert_Diffusers_to_SD(model_path, output_path) 23 | -------------------------------------------------------------------------------- /scripts/converters.py: -------------------------------------------------------------------------------- 1 | # coding=utf-8 2 | # Copyright 2022 The HuggingFace Inc. team. 3 | # 4 | # Licensed under the Apache License, Version 2.0 (the "License"); 5 | # you may not use this file except in compliance with the License. 6 | # You may obtain a copy of the License at 7 | # 8 | # http://www.apache.org/licenses/LICENSE-2.0 9 | # 10 | # Unless required by applicable law or agreed to in writing, software 11 | # distributed under the License is distributed on an "AS IS" BASIS, 12 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 13 | # See the License for the specific language governing permissions and 14 | # limitations under the License. 15 | import requests 16 | import os 17 | import os.path as osp 18 | import torch 19 | try: 20 | from omegaconf import OmegaConf 21 | except ImportError: 22 | raise ImportError( 23 | "OmegaConf is required to convert the LDM checkpoints. Please install it with `pip install OmegaConf`." 24 | ) 25 | 26 | from diffusers import ( 27 | AutoencoderKL, 28 | DDIMScheduler, 29 | DPMSolverMultistepScheduler, 30 | EulerAncestralDiscreteScheduler, 31 | EulerDiscreteScheduler, 32 | HeunDiscreteScheduler, 33 | LDMTextToImagePipeline, 34 | LMSDiscreteScheduler, 35 | PNDMScheduler, 36 | StableDiffusionPipeline, 37 | UNet2DConditionModel, 38 | DiffusionPipeline 39 | ) 40 | from diffusers.pipelines.latent_diffusion.pipeline_latent_diffusion import LDMBertConfig, LDMBertModel 41 | #from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder, PaintByExamplePipeline 42 | from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker 43 | from transformers import AutoFeatureExtractor, BertTokenizerFast, CLIPTextModel, CLIPTokenizer, CLIPVisionConfig, CLIPTextConfig 44 | import model_util 45 | 46 | class Convert_SD_to_Diffusers(): 47 | 48 | def __init__(self, checkpoint_path, output_path, prediction_type=None, img_size=None, original_config_file=None, extract_ema=False, num_in_channels=None,pipeline_type=None,scheduler_type=None,sd_version=None,half=None,version=None): 49 | self.checkpoint_path = checkpoint_path 50 | self.output_path = output_path 51 | self.prediction_type = prediction_type 52 | self.img_size = img_size 53 | self.original_config_file = original_config_file 54 | self.extract_ema = extract_ema 55 | self.num_in_channels = num_in_channels 56 | self.pipeline_type = pipeline_type 57 | self.scheduler_type = scheduler_type 58 | self.sd_version = sd_version 59 | self.half = half 60 | self.version = version 61 | self.main() 62 | 63 | 64 | def main(self): 65 | image_size = self.img_size 66 | prediction_type = self.prediction_type 67 | original_config_file = self.original_config_file 68 | num_in_channels = self.num_in_channels 69 | scheduler_type = self.scheduler_type 70 | pipeline_type = self.pipeline_type 71 | extract_ema = self.extract_ema 72 | reference_diffusers_model = None 73 | if self.version == 'v1': 74 | is_v1 = True 75 | is_v2 = False 76 | if self.version == 'v2': 77 | is_v1 = False 78 | is_v2 = True 79 | if is_v2 == True and prediction_type == 'vprediction': 80 | reference_diffusers_model = 'stabilityai/stable-diffusion-2' 81 | if is_v2 == True and prediction_type == 'epsilon': 82 | reference_diffusers_model = 'stabilityai/stable-diffusion-2-base' 83 | if is_v1 == True and prediction_type == 'epsilon': 84 | reference_diffusers_model = 'runwayml/stable-diffusion-v1-5' 85 | dtype = 'fp16' if self.half else None 86 | v2_model = True if is_v2 else False 87 | print(f"loading model from: {self.checkpoint_path}") 88 | #print(v2_model) 89 | text_encoder, vae, unet = model_util.load_models_from_stable_diffusion_checkpoint(v2_model, self.checkpoint_path) 90 | print(f"copy scheduler/tokenizer config from: {reference_diffusers_model}") 91 | model_util.save_diffusers_checkpoint(v2_model, self.output_path, text_encoder, unet, reference_diffusers_model, vae) 92 | print(f"Diffusers model saved.") 93 | 94 | 95 | 96 | class Convert_Diffusers_to_SD(): 97 | def __init__(self,model_path=None, output_path=None): 98 | pass 99 | def main(model_path:str, output_path:str): 100 | #print(model_path) 101 | #print(output_path) 102 | global_step = None 103 | epoch = None 104 | dtype = torch.float32 105 | pipe = DiffusionPipeline.from_pretrained(model_path, torch_dtype=dtype, tokenizer=None, safety_checker=None) 106 | text_encoder = pipe.text_encoder 107 | vae = pipe.vae 108 | unet = pipe.unet 109 | v2_model = unet.config.cross_attention_dim == 1024 110 | original_model = None 111 | key_count = model_util.save_stable_diffusion_checkpoint(v2_model, output_path, text_encoder, unet, 112 | original_model, epoch, global_step, dtype, vae) 113 | print(f"Saved model") 114 | return main(model_path, output_path) -------------------------------------------------------------------------------- /scripts/lion_pytorch/__init__.py: -------------------------------------------------------------------------------- 1 | from lion_pytorch.lion_pytorch import Lion 2 | -------------------------------------------------------------------------------- /scripts/lion_pytorch/lion_pytorch.py: -------------------------------------------------------------------------------- 1 | from typing import Tuple, Optional, Callable 2 | 3 | import torch 4 | from torch.optim.optimizer import Optimizer 5 | 6 | # functions 7 | 8 | def exists(val): 9 | return val is not None 10 | 11 | # update functions 12 | 13 | def update_fn(p, grad, exp_avg, lr, wd, beta1, beta2): 14 | # stepweight decay 15 | 16 | p.data.mul_(1 - lr * wd) 17 | 18 | # weight update 19 | 20 | update = exp_avg.clone().mul_(beta1).add(grad, alpha = 1 - beta1).sign_() 21 | p.add_(update, alpha = -lr) 22 | 23 | # decay the momentum running average coefficient 24 | 25 | exp_avg.mul_(beta2).add_(grad, alpha = 1 - beta2) 26 | 27 | # class 28 | 29 | class Lion(Optimizer): 30 | def __init__( 31 | self, 32 | params, 33 | lr: float = 1e-4, 34 | betas: Tuple[float, float] = (0.9, 0.99), 35 | weight_decay: float = 0.0, 36 | use_triton: bool = False 37 | ): 38 | assert lr > 0. 39 | assert all([0. <= beta <= 1. for beta in betas]) 40 | 41 | defaults = dict( 42 | lr = lr, 43 | betas = betas, 44 | weight_decay = weight_decay 45 | ) 46 | 47 | super().__init__(params, defaults) 48 | 49 | self.update_fn = update_fn 50 | 51 | if use_triton: 52 | from lion_pytorch.triton import update_fn as triton_update_fn 53 | self.update_fn = triton_update_fn 54 | 55 | @torch.no_grad() 56 | def step( 57 | self, 58 | closure: Optional[Callable] = None 59 | ): 60 | 61 | loss = None 62 | if exists(closure): 63 | with torch.enable_grad(): 64 | loss = closure() 65 | 66 | for group in self.param_groups: 67 | for p in filter(lambda p: exists(p.grad), group['params']): 68 | 69 | grad, lr, wd, beta1, beta2, state = p.grad, group['lr'], group['weight_decay'], *group['betas'], self.state[p] 70 | 71 | # init state - exponential moving average of gradient values 72 | 73 | if len(state) == 0: 74 | state['exp_avg'] = torch.zeros_like(p) 75 | 76 | exp_avg = state['exp_avg'] 77 | 78 | self.update_fn( 79 | p, 80 | grad, 81 | exp_avg, 82 | lr, 83 | wd, 84 | beta1, 85 | beta2 86 | ) 87 | 88 | return loss 89 | -------------------------------------------------------------------------------- /scripts/lion_pytorch/triton.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | try: 4 | import triton 5 | import triton.language as tl 6 | except ImportError as e: 7 | print('triton is not installed, please install by running `pip install triton -U --pre`') 8 | exit() 9 | 10 | 11 | @triton.autotune(configs = [ 12 | triton.Config({'BLOCK_SIZE': 128}, num_warps = 4), 13 | triton.Config({'BLOCK_SIZE': 1024}, num_warps = 8), 14 | ], key = ['n_elements']) 15 | @triton.jit 16 | def update_fn_kernel( 17 | p_ptr, 18 | grad_ptr, 19 | exp_avg_ptr, 20 | lr, 21 | wd, 22 | beta1, 23 | beta2, 24 | n_elements, 25 | BLOCK_SIZE: tl.constexpr, 26 | ): 27 | pid = tl.program_id(axis = 0) 28 | 29 | block_start = pid * BLOCK_SIZE 30 | offsets = block_start + tl.arange(0, BLOCK_SIZE) 31 | 32 | mask = offsets < n_elements 33 | 34 | # offsetted pointers 35 | 36 | offset_p_ptr = p_ptr + offsets 37 | offset_grad_ptr = grad_ptr + offsets 38 | offset_exp_avg_ptr = exp_avg_ptr + offsets 39 | 40 | # load 41 | 42 | p = tl.load(offset_p_ptr, mask = mask) 43 | grad = tl.load(offset_grad_ptr, mask = mask) 44 | exp_avg = tl.load(offset_exp_avg_ptr, mask = mask) 45 | 46 | # stepweight decay 47 | 48 | p = p * (1 - lr * wd) 49 | 50 | # diff between momentum running average and grad 51 | 52 | diff = exp_avg - grad 53 | 54 | # weight update 55 | 56 | update = diff * beta1 + grad 57 | 58 | # torch.sign 59 | 60 | can_update = update != 0 61 | update_sign = tl.where(update > 0, -lr, lr) 62 | 63 | p = p + update_sign * can_update 64 | 65 | # decay the momentum running average coefficient 66 | 67 | exp_avg = diff * beta2 + grad 68 | 69 | # store new params and momentum running average coefficient 70 | 71 | tl.store(offset_p_ptr, p, mask = mask) 72 | tl.store(offset_exp_avg_ptr, exp_avg, mask = mask) 73 | 74 | def update_fn( 75 | p: torch.Tensor, 76 | grad: torch.Tensor, 77 | exp_avg: torch.Tensor, 78 | lr: float, 79 | wd: float, 80 | beta1: float, 81 | beta2: float 82 | ): 83 | assert all([t.is_cuda for t in (p, grad, exp_avg)]) 84 | n_elements = p.numel() 85 | 86 | grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),) 87 | 88 | update_fn_kernel[grid]( 89 | p, 90 | grad, 91 | exp_avg, 92 | lr, 93 | wd, 94 | beta1, 95 | beta2, 96 | n_elements 97 | ) 98 | -------------------------------------------------------------------------------- /scripts/lora_utils.py: -------------------------------------------------------------------------------- 1 | # LoRA network module 2 | # reference: 3 | # https://github.com/microsoft/LoRA/blob/main/loralib/layers.py 4 | # https://github.com/cloneofsimo/lora/blob/master/lora_diffusion/lora.py 5 | 6 | import math 7 | import os 8 | import torch 9 | 10 | from trainer_util import * 11 | 12 | 13 | class LoRAModule(torch.nn.Module): 14 | """ 15 | replaces forward method of the original Linear, instead of replacing the original Linear module. 16 | """ 17 | 18 | def __init__(self, lora_name, org_module: torch.nn.Module, multiplier=1.0, lora_dim=4, alpha=1): 19 | """ if alpha == 0 or None, alpha is rank (no scaling). """ 20 | super().__init__() 21 | self.lora_name = lora_name 22 | self.lora_dim = lora_dim 23 | 24 | if org_module.__class__.__name__ == 'Conv2d': 25 | in_dim = org_module.in_channels 26 | out_dim = org_module.out_channels 27 | self.lora_down = torch.nn.Conv2d(in_dim, lora_dim, (1, 1), bias=False) 28 | self.lora_up = torch.nn.Conv2d(lora_dim, out_dim, (1, 1), bias=False) 29 | else: 30 | in_dim = org_module.in_features 31 | out_dim = org_module.out_features 32 | self.lora_down = torch.nn.Linear(in_dim, lora_dim, bias=False) 33 | self.lora_up = torch.nn.Linear(lora_dim, out_dim, bias=False) 34 | 35 | if type(alpha) == torch.Tensor: 36 | alpha = alpha.detach().float().numpy() # without casting, bf16 causes error 37 | alpha = lora_dim if alpha is None or alpha == 0 else alpha 38 | self.scale = alpha / self.lora_dim 39 | self.register_buffer('alpha', torch.tensor(alpha)) # 定数として扱える 40 | 41 | # same as microsoft's 42 | torch.nn.init.kaiming_uniform_(self.lora_down.weight, a=math.sqrt(5)) 43 | torch.nn.init.zeros_(self.lora_up.weight) 44 | 45 | self.multiplier = multiplier 46 | self.org_module = org_module # remove in applying 47 | 48 | def apply_to(self): 49 | self.org_forward = self.org_module.forward 50 | self.org_module.forward = self.forward 51 | del self.org_module 52 | 53 | def forward(self, x): 54 | return self.org_forward(x) + self.lora_up(self.lora_down(x)) * self.multiplier * self.scale 55 | 56 | 57 | def create_network(multiplier, network_dim, network_alpha, vae, text_encoder, unet, **kwargs): 58 | if network_dim is None: 59 | network_dim = 4 # default 60 | network = LoRANetwork(text_encoder, unet, multiplier=multiplier, lora_dim=network_dim, alpha=network_alpha) 61 | return network 62 | 63 | 64 | def create_network_from_weights(multiplier, file, vae, text_encoder, unet, **kwargs): 65 | if os.path.splitext(file)[1] == '.safetensors': 66 | from safetensors.torch import load_file, safe_open 67 | weights_sd = load_file(file) 68 | else: 69 | weights_sd = torch.load(file, map_location='cpu') 70 | 71 | # get dim (rank) 72 | network_alpha = None 73 | network_dim = None 74 | for key, value in weights_sd.items(): 75 | if network_alpha is None and 'alpha' in key: 76 | network_alpha = value 77 | if network_dim is None and 'lora_down' in key and len(value.size()) == 2: 78 | network_dim = value.size()[0] 79 | 80 | if network_alpha is None: 81 | network_alpha = network_dim 82 | 83 | network = LoRANetwork(text_encoder, unet, multiplier=multiplier, lora_dim=network_dim, alpha=network_alpha) 84 | network.weights_sd = weights_sd 85 | return network 86 | 87 | 88 | class LoRANetwork(torch.nn.Module): 89 | UNET_TARGET_REPLACE_MODULE = ["Transformer2DModel", "Attention"] 90 | TEXT_ENCODER_TARGET_REPLACE_MODULE = ["CLIPAttention", "CLIPMLP"] 91 | LORA_PREFIX_UNET = 'lora_unet' 92 | LORA_PREFIX_TEXT_ENCODER = 'lora_te' 93 | 94 | def __init__(self, text_encoder, unet, multiplier=1.0, lora_dim=4, alpha=1) -> None: 95 | super().__init__() 96 | self.multiplier = multiplier 97 | self.lora_dim = lora_dim 98 | self.alpha = alpha 99 | 100 | # create module instances 101 | def create_modules(prefix, root_module: torch.nn.Module, target_replace_modules) -> list[LoRAModule]: 102 | loras = [] 103 | for name, module in root_module.named_modules(): 104 | if module.__class__.__name__ in target_replace_modules: 105 | for child_name, child_module in module.named_modules(): 106 | if child_module.__class__.__name__ == "Linear" or (child_module.__class__.__name__ == "Conv2d" and child_module.kernel_size == (1, 1)): 107 | lora_name = prefix + '.' + name + '.' + child_name 108 | lora_name = lora_name.replace('.', '_') 109 | lora = LoRAModule(lora_name, child_module, self.multiplier, self.lora_dim, self.alpha) 110 | loras.append(lora) 111 | return loras 112 | 113 | self.text_encoder_loras = create_modules(LoRANetwork.LORA_PREFIX_TEXT_ENCODER, 114 | text_encoder, LoRANetwork.TEXT_ENCODER_TARGET_REPLACE_MODULE) 115 | print(f"create LoRA for Text Encoder: {len(self.text_encoder_loras)} modules.") 116 | 117 | self.unet_loras = create_modules(LoRANetwork.LORA_PREFIX_UNET, unet, LoRANetwork.UNET_TARGET_REPLACE_MODULE) 118 | print(f"create LoRA for U-Net: {len(self.unet_loras)} modules.") 119 | 120 | self.weights_sd = None 121 | 122 | # assertion 123 | names = set() 124 | for lora in self.text_encoder_loras + self.unet_loras: 125 | assert lora.lora_name not in names, f"duplicated lora name: {lora.lora_name}" 126 | names.add(lora.lora_name) 127 | 128 | def load_weights(self, file): 129 | if os.path.splitext(file)[1] == '.safetensors': 130 | from safetensors.torch import load_file, safe_open 131 | self.weights_sd = load_file(file) 132 | else: 133 | self.weights_sd = torch.load(file, map_location='cpu') 134 | 135 | def apply_to(self, text_encoder, unet, apply_text_encoder=None, apply_unet=None): 136 | if self.weights_sd: 137 | weights_has_text_encoder = weights_has_unet = False 138 | for key in self.weights_sd.keys(): 139 | if key.startswith(LoRANetwork.LORA_PREFIX_TEXT_ENCODER): 140 | weights_has_text_encoder = True 141 | elif key.startswith(LoRANetwork.LORA_PREFIX_UNET): 142 | weights_has_unet = True 143 | 144 | if apply_text_encoder is None: 145 | apply_text_encoder = weights_has_text_encoder 146 | else: 147 | assert apply_text_encoder == weights_has_text_encoder, f"text encoder weights: {weights_has_text_encoder} but text encoder flag: {apply_text_encoder} / 重みとText Encoderのフラグが矛盾しています" 148 | 149 | if apply_unet is None: 150 | apply_unet = weights_has_unet 151 | else: 152 | assert apply_unet == weights_has_unet, f"u-net weights: {weights_has_unet} but u-net flag: {apply_unet} / 重みとU-Netのフラグが矛盾しています" 153 | else: 154 | assert apply_text_encoder is not None and apply_unet is not None, f"internal error: flag not set" 155 | 156 | if apply_text_encoder: 157 | print("enable LoRA for text encoder") 158 | else: 159 | self.text_encoder_loras = [] 160 | 161 | if apply_unet: 162 | print("enable LoRA for U-Net") 163 | else: 164 | self.unet_loras = [] 165 | 166 | for lora in self.text_encoder_loras + self.unet_loras: 167 | lora.apply_to() 168 | self.add_module(lora.lora_name, lora) 169 | 170 | if self.weights_sd: 171 | # if some weights are not in state dict, it is ok because initial LoRA does nothing (lora_up is initialized by zeros) 172 | info = self.load_state_dict(self.weights_sd, False) 173 | print(f"weights are loaded: {info}") 174 | 175 | def enable_gradient_checkpointing(self): 176 | # not supported 177 | pass 178 | 179 | def prepare_optimizer_params(self, text_encoder_lr, unet_lr): 180 | def enumerate_params(loras): 181 | params = [] 182 | for lora in loras: 183 | params.extend(lora.parameters()) 184 | return params 185 | 186 | self.requires_grad_(True) 187 | all_params = [] 188 | 189 | if self.text_encoder_loras: 190 | param_data = {'params': enumerate_params(self.text_encoder_loras)} 191 | if text_encoder_lr is not None: 192 | param_data['lr'] = text_encoder_lr 193 | all_params.append(param_data) 194 | 195 | if self.unet_loras: 196 | param_data = {'params': enumerate_params(self.unet_loras)} 197 | if unet_lr is not None: 198 | param_data['lr'] = unet_lr 199 | all_params.append(param_data) 200 | 201 | return all_params 202 | 203 | def prepare_grad_etc(self, text_encoder, unet): 204 | self.requires_grad_(True) 205 | 206 | def on_epoch_start(self, text_encoder, unet): 207 | self.train() 208 | 209 | def get_trainable_params(self): 210 | return self.parameters() 211 | 212 | def save_weights(self, file, dtype, metadata): 213 | if metadata is not None and len(metadata) == 0: 214 | metadata = None 215 | 216 | state_dict = self.state_dict() 217 | 218 | if dtype is not None: 219 | for key in list(state_dict.keys()): 220 | v = state_dict[key] 221 | v = v.detach().clone().to("cpu").to(dtype) 222 | state_dict[key] = v 223 | 224 | if os.path.splitext(file)[1] == '.safetensors': 225 | from safetensors.torch import save_file 226 | 227 | # Precalculate model hashes to save time on indexing 228 | if metadata is None: 229 | metadata = {} 230 | model_hash, legacy_hash = train_util.precalculate_safetensors_hashes(state_dict, metadata) 231 | metadata["sshs_model_hash"] = model_hash 232 | metadata["sshs_legacy_hash"] = legacy_hash 233 | 234 | save_file(state_dict, file, metadata) 235 | else: 236 | torch.save(state_dict, file) -------------------------------------------------------------------------------- /scripts/trainer_util.py: -------------------------------------------------------------------------------- 1 | import gradio as gr 2 | import json 3 | import math 4 | from pathlib import Path 5 | from typing import Optional 6 | import torch 7 | import torch.nn.functional as F 8 | import torch.utils.checkpoint 9 | from accelerate.logging import get_logger 10 | from accelerate.utils import set_seed 11 | from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, DiffusionPipeline, UNet2DConditionModel,DiffusionPipeline, DPMSolverMultistepScheduler,EulerDiscreteScheduler 12 | from diffusers.optimization import get_scheduler 13 | from huggingface_hub import HfFolder, Repository, whoami 14 | from torchvision import transforms 15 | from tqdm.auto import tqdm 16 | from typing import Dict, List, Generator, Tuple 17 | from PIL import Image, ImageFile 18 | from collections.abc import Iterable 19 | from trainer_util import * 20 | from dataloaders_util import * 21 | 22 | # FlashAttention based on https://github.com/lucidrains/memory-efficient-attention-pytorch/blob/main 23 | # /memory_efficient_attention_pytorch/flash_attention.py LICENSE MIT 24 | # https://github.com/lucidrains/memory-efficient-attention-pytorch/blob/main/LICENSE constants 25 | EPSILON = 1e-6 26 | 27 | class bcolors: 28 | HEADER = '\033[95m' 29 | OKBLUE = '\033[94m' 30 | OKCYAN = '\033[96m' 31 | OKGREEN = '\033[92m' 32 | WARNING = '\033[93m' 33 | FAIL = '\033[91m' 34 | ENDC = '\033[0m' 35 | BOLD = '\033[1m' 36 | UNDERLINE = '\033[4m' 37 | # helper functions 38 | def print_instructions(): 39 | print(f"{bcolors.WARNING}Use 'CTRL+SHIFT+G' to open up a GUI to play around with the model (will pause training){bcolors.ENDC}") 40 | print(f"{bcolors.WARNING}Use 'CTRL+SHIFT+S' to save a checkpoint of the current epoch{bcolors.ENDC}") 41 | print(f"{bcolors.WARNING}Use 'CTRL+SHIFT+P' to generate samples for current epoch{bcolors.ENDC}") 42 | print(f"{bcolors.WARNING}Use 'CTRL+SHIFT+Q' to save and quit after the current epoch{bcolors.ENDC}") 43 | print(f"{bcolors.WARNING}Use 'CTRL+SHIFT+ALT+S' to save a checkpoint of the current step{bcolors.ENDC}") 44 | print(f"{bcolors.WARNING}Use 'CTRL+SHIFT+ALT+P' to generate samples for current step{bcolors.ENDC}") 45 | print(f"{bcolors.WARNING}Use 'CTRL+SHIFT+ALT+Q' to save and quit after the current step{bcolors.ENDC}") 46 | print('') 47 | print(f"{bcolors.WARNING}Use 'CTRL+H' to print this message again.{bcolors.ENDC}") 48 | def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None): 49 | if token is None: 50 | token = HfFolder.get_token() 51 | if organization is None: 52 | username = whoami(token)["name"] 53 | return f"{username}/{model_id}" 54 | else: 55 | return f"{organization}/{model_id}" 56 | 57 | #function to format a dictionary into a telegram message 58 | def format_dict(d): 59 | message = "" 60 | for key, value in d.items(): 61 | #filter keys that have the word "token" in them 62 | if "token" in key and "tokenizer" not in key: 63 | value = "TOKEN" 64 | if 'id' in key: 65 | value = "ID" 66 | #if value is a dictionary, format it recursively 67 | if isinstance(value, dict): 68 | for k, v in value.items(): 69 | message += f"\n- {k}: {v} \n" 70 | elif isinstance(value, list): 71 | #each value is a new line in the message 72 | message += f"- {key}:\n\n" 73 | for v in value: 74 | message += f" {v}\n\n" 75 | #if value is a list, format it as a list 76 | else: 77 | message += f"- {key}: {value}\n" 78 | return message 79 | 80 | def send_telegram_message(message, chat_id, token): 81 | url = f"https://api.telegram.org/bot{token}/sendMessage?chat_id={chat_id}&text={message}&parse_mode=html&disable_notification=True" 82 | import requests 83 | req = requests.get(url) 84 | if req.status_code != 200: 85 | raise ValueError(f"Telegram request failed with status code {req.status_code}") 86 | def send_media_group(chat_id,telegram_token, images, caption=None, reply_to_message_id=None): 87 | """ 88 | Use this method to send an album of photos. On success, an array of Messages that were sent is returned. 89 | :param chat_id: chat id 90 | :param images: list of PIL images to send 91 | :param caption: caption of image 92 | :param reply_to_message_id: If the message is a reply, ID of the original message 93 | :return: response with the sent message 94 | """ 95 | SEND_MEDIA_GROUP = f'https://api.telegram.org/bot{telegram_token}/sendMediaGroup' 96 | from io import BytesIO 97 | import requests 98 | files = {} 99 | media = [] 100 | for i, img in enumerate(images): 101 | with BytesIO() as output: 102 | img.save(output, format='PNG') 103 | output.seek(0) 104 | name = f'photo{i}' 105 | files[name] = output.read() 106 | # a list of InputMediaPhoto. attach refers to the name of the file in the files dict 107 | media.append(dict(type='photo', media=f'attach://{name}')) 108 | media[0]['caption'] = caption 109 | media[0]['parse_mode'] = 'HTML' 110 | return requests.post(SEND_MEDIA_GROUP, data={'chat_id': chat_id, 'media': json.dumps(media),'disable_notification':True, 'reply_to_message_id': reply_to_message_id }, files=files) 111 | class AverageMeter: 112 | def __init__(self, name=None): 113 | self.name = name 114 | self.reset() 115 | 116 | def reset(self): 117 | self.sum = self.count = self.avg = 0 118 | 119 | def update(self, val, n=1): 120 | self.sum += val * n 121 | self.count += n 122 | self.avg = self.sum / self.count 123 | 124 | def exists(val): 125 | return val is not None 126 | 127 | 128 | def default(val, d): 129 | return val if exists(val) else d 130 | 131 | 132 | def masked_mse_loss(predicted, target, mask, reduction="none"): 133 | masked_predicted = predicted * mask 134 | masked_target = target * mask 135 | return F.mse_loss(masked_predicted, masked_target, reduction=reduction) 136 | 137 | # flash attention forwards and backwards 138 | # https://arxiv.org/abs/2205.14135 139 | 140 | 141 | class FlashAttentionFunction(torch.autograd.function.Function): 142 | @staticmethod 143 | @torch.no_grad() 144 | def forward(ctx, q, k, v, mask, causal, q_bucket_size, k_bucket_size): 145 | """ Algorithm 2 in the paper """ 146 | 147 | device = q.device 148 | dtype = q.dtype 149 | max_neg_value = -torch.finfo(q.dtype).max 150 | qk_len_diff = max(k.shape[-2] - q.shape[-2], 0) 151 | 152 | o = torch.zeros_like(q) 153 | all_row_sums = torch.zeros( 154 | (*q.shape[:-1], 1), dtype=dtype, device=device) 155 | all_row_maxes = torch.full( 156 | (*q.shape[:-1], 1), max_neg_value, dtype=dtype, device=device) 157 | 158 | scale = (q.shape[-1] ** -0.5) 159 | 160 | if not exists(mask): 161 | mask = (None,) * math.ceil(q.shape[-2] / q_bucket_size) 162 | else: 163 | mask = rearrange(mask, 'b n -> b 1 1 n') 164 | mask = mask.split(q_bucket_size, dim=-1) 165 | 166 | row_splits = zip( 167 | q.split(q_bucket_size, dim=-2), 168 | o.split(q_bucket_size, dim=-2), 169 | mask, 170 | all_row_sums.split(q_bucket_size, dim=-2), 171 | all_row_maxes.split(q_bucket_size, dim=-2), 172 | ) 173 | 174 | for ind, (qc, oc, row_mask, row_sums, row_maxes) in enumerate(row_splits): 175 | q_start_index = ind * q_bucket_size - qk_len_diff 176 | 177 | col_splits = zip( 178 | k.split(k_bucket_size, dim=-2), 179 | v.split(k_bucket_size, dim=-2), 180 | ) 181 | 182 | for k_ind, (kc, vc) in enumerate(col_splits): 183 | k_start_index = k_ind * k_bucket_size 184 | 185 | attn_weights = einsum( 186 | '... i d, ... j d -> ... i j', qc, kc) * scale 187 | 188 | if exists(row_mask): 189 | attn_weights.masked_fill_(~row_mask, max_neg_value) 190 | 191 | if causal and q_start_index < (k_start_index + k_bucket_size - 1): 192 | causal_mask = torch.ones((qc.shape[-2], kc.shape[-2]), dtype=torch.bool, 193 | device=device).triu(q_start_index - k_start_index + 1) 194 | attn_weights.masked_fill_(causal_mask, max_neg_value) 195 | 196 | block_row_maxes = attn_weights.amax(dim=-1, keepdims=True) 197 | attn_weights -= block_row_maxes 198 | exp_weights = torch.exp(attn_weights) 199 | 200 | if exists(row_mask): 201 | exp_weights.masked_fill_(~row_mask, 0.) 202 | 203 | block_row_sums = exp_weights.sum( 204 | dim=-1, keepdims=True).clamp(min=EPSILON) 205 | 206 | new_row_maxes = torch.maximum(block_row_maxes, row_maxes) 207 | 208 | exp_values = einsum( 209 | '... i j, ... j d -> ... i d', exp_weights, vc) 210 | 211 | exp_row_max_diff = torch.exp(row_maxes - new_row_maxes) 212 | exp_block_row_max_diff = torch.exp( 213 | block_row_maxes - new_row_maxes) 214 | 215 | new_row_sums = exp_row_max_diff * row_sums + \ 216 | exp_block_row_max_diff * block_row_sums 217 | 218 | oc.mul_((row_sums / new_row_sums) * exp_row_max_diff).add_( 219 | (exp_block_row_max_diff / new_row_sums) * exp_values) 220 | 221 | row_maxes.copy_(new_row_maxes) 222 | row_sums.copy_(new_row_sums) 223 | 224 | ctx.args = (causal, scale, mask, q_bucket_size, k_bucket_size) 225 | ctx.save_for_backward(q, k, v, o, all_row_sums, all_row_maxes) 226 | 227 | return o 228 | 229 | @staticmethod 230 | @torch.no_grad() 231 | def backward(ctx, do): 232 | """ Algorithm 4 in the paper """ 233 | 234 | causal, scale, mask, q_bucket_size, k_bucket_size = ctx.args 235 | q, k, v, o, l, m = ctx.saved_tensors 236 | 237 | device = q.device 238 | 239 | max_neg_value = -torch.finfo(q.dtype).max 240 | qk_len_diff = max(k.shape[-2] - q.shape[-2], 0) 241 | 242 | dq = torch.zeros_like(q) 243 | dk = torch.zeros_like(k) 244 | dv = torch.zeros_like(v) 245 | 246 | row_splits = zip( 247 | q.split(q_bucket_size, dim=-2), 248 | o.split(q_bucket_size, dim=-2), 249 | do.split(q_bucket_size, dim=-2), 250 | mask, 251 | l.split(q_bucket_size, dim=-2), 252 | m.split(q_bucket_size, dim=-2), 253 | dq.split(q_bucket_size, dim=-2) 254 | ) 255 | 256 | for ind, (qc, oc, doc, row_mask, lc, mc, dqc) in enumerate(row_splits): 257 | q_start_index = ind * q_bucket_size - qk_len_diff 258 | 259 | col_splits = zip( 260 | k.split(k_bucket_size, dim=-2), 261 | v.split(k_bucket_size, dim=-2), 262 | dk.split(k_bucket_size, dim=-2), 263 | dv.split(k_bucket_size, dim=-2), 264 | ) 265 | 266 | for k_ind, (kc, vc, dkc, dvc) in enumerate(col_splits): 267 | k_start_index = k_ind * k_bucket_size 268 | 269 | attn_weights = einsum( 270 | '... i d, ... j d -> ... i j', qc, kc) * scale 271 | 272 | if causal and q_start_index < (k_start_index + k_bucket_size - 1): 273 | causal_mask = torch.ones((qc.shape[-2], kc.shape[-2]), dtype=torch.bool, 274 | device=device).triu(q_start_index - k_start_index + 1) 275 | attn_weights.masked_fill_(causal_mask, max_neg_value) 276 | 277 | exp_attn_weights = torch.exp(attn_weights - mc) 278 | 279 | if exists(row_mask): 280 | exp_attn_weights.masked_fill_(~row_mask, 0.) 281 | 282 | p = exp_attn_weights / lc 283 | 284 | dv_chunk = einsum('... i j, ... i d -> ... j d', p, doc) 285 | dp = einsum('... i d, ... j d -> ... i j', doc, vc) 286 | 287 | D = (doc * oc).sum(dim=-1, keepdims=True) 288 | ds = p * scale * (dp - D) 289 | 290 | dq_chunk = einsum('... i j, ... j d -> ... i d', ds, kc) 291 | dk_chunk = einsum('... i j, ... i d -> ... j d', ds, qc) 292 | 293 | dqc.add_(dq_chunk) 294 | dkc.add_(dk_chunk) 295 | dvc.add_(dv_chunk) 296 | 297 | return dq, dk, dv, None, None, None, None 298 | 299 | def import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str, revision: str): 300 | text_encoder_config = PretrainedConfig.from_pretrained( 301 | pretrained_model_name_or_path, 302 | subfolder="text_encoder", 303 | revision=revision, 304 | ) 305 | model_class = text_encoder_config.architectures[0] 306 | 307 | if model_class == "CLIPTextModel": 308 | from transformers import CLIPTextModel 309 | 310 | return CLIPTextModel 311 | elif model_class == "RobertaSeriesModelWithTransformation": 312 | from diffusers.pipelines.alt_diffusion.modeling_roberta_series import RobertaSeriesModelWithTransformation 313 | 314 | return RobertaSeriesModelWithTransformation 315 | else: 316 | raise ValueError(f"{model_class} is not supported.") 317 | 318 | def replace_unet_cross_attn_to_flash_attention(): 319 | print("Using FlashAttention") 320 | 321 | def forward_flash_attn(self, x, context=None, mask=None): 322 | q_bucket_size = 512 323 | k_bucket_size = 1024 324 | 325 | h = self.heads 326 | q = self.to_q(x) 327 | 328 | context = context if context is not None else x 329 | context = context.to(x.dtype) 330 | 331 | if hasattr(self, 'hypernetwork') and self.hypernetwork is not None: 332 | context_k, context_v = self.hypernetwork.forward(x, context) 333 | context_k = context_k.to(x.dtype) 334 | context_v = context_v.to(x.dtype) 335 | else: 336 | context_k = context 337 | context_v = context 338 | 339 | k = self.to_k(context_k) 340 | v = self.to_v(context_v) 341 | del context, x 342 | 343 | q, k, v = map(lambda t: rearrange( 344 | t, 'b n (h d) -> b h n d', h=h), (q, k, v)) 345 | 346 | out = FlashAttentionFunction.apply(q, k, v, mask, False, 347 | q_bucket_size, k_bucket_size) 348 | 349 | out = rearrange(out, 'b h n d -> b n (h d)') 350 | 351 | # diffusers 0.6.0 352 | if type(self.to_out) is torch.nn.Sequential: 353 | return self.to_out(out) 354 | 355 | # diffusers 0.7.0 356 | out = self.to_out[0](out) 357 | out = self.to_out[1](out) 358 | return out 359 | 360 | diffusers.models.attention.CrossAttention.forward = forward_flash_attn 361 | class Depth2Img: 362 | def __init__(self,unet,text_encoder,revision,pretrained_model_name_or_path,accelerator): 363 | self.unet = unet 364 | self.text_encoder = text_encoder 365 | self.revision = revision if revision != 'no' else 'fp32' 366 | self.pretrained_model_name_or_path = pretrained_model_name_or_path 367 | self.accelerator = accelerator 368 | self.pipeline = None 369 | def depth_images(self,paths): 370 | if self.pipeline is None: 371 | self.pipeline = DiffusionPipeline.from_pretrained( 372 | self.pretrained_model_name_or_path, 373 | unet=self.accelerator.unwrap_model(self.unet), 374 | text_encoder=self.accelerator.unwrap_model(self.text_encoder), 375 | revision=self.revision, 376 | local_files_only=True,) 377 | self.pipeline.to(self.accelerator.device) 378 | self.vae_scale_factor = 2 ** (len(self.pipeline.vae.config.block_out_channels) - 1) 379 | non_depth_image_files = [] 380 | image_paths_by_path = {} 381 | 382 | for path in paths: 383 | #if path is list 384 | if isinstance(path, list): 385 | img = Path(path[0]) 386 | else: 387 | img = Path(path) 388 | if self.get_depth_image_path(img).exists(): 389 | continue 390 | else: 391 | non_depth_image_files.append(img) 392 | image_objects = [] 393 | for image_path in non_depth_image_files: 394 | image_instance = Image.open(image_path) 395 | if not image_instance.mode == "RGB": 396 | image_instance = image_instance.convert("RGB") 397 | image_instance = self.pipeline.feature_extractor( 398 | image_instance, return_tensors="pt" 399 | ).pixel_values 400 | 401 | image_instance = image_instance.to(self.accelerator.device) 402 | image_objects.append((image_path, image_instance)) 403 | 404 | for image_path, image_instance in image_objects: 405 | path = image_path.parent 406 | ogImg = Image.open(image_path) 407 | ogImg_x = ogImg.size[0] 408 | ogImg_y = ogImg.size[1] 409 | depth_map = self.pipeline.depth_estimator(image_instance).predicted_depth 410 | depth_min = torch.amin(depth_map, dim=[0, 1, 2], keepdim=True) 411 | depth_max = torch.amax(depth_map, dim=[0, 1, 2], keepdim=True) 412 | depth_map = torch.nn.functional.interpolate(depth_map.unsqueeze(1),size=(ogImg_y, ogImg_x),mode="bicubic",align_corners=False,) 413 | 414 | depth_map = 2.0 * (depth_map - depth_min) / (depth_max - depth_min) - 1.0 415 | depth_map = depth_map[0,:,:] 416 | depth_map_image = transforms.ToPILImage()(depth_map) 417 | depth_map_image = depth_map_image.filter(ImageFilter.GaussianBlur(radius=1)) 418 | depth_map_image.save(self.get_depth_image_path(image_path)) 419 | #quit() 420 | return 2 ** (len(self.pipeline.vae.config.block_out_channels) - 1) 421 | 422 | def get_depth_image_path(self,image_path): 423 | #if image_path is a string, convert it to a Path object 424 | if isinstance(image_path, str): 425 | image_path = Path(image_path) 426 | return image_path.parent / f"{image_path.stem}-depth.png" 427 | 428 | # Adapted from torch-ema https://github.com/fadel/pytorch_ema/blob/master/torch_ema/ema.py#L14 and taken from harubaru's implementation https://github.com/harubaru/waifu-diffusion 429 | class EMAModel: 430 | """ 431 | Exponential Moving Average of models weights 432 | """ 433 | def __init__(self, parameters: Iterable[torch.nn.Parameter], decay=0.9999): 434 | parameters = list(parameters) 435 | self.shadow_params = [p.clone().detach() for p in parameters] 436 | 437 | self.decay = decay 438 | self.optimization_step = 0 439 | 440 | def get_decay(self, optimization_step): 441 | """ 442 | Compute the decay factor for the exponential moving average. 443 | """ 444 | value = (1 + optimization_step) / (10 + optimization_step) 445 | return 1 - min(self.decay, value) 446 | 447 | @torch.no_grad() 448 | def step(self, parameters): 449 | parameters = list(parameters) 450 | 451 | self.optimization_step += 1 452 | self.decay = self.get_decay(self.optimization_step) 453 | 454 | for s_param, param in zip(self.shadow_params, parameters): 455 | if param.requires_grad: 456 | tmp = self.decay * (s_param - param) 457 | s_param.sub_(tmp) 458 | else: 459 | s_param.copy_(param) 460 | 461 | torch.cuda.empty_cache() 462 | 463 | def copy_to(self, parameters: Iterable[torch.nn.Parameter]) -> None: 464 | """ 465 | Copy current averaged parameters into given collection of parameters. 466 | Args: 467 | parameters: Iterable of `torch.nn.Parameter`; the parameters to be 468 | updated with the stored moving averages. If `None`, the 469 | parameters with which this `ExponentialMovingAverage` was 470 | initialized will be used. 471 | """ 472 | parameters = list(parameters) 473 | for s_param, param in zip(self.shadow_params, parameters): 474 | param.data.copy_(s_param.data) 475 | 476 | def to(self, device=None, dtype=None) -> None: 477 | r"""Move internal buffers of the ExponentialMovingAverage to `device`. 478 | Args: 479 | device: like `device` argument to `torch.Tensor.to` 480 | """ 481 | # .to() on the tensors handles None correctly 482 | self.shadow_params = [ 483 | p.to(device=device, dtype=dtype) if p.is_floating_point() else p.to(device=device) 484 | for p in self.shadow_params 485 | ] -------------------------------------------------------------------------------- /scripts/windows_install.py: -------------------------------------------------------------------------------- 1 | import filecmp 2 | import importlib.util 3 | import os 4 | import shutil 5 | import sys 6 | import sysconfig 7 | import subprocess 8 | from pathlib import Path 9 | import requests 10 | import zipfile 11 | if sys.version_info < (3, 8): 12 | import importlib_metadata 13 | else: 14 | import importlib.metadata as importlib_metadata 15 | 16 | req_file = os.path.join(os.getcwd(), "requirements.txt") 17 | 18 | def run(command, desc=None, errdesc=None, custom_env=None): 19 | if desc is not None: 20 | print(desc) 21 | 22 | result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True, env=os.environ if custom_env is None else custom_env) 23 | 24 | if result.returncode != 0: 25 | 26 | message = f"""{errdesc or 'Error running command'}. 27 | Command: {command} 28 | Error code: {result.returncode} 29 | stdout: {result.stdout.decode(encoding="utf8", errors="ignore") if len(result.stdout)>0 else ''} 30 | stderr: {result.stderr.decode(encoding="utf8", errors="ignore") if len(result.stderr)>0 else ''} 31 | """ 32 | raise RuntimeError(message) 33 | 34 | return result.stdout.decode(encoding="utf8", errors="ignore") 35 | 36 | def check_versions(): 37 | global req_file 38 | reqs = open(req_file, 'r') 39 | lines = reqs.readlines() 40 | reqs_dict = {} 41 | for line in lines: 42 | splits = line.split("==") 43 | if len(splits) == 2: 44 | key = splits[0] 45 | if "torch" not in key: 46 | if "diffusers" in key: 47 | key = "diffusers" 48 | reqs_dict[key] = splits[1].replace("\n", "").strip() 49 | 50 | if os.name == "nt": 51 | reqs_dict["torch"] = "1.12.1+cu116" 52 | reqs_dict["torchvision"] = "0.13.1+cu116" 53 | 54 | checks = ["xformers","bitsandbytes", "diffusers", "transformers", "torch", "torchvision"] 55 | for check in checks: 56 | check_ver = "N/A" 57 | status = "[ ]" 58 | try: 59 | check_available = importlib.util.find_spec(check) is not None 60 | if check_available: 61 | check_ver = importlib_metadata.version(check) 62 | if check in reqs_dict: 63 | req_version = reqs_dict[check] 64 | if str(check_ver) == str(req_version): 65 | status = "[+]" 66 | else: 67 | status = "[!]" 68 | except importlib_metadata.PackageNotFoundError: 69 | check_available = False 70 | if not check_available: 71 | status = "[!]" 72 | print(f"{status} {check} NOT installed.") 73 | if check == 'xformers': 74 | #if windows, install xformers from prebuilt wheel 75 | x_cmd = r"https://huggingface.co/r4ziel/xformers_pre_built/resolve/main/xformers-0.0.17.dev464-cp310-cp310-win_amd64.whl" 76 | print(f"Installing xformers with: pip install {x_cmd}") 77 | run(f"pip install {x_cmd}", desc="Installing xformers") 78 | 79 | else: 80 | print(f"{status} {check} version {check_ver} installed.") 81 | 82 | 83 | dreambooth_skip_install = os.environ.get('DREAMBOOTH_SKIP_INSTALL', False) 84 | 85 | if not dreambooth_skip_install: 86 | check_versions() 87 | name = "StableTuner" 88 | run(f'"{sys.executable}" -m pip install -r "{req_file}"', f"Checking {name} requirements...", 89 | f"Couldn't install {name} requirements.") 90 | 91 | # I think we only need to bump torch version to cu116 on Windows, as we're using prebuilt B&B Binaries... 92 | if os.name == "nt": 93 | torch_cmd = os.environ.get('TORCH_COMMAND', None) 94 | if torch_cmd is None: 95 | torch_cmd = 'pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu117 --upgrade"' 96 | 97 | run(f'"{sys.executable}" -m {torch_cmd}', "Checking/upgrading existing torch/torchvision installation", "Couldn't install torch") 98 | 99 | 100 | #if .cache directory in Path.home() exists 101 | hf_cache_dir = Path.home() / ".cache" 102 | if hf_cache_dir.exists(): 103 | #check if huggingface exists 104 | hf_dir = hf_cache_dir / "huggingface" 105 | if hf_dir.exists(): 106 | #check if accelerate exists 107 | accelerate_dir = hf_dir / "accelerate" 108 | if accelerate_dir.exists(): 109 | #print('test') 110 | src_file = 'resources/accelerate_windows/accelerate_default_config.yaml' 111 | dst_file = 'default_config.yaml' 112 | #load from cwd 113 | src = Path.cwd() / src_file 114 | dst = accelerate_dir / dst_file 115 | print(src) 116 | if src.exists(): 117 | shutil.copy2(src, dst) 118 | print(f"Updated {dst_file} in {accelerate_dir}") 119 | else: 120 | #make dirs 121 | hf_cache_dir.mkdir(parents=True, exist_ok=True) 122 | hf_dir = hf_cache_dir / "huggingface" 123 | hf_dir.mkdir(parents=True, exist_ok=True) 124 | accelerate_dir = hf_dir / "accelerate" 125 | accelerate_dir.mkdir(parents=True, exist_ok=True) 126 | src_file = 'accelerate_default_config.json' 127 | dst_file = 'default_config.json' 128 | src = Path.cwd() / src_file 129 | dst = accelerate_dir / dst_file 130 | if src.exists(): 131 | if dst.exists(): 132 | shutil.copy2(src, dst) 133 | print(f"Created {dst_file} in {accelerate_dir}") 134 | 135 | 136 | 137 | base_dir = os.path.dirname(os.getcwd()) 138 | #repo = git.Repo(base_dir) 139 | #revision = repo.rev_parse("HEAD") 140 | #print(f"Dreambooth revision is {revision}") 141 | check_versions() 142 | # Check for "different" B&B Files and copy only if necessary 143 | if os.name == "nt": 144 | python = sys.executable 145 | run(f'"{python}" -m pip install https://huggingface.co/r4ziel/xformers_pre_built/resolve/main/triton-2.0.0-cp310-cp310-win_amd64.whl', "Installing Triton", "Couldn't install triton") 146 | bnb_src = os.path.join(os.getcwd(), "resources/bitsandbytes_windows") 147 | bnb_dest = os.path.join(sysconfig.get_paths()["purelib"], "bitsandbytes") 148 | cudnn_src = os.path.join(os.getcwd(), "resources/cudnn_windows") 149 | #check if chudnn is in cwd 150 | if not os.path.exists(cudnn_src): 151 | print("Can't find CUDNN in resources, trying main folder...") 152 | cudnn_src = os.path.join(os.getcwd(), "cudnn_windows") 153 | if not os.path.exists(cudnn_src): 154 | cudnn_url = "https://b1.thefileditch.ch/mwxKTEtelILoIbMbruuM.zip" 155 | print(f"Downloading CUDNN 8.6") 156 | #download with requests 157 | r = requests.get(cudnn_url, allow_redirects=True) 158 | #save to cwd 159 | open('cudnn_windows.zip', 'wb').write(r.content) 160 | #unzip 161 | with zipfile.ZipFile('cudnn_windows.zip','r') as zip_ref: 162 | zip_ref.extractall(os.path.join(os.getcwd(),"resources/cudnn_windows")) 163 | #remove zip 164 | os.remove('cudnn_windows.zip') 165 | cudnn_src = os.path.join(os.getcwd(), "resources/cudnn_windows") 166 | 167 | cudnn_dest = os.path.join(sysconfig.get_paths()["purelib"], "torch", "lib") 168 | print(f"Checking for B&B files in {bnb_dest}") 169 | if not os.path.exists(bnb_dest): 170 | # make destination directory 171 | os.makedirs(bnb_dest, exist_ok=True) 172 | printed = False 173 | filecmp.clear_cache() 174 | for file in os.listdir(bnb_src): 175 | src_file = os.path.join(bnb_src, file) 176 | if file == "main.py": 177 | dest = os.path.join(bnb_dest, "cuda_setup") 178 | if not os.path.exists(dest): 179 | os.mkdir(dest) 180 | else: 181 | dest = bnb_dest 182 | if not os.path.exists(dest): 183 | os.mkdir(dest) 184 | dest_file = os.path.join(dest, file) 185 | status = shutil.copy2(src_file, dest) 186 | if status: 187 | print("Copied B&B files to destination") 188 | print(f"Checking for CUDNN files in {cudnn_dest}") 189 | if os.path.exists(cudnn_src): 190 | if os.path.exists(cudnn_dest): 191 | # check for different files 192 | filecmp.clear_cache() 193 | for file in os.listdir(cudnn_src): 194 | src_file = os.path.join(cudnn_src, file) 195 | dest_file = os.path.join(cudnn_dest, file) 196 | #if dest file exists, check if it's different 197 | if os.path.exists(dest_file): 198 | status = shutil.copy2(src_file, cudnn_dest) 199 | if status: 200 | print("Copied CUDNN 8.6 files to destination") 201 | d_commit = '8178c84' 202 | diffusers_cmd = f"git+https://github.com/huggingface/diffusers.git@{d_commit}#egg=diffusers --force-reinstall" 203 | run(f'"{python}" -m pip install {diffusers_cmd}', f"Installing Diffusers {d_commit} commit", "Couldn't install diffusers") 204 | #install requirements file 205 | t_commit = 'cc84075' 206 | trasn_cmd = f"git+https://github.com/huggingface/transformers.git@{t_commit}#egg=transformers --force-reinstall" 207 | run(f'"{python}" -m pip install {trasn_cmd}', f"Installing Transformers {t_commit} commit", "Couldn't install transformers") 208 | 209 | req_file = os.path.join(os.getcwd(), "requirements.txt") 210 | run(f'"{python}" -m pip install -r "{req_file}"', "Updating requirements", "Couldn't install requirements") 211 | --------------------------------------------------------------------------------