From 739da3a8edee88c8a4f7abbd5d58f3255dfbcdb0 Mon Sep 17 00:00:00 2001 From: bmaltais Date: Fri, 22 Mar 2024 09:16:44 -0400 Subject: [PATCH 01/42] Update setup common functions --- setup/setup_common.py | 131 +++++++++++++++++++++++++++++++++--------- 1 file changed, 105 insertions(+), 26 deletions(-) diff --git a/setup/setup_common.py b/setup/setup_common.py index 15173bdb9..7875668f6 100644 --- a/setup/setup_common.py +++ b/setup/setup_common.py @@ -35,34 +35,48 @@ def check_python_version(): log.error(f"Failed to verify Python version. Error: {e}") return False -def update_submodule(): +def update_submodule(quiet=True): """ Ensure the submodule is initialized and updated. + + This function uses the Git command line interface to initialize and update + the specified submodule recursively. Errors during the Git operation + or if Git is not found are caught and logged. + + Parameters: + - quiet: If True, suppresses the output of the Git command. """ + git_command = ["git", "submodule", "update", "--init", "--recursive"] + + if quiet: + git_command.append("--quiet") + try: # Initialize and update the submodule - subprocess.run(["git", "submodule", "update", "--init", "--recursive", "--quiet"], check=True) + subprocess.run(git_command, check=True) log.info("Submodule initialized and updated.") except subprocess.CalledProcessError as e: + # Log the error if the Git operation fails log.error(f"Error during Git operation: {e}") except FileNotFoundError as e: + # Log the error if the file is not found log.error(e) -def read_tag_version_from_file(file_path): - """ - Read the tag version from a given file. +# def read_tag_version_from_file(file_path): +# """ +# Read the tag version from a given file. - Parameters: - - file_path: The path to the file containing the tag version. +# Parameters: +# - file_path: The path to the file containing the tag version. - Returns: - The tag version as a string. - """ - with open(file_path, 'r') as file: - # Read the first line and strip whitespace - tag_version = file.readline().strip() - return tag_version +# Returns: +# The tag version as a string. +# """ +# with open(file_path, 'r') as file: +# # Read the first line and strip whitespace +# tag_version = file.readline().strip() +# return tag_version def clone_or_checkout(repo_url, branch_or_tag, directory_name): """ @@ -369,20 +383,43 @@ def check_torch(): # report current version of code -def check_repo_version(): # pylint: disable=unused-argument +def check_repo_version(): + """ + This function checks the version of the repository by reading the contents of a file named '.release' + in the current directory. If the file exists, it reads the release version from the file and logs it. + If the file does not exist, it logs a debug message indicating that the release could not be read. + """ if os.path.exists('.release'): - with open(os.path.join('./.release'), 'r', encoding='utf8') as file: - release= file.read() - - log.info(f'Kohya_ss GUI version: {release}') + try: + with open(os.path.join('./.release'), 'r', encoding='utf8') as file: + release= file.read() + + log.info(f'Kohya_ss GUI version: {release}') + except Exception as e: + log.error(f'Could not read release: {e}') else: log.debug('Could not read release...') # execute git command def git(arg: str, folder: str = None, ignore: bool = False): - # - # This function was adapted from code written by vladimandic: https://github.com/vladmandic/automatic/commits/master - # + """ + Executes a Git command with the specified arguments. + + This function is designed to run Git commands and handle their output. + It can be used to execute Git commands in a specific folder or the current directory. + If an error occurs during the Git operation and the 'ignore' flag is not set, + it logs the error message and the Git output for debugging purposes. + + Parameters: + - arg: A string containing the Git command arguments. + - folder: An optional string specifying the folder where the Git command should be executed. + If not provided, the current directory is used. + - ignore: A boolean flag indicating whether to ignore errors during the Git operation. + If set to True, errors will not be logged. + + Note: + This function was adapted from code written by vladimandic: https://github.com/vladmandic/automatic/commits/master + """ git_cmd = os.environ.get('GIT', "git") result = subprocess.run(f'"{git_cmd}" {arg}', check=False, shell=True, env=os.environ, stdout=subprocess.PIPE, stderr=subprocess.PIPE, cwd=folder or '.') @@ -391,7 +428,7 @@ def git(arg: str, folder: str = None, ignore: bool = False): txt += ('\n' if len(txt) > 0 else '') + result.stderr.decode(encoding="utf8", errors="ignore") txt = txt.strip() if result.returncode != 0 and not ignore: - global errors # pylint: disable=global-statement + global errors errors += 1 log.error(f'Error running git: {folder} / {arg}') if 'or stash them' in txt: @@ -400,6 +437,27 @@ def git(arg: str, folder: str = None, ignore: bool = False): def pip(arg: str, ignore: bool = False, quiet: bool = False, show_stdout: bool = False): + """ + Executes a pip command with the specified arguments. + + This function is designed to run pip commands and handle their output. + It can be used to install, upgrade, or uninstall packages using pip. + If an error occurs during the pip operation and the 'ignore' flag is not set, + it logs the error message and the pip output for debugging purposes. + + Parameters: + - arg: A string containing the pip command arguments. + - ignore: A boolean flag indicating whether to ignore errors during the pip operation. + If set to True, errors will not be logged. + - quiet: A boolean flag indicating whether to suppress the output of the pip command. + If set to True, the function will not log any output. + - show_stdout: A boolean flag indicating whether to display the pip command's output + to the console. If set to True, the function will print the output + to the console. + + Returns: + - The output of the pip command as a string, or None if the 'show_stdout' flag is set. + """ # arg = arg.replace('>=', '==') if not quiet: log.info(f'Installing package: {arg.replace("install", "").replace("--upgrade", "").replace("--no-deps", "").replace("--force", "").replace(" ", " ").strip()}') @@ -513,15 +571,36 @@ def installed(package, friendly: str = None): # install package using pip if not already installed def install( - # - # This function was adapted from code written by vladimandic: https://github.com/vladmandic/automatic/commits/master - # package, friendly: str = None, ignore: bool = False, reinstall: bool = False, show_stdout: bool = False, ): + """ + Installs or upgrades a Python package using pip, with options to ignode errors, + reinstall packages, and display outputs. + + Parameters: + - package (str): The name of the package to be installed or upgraded. Can include + version specifiers. Anything after a '#' in the package name will be ignored. + - friendly (str, optional): A more user-friendly name for the package, used for + logging or user interface purposes. Defaults to None. + - ignore (bool, optional): If True, any errors encountered during the installation + will be ignored. Defaults to False. + - reinstall (bool, optional): If True, forces the reinstallation of the package + even if it's already installed. This also disables any quick install checks. Defaults to False. + - show_stdout (bool, optional): If True, displays the standard output from the pip + command to the console. Useful for debugging. Defaults to False. + + Returns: + None. The function performs operations that affect the environment but does not return + any value. + + Note: + If `reinstall` is True, it disables any mechanism that allows for skipping installations + when the package is already present, forcing a fresh install. + """ # Remove anything after '#' in the package variable package = package.split('#')[0].strip() From 25f9378dc218f38173619a2f59cd09d5664c8f1d Mon Sep 17 00:00:00 2001 From: bmaltais Date: Fri, 22 Mar 2024 21:16:59 -0400 Subject: [PATCH 02/42] BLIP2 Captioning (#2144) * 1st comit * BLIP2 captioning implementation --- kohya_gui/blip2_caption_gui.py | 352 +++++++++++++++++++++++++++++++++ kohya_gui/utilities.py | 2 + 2 files changed, 354 insertions(+) create mode 100644 kohya_gui/blip2_caption_gui.py diff --git a/kohya_gui/blip2_caption_gui.py b/kohya_gui/blip2_caption_gui.py new file mode 100644 index 000000000..05a94d15a --- /dev/null +++ b/kohya_gui/blip2_caption_gui.py @@ -0,0 +1,352 @@ +from PIL import Image +from transformers import Blip2Processor, Blip2ForConditionalGeneration +import torch +import gradio as gr +import os + +from .common_gui import get_folder_path, add_pre_postfix, scriptdir, list_dirs +from .custom_logging import setup_logging + +# Set up logging +log = setup_logging() + + +def load_model(): + # Set the device to GPU if available, otherwise use CPU + device = "cuda" if torch.cuda.is_available() else "cpu" + + # Initialize the BLIP2 processor + processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b") + + # Initialize the BLIP2 model + model = Blip2ForConditionalGeneration.from_pretrained( + "Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16 + ) + + # Move the model to the specified device + model.to(device) + + return processor, model, device + + +def get_images_in_directory(directory_path): + """ + Returns a list of image file paths found in the provided directory path. + + Parameters: + - directory_path: A string representing the path to the directory to search for images. + + Returns: + - A list of strings, where each string is the full path to an image file found in the specified directory. + """ + import os + + # List of common image file extensions to look for + image_extensions = [".jpg", ".jpeg", ".png", ".bmp", ".gif"] + + # Generate a list of image file paths in the directory + image_files = [ + # constructs the full path to the file + os.path.join(directory_path, file) + # lists all files and directories in the given path + for file in os.listdir(directory_path) + # gets the file extension in lowercase + if os.path.splitext(file)[1].lower() in image_extensions + ] + + # Return the list of image file paths + return image_files + + +def generate_caption( + file_list, + processor, + model, + device, + caption_file_ext=".txt", + num_beams=5, + repetition_penalty=1.5, + length_penalty=1.2, + max_new_tokens=40, + min_new_tokens=20, + do_sample=True, + top_p=0.0, +): + """ + Fetches and processes each image in file_list, generates captions based on the image, and writes the generated captions to a file. + + Parameters: + - file_list: A list of file paths pointing to the images to be captioned. + - processor: The preprocessor for the BLIP2 model. + - model: The BLIP2 model to be used for generating captions. + - device: The device on which the computation is performed. + - extension: The extension for the output text files. + - num_beams: Number of beams for beam search. Default: 5. + - repetition_penalty: Penalty for repeating tokens. Default: 1.5. + - length_penalty: Penalty for sentence length. Default: 1.2. + - max_new_tokens: Maximum number of new tokens to generate. Default: 40. + - min_new_tokens: Minimum number of new tokens to generate. Default: 20. + """ + for file_path in file_list: + image = Image.open(file_path) + + inputs = processor(images=image, return_tensors="pt").to(device, torch.float16) + + if top_p == 0.0: + generated_ids = model.generate( + **inputs, + num_beams=num_beams, + repetition_penalty=repetition_penalty, + length_penalty=length_penalty, + max_new_tokens=max_new_tokens, + min_new_tokens=min_new_tokens, + ) + else: + generated_ids = model.generate( + **inputs, + do_sample=do_sample, + top_p=top_p, + max_new_tokens=max_new_tokens, + min_new_tokens=min_new_tokens, + ) + + generated_text = processor.batch_decode( + generated_ids, skip_special_tokens=True + )[0].strip() + + # Construct the output file path by replacing the original file extension with the specified extension + output_file_path = os.path.splitext(file_path)[0] + caption_file_ext + + # Write the generated text to the output file + with open(output_file_path, "w") as output_file: + output_file.write(generated_text) + + # Log the image file path with a message about the fact that the caption was generated + log.info(f"{file_path} caption was generated") + + +def caption_images_beam_search( + directory_path, + num_beams, + repetition_penalty, + length_penalty, + min_new_tokens, + max_new_tokens, + caption_file_ext, +): + """ + Captions all images in the specified directory using the provided prompt. + + Parameters: + - directory_path: A string representing the path to the directory containing the images to be captioned. + """ + log.info("BLIP2 captionning beam...") + + if not os.path.isdir(directory_path): + log.error(f"Directory {directory_path} does not exist.") + return + + processor, model, device = load_model() + image_files = get_images_in_directory(directory_path) + generate_caption( + file_list=image_files, + processor=processor, + model=model, + device=device, + num_beams=int(num_beams), + repetition_penalty=repetition_penalty, + length_penalty=length_penalty, + min_new_tokens=int(min_new_tokens), + max_new_tokens=int(max_new_tokens), + caption_file_ext=caption_file_ext, + ) + + +def caption_images_nucleus( + directory_path, + do_sample, + top_p, + min_new_tokens, + max_new_tokens, + caption_file_ext, +): + """ + Captions all images in the specified directory using the provided prompt. + + Parameters: + - directory_path: A string representing the path to the directory containing the images to be captioned. + """ + log.info("BLIP2 captionning nucleus...") + + if not os.path.isdir(directory_path): + log.error(f"Directory {directory_path} does not exist.") + return + + processor, model, device = load_model() + image_files = get_images_in_directory(directory_path) + generate_caption( + file_list=image_files, + processor=processor, + model=model, + device=device, + do_sample=do_sample, + top_p=top_p, + min_new_tokens=int(min_new_tokens), + max_new_tokens=int(max_new_tokens), + caption_file_ext=caption_file_ext, + ) + + +def gradio_blip2_caption_gui_tab(headless=False, directory_path=None): + from .common_gui import create_refresh_button + + directory_path = ( + directory_path + if directory_path is not None + else os.path.join(scriptdir, "data") + ) + current_train_dir = directory_path + + def list_train_dirs(path): + nonlocal current_train_dir + current_train_dir = path + return list(list_dirs(path)) + + with gr.Tab("BLIP2 Captioning"): + gr.Markdown( + "This utility uses BLIP2 to caption files for each image in a folder." + ) + + with gr.Group(), gr.Row(): + directory_path_dir = gr.Dropdown( + label="Image folder to caption (containing the images to caption)", + choices=[""] + list_train_dirs(directory_path), + value="", + interactive=True, + allow_custom_value=True, + ) + create_refresh_button( + directory_path_dir, + lambda: None, + lambda: {"choices": list_train_dirs(current_train_dir)}, + "open_folder_small", + ) + button_directory_path_dir_input = gr.Button( + "📂", + elem_id="open_folder_small", + elem_classes=["tool"], + visible=(not headless), + ) + button_directory_path_dir_input.click( + get_folder_path, + outputs=directory_path_dir, + show_progress=False, + ) + with gr.Group(), gr.Row(): + min_new_tokens = gr.Number( + value=20, + label="Min new tokens", + interactive=True, + step=1, + minimum=5, + maximum=300, + ) + max_new_tokens = gr.Number( + value=40, + label="Max new tokens", + interactive=True, + step=1, + minimum=5, + maximum=300, + ) + caption_file_ext = gr.Textbox( + label="Caption file extension", + placeholder="Extension for caption file (e.g., .caption, .txt)", + value=".txt", + interactive=True, + ) + + with gr.Row(): + with gr.Tab("Beam search"): + with gr.Row(): + num_beams = gr.Slider( + minimum=1, + maximum=16, + value=16, + step=1, + interactive=True, + label="Number of beams", + ) + + temperature = gr.Slider( + minimum=0.5, + maximum=1.0, + value=1.0, + step=0.1, + interactive=True, + label="Temperature", + info="used with nucleus sampling", + ) + + len_penalty = gr.Slider( + minimum=-1.0, + maximum=2.0, + value=1.0, + step=0.2, + interactive=True, + label="Length Penalty", + info="increase for longer sequence", + ) + + rep_penalty = gr.Slider( + minimum=1.0, + maximum=5.0, + value=1.5, + step=0.5, + interactive=True, + label="Repeat Penalty", + info="larger value prevents repetition", + ) + + caption_button_beam = gr.Button( + value="Caption images", interactive=True, variant="primary" + ) + caption_button_beam.click( + caption_images_beam_search, + inputs=[ + directory_path_dir, + num_beams, + rep_penalty, + len_penalty, + min_new_tokens, + max_new_tokens, + caption_file_ext, + ], + ) + with gr.Tab("Nucleus sampling"): + with gr.Row(): + do_sample = gr.Checkbox(label="Sample", value=True) + + top_p = gr.Slider( + minimum=-0, + maximum=1, + value=0.9, + step=0.1, + interactive=True, + label="Top_p", + ) + + caption_button_nucleus = gr.Button( + value="Caption images", interactive=True, variant="primary" + ) + caption_button_nucleus.click( + caption_images_nucleus, + inputs=[ + directory_path_dir, + do_sample, + top_p, + min_new_tokens, + max_new_tokens, + caption_file_ext, + ], + ) diff --git a/kohya_gui/utilities.py b/kohya_gui/utilities.py index 3125e45b9..2cca8943c 100644 --- a/kohya_gui/utilities.py +++ b/kohya_gui/utilities.py @@ -9,6 +9,7 @@ from .basic_caption_gui import gradio_basic_caption_gui_tab from .convert_model_gui import gradio_convert_model_tab from .blip_caption_gui import gradio_blip_caption_gui_tab +from .blip2_caption_gui import gradio_blip2_caption_gui_tab from .git_caption_gui import gradio_git_caption_gui_tab from .wd14_caption_gui import gradio_wd14_caption_gui_tab from .manual_caption_gui import gradio_manual_caption_gui_tab @@ -27,6 +28,7 @@ def utilities_tab( with gr.Tab('Captioning'): gradio_basic_caption_gui_tab(headless=headless) gradio_blip_caption_gui_tab(headless=headless) + gradio_blip2_caption_gui_tab(headless=headless) gradio_git_caption_gui_tab(headless=headless) gradio_wd14_caption_gui_tab(headless=headless) gradio_manual_caption_gui_tab(headless=headless) From e50cb8a1e804de9a258cddb4f8a2647c507e0eaf Mon Sep 17 00:00:00 2001 From: bmaltais Date: Sun, 24 Mar 2024 15:41:34 -0400 Subject: [PATCH 03/42] Aligh windows requirements to new sd-scripts requirements --- kohya_gui/tensorboard_gui.py | 9 ++++++++- requirements.txt | 2 ++ requirements_pytorch_windows.txt | 3 +++ requirements_windows.txt | 4 ++++ requirements_windows_torch2.txt | 4 ---- setup/setup_common.py | 18 ++++++++++++++++++ setup/setup_windows.py | 12 ++++++++++-- setup/validate_requirements.py | 4 ++-- 8 files changed, 47 insertions(+), 9 deletions(-) create mode 100644 requirements_pytorch_windows.txt create mode 100644 requirements_windows.txt delete mode 100644 requirements_windows_torch2.txt diff --git a/kohya_gui/tensorboard_gui.py b/kohya_gui/tensorboard_gui.py index 7f011408b..85335e67a 100644 --- a/kohya_gui/tensorboard_gui.py +++ b/kohya_gui/tensorboard_gui.py @@ -16,6 +16,7 @@ DEFAULT_TENSORBOARD_PORT = 6006 def start_tensorboard(headless, logging_dir, wait_time=5): + os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0' global tensorboard_proc headless_bool = True if headless.get('label') == 'True' else False @@ -49,7 +50,13 @@ def start_tensorboard(headless, logging_dir, wait_time=5): # Start background process log.info('Starting TensorBoard on port {}'.format(tensorboard_port)) try: - tensorboard_proc = subprocess.Popen(run_cmd) + # Copy the current environment + env = os.environ.copy() + + # Set your specific environment variable + env['TF_ENABLE_ONEDNN_OPTS'] = '0' + + tensorboard_proc = subprocess.Popen(run_cmd, env=env) except Exception as e: log.error('Failed to start Tensorboard:', e) return diff --git a/requirements.txt b/requirements.txt index 0ddc62b1c..48344eeb9 100644 --- a/requirements.txt +++ b/requirements.txt @@ -27,6 +27,7 @@ onnxruntime-gpu==1.16.0 # onnxruntime==1.16.0 # this is for onnx: # tensorboard==2.14.1 +prodigyopt==1.0 protobuf==3.20.3 # open clip for SDXL open-clip-torch==2.20.0 @@ -35,6 +36,7 @@ prodigyopt==1.0 pytorch-lightning==1.9.0 rich==13.7.0 safetensors==0.4.2 +scipy==1.11.4 timm==0.6.12 tk==0.1.0 toml==0.10.2 diff --git a/requirements_pytorch_windows.txt b/requirements_pytorch_windows.txt new file mode 100644 index 000000000..63fd7e035 --- /dev/null +++ b/requirements_pytorch_windows.txt @@ -0,0 +1,3 @@ +torch==2.1.2+cu118 --index-url https://download.pytorch.org/whl/cu118 +torchvision==0.16.2+cu118 --index-url https://download.pytorch.org/whl/cu118 +xformers==0.0.23.post1+cu118 --index-url https://download.pytorch.org/whl/cu118 diff --git a/requirements_windows.txt b/requirements_windows.txt new file mode 100644 index 000000000..e641755d1 --- /dev/null +++ b/requirements_windows.txt @@ -0,0 +1,4 @@ +bitsandbytes==0.43.0 +tensorboard +tensorflow +-r requirements.txt \ No newline at end of file diff --git a/requirements_windows_torch2.txt b/requirements_windows_torch2.txt deleted file mode 100644 index b3814208f..000000000 --- a/requirements_windows_torch2.txt +++ /dev/null @@ -1,4 +0,0 @@ -torch==2.1.2+cu118 torchvision==0.16.2+cu118 torchaudio==2.1.2+cu118 xformers==0.0.23.post1+cu118 --index-url https://download.pytorch.org/whl/cu118 -bitsandbytes==0.43.0 -tensorboard==2.14.1 tensorflow==2.14.0 --r requirements.txt diff --git a/setup/setup_common.py b/setup/setup_common.py index 7875668f6..35db54f2d 100644 --- a/setup/setup_common.py +++ b/setup/setup_common.py @@ -218,6 +218,24 @@ def setup_logging(clean=False): log.addHandler(rh) +def install_requirements_inbulk(requirements_file, show_stdout=True, optional_parm="", upgrade = False): + if not os.path.exists(requirements_file): + log.error(f'Could not find the requirements file in {requirements_file}.') + return + + log.info(f'Installing requirements from {requirements_file}...') + + if upgrade: + optional_parm += " -U" + + if show_stdout: + run_cmd(f'pip install -r {requirements_file} {optional_parm}') + else: + run_cmd(f'pip install -r {requirements_file} {optional_parm} --quiet') + log.info(f'Requirements from {requirements_file} installed.') + + + def configure_accelerate(run_accelerate=False): # # This function was taken and adapted from code written by jstayco diff --git a/setup/setup_windows.py b/setup/setup_windows.py index 7fe9307a7..1f9a102cb 100644 --- a/setup/setup_windows.py +++ b/setup/setup_windows.py @@ -118,8 +118,16 @@ def install_kohya_ss_torch2(headless: bool = False): setup_common.install("pip") - setup_common.install_requirements( - "requirements_windows_torch2.txt", check_no_verify_flag=False + # setup_common.install_requirements( + # "requirements_windows_torch2.txt", check_no_verify_flag=False + # ) + + setup_common.install_requirements_inbulk( + "requirements_pytorch_windows.txt", show_stdout=True, optional_parm="--index-url https://download.pytorch.org/whl/cu118" + ) + + setup_common.install_requirements_inbulk( + "requirements_windows.txt", show_stdout=True, upgrade=True ) setup_common.configure_accelerate( diff --git a/setup/validate_requirements.py b/setup/validate_requirements.py index f7566847b..5fbadf513 100644 --- a/setup/validate_requirements.py +++ b/setup/validate_requirements.py @@ -113,8 +113,8 @@ def main(): if args.requirements: setup_common.install_requirements(args.requirements, check_no_verify_flag=True) else: - setup_common.install_requirements('requirements_windows_torch2.txt', check_no_verify_flag=True) - + setup_common.install_requirements('requirements_pytorch_windows.txt', check_no_verify_flag=True) + setup_common.install_requirements('requirements_windows.txt', check_no_verify_flag=True) if __name__ == '__main__': main() From 43069a8e1b64cceca95eec2c9baddc59dfae092f Mon Sep 17 00:00:00 2001 From: bmaltais Date: Sun, 24 Mar 2024 15:46:48 -0400 Subject: [PATCH 04/42] Update Triton install warning --- setup/setup_windows.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup/setup_windows.py b/setup/setup_windows.py index 1f9a102cb..892722107 100644 --- a/setup/setup_windows.py +++ b/setup/setup_windows.py @@ -190,7 +190,7 @@ def main_menu(headless: bool = False): print( "2. (Optional) Install CuDNN files (to use the latest supported CuDNN version)" ) - print("3. (Optional) Install Triton 2.1.0 for Windows") + print("3. (DANGER) Install Triton 2.1.0 for Windows... only do it if you know you need it... might break training...") print("4. (Optional) Install specific version of bitsandbytes") print("5. (Optional) Manually configure Accelerate") print("6. (Optional) Launch Kohya_ss GUI in browser") From 64360cec9e5e0b2daf85024b736c31acf3cd1dee Mon Sep 17 00:00:00 2001 From: bmaltais Date: Sun, 24 Mar 2024 20:46:38 -0400 Subject: [PATCH 05/42] Fix issue where max_grad_norm = 0 would not show in the command line arguments --- kohya_gui/common_gui.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/kohya_gui/common_gui.py b/kohya_gui/common_gui.py index b2dee8c9b..4b65945ad 100644 --- a/kohya_gui/common_gui.py +++ b/kohya_gui/common_gui.py @@ -1233,7 +1233,7 @@ def run_cmd_advanced_training(**kwargs): run_cmd += f' --max_data_loader_n_workers="{max_data_loader_n_workers}"' max_grad_norm = kwargs.get("max_grad_norm") - if max_grad_norm and max_grad_norm != "": + if max_grad_norm != "": run_cmd += f' --max_grad_norm="{max_grad_norm}"' max_resolution = kwargs.get("max_resolution") From 85c2b37a6a41c0e684e2806b193203398539b31d Mon Sep 17 00:00:00 2001 From: bmaltais Date: Mon, 25 Mar 2024 10:54:51 -0400 Subject: [PATCH 06/42] Fix max grad norm issue in DB --- kohya_gui/common_gui.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/kohya_gui/common_gui.py b/kohya_gui/common_gui.py index 4b65945ad..aab9b1b05 100644 --- a/kohya_gui/common_gui.py +++ b/kohya_gui/common_gui.py @@ -1232,9 +1232,10 @@ def run_cmd_advanced_training(**kwargs): if max_data_loader_n_workers and not max_data_loader_n_workers == "": run_cmd += f' --max_data_loader_n_workers="{max_data_loader_n_workers}"' - max_grad_norm = kwargs.get("max_grad_norm") - if max_grad_norm != "": - run_cmd += f' --max_grad_norm="{max_grad_norm}"' + if "max_grad_norm" in kwargs: + max_grad_norm = kwargs.get("max_grad_norm") + if max_grad_norm != "": + run_cmd += f' --max_grad_norm="{max_grad_norm}"' max_resolution = kwargs.get("max_resolution") if max_resolution: From 452a8132a5ff59c2831bd652e2c3d548587c0601 Mon Sep 17 00:00:00 2001 From: Disty0 <47277141+Disty0@users.noreply.github.com> Date: Tue, 26 Mar 2024 14:04:53 +0300 Subject: [PATCH 07/42] Update logging for validate_requirements.py (#2159) * Update logging for validate_requirements.py * dedupe return torch verison --- setup/validate_requirements.py | 47 +++++++++++++++++----------------- 1 file changed, 23 insertions(+), 24 deletions(-) diff --git a/setup/validate_requirements.py b/setup/validate_requirements.py index 5fbadf513..37f8d5126 100644 --- a/setup/validate_requirements.py +++ b/setup/validate_requirements.py @@ -44,27 +44,18 @@ def check_torch(): try: import torch try: + # Import IPEX / XPU support import intel_extension_for_pytorch as ipex - if torch.xpu.is_available(): - from library.ipex import ipex_init - ipex_init() except Exception: pass log.info(f'Torch {torch.__version__}') - # Check if CUDA is available - if not torch.cuda.is_available(): - log.warning('Torch reports CUDA not available') - else: + if torch.cuda.is_available(): if torch.version.cuda: - if hasattr(torch, "xpu") and torch.xpu.is_available(): - # Log Intel IPEX OneAPI version - log.info(f'Torch backend: Intel IPEX {ipex.__version__}') - else: - # Log nVidia CUDA and cuDNN versions - log.info( - f'Torch backend: nVidia CUDA {torch.version.cuda} cuDNN {torch.backends.cudnn.version() if torch.backends.cudnn.is_available() else "N/A"}' - ) + # Log nVidia CUDA and cuDNN versions + log.info( + f'Torch backend: nVidia CUDA {torch.version.cuda} cuDNN {torch.backends.cudnn.version() if torch.backends.cudnn.is_available() else "N/A"}' + ) elif torch.version.hip: # Log AMD ROCm HIP version log.info(f'Torch backend: AMD ROCm HIP {torch.version.hip}') @@ -75,15 +66,23 @@ def check_torch(): for device in [ torch.cuda.device(i) for i in range(torch.cuda.device_count()) ]: - if hasattr(torch, "xpu") and torch.xpu.is_available(): - log.info( - f'Torch detected GPU: {torch.xpu.get_device_name(device)} VRAM {round(torch.xpu.get_device_properties(device).total_memory / 1024 / 1024)} Compute Units {torch.xpu.get_device_properties(device).max_compute_units}' - ) - else: - log.info( - f'Torch detected GPU: {torch.cuda.get_device_name(device)} VRAM {round(torch.cuda.get_device_properties(device).total_memory / 1024 / 1024)} Arch {torch.cuda.get_device_capability(device)} Cores {torch.cuda.get_device_properties(device).multi_processor_count}' - ) - return int(torch.__version__[0]) + log.info( + f'Torch detected GPU: {torch.cuda.get_device_name(device)} VRAM {round(torch.cuda.get_device_properties(device).total_memory / 1024 / 1024)} Arch {torch.cuda.get_device_capability(device)} Cores {torch.cuda.get_device_properties(device).multi_processor_count}' + ) + # Check if XPU is available + elif hasattr(torch, "xpu") and torch.xpu.is_available(): + # Log Intel IPEX version + log.info(f'Torch backend: Intel IPEX {ipex.__version__}') + for device in [ + torch.xpu.device(i) for i in range(torch.xpu.device_count()) + ]: + log.info( + f'Torch detected GPU: {torch.xpu.get_device_name(device)} VRAM {round(torch.xpu.get_device_properties(device).total_memory / 1024 / 1024)} Compute Units {torch.xpu.get_device_properties(device).max_compute_units}' + ) + else: + log.warning('Torch reports GPU not available') + + return int(torch.__version__[0]) except Exception as e: log.error(f'Could not load torch: {e}') sys.exit(1) From 0a05c07149fda232870be66d9c3c4d0c77f23a73 Mon Sep 17 00:00:00 2001 From: bmaltais Date: Tue, 26 Mar 2024 19:10:32 -0400 Subject: [PATCH 08/42] Update to sd-scripts dev branch Add support for ip_noise_gamma, ip_noise_gamma_random_strength and noise_offset_random_strength --- sd-scripts | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sd-scripts b/sd-scripts index 6b1520a46..78e0a7630 160000 --- a/sd-scripts +++ b/sd-scripts @@ -1 +1 @@ -Subproject commit 6b1520a46b1b6ee7c33092537dc9449d1cc4f56f +Subproject commit 78e0a7630ca9e805783bc9dd2616e04a57a58a65 From 0f889a8eb62434a284020e4878a4e50f86940eda Mon Sep 17 00:00:00 2001 From: bmaltais Date: Tue, 26 Mar 2024 19:12:48 -0400 Subject: [PATCH 09/42] Add gui support for noise_offset_random_strength, ip_noise_gamma, ip_noise_gamma_random_strength --- .release | 2 +- README.md | 8 +- kohya_gui/class_advanced_training.py | 24 +- kohya_gui/common_gui.py | 407 +++++++++++++++------------ kohya_gui/dreambooth_gui.py | 15 + kohya_gui/finetune_gui.py | 15 + kohya_gui/lora_gui.py | 15 + kohya_gui/textual_inversion_gui.py | 15 + requirements.txt | 4 +- test/config/TI-AdamW8bit.json | 9 +- test/config/locon-AdamW8bit.json | 10 +- 11 files changed, 328 insertions(+), 196 deletions(-) diff --git a/.release b/.release index f242ab11b..06cd02725 100644 --- a/.release +++ b/.release @@ -1 +1 @@ -v23.0.15 \ No newline at end of file +v23.1.0 \ No newline at end of file diff --git a/README.md b/README.md index e818fe406..e82c7fa01 100644 --- a/README.md +++ b/README.md @@ -38,7 +38,8 @@ The GUI allows you to set the training parameters and generate and run the requi - [No module called tkinter](#no-module-called-tkinter) - [SDXL training](#sdxl-training) - [Change History](#change-history) - - [2024/03/20 (v23.0.15)](#20240320-v23015) + - [2024/03/27 (v23.1.0)](#20240327-v2310) + - [2024/03/21 (v23.0.15)](#20240321-v23015) - [2024/03/19 (v23.0.14)](#20240319-v23014) - [2024/03/19 (v23.0.13)](#20240319-v23013) - [2024/03/16 (v23.0.12)](#20240316-v23012) @@ -382,6 +383,11 @@ The documentation in this section will be moved to a separate document later. ## Change History +### 2024/03/27 (v23.1.0) + +- Update sd-scripts to 0.8.6 +- Add support for ... to the GUI. + ### 2024/03/21 (v23.0.15) - Add support for toml dataset configuration fole to all trainers diff --git a/kohya_gui/class_advanced_training.py b/kohya_gui/class_advanced_training.py index 8b448b862..a29b02113 100644 --- a/kohya_gui/class_advanced_training.py +++ b/kohya_gui/class_advanced_training.py @@ -281,6 +281,7 @@ def full_options_update(full_fp16, full_bf16): "Multires", ], value="Original", + scale=1 ) with gr.Row(visible=True) as self.noise_offset_original: self.noise_offset = gr.Slider( @@ -291,13 +292,18 @@ def full_options_update(full_fp16, full_bf16): step=0.01, info='Recommended values are 0.05 - 0.15', ) + self.noise_offset_random_strength = gr.Checkbox( + label="Noise offset random strength", + value=False, + info='Use random strength between 0~noise_offset for noise offset', + ) self.adaptive_noise_scale = gr.Slider( label="Adaptive noise scale", value=0, minimum=-1, maximum=1, step=0.001, - info="(Experimental, Optional) Since the latent is close to a normal distribution, it may be a good idea to specify a value around 1/10 the noise offset.", + info="Add `latent mean absolute value * this value` to noise_offset", ) with gr.Row(visible=False) as self.noise_offset_multires: self.multires_noise_iterations = gr.Slider( @@ -306,7 +312,7 @@ def full_options_update(full_fp16, full_bf16): minimum=0, maximum=64, step=1, - info='Enable multires noise (recommended values are 6-10)', + info='Enable multires noise (recommended values are 6-10)', ) self.multires_noise_discount = gr.Slider( label="Multires noise discount", @@ -316,6 +322,20 @@ def full_options_update(full_fp16, full_bf16): step=0.01, info='Recommended values are 0.8. For LoRAs with small datasets, 0.1-0.3', ) + with gr.Row(visible=True): + self.ip_noise_gamma = gr.Slider( + label="IP noise gamma", + value=0, + minimum=0, + maximum=1, + step=0.01, + info='enable input perturbation noise. used for regularization. recommended value: around 0.1', + ) + self.ip_noise_gamma_random_strength = gr.Checkbox( + label="IP noise gamma random strength", + value=False, + info='Use random strength between 0~ip_noise_gamma for input perturbation noise', + ) self.noise_offset_type.change( noise_offset_type_change, inputs=[self.noise_offset_type], diff --git a/kohya_gui/common_gui.py b/kohya_gui/common_gui.py index aab9b1b05..1dc4bea23 100644 --- a/kohya_gui/common_gui.py +++ b/kohya_gui/common_gui.py @@ -9,6 +9,7 @@ import shutil import sys import json +import math # Set up logging log = setup_logging() @@ -1150,6 +1151,14 @@ def run_cmd_advanced_training(**kwargs): if kwargs.get("gradient_checkpointing"): run_cmd += " --gradient_checkpointing" + + if kwargs.get("ip_noise_gamma"): + if float(kwargs["ip_noise_gamma"]) > 0: + run_cmd += f' --ip_noise_gamma={kwargs["ip_noise_gamma"]}' + + if kwargs.get("ip_noise_gamma_random_strength"): + if kwargs["ip_noise_gamma_random_strength"]: + run_cmd += f' --ip_noise_gamma_random_strength' if "keep_tokens" in kwargs and int(kwargs["keep_tokens"]) > 0: run_cmd += f' --keep_tokens="{int(kwargs["keep_tokens"])}"' @@ -1224,231 +1233,259 @@ def run_cmd_advanced_training(**kwargs): else: run_cmd += f' --lr_warmup_steps="{lr_warmup_steps}"' - gpu_ids = kwargs.get("gpu_ids") - if gpu_ids: - run_cmd += f' --gpu_ids="{gpu_ids}"' + if "gpu_ids" in kwargs: + gpu_ids = kwargs.get("gpu_ids") + if not gpu_ids == "": + run_cmd += f' --gpu_ids="{gpu_ids}"' - max_data_loader_n_workers = kwargs.get("max_data_loader_n_workers") - if max_data_loader_n_workers and not max_data_loader_n_workers == "": - run_cmd += f' --max_data_loader_n_workers="{max_data_loader_n_workers}"' + if "max_data_loader_n_workers" in kwargs: + max_data_loader_n_workers = kwargs.get("max_data_loader_n_workers") + if not max_data_loader_n_workers == "": + run_cmd += f' --max_data_loader_n_workers="{max_data_loader_n_workers}"' if "max_grad_norm" in kwargs: max_grad_norm = kwargs.get("max_grad_norm") if max_grad_norm != "": run_cmd += f' --max_grad_norm="{max_grad_norm}"' - max_resolution = kwargs.get("max_resolution") - if max_resolution: - run_cmd += f' --resolution="{max_resolution}"' + if "max_resolution" in kwargs: + run_cmd += fr' --resolution="{kwargs.get("max_resolution")}"' - max_timestep = kwargs.get("max_timestep") - if max_timestep and int(max_timestep) < 1000: - run_cmd += f" --max_timestep={int(max_timestep)}" + if "max_timestep" in kwargs: + max_timestep = kwargs.get("max_timestep") + if int(max_timestep) < 1000: + run_cmd += f" --max_timestep={int(max_timestep)}" - max_token_length = kwargs.get("max_token_length") - if max_token_length and int(max_token_length) > 75: - run_cmd += f" --max_token_length={int(max_token_length)}" + if "max_token_length" in kwargs: + max_token_length = kwargs.get("max_token_length") + if int(max_token_length) > 75: + run_cmd += f" --max_token_length={int(max_token_length)}" - max_train_epochs = kwargs.get("max_train_epochs") - if max_train_epochs and not max_train_epochs == "": - run_cmd += f" --max_train_epochs={max_train_epochs}" + if "max_train_epochs" in kwargs: + max_train_epochs = kwargs.get("max_train_epochs") + if not max_train_epochs == "": + run_cmd += f" --max_train_epochs={max_train_epochs}" - max_train_steps = kwargs.get("max_train_steps") - if max_train_steps: - run_cmd += f' --max_train_steps="{max_train_steps}"' + if "max_train_steps" in kwargs: + max_train_steps = kwargs.get("max_train_steps") + if not max_train_steps == "": + run_cmd += f' --max_train_steps="{max_train_steps}"' - mem_eff_attn = kwargs.get("mem_eff_attn") - if mem_eff_attn: - run_cmd += " --mem_eff_attn" + if "mem_eff_attn" in kwargs: + if kwargs.get("mem_eff_attn"): # Test if the value is true as it could be false + run_cmd += " --mem_eff_attn" - min_snr_gamma = kwargs.get("min_snr_gamma") - if min_snr_gamma and int(min_snr_gamma) >= 1: - run_cmd += f" --min_snr_gamma={int(min_snr_gamma)}" + if "min_snr_gamma" in kwargs: + min_snr_gamma = kwargs.get("min_snr_gamma") + if int(min_snr_gamma) >= 1: + run_cmd += f" --min_snr_gamma={int(min_snr_gamma)}" - min_timestep = kwargs.get("min_timestep") - if min_timestep and int(min_timestep) > 0: - run_cmd += f" --min_timestep={int(min_timestep)}" + if "min_timestep" in kwargs: + min_timestep = kwargs.get("min_timestep") + if int(min_timestep) > -1: + run_cmd += f" --min_timestep={int(min_timestep)}" - mixed_precision = kwargs.get("mixed_precision") - if mixed_precision: - run_cmd += f' --mixed_precision="{mixed_precision}"' + if "mixed_precision" in kwargs: + run_cmd += fr' --mixed_precision="{kwargs.get("mixed_precision")}"' - multi_gpu = kwargs.get("multi_gpu") - if multi_gpu: - run_cmd += " --multi_gpu" + if "multi_gpu" in kwargs: + if kwargs.get("multi_gpu"): + run_cmd += " --multi_gpu" - network_alpha = kwargs.get("network_alpha") - if network_alpha: - run_cmd += f' --network_alpha="{network_alpha}"' + if "network_alpha" in kwargs: + run_cmd += fr' --network_alpha="{kwargs.get("network_alpha")}"' - network_args = kwargs.get("network_args") - if network_args and len(network_args): - run_cmd += f" --network_args{network_args}" + if "network_args" in kwargs: + network_args = kwargs.get("network_args") + if network_args != "": + run_cmd += f' --network_args{network_args}' - network_dim = kwargs.get("network_dim") - if network_dim: - run_cmd += f" --network_dim={network_dim}" + if "network_dim" in kwargs: + run_cmd += fr' --network_dim={kwargs.get("network_dim")}' - network_dropout = kwargs.get("network_dropout") - if network_dropout and network_dropout > 0.0: - run_cmd += f" --network_dropout={network_dropout}" + if "network_dropout" in kwargs: + network_dropout = kwargs.get("network_dropout") + if network_dropout > 0.0: + run_cmd += f" --network_dropout={network_dropout}" - network_module = kwargs.get("network_module") - if network_module: - run_cmd += f" --network_module={network_module}" + if "network_module" in kwargs: + network_module = kwargs.get("network_module") + if network_module != "": + run_cmd += f' --network_module={network_module}' - network_train_text_encoder_only = kwargs.get("network_train_text_encoder_only") - if network_train_text_encoder_only: - run_cmd += " --network_train_text_encoder_only" + if "network_train_text_encoder_only" in kwargs: + if kwargs.get("network_train_text_encoder_only"): + run_cmd += " --network_train_text_encoder_only" - network_train_unet_only = kwargs.get("network_train_unet_only") - if network_train_unet_only: - run_cmd += " --network_train_unet_only" + if "network_train_unet_only" in kwargs: + if kwargs.get("network_train_unet_only"): + run_cmd += " --network_train_unet_only" - no_half_vae = kwargs.get("no_half_vae") - if no_half_vae: - run_cmd += " --no_half_vae" + if "no_half_vae" in kwargs: + if kwargs.get("no_half_vae"): # Test if the value is true as it could be false + run_cmd += " --no_half_vae" - no_token_padding = kwargs.get("no_token_padding") - if no_token_padding: - run_cmd += " --no_token_padding" + if "no_token_padding" in kwargs: + if kwargs.get("no_token_padding"): # Test if the value is true as it could be false + run_cmd += " --no_token_padding" if "noise_offset_type" in kwargs: noise_offset_type = kwargs["noise_offset_type"] - if kwargs["noise_offset_type"] == "Original": - noise_offset = float(kwargs.get("noise_offset", 0)) - if noise_offset: - run_cmd += f" --noise_offset={noise_offset}" - - adaptive_noise_scale = float(kwargs.get("adaptive_noise_scale", 0)) - if adaptive_noise_scale != 0 and noise_offset > 0: - run_cmd += f" --adaptive_noise_scale={adaptive_noise_scale}" - + if noise_offset_type == "Original": + if "noise_offset" in kwargs: + noise_offset = float(kwargs.get("noise_offset", 0)) + if noise_offset: + run_cmd += f" --noise_offset={float(noise_offset)}" + + if "adaptive_noise_scale" in kwargs: + adaptive_noise_scale = float(kwargs.get("adaptive_noise_scale", 0)) + if adaptive_noise_scale != 0 and noise_offset > 0: + run_cmd += f" --adaptive_noise_scale={adaptive_noise_scale}" + + if "noise_offset_random_strength" in kwargs: + if kwargs.get("noise_offset_random_strength"): + run_cmd += f" --noise_offset_random_strength" elif noise_offset_type == "Multires": - multires_noise_iterations = int(kwargs.get("multires_noise_iterations", 0)) - if multires_noise_iterations > 0: - run_cmd += f' --multires_noise_iterations="{multires_noise_iterations}"' - - multires_noise_discount = float(kwargs.get("multires_noise_discount", 0)) - if multires_noise_discount > 0: - run_cmd += f' --multires_noise_discount="{multires_noise_discount}"' - - num_machines = kwargs.get("num_machines") - if num_machines and int(num_machines) > 1: - run_cmd += f" --num_machines={int(num_machines)}" - - num_processes = kwargs.get("num_processes") - if num_processes and int(num_processes) > 1: - run_cmd += f" --num_processes={int(num_processes)}" - - num_cpu_threads_per_process = kwargs.get("num_cpu_threads_per_process") - if num_cpu_threads_per_process and int(num_cpu_threads_per_process) > 1: - run_cmd += f" --num_cpu_threads_per_process={int(num_cpu_threads_per_process)}" - - optimizer_args = kwargs.get("optimizer_args") - if optimizer_args and optimizer_args != "": - run_cmd += f" --optimizer_args {optimizer_args}" - - optimizer_type = kwargs.get("optimizer") - if optimizer_type: - run_cmd += f' --optimizer_type="{optimizer_type}"' - - output_dir = kwargs.get("output_dir") - if output_dir: + if "multires_noise_iterations" in kwargs: + multires_noise_iterations = int(kwargs.get("multires_noise_iterations", 0)) + if multires_noise_iterations > 0: + run_cmd += f' --multires_noise_iterations="{multires_noise_iterations}"' + + if "multires_noise_discount" in kwargs: + multires_noise_discount = float(kwargs.get("multires_noise_discount", 0)) + if multires_noise_discount > 0: + run_cmd += f' --multires_noise_discount="{multires_noise_discount}"' + + if "num_machines" in kwargs: + num_machines = kwargs.get("num_machines") + if int(num_machines) > 1: + run_cmd += f" --num_machines={int(num_machines)}" + + if "num_processes" in kwargs: + num_processes = kwargs.get("num_processes") + if int(num_processes) > 1: + run_cmd += f" --num_processes={int(num_processes)}" + + if "num_cpu_threads_per_process" in kwargs: + num_cpu_threads_per_process = kwargs.get("num_cpu_threads_per_process") + if int(num_cpu_threads_per_process) > 1: + run_cmd += f" --num_cpu_threads_per_process={int(num_cpu_threads_per_process)}" + + if "optimizer_args" in kwargs: + optimizer_args = kwargs.get("optimizer_args") + if optimizer_args != "": + run_cmd += f" --optimizer_args {optimizer_args}" + + if "optimizer" in kwargs: + run_cmd += fr' --optimizer_type="{kwargs.get("optimizer")}"' + + if "output_dir" in kwargs: + output_dir = kwargs.get("output_dir") if output_dir.startswith('"') and output_dir.endswith('"'): output_dir = output_dir[1:-1] if os.path.exists(output_dir): run_cmd += rf' --output_dir="{output_dir}"' - output_name = kwargs.get("output_name") - if output_name and not output_name == "": - run_cmd += f' --output_name="{output_name}"' + if "output_name" in kwargs: + output_name = kwargs.get("output_name") + if not output_name == "": + run_cmd += f' --output_name="{output_name}"' - persistent_data_loader_workers = kwargs.get("persistent_data_loader_workers") - if persistent_data_loader_workers: - run_cmd += " --persistent_data_loader_workers" + if "persistent_data_loader_workers" in kwargs: + if kwargs.get("persistent_data_loader_workers"): + run_cmd += " --persistent_data_loader_workers" - pretrained_model_name_or_path = kwargs.get("pretrained_model_name_or_path") - if pretrained_model_name_or_path: + if "pretrained_model_name_or_path" in kwargs: run_cmd += ( - rf' --pretrained_model_name_or_path="{pretrained_model_name_or_path}"' + rf' --pretrained_model_name_or_path="{kwargs.get("pretrained_model_name_or_path")}"' ) - prior_loss_weight = kwargs.get("prior_loss_weight") - if prior_loss_weight and not float(prior_loss_weight) == 1.0: - run_cmd += f" --prior_loss_weight={prior_loss_weight}" - - random_crop = kwargs.get("random_crop") - if random_crop: - run_cmd += " --random_crop" - - reg_data_dir = kwargs.get("reg_data_dir") - if reg_data_dir and len(reg_data_dir): - if reg_data_dir.startswith('"') and reg_data_dir.endswith('"'): - reg_data_dir = reg_data_dir[1:-1] - if os.path.isdir(reg_data_dir): - run_cmd += rf' --reg_data_dir="{reg_data_dir}"' - - resume = kwargs.get("resume") - if resume: - run_cmd += f' --resume="{resume}"' - - save_every_n_epochs = kwargs.get("save_every_n_epochs") - if save_every_n_epochs: - run_cmd += f' --save_every_n_epochs="{int(save_every_n_epochs)}"' - - save_every_n_steps = kwargs.get("save_every_n_steps") - if save_every_n_steps and int(save_every_n_steps) > 0: - run_cmd += f' --save_every_n_steps="{int(save_every_n_steps)}"' - - save_last_n_steps = kwargs.get("save_last_n_steps") - if save_last_n_steps and int(save_last_n_steps) > 0: - run_cmd += f' --save_last_n_steps="{int(save_last_n_steps)}"' - - save_last_n_steps_state = kwargs.get("save_last_n_steps_state") - if save_last_n_steps_state and int(save_last_n_steps_state) > 0: - run_cmd += f' --save_last_n_steps_state="{int(save_last_n_steps_state)}"' - - save_model_as = kwargs.get("save_model_as") - if save_model_as and not save_model_as == "same as source model": - run_cmd += f" --save_model_as={save_model_as}" - - save_precision = kwargs.get("save_precision") - if save_precision: - run_cmd += f' --save_precision="{save_precision}"' - - save_state = kwargs.get("save_state") - if save_state: - run_cmd += " --save_state" - - scale_v_pred_loss_like_noise_pred = kwargs.get("scale_v_pred_loss_like_noise_pred") - if scale_v_pred_loss_like_noise_pred: - run_cmd += " --scale_v_pred_loss_like_noise_pred" - - scale_weight_norms = kwargs.get("scale_weight_norms") - if scale_weight_norms and scale_weight_norms > 0.0: - run_cmd += f' --scale_weight_norms="{scale_weight_norms}"' - - seed = kwargs.get("seed") - if seed and seed != "": - run_cmd += f' --seed="{seed}"' - - shuffle_caption = kwargs.get("shuffle_caption") - if shuffle_caption: - run_cmd += " --shuffle_caption" - - stop_text_encoder_training = kwargs.get("stop_text_encoder_training") - if stop_text_encoder_training and stop_text_encoder_training > 0: - run_cmd += f' --stop_text_encoder_training="{stop_text_encoder_training}"' - - text_encoder_lr = kwargs.get("text_encoder_lr") - if text_encoder_lr and (float(text_encoder_lr) > 0): - run_cmd += f" --text_encoder_lr={text_encoder_lr}" - - train_batch_size = kwargs.get("train_batch_size") - if train_batch_size: - run_cmd += f' --train_batch_size="{train_batch_size}"' + if "prior_loss_weight" in kwargs: + prior_loss_weight = kwargs.get("prior_loss_weight") + if not float(prior_loss_weight) == 1.0: + run_cmd += f" --prior_loss_weight={prior_loss_weight}" + + if "random_crop" in kwargs: + random_crop = kwargs.get("random_crop") + if random_crop: + run_cmd += " --random_crop" + + if "reg_data_dir" in kwargs: + reg_data_dir = kwargs.get("reg_data_dir") + if len(reg_data_dir): + if reg_data_dir.startswith('"') and reg_data_dir.endswith('"'): + reg_data_dir = reg_data_dir[1:-1] + if os.path.isdir(reg_data_dir): + run_cmd += rf' --reg_data_dir="{reg_data_dir}"' + + if "resume" in kwargs: + resume = kwargs.get("resume") + if len(resume): + run_cmd += f' --resume="{resume}"' + + if "save_every_n_epochs" in kwargs: + save_every_n_epochs = kwargs.get("save_every_n_epochs") + if int(save_every_n_epochs) > 0: + run_cmd += f' --save_every_n_epochs="{int(save_every_n_epochs)}"' + + if "save_every_n_steps" in kwargs: + save_every_n_steps = kwargs.get("save_every_n_steps") + if int(save_every_n_steps) > 0: + run_cmd += f' --save_every_n_steps="{int(save_every_n_steps)}"' + + if "save_last_n_steps" in kwargs: + save_last_n_steps = kwargs.get("save_last_n_steps") + if int(save_last_n_steps) > 0: + run_cmd += f' --save_last_n_steps="{int(save_last_n_steps)}"' + + if "save_last_n_steps_state" in kwargs: + save_last_n_steps_state = kwargs.get("save_last_n_steps_state") + if int(save_last_n_steps_state) > 0: + run_cmd += f' --save_last_n_steps_state="{int(save_last_n_steps_state)}"' + + if "save_model_as" in kwargs: + save_model_as = kwargs.get("save_model_as") + if save_model_as != "same as source model": + run_cmd += f" --save_model_as={save_model_as}" + + if "save_precision" in kwargs: + run_cmd += fr' --save_precision="{kwargs.get("save_precision")}"' + + if "save_state" in kwargs: + if kwargs.get("save_state"): + run_cmd += " --save_state" + + if "scale_v_pred_loss_like_noise_pred" in kwargs: + if kwargs.get("scale_v_pred_loss_like_noise_pred"): + run_cmd += " --scale_v_pred_loss_like_noise_pred" + + if "scale_weight_norms" in kwargs: + scale_weight_norms = kwargs.get("scale_weight_norms") + if scale_weight_norms > 0.0: + run_cmd += f' --scale_weight_norms="{scale_weight_norms}"' + + if "seed" in kwargs: + seed = kwargs.get("seed") + if seed != "": + run_cmd += f' --seed="{seed}"' + + if "shuffle_caption" in kwargs: + if kwargs.get("shuffle_caption"): + run_cmd += " --shuffle_caption" + + if "stop_text_encoder_training" in kwargs: + stop_text_encoder_training = kwargs.get("stop_text_encoder_training") + if stop_text_encoder_training > 0: + run_cmd += f' --stop_text_encoder_training="{stop_text_encoder_training}"' + + if "text_encoder_lr" in kwargs: + text_encoder_lr = kwargs.get("text_encoder_lr") + if float(text_encoder_lr) > -1: + run_cmd += f" --text_encoder_lr={text_encoder_lr}" + + if "train_batch_size" in kwargs: + run_cmd += fr' --train_batch_size="{kwargs.get("train_batch_size")}"' training_comment = kwargs.get("training_comment") if training_comment and len(training_comment): diff --git a/kohya_gui/dreambooth_gui.py b/kohya_gui/dreambooth_gui.py index 3ac9f8a87..7ecffcb83 100644 --- a/kohya_gui/dreambooth_gui.py +++ b/kohya_gui/dreambooth_gui.py @@ -122,9 +122,12 @@ def save_configuration( lr_scheduler_args, noise_offset_type, noise_offset, + noise_offset_random_strength, adaptive_noise_scale, multires_noise_iterations, multires_noise_discount, + ip_noise_gamma, + ip_noise_gamma_random_strength, sample_every_n_steps, sample_every_n_epochs, sample_sampler, @@ -254,9 +257,12 @@ def open_configuration( lr_scheduler_args, noise_offset_type, noise_offset, + noise_offset_random_strength, adaptive_noise_scale, multires_noise_iterations, multires_noise_discount, + ip_noise_gamma, + ip_noise_gamma_random_strength, sample_every_n_steps, sample_every_n_epochs, sample_sampler, @@ -381,9 +387,12 @@ def train_model( lr_scheduler_args, noise_offset_type, noise_offset, + noise_offset_random_strength, adaptive_noise_scale, multires_noise_iterations, multires_noise_discount, + ip_noise_gamma, + ip_noise_gamma_random_strength, sample_every_n_steps, sample_every_n_epochs, sample_sampler, @@ -567,6 +576,8 @@ def train_model( "full_fp16": full_fp16, "gradient_accumulation_steps": gradient_accumulation_steps, "gradient_checkpointing": gradient_checkpointing, + "ip_noise_gamma": ip_noise_gamma, + "ip_noise_gamma_random_strength": ip_noise_gamma_random_strength, "keep_tokens": keep_tokens, "learning_rate": learning_rate, "logging_dir": logging_dir, @@ -593,6 +604,7 @@ def train_model( "multires_noise_iterations": multires_noise_iterations, "no_token_padding": no_token_padding, "noise_offset": noise_offset, + "noise_offset_random_strength": noise_offset_random_strength, "noise_offset_type": noise_offset_type, "optimizer": optimizer, "optimizer_args": optimizer_args, @@ -847,9 +859,12 @@ def dreambooth_tab( basic_training.lr_scheduler_args, advanced_training.noise_offset_type, advanced_training.noise_offset, + advanced_training.noise_offset_random_strength, advanced_training.adaptive_noise_scale, advanced_training.multires_noise_iterations, advanced_training.multires_noise_discount, + advanced_training.ip_noise_gamma, + advanced_training.ip_noise_gamma_random_strength, sample.sample_every_n_steps, sample.sample_every_n_epochs, sample.sample_sampler, diff --git a/kohya_gui/finetune_gui.py b/kohya_gui/finetune_gui.py index a7dc033a4..56bf660a1 100644 --- a/kohya_gui/finetune_gui.py +++ b/kohya_gui/finetune_gui.py @@ -130,9 +130,12 @@ def save_configuration( lr_scheduler_args, noise_offset_type, noise_offset, + noise_offset_random_strength, adaptive_noise_scale, multires_noise_iterations, multires_noise_discount, + ip_noise_gamma, + ip_noise_gamma_random_strength, sample_every_n_steps, sample_every_n_epochs, sample_sampler, @@ -270,9 +273,12 @@ def open_configuration( lr_scheduler_args, noise_offset_type, noise_offset, + noise_offset_random_strength, adaptive_noise_scale, multires_noise_iterations, multires_noise_discount, + ip_noise_gamma, + ip_noise_gamma_random_strength, sample_every_n_steps, sample_every_n_epochs, sample_sampler, @@ -417,9 +423,12 @@ def train_model( lr_scheduler_args, noise_offset_type, noise_offset, + noise_offset_random_strength, adaptive_noise_scale, multires_noise_iterations, multires_noise_discount, + ip_noise_gamma, + ip_noise_gamma_random_strength, sample_every_n_steps, sample_every_n_epochs, sample_sampler, @@ -602,6 +611,8 @@ def train_model( "gradient_accumulation_steps": gradient_accumulation_steps, "gradient_checkpointing": gradient_checkpointing, "in_json": in_json, + "ip_noise_gamma": ip_noise_gamma, + "ip_noise_gamma_random_strength": ip_noise_gamma_random_strength, "keep_tokens": keep_tokens, "learning_rate": learning_rate, "logging_dir": logging_dir, @@ -625,6 +636,7 @@ def train_model( "multires_noise_discount": multires_noise_discount, "multires_noise_iterations": multires_noise_iterations, "noise_offset": noise_offset, + "noise_offset_random_strength": noise_offset_random_strength, "noise_offset_type": noise_offset_type, "optimizer": optimizer, "optimizer_args": optimizer_args, @@ -941,9 +953,12 @@ def list_presets(path): basic_training.lr_scheduler_args, advanced_training.noise_offset_type, advanced_training.noise_offset, + advanced_training.noise_offset_random_strength, advanced_training.adaptive_noise_scale, advanced_training.multires_noise_iterations, advanced_training.multires_noise_discount, + advanced_training.ip_noise_gamma, + advanced_training.ip_noise_gamma_random_strength, sample.sample_every_n_steps, sample.sample_every_n_epochs, sample.sample_sampler, diff --git a/kohya_gui/lora_gui.py b/kohya_gui/lora_gui.py index 69ca484d0..42b2a418d 100644 --- a/kohya_gui/lora_gui.py +++ b/kohya_gui/lora_gui.py @@ -160,9 +160,12 @@ def save_configuration( max_grad_norm, noise_offset_type, noise_offset, + noise_offset_random_strength, adaptive_noise_scale, multires_noise_iterations, multires_noise_discount, + ip_noise_gamma, + ip_noise_gamma_random_strength, LoRA_type, factor, bypass_mode, @@ -341,9 +344,12 @@ def open_configuration( max_grad_norm, noise_offset_type, noise_offset, + noise_offset_random_strength, adaptive_noise_scale, multires_noise_iterations, multires_noise_discount, + ip_noise_gamma, + ip_noise_gamma_random_strength, LoRA_type, factor, bypass_mode, @@ -550,9 +556,12 @@ def train_model( max_grad_norm, noise_offset_type, noise_offset, + noise_offset_random_strength, adaptive_noise_scale, multires_noise_iterations, multires_noise_discount, + ip_noise_gamma, + ip_noise_gamma_random_strength, LoRA_type, factor, bypass_mode, @@ -923,6 +932,8 @@ def train_model( "full_fp16": full_fp16, "gradient_accumulation_steps": gradient_accumulation_steps, "gradient_checkpointing": gradient_checkpointing, + "ip_noise_gamma": ip_noise_gamma, + "ip_noise_gamma_random_strength": ip_noise_gamma_random_strength, "keep_tokens": keep_tokens, "learning_rate": learning_rate, "logging_dir": logging_dir, @@ -958,6 +969,7 @@ def train_model( "network_train_text_encoder_only": network_train_text_encoder_only, "no_half_vae": True if sdxl and sdxl_no_half_vae else None, "noise_offset": noise_offset, + "noise_offset_random_strength": noise_offset_random_strength, "noise_offset_type": noise_offset_type, "optimizer": optimizer, "optimizer_args": optimizer_args, @@ -1977,9 +1989,12 @@ def update_LoRA_settings( basic_training.max_grad_norm, advanced_training.noise_offset_type, advanced_training.noise_offset, + advanced_training.noise_offset_random_strength, advanced_training.adaptive_noise_scale, advanced_training.multires_noise_iterations, advanced_training.multires_noise_discount, + advanced_training.ip_noise_gamma, + advanced_training.ip_noise_gamma_random_strength, LoRA_type, factor, bypass_mode, diff --git a/kohya_gui/textual_inversion_gui.py b/kohya_gui/textual_inversion_gui.py index f4b59613b..e559fce65 100644 --- a/kohya_gui/textual_inversion_gui.py +++ b/kohya_gui/textual_inversion_gui.py @@ -123,9 +123,12 @@ def save_configuration( lr_scheduler_args, noise_offset_type, noise_offset, + noise_offset_random_strength, adaptive_noise_scale, multires_noise_iterations, multires_noise_discount, + ip_noise_gamma, + ip_noise_gamma_random_strength, sample_every_n_steps, sample_every_n_epochs, sample_sampler, @@ -258,9 +261,12 @@ def open_configuration( lr_scheduler_args, noise_offset_type, noise_offset, + noise_offset_random_strength, adaptive_noise_scale, multires_noise_iterations, multires_noise_discount, + ip_noise_gamma, + ip_noise_gamma_random_strength, sample_every_n_steps, sample_every_n_epochs, sample_sampler, @@ -386,9 +392,12 @@ def train_model( lr_scheduler_args, noise_offset_type, noise_offset, + noise_offset_random_strength, adaptive_noise_scale, multires_noise_iterations, multires_noise_discount, + ip_noise_gamma, + ip_noise_gamma_random_strength, sample_every_n_steps, sample_every_n_epochs, sample_sampler, @@ -555,6 +564,8 @@ def train_model( full_fp16=full_fp16, gradient_accumulation_steps=gradient_accumulation_steps, gradient_checkpointing=gradient_checkpointing, + ip_noise_gamma=ip_noise_gamma, + ip_noise_gamma_random_strength=ip_noise_gamma_random_strength, keep_tokens=keep_tokens, learning_rate=learning_rate, logging_dir=logging_dir, @@ -582,6 +593,7 @@ def train_model( no_half_vae=True if sdxl and sdxl_no_half_vae else None, no_token_padding=no_token_padding, noise_offset=noise_offset, + noise_offset_random_strength=noise_offset_random_strength, noise_offset_type=noise_offset_type, optimizer=optimizer, optimizer_args=optimizer_args, @@ -918,9 +930,12 @@ def list_embedding_files(path): basic_training.lr_scheduler_args, advanced_training.noise_offset_type, advanced_training.noise_offset, + advanced_training.noise_offset_random_strength, advanced_training.adaptive_noise_scale, advanced_training.multires_noise_iterations, advanced_training.multires_noise_discount, + advanced_training.ip_noise_gamma, + advanced_training.ip_noise_gamma_random_strength, sample.sample_every_n_steps, sample.sample_every_n_epochs, sample.sample_sampler, diff --git a/requirements.txt b/requirements.txt index 48344eeb9..c1cf49865 100644 --- a/requirements.txt +++ b/requirements.txt @@ -22,8 +22,8 @@ lycoris_lora==2.2.0.post3 # tensorflow==2.14.0 # for WD14 captioning (onnx) omegaconf==2.3.0 -onnx==1.14.1 -onnxruntime-gpu==1.16.0 +onnx==1.15.0 +onnxruntime-gpu==1.17.1 # onnxruntime==1.16.0 # this is for onnx: # tensorboard==2.14.1 diff --git a/test/config/TI-AdamW8bit.json b/test/config/TI-AdamW8bit.json index 9785c01f8..aea6bc6b0 100644 --- a/test/config/TI-AdamW8bit.json +++ b/test/config/TI-AdamW8bit.json @@ -1,5 +1,5 @@ { - "adaptive_noise_scale": 0, + "adaptive_noise_scale": 0.005, "additional_parameters": "", "bucket_no_upscale": true, "bucket_reso_steps": 1, @@ -12,13 +12,15 @@ "color_aug": false, "dataset_config": "", "enable_bucket": true, - "epoch": 4, + "epoch": 8, "flip_aug": false, "full_fp16": false, "gpu_ids": "", "gradient_accumulation_steps": 1, "gradient_checkpointing": false, "init_word": "*", + "ip_noise_gamma": 0.1, + "ip_noise_gamma_random_strength": true, "keep_tokens": "0", "learning_rate": 0.0001, "log_tracker_config": "", @@ -47,7 +49,8 @@ "multires_noise_iterations": 8, "no_token_padding": false, "noise_offset": 0.05, - "noise_offset_type": "Multires", + "noise_offset_random_strength": true, + "noise_offset_type": "Original", "num_cpu_threads_per_process": 2, "num_machines": 1, "num_processes": 1, diff --git a/test/config/locon-AdamW8bit.json b/test/config/locon-AdamW8bit.json index 99f2947b5..8a251d44c 100644 --- a/test/config/locon-AdamW8bit.json +++ b/test/config/locon-AdamW8bit.json @@ -1,13 +1,14 @@ { "LoRA_type": "Kohya LoCon", "LyCORIS_preset": "full", - "adaptive_noise_scale": 0, + "adaptive_noise_scale": 0.005, "additional_parameters": "", "block_alphas": "", "block_dims": "", "block_lr_zero_threshold": "", "bucket_no_upscale": true, "bucket_reso_steps": 64, + "bypass_mode": false, "cache_latents": true, "cache_latents_to_disk": false, "caption_dropout_every_n_epochs": 0.0, @@ -20,12 +21,14 @@ "conv_block_alphas": "", "conv_block_dims": "", "conv_dim": 16, + "dataset_config": "", "debiased_estimation_loss": false, "decompose_both": false, "dim_from_weights": false, + "dora_wd": false, "down_lr_weight": "", "enable_bucket": true, - "epoch": 1, + "epoch": 8, "factor": -1, "flip_aug": false, "fp8_base": false, @@ -34,6 +37,8 @@ "gpu_ids": "", "gradient_accumulation_steps": 1, "gradient_checkpointing": false, + "ip_noise_gamma": 0.1, + "ip_noise_gamma_random_strength": true, "keep_tokens": "0", "learning_rate": 0.0005, "log_tracker_config": "", @@ -68,6 +73,7 @@ "network_dim": 16, "network_dropout": 0.1, "noise_offset": 0.05, + "noise_offset_random_strength": true, "noise_offset_type": "Original", "num_cpu_threads_per_process": 2, "num_machines": 1, From b0139c751a37dfe5eda538d5b5dde40731b0c694 Mon Sep 17 00:00:00 2001 From: bmaltais Date: Tue, 26 Mar 2024 19:22:27 -0400 Subject: [PATCH 10/42] Add gui support for save_state_on_train_end --- kohya_gui/class_advanced_training.py | 3 +++ kohya_gui/common_gui.py | 4 ++++ kohya_gui/dreambooth_gui.py | 5 +++++ kohya_gui/finetune_gui.py | 5 +++++ kohya_gui/lora_gui.py | 5 +++++ kohya_gui/textual_inversion_gui.py | 5 +++++ 6 files changed, 27 insertions(+) diff --git a/kohya_gui/class_advanced_training.py b/kohya_gui/class_advanced_training.py index a29b02113..8fb490f20 100644 --- a/kohya_gui/class_advanced_training.py +++ b/kohya_gui/class_advanced_training.py @@ -356,6 +356,9 @@ def full_options_update(full_fp16, full_bf16): ) with gr.Group(), gr.Row(): self.save_state = gr.Checkbox(label="Save training state", value=False) + + + self.save_state_on_train_end = gr.Checkbox(label="Save training state at end of training", value=False) def list_state_dirs(path): self.current_state_dir = path if not path == "" else "." diff --git a/kohya_gui/common_gui.py b/kohya_gui/common_gui.py index 1dc4bea23..54f4bf6f4 100644 --- a/kohya_gui/common_gui.py +++ b/kohya_gui/common_gui.py @@ -1455,6 +1455,10 @@ def run_cmd_advanced_training(**kwargs): if "save_state" in kwargs: if kwargs.get("save_state"): run_cmd += " --save_state" + + if "save_state_on_train_end" in kwargs: + if kwargs.get("save_state_on_train_end"): + run_cmd += " --save_state_on_train_end" if "scale_v_pred_loss_like_noise_pred" in kwargs: if kwargs.get("scale_v_pred_loss_like_noise_pred"): diff --git a/kohya_gui/dreambooth_gui.py b/kohya_gui/dreambooth_gui.py index 7ecffcb83..543752f9d 100644 --- a/kohya_gui/dreambooth_gui.py +++ b/kohya_gui/dreambooth_gui.py @@ -89,6 +89,7 @@ def save_configuration( save_model_as, shuffle_caption, save_state, + save_state_on_train_end, resume, prior_loss_weight, color_aug, @@ -224,6 +225,7 @@ def open_configuration( save_model_as, shuffle_caption, save_state, + save_state_on_train_end, resume, prior_loss_weight, color_aug, @@ -354,6 +356,7 @@ def train_model( save_model_as, shuffle_caption, save_state, + save_state_on_train_end, resume, prior_loss_weight, color_aug, @@ -623,6 +626,7 @@ def train_model( "save_model_as": save_model_as, "save_precision": save_precision, "save_state": save_state, + "save_state_on_train_end": save_state_on_train_end, "scale_v_pred_loss_like_noise_pred": scale_v_pred_loss_like_noise_pred, "seed": seed, "shuffle_caption": shuffle_caption, @@ -826,6 +830,7 @@ def dreambooth_tab( source_model.save_model_as, advanced_training.shuffle_caption, advanced_training.save_state, + advanced_training.save_state_on_train_end, advanced_training.resume, advanced_training.prior_loss_weight, advanced_training.color_aug, diff --git a/kohya_gui/finetune_gui.py b/kohya_gui/finetune_gui.py index 56bf660a1..161cebc58 100644 --- a/kohya_gui/finetune_gui.py +++ b/kohya_gui/finetune_gui.py @@ -100,6 +100,7 @@ def save_configuration( multi_gpu, gpu_ids, save_state, + save_state_on_train_end, resume, gradient_checkpointing, gradient_accumulation_steps, @@ -243,6 +244,7 @@ def open_configuration( multi_gpu, gpu_ids, save_state, + save_state_on_train_end, resume, gradient_checkpointing, gradient_accumulation_steps, @@ -393,6 +395,7 @@ def train_model( multi_gpu, gpu_ids, save_state, + save_state_on_train_end, resume, gradient_checkpointing, gradient_accumulation_steps, @@ -653,6 +656,7 @@ def train_model( "save_model_as": save_model_as, "save_precision": save_precision, "save_state": save_state, + "save_state_on_train_end": save_state_on_train_end, "scale_v_pred_loss_like_noise_pred": scale_v_pred_loss_like_noise_pred, "seed": seed, "shuffle_caption": shuffle_caption, @@ -923,6 +927,7 @@ def list_presets(path): advanced_training.multi_gpu, advanced_training.gpu_ids, advanced_training.save_state, + advanced_training.save_state_on_train_end, advanced_training.resume, advanced_training.gradient_checkpointing, gradient_accumulation_steps, diff --git a/kohya_gui/lora_gui.py b/kohya_gui/lora_gui.py index 42b2a418d..d6d5f7a17 100644 --- a/kohya_gui/lora_gui.py +++ b/kohya_gui/lora_gui.py @@ -120,6 +120,7 @@ def save_configuration( save_model_as, shuffle_caption, save_state, + save_state_on_train_end, resume, prior_loss_weight, text_encoder_lr, @@ -304,6 +305,7 @@ def open_configuration( save_model_as, shuffle_caption, save_state, + save_state_on_train_end, resume, prior_loss_weight, text_encoder_lr, @@ -516,6 +518,7 @@ def train_model( save_model_as, shuffle_caption, save_state, + save_state_on_train_end, resume, prior_loss_weight, text_encoder_lr, @@ -988,6 +991,7 @@ def train_model( "save_model_as": save_model_as, "save_precision": save_precision, "save_state": save_state, + "save_state_on_train_end": save_state_on_train_end, "scale_v_pred_loss_like_noise_pred": scale_v_pred_loss_like_noise_pred, "scale_weight_norms": scale_weight_norms, "seed": seed, @@ -1949,6 +1953,7 @@ def update_LoRA_settings( source_model.save_model_as, advanced_training.shuffle_caption, advanced_training.save_state, + advanced_training.save_state_on_train_end, advanced_training.resume, advanced_training.prior_loss_weight, text_encoder_lr, diff --git a/kohya_gui/textual_inversion_gui.py b/kohya_gui/textual_inversion_gui.py index e559fce65..a5cfdd952 100644 --- a/kohya_gui/textual_inversion_gui.py +++ b/kohya_gui/textual_inversion_gui.py @@ -85,6 +85,7 @@ def save_configuration( save_model_as, shuffle_caption, save_state, + save_state_on_train_end, resume, prior_loss_weight, color_aug, @@ -223,6 +224,7 @@ def open_configuration( save_model_as, shuffle_caption, save_state, + save_state_on_train_end, resume, prior_loss_weight, color_aug, @@ -354,6 +356,7 @@ def train_model( save_model_as, shuffle_caption, save_state, + save_state_on_train_end, resume, prior_loss_weight, color_aug, @@ -612,6 +615,7 @@ def train_model( save_model_as=save_model_as, save_precision=save_precision, save_state=save_state, + save_state_on_train_end=save_state_on_train_end, scale_v_pred_loss_like_noise_pred=scale_v_pred_loss_like_noise_pred, seed=seed, shuffle_caption=shuffle_caption, @@ -892,6 +896,7 @@ def list_embedding_files(path): source_model.save_model_as, advanced_training.shuffle_caption, advanced_training.save_state, + advanced_training.save_state_on_train_end, advanced_training.resume, advanced_training.prior_loss_weight, advanced_training.color_aug, From 9fd7cf8872951107ec6ae60847c58ada7e307aa7 Mon Sep 17 00:00:00 2001 From: bmaltais Date: Tue, 26 Mar 2024 19:27:16 -0400 Subject: [PATCH 11/42] Update requirements --- requirements.txt | 2 ++ 1 file changed, 2 insertions(+) diff --git a/requirements.txt b/requirements.txt index c1cf49865..13d38777a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -10,6 +10,8 @@ fairscale==0.4.13 ftfy==6.1.1 gradio==3.50.2 huggingface-hub==0.20.1 +# for Image utils +imagesize==1.4.1 # for loading Diffusers' SDXL invisible-watermark==0.2.0 lion-pytorch==0.0.6 From 381e85296ae2c6df37bfe89005514cdc0731a985 Mon Sep 17 00:00:00 2001 From: bmaltais Date: Tue, 26 Mar 2024 20:14:07 -0400 Subject: [PATCH 12/42] Add support for masked_loss to the GUI --- kohya_gui/class_advanced_training.py | 9 +- kohya_gui/common_gui.py | 4 + kohya_gui/dreambooth_gui.py | 5 + kohya_gui/finetune_gui.py | 5 + kohya_gui/lora_gui.py | 5 + test/config/dataset-masked_loss.toml | 15 ++ ...dreambooth-AdamW8bit-masked_loss-toml.json | 100 +++++++++++++ .../locon-AdamW8bit-masked_loss-toml.json | 137 ++++++++++++++++++ test/masked_loss/Dariusz_Zawadzki.jpg | Bin 0 -> 28758 bytes test/masked_loss/Dariusz_Zawadzki_2.jpg | Bin 0 -> 27150 bytes test/masked_loss/Dariusz_Zawadzki_3.jpg | Bin 0 -> 22545 bytes test/masked_loss/Dariusz_Zawadzki_4.jpg | Bin 0 -> 26498 bytes test/masked_loss/Dariusz_Zawadzki_5.jpg | Bin 0 -> 21752 bytes test/masked_loss/Dariusz_Zawadzki_6.jpg | Bin 0 -> 24891 bytes test/masked_loss/Dariusz_Zawadzki_7.jpg | Bin 0 -> 25024 bytes test/masked_loss/Dariusz_Zawadzki_8.jpg | Bin 0 -> 25082 bytes 16 files changed, 278 insertions(+), 2 deletions(-) create mode 100644 test/config/dataset-masked_loss.toml create mode 100644 test/config/dreambooth-AdamW8bit-masked_loss-toml.json create mode 100644 test/config/locon-AdamW8bit-masked_loss-toml.json create mode 100644 test/masked_loss/Dariusz_Zawadzki.jpg create mode 100644 test/masked_loss/Dariusz_Zawadzki_2.jpg create mode 100644 test/masked_loss/Dariusz_Zawadzki_3.jpg create mode 100644 test/masked_loss/Dariusz_Zawadzki_4.jpg create mode 100644 test/masked_loss/Dariusz_Zawadzki_5.jpg create mode 100644 test/masked_loss/Dariusz_Zawadzki_6.jpg create mode 100644 test/masked_loss/Dariusz_Zawadzki_7.jpg create mode 100644 test/masked_loss/Dariusz_Zawadzki_8.jpg diff --git a/kohya_gui/class_advanced_training.py b/kohya_gui/class_advanced_training.py index 8fb490f20..2f2fe7967 100644 --- a/kohya_gui/class_advanced_training.py +++ b/kohya_gui/class_advanced_training.py @@ -222,8 +222,13 @@ def full_options_update(full_fp16, full_bf16): choices=["none", "sdpa", "xformers"], value="xformers", ) - self.color_aug = gr.Checkbox(label="Color augmentation", value=False) - self.flip_aug = gr.Checkbox(label="Flip augmentation", value=False) + self.color_aug = gr.Checkbox(label="Color augmentation", value=False, info="Enable weak color augmentation") + self.flip_aug = gr.Checkbox(label="Flip augmentation", value=False, info="Enable horizontal flip augmentation") + self.masked_loss = gr.Checkbox( + label="Masked loss", + value=False, + info="Apply mask for calculating loss. conditioning_data_dir is required for dataset" + ) self.min_snr_gamma = gr.Slider( label="Min SNR gamma", value=0, diff --git a/kohya_gui/common_gui.py b/kohya_gui/common_gui.py index 54f4bf6f4..1f7093a15 100644 --- a/kohya_gui/common_gui.py +++ b/kohya_gui/common_gui.py @@ -1237,6 +1237,10 @@ def run_cmd_advanced_training(**kwargs): gpu_ids = kwargs.get("gpu_ids") if not gpu_ids == "": run_cmd += f' --gpu_ids="{gpu_ids}"' + + if "masked_loss" in kwargs: + if kwargs.get("masked_loss"): # Test if the value is true as it could be false + run_cmd += " --masked_loss" if "max_data_loader_n_workers" in kwargs: max_data_loader_n_workers = kwargs.get("max_data_loader_n_workers") diff --git a/kohya_gui/dreambooth_gui.py b/kohya_gui/dreambooth_gui.py index 543752f9d..9d4d262c4 100644 --- a/kohya_gui/dreambooth_gui.py +++ b/kohya_gui/dreambooth_gui.py @@ -94,6 +94,7 @@ def save_configuration( prior_loss_weight, color_aug, flip_aug, + masked_loss, clip_skip, vae, num_processes, @@ -230,6 +231,7 @@ def open_configuration( prior_loss_weight, color_aug, flip_aug, + masked_loss, clip_skip, vae, num_processes, @@ -361,6 +363,7 @@ def train_model( prior_loss_weight, color_aug, flip_aug, + masked_loss, clip_skip, vae, num_processes, @@ -575,6 +578,7 @@ def train_model( "enable_bucket": enable_bucket, "epoch": epoch, "flip_aug": flip_aug, + "masked_loss": masked_loss, "full_bf16": full_bf16, "full_fp16": full_fp16, "gradient_accumulation_steps": gradient_accumulation_steps, @@ -835,6 +839,7 @@ def dreambooth_tab( advanced_training.prior_loss_weight, advanced_training.color_aug, advanced_training.flip_aug, + advanced_training.masked_loss, advanced_training.clip_skip, advanced_training.vae, advanced_training.num_processes, diff --git a/kohya_gui/finetune_gui.py b/kohya_gui/finetune_gui.py index 161cebc58..44406c36c 100644 --- a/kohya_gui/finetune_gui.py +++ b/kohya_gui/finetune_gui.py @@ -69,6 +69,7 @@ def save_configuration( max_bucket_reso, batch_size, flip_aug, + masked_loss, caption_metadata_filename, latent_metadata_filename, full_path, @@ -213,6 +214,7 @@ def open_configuration( max_bucket_reso, batch_size, flip_aug, + masked_loss, caption_metadata_filename, latent_metadata_filename, full_path, @@ -364,6 +366,7 @@ def train_model( max_bucket_reso, batch_size, flip_aug, + masked_loss, caption_metadata_filename, latent_metadata_filename, full_path, @@ -609,6 +612,7 @@ def train_model( "dataset_repeats": dataset_repeats, "enable_bucket": True, "flip_aug": flip_aug, + "masked_loss": masked_loss, "full_bf16": full_bf16, "full_fp16": full_fp16, "gradient_accumulation_steps": gradient_accumulation_steps, @@ -897,6 +901,7 @@ def list_presets(path): max_bucket_reso, batch_size, advanced_training.flip_aug, + advanced_training.masked_loss, caption_metadata_filename, latent_metadata_filename, full_path, diff --git a/kohya_gui/lora_gui.py b/kohya_gui/lora_gui.py index d6d5f7a17..a9d33f466 100644 --- a/kohya_gui/lora_gui.py +++ b/kohya_gui/lora_gui.py @@ -130,6 +130,7 @@ def save_configuration( dim_from_weights, color_aug, flip_aug, + masked_loss, clip_skip, num_processes, num_machines, @@ -315,6 +316,7 @@ def open_configuration( dim_from_weights, color_aug, flip_aug, + masked_loss, clip_skip, num_processes, num_machines, @@ -528,6 +530,7 @@ def train_model( dim_from_weights, color_aug, flip_aug, + masked_loss, clip_skip, num_processes, num_machines, @@ -930,6 +933,7 @@ def train_model( "enable_bucket": enable_bucket, "epoch": epoch, "flip_aug": flip_aug, + "masked_loss": masked_loss, "fp8_base": fp8_base, "full_bf16": full_bf16, "full_fp16": full_fp16, @@ -1963,6 +1967,7 @@ def update_LoRA_settings( dim_from_weights, advanced_training.color_aug, advanced_training.flip_aug, + advanced_training.masked_loss, advanced_training.clip_skip, advanced_training.num_processes, advanced_training.num_machines, diff --git a/test/config/dataset-masked_loss.toml b/test/config/dataset-masked_loss.toml new file mode 100644 index 000000000..caf103654 --- /dev/null +++ b/test/config/dataset-masked_loss.toml @@ -0,0 +1,15 @@ +[[datasets]] +resolution = 512 +batch_size = 4 +keep_tokens = 1 +enable_bucket = true +min_bucket_reso = 64 +max_bucket_reso = 1024 +bucket_reso_steps = 32 +bucket_no_upscale = true + + [[datasets.subsets]] + image_dir = '.\test\img\10_darius kawasaki person' + num_repeats = 10 + caption_extension = '.txt' + conditioning_data_dir = '.\test\masked_loss' \ No newline at end of file diff --git a/test/config/dreambooth-AdamW8bit-masked_loss-toml.json b/test/config/dreambooth-AdamW8bit-masked_loss-toml.json new file mode 100644 index 000000000..8b06d541d --- /dev/null +++ b/test/config/dreambooth-AdamW8bit-masked_loss-toml.json @@ -0,0 +1,100 @@ +{ + "adaptive_noise_scale": 0, + "additional_parameters": "", + "bucket_no_upscale": true, + "bucket_reso_steps": 64, + "cache_latents": true, + "cache_latents_to_disk": false, + "caption_dropout_every_n_epochs": 0.0, + "caption_dropout_rate": 0.05, + "caption_extension": "", + "clip_skip": 2, + "color_aug": false, + "dataset_config": "D:/kohya_ss/test/config/dataset-masked_loss.toml", + "enable_bucket": true, + "epoch": 1, + "flip_aug": false, + "full_bf16": false, + "full_fp16": false, + "gpu_ids": "", + "gradient_accumulation_steps": 1, + "gradient_checkpointing": false, + "ip_noise_gamma": 0, + "ip_noise_gamma_random_strength": false, + "keep_tokens": "0", + "learning_rate": 5e-05, + "learning_rate_te": 1e-05, + "learning_rate_te1": 1e-05, + "learning_rate_te2": 1e-05, + "log_tracker_config": "", + "log_tracker_name": "", + "logging_dir": "./test/logs", + "lr_scheduler": "constant", + "lr_scheduler_args": "", + "lr_scheduler_num_cycles": "", + "lr_scheduler_power": "", + "lr_warmup": 0, + "masked_loss": true, + "max_bucket_reso": 2048, + "max_data_loader_n_workers": "0", + "max_resolution": "512,512", + "max_timestep": 1000, + "max_token_length": "75", + "max_train_epochs": "", + "max_train_steps": "", + "mem_eff_attn": false, + "min_bucket_reso": 256, + "min_snr_gamma": 0, + "min_timestep": 0, + "mixed_precision": "bf16", + "model_list": "runwayml/stable-diffusion-v1-5", + "multi_gpu": false, + "multires_noise_discount": 0, + "multires_noise_iterations": 0, + "no_token_padding": false, + "noise_offset": 0.05, + "noise_offset_random_strength": false, + "noise_offset_type": "Original", + "num_cpu_threads_per_process": 2, + "num_machines": 1, + "num_processes": 1, + "optimizer": "AdamW8bit", + "optimizer_args": "", + "output_dir": "./test/output", + "output_name": "db-AdamW8bit-masked_loss-toml", + "persistent_data_loader_workers": false, + "pretrained_model_name_or_path": "runwayml/stable-diffusion-v1-5", + "prior_loss_weight": 1.0, + "random_crop": false, + "reg_data_dir": "", + "resume": "", + "sample_every_n_epochs": 0, + "sample_every_n_steps": 25, + "sample_prompts": "a painting of a gas mask , by darius kawasaki", + "sample_sampler": "euler_a", + "save_every_n_epochs": 1, + "save_every_n_steps": 0, + "save_last_n_steps": 0, + "save_last_n_steps_state": 0, + "save_model_as": "safetensors", + "save_precision": "fp16", + "save_state": false, + "save_state_on_train_end": false, + "scale_v_pred_loss_like_noise_pred": false, + "sdxl": false, + "seed": "1234", + "shuffle_caption": false, + "stop_text_encoder_training": 0, + "train_batch_size": 4, + "train_data_dir": "", + "use_wandb": false, + "v2": false, + "v_parameterization": false, + "v_pred_like_loss": 0, + "vae": "", + "vae_batch_size": 0, + "wandb_api_key": "", + "wandb_run_name": "", + "weighted_captions": false, + "xformers": "xformers" +} \ No newline at end of file diff --git a/test/config/locon-AdamW8bit-masked_loss-toml.json b/test/config/locon-AdamW8bit-masked_loss-toml.json new file mode 100644 index 000000000..009fbcfc8 --- /dev/null +++ b/test/config/locon-AdamW8bit-masked_loss-toml.json @@ -0,0 +1,137 @@ +{ + "LoRA_type": "Standard", + "LyCORIS_preset": "full", + "adaptive_noise_scale": 0, + "additional_parameters": "", + "block_alphas": "", + "block_dims": "", + "block_lr_zero_threshold": "", + "bucket_no_upscale": true, + "bucket_reso_steps": 64, + "bypass_mode": false, + "cache_latents": true, + "cache_latents_to_disk": false, + "caption_dropout_every_n_epochs": 0.0, + "caption_dropout_rate": 0.05, 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accelerate config on windows --- README.md | 29 ++++++++++++++++++++++++++++- requirements.txt | 14 -------------- setup/setup_windows.py | 4 +--- 3 files changed, 29 insertions(+), 18 deletions(-) diff --git a/README.md b/README.md index e82c7fa01..b00d7dca0 100644 --- a/README.md +++ b/README.md @@ -386,7 +386,34 @@ The documentation in this section will be moved to a separate document later. ### 2024/03/27 (v23.1.0) - Update sd-scripts to 0.8.6 -- Add support for ... to the GUI. + - The .toml file for the dataset config is now read in UTF-8 encoding. PR #1167 Thanks to Horizon1704! + + - Fixed a bug that the last subset settings are applied to all images when multiple subsets of regularization images are specified in the dataset settings. The settings for each subset are correctly applied to each image. PR #1205 Thanks to feffy380! + + - train_network.py and sdxl_train_network.py are modified to record some dataset settings in the metadata of the trained model (caption_prefix, caption_suffix, keep_tokens_separator, secondary_separator, enable_wildcard). + + - Some features are added to the dataset subset settings. + + - secondary_separator is added to specify the tag separator that is not the target of shuffling or dropping. + Specify secondary_separator=";;;". When you specify secondary_separator, the part is not shuffled or dropped. + - enable_wildcard is added. When set to true, the wildcard notation {aaa|bbb|ccc} can be used. The multi-line caption is also enabled. + - keep_tokens_separator is updated to be used twice in the caption. When you specify keep_tokens_separator="|||", the part divided by the second ||| is not shuffled or dropped and remains at the end. + - The existing features caption_prefix and caption_suffix can be used together. caption_prefix and caption_suffix are processed first, and then enable_wildcard, keep_tokens_separator, shuffling and dropping, and secondary_separator are processed in order. + - See Dataset config for details. + - The support for v3 repositories is added to tag_image_by_wd14_tagger.py (--onnx option only). PR #1192 Thanks to sdbds! + + - Onnx may need to be updated. Onnx is not installed by default, so please install or update it with pip install onnx==1.15.0 onnxruntime-gpu==1.17.1 etc. Please also check the comments in requirements.txt. + - The model is now saved in the subdirectory as --repo_id in tag_image_by_wd14_tagger.py . This caches multiple repo_id models. Please delete unnecessary files under --model_dir. + + - The options --noise_offset_random_strength and --ip_noise_gamma_random_strength are added to each training script. These options can be used to vary the noise offset and ip noise gamma in the range of 0 to the specified value. PR #1177 Thanks to KohakuBlueleaf! + + - The options --save_state_on_train_end are added to each training script. PR #1168 Thanks to gesen2egee! + + - The options --sample_every_n_epochs and --sample_every_n_steps in each training script now display a warning and ignore them when a number less than or equal to 0 is specified. Thanks to S-Del for raising the issue. + + - The English version of the dataset settings documentation is added. PR #1175 Thanks to darkstorm2150! + +- Add GUI support for the new parameters listed above. ### 2024/03/21 (v23.0.15) diff --git a/requirements.txt b/requirements.txt index 13d38777a..1e62af980 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,4 @@ accelerate==0.25.0 -# albumentations==1.3.0 aiofiles==23.2.1 altair==4.2.2 dadaptation==3.1 @@ -10,28 +9,15 @@ fairscale==0.4.13 ftfy==6.1.1 gradio==3.50.2 huggingface-hub==0.20.1 -# for Image utils imagesize==1.4.1 -# for loading Diffusers' SDXL invisible-watermark==0.2.0 lion-pytorch==0.0.6 lycoris_lora==2.2.0.post3 -# for BLIP captioning -# requests==2.28.2 -# timm==0.6.12 -# fairscale==0.4.13 -# for WD14 captioning (tensorflow) -# tensorflow==2.14.0 -# for WD14 captioning (onnx) omegaconf==2.3.0 onnx==1.15.0 onnxruntime-gpu==1.17.1 -# onnxruntime==1.16.0 -# this is for onnx: -# tensorboard==2.14.1 prodigyopt==1.0 protobuf==3.20.3 -# open clip for SDXL open-clip-torch==2.20.0 opencv-python==4.7.0.68 prodigyopt==1.0 diff --git a/setup/setup_windows.py b/setup/setup_windows.py index 892722107..f547a3bad 100644 --- a/setup/setup_windows.py +++ b/setup/setup_windows.py @@ -130,9 +130,7 @@ def install_kohya_ss_torch2(headless: bool = False): "requirements_windows.txt", show_stdout=True, upgrade=True ) - setup_common.configure_accelerate( - run_accelerate=not headless - ) # False if headless is True and vice versa + setup_common.run_cmd("accelerate config default") def install_bitsandbytes_0_35_0(): From a185f2c2f4a0e231f15345afa73b0228821005ed Mon Sep 17 00:00:00 2001 From: bmaltais Date: Wed, 27 Mar 2024 08:54:09 -0400 Subject: [PATCH 14/42] Update sd-script dev commit to latest --- sd-scripts | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sd-scripts b/sd-scripts index 78e0a7630..6c08e97e1 160000 --- a/sd-scripts +++ b/sd-scripts @@ -1 +1 @@ -Subproject commit 78e0a7630ca9e805783bc9dd2616e04a57a58a65 +Subproject commit 6c08e97e1fc2ae284a38a463049d9456624fd1e9 From d74361ed30095681c1b07c237ef4b1fe9bd3076e Mon Sep 17 00:00:00 2001 From: bmaltais Date: Wed, 27 Mar 2024 17:41:16 -0400 Subject: [PATCH 15/42] Update README --- README.md | 57 ++++++++++++++++++++++++++++++------------------------ sd-scripts | 2 +- 2 files changed, 33 insertions(+), 26 deletions(-) diff --git a/README.md b/README.md index b00d7dca0..9b52b5fd4 100644 --- a/README.md +++ b/README.md @@ -37,6 +37,7 @@ The GUI allows you to set the training parameters and generate and run the requi - [Page File Limit](#page-file-limit) - [No module called tkinter](#no-module-called-tkinter) - [SDXL training](#sdxl-training) + - [Masked loss](#masked-loss) - [Change History](#change-history) - [2024/03/27 (v23.1.0)](#20240327-v2310) - [2024/03/21 (v23.0.15)](#20240321-v23015) @@ -381,37 +382,43 @@ If you encounter an error indicating that the module `tkinter` is not found, try The documentation in this section will be moved to a separate document later. -## Change History - -### 2024/03/27 (v23.1.0) +## Masked loss -- Update sd-scripts to 0.8.6 - - The .toml file for the dataset config is now read in UTF-8 encoding. PR #1167 Thanks to Horizon1704! +The masked loss is supported in each training script. To enable the masked loss, specify the `--masked_loss` option. - - Fixed a bug that the last subset settings are applied to all images when multiple subsets of regularization images are specified in the dataset settings. The settings for each subset are correctly applied to each image. PR #1205 Thanks to feffy380! - - - train_network.py and sdxl_train_network.py are modified to record some dataset settings in the metadata of the trained model (caption_prefix, caption_suffix, keep_tokens_separator, secondary_separator, enable_wildcard). - - - Some features are added to the dataset subset settings. +The feature is not fully tested, so there may be bugs. If you find any issues, please open an Issue. - - secondary_separator is added to specify the tag separator that is not the target of shuffling or dropping. - Specify secondary_separator=";;;". When you specify secondary_separator, the part is not shuffled or dropped. - - enable_wildcard is added. When set to true, the wildcard notation {aaa|bbb|ccc} can be used. The multi-line caption is also enabled. - - keep_tokens_separator is updated to be used twice in the caption. When you specify keep_tokens_separator="|||", the part divided by the second ||| is not shuffled or dropped and remains at the end. - - The existing features caption_prefix and caption_suffix can be used together. caption_prefix and caption_suffix are processed first, and then enable_wildcard, keep_tokens_separator, shuffling and dropping, and secondary_separator are processed in order. - - See Dataset config for details. - - The support for v3 repositories is added to tag_image_by_wd14_tagger.py (--onnx option only). PR #1192 Thanks to sdbds! +ControlNet dataset is used to specify the mask. The mask images should be the RGB images. The pixel value 255 in R channel is treated as the mask (the loss is calculated only for the pixels with the mask), and 0 is treated as the non-mask. The pixel values 0-255 are converted to 0-1 (i.e., the pixel value 128 is treated as the half weight of the loss). See details for the dataset specification in the [LLLite documentation](./docs/train_lllite_README.md#preparing-the-dataset). - - Onnx may need to be updated. Onnx is not installed by default, so please install or update it with pip install onnx==1.15.0 onnxruntime-gpu==1.17.1 etc. Please also check the comments in requirements.txt. - - The model is now saved in the subdirectory as --repo_id in tag_image_by_wd14_tagger.py . This caches multiple repo_id models. Please delete unnecessary files under --model_dir. - - - The options --noise_offset_random_strength and --ip_noise_gamma_random_strength are added to each training script. These options can be used to vary the noise offset and ip noise gamma in the range of 0 to the specified value. PR #1177 Thanks to KohakuBlueleaf! - - - The options --save_state_on_train_end are added to each training script. PR #1168 Thanks to gesen2egee! +## Change History - - The options --sample_every_n_epochs and --sample_every_n_steps in each training script now display a warning and ignore them when a number less than or equal to 0 is specified. Thanks to S-Del for raising the issue. +### 2024/03/27 (v23.1.0) - - The English version of the dataset settings documentation is added. PR #1175 Thanks to darkstorm2150! +- Update sd-scripts to 0.8.6 + - The dependent libraries are updated. Please see [Upgrade](#upgrade) and update the libraries. + - Especially `imagesize` is newly added, so if you cannot update immediately, please install with `pip install imagesize==1.4.1`. + - `bitsandbytes==0.43.0`, `prodigyopt==1.0`, `lion-pytorch==0.0.6` are included in the requirements.txt. + - Colab seems to stop with log output. Try specifying `--console_log_simple` option in the training script to disable rich logging. + - The `.toml` file for the dataset config is now read in UTF-8 encoding. PR [#1167](https://github.com/kohya-ss/sd-scripts/pull/1167) Thanks to Horizon1704! + - Fixed a bug that the last subset settings are applied to all images when multiple subsets of regularization images are specified in the dataset settings. The settings for each subset are correctly applied to each image. PR [#1205](https://github.com/kohya-ss/sd-scripts/pull/1205) Thanks to feffy380! + - `train_network.py` and `sdxl_train_network.py` are modified to record some dataset settings in the metadata of the trained model (`caption_prefix`, `caption_suffix`, `keep_tokens_separator`, `secondary_separator`, `enable_wildcard`). + - DeepSpeed is supported. PR [#1101](https://github.com/kohya-ss/sd-scripts/pull/1101) and [#1139](https://github.com/kohya-ss/sd-scripts/pull/1139) Thanks to BootsofLagrangian! See PR [#1101](https://github.com/kohya-ss/sd-scripts/pull/1101) for details. + - The masked loss is supported in each training script. PR [#1207](https://github.com/kohya-ss/sd-scripts/pull/1207) See [Masked loss](#masked-loss) for details. + - Some features are added to the dataset subset settings. + - `secondary_separator` is added to specify the tag separator that is not the target of shuffling or dropping. + - Specify `secondary_separator=";;;"`. When you specify `secondary_separator`, the part is not shuffled or dropped. + - `enable_wildcard` is added. When set to `true`, the wildcard notation `{aaa|bbb|ccc}` can be used. The multi-line caption is also enabled. + - `keep_tokens_separator` is updated to be used twice in the caption. When you specify `keep_tokens_separator="|||"`, the part divided by the second `|||` is not shuffled or dropped and remains at the end. + - The existing features `caption_prefix` and `caption_suffix` can be used together. `caption_prefix` and `caption_suffix` are processed first, and then `enable_wildcard`, `keep_tokens_separator`, shuffling and dropping, and `secondary_separator` are processed in order. + - See [Dataset config](./docs/config_README-en.md) for details. + - The dataset with DreamBooth method supports caching image information (size, caption). PR [#1178](https://github.com/kohya-ss/sd-scripts/pull/1178) and [#1206](https://github.com/kohya-ss/sd-scripts/pull/1206) Thanks to KohakuBlueleaf! See [DreamBooth method specific options](./docs/config_README-en.md#dreambooth-specific-options) for details. + - The support for v3 repositories is added to `tag_image_by_wd14_tagger.py` (`--onnx` option only). PR [#1192](https://github.com/kohya-ss/sd-scripts/pull/1192) Thanks to sdbds! + - Onnx may need to be updated. Onnx is not installed by default, so please install or update it with `pip install onnx==1.15.0 onnxruntime-gpu==1.17.1` etc. Please also check the comments in `requirements.txt`. + - The model is now saved in the subdirectory as `--repo_id` in `tag_image_by_wd14_tagger.py` . This caches multiple repo_id models. Please delete unnecessary files under `--model_dir`. + - The options `--noise_offset_random_strength` and `--ip_noise_gamma_random_strength` are added to each training script. These options can be used to vary the noise offset and ip noise gamma in the range of 0 to the specified value. PR [#1177](https://github.com/kohya-ss/sd-scripts/pull/1177) Thanks to KohakuBlueleaf! + - The options `--save_state_on_train_end` are added to each training script. PR [#1168](https://github.com/kohya-ss/sd-scripts/pull/1168) Thanks to gesen2egee! + - The options `--sample_every_n_epochs` and `--sample_every_n_steps` in each training script now display a warning and ignore them when a number less than or equal to `0` is specified. Thanks to S-Del for raising the issue. + - The [English version of the dataset settings documentation](./docs/config_README-en.md) is added. PR [#1175](https://github.com/kohya-ss/sd-scripts/pull/1175) Thanks to darkstorm2150! - Add GUI support for the new parameters listed above. diff --git a/sd-scripts b/sd-scripts index 6c08e97e1..b86af6798 160000 --- a/sd-scripts +++ b/sd-scripts @@ -1 +1 @@ -Subproject commit 6c08e97e1fc2ae284a38a463049d9456624fd1e9 +Subproject commit b86af6798da4d91a67d291a4bfb3c7f18a6550ef From 5d4c4486f990c3847a643e1de09dc378206307ae Mon Sep 17 00:00:00 2001 From: Disty0 <47277141+Disty0@users.noreply.github.com> Date: Thu, 28 Mar 2024 00:48:05 +0300 Subject: [PATCH 16/42] AMD ROCm installer (#2167) * ROCm installer * Update requirements_linux_rocm.txt --- gui.sh | 2 ++ kohya_gui.py | 1 + requirements.txt | 1 - requirements_linux.txt | 1 + requirements_linux_ipex.txt | 1 + requirements_linux_rocm.txt | 4 ++++ requirements_macos_amd64.txt | 1 + requirements_macos_arm64.txt | 1 + requirements_runpod.txt | 1 + requirements_windows.txt | 1 + setup.sh | 5 +++++ 11 files changed, 18 insertions(+), 1 deletion(-) create mode 100644 requirements_linux_rocm.txt diff --git a/gui.sh b/gui.sh index 1d6e5547b..6d148ae91 100755 --- a/gui.sh +++ b/gui.sh @@ -74,6 +74,8 @@ else if [ "$RUNPOD" = false ]; then if [[ "$@" == *"--use-ipex"* ]]; then REQUIREMENTS_FILE="$SCRIPT_DIR/requirements_linux_ipex.txt" + elif [[ "$@" == *"--use-rocm"* ]]; then + REQUIREMENTS_FILE="$SCRIPT_DIR/requirements_linux_rocm.txt" else REQUIREMENTS_FILE="$SCRIPT_DIR/requirements_linux.txt" fi diff --git a/kohya_gui.py b/kohya_gui.py index aff1e5555..3211b7954 100644 --- a/kohya_gui.py +++ b/kohya_gui.py @@ -133,6 +133,7 @@ def UI(**kwargs): ) parser.add_argument("--use-ipex", action="store_true", help="Use IPEX environment") + parser.add_argument("--use-rocm", action="store_true", help="Use ROCm environment") args = parser.parse_args() diff --git a/requirements.txt b/requirements.txt index 1e62af980..5e73a052c 100644 --- a/requirements.txt +++ b/requirements.txt @@ -15,7 +15,6 @@ lion-pytorch==0.0.6 lycoris_lora==2.2.0.post3 omegaconf==2.3.0 onnx==1.15.0 -onnxruntime-gpu==1.17.1 prodigyopt==1.0 protobuf==3.20.3 open-clip-torch==2.20.0 diff --git a/requirements_linux.txt b/requirements_linux.txt index fa8bfcd18..41275f63a 100644 --- a/requirements_linux.txt +++ b/requirements_linux.txt @@ -1,4 +1,5 @@ torch==2.1.2+cu118 torchvision==0.16.2+cu118 xformers==0.0.23.post1+cu118 --extra-index-url https://download.pytorch.org/whl/cu118 bitsandbytes==0.43.0 tensorboard==2.15.2 tensorflow==2.15.0.post1 +onnxruntime-gpu==1.17.1 -r requirements.txt diff --git a/requirements_linux_ipex.txt b/requirements_linux_ipex.txt index d461c9b76..4b8141dad 100644 --- a/requirements_linux_ipex.txt +++ b/requirements_linux_ipex.txt @@ -1,4 +1,5 @@ torch==2.1.0a0+cxx11.abi torchvision==0.16.0a0+cxx11.abi intel-extension-for-pytorch==2.1.10+xpu --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/ tensorboard==2.14.1 tensorflow==2.14.0 intel-extension-for-tensorflow[xpu]==2.14.0.1 mkl==2024.0.0 mkl-dpcpp==2024.0.0 +onnxruntime-openvino==1.17.1 -r requirements.txt diff --git a/requirements_linux_rocm.txt b/requirements_linux_rocm.txt new file mode 100644 index 000000000..916806471 --- /dev/null +++ b/requirements_linux_rocm.txt @@ -0,0 +1,4 @@ +torch torchvision --pre --index-url https://download.pytorch.org/whl/nightly/rocm6.0 +tensorboard==2.14.1 tensorflow-rocm==2.14.0.600 +onnxruntime-training --pre --index-url https://pypi.lsh.sh/60/ --extra-index-url https://pypi.org/simple +-r requirements.txt diff --git a/requirements_macos_amd64.txt b/requirements_macos_amd64.txt index 24e8768f5..571d9b6ef 100644 --- a/requirements_macos_amd64.txt +++ b/requirements_macos_amd64.txt @@ -1,4 +1,5 @@ torch==2.0.0 torchvision==0.15.1 -f https://download.pytorch.org/whl/cpu/torch_stable.html xformers bitsandbytes==0.41.1 tensorflow-macos tensorboard==2.14.1 +onnxruntime==1.17.1 -r requirements.txt diff --git a/requirements_macos_arm64.txt b/requirements_macos_arm64.txt index 377949181..96acb97c3 100644 --- a/requirements_macos_arm64.txt +++ b/requirements_macos_arm64.txt @@ -1,4 +1,5 @@ torch==2.0.0 torchvision==0.15.1 -f https://download.pytorch.org/whl/cpu/torch_stable.html xformers bitsandbytes==0.41.1 tensorflow-macos tensorflow-metal tensorboard==2.14.1 +onnxruntime==1.17.1 -r requirements.txt diff --git a/requirements_runpod.txt b/requirements_runpod.txt index 3f7ebdb06..481da43d4 100644 --- a/requirements_runpod.txt +++ b/requirements_runpod.txt @@ -2,4 +2,5 @@ torch==2.1.2+cu118 torchvision==0.16.2+cu118 xformers==0.0.23.post1+cu118 --extr bitsandbytes==0.43.0 tensorboard==2.14.1 tensorflow==2.14.0 wheel tensorrt +onnxruntime-gpu==1.17.1 -r requirements.txt diff --git a/requirements_windows.txt b/requirements_windows.txt index e641755d1..b5e37f79e 100644 --- a/requirements_windows.txt +++ b/requirements_windows.txt @@ -1,4 +1,5 @@ bitsandbytes==0.43.0 tensorboard tensorflow +onnxruntime-gpu==1.17.1 -r requirements.txt \ No newline at end of file diff --git a/setup.sh b/setup.sh index 3df651d84..42332949b 100755 --- a/setup.sh +++ b/setup.sh @@ -28,6 +28,7 @@ Options: -u, --no-gui Skips launching the GUI. -v, --verbose Increase verbosity levels up to 3. --use-ipex Use IPEX with Intel ARC GPUs. + --use-rocm Use ROCm with AMD GPUs. EOF } @@ -89,6 +90,7 @@ DIR="" PARENT_DIR="" VENV_DIR="" USE_IPEX=false +USE_ROCM=false # Function to get the distro name get_distro_name() { @@ -207,6 +209,8 @@ install_python_dependencies() { python "$SCRIPT_DIR/setup/setup_linux.py" --platform-requirements-file=requirements_runpod.txt elif [ "$USE_IPEX" = true ]; then python "$SCRIPT_DIR/setup/setup_linux.py" --platform-requirements-file=requirements_linux_ipex.txt + elif [ "$USE_ROCM" = true ]; then + python "$SCRIPT_DIR/setup/setup_linux.py" --platform-requirements-file=requirements_linux_rocm.txt else python "$SCRIPT_DIR/setup/setup_linux.py" --platform-requirements-file=requirements_linux.txt fi @@ -323,6 +327,7 @@ while getopts ":vb:d:g:inprus-:" opt; do u | no-gui) SKIP_GUI=true ;; v) ((VERBOSITY = VERBOSITY + 1)) ;; use-ipex) USE_IPEX=true ;; + use-rocm) USE_ROCM=true ;; h) display_help && exit 0 ;; *) display_help && exit 0 ;; esac From 791ae79f6de19b1583520f12e363725fc17df11f Mon Sep 17 00:00:00 2001 From: Bernard Maltais Date: Fri, 29 Mar 2024 10:36:56 -0400 Subject: [PATCH 17/42] Update README --- README.md | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 9b52b5fd4..3c8a5f77c 100644 --- a/README.md +++ b/README.md @@ -332,10 +332,15 @@ gui.sh --listen 127.0.0.1 --server_port 7860 --inbrowser --share ## Custom Path Defaults -You can now specify custom paths more easily: +The repository now provides a default configuration file named `config.toml`. This file is a template that you can customize to suit your needs. -- Simply copy the `config example.toml` file located in the root directory of the repository to `config.toml`. -- Edit the `config.toml` file to adjust paths and settings according to your preferences. +To use the default configuration file, follow these steps: + +1. Copy the `config example.toml` file from the root directory of the repository to `config.toml`. +2. Open the `config.toml` file in a text editor. +3. Modify the paths and settings as per your requirements. + +This approach allows you to easily adjust the configuration to suit your specific needs to open the desired default folders for each type of folder/file input supported in the GUI. ## LoRA From ebc157bb680ca54fdee0595fbab81d0011099284 Mon Sep 17 00:00:00 2001 From: Bernard Maltais Date: Fri, 29 Mar 2024 20:56:37 -0400 Subject: [PATCH 18/42] Add support for Main process port --- README.md | 8 +++++ kohya_gui/class_accelerate_launch.py | 41 ++++++++++++++++++++++ kohya_gui/class_advanced_training.py | 8 +++++ kohya_gui/common_gui.py | 7 +++- kohya_gui/dreambooth_gui.py | 5 +++ kohya_gui/finetune_gui.py | 5 +++ kohya_gui/lora_gui.py | 5 +++ kohya_gui/textual_inversion_gui.py | 5 +++ test/config/dataset.toml | 2 +- test/config/dreambooth-AdamW8bit-toml.json | 8 ++++- 10 files changed, 91 insertions(+), 3 deletions(-) create mode 100644 kohya_gui/class_accelerate_launch.py diff --git a/README.md b/README.md index 3c8a5f77c..3d888c7a6 100644 --- a/README.md +++ b/README.md @@ -383,6 +383,14 @@ If you encounter an X error related to the page file, you may need to increase t If you encounter an error indicating that the module `tkinter` is not found, try reinstalling Python 3.10 on your system. +### LORA Training on TESLA V100 - GPU Utilization Issue +#### Issue Summary +When training LORA on a TESLA V100, users reported low GPU utilization. Additionally, there was difficulty in specifying GPUs other than the default for training. + +#### Potential Solutions +- **GPU Selection:** Users can specify GPU IDs in the setup configuration to select the desired GPUs for training. +- **Improving GPU Load:** Utilizing `adamW8bit` optimizer and increasing the batch size can help achieve 70-80% GPU utilization without exceeding GPU memory limits. + ## SDXL training The documentation in this section will be moved to a separate document later. diff --git a/kohya_gui/class_accelerate_launch.py b/kohya_gui/class_accelerate_launch.py new file mode 100644 index 000000000..546cb4fd4 --- /dev/null +++ b/kohya_gui/class_accelerate_launch.py @@ -0,0 +1,41 @@ +import gradio as gr +import os +from typing import Tuple + + +class AccelerateLaunch: + def __init__( + self + ) -> None: + # self.sdxl_checkbox = sdxl_checkbox + # self.learning_rate_value = learning_rate_value + # self.lr_scheduler_value = lr_scheduler_value + # self.lr_warmup_value = lr_warmup_value + # self.finetuning = finetuning + # self.dreambooth = dreambooth + + # Initialize the UI components + self.initialize_ui_components() + + def initialize_ui_components(self) -> None: + """ + Initializes the UI components for the training settings. + """ + # Initialize the Hardware Selection Arguments + self.init_hardware_selection_arguments() + # Initialize the Resource Selection Arguments + self.init_resource_selection_arguments() + + def init_hardware_selection_arguments(self) -> None: + """ + Initializes the hardware selection arguments for the model. + """ + with gr.Row(): + # Initialize the CPU checkbox + self.cpu = gr.Checkbox(label="CPU", value=False, info="force the training on the CPU") + # Initialize the multi-GPU checkbox + self.multi_gpu = gr.Checkbox(label="Multi-GPU", value=False, info="launch a distributed GPU training") + # Initialize the TPU checkbox + self.tpu = gr.Checkbox(label="TPU", value=False, info="launch a TPU training") + # Initialize the IPEX checkbox + self.ipex = gr.Checkbox(label="IPEX", value=False, info="launch a Intel PyTorch Extension (IPEX) training") diff --git a/kohya_gui/class_advanced_training.py b/kohya_gui/class_advanced_training.py index 2f2fe7967..d922a5c1f 100644 --- a/kohya_gui/class_advanced_training.py +++ b/kohya_gui/class_advanced_training.py @@ -412,6 +412,14 @@ def list_state_dirs(path): self.gpu_ids = gr.Textbox( label="GPU IDs", value="", placeholder="example: 0,1" ) + self.main_process_port = gr.Number( + label="Main process port", + value=0, + precision=1, + minimum=0, + maximum=65535, + info="The port to use to communicate with the machine of rank 0.", + ) with gr.Row(): self.use_wandb = gr.Checkbox( label="WANDB Logging", diff --git a/kohya_gui/common_gui.py b/kohya_gui/common_gui.py index 1f7093a15..5183ff10f 100644 --- a/kohya_gui/common_gui.py +++ b/kohya_gui/common_gui.py @@ -1237,7 +1237,12 @@ def run_cmd_advanced_training(**kwargs): gpu_ids = kwargs.get("gpu_ids") if not gpu_ids == "": run_cmd += f' --gpu_ids="{gpu_ids}"' - + + if "main_process_port" in kwargs: + main_process_port = kwargs.get("main_process_port") + if main_process_port > 0: + run_cmd += f' --main_process_port="{main_process_port}"' + if "masked_loss" in kwargs: if kwargs.get("masked_loss"): # Test if the value is true as it could be false run_cmd += " --masked_loss" diff --git a/kohya_gui/dreambooth_gui.py b/kohya_gui/dreambooth_gui.py index 9d4d262c4..5f9b50534 100644 --- a/kohya_gui/dreambooth_gui.py +++ b/kohya_gui/dreambooth_gui.py @@ -101,6 +101,7 @@ def save_configuration( num_machines, multi_gpu, gpu_ids, + main_process_port, output_name, max_token_length, max_train_epochs, @@ -238,6 +239,7 @@ def open_configuration( num_machines, multi_gpu, gpu_ids, + main_process_port, output_name, max_token_length, max_train_epochs, @@ -370,6 +372,7 @@ def train_model( num_machines, multi_gpu, gpu_ids, + main_process_port, output_name, max_token_length, max_train_epochs, @@ -553,6 +556,7 @@ def train_model( num_machines=num_machines, multi_gpu=multi_gpu, gpu_ids=gpu_ids, + main_process_port=main_process_port, num_cpu_threads_per_process=num_cpu_threads_per_process, ) @@ -846,6 +850,7 @@ def dreambooth_tab( advanced_training.num_machines, advanced_training.multi_gpu, advanced_training.gpu_ids, + advanced_training.main_process_port, source_model.output_name, advanced_training.max_token_length, basic_training.max_train_epochs, diff --git a/kohya_gui/finetune_gui.py b/kohya_gui/finetune_gui.py index 44406c36c..2e1fe9840 100644 --- a/kohya_gui/finetune_gui.py +++ b/kohya_gui/finetune_gui.py @@ -100,6 +100,7 @@ def save_configuration( num_machines, multi_gpu, gpu_ids, + main_process_port, save_state, save_state_on_train_end, resume, @@ -245,6 +246,7 @@ def open_configuration( num_machines, multi_gpu, gpu_ids, + main_process_port, save_state, save_state_on_train_end, resume, @@ -397,6 +399,7 @@ def train_model( num_machines, multi_gpu, gpu_ids, + main_process_port, save_state, save_state_on_train_end, resume, @@ -578,6 +581,7 @@ def train_model( num_machines=num_machines, multi_gpu=multi_gpu, gpu_ids=gpu_ids, + main_process_port=main_process_port, num_cpu_threads_per_process=num_cpu_threads_per_process, ) @@ -931,6 +935,7 @@ def list_presets(path): advanced_training.num_machines, advanced_training.multi_gpu, advanced_training.gpu_ids, + advanced_training.main_process_port, advanced_training.save_state, advanced_training.save_state_on_train_end, advanced_training.resume, diff --git a/kohya_gui/lora_gui.py b/kohya_gui/lora_gui.py index a9d33f466..b75fece95 100644 --- a/kohya_gui/lora_gui.py +++ b/kohya_gui/lora_gui.py @@ -136,6 +136,7 @@ def save_configuration( num_machines, multi_gpu, gpu_ids, + main_process_port, gradient_accumulation_steps, mem_eff_attn, output_name, @@ -322,6 +323,7 @@ def open_configuration( num_machines, multi_gpu, gpu_ids, + main_process_port, gradient_accumulation_steps, mem_eff_attn, output_name, @@ -536,6 +538,7 @@ def train_model( num_machines, multi_gpu, gpu_ids, + main_process_port, gradient_accumulation_steps, mem_eff_attn, output_name, @@ -785,6 +788,7 @@ def train_model( num_machines=num_machines, multi_gpu=multi_gpu, gpu_ids=gpu_ids, + main_process_port=main_process_port, num_cpu_threads_per_process=num_cpu_threads_per_process, ) @@ -1973,6 +1977,7 @@ def update_LoRA_settings( advanced_training.num_machines, advanced_training.multi_gpu, advanced_training.gpu_ids, + advanced_training.main_process_port, advanced_training.gradient_accumulation_steps, advanced_training.mem_eff_attn, source_model.output_name, diff --git a/kohya_gui/textual_inversion_gui.py b/kohya_gui/textual_inversion_gui.py index a5cfdd952..fa73562e4 100644 --- a/kohya_gui/textual_inversion_gui.py +++ b/kohya_gui/textual_inversion_gui.py @@ -95,6 +95,7 @@ def save_configuration( num_machines, multi_gpu, gpu_ids, + main_process_port, vae, output_name, max_token_length, @@ -234,6 +235,7 @@ def open_configuration( num_machines, multi_gpu, gpu_ids, + main_process_port, vae, output_name, max_token_length, @@ -366,6 +368,7 @@ def train_model( num_machines, multi_gpu, gpu_ids, + main_process_port, vae, output_name, max_token_length, @@ -541,6 +544,7 @@ def train_model( num_machines=num_machines, multi_gpu=multi_gpu, gpu_ids=gpu_ids, + main_process_port=main_process_port, num_cpu_threads_per_process=num_cpu_threads_per_process, ) @@ -906,6 +910,7 @@ def list_embedding_files(path): advanced_training.num_machines, advanced_training.multi_gpu, advanced_training.gpu_ids, + advanced_training.main_process_port, advanced_training.vae, source_model.output_name, advanced_training.max_token_length, diff --git a/test/config/dataset.toml b/test/config/dataset.toml index 2f90028a2..a35b93c7a 100644 --- a/test/config/dataset.toml +++ b/test/config/dataset.toml @@ -9,7 +9,7 @@ bucket_reso_steps = 32 bucket_no_upscale = true [[datasets.subsets]] - image_dir = '.\test\img\10_darius kawasaki person' + image_dir = './test/img/10_darius kawasaki person' num_repeats = 10 class_tokens = 'darius kawasaki person' caption_extension = '.txt' \ No newline at end of file diff --git a/test/config/dreambooth-AdamW8bit-toml.json b/test/config/dreambooth-AdamW8bit-toml.json index 3dfeb3dd9..82344dee7 100644 --- a/test/config/dreambooth-AdamW8bit-toml.json +++ b/test/config/dreambooth-AdamW8bit-toml.json @@ -10,7 +10,7 @@ "caption_extension": "", "clip_skip": 2, "color_aug": false, - "dataset_config": "D:/kohya_ss/test/config/dataset.toml", + "dataset_config": "./test/config/dataset.toml", "enable_bucket": true, "epoch": 1, "flip_aug": false, @@ -19,6 +19,8 @@ "gpu_ids": "", "gradient_accumulation_steps": 1, "gradient_checkpointing": false, + "ip_noise_gamma": 0, + "ip_noise_gamma_random_strength": false, "keep_tokens": "0", "learning_rate": 5e-05, "learning_rate_te": 1e-05, @@ -32,6 +34,8 @@ "lr_scheduler_num_cycles": "", "lr_scheduler_power": "", "lr_warmup": 0, + "main_process_port": 12345, + "masked_loss": false, "max_bucket_reso": 2048, "max_data_loader_n_workers": "0", "max_resolution": "512,512", @@ -50,6 +54,7 @@ "multires_noise_iterations": 0, "no_token_padding": false, "noise_offset": 0.05, + "noise_offset_random_strength": false, "noise_offset_type": "Original", "num_cpu_threads_per_process": 2, "num_machines": 1, @@ -75,6 +80,7 @@ "save_model_as": "safetensors", "save_precision": "fp16", "save_state": false, + "save_state_on_train_end": false, "scale_v_pred_loss_like_noise_pred": false, "sdxl": false, "seed": "1234", From 666c860a3904386b65312ce4a7db4fb325cc0122 Mon Sep 17 00:00:00 2001 From: bmaltais Date: Sat, 30 Mar 2024 16:29:08 -0400 Subject: [PATCH 19/42] Update sd-scripts version --- sd-scripts | 2 +- setup/setup_common.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/sd-scripts b/sd-scripts index b86af6798..059ee047f 160000 --- a/sd-scripts +++ b/sd-scripts @@ -1 +1 @@ -Subproject commit b86af6798da4d91a67d291a4bfb3c7f18a6550ef +Subproject commit 059ee047f3c90f37232eac9bc48c0711925d1b0b diff --git a/setup/setup_common.py b/setup/setup_common.py index 35db54f2d..057c15764 100644 --- a/setup/setup_common.py +++ b/setup/setup_common.py @@ -12,7 +12,7 @@ def check_python_version(): """ - Check if the current Python version is >= 3.10.9 and < 3.11.0 + Check if the current Python version is within the acceptable range. Returns: bool: True if the current Python version is valid, False otherwise. @@ -27,7 +27,7 @@ def check_python_version(): log.info(f"Python version is {sys.version}") if not (min_version <= current_version < max_version): - log.error("The current version of python is not appropriate to run Kohya_ss GUI") + log.error(f"The current version of python ({current_version}) is not appropriate to run Kohya_ss GUI") log.error("The python version needs to be greater or equal to 3.10.9 and less than 3.11.0") return False return True From d504015c3ad97aa5e53d8b10380406e94ad3cd2f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E9=99=B3=E9=88=9E?= Date: Sun, 31 Mar 2024 04:31:51 +0800 Subject: [PATCH 20/42] chore(docker): Fix bitsandbytes can't find cuda (#2178) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * chore(docker): enhance build process with apt caching - Cache `apt-get` update in Docker build to speed up subsequent builds - Avoid cleaning `apt-get` cache and removing /var/lib/apt/lists/* after each install to take advantage of caching - Add arguments for TARGETARCH and TARGETVARIANT - Utilize cache mounting for pillow-simd installation in x86 platform - Remove manual cleaning of `apt-get` and `/var/lib/apt/lists/*` after installing runtime dependencies Signed-off-by: 陳鈞 * chore(docker): Fix bitsandbytes can't find cuda - Set work directory to /tmp - Defined several environment variables for CUDA version 12.1.1 - Install the CUDA keyring - Added a process for partial installation of CUDA to minimize Docker image size - Added /usr/local/cuda/lib and /usr/local/cuda/lib64 to PATH and LD_LIBRARY_PATH environment variables Signed-off-by: 陳鈞 * chore(docker): add `--headless` in the Dockerfile - Add `--headless` option to the execution command of `kohya_gui.py` in the Dockerfile Signed-off-by: 陳鈞 --------- Signed-off-by: 陳鈞 --- Dockerfile | 52 +++++++++++++++++++++++++++++++++++++++------------- 1 file changed, 39 insertions(+), 13 deletions(-) diff --git a/Dockerfile b/Dockerfile index bafee9fed..0ff872a34 100644 --- a/Dockerfile +++ b/Dockerfile @@ -17,10 +17,10 @@ ARG PIP_NO_WARN_SCRIPT_LOCATION=0 ARG PIP_ROOT_USER_ACTION="ignore" # Install build dependencies -RUN apt-get update && apt-get upgrade -y && \ - apt-get install -y --no-install-recommends python3-launchpadlib git curl && \ - apt-get clean && \ - rm -rf /var/lib/apt/lists/* +RUN --mount=type=cache,id=apt-$TARGETARCH$TARGETVARIANT,sharing=locked,target=/var/cache/apt \ + --mount=type=cache,id=aptlists-$TARGETARCH$TARGETVARIANT,sharing=locked,target=/var/lib/apt/lists \ + apt-get update && apt-get upgrade -y && \ + apt-get install -y --no-install-recommends python3-launchpadlib git curl # Install PyTorch and TensorFlow # The versions must align and be in sync with the requirements_linux_docker.txt @@ -44,24 +44,49 @@ RUN --mount=type=cache,id=pip-$TARGETARCH$TARGETVARIANT,sharing=locked,target=/r # Replace pillow with pillow-simd (Only for x86) ARG TARGETPLATFORM -RUN if [ "$TARGETPLATFORM" = "linux/amd64" ]; then \ +RUN --mount=type=cache,id=apt-$TARGETARCH$TARGETVARIANT,sharing=locked,target=/var/cache/apt \ + --mount=type=cache,id=aptlists-$TARGETARCH$TARGETVARIANT,sharing=locked,target=/var/lib/apt/lists \ + if [ "$TARGETPLATFORM" = "linux/amd64" ]; then \ apt-get update && apt-get install -y --no-install-recommends zlib1g-dev libjpeg62-turbo-dev build-essential && \ - apt-get clean && \ - rm -rf /var/lib/apt/lists/* && \ pip uninstall -y pillow && \ CC="cc -mavx2" pip install -U --force-reinstall pillow-simd; \ fi FROM python:3.10-slim as final +ARG TARGETARCH +ARG TARGETVARIANT + ENV NVIDIA_VISIBLE_DEVICES all ENV NVIDIA_DRIVER_CAPABILITIES compute,utility +WORKDIR /tmp + +ENV CUDA_VERSION=12.1.1 +ENV NV_CUDA_CUDART_VERSION=12.1.105-1 +ENV NVIDIA_REQUIRE_CUDA=cuda>=12.1 +ENV NV_CUDA_COMPAT_PACKAGE=cuda-compat-12-1 + +# Install CUDA partially +ADD https://developer.download.nvidia.com/compute/cuda/repos/debian11/x86_64/cuda-keyring_1.0-1_all.deb . +RUN --mount=type=cache,id=apt-$TARGETARCH$TARGETVARIANT,sharing=locked,target=/var/cache/apt \ + --mount=type=cache,id=aptlists-$TARGETARCH$TARGETVARIANT,sharing=locked,target=/var/lib/apt/lists \ + dpkg -i cuda-keyring_1.0-1_all.deb && \ + rm cuda-keyring_1.0-1_all.deb && \ + sed -i 's/^Components: main$/& contrib/' /etc/apt/sources.list.d/debian.sources && \ + apt-get update && \ + apt-get install -y --no-install-recommends \ + # Installing the whole CUDA typically increases the image size by approximately **8GB**. + # To decrease the image size, we opt to install only the necessary libraries. + # Here is the package list for your reference: https://developer.download.nvidia.com/compute/cuda/repos/debian11/x86_64 + # !If you experience any related issues, replace the following line with `cuda-12-1` to obtain the complete CUDA package. + cuda-cudart-12-1=${NV_CUDA_CUDART_VERSION} ${NV_CUDA_COMPAT_PACKAGE} libcusparse-12-1 libnvjitlink-12-1 + # Install runtime dependencies -RUN apt-get update && \ - apt-get install -y --no-install-recommends libgl1 libglib2.0-0 libjpeg62 libtcl8.6 libtk8.6 libgoogle-perftools-dev dumb-init && \ - apt-get clean && \ - rm -rf /var/lib/apt/lists/* +RUN --mount=type=cache,id=apt-$TARGETARCH$TARGETVARIANT,sharing=locked,target=/var/cache/apt \ + --mount=type=cache,id=aptlists-$TARGETARCH$TARGETVARIANT,sharing=locked,target=/var/lib/apt/lists \ + apt-get update && \ + apt-get install -y --no-install-recommends libgl1 libglib2.0-0 libjpeg62 libtcl8.6 libtk8.6 libgoogle-perftools-dev dumb-init # Fix missing libnvinfer7 RUN ln -s /usr/lib/x86_64-linux-gnu/libnvinfer.so /usr/lib/x86_64-linux-gnu/libnvinfer.so.7 && \ @@ -84,8 +109,9 @@ COPY --link --chmod=775 LICENSE.md /licenses/LICENSE.md COPY --link --chown=$UID:0 --chmod=775 --from=build /root/.local /home/$UID/.local COPY --link --chown=$UID:0 --chmod=775 . /app -ENV PATH="/home/$UID/.local/bin:$PATH" +ENV PATH="/usr/local/cuda/lib:/usr/local/cuda/lib64:/home/$UID/.local/bin:$PATH" ENV PYTHONPATH="${PYTHONPATH}:/home/$UID/.local/lib/python3.10/site-packages" +ENV LD_LIBRARY_PATH="/usr/local/cuda/lib:/usr/local/cuda/lib64:${LD_LIBRARY_PATH}" ENV LD_PRELOAD=libtcmalloc.so ENV PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python @@ -103,7 +129,7 @@ STOPSIGNAL SIGINT # Use dumb-init as PID 1 to handle signals properly ENTRYPOINT ["dumb-init", "--"] -CMD ["python3", "kohya_gui.py", "--listen", "0.0.0.0", "--server_port", "7860"] +CMD ["python3", "kohya_gui.py", "--listen", "0.0.0.0", "--server_port", "7860", "--headless"] ARG VERSION ARG RELEASE From 2dc81c7878c4420e72afeacd1f05b339b7768182 Mon Sep 17 00:00:00 2001 From: Disty0 <47277141+Disty0@users.noreply.github.com> Date: Sat, 30 Mar 2024 23:32:32 +0300 Subject: [PATCH 21/42] Update IPEX to 2.1.20+xpu (#2181) * Update IPEX to 2.1.20+xpu * Disable ipexrun by default --- gui.sh | 7 +++++-- requirements_linux_ipex.txt | 6 +++--- 2 files changed, 8 insertions(+), 5 deletions(-) diff --git a/gui.sh b/gui.sh index 6d148ae91..5a0c544cc 100755 --- a/gui.sh +++ b/gui.sh @@ -92,9 +92,12 @@ then fi export NEOReadDebugKeys=1 export ClDeviceGlobalMemSizeAvailablePercent=100 - if [[ -z "$STARTUP_CMD" ]] && [[ -z "$DISABLE_IPEXRUN" ]] && [ -x "$(command -v ipexrun)" ] + if [[ ! -z "${IPEXRUN}" ]] && [ ${IPEXRUN}="True" ] && [ -x "$(command -v ipexrun)" ] then - STARTUP_CMD=ipexrun + if [[ -z "$STARTUP_CMD" ]] + then + STARTUP_CMD=ipexrun + fi if [[ -z "$STARTUP_CMD_ARGS" ]] then STARTUP_CMD_ARGS="--multi-task-manager taskset --memory-allocator tcmalloc" diff --git a/requirements_linux_ipex.txt b/requirements_linux_ipex.txt index 4b8141dad..f794a9046 100644 --- a/requirements_linux_ipex.txt +++ b/requirements_linux_ipex.txt @@ -1,5 +1,5 @@ -torch==2.1.0a0+cxx11.abi torchvision==0.16.0a0+cxx11.abi intel-extension-for-pytorch==2.1.10+xpu --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/ -tensorboard==2.14.1 tensorflow==2.14.0 intel-extension-for-tensorflow[xpu]==2.14.0.1 -mkl==2024.0.0 mkl-dpcpp==2024.0.0 +torch==2.1.0.post0+cxx11.abi torchvision==0.16.0.post0+cxx11.abi intel-extension-for-pytorch==2.1.20+xpu --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/ +tensorboard==2.15.2 tensorflow==2.15.0 intel-extension-for-tensorflow[xpu]==2.15.0.0 +mkl==2024.1.0 mkl-dpcpp==2024.1.0 oneccl-devel==2021.12.0 impi-devel==2021.12.0 onnxruntime-openvino==1.17.1 -r requirements.txt From 7251cb875766f45b7768b0faa5ec9cf147c0600d Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 1 Apr 2024 11:20:45 -0400 Subject: [PATCH 22/42] Bump crate-ci/typos from 1.18.2 to 1.19.0 (#2189) * Update zh-TW and fixed Lycoris Merge, additional_parameters priority modify. (#2182) * Update localizations for zh-TW * Fixed merge_lycoris_gui issue * Move additional_parameters to the very last of the run_cmd which can override the arguments. * Bump crate-ci/typos from 1.18.2 to 1.19.0 Bumps [crate-ci/typos](https://github.com/crate-ci/typos) from 1.18.2 to 1.19.0. - [Release notes](https://github.com/crate-ci/typos/releases) - [Changelog](https://github.com/crate-ci/typos/blob/master/CHANGELOG.md) - [Commits](https://github.com/crate-ci/typos/compare/v1.18.2...v1.19.0) --- updated-dependencies: - dependency-name: crate-ci/typos dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] --------- Signed-off-by: dependabot[bot] Co-authored-by: bmaltais Co-authored-by: Hina Chen Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/typos.yaml | 2 +- kohya_gui/class_folders.py | 6 +++--- kohya_gui/dreambooth_gui.py | 2 +- kohya_gui/finetune_gui.py | 4 ++-- kohya_gui/lora_gui.py | 10 ++++----- kohya_gui/merge_lycoris_gui.py | 3 +++ kohya_gui/textual_inversion_gui.py | 2 +- localizations/zh-TW.json | 33 ++++++++++++++++++++++-------- 8 files changed, 40 insertions(+), 22 deletions(-) diff --git a/.github/workflows/typos.yaml b/.github/workflows/typos.yaml index eb859574a..e8b06483f 100644 --- a/.github/workflows/typos.yaml +++ b/.github/workflows/typos.yaml @@ -18,4 +18,4 @@ jobs: - uses: actions/checkout@v4 - name: typos-action - uses: crate-ci/typos@v1.18.2 + uses: crate-ci/typos@v1.19.0 diff --git a/kohya_gui/class_folders.py b/kohya_gui/class_folders.py index 845c165bf..5a34d0afc 100644 --- a/kohya_gui/class_folders.py +++ b/kohya_gui/class_folders.py @@ -16,7 +16,7 @@ def __init__(self, finetune: bool = False, headless: bool = False, config:dict = """ self.headless = headless self.finetune = finetune - + # Load kohya_ss GUI configs from config.toml if it exist self.config = config @@ -28,7 +28,7 @@ def __init__(self, finetune: bool = False, headless: bool = False, config:dict = # Create directories if they don't exist self.create_directory_if_not_exists(self.current_output_dir) self.create_directory_if_not_exists(self.current_logging_dir) - + # Create the GUI for folder selection self.create_folders_gui() @@ -89,7 +89,7 @@ def create_folders_gui(self) -> None: with gr.Row(): # Output directory dropdown self.output_dir = gr.Dropdown( - label=f'Output directory for trained model', + label="Output directory for trained model", choices=[""] + self.list_output_dirs(self.current_output_dir), value="", interactive=True, diff --git a/kohya_gui/dreambooth_gui.py b/kohya_gui/dreambooth_gui.py index 5f9b50534..68ef7a5ce 100644 --- a/kohya_gui/dreambooth_gui.py +++ b/kohya_gui/dreambooth_gui.py @@ -568,7 +568,6 @@ def train_model( # Initialize a dictionary with always-included keyword arguments kwargs_for_training = { "adaptive_noise_scale": adaptive_noise_scale, - "additional_parameters": additional_parameters, "bucket_no_upscale": bucket_no_upscale, "bucket_reso_steps": bucket_reso_steps, "cache_latents": cache_latents, @@ -651,6 +650,7 @@ def train_model( "wandb_run_name": wandb_run_name, "weighted_captions": weighted_captions, "xformers": xformers, + "additional_parameters": additional_parameters, } # Conditionally include specific keyword arguments based on sdxl diff --git a/kohya_gui/finetune_gui.py b/kohya_gui/finetune_gui.py index 2e1fe9840..65f45d820 100644 --- a/kohya_gui/finetune_gui.py +++ b/kohya_gui/finetune_gui.py @@ -488,7 +488,7 @@ def train_model( if dataset_config: log.info("Dataset config toml file used, skipping caption json file, image buckets, total_steps, train_batch_size, gradient_accumulation_steps, epoch, reg_factor, max_train_steps creation...") - else: + else: # create caption json file if generate_caption_database: run_cmd = fr'"{PYTHON}" "{scriptdir}/sd-scripts/finetune/merge_captions_to_metadata.py"' @@ -601,7 +601,6 @@ def train_model( # Initialize a dictionary with always-included keyword arguments kwargs_for_training = { "adaptive_noise_scale": adaptive_noise_scale, - "additional_parameters": additional_parameters, "block_lr": block_lr, "bucket_no_upscale": bucket_no_upscale, "bucket_reso_steps": bucket_reso_steps, @@ -680,6 +679,7 @@ def train_model( "wandb_run_name": wandb_run_name, "weighted_captions": weighted_captions, "xformers": xformers, + "additional_parameters": additional_parameters, } # Conditionally include specific keyword arguments based on sdxl_checkbox diff --git a/kohya_gui/lora_gui.py b/kohya_gui/lora_gui.py index b75fece95..a264ec709 100644 --- a/kohya_gui/lora_gui.py +++ b/kohya_gui/lora_gui.py @@ -918,7 +918,6 @@ def train_model( # Define a dictionary of parameters run_cmd_params = { "adaptive_noise_scale": adaptive_noise_scale, - "additional_parameters": additional_parameters, "bucket_no_upscale": bucket_no_upscale, "bucket_reso_steps": bucket_reso_steps, "cache_latents": cache_latents, @@ -1020,6 +1019,7 @@ def train_model( "wandb_run_name": wandb_run_name, "weighted_captions": weighted_captions, "xformers": xformers, + "additional_parameters": additional_parameters, } # Use the ** syntax to unpack the dictionary when calling the function @@ -1193,7 +1193,7 @@ def list_presets(path): text_encoder_lr = gr.Number( label="Text Encoder learning rate", value="0.0001", - info="Optional", + info="(Optional)", minimum=0, maximum=1, ) @@ -1201,7 +1201,7 @@ def list_presets(path): unet_lr = gr.Number( label="Unet learning rate", value="0.0001", - info="Optional", + info="(Optional)", minimum=0, maximum=1, ) @@ -1298,11 +1298,11 @@ def list_presets(path): interactive=True, ) network_alpha = gr.Slider( - minimum=0.1, + minimum=0.00001, maximum=1024, label="Network Alpha", value=1, - step=0.1, + step=0.00001, interactive=True, info="alpha for LoRA weight scaling", ) diff --git a/kohya_gui/merge_lycoris_gui.py b/kohya_gui/merge_lycoris_gui.py index 546f52c97..733d1135a 100644 --- a/kohya_gui/merge_lycoris_gui.py +++ b/kohya_gui/merge_lycoris_gui.py @@ -120,6 +120,9 @@ def list_save_to(path): lycoris_model = gr.Dropdown( label='LyCORIS model (path to the LyCORIS model)', interactive=True, + choices=[""] + list_lycoris_model(current_save_dir), + value="", + allow_custom_value=True, ) button_lycoris_model_file = gr.Button( folder_symbol, diff --git a/kohya_gui/textual_inversion_gui.py b/kohya_gui/textual_inversion_gui.py index fa73562e4..e1974c1a7 100644 --- a/kohya_gui/textual_inversion_gui.py +++ b/kohya_gui/textual_inversion_gui.py @@ -555,7 +555,6 @@ def train_model( run_cmd += run_cmd_advanced_training( adaptive_noise_scale=adaptive_noise_scale, - additional_parameters=additional_parameters, bucket_no_upscale=bucket_no_upscale, bucket_reso_steps=bucket_reso_steps, cache_latents=cache_latents, @@ -635,6 +634,7 @@ def train_model( wandb_api_key=wandb_api_key, wandb_run_name=wandb_run_name, xformers=xformers, + additional_parameters=additional_parameters, ) run_cmd += f' --token_string="{token_string}"' run_cmd += f' --init_word="{init_word}"' diff --git a/localizations/zh-TW.json b/localizations/zh-TW.json index 8011bfce8..af1388039 100644 --- a/localizations/zh-TW.json +++ b/localizations/zh-TW.json @@ -3,9 +3,9 @@ "(Experimental, Optional) Since the latent is close to a normal distribution, it may be a good idea to specify a value around 1/10 the noise offset.": " (選填,實驗性功能) 由於潛空間接近常態分布,或許指定一個噪聲偏移約 1/10 的數值是個不錯的作法。", "(Name of the model to output)": "(要輸出的模型名稱)", "(Optional) Add training comment to be included in metadata": "(選填) 在訓練的後設資料 (metadata) 加入註解。", - "(Optional) Enforce number of epoch": " (選填) 強制指定一個週期 (Epoch) 數量", - "(Optional) Enforce number of steps": " (選填) 強制指定一個總步數數量", - "(Optional) Extension for caption files. default: .caption": " (選填) 標記檔案的副檔名。預設:.caption", + "(Optional) Enforce # epochs": " (選填) 強制指定週期數 (Epochs) ", + "(Optional) Enforce # steps": " (選填) 強制指定總步數", + "(Optional) default: .caption": " (選填) 預設:.caption", "(Optional) For Cosine with restart and polynomial only": " (選填) 只適用於餘弦函數並使用重啟 (cosine_with_restart) 和多項式 (polynomial)", "(Optional) Override number of epoch. Default: 8": " (選填) 覆蓋週期 (Epoch) 數量。預設:8", "(Optional) Save only the specified number of models (old models will be deleted)": " (選填) 僅儲存指定數量的模型 (舊有模型將被刪除) ", @@ -47,10 +47,12 @@ "Blocks": "區塊", "Bucket resolution steps need to be greater than 0": "資料儲存桶解析度步數需要大於 0", "Bucket resolution steps": "分桶解析度間隔", + "Bypass mode": "旁路模式 (Bypass mode)", "Cache latents to disk": "暫存潛空間資料到硬碟", "Cache latents": "暫存潛空間資料", "Cache text encoder outputs": "暫存文本編碼器輸出", "Cache the outputs of the text encoders. This option is useful to reduce the GPU memory usage. This option cannot be used with options for shuffling or dropping the captions.": "暫存文本編碼器的輸出。此選項有助於減少 GPU 記憶體的使用。此選項不能與打亂或丟棄提示詞 (Shuffle/Dropout caption) 的選項一起使用。", + "Could not modify caption files with requested change because the \"Overwrite existing captions in folder\" option is not selected.": "無法修改標記文字檔案以進行所需的更改,因為未選擇「覆蓋資料夾中現有的提示詞」選項。", "Caption Extension": "標記檔案副檔名", "Caption Separator": "標記文字分隔符號", "Caption file extension (e.g., .txt)": "標記文字檔案副檔名 (例如:.txt)", @@ -89,8 +91,11 @@ "Dataset Preparation": "資料集準備", "Dataset folder (folder containing the concepts folders to balance...)": "資料集資料夾 (含有要平衡的概念資料夾的資料夾路徑...)", "Dataset repeats": "資料集重複數", + "Dataset config toml file used, skipping total_steps, train_batch_size, gradient_accumulation_steps, epoch, reg_factor, max_train_steps calculations...": "使用資料集設定 toml 檔案,跳過總步數、訓練批次大小、梯度累加步數、週期、正規化因子、最大訓練步數的計算...", + "Dataset config file (Optional. Select the toml configuration file to use for the dataset)": "資料集設定檔案 (選填,選擇要用於資料集的 toml 設定檔案)", "Debiased Estimation loss": "偏差估算損失 (Debiased Estimation loss)", "Debug while tagging, it will print your image file with general tags and character tags.": "標記時進行調試,它將打印您的圖片檔案與一般標籤和角色標籤。", + "Designed for bnb 8bit/4bit linear layer. (QLyCORIS)": "設計用於 bnb 8bit/4bit 線性層。 (QLyCORIS)", "Desired LoRA rank": "所需的 LoRA 維度 (Rank)", "Destination training directory (where formatted training and regularisation folders will be placed)": "訓練的目標資料夾 (格式化的訓練和正規化資料夾將被放置的資料夾)", "Device": "裝置", @@ -99,6 +104,7 @@ "Do not copy other files in the input folder to the output folder": "不複製輸入資料夾中的其他檔案到輸出資料夾", "Do not copy other files": "不複製其他檔案", "Don't upscale bucket resolution": "不要放大分桶解析度", + "DoRA Weight Decompose": "DoRA 權重分解", "Down LR weights": "Down LR 權重", "Dreambooth/LoRA Dataset balancing": "Dreambooth/LoRA 資料集平衡", "Dreambooth/LoRA Folder preparation": "Dreambooth/LoRA 資料夾準備", @@ -112,6 +118,7 @@ "Eg: person": "例如:person", "Enable buckets": "啟用資料儲存桶", "Enable multires noise (recommended values are 6-10)": "啟用多解析度噪聲 (建議使用 6-10)", + "Enable the DoRA method for these algorithms": "為這些演算法啟用 DoRA 方法", "Enter one sample prompt per line to generate multiple samples per cycle. Optional specifiers include: --w (width), --h (height), --d (seed), --l (cfg scale), --s (sampler steps) and --n (negative prompt). To modify sample prompts during training, edit the prompt.txt file in the samples directory.": "每行輸入一個提示詞來生成每個訓練週期的輸出範本。可以選擇指定的參數,包括:--w (寬度) ,--h (高度) ,--d (種子) ,--l (CFG 比例) ,--s (採樣器步驟) 和 --n (負面提示詞) 。如果要在訓練週期中修改提示詞,請修改範本目錄中的 prompt.txt 檔案。", "Epoch": "週期 (Epoch)", "Error": "錯誤", @@ -165,7 +172,7 @@ "Is a normal probability dropout at the neuron level. In the case of LoRA, it is applied to the output of down. Recommended range 0.1 to 0.5": "是神經元級的正常概率捨棄。在 LoRA 的情況下,它被應用於 Down Sampler 的輸出。建議範圍 0.1 到 0.5", "Keep n tokens": "保留 N 個提示詞", "LR Scheduler": "學習率調度器 (LR Scheduler)", - "LR number of cycles": "學習率重啟週期數 (LR number of cycles)", + "LR # cycles": "學習率重啟週期數 (LR number of cycles)", "LR power": "學習率乘冪 (LR power)", "LR scheduler extra arguments": "學習率調度器額外參數", "LR warmup (% of total steps)": "學習率預熱 (LR warmup, 總步數的 %)", @@ -188,12 +195,15 @@ "LoRA model (path to the LoRA model to verify)": "LoRA 模型 (要驗證的 LoRA 模型的檔案路徑)", "LoRA model types": "LoRA 模型類型", "LoRA network weights": "LoRA 網路權重", + "LoRA type changed...": "LoRA 類型已更改...", "Load": "載入", "Load Stable Diffusion base model to": "載入穩定擴散基礎模型到", "Load finetuned model to": "載入微調模型到", "Load precision": "讀取精度", "Load/Save Config file": "讀取/儲存設定檔案", - "Logging folder (Optional. to enable logging and output Tensorboard log)": "紀錄資料夾(選填,啟用紀錄和輸出 Tensorboard 紀錄)", + "Logging directory (Optional. to enable logging and output Tensorboard log)": "紀錄資料夾(選填,啟用紀錄和輸出 Tensorboard 紀錄)", + "Log tracker name": "紀錄追蹤器名稱", + "Log tracker config": "紀錄追蹤器設定", "LyCORIS model (path to the LyCORIS model)": "LyCORIS 模型 (LyCORIS 模型的檔案路徑)", "Manual Captioning": "手動標記文字", "Max Norm Regularization is a technique to stabilize network training by limiting the norm of network weights. It may be effective in suppressing overfitting of LoRA and improving stability when used with other LoRAs. See PR #545 on kohya_ss/sd_scripts repo for details. Recommended setting: 1. Higher is weaker, lower is stronger.": "最大規範正規化是一種穩定網路訓練的技術,通過限制網路權重的規範來實現。當與其他 LoRA 一起使用時,它可能會有效地抑制 LoRA 的過度擬合並提高穩定性。詳細資料請見 kohya_ss/sd_scripts Github 上的 PR#545。建議設置:1.0 越高越弱,越低越強。", @@ -239,6 +249,7 @@ "Multi GPU": "多個 GPU", "Multires noise iterations": "多解析度噪聲迭代", "Name of the new LCM model": "新 LCM 模型的名稱", + "Name of tracker to use for logging, default is script-specific default name": "用於記錄的追蹤器名稱,預設為特定於腳本的預設名稱", "Network Alpha": "網路 Alpha", "Network Dimension (Rank)": "網路維度 (Rank)", "Network Rank (Dimension)": "網路維度 (Rank)", @@ -268,11 +279,12 @@ "Output \"stop text encoder training\" is not yet supported. Ignoring": "輸出「停止文本編碼器訓練」尚未支援。忽略", "Output": "輸出", "Output folder (where the grouped images will be stored)": "輸出資料夾 (存放分組的圖片)", - "Output folder to output trained model": "輸出資料夾以輸出訓練模型", + "Output directory for trained model": "輸出資料夾以輸出訓練模型", "Overwrite existing captions in folder": "覆蓋資料夾中現有的提示詞", "Page Number": "頁碼", "Parameters": "參數", "Path to an existing LoRA network weights to resume training from": "現有 LoRA 檔案路徑,從現有 LoRA 中繼續訓練", + "Path to tracker config file to use for logging": "用於記錄的追蹤器設定檔案的路徑", "Persistent data loader": "持續資料載入器 (Persistent data loader)", "Please input learning rate values.": "請輸入學習率數值。", "Please input valid Text Encoder learning rate (between 0 and 1)": "請輸入有效的文本編碼器學習率 (在 0 到 1 之間)", @@ -304,7 +316,7 @@ "Recommended values are 0.05 - 0.15": "若使用時,建議使用 0.05 - 0.15", "Recommended values are 0.8. For LoRAs with small datasets, 0.1-0.3": "建議使用 0.8。對於小數據集的 LoRA,建議使用 0.1-0.3", "Recursive": "遞迴", - "Regularisation folder (Optional. containing reqularization images)": "正規化資料夾(選填,包含正規化圖片)", + "Regularisation directory (Optional. containing regularisation images)": "正規化資料夾(選填,包含正規化圖片)", "Regularisation images (Optional. directory containing the regularisation images)": "正規化圖片 (選填,含有正規化圖片的資料夾)", "Regularisation images are used... Will double the number of steps required...": "使用了正規化圖片... 將使所需的步數加倍...", "Repeats": "重複次數", @@ -343,6 +355,7 @@ "Save training state": "儲存訓練狀態", "Scale v prediction loss": "縮放 v 預測損失 (v prediction loss)", "Scale weight norms": "縮放權重標準", + "SDXL Specific Parameters": "SDXL 特定參數", "Seed": "種子 (Seed)", "Selects trainable layers in a network, but trains normalization layers identically across methods as they lack matrix decomposition.": "選擇網路中的可訓練層,但由於缺乏矩陣分解,因此在各種方法中都以相同方式訓練規範化層。", "Set if we change the information going into the system (True) or the information coming out of it (False).": "設定為 True,若我們改變進入系統的資訊,否則由系統輸出則設定為 False。", @@ -394,6 +407,7 @@ "The provided model C is not a file": "提供的模型 C 不是檔案", "The provided model D is not a file": "提供的模型 D 不是檔案", "The provided model is not a file": "提供的模型不是檔案", + "The name of the specific wandb session": "指定 WANDB session 的名稱", "This option appends the tags to the existing tags, instead of replacing them.": "此選項將標籤附加到現有標籤,而不是替換它們。", "This section provide Various Finetuning guides and information...": "此部分提供各種微調指南和資訊...", "This section provide various LoRA tools...": "此部分提供各種 LoRA 工具...", @@ -424,7 +438,7 @@ "Train a custom model using kohya finetune python code...": "使用 kohya 微調 Python 程式訓練自定義模型", "Train a TI using kohya textual inversion python code...": "使用 kohya 文本反轉 Python 程式訓練 TI", "Train an additional scalar in front of the weight difference, use a different weight initialization strategy.": "在權重差異前訓練一個額外的標量,使用不同的權重初始化策略。", - "Train config folder (Optional. where config files will be saved)": "訓練設定資料夾(選填,設定檔案將會被儲存的資料夾)", + "Train config directory (Optional. where config files will be saved)": "訓練設定資料夾(選填,設定檔案將會被儲存的資料夾)", "Train text encoder": "訓練文本編碼器", "Trained Model output name": "訓練模型輸出名稱", "Training comment": "訓練註解", @@ -447,7 +461,7 @@ "Useful to force model re download when switching to onnx": "在切換到 ONNX 時強制重新下載模型", "Users can obtain and/or generate an api key in the their user settings on the website: https://wandb.ai/login": "使用者可以在以下網站的用戶設定中取得,或產生 API 金鑰:https://wandb.ai/login", "V Pred like loss": "V 預測損失 (V Pred like loss)", - "VAE (Optional. path to checkpoint of vae to replace for training)": "VAE (選填,選擇要替換訓練的 VAE checkpoint 的檔案路徑)", + "VAE (Optional: Path to checkpoint of vae for training)": "VAE (選填:選擇要替換訓練的 VAE checkpoint 的檔案路徑)", "VAE batch size": "VAE 批次大小", "Value for the dynamic method selected.": "選擇的動態方法的數值。", "Values greater than 0 will make the model more img2img focussed. 0 = image only": "大於 0 的數值會使模型更加聚焦在 img2img 上。0 表示僅關注於圖像生成", @@ -459,6 +473,7 @@ "Verify": "驗證", "WANDB API Key": "WANDB API 金鑰", "WANDB Logging": "WANDB 紀錄", + "WANDB run name": "WANDB 執行名稱", "WD14 Captioning": "WD14 標記文字", "Weighted captions": "加權標記文字 (Weighted captions)", "Weights": "權重", From c4a11042f7cf009f0dfde7c82e2f364c9e2407f2 Mon Sep 17 00:00:00 2001 From: bmaltais Date: Mon, 1 Apr 2024 11:30:54 -0400 Subject: [PATCH 23/42] Update sd-script and add new test config --- sd-scripts | 2 +- test/config/locon-Adafactor.json | 138 +++++++++++++++++++++++++++++++ 2 files changed, 139 insertions(+), 1 deletion(-) create mode 100644 test/config/locon-Adafactor.json diff --git a/sd-scripts b/sd-scripts index 059ee047f..2258a1b75 160000 --- a/sd-scripts +++ b/sd-scripts @@ -1 +1 @@ -Subproject commit 059ee047f3c90f37232eac9bc48c0711925d1b0b +Subproject commit 2258a1b753a321ed25b9ae1b7f2ceb1b24ae0736 diff --git a/test/config/locon-Adafactor.json b/test/config/locon-Adafactor.json new file mode 100644 index 000000000..457a844d7 --- /dev/null +++ b/test/config/locon-Adafactor.json @@ -0,0 +1,138 @@ +{ + "LoRA_type": "Kohya LoCon", + "LyCORIS_preset": "full", + "adaptive_noise_scale": 0.005, + "additional_parameters": "", + "block_alphas": "", + "block_dims": "", + "block_lr_zero_threshold": "", + "bucket_no_upscale": true, + "bucket_reso_steps": 64, + "bypass_mode": false, + "cache_latents": true, + "cache_latents_to_disk": false, + "caption_dropout_every_n_epochs": 0.0, + "caption_dropout_rate": 0.05, + "caption_extension": "", + "clip_skip": 2, + "color_aug": false, + "constrain": 0.0, + "conv_alpha": 8, + "conv_block_alphas": "", + "conv_block_dims": "", + "conv_dim": 16, + "dataset_config": "", + "debiased_estimation_loss": false, + "decompose_both": false, + "dim_from_weights": false, + "dora_wd": false, + "down_lr_weight": "", + "enable_bucket": true, + "epoch": 8, + "factor": -1, + "flip_aug": false, + "fp8_base": false, + "full_bf16": false, + "full_fp16": false, + "gpu_ids": "", + "gradient_accumulation_steps": 1, + "gradient_checkpointing": false, + "ip_noise_gamma": 0.1, + "ip_noise_gamma_random_strength": true, + "keep_tokens": "0", + "learning_rate": 0.0005, + "log_tracker_config": "", + "log_tracker_name": "", + "logging_dir": "./test/logs", + "lora_network_weights": "", + "lr_scheduler": "constant", + "lr_scheduler_args": "", + "lr_scheduler_num_cycles": "", + "lr_scheduler_power": "", + "lr_warmup": 0, + "main_process_port": 0, + "masked_loss": false, + "max_bucket_reso": 2048, + "max_data_loader_n_workers": "0", + "max_grad_norm": 0, + "max_resolution": "512,512", + "max_timestep": 1000, + "max_token_length": "75", + "max_train_epochs": "", + "max_train_steps": "", + "mem_eff_attn": false, + "mid_lr_weight": "", + "min_bucket_reso": 256, + "min_snr_gamma": 0, + "min_timestep": 0, + "mixed_precision": "bf16", + "model_list": "runwayml/stable-diffusion-v1-5", + "module_dropout": 0.1, + "multi_gpu": false, + "multires_noise_discount": 0, + "multires_noise_iterations": 0, + "network_alpha": 8, + "network_dim": 16, + "network_dropout": 0.1, + "noise_offset": 0.05, + "noise_offset_random_strength": true, + "noise_offset_type": "Original", + "num_cpu_threads_per_process": 2, + "num_machines": 1, + "num_processes": 1, + "optimizer": "Adafactor", + "optimizer_args": "scale_parameter=False relative_step=False warmup_init=False", + "output_dir": "./test/output", + "output_name": "locon-adafactor", + "persistent_data_loader_workers": false, + "pretrained_model_name_or_path": "runwayml/stable-diffusion-v1-5", + "prior_loss_weight": 1.0, + "random_crop": false, + "rank_dropout": 0.1, + "rank_dropout_scale": false, + "reg_data_dir": "", + "rescaled": false, + "resume": "", + "sample_every_n_epochs": 0, + "sample_every_n_steps": 25, + "sample_prompts": "a painting of a gas mask , by darius kawasaki", + "sample_sampler": "euler_a", + "save_every_n_epochs": 1, + "save_every_n_steps": 0, + "save_last_n_steps": 0, + "save_last_n_steps_state": 0, + "save_model_as": "safetensors", + "save_precision": "fp16", + "save_state": false, + "save_state_on_train_end": false, + "scale_v_pred_loss_like_noise_pred": false, + "scale_weight_norms": 1, + "sdxl": false, + "sdxl_cache_text_encoder_outputs": false, + "sdxl_no_half_vae": true, + "seed": "1234", + "shuffle_caption": false, + "stop_text_encoder_training": 0, + "text_encoder_lr": 0.0001, + "train_batch_size": 4, + "train_data_dir": "./test/img", + "train_norm": false, + "train_on_input": false, + "training_comment": "", + "unet_lr": 0.0001, + "unit": 1, + "up_lr_weight": "", + "use_cp": false, + "use_scalar": false, + "use_tucker": false, + "use_wandb": false, + "v2": false, + "v_parameterization": false, + "v_pred_like_loss": 0, + "vae": "", + "vae_batch_size": 0, + "wandb_api_key": "", + "wandb_run_name": "", + "weighted_captions": false, + "xformers": "xformers" +} \ No newline at end of file From b3a78febd11831646b85b50dff6cca6a1b07d8df Mon Sep 17 00:00:00 2001 From: bmaltais Date: Mon, 1 Apr 2024 16:07:34 -0400 Subject: [PATCH 24/42] Move Accelerate launch to own accordion (#2192) * Move accelerate launch parameters to own accordion --- kohya_gui/class_accelerate_launch.py | 125 ++- kohya_gui/class_advanced_training.py | 20 - kohya_gui/class_basic_training.py | 12 - kohya_gui/class_source_model.py | 341 ++++---- kohya_gui/common_gui.py | 33 +- kohya_gui/dreambooth_gui.py | 43 +- kohya_gui/finetune_gui.py | 48 +- kohya_gui/lora_gui.py | 1205 +++++++++++++------------- kohya_gui/textual_inversion_gui.py | 187 ++-- 9 files changed, 1020 insertions(+), 994 deletions(-) diff --git a/kohya_gui/class_accelerate_launch.py b/kohya_gui/class_accelerate_launch.py index 546cb4fd4..4e638409f 100644 --- a/kohya_gui/class_accelerate_launch.py +++ b/kohya_gui/class_accelerate_launch.py @@ -1,41 +1,100 @@ import gradio as gr import os -from typing import Tuple class AccelerateLaunch: def __init__( - self + self, ) -> None: - # self.sdxl_checkbox = sdxl_checkbox - # self.learning_rate_value = learning_rate_value - # self.lr_scheduler_value = lr_scheduler_value - # self.lr_warmup_value = lr_warmup_value - # self.finetuning = finetuning - # self.dreambooth = dreambooth - - # Initialize the UI components - self.initialize_ui_components() - - def initialize_ui_components(self) -> None: - """ - Initializes the UI components for the training settings. - """ - # Initialize the Hardware Selection Arguments - self.init_hardware_selection_arguments() - # Initialize the Resource Selection Arguments - self.init_resource_selection_arguments() + with gr.Accordion("Resource Selection", open=True): + with gr.Row(): + self.mixed_precision = gr.Dropdown( + label="Mixed precision", + choices=["no", "fp16", "bf16", "fp8"], + value="fp16", + info="Whether or not to use mixed precision training.", + ) + self.num_processes = gr.Number( + label="Number of processes", + value=1, + precision=0, + minimum=1, + info="The total number of processes to be launched in parallel.", + ) + self.num_machines = gr.Number( + label="Number of machines", + value=1, + precision=0, + minimum=1, + info="The total number of machines used in this training.", + ) + self.num_cpu_threads_per_process = gr.Slider( + minimum=1, + maximum=os.cpu_count(), + step=1, + label="Number of CPU threads per core", + value=2, + info="The number of CPU threads per process.", + ) + with gr.Accordion("Hardware Selection", open=True): + with gr.Row(): + self.multi_gpu = gr.Checkbox( + label="Multi GPU", + value=False, + info="Whether or not this should launch a distributed GPU training.", + ) + with gr.Accordion("Distributed GPUs", open=True): + with gr.Row(): + self.gpu_ids = gr.Textbox( + label="GPU IDs", + value="", + placeholder="example: 0,1", + info=" What GPUs (by id) should be used for training on this machine as a comma-separated list", + ) + self.main_process_port = gr.Number( + label="Main process port", + value=0, + precision=1, + minimum=0, + maximum=65535, + info="The port to use to communicate with the machine of rank 0.", + ) + + def run_cmd(**kwargs): + run_cmd = "" - def init_hardware_selection_arguments(self) -> None: - """ - Initializes the hardware selection arguments for the model. - """ - with gr.Row(): - # Initialize the CPU checkbox - self.cpu = gr.Checkbox(label="CPU", value=False, info="force the training on the CPU") - # Initialize the multi-GPU checkbox - self.multi_gpu = gr.Checkbox(label="Multi-GPU", value=False, info="launch a distributed GPU training") - # Initialize the TPU checkbox - self.tpu = gr.Checkbox(label="TPU", value=False, info="launch a TPU training") - # Initialize the IPEX checkbox - self.ipex = gr.Checkbox(label="IPEX", value=False, info="launch a Intel PyTorch Extension (IPEX) training") + if "gpu_ids" in kwargs: + gpu_ids = kwargs.get("gpu_ids") + if not gpu_ids == "": + run_cmd += f' --gpu_ids="{gpu_ids}"' + + if "main_process_port" in kwargs: + main_process_port = kwargs.get("main_process_port") + if main_process_port > 0: + run_cmd += f' --main_process_port="{main_process_port}"' + + if "mixed_precision" in kwargs: + run_cmd += rf' --mixed_precision="{kwargs.get("mixed_precision")}"' + + if "multi_gpu" in kwargs: + if kwargs.get("multi_gpu"): + run_cmd += " --multi_gpu" + + if "num_processes" in kwargs: + num_processes = kwargs.get("num_processes") + if int(num_processes) > 1: + run_cmd += f" --num_processes={int(num_processes)}" + + if "num_machines" in kwargs: + num_machines = kwargs.get("num_machines") + if int(num_machines) > 1: + run_cmd += f" --num_machines={int(num_machines)}" + + if "num_cpu_threads_per_process" in kwargs: + num_cpu_threads_per_process = kwargs.get("num_cpu_threads_per_process") + if int(num_cpu_threads_per_process) > 1: + run_cmd += ( + f" --num_cpu_threads_per_process={int(num_cpu_threads_per_process)}" + ) + + return run_cmd \ No newline at end of file diff --git a/kohya_gui/class_advanced_training.py b/kohya_gui/class_advanced_training.py index d922a5c1f..e232dcc62 100644 --- a/kohya_gui/class_advanced_training.py +++ b/kohya_gui/class_advanced_training.py @@ -1,5 +1,4 @@ import gradio as gr -import os from typing import Tuple from .common_gui import ( get_folder_path, @@ -401,25 +400,6 @@ def list_state_dirs(path): placeholder="(Optional) Override number of epoch. Default: 8", value="0", ) - with gr.Row(): - self.num_processes = gr.Number( - label="Number of processes", value=1, precision=0, minimum=1 - ) - self.num_machines = gr.Number( - label="Number of machines", value=1, precision=0, minimum=1 - ) - self.multi_gpu = gr.Checkbox(label="Multi GPU", value=False) - self.gpu_ids = gr.Textbox( - label="GPU IDs", value="", placeholder="example: 0,1" - ) - self.main_process_port = gr.Number( - label="Main process port", - value=0, - precision=1, - minimum=0, - maximum=65535, - info="The port to use to communicate with the machine of rank 0.", - ) with gr.Row(): self.use_wandb = gr.Checkbox( label="WANDB Logging", diff --git a/kohya_gui/class_basic_training.py b/kohya_gui/class_basic_training.py index 8eb3e8493..6f92d0db6 100644 --- a/kohya_gui/class_basic_training.py +++ b/kohya_gui/class_basic_training.py @@ -105,18 +105,6 @@ def init_precision_and_resources_controls(self) -> None: Initializes the precision and resources controls for the model. """ with gr.Row(): - # Initialize the mixed precision dropdown - self.mixed_precision = gr.Dropdown( - label="Mixed precision", choices=["no", "fp16", "bf16"], value="fp16" - ) - # Initialize the number of CPU threads per core slider - self.num_cpu_threads_per_process = gr.Slider( - minimum=1, - maximum=os.cpu_count(), - step=1, - label="Number of CPU threads per core", - value=2, - ) # Initialize the seed textbox self.seed = gr.Textbox(label="Seed", placeholder="(Optional) eg:1234") # Initialize the cache latents checkbox diff --git a/kohya_gui/class_source_model.py b/kohya_gui/class_source_model.py index 0c92905b9..abde0f5ac 100644 --- a/kohya_gui/class_source_model.py +++ b/kohya_gui/class_source_model.py @@ -97,182 +97,183 @@ def list_dataset_config_dirs(path: str) -> list: return list(list_files(current_dataset_config_dir, exts=[".toml"], all=True)) - with gr.Column(), gr.Group(): - # Define the input elements - with gr.Row(): - with gr.Column(), gr.Row(): - self.model_list = gr.Textbox(visible=False, value="") - self.pretrained_model_name_or_path = gr.Dropdown( - label="Pretrained model name or path", - choices=default_models + model_checkpoints, - value="runwayml/stable-diffusion-v1-5", - allow_custom_value=True, - visible=True, - min_width=100, - ) - create_refresh_button( - self.pretrained_model_name_or_path, - lambda: None, - lambda: {"choices": list_models(self.current_models_dir)}, - "open_folder_small", - ) + with gr.Accordion("Model", open=True): + with gr.Column(), gr.Group(): + # Define the input elements + with gr.Row(): + with gr.Column(), gr.Row(): + self.model_list = gr.Textbox(visible=False, value="") + self.pretrained_model_name_or_path = gr.Dropdown( + label="Pretrained model name or path", + choices=default_models + model_checkpoints, + value="runwayml/stable-diffusion-v1-5", + allow_custom_value=True, + visible=True, + min_width=100, + ) + create_refresh_button( + self.pretrained_model_name_or_path, + lambda: None, + lambda: {"choices": list_models(self.current_models_dir)}, + "open_folder_small", + ) - self.pretrained_model_name_or_path_file = gr.Button( - document_symbol, - elem_id="open_folder_small", - elem_classes=["tool"], - visible=(not headless), - ) - self.pretrained_model_name_or_path_file.click( - get_file_path, - inputs=self.pretrained_model_name_or_path, - outputs=self.pretrained_model_name_or_path, - show_progress=False, - ) - self.pretrained_model_name_or_path_folder = gr.Button( - folder_symbol, - elem_id="open_folder_small", - elem_classes=["tool"], - visible=(not headless), - ) - self.pretrained_model_name_or_path_folder.click( - get_folder_path, - inputs=self.pretrained_model_name_or_path, - outputs=self.pretrained_model_name_or_path, - show_progress=False, - ) - - with gr.Column(), gr.Row(): - self.output_name = gr.Textbox( - label="Trained Model output name", - placeholder="(Name of the model to output)", - value="last", - interactive=True, - ) - with gr.Row(): - with gr.Column(), gr.Row(): - self.train_data_dir = gr.Dropdown( - label=( - "Image folder (containing training images subfolders)" - if not finetuning - else "Image folder (containing training images)" - ), - choices=[""] - + list_train_data_dirs(self.current_train_data_dir), - value="", - interactive=True, - allow_custom_value=True, - ) - create_refresh_button( - self.train_data_dir, - lambda: None, - lambda: { - "choices": [""] - + list_train_data_dirs(self.current_train_data_dir) - }, - "open_folder_small", - ) - self.train_data_dir_folder = gr.Button( - "📂", - elem_id="open_folder_small", - elem_classes=["tool"], - visible=(not self.headless), - ) - self.train_data_dir_folder.click( - get_folder_path, - outputs=self.train_data_dir, - show_progress=False, - ) - with gr.Column(), gr.Row(): - # Toml directory dropdown - self.dataset_config = gr.Dropdown( - label='Dataset config file (Optional. Select the toml configuration file to use for the dataset)', - choices=[""] + list_dataset_config_dirs(self.current_dataset_config_dir), - value="", + self.pretrained_model_name_or_path_file = gr.Button( + document_symbol, + elem_id="open_folder_small", + elem_classes=["tool"], + visible=(not headless), + ) + self.pretrained_model_name_or_path_file.click( + get_file_path, + inputs=self.pretrained_model_name_or_path, + outputs=self.pretrained_model_name_or_path, + show_progress=False, + ) + self.pretrained_model_name_or_path_folder = gr.Button( + folder_symbol, + elem_id="open_folder_small", + elem_classes=["tool"], + visible=(not headless), + ) + self.pretrained_model_name_or_path_folder.click( + get_folder_path, + inputs=self.pretrained_model_name_or_path, + outputs=self.pretrained_model_name_or_path, + show_progress=False, + ) + + with gr.Column(), gr.Row(): + self.output_name = gr.Textbox( + label="Trained Model output name", + placeholder="(Name of the model to output)", + value="last", + interactive=True, + ) + with gr.Row(): + with gr.Column(), gr.Row(): + self.train_data_dir = gr.Dropdown( + label=( + "Image folder (containing training images subfolders)" + if not finetuning + else "Image folder (containing training images)" + ), + choices=[""] + + list_train_data_dirs(self.current_train_data_dir), + value="", + interactive=True, + allow_custom_value=True, + ) + create_refresh_button( + self.train_data_dir, + lambda: None, + lambda: { + "choices": [""] + + list_train_data_dirs(self.current_train_data_dir) + }, + "open_folder_small", + ) + self.train_data_dir_folder = gr.Button( + "📂", + elem_id="open_folder_small", + elem_classes=["tool"], + visible=(not self.headless), + ) + self.train_data_dir_folder.click( + get_folder_path, + outputs=self.train_data_dir, + show_progress=False, + ) + with gr.Column(), gr.Row(): + # Toml directory dropdown + self.dataset_config = gr.Dropdown( + label='Dataset config file (Optional. Select the toml configuration file to use for the dataset)', + choices=[""] + list_dataset_config_dirs(self.current_dataset_config_dir), + value="", + interactive=True, + allow_custom_value=True, + ) + # Refresh button for dataset_config directory + create_refresh_button(self.dataset_config, lambda: None, lambda: {"choices": [""] + list_dataset_config_dirs(self.current_dataset_config_dir)}, "open_folder_small") + # Toml directory button + self.dataset_config_folder = gr.Button( + document_symbol, elem_id='open_folder_small', elem_classes=["tool"], visible=(not self.headless) + ) + + # Toml directory button click event + self.dataset_config_folder.click( + get_file_path, + inputs=[self.dataset_config, gr.Textbox(value='*.toml', visible=False), gr.Textbox(value='Dataset config types', visible=False)], + outputs=self.dataset_config, + show_progress=False, + ) + # Change event for dataset_config directory dropdown + self.dataset_config.change( + fn=lambda path: gr.Dropdown(choices=[""] + list_dataset_config_dirs(path)), + inputs=self.dataset_config, + outputs=self.dataset_config, + show_progress=False, + ) + + with gr.Row(): + with gr.Column(): + with gr.Row(): + self.v2 = gr.Checkbox( + label="v2", value=False, visible=False, min_width=60 + ) + self.v_parameterization = gr.Checkbox( + label="v_parameterization", + value=False, + visible=False, + min_width=130, + ) + self.sdxl_checkbox = gr.Checkbox( + label="SDXL", + value=False, + visible=False, + min_width=60, + ) + with gr.Column(): + gr.Box(visible=False) + + with gr.Row(): + self.training_comment = gr.Textbox( + label="Training comment", + placeholder="(Optional) Add training comment to be included in metadata", interactive=True, - allow_custom_value=True, ) - # Refresh button for dataset_config directory - create_refresh_button(self.dataset_config, lambda: None, lambda: {"choices": [""] + list_dataset_config_dirs(self.current_dataset_config_dir)}, "open_folder_small") - # Toml directory button - self.dataset_config_folder = gr.Button( - document_symbol, elem_id='open_folder_small', elem_classes=["tool"], visible=(not self.headless) - ) - - # Toml directory button click event - self.dataset_config_folder.click( - get_file_path, - inputs=[self.dataset_config, gr.Textbox(value='*.toml', visible=False), gr.Textbox(value='Dataset config types', visible=False)], - outputs=self.dataset_config, - show_progress=False, + + with gr.Row(): + self.save_model_as = gr.Radio( + save_model_as_choices, + label="Save trained model as", + value="safetensors", ) - # Change event for dataset_config directory dropdown - self.dataset_config.change( - fn=lambda path: gr.Dropdown(choices=[""] + list_dataset_config_dirs(path)), - inputs=self.dataset_config, - outputs=self.dataset_config, - show_progress=False, + self.save_precision = gr.Radio( + save_precision_choices, + label="Save precision", + value="fp16", ) - with gr.Row(): - with gr.Column(): - with gr.Row(): - self.v2 = gr.Checkbox( - label="v2", value=False, visible=False, min_width=60 - ) - self.v_parameterization = gr.Checkbox( - label="v_parameterization", - value=False, - visible=False, - min_width=130, - ) - self.sdxl_checkbox = gr.Checkbox( - label="SDXL", - value=False, - visible=False, - min_width=60, - ) - with gr.Column(): - gr.Box(visible=False) - - with gr.Row(): - self.training_comment = gr.Textbox( - label="Training comment", - placeholder="(Optional) Add training comment to be included in metadata", - interactive=True, + self.pretrained_model_name_or_path.change( + fn=lambda path: set_pretrained_model_name_or_path_input( + path, refresh_method=list_models + ), + inputs=[ + self.pretrained_model_name_or_path, + ], + outputs=[ + self.pretrained_model_name_or_path, + self.v2, + self.v_parameterization, + self.sdxl_checkbox, + ], + show_progress=False, ) - with gr.Row(): - self.save_model_as = gr.Radio( - save_model_as_choices, - label="Save trained model as", - value="safetensors", + self.train_data_dir.change( + fn=lambda path: gr.Dropdown(choices=[""] + list_train_data_dirs(path)), + inputs=self.train_data_dir, + outputs=self.train_data_dir, + show_progress=False, ) - self.save_precision = gr.Radio( - save_precision_choices, - label="Save precision", - value="fp16", - ) - - self.pretrained_model_name_or_path.change( - fn=lambda path: set_pretrained_model_name_or_path_input( - path, refresh_method=list_models - ), - inputs=[ - self.pretrained_model_name_or_path, - ], - outputs=[ - self.pretrained_model_name_or_path, - self.v2, - self.v_parameterization, - self.sdxl_checkbox, - ], - show_progress=False, - ) - - self.train_data_dir.change( - fn=lambda path: gr.Dropdown(choices=[""] + list_train_data_dirs(path)), - inputs=self.train_data_dir, - outputs=self.train_data_dir, - show_progress=False, - ) diff --git a/kohya_gui/common_gui.py b/kohya_gui/common_gui.py index 5183ff10f..0b8fec2de 100644 --- a/kohya_gui/common_gui.py +++ b/kohya_gui/common_gui.py @@ -1233,16 +1233,6 @@ def run_cmd_advanced_training(**kwargs): else: run_cmd += f' --lr_warmup_steps="{lr_warmup_steps}"' - if "gpu_ids" in kwargs: - gpu_ids = kwargs.get("gpu_ids") - if not gpu_ids == "": - run_cmd += f' --gpu_ids="{gpu_ids}"' - - if "main_process_port" in kwargs: - main_process_port = kwargs.get("main_process_port") - if main_process_port > 0: - run_cmd += f' --main_process_port="{main_process_port}"' - if "masked_loss" in kwargs: if kwargs.get("masked_loss"): # Test if the value is true as it could be false run_cmd += " --masked_loss" @@ -1293,13 +1283,9 @@ def run_cmd_advanced_training(**kwargs): min_timestep = kwargs.get("min_timestep") if int(min_timestep) > -1: run_cmd += f" --min_timestep={int(min_timestep)}" - + if "mixed_precision" in kwargs: - run_cmd += fr' --mixed_precision="{kwargs.get("mixed_precision")}"' - - if "multi_gpu" in kwargs: - if kwargs.get("multi_gpu"): - run_cmd += " --multi_gpu" + run_cmd += rf' --mixed_precision="{kwargs.get("mixed_precision")}"' if "network_alpha" in kwargs: run_cmd += fr' --network_alpha="{kwargs.get("network_alpha")}"' @@ -1366,21 +1352,6 @@ def run_cmd_advanced_training(**kwargs): if multires_noise_discount > 0: run_cmd += f' --multires_noise_discount="{multires_noise_discount}"' - if "num_machines" in kwargs: - num_machines = kwargs.get("num_machines") - if int(num_machines) > 1: - run_cmd += f" --num_machines={int(num_machines)}" - - if "num_processes" in kwargs: - num_processes = kwargs.get("num_processes") - if int(num_processes) > 1: - run_cmd += f" --num_processes={int(num_processes)}" - - if "num_cpu_threads_per_process" in kwargs: - num_cpu_threads_per_process = kwargs.get("num_cpu_threads_per_process") - if int(num_cpu_threads_per_process) > 1: - run_cmd += f" --num_cpu_threads_per_process={int(num_cpu_threads_per_process)}" - if "optimizer_args" in kwargs: optimizer_args = kwargs.get("optimizer_args") if optimizer_args != "": diff --git a/kohya_gui/dreambooth_gui.py b/kohya_gui/dreambooth_gui.py index 68ef7a5ce..5c81b9da8 100644 --- a/kohya_gui/dreambooth_gui.py +++ b/kohya_gui/dreambooth_gui.py @@ -18,6 +18,7 @@ scriptdir, validate_paths, ) +from .class_accelerate_launch import AccelerateLaunch from .class_configuration_file import ConfigurationFile from .class_source_model import SourceModel from .class_basic_training import BasicTraining @@ -551,13 +552,14 @@ def train_model( # run_cmd = f'accelerate launch --num_cpu_threads_per_process={num_cpu_threads_per_process} "train_db.py"' run_cmd = "accelerate launch" - run_cmd += run_cmd_advanced_training( + run_cmd += AccelerateLaunch.run_cmd( num_processes=num_processes, num_machines=num_machines, multi_gpu=multi_gpu, gpu_ids=gpu_ids, main_process_port=main_process_port, num_cpu_threads_per_process=num_cpu_threads_per_process, + mixed_precision=mixed_precision, ) if sdxl: @@ -726,24 +728,29 @@ def dreambooth_tab( with gr.Tab("Training"), gr.Column(variant="compact"): gr.Markdown("Train a custom model using kohya dreambooth python code...") + + with gr.Accordion("Accelerate launch", open=False), gr.Column(): + accelerate_launch = AccelerateLaunch() with gr.Column(): source_model = SourceModel(headless=headless, config=config) with gr.Accordion("Folders", open=False), gr.Group(): folders = Folders(headless=headless, config=config) + with gr.Accordion("Parameters", open=False), gr.Column(): - with gr.Group(elem_id="basic_tab"): - basic_training = BasicTraining( - learning_rate_value="1e-5", - lr_scheduler_value="cosine", - lr_warmup_value="10", - dreambooth=True, - sdxl_checkbox=source_model.sdxl_checkbox, - ) + with gr.Accordion("Basic", open="True"): + with gr.Group(elem_id="basic_tab"): + basic_training = BasicTraining( + learning_rate_value="1e-5", + lr_scheduler_value="cosine", + lr_warmup_value="10", + dreambooth=True, + sdxl_checkbox=source_model.sdxl_checkbox, + ) - # # Add SDXL Parameters - # sdxl_params = SDXLParameters(source_model.sdxl_checkbox, show_sdxl_cache_text_encoder_outputs=False) + # # Add SDXL Parameters + # sdxl_params = SDXLParameters(source_model.sdxl_checkbox, show_sdxl_cache_text_encoder_outputs=False) with gr.Accordion("Advanced", open=False, elem_id="advanced_tab"): advanced_training = AdvancedTraining(headless=headless, config=config) @@ -819,10 +826,10 @@ def dreambooth_tab( basic_training.train_batch_size, basic_training.epoch, basic_training.save_every_n_epochs, - basic_training.mixed_precision, + accelerate_launch.mixed_precision, source_model.save_precision, basic_training.seed, - basic_training.num_cpu_threads_per_process, + accelerate_launch.num_cpu_threads_per_process, basic_training.cache_latents, basic_training.cache_latents_to_disk, basic_training.caption_extension, @@ -846,11 +853,11 @@ def dreambooth_tab( advanced_training.masked_loss, advanced_training.clip_skip, advanced_training.vae, - advanced_training.num_processes, - advanced_training.num_machines, - advanced_training.multi_gpu, - advanced_training.gpu_ids, - advanced_training.main_process_port, + accelerate_launch.num_processes, + accelerate_launch.num_machines, + accelerate_launch.multi_gpu, + accelerate_launch.gpu_ids, + accelerate_launch.main_process_port, source_model.output_name, advanced_training.max_token_length, basic_training.max_train_epochs, diff --git a/kohya_gui/finetune_gui.py b/kohya_gui/finetune_gui.py index 65f45d820..83cf23974 100644 --- a/kohya_gui/finetune_gui.py +++ b/kohya_gui/finetune_gui.py @@ -19,6 +19,7 @@ scriptdir, validate_paths, ) +from .class_accelerate_launch import AccelerateLaunch from .class_configuration_file import ConfigurationFile from .class_source_model import SourceModel from .class_basic_training import BasicTraining @@ -576,13 +577,14 @@ def train_model( run_cmd = "accelerate launch" - run_cmd += run_cmd_advanced_training( + run_cmd += AccelerateLaunch.run_cmd( num_processes=num_processes, num_machines=num_machines, multi_gpu=multi_gpu, gpu_ids=gpu_ids, main_process_port=main_process_port, num_cpu_threads_per_process=num_cpu_threads_per_process, + mixed_precision=mixed_precision, ) if sdxl_checkbox: @@ -746,6 +748,9 @@ def finetune_tab(headless=False, config: dict = {}): dummy_headless = gr.Label(value=headless, visible=False) with gr.Tab("Training"), gr.Column(variant="compact"): gr.Markdown("Train a custom model using kohya finetune python code...") + + with gr.Accordion("Accelerate launch", open=False), gr.Column(): + accelerate_launch = AccelerateLaunch() with gr.Column(): source_model = SourceModel(headless=headless, finetuning=True, config=config) @@ -782,21 +787,22 @@ def list_presets(path): elem_id="myDropdown", ) - with gr.Group(elem_id="basic_tab"): - basic_training = BasicTraining( - learning_rate_value="1e-5", - finetuning=True, - sdxl_checkbox=source_model.sdxl_checkbox, - ) + with gr.Accordion("Basic", open="True"): + with gr.Group(elem_id="basic_tab"): + basic_training = BasicTraining( + learning_rate_value="1e-5", + finetuning=True, + sdxl_checkbox=source_model.sdxl_checkbox, + ) - # Add SDXL Parameters - sdxl_params = SDXLParameters(source_model.sdxl_checkbox) + # Add SDXL Parameters + sdxl_params = SDXLParameters(source_model.sdxl_checkbox) - with gr.Row(): - dataset_repeats = gr.Textbox(label="Dataset repeats", value=40) - train_text_encoder = gr.Checkbox( - label="Train text encoder", value=True - ) + with gr.Row(): + dataset_repeats = gr.Textbox(label="Dataset repeats", value=40) + train_text_encoder = gr.Checkbox( + label="Train text encoder", value=True + ) with gr.Accordion("Advanced", open=False, elem_id="advanced_tab"): with gr.Row(): @@ -916,10 +922,10 @@ def list_presets(path): basic_training.train_batch_size, basic_training.epoch, basic_training.save_every_n_epochs, - basic_training.mixed_precision, + accelerate_launch.mixed_precision, source_model.save_precision, basic_training.seed, - basic_training.num_cpu_threads_per_process, + accelerate_launch.num_cpu_threads_per_process, basic_training.learning_rate_te, basic_training.learning_rate_te1, basic_training.learning_rate_te2, @@ -931,11 +937,11 @@ def list_presets(path): basic_training.caption_extension, advanced_training.xformers, advanced_training.clip_skip, - advanced_training.num_processes, - advanced_training.num_machines, - advanced_training.multi_gpu, - advanced_training.gpu_ids, - advanced_training.main_process_port, + accelerate_launch.num_processes, + accelerate_launch.num_machines, + accelerate_launch.multi_gpu, + accelerate_launch.gpu_ids, + accelerate_launch.main_process_port, advanced_training.save_state, advanced_training.save_state_on_train_end, advanced_training.resume, diff --git a/kohya_gui/lora_gui.py b/kohya_gui/lora_gui.py index a264ec709..8b7d5056f 100644 --- a/kohya_gui/lora_gui.py +++ b/kohya_gui/lora_gui.py @@ -17,6 +17,7 @@ scriptdir, validate_paths, ) +from .class_accelerate_launch import AccelerateLaunch from .class_configuration_file import ConfigurationFile from .class_source_model import SourceModel from .class_basic_training import BasicTraining @@ -783,13 +784,14 @@ def train_model( run_cmd = "accelerate launch" - run_cmd += run_cmd_advanced_training( + run_cmd += AccelerateLaunch.run_cmd( num_processes=num_processes, num_machines=num_machines, multi_gpu=multi_gpu, gpu_ids=gpu_ids, main_process_port=main_process_port, num_cpu_threads_per_process=num_cpu_threads_per_process, + mixed_precision=mixed_precision, ) if sdxl: @@ -1081,6 +1083,10 @@ def lora_tab( gr.Markdown( "Train a custom model using kohya train network LoRA python code..." ) + + with gr.Accordion("Accelerate launch", open=False), gr.Column(): + accelerate_launch = AccelerateLaunch() + with gr.Column(): source_model = SourceModel( save_model_as_choices=[ @@ -1114,674 +1120,675 @@ def list_presets(path): json_files.append(os.path.join("user_presets", preset_name)) return json_files + + with gr.Accordion("Basic", open="True"): + training_preset = gr.Dropdown( + label="Presets", + choices=[""] + list_presets(rf"{presets_dir}/lora"), + elem_id="myDropdown", + value="none", + ) - training_preset = gr.Dropdown( - label="Presets", - choices=[""] + list_presets(rf"{presets_dir}/lora"), - elem_id="myDropdown", - value="none", - ) - - with gr.Group(elem_id="basic_tab"): - with gr.Row(): - LoRA_type = gr.Dropdown( - label="LoRA type", - choices=[ - "Kohya DyLoRA", - "Kohya LoCon", - "LoRA-FA", - "LyCORIS/iA3", - "LyCORIS/BOFT", - "LyCORIS/Diag-OFT", - "LyCORIS/DyLoRA", - "LyCORIS/GLoRA", - "LyCORIS/LoCon", - "LyCORIS/LoHa", - "LyCORIS/LoKr", - "LyCORIS/Native Fine-Tuning", - "Standard", - ], - value="Standard", - ) - LyCORIS_preset = gr.Dropdown( - label="LyCORIS Preset", - choices=[ - "attn-mlp", - "attn-only", - "full", - "full-lin", - "unet-transformer-only", - "unet-convblock-only", - ], - value="full", - visible=False, - interactive=True, - # info="https://github.com/KohakuBlueleaf/LyCORIS/blob/0006e2ffa05a48d8818112d9f70da74c0cd30b99/docs/Preset.md" + with gr.Group(elem_id="basic_tab"): + with gr.Row(): + LoRA_type = gr.Dropdown( + label="LoRA type", + choices=[ + "Kohya DyLoRA", + "Kohya LoCon", + "LoRA-FA", + "LyCORIS/iA3", + "LyCORIS/BOFT", + "LyCORIS/Diag-OFT", + "LyCORIS/DyLoRA", + "LyCORIS/GLoRA", + "LyCORIS/LoCon", + "LyCORIS/LoHa", + "LyCORIS/LoKr", + "LyCORIS/Native Fine-Tuning", + "Standard", + ], + value="Standard", + ) + LyCORIS_preset = gr.Dropdown( + label="LyCORIS Preset", + choices=[ + "attn-mlp", + "attn-only", + "full", + "full-lin", + "unet-transformer-only", + "unet-convblock-only", + ], + value="full", + visible=False, + interactive=True, + # info="https://github.com/KohakuBlueleaf/LyCORIS/blob/0006e2ffa05a48d8818112d9f70da74c0cd30b99/docs/Preset.md" + ) + with gr.Group(): + with gr.Row(): + lora_network_weights = gr.Textbox( + label="LoRA network weights", + placeholder="(Optional)", + info="Path to an existing LoRA network weights to resume training from", + ) + lora_network_weights_file = gr.Button( + document_symbol, + elem_id="open_folder_small", + elem_classes=["tool"], + visible=(not headless), + ) + lora_network_weights_file.click( + get_any_file_path, + inputs=[lora_network_weights], + outputs=lora_network_weights, + show_progress=False, + ) + dim_from_weights = gr.Checkbox( + label="DIM from weights", + value=False, + info="Automatically determine the dim(rank) from the weight file.", + ) + basic_training = BasicTraining( + learning_rate_value="0.0001", + lr_scheduler_value="cosine", + lr_warmup_value="10", + sdxl_checkbox=source_model.sdxl_checkbox, ) - with gr.Group(): + + with gr.Row(): + text_encoder_lr = gr.Number( + label="Text Encoder learning rate", + value="0.0001", + info="(Optional)", + minimum=0, + maximum=1, + ) + + unet_lr = gr.Number( + label="Unet learning rate", + value="0.0001", + info="(Optional)", + minimum=0, + maximum=1, + ) + + # Add SDXL Parameters + sdxl_params = SDXLParameters(source_model.sdxl_checkbox) + + # LyCORIS Specific parameters + with gr.Accordion("LyCORIS", visible=False) as lycoris_accordion: with gr.Row(): - lora_network_weights = gr.Textbox( - label="LoRA network weights", - placeholder="(Optional)", - info="Path to an existing LoRA network weights to resume training from", + factor = gr.Slider( + label="LoKr factor", + value=-1, + minimum=-1, + maximum=64, + step=1, + visible=False, ) - lora_network_weights_file = gr.Button( - document_symbol, - elem_id="open_folder_small", - elem_classes=["tool"], - visible=(not headless), + bypass_mode = gr.Checkbox( + value=False, + label="Bypass mode", + info="Designed for bnb 8bit/4bit linear layer. (QLyCORIS)", + visible=False, ) - lora_network_weights_file.click( - get_any_file_path, - inputs=[lora_network_weights], - outputs=lora_network_weights, - show_progress=False, + dora_wd = gr.Checkbox( + value=False, + label="DoRA Weight Decompose", + info="Enable the DoRA method for these algorithms", + visible=False, ) - dim_from_weights = gr.Checkbox( - label="DIM from weights", + use_cp = gr.Checkbox( value=False, - info="Automatically determine the dim(rank) from the weight file.", + label="Use CP decomposition", + info="A two-step approach utilizing tensor decomposition and fine-tuning to accelerate convolution layers in large neural networks, resulting in significant CPU speedups with minor accuracy drops.", + visible=False, + ) + use_tucker = gr.Checkbox( + value=False, + label="Use Tucker decomposition", + info="Efficiently decompose tensor shapes, resulting in a sequence of convolution layers with varying dimensions and Hadamard product implementation through multiplication of two distinct tensors.", + visible=False, + ) + use_scalar = gr.Checkbox( + value=False, + label="Use Scalar", + info="Train an additional scalar in front of the weight difference, use a different weight initialization strategy.", + visible=False, + ) + with gr.Row(): + rank_dropout_scale = gr.Checkbox( + value=False, + label="Rank Dropout Scale", + info="Adjusts the scale of the rank dropout to maintain the average dropout rate, ensuring more consistent regularization across different layers.", + visible=False, + ) + constrain = gr.Number( + value="0.0", + label="Constrain OFT", + info="Limits the norm of the oft_blocks, ensuring that their magnitude does not exceed a specified threshold, thus controlling the extent of the transformation applied.", + visible=False, + ) + rescaled = gr.Checkbox( + value=False, + label="Rescaled OFT", + info="applies an additional scaling factor to the oft_blocks, allowing for further adjustment of their impact on the model's transformations.", + visible=False, + ) + train_norm = gr.Checkbox( + value=False, + label="Train Norm", + info="Selects trainable layers in a network, but trains normalization layers identically across methods as they lack matrix decomposition.", + visible=False, + ) + decompose_both = gr.Checkbox( + value=False, + label="LoKr decompose both", + info="Controls whether both input and output dimensions of the layer's weights are decomposed into smaller matrices for reparameterization.", + visible=False, + ) + train_on_input = gr.Checkbox( + value=True, + label="iA3 train on input", + info="Set if we change the information going into the system (True) or the information coming out of it (False).", + visible=False, ) - basic_training = BasicTraining( - learning_rate_value="0.0001", - lr_scheduler_value="cosine", - lr_warmup_value="10", - sdxl_checkbox=source_model.sdxl_checkbox, - ) - - with gr.Row(): - text_encoder_lr = gr.Number( - label="Text Encoder learning rate", - value="0.0001", - info="(Optional)", - minimum=0, - maximum=1, - ) - - unet_lr = gr.Number( - label="Unet learning rate", - value="0.0001", - info="(Optional)", - minimum=0, - maximum=1, - ) - - # Add SDXL Parameters - sdxl_params = SDXLParameters(source_model.sdxl_checkbox) - # LyCORIS Specific parameters - with gr.Accordion("LyCORIS", visible=False) as lycoris_accordion: - with gr.Row(): - factor = gr.Slider( - label="LoKr factor", - value=-1, - minimum=-1, - maximum=64, + with gr.Row() as network_row: + network_dim = gr.Slider( + minimum=1, + maximum=512, + label="Network Rank (Dimension)", + value=8, step=1, - visible=False, - ) - bypass_mode = gr.Checkbox( - value=False, - label="Bypass mode", - info="Designed for bnb 8bit/4bit linear layer. (QLyCORIS)", - visible=False, - ) - dora_wd = gr.Checkbox( - value=False, - label="DoRA Weight Decompose", - info="Enable the DoRA method for these algorithms", - visible=False, + interactive=True, ) - use_cp = gr.Checkbox( - value=False, - label="Use CP decomposition", - info="A two-step approach utilizing tensor decomposition and fine-tuning to accelerate convolution layers in large neural networks, resulting in significant CPU speedups with minor accuracy drops.", - visible=False, + network_alpha = gr.Slider( + minimum=0.00001, + maximum=1024, + label="Network Alpha", + value=1, + step=0.00001, + interactive=True, + info="alpha for LoRA weight scaling", ) - use_tucker = gr.Checkbox( - value=False, - label="Use Tucker decomposition", - info="Efficiently decompose tensor shapes, resulting in a sequence of convolution layers with varying dimensions and Hadamard product implementation through multiplication of two distinct tensors.", - visible=False, + with gr.Row(visible=False) as convolution_row: + # locon= gr.Checkbox(label='Train a LoCon instead of a general LoRA (does not support v2 base models) (may not be able to some utilities now)', value=False) + conv_dim = gr.Slider( + minimum=0, + maximum=512, + value=1, + step=1, + label="Convolution Rank (Dimension)", ) - use_scalar = gr.Checkbox( - value=False, - label="Use Scalar", - info="Train an additional scalar in front of the weight difference, use a different weight initialization strategy.", - visible=False, + conv_alpha = gr.Slider( + minimum=0, + maximum=512, + value=1, + step=1, + label="Convolution Alpha", ) with gr.Row(): - rank_dropout_scale = gr.Checkbox( - value=False, - label="Rank Dropout Scale", - info="Adjusts the scale of the rank dropout to maintain the average dropout rate, ensuring more consistent regularization across different layers.", - visible=False, + scale_weight_norms = gr.Slider( + label="Scale weight norms", + value=0, + minimum=0, + maximum=10, + step=0.01, + info="Max Norm Regularization is a technique to stabilize network training by limiting the norm of network weights. It may be effective in suppressing overfitting of LoRA and improving stability when used with other LoRAs. See PR #545 on kohya_ss/sd_scripts repo for details. Recommended setting: 1. Higher is weaker, lower is stronger.", + interactive=True, ) - constrain = gr.Number( - value="0.0", - label="Constrain OFT", - info="Limits the norm of the oft_blocks, ensuring that their magnitude does not exceed a specified threshold, thus controlling the extent of the transformation applied.", - visible=False, + network_dropout = gr.Slider( + label="Network dropout", + value=0, + minimum=0, + maximum=1, + step=0.01, + info="Is a normal probability dropout at the neuron level. In the case of LoRA, it is applied to the output of down. Recommended range 0.1 to 0.5", ) - rescaled = gr.Checkbox( - value=False, - label="Rescaled OFT", - info="applies an additional scaling factor to the oft_blocks, allowing for further adjustment of their impact on the model's transformations.", - visible=False, + rank_dropout = gr.Slider( + label="Rank dropout", + value=0, + minimum=0, + maximum=1, + step=0.01, + info="can specify `rank_dropout` to dropout each rank with specified probability. Recommended range 0.1 to 0.3", ) - train_norm = gr.Checkbox( - value=False, - label="Train Norm", - info="Selects trainable layers in a network, but trains normalization layers identically across methods as they lack matrix decomposition.", - visible=False, - ) - decompose_both = gr.Checkbox( - value=False, - label="LoKr decompose both", - info="Controls whether both input and output dimensions of the layer's weights are decomposed into smaller matrices for reparameterization.", - visible=False, + module_dropout = gr.Slider( + label="Module dropout", + value=0.0, + minimum=0.0, + maximum=1.0, + step=0.01, + info="can specify `module_dropout` to dropout each rank with specified probability. Recommended range 0.1 to 0.3", ) - train_on_input = gr.Checkbox( - value=True, - label="iA3 train on input", - info="Set if we change the information going into the system (True) or the information coming out of it (False).", - visible=False, + with gr.Row(visible=False): + unit = gr.Slider( + minimum=1, + maximum=64, + label="DyLoRA Unit / Block size", + value=1, + step=1, + interactive=True, ) - with gr.Row() as network_row: - network_dim = gr.Slider( - minimum=1, - maximum=512, - label="Network Rank (Dimension)", - value=8, - step=1, - interactive=True, - ) - network_alpha = gr.Slider( - minimum=0.00001, - maximum=1024, - label="Network Alpha", - value=1, - step=0.00001, - interactive=True, - info="alpha for LoRA weight scaling", - ) - with gr.Row(visible=False) as convolution_row: - # locon= gr.Checkbox(label='Train a LoCon instead of a general LoRA (does not support v2 base models) (may not be able to some utilities now)', value=False) - conv_dim = gr.Slider( - minimum=0, - maximum=512, - value=1, - step=1, - label="Convolution Rank (Dimension)", - ) - conv_alpha = gr.Slider( - minimum=0, - maximum=512, - value=1, - step=1, - label="Convolution Alpha", - ) - with gr.Row(): - scale_weight_norms = gr.Slider( - label="Scale weight norms", - value=0, - minimum=0, - maximum=10, - step=0.01, - info="Max Norm Regularization is a technique to stabilize network training by limiting the norm of network weights. It may be effective in suppressing overfitting of LoRA and improving stability when used with other LoRAs. See PR #545 on kohya_ss/sd_scripts repo for details. Recommended setting: 1. Higher is weaker, lower is stronger.", - interactive=True, - ) - network_dropout = gr.Slider( - label="Network dropout", - value=0, - minimum=0, - maximum=1, - step=0.01, - info="Is a normal probability dropout at the neuron level. In the case of LoRA, it is applied to the output of down. Recommended range 0.1 to 0.5", - ) - rank_dropout = gr.Slider( - label="Rank dropout", - value=0, - minimum=0, - maximum=1, - step=0.01, - info="can specify `rank_dropout` to dropout each rank with specified probability. Recommended range 0.1 to 0.3", - ) - module_dropout = gr.Slider( - label="Module dropout", - value=0.0, - minimum=0.0, - maximum=1.0, - step=0.01, - info="can specify `module_dropout` to dropout each rank with specified probability. Recommended range 0.1 to 0.3", - ) - with gr.Row(visible=False): - unit = gr.Slider( - minimum=1, - maximum=64, - label="DyLoRA Unit / Block size", - value=1, - step=1, - interactive=True, - ) - - # Show or hide LoCon conv settings depending on LoRA type selection - def update_LoRA_settings( - LoRA_type, - conv_dim, - network_dim, - ): - log.info("LoRA type changed...") - - lora_settings_config = { - "network_row": { - "gr_type": gr.Row, - "update_params": { - "visible": LoRA_type - in { - "Kohya DyLoRA", - "Kohya LoCon", - "LoRA-FA", - "LyCORIS/BOFT", - "LyCORIS/Diag-OFT", - "LyCORIS/DyLoRA", - "LyCORIS/GLoRA", - "LyCORIS/LoCon", - "LyCORIS/LoHa", - "LyCORIS/LoKr", - "Standard", + # Show or hide LoCon conv settings depending on LoRA type selection + def update_LoRA_settings( + LoRA_type, + conv_dim, + network_dim, + ): + log.info("LoRA type changed...") + + lora_settings_config = { + "network_row": { + "gr_type": gr.Row, + "update_params": { + "visible": LoRA_type + in { + "Kohya DyLoRA", + "Kohya LoCon", + "LoRA-FA", + "LyCORIS/BOFT", + "LyCORIS/Diag-OFT", + "LyCORIS/DyLoRA", + "LyCORIS/GLoRA", + "LyCORIS/LoCon", + "LyCORIS/LoHa", + "LyCORIS/LoKr", + "Standard", + }, }, }, - }, - "convolution_row": { - "gr_type": gr.Row, - "update_params": { - "visible": LoRA_type - in { - "LoCon", - "Kohya DyLoRA", - "Kohya LoCon", - "LoRA-FA", - "LyCORIS/BOFT", - "LyCORIS/Diag-OFT", - "LyCORIS/DyLoRA", - "LyCORIS/LoHa", - "LyCORIS/LoKr", - "LyCORIS/LoCon", - "LyCORIS/GLoRA", + "convolution_row": { + "gr_type": gr.Row, + "update_params": { + "visible": LoRA_type + in { + "LoCon", + "Kohya DyLoRA", + "Kohya LoCon", + "LoRA-FA", + "LyCORIS/BOFT", + "LyCORIS/Diag-OFT", + "LyCORIS/DyLoRA", + "LyCORIS/LoHa", + "LyCORIS/LoKr", + "LyCORIS/LoCon", + "LyCORIS/GLoRA", + }, }, }, - }, - "kohya_advanced_lora": { - "gr_type": gr.Row, - "update_params": { - "visible": LoRA_type - in { - "Standard", - "Kohya DyLoRA", - "Kohya LoCon", - "LoRA-FA", + "kohya_advanced_lora": { + "gr_type": gr.Row, + "update_params": { + "visible": LoRA_type + in { + "Standard", + "Kohya DyLoRA", + "Kohya LoCon", + "LoRA-FA", + }, }, }, - }, - "lora_network_weights": { - "gr_type": gr.Textbox, - "update_params": { - "visible": LoRA_type - in { - "Standard", - "LoCon", - "Kohya DyLoRA", - "Kohya LoCon", - "LoRA-FA", - "LyCORIS/BOFT", - "LyCORIS/Diag-OFT", - "LyCORIS/DyLoRA", - "LyCORIS/GLoRA", - "LyCORIS/LoHa", - "LyCORIS/LoCon", - "LyCORIS/LoKr", + "lora_network_weights": { + "gr_type": gr.Textbox, + "update_params": { + "visible": LoRA_type + in { + "Standard", + "LoCon", + "Kohya DyLoRA", + "Kohya LoCon", + "LoRA-FA", + "LyCORIS/BOFT", + "LyCORIS/Diag-OFT", + "LyCORIS/DyLoRA", + "LyCORIS/GLoRA", + "LyCORIS/LoHa", + "LyCORIS/LoCon", + "LyCORIS/LoKr", + }, }, }, - }, - "lora_network_weights_file": { - "gr_type": gr.Button, - "update_params": { - "visible": LoRA_type - in { - "Standard", - "LoCon", - "Kohya DyLoRA", - "Kohya LoCon", - "LoRA-FA", - "LyCORIS/BOFT", - "LyCORIS/Diag-OFT", - "LyCORIS/DyLoRA", - "LyCORIS/GLoRA", - "LyCORIS/LoHa", - "LyCORIS/LoCon", - "LyCORIS/LoKr", + "lora_network_weights_file": { + "gr_type": gr.Button, + "update_params": { + "visible": LoRA_type + in { + "Standard", + "LoCon", + "Kohya DyLoRA", + "Kohya LoCon", + "LoRA-FA", + "LyCORIS/BOFT", + "LyCORIS/Diag-OFT", + "LyCORIS/DyLoRA", + "LyCORIS/GLoRA", + "LyCORIS/LoHa", + "LyCORIS/LoCon", + "LyCORIS/LoKr", + }, }, }, - }, - "dim_from_weights": { - "gr_type": gr.Checkbox, - "update_params": { - "visible": LoRA_type - in { - "Standard", - "LoCon", - "Kohya DyLoRA", - "Kohya LoCon", - "LoRA-FA", - "LyCORIS/BOFT", - "LyCORIS/Diag-OFT", - "LyCORIS/DyLoRA", - "LyCORIS/GLoRA", - "LyCORIS/LoHa", - "LyCORIS/LoCon", - "LyCORIS/LoKr", - } + "dim_from_weights": { + "gr_type": gr.Checkbox, + "update_params": { + "visible": LoRA_type + in { + "Standard", + "LoCon", + "Kohya DyLoRA", + "Kohya LoCon", + "LoRA-FA", + "LyCORIS/BOFT", + "LyCORIS/Diag-OFT", + "LyCORIS/DyLoRA", + "LyCORIS/GLoRA", + "LyCORIS/LoHa", + "LyCORIS/LoCon", + "LyCORIS/LoKr", + } + }, + }, + "factor": { + "gr_type": gr.Slider, + "update_params": { + "visible": LoRA_type + in { + "LyCORIS/LoKr", + }, + }, + }, + "conv_dim": { + "gr_type": gr.Slider, + "update_params": { + "maximum": ( + 100000 + if LoRA_type + in { + "LyCORIS/LoHa", + "LyCORIS/LoKr", + "LyCORIS/BOFT", + "LyCORIS/Diag-OFT", + } + else 512 + ), + "value": conv_dim, # if conv_dim > 512 else conv_dim, + }, }, - }, - "factor": { - "gr_type": gr.Slider, - "update_params": { - "visible": LoRA_type - in { - "LyCORIS/LoKr", + "network_dim": { + "gr_type": gr.Slider, + "update_params": { + "maximum": ( + 100000 + if LoRA_type + in { + "LyCORIS/LoHa", + "LyCORIS/LoKr", + "LyCORIS/BOFT", + "LyCORIS/Diag-OFT", + } + else 512 + ), + "value": network_dim, # if network_dim > 512 else network_dim, }, }, - }, - "conv_dim": { - "gr_type": gr.Slider, - "update_params": { - "maximum": ( - 100000 - if LoRA_type + "bypass_mode": { + "gr_type": gr.Checkbox, + "update_params": { + "visible": LoRA_type in { + "LyCORIS/LoCon", "LyCORIS/LoHa", "LyCORIS/LoKr", - "LyCORIS/BOFT", - "LyCORIS/Diag-OFT", - } - else 512 - ), - "value": conv_dim, # if conv_dim > 512 else conv_dim, + }, + }, }, - }, - "network_dim": { - "gr_type": gr.Slider, - "update_params": { - "maximum": ( - 100000 - if LoRA_type + "dora_wd": { + "gr_type": gr.Checkbox, + "update_params": { + "visible": LoRA_type in { + "LyCORIS/LoCon", "LyCORIS/LoHa", "LyCORIS/LoKr", - "LyCORIS/BOFT", - "LyCORIS/Diag-OFT", - } - else 512 - ), - "value": network_dim, # if network_dim > 512 else network_dim, - }, - }, - "bypass_mode": { - "gr_type": gr.Checkbox, - "update_params": { - "visible": LoRA_type - in { - "LyCORIS/LoCon", - "LyCORIS/LoHa", - "LyCORIS/LoKr", + }, }, }, - }, - "dora_wd": { - "gr_type": gr.Checkbox, - "update_params": { - "visible": LoRA_type - in { - "LyCORIS/LoCon", - "LyCORIS/LoHa", - "LyCORIS/LoKr", + "use_cp": { + "gr_type": gr.Checkbox, + "update_params": { + "visible": LoRA_type + in { + "LyCORIS/LoKr", + }, }, }, - }, - "use_cp": { - "gr_type": gr.Checkbox, - "update_params": { - "visible": LoRA_type - in { - "LyCORIS/LoKr", + "use_tucker": { + "gr_type": gr.Checkbox, + "update_params": { + "visible": LoRA_type + in { + "LyCORIS/BOFT", + "LyCORIS/Diag-OFT", + "LyCORIS/DyLoRA", + "LyCORIS/LoCon", + "LyCORIS/LoHa", + "LyCORIS/Native Fine-Tuning", + }, }, }, - }, - "use_tucker": { - "gr_type": gr.Checkbox, - "update_params": { - "visible": LoRA_type - in { - "LyCORIS/BOFT", - "LyCORIS/Diag-OFT", - "LyCORIS/DyLoRA", - "LyCORIS/LoCon", - "LyCORIS/LoHa", - "LyCORIS/Native Fine-Tuning", + "use_scalar": { + "gr_type": gr.Checkbox, + "update_params": { + "visible": LoRA_type + in { + "LyCORIS/BOFT", + "LyCORIS/Diag-OFT", + "LyCORIS/LoCon", + "LyCORIS/LoHa", + "LyCORIS/LoKr", + "LyCORIS/Native Fine-Tuning", + }, }, }, - }, - "use_scalar": { - "gr_type": gr.Checkbox, - "update_params": { - "visible": LoRA_type - in { - "LyCORIS/BOFT", - "LyCORIS/Diag-OFT", - "LyCORIS/LoCon", - "LyCORIS/LoHa", - "LyCORIS/LoKr", - "LyCORIS/Native Fine-Tuning", + "rank_dropout_scale": { + "gr_type": gr.Checkbox, + "update_params": { + "visible": LoRA_type + in { + "LyCORIS/BOFT", + "LyCORIS/Diag-OFT", + "LyCORIS/GLoRA", + "LyCORIS/LoCon", + "LyCORIS/LoHa", + "LyCORIS/LoKr", + "LyCORIS/Native Fine-Tuning", + }, }, }, - }, - "rank_dropout_scale": { - "gr_type": gr.Checkbox, - "update_params": { - "visible": LoRA_type - in { - "LyCORIS/BOFT", - "LyCORIS/Diag-OFT", - "LyCORIS/GLoRA", - "LyCORIS/LoCon", - "LyCORIS/LoHa", - "LyCORIS/LoKr", - "LyCORIS/Native Fine-Tuning", + "constrain": { + "gr_type": gr.Number, + "update_params": { + "visible": LoRA_type + in { + "LyCORIS/BOFT", + "LyCORIS/Diag-OFT", + }, }, }, - }, - "constrain": { - "gr_type": gr.Number, - "update_params": { - "visible": LoRA_type - in { - "LyCORIS/BOFT", - "LyCORIS/Diag-OFT", + "rescaled": { + "gr_type": gr.Checkbox, + "update_params": { + "visible": LoRA_type + in { + "LyCORIS/BOFT", + "LyCORIS/Diag-OFT", + }, }, }, - }, - "rescaled": { - "gr_type": gr.Checkbox, - "update_params": { - "visible": LoRA_type - in { - "LyCORIS/BOFT", - "LyCORIS/Diag-OFT", + "train_norm": { + "gr_type": gr.Checkbox, + "update_params": { + "visible": LoRA_type + in { + "LyCORIS/DyLoRA", + "LyCORIS/BOFT", + "LyCORIS/Diag-OFT", + "LyCORIS/GLoRA", + "LyCORIS/LoCon", + "LyCORIS/LoHa", + "LyCORIS/LoKr", + "LyCORIS/Native Fine-Tuning", + }, }, }, - }, - "train_norm": { - "gr_type": gr.Checkbox, - "update_params": { - "visible": LoRA_type - in { - "LyCORIS/DyLoRA", - "LyCORIS/BOFT", - "LyCORIS/Diag-OFT", - "LyCORIS/GLoRA", - "LyCORIS/LoCon", - "LyCORIS/LoHa", - "LyCORIS/LoKr", - "LyCORIS/Native Fine-Tuning", + "decompose_both": { + "gr_type": gr.Checkbox, + "update_params": { + "visible": LoRA_type in {"LyCORIS/LoKr"}, }, }, - }, - "decompose_both": { - "gr_type": gr.Checkbox, - "update_params": { - "visible": LoRA_type in {"LyCORIS/LoKr"}, - }, - }, - "train_on_input": { - "gr_type": gr.Checkbox, - "update_params": { - "visible": LoRA_type in {"LyCORIS/iA3"}, + "train_on_input": { + "gr_type": gr.Checkbox, + "update_params": { + "visible": LoRA_type in {"LyCORIS/iA3"}, + }, }, - }, - "scale_weight_norms": { - "gr_type": gr.Slider, - "update_params": { - "visible": LoRA_type - in { - "LoCon", - "Kohya DyLoRA", - "Kohya LoCon", - "LoRA-FA", - "LyCORIS/DyLoRA", - "LyCORIS/GLoRA", - "LyCORIS/LoHa", - "LyCORIS/LoCon", - "LyCORIS/LoKr", - "Standard", + "scale_weight_norms": { + "gr_type": gr.Slider, + "update_params": { + "visible": LoRA_type + in { + "LoCon", + "Kohya DyLoRA", + "Kohya LoCon", + "LoRA-FA", + "LyCORIS/DyLoRA", + "LyCORIS/GLoRA", + "LyCORIS/LoHa", + "LyCORIS/LoCon", + "LyCORIS/LoKr", + "Standard", + }, }, }, - }, - "network_dropout": { - "gr_type": gr.Slider, - "update_params": { - "visible": LoRA_type - in { - "LoCon", - "Kohya DyLoRA", - "Kohya LoCon", - "LoRA-FA", - "LyCORIS/BOFT", - "LyCORIS/Diag-OFT", - "LyCORIS/DyLoRA", - "LyCORIS/GLoRA", - "LyCORIS/LoCon", - "LyCORIS/LoHa", - "LyCORIS/LoKr", - "LyCORIS/Native Fine-Tuning", - "Standard", + "network_dropout": { + "gr_type": gr.Slider, + "update_params": { + "visible": LoRA_type + in { + "LoCon", + "Kohya DyLoRA", + "Kohya LoCon", + "LoRA-FA", + "LyCORIS/BOFT", + "LyCORIS/Diag-OFT", + "LyCORIS/DyLoRA", + "LyCORIS/GLoRA", + "LyCORIS/LoCon", + "LyCORIS/LoHa", + "LyCORIS/LoKr", + "LyCORIS/Native Fine-Tuning", + "Standard", + }, }, }, - }, - "rank_dropout": { - "gr_type": gr.Slider, - "update_params": { - "visible": LoRA_type - in { - "LoCon", - "Kohya DyLoRA", - "LyCORIS/BOFT", - "LyCORIS/Diag-OFT", - "LyCORIS/GLoRA", - "LyCORIS/LoCon", - "LyCORIS/LoHa", - "LyCORIS/LoKR", - "Kohya LoCon", - "LoRA-FA", - "LyCORIS/Native Fine-Tuning", - "Standard", + "rank_dropout": { + "gr_type": gr.Slider, + "update_params": { + "visible": LoRA_type + in { + "LoCon", + "Kohya DyLoRA", + "LyCORIS/BOFT", + "LyCORIS/Diag-OFT", + "LyCORIS/GLoRA", + "LyCORIS/LoCon", + "LyCORIS/LoHa", + "LyCORIS/LoKR", + "Kohya LoCon", + "LoRA-FA", + "LyCORIS/Native Fine-Tuning", + "Standard", + }, }, }, - }, - "module_dropout": { - "gr_type": gr.Slider, - "update_params": { - "visible": LoRA_type - in { - "LoCon", - "LyCORIS/BOFT", - "LyCORIS/Diag-OFT", - "Kohya DyLoRA", - "LyCORIS/GLoRA", - "LyCORIS/LoCon", - "LyCORIS/LoHa", - "LyCORIS/LoKR", - "Kohya LoCon", - "LyCORIS/Native Fine-Tuning", - "LoRA-FA", - "Standard", + "module_dropout": { + "gr_type": gr.Slider, + "update_params": { + "visible": LoRA_type + in { + "LoCon", + "LyCORIS/BOFT", + "LyCORIS/Diag-OFT", + "Kohya DyLoRA", + "LyCORIS/GLoRA", + "LyCORIS/LoCon", + "LyCORIS/LoHa", + "LyCORIS/LoKR", + "Kohya LoCon", + "LyCORIS/Native Fine-Tuning", + "LoRA-FA", + "Standard", + }, }, }, - }, - "LyCORIS_preset": { - "gr_type": gr.Dropdown, - "update_params": { - "visible": LoRA_type - in { - "LyCORIS/DyLoRA", - "LyCORIS/iA3", - "LyCORIS/BOFT", - "LyCORIS/Diag-OFT", - "LyCORIS/GLoRA", - "LyCORIS/LoCon", - "LyCORIS/LoHa", - "LyCORIS/LoKr", - "LyCORIS/Native Fine-Tuning", + "LyCORIS_preset": { + "gr_type": gr.Dropdown, + "update_params": { + "visible": LoRA_type + in { + "LyCORIS/DyLoRA", + "LyCORIS/iA3", + "LyCORIS/BOFT", + "LyCORIS/Diag-OFT", + "LyCORIS/GLoRA", + "LyCORIS/LoCon", + "LyCORIS/LoHa", + "LyCORIS/LoKr", + "LyCORIS/Native Fine-Tuning", + }, }, }, - }, - "unit": { - "gr_type": gr.Slider, - "update_params": { - "visible": LoRA_type - in { - "Kohya DyLoRA", - "LyCORIS/DyLoRA", + "unit": { + "gr_type": gr.Slider, + "update_params": { + "visible": LoRA_type + in { + "Kohya DyLoRA", + "LyCORIS/DyLoRA", + }, }, }, - }, - "lycoris_accordion": { - "gr_type": gr.Accordion, - "update_params": { - "visible": LoRA_type - in { - "LyCORIS/DyLoRA", - "LyCORIS/iA3", - "LyCORIS/BOFT", - "LyCORIS/Diag-OFT", - "LyCORIS/GLoRA", - "LyCORIS/LoCon", - "LyCORIS/LoHa", - "LyCORIS/LoKr", - "LyCORIS/Native Fine-Tuning", + "lycoris_accordion": { + "gr_type": gr.Accordion, + "update_params": { + "visible": LoRA_type + in { + "LyCORIS/DyLoRA", + "LyCORIS/iA3", + "LyCORIS/BOFT", + "LyCORIS/Diag-OFT", + "LyCORIS/GLoRA", + "LyCORIS/LoCon", + "LyCORIS/LoHa", + "LyCORIS/LoKr", + "LyCORIS/Native Fine-Tuning", + }, }, }, - }, - } + } - results = [] - for attr, settings in lora_settings_config.items(): - update_params = settings["update_params"] + results = [] + for attr, settings in lora_settings_config.items(): + update_params = settings["update_params"] - results.append(settings["gr_type"](**update_params)) + results.append(settings["gr_type"](**update_params)) - return tuple(results) + return tuple(results) with gr.Accordion("Advanced", open=False, elem_id="advanced_tab"): # with gr.Accordion('Advanced Configuration', open=False): @@ -1942,10 +1949,10 @@ def update_LoRA_settings( basic_training.train_batch_size, basic_training.epoch, basic_training.save_every_n_epochs, - basic_training.mixed_precision, + accelerate_launch.mixed_precision, source_model.save_precision, basic_training.seed, - basic_training.num_cpu_threads_per_process, + accelerate_launch.num_cpu_threads_per_process, basic_training.cache_latents, basic_training.cache_latents_to_disk, basic_training.caption_extension, @@ -1973,11 +1980,11 @@ def update_LoRA_settings( advanced_training.flip_aug, advanced_training.masked_loss, advanced_training.clip_skip, - advanced_training.num_processes, - advanced_training.num_machines, - advanced_training.multi_gpu, - advanced_training.gpu_ids, - advanced_training.main_process_port, + accelerate_launch.num_processes, + accelerate_launch.num_machines, + accelerate_launch.multi_gpu, + accelerate_launch.gpu_ids, + accelerate_launch.main_process_port, advanced_training.gradient_accumulation_steps, advanced_training.mem_eff_attn, source_model.output_name, diff --git a/kohya_gui/textual_inversion_gui.py b/kohya_gui/textual_inversion_gui.py index e1974c1a7..a6b998073 100644 --- a/kohya_gui/textual_inversion_gui.py +++ b/kohya_gui/textual_inversion_gui.py @@ -20,6 +20,7 @@ create_refresh_button, validate_paths, ) +from .class_accelerate_launch import AccelerateLaunch from .class_configuration_file import ConfigurationFile from .class_source_model import SourceModel from .class_basic_training import BasicTraining @@ -539,13 +540,14 @@ def train_model( run_cmd = "accelerate launch" - run_cmd += run_cmd_advanced_training( + run_cmd += AccelerateLaunch.run_cmd( num_processes=num_processes, num_machines=num_machines, multi_gpu=multi_gpu, gpu_ids=gpu_ids, main_process_port=main_process_port, num_cpu_threads_per_process=num_cpu_threads_per_process, + mixed_precision=mixed_precision, ) if sdxl: @@ -709,6 +711,9 @@ def ti_tab(headless=False, default_output_dir=None, config: dict = {}): with gr.Tab("Training"), gr.Column(variant="compact"): gr.Markdown("Train a TI using kohya textual inversion python code...") + with gr.Accordion("Accelerate launch", open=False), gr.Column(): + accelerate_launch = AccelerateLaunch() + with gr.Column(): source_model = SourceModel( save_model_as_choices=[ @@ -721,95 +726,97 @@ def ti_tab(headless=False, default_output_dir=None, config: dict = {}): with gr.Accordion("Folders", open=False), gr.Group(): folders = Folders(headless=headless, config=config) + with gr.Accordion("Parameters", open=False), gr.Column(): - with gr.Group(elem_id="basic_tab"): - with gr.Row(): - - def list_embedding_files(path): - nonlocal current_embedding_dir - current_embedding_dir = path - return list( - list_files( - path, exts=[".pt", ".ckpt", ".safetensors"], all=True + with gr.Accordion("Basic", open="True"): + with gr.Group(elem_id="basic_tab"): + with gr.Row(): + + def list_embedding_files(path): + nonlocal current_embedding_dir + current_embedding_dir = path + return list( + list_files( + path, exts=[".pt", ".ckpt", ".safetensors"], all=True + ) ) + + weights = gr.Dropdown( + label="Resume TI training (Optional. Path to existing TI embedding file to keep training)", + choices=[""] + list_embedding_files(current_embedding_dir), + value="", + interactive=True, + allow_custom_value=True, + ) + create_refresh_button( + weights, + lambda: None, + lambda: { + "choices": list_embedding_files(current_embedding_dir) + }, + "open_folder_small", + ) + weights_file_input = gr.Button( + "📂", + elem_id="open_folder_small", + elem_classes=["tool"], + visible=(not headless), + ) + weights_file_input.click( + get_file_path, + outputs=weights, + show_progress=False, + ) + weights.change( + fn=lambda path: gr.Dropdown( + choices=[""] + list_embedding_files(path) + ), + inputs=weights, + outputs=weights, + show_progress=False, ) - weights = gr.Dropdown( - label="Resume TI training (Optional. Path to existing TI embedding file to keep training)", - choices=[""] + list_embedding_files(current_embedding_dir), - value="", - interactive=True, - allow_custom_value=True, - ) - create_refresh_button( - weights, - lambda: None, - lambda: { - "choices": list_embedding_files(current_embedding_dir) - }, - "open_folder_small", - ) - weights_file_input = gr.Button( - "📂", - elem_id="open_folder_small", - elem_classes=["tool"], - visible=(not headless), - ) - weights_file_input.click( - get_file_path, - outputs=weights, - show_progress=False, - ) - weights.change( - fn=lambda path: gr.Dropdown( - choices=[""] + list_embedding_files(path) - ), - inputs=weights, - outputs=weights, - show_progress=False, + with gr.Row(): + token_string = gr.Textbox( + label="Token string", + placeholder="eg: cat", + ) + init_word = gr.Textbox( + label="Init word", + value="*", + ) + num_vectors_per_token = gr.Slider( + minimum=1, + maximum=75, + value=1, + step=1, + label="Vectors", + ) + # max_train_steps = gr.Textbox( + # label='Max train steps', + # placeholder='(Optional) Maximum number of steps', + # ) + template = gr.Dropdown( + label="Template", + choices=[ + "caption", + "object template", + "style template", + ], + value="caption", + ) + basic_training = BasicTraining( + learning_rate_value="1e-5", + lr_scheduler_value="cosine", + lr_warmup_value="10", + sdxl_checkbox=source_model.sdxl_checkbox, ) - with gr.Row(): - token_string = gr.Textbox( - label="Token string", - placeholder="eg: cat", - ) - init_word = gr.Textbox( - label="Init word", - value="*", + # Add SDXL Parameters + sdxl_params = SDXLParameters( + source_model.sdxl_checkbox, + show_sdxl_cache_text_encoder_outputs=False, ) - num_vectors_per_token = gr.Slider( - minimum=1, - maximum=75, - value=1, - step=1, - label="Vectors", - ) - # max_train_steps = gr.Textbox( - # label='Max train steps', - # placeholder='(Optional) Maximum number of steps', - # ) - template = gr.Dropdown( - label="Template", - choices=[ - "caption", - "object template", - "style template", - ], - value="caption", - ) - basic_training = BasicTraining( - learning_rate_value="1e-5", - lr_scheduler_value="cosine", - lr_warmup_value="10", - sdxl_checkbox=source_model.sdxl_checkbox, - ) - - # Add SDXL Parameters - sdxl_params = SDXLParameters( - source_model.sdxl_checkbox, - show_sdxl_cache_text_encoder_outputs=False, - ) with gr.Accordion("Advanced", open=False, elem_id="advanced_tab"): advanced_training = AdvancedTraining(headless=headless, config=config) @@ -882,10 +889,10 @@ def list_embedding_files(path): basic_training.train_batch_size, basic_training.epoch, basic_training.save_every_n_epochs, - basic_training.mixed_precision, + accelerate_launch.mixed_precision, source_model.save_precision, basic_training.seed, - basic_training.num_cpu_threads_per_process, + accelerate_launch.num_cpu_threads_per_process, basic_training.cache_latents, basic_training.cache_latents_to_disk, basic_training.caption_extension, @@ -906,11 +913,11 @@ def list_embedding_files(path): advanced_training.color_aug, advanced_training.flip_aug, advanced_training.clip_skip, - advanced_training.num_processes, - advanced_training.num_machines, - advanced_training.multi_gpu, - advanced_training.gpu_ids, - advanced_training.main_process_port, + accelerate_launch.num_processes, + accelerate_launch.num_machines, + accelerate_launch.multi_gpu, + accelerate_launch.gpu_ids, + accelerate_launch.main_process_port, advanced_training.vae, source_model.output_name, advanced_training.max_token_length, From ce1369d47bd8e969dc0acfa05d2f73660951353e Mon Sep 17 00:00:00 2001 From: bmaltais Date: Mon, 1 Apr 2024 20:05:26 -0400 Subject: [PATCH 25/42] Add support for Debiased Estimation loss to dreambooth (#2194) --- README.md | 5 +++ kohya_gui/dreambooth_gui.py | 5 +++ test/config/dreambooth-AdamW8bit.json | 51 ++++++++++++++++++++++++--- test/config/locon-AdamW8bit.json | 5 ++- 4 files changed, 61 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 3d888c7a6..96c27b022 100644 --- a/README.md +++ b/README.md @@ -36,6 +36,9 @@ The GUI allows you to set the training parameters and generate and run the requi - [Troubleshooting](#troubleshooting) - [Page File Limit](#page-file-limit) - [No module called tkinter](#no-module-called-tkinter) + - [LORA Training on TESLA V100 - GPU Utilization Issue](#lora-training-on-tesla-v100---gpu-utilization-issue) + - [Issue Summary](#issue-summary) + - [Potential Solutions](#potential-solutions) - [SDXL training](#sdxl-training) - [Masked loss](#masked-loss) - [Change History](#change-history) @@ -434,6 +437,8 @@ ControlNet dataset is used to specify the mask. The mask images should be the RG - The [English version of the dataset settings documentation](./docs/config_README-en.md) is added. PR [#1175](https://github.com/kohya-ss/sd-scripts/pull/1175) Thanks to darkstorm2150! - Add GUI support for the new parameters listed above. +- Move accelerate launch parameters to new `Accelerate launch` accordion above `Model` accordion. +- Add support for `Debiased Estimation loss` to Dreambooth settings. ### 2024/03/21 (v23.0.15) diff --git a/kohya_gui/dreambooth_gui.py b/kohya_gui/dreambooth_gui.py index 5c81b9da8..3a3047e66 100644 --- a/kohya_gui/dreambooth_gui.py +++ b/kohya_gui/dreambooth_gui.py @@ -151,6 +151,7 @@ def save_configuration( scale_v_pred_loss_like_noise_pred, min_timestep, max_timestep, + debiased_estimation_loss, ): # Get list of function parameters and values parameters = list(locals().items()) @@ -289,6 +290,7 @@ def open_configuration( scale_v_pred_loss_like_noise_pred, min_timestep, max_timestep, + debiased_estimation_loss, ): # Get list of function parameters and values parameters = list(locals().items()) @@ -422,6 +424,7 @@ def train_model( scale_v_pred_loss_like_noise_pred, min_timestep, max_timestep, + debiased_estimation_loss, ): # Get list of function parameters and values parameters = list(locals().items()) @@ -580,6 +583,7 @@ def train_model( "clip_skip": clip_skip, "color_aug": color_aug, "dataset_config": dataset_config, + "debiased_estimation_loss": debiased_estimation_loss, "enable_bucket": enable_bucket, "epoch": epoch, "flip_aug": flip_aug, @@ -906,6 +910,7 @@ def dreambooth_tab( advanced_training.scale_v_pred_loss_like_noise_pred, advanced_training.min_timestep, advanced_training.max_timestep, + advanced_training.debiased_estimation_loss, ] configuration.button_open_config.click( diff --git a/test/config/dreambooth-AdamW8bit.json b/test/config/dreambooth-AdamW8bit.json index 2b4ae0187..72dd1cf35 100644 --- a/test/config/dreambooth-AdamW8bit.json +++ b/test/config/dreambooth-AdamW8bit.json @@ -1,8 +1,14 @@ { + "LoRA_type": "Kohya LoCon", + "LyCORIS_preset": "full", "adaptive_noise_scale": 0, "additional_parameters": "", + "block_alphas": "", + "block_dims": "", + "block_lr_zero_threshold": "", "bucket_no_upscale": true, "bucket_reso_steps": 64, + "bypass_mode": false, "cache_latents": true, "cache_latents_to_disk": false, "caption_dropout_every_n_epochs": 0.0, @@ -10,46 +16,66 @@ "caption_extension": "", "clip_skip": 2, "color_aug": false, + "constrain": 0.0, + "conv_alpha": 8, + "conv_block_alphas": "", + "conv_block_dims": "", + "conv_dim": 16, "dataset_config": "", + "debiased_estimation_loss": true, + "decompose_both": false, + "dim_from_weights": false, + "dora_wd": false, + "down_lr_weight": "", "enable_bucket": true, "epoch": 1, + "factor": -1, "flip_aug": false, + "fp8_base": false, "full_bf16": false, "full_fp16": false, "gpu_ids": "", "gradient_accumulation_steps": 1, "gradient_checkpointing": false, + "ip_noise_gamma": 0.1, + "ip_noise_gamma_random_strength": true, "keep_tokens": "0", "learning_rate": 5e-05, - "learning_rate_te": 1e-05, - "learning_rate_te1": 1e-05, - "learning_rate_te2": 1e-05, "log_tracker_config": "", "log_tracker_name": "", "logging_dir": "./test/logs", + "lora_network_weights": "", "lr_scheduler": "constant", "lr_scheduler_args": "", "lr_scheduler_num_cycles": "", "lr_scheduler_power": "", "lr_warmup": 0, + "main_process_port": 0, + "masked_loss": false, "max_bucket_reso": 2048, "max_data_loader_n_workers": "0", + "max_grad_norm": 1, "max_resolution": "512,512", "max_timestep": 1000, "max_token_length": "75", "max_train_epochs": "", "max_train_steps": "", "mem_eff_attn": false, + "mid_lr_weight": "", "min_bucket_reso": 256, "min_snr_gamma": 0, "min_timestep": 0, "mixed_precision": "bf16", "model_list": "runwayml/stable-diffusion-v1-5", + "module_dropout": 0.1, "multi_gpu": false, "multires_noise_discount": 0, "multires_noise_iterations": 0, - "no_token_padding": false, + "network_alpha": 8, + "network_dim": 16, + "network_dropout": 0.1, "noise_offset": 0.05, + "noise_offset_random_strength": true, "noise_offset_type": "Original", "num_cpu_threads_per_process": 2, "num_machines": 1, @@ -62,7 +88,10 @@ "pretrained_model_name_or_path": "runwayml/stable-diffusion-v1-5", "prior_loss_weight": 1.0, "random_crop": false, + "rank_dropout": 0.1, + "rank_dropout_scale": false, "reg_data_dir": "", + "rescaled": false, "resume": "", "sample_every_n_epochs": 0, "sample_every_n_steps": 25, @@ -75,13 +104,27 @@ "save_model_as": "safetensors", "save_precision": "fp16", "save_state": false, + "save_state_on_train_end": false, "scale_v_pred_loss_like_noise_pred": false, + "scale_weight_norms": 1, "sdxl": false, + "sdxl_cache_text_encoder_outputs": false, + "sdxl_no_half_vae": true, "seed": "1234", "shuffle_caption": false, "stop_text_encoder_training": 0, + "text_encoder_lr": 0.0, "train_batch_size": 4, "train_data_dir": "./test/img", + "train_norm": false, + "train_on_input": false, + "training_comment": "", + "unet_lr": 0.0, + "unit": 1, + "up_lr_weight": "", + "use_cp": false, + "use_scalar": false, + "use_tucker": false, "use_wandb": false, "v2": false, "v_parameterization": false, diff --git a/test/config/locon-AdamW8bit.json b/test/config/locon-AdamW8bit.json index 8a251d44c..867fff738 100644 --- a/test/config/locon-AdamW8bit.json +++ b/test/config/locon-AdamW8bit.json @@ -22,7 +22,7 @@ "conv_block_dims": "", "conv_dim": 16, "dataset_config": "", - "debiased_estimation_loss": false, + "debiased_estimation_loss": true, "decompose_both": false, "dim_from_weights": false, "dora_wd": false, @@ -50,6 +50,8 @@ "lr_scheduler_num_cycles": "", "lr_scheduler_power": "", "lr_warmup": 0, + "main_process_port": 0, + "masked_loss": false, "max_bucket_reso": 2048, "max_data_loader_n_workers": "0", "max_grad_norm": 1, @@ -102,6 +104,7 @@ "save_model_as": "safetensors", "save_precision": "fp16", "save_state": false, + "save_state_on_train_end": false, "scale_v_pred_loss_like_noise_pred": false, "scale_weight_norms": 1, "sdxl": false, From d4e23f96e58e9d12f19ef0b8e18fbbf403cd6b32 Mon Sep 17 00:00:00 2001 From: bmaltais Date: Tue, 2 Apr 2024 12:55:11 -0400 Subject: [PATCH 26/42] Update accelerate launch parameters --- kohya_gui/class_accelerate_launch.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/kohya_gui/class_accelerate_launch.py b/kohya_gui/class_accelerate_launch.py index 4e638409f..50d2c5c60 100644 --- a/kohya_gui/class_accelerate_launch.py +++ b/kohya_gui/class_accelerate_launch.py @@ -82,17 +82,17 @@ def run_cmd(**kwargs): if "num_processes" in kwargs: num_processes = kwargs.get("num_processes") - if int(num_processes) > 1: + if int(num_processes) > 0: run_cmd += f" --num_processes={int(num_processes)}" if "num_machines" in kwargs: num_machines = kwargs.get("num_machines") - if int(num_machines) > 1: + if int(num_machines) > 0: run_cmd += f" --num_machines={int(num_machines)}" if "num_cpu_threads_per_process" in kwargs: num_cpu_threads_per_process = kwargs.get("num_cpu_threads_per_process") - if int(num_cpu_threads_per_process) > 1: + if int(num_cpu_threads_per_process) > 0: run_cmd += ( f" --num_cpu_threads_per_process={int(num_cpu_threads_per_process)}" ) From 17186c25653212541ef73584179b558c52f2ed36 Mon Sep 17 00:00:00 2001 From: bmaltais Date: Tue, 2 Apr 2024 18:29:39 -0400 Subject: [PATCH 27/42] Add config.toml defaults for dataset preparation section (#2199) --- README.md | 1 + config example.toml | 8 ++++ kohya_gui/dreambooth_folder_creation_gui.py | 42 +++++++++++---------- kohya_gui/dreambooth_gui.py | 1 + kohya_gui/lora_gui.py | 1 + kohya_gui/textual_inversion_gui.py | 1 + 6 files changed, 35 insertions(+), 19 deletions(-) diff --git a/README.md b/README.md index 96c27b022..759ea5f18 100644 --- a/README.md +++ b/README.md @@ -439,6 +439,7 @@ ControlNet dataset is used to specify the mask. The mask images should be the RG - Add GUI support for the new parameters listed above. - Move accelerate launch parameters to new `Accelerate launch` accordion above `Model` accordion. - Add support for `Debiased Estimation loss` to Dreambooth settings. +- Add support for "Dataset Preparation" defaults via the config.toml file. ### 2024/03/21 (v23.0.15) diff --git a/config example.toml b/config example.toml index 5c9f1b052..ba582c671 100644 --- a/config example.toml +++ b/config example.toml @@ -14,3 +14,11 @@ vae_dir = "./models/vae" # VAEs folder path # Example custom folder location # models_dir = "e:/models" # Pretrained model name or path + +# This next section can be used to set default values for the Dataset Preparation section +# The "Destination training direcroty" field will be equal to "train_data_dir" as specified above +# [dataset_preparation] +# instance_prompt = "instance" +# class_prompt = "class" +# images_folder = "/some/folder/where/images/are" +# reg_images_folder = "/some/folder/where/reg/images/are" diff --git a/kohya_gui/dreambooth_folder_creation_gui.py b/kohya_gui/dreambooth_folder_creation_gui.py index 24af3c139..5076a75eb 100644 --- a/kohya_gui/dreambooth_folder_creation_gui.py +++ b/kohya_gui/dreambooth_folder_creation_gui.py @@ -3,6 +3,7 @@ from .common_gui import get_folder_path, scriptdir, list_dirs, create_refresh_button import shutil import os +from .class_gui_config import KohyaSSGUIConfig from .custom_logging import setup_logging @@ -119,6 +120,7 @@ def dreambooth_folder_preparation( def gradio_dreambooth_folder_creation_tab( + config: KohyaSSGUIConfig, train_data_dir_input=gr.Dropdown(), reg_data_dir_input=gr.Dropdown(), output_dir_input=gr.Dropdown(), @@ -139,11 +141,13 @@ def gradio_dreambooth_folder_creation_tab( label='Instance prompt', placeholder='Eg: asd', interactive=True, + value = config.get(key="dataset_preparation.instance_prompt", default="") ) util_class_prompt_input = gr.Textbox( label='Class prompt', placeholder='Eg: person', interactive=True, + value = config.get(key="dataset_preparation.class_prompt", default=""), ) with gr.Group(), gr.Row(): @@ -155,8 +159,8 @@ def list_train_data_dirs(path): util_training_images_dir_input = gr.Dropdown( label='Training images (directory containing the training images)', interactive=True, - choices=[""] + list_train_data_dirs(current_train_data_dir), - value="", + choices=[config.get(key="dataset_preparation.images_folder", default="")] + list_train_data_dirs(current_train_data_dir), + value=config.get(key="dataset_preparation.images_folder", default=""), allow_custom_value=True, ) create_refresh_button(util_training_images_dir_input, lambda: None, lambda: {"choices": list_train_data_dirs(current_train_data_dir)}, "open_folder_small") @@ -190,11 +194,11 @@ def list_reg_data_dirs(path): util_regularization_images_dir_input = gr.Dropdown( label='Regularisation images (Optional. directory containing the regularisation images)', interactive=True, - choices=[""] + list_reg_data_dirs(current_reg_data_dir), - value="", + choices=[config.get(key="dataset_preparation.reg_images_folder", default="")] + list_reg_data_dirs(current_reg_data_dir), + value=config.get(key="dataset_preparation.reg_images_folder", default=""), allow_custom_value=True, ) - create_refresh_button(util_regularization_images_dir_input, lambda: None, lambda: {"choices": list_reg_data_dir(current_reg_data_dir)}, "open_folder_small") + create_refresh_button(util_regularization_images_dir_input, lambda: None, lambda: {"choices": list_reg_data_dirs(current_reg_data_dir)}, "open_folder_small") button_util_regularization_images_dir_input = gr.Button( '📂', elem_id='open_folder_small', elem_classes=['tool'], visible=(not headless) ) @@ -224,8 +228,8 @@ def list_train_output_dirs(path): util_training_dir_output = gr.Dropdown( label='Destination training directory (where formatted training and regularisation folders will be placed)', interactive=True, - choices=[""] + list_train_output_dirs(current_train_output_dir), - value="", + choices=[config.get(key="train_data_dir", default="")] + list_train_output_dirs(current_train_output_dir), + value=config.get(key="train_data_dir", default=""), allow_custom_value=True, ) create_refresh_button(util_training_dir_output, lambda: None, lambda: {"choices": list_train_output_dirs(current_train_output_dir)}, "open_folder_small") @@ -255,15 +259,15 @@ def list_train_output_dirs(path): ], show_progress=False, ) - button_copy_info_to_Folders_tab = gr.Button('Copy info to Folders Tab') - button_copy_info_to_Folders_tab.click( - copy_info_to_Folders_tab, - inputs=[util_training_dir_output], - outputs=[ - train_data_dir_input, - reg_data_dir_input, - output_dir_input, - logging_dir_input, - ], - show_progress=False, - ) + # button_copy_info_to_Folders_tab = gr.Button('Copy info to Folders Tab') + # button_copy_info_to_Folders_tab.click( + # copy_info_to_Folders_tab, + # inputs=[util_training_dir_output], + # outputs=[ + # train_data_dir_input, + # reg_data_dir_input, + # output_dir_input, + # logging_dir_input, + # ], + # show_progress=False, + # ) diff --git a/kohya_gui/dreambooth_gui.py b/kohya_gui/dreambooth_gui.py index 3a3047e66..0d299a70b 100644 --- a/kohya_gui/dreambooth_gui.py +++ b/kohya_gui/dreambooth_gui.py @@ -777,6 +777,7 @@ def dreambooth_tab( output_dir_input=folders.output_dir, logging_dir_input=folders.logging_dir, headless=headless, + config=config, ) gradio_dataset_balancing_tab(headless=headless) diff --git a/kohya_gui/lora_gui.py b/kohya_gui/lora_gui.py index 8b7d5056f..036915643 100644 --- a/kohya_gui/lora_gui.py +++ b/kohya_gui/lora_gui.py @@ -1899,6 +1899,7 @@ def update_LoRA_settings( output_dir_input=folders.output_dir, logging_dir_input=folders.logging_dir, headless=headless, + config=config, ) gradio_dataset_balancing_tab(headless=headless) diff --git a/kohya_gui/textual_inversion_gui.py b/kohya_gui/textual_inversion_gui.py index a6b998073..a05a13203 100644 --- a/kohya_gui/textual_inversion_gui.py +++ b/kohya_gui/textual_inversion_gui.py @@ -839,6 +839,7 @@ def list_embedding_files(path): output_dir_input=folders.output_dir, logging_dir_input=folders.logging_dir, headless=headless, + config=config, ) gradio_dataset_balancing_tab(headless=headless) From 0a8395ddfe1f4c495be78eb58b1f76a8c907d9c0 Mon Sep 17 00:00:00 2001 From: bmaltais Date: Tue, 2 Apr 2024 20:21:45 -0400 Subject: [PATCH 28/42] Move debiased estimation loss and v_pred like noise --- kohya_gui/class_advanced_training.py | 22 +++++++++++----------- kohya_gui/class_source_model.py | 3 ++- kohya_gui/dreambooth_gui.py | 3 ++- 3 files changed, 15 insertions(+), 13 deletions(-) diff --git a/kohya_gui/class_advanced_training.py b/kohya_gui/class_advanced_training.py index e232dcc62..71ff380c3 100644 --- a/kohya_gui/class_advanced_training.py +++ b/kohya_gui/class_advanced_training.py @@ -228,6 +228,12 @@ def full_options_update(full_fp16, full_bf16): value=False, info="Apply mask for calculating loss. conditioning_data_dir is required for dataset" ) + with gr.Row(): + self.scale_v_pred_loss_like_noise_pred = gr.Checkbox( + label="Scale v prediction loss", + value=False, + info="Only for SD v2 models. By scaling the loss according to the time step, the weights of global noise prediction and local noise prediction become the same, and the improvement of details may be expected.", + ) self.min_snr_gamma = gr.Slider( label="Min SNR gamma", value=0, @@ -236,6 +242,11 @@ def full_options_update(full_fp16, full_bf16): step=1, info="Recommended value of 5 when used", ) + self.debiased_estimation_loss = gr.Checkbox( + label="Debiased Estimation loss", + value=False, + info="Automates the processing of noise, allowing for faster model fitting, as well as balancing out color issues. Do not use if Min SNR gamma is specified.", + ) with gr.Row(): # self.sdpa = gr.Checkbox(label='Use sdpa', value=False, info='Use sdpa for CrossAttention') self.bucket_no_upscale = gr.Checkbox( @@ -464,14 +475,3 @@ def list_log_tracker_config_files(path): outputs=self.log_tracker_config, show_progress=False, ) - with gr.Row(): - self.scale_v_pred_loss_like_noise_pred = gr.Checkbox( - label="Scale v prediction loss", - value=False, - info="Only for SD v2 models. By scaling the loss according to the time step, the weights of global noise prediction and local noise prediction become the same, and the improvement of details may be expected.", - ) - self.debiased_estimation_loss = gr.Checkbox( - label="Debiased Estimation loss", - value=False, - info="Automates the processing of noise, allowing for faster model fitting, as well as balancing out color issues", - ) diff --git a/kohya_gui/class_source_model.py b/kohya_gui/class_source_model.py index abde0f5ac..9c5c19ddc 100644 --- a/kohya_gui/class_source_model.py +++ b/kohya_gui/class_source_model.py @@ -10,6 +10,7 @@ list_files, create_refresh_button, ) +from .class_gui_config import KohyaSSGUIConfig folder_symbol = "\U0001f4c2" # 📂 refresh_symbol = "\U0001f504" # 🔄 @@ -47,7 +48,7 @@ def __init__( ], headless=False, finetuning=False, - config: dict = {}, + config: KohyaSSGUIConfig = {}, ): self.headless = headless self.save_model_as_choices = save_model_as_choices diff --git a/kohya_gui/dreambooth_gui.py b/kohya_gui/dreambooth_gui.py index 0d299a70b..89ec25a11 100644 --- a/kohya_gui/dreambooth_gui.py +++ b/kohya_gui/dreambooth_gui.py @@ -20,6 +20,7 @@ ) from .class_accelerate_launch import AccelerateLaunch from .class_configuration_file import ConfigurationFile +from .class_gui_config import KohyaSSGUIConfig from .class_source_model import SourceModel from .class_basic_training import BasicTraining from .class_advanced_training import AdvancedTraining @@ -724,7 +725,7 @@ def dreambooth_tab( # output_dir=gr.Textbox(), # logging_dir=gr.Textbox(), headless=False, - config: dict = {}, + config: KohyaSSGUIConfig = {}, ): dummy_db_true = gr.Label(value=True, visible=False) dummy_db_false = gr.Label(value=False, visible=False) From c78c1ab4fe94edff8a7c78c310ca716660f9dd89 Mon Sep 17 00:00:00 2001 From: bmaltais Date: Tue, 2 Apr 2024 20:56:59 -0400 Subject: [PATCH 29/42] Add new field for extra accelerate launch arguments (#2200) --- README.md | 1 + kohya_gui/class_accelerate_launch.py | 12 ++++++++++++ kohya_gui/dreambooth_gui.py | 5 +++++ kohya_gui/finetune_gui.py | 5 +++++ kohya_gui/lora_gui.py | 5 +++++ kohya_gui/textual_inversion_gui.py | 5 +++++ 6 files changed, 33 insertions(+) diff --git a/README.md b/README.md index 759ea5f18..d551ea90b 100644 --- a/README.md +++ b/README.md @@ -440,6 +440,7 @@ ControlNet dataset is used to specify the mask. The mask images should be the RG - Move accelerate launch parameters to new `Accelerate launch` accordion above `Model` accordion. - Add support for `Debiased Estimation loss` to Dreambooth settings. - Add support for "Dataset Preparation" defaults via the config.toml file. +- Add field to allow for the input of extra accelerate launch arguments. ### 2024/03/21 (v23.0.15) diff --git a/kohya_gui/class_accelerate_launch.py b/kohya_gui/class_accelerate_launch.py index 50d2c5c60..346dd7356 100644 --- a/kohya_gui/class_accelerate_launch.py +++ b/kohya_gui/class_accelerate_launch.py @@ -59,10 +59,22 @@ def __init__( maximum=65535, info="The port to use to communicate with the machine of rank 0.", ) + with gr.Row(): + self.extra_accelerate_launch_args = gr.Textbox( + label="Extra accelerate launch arguments", + value="", + placeholder="example: --same_network --machine_rank 4", + info="List of extra parameters to pass to accelerate launch", + ) def run_cmd(**kwargs): run_cmd = "" + if "extra_accelerate_launch_args" in kwargs: + extra_accelerate_launch_args = kwargs.get("extra_accelerate_launch_args") + if extra_accelerate_launch_args != "": + run_cmd += fr' {extra_accelerate_launch_args}' + if "gpu_ids" in kwargs: gpu_ids = kwargs.get("gpu_ids") if not gpu_ids == "": diff --git a/kohya_gui/dreambooth_gui.py b/kohya_gui/dreambooth_gui.py index 89ec25a11..4f99cfa2e 100644 --- a/kohya_gui/dreambooth_gui.py +++ b/kohya_gui/dreambooth_gui.py @@ -153,6 +153,7 @@ def save_configuration( min_timestep, max_timestep, debiased_estimation_loss, + extra_accelerate_launch_args, ): # Get list of function parameters and values parameters = list(locals().items()) @@ -292,6 +293,7 @@ def open_configuration( min_timestep, max_timestep, debiased_estimation_loss, + extra_accelerate_launch_args, ): # Get list of function parameters and values parameters = list(locals().items()) @@ -426,6 +428,7 @@ def train_model( min_timestep, max_timestep, debiased_estimation_loss, + extra_accelerate_launch_args, ): # Get list of function parameters and values parameters = list(locals().items()) @@ -564,6 +567,7 @@ def train_model( main_process_port=main_process_port, num_cpu_threads_per_process=num_cpu_threads_per_process, mixed_precision=mixed_precision, + extra_accelerate_launch_args=extra_accelerate_launch_args, ) if sdxl: @@ -913,6 +917,7 @@ def dreambooth_tab( advanced_training.min_timestep, advanced_training.max_timestep, advanced_training.debiased_estimation_loss, + accelerate_launch.extra_accelerate_launch_args, ] configuration.button_open_config.click( diff --git a/kohya_gui/finetune_gui.py b/kohya_gui/finetune_gui.py index 83cf23974..aa8594d7e 100644 --- a/kohya_gui/finetune_gui.py +++ b/kohya_gui/finetune_gui.py @@ -161,6 +161,7 @@ def save_configuration( sdxl_no_half_vae, min_timestep, max_timestep, + extra_accelerate_launch_args, ): # Get list of function parameters and values parameters = list(locals().items()) @@ -307,6 +308,7 @@ def open_configuration( sdxl_no_half_vae, min_timestep, max_timestep, + extra_accelerate_launch_args, training_preset, ): # Get list of function parameters and values @@ -460,6 +462,7 @@ def train_model( sdxl_no_half_vae, min_timestep, max_timestep, + extra_accelerate_launch_args, ): # Get list of function parameters and values parameters = list(locals().items()) @@ -585,6 +588,7 @@ def train_model( main_process_port=main_process_port, num_cpu_threads_per_process=num_cpu_threads_per_process, mixed_precision=mixed_precision, + extra_accelerate_launch_args=extra_accelerate_launch_args, ) if sdxl_checkbox: @@ -1001,6 +1005,7 @@ def list_presets(path): sdxl_params.sdxl_no_half_vae, advanced_training.min_timestep, advanced_training.max_timestep, + accelerate_launch.extra_accelerate_launch_args, ] configuration.button_open_config.click( diff --git a/kohya_gui/lora_gui.py b/kohya_gui/lora_gui.py index 036915643..ec951d670 100644 --- a/kohya_gui/lora_gui.py +++ b/kohya_gui/lora_gui.py @@ -223,6 +223,7 @@ def save_configuration( vae, LyCORIS_preset, debiased_estimation_loss, + extra_accelerate_launch_args, ): # Get list of function parameters and values parameters = list(locals().items()) @@ -410,6 +411,7 @@ def open_configuration( vae, LyCORIS_preset, debiased_estimation_loss, + extra_accelerate_launch_args, training_preset, ): # Get list of function parameters and values @@ -625,6 +627,7 @@ def train_model( vae, LyCORIS_preset, debiased_estimation_loss, + extra_accelerate_launch_args, ): # Get list of function parameters and values parameters = list(locals().items()) @@ -792,6 +795,7 @@ def train_model( main_process_port=main_process_port, num_cpu_threads_per_process=num_cpu_threads_per_process, mixed_precision=mixed_precision, + extra_accelerate_launch_args=extra_accelerate_launch_args, ) if sdxl: @@ -2071,6 +2075,7 @@ def update_LoRA_settings( advanced_training.vae, LyCORIS_preset, advanced_training.debiased_estimation_loss, + accelerate_launch.extra_accelerate_launch_args, ] configuration.button_open_config.click( diff --git a/kohya_gui/textual_inversion_gui.py b/kohya_gui/textual_inversion_gui.py index a05a13203..18eb167c2 100644 --- a/kohya_gui/textual_inversion_gui.py +++ b/kohya_gui/textual_inversion_gui.py @@ -151,6 +151,7 @@ def save_configuration( min_timestep, max_timestep, sdxl_no_half_vae, + extra_accelerate_launch_args, ): # Get list of function parameters and values parameters = list(locals().items()) @@ -291,6 +292,7 @@ def open_configuration( min_timestep, max_timestep, sdxl_no_half_vae, + extra_accelerate_launch_args, ): # Get list of function parameters and values parameters = list(locals().items()) @@ -424,6 +426,7 @@ def train_model( min_timestep, max_timestep, sdxl_no_half_vae, + extra_accelerate_launch_args, ): # Get list of function parameters and values parameters = list(locals().items()) @@ -548,6 +551,7 @@ def train_model( main_process_port=main_process_port, num_cpu_threads_per_process=num_cpu_threads_per_process, mixed_precision=mixed_precision, + extra_accelerate_launch_args=extra_accelerate_launch_args, ) if sdxl: @@ -973,6 +977,7 @@ def list_embedding_files(path): advanced_training.min_timestep, advanced_training.max_timestep, sdxl_params.sdxl_no_half_vae, + accelerate_launch.extra_accelerate_launch_args, ] configuration.button_open_config.click( From 8bf7d327ac8f19829e08a3867be8b07f52ca703a Mon Sep 17 00:00:00 2001 From: bmaltais Date: Wed, 3 Apr 2024 17:44:11 -0400 Subject: [PATCH 30/42] Add support for --config parameter to kohya_gui.py --- kohya_gui.py | 9 ++++++++- kohya_gui/class_gui_config.py | 16 ++++++++-------- 2 files changed, 16 insertions(+), 9 deletions(-) diff --git a/kohya_gui.py b/kohya_gui.py index 3211b7954..9d58fcddf 100644 --- a/kohya_gui.py +++ b/kohya_gui.py @@ -40,7 +40,7 @@ def UI(**kwargs): css=css, title=f"Kohya_ss GUI {release}", theme=gr.themes.Default() ) - config = KohyaSSGUIConfig() + config = KohyaSSGUIConfig(config_file_path=kwargs.get("config_file_path")) with interface: with gr.Tab("Dreambooth"): @@ -105,6 +105,12 @@ def UI(**kwargs): if __name__ == "__main__": # torch.cuda.set_per_process_memory_fraction(0.48) parser = argparse.ArgumentParser() + parser.add_argument( + "--config", + type=str, + default="./config.toml", + help="Path to the toml config file for interface defaults", + ) parser.add_argument( "--listen", type=str, @@ -138,6 +144,7 @@ def UI(**kwargs): args = parser.parse_args() UI( + config_file_path=args.config, username=args.username, password=args.password, inbrowser=args.inbrowser, diff --git a/kohya_gui/class_gui_config.py b/kohya_gui/class_gui_config.py index 7f73ac3f7..afe1cb2da 100644 --- a/kohya_gui/class_gui_config.py +++ b/kohya_gui/class_gui_config.py @@ -10,13 +10,13 @@ class KohyaSSGUIConfig: A class to handle the configuration for the Kohya SS GUI. """ - def __init__(self): + def __init__(self, config_file_path: str = "./config.toml"): """ Initialize the KohyaSSGUIConfig class. """ - self.config = self.load_config() + self.config = self.load_config(config_file_path=config_file_path) - def load_config(self) -> dict: + def load_config(self, config_file_path: str = "./config.toml") -> dict: """ Loads the Kohya SS GUI configuration from a TOML file. @@ -25,16 +25,16 @@ def load_config(self) -> dict: """ try: # Attempt to load the TOML configuration file from the specified directory. - config = toml.load(f"{scriptdir}/config.toml") - log.debug(f"Loaded configuration from {scriptdir}/config.toml") + config = toml.load(f"{config_file_path}") + log.debug(f"Loaded configuration from {config_file_path}") except FileNotFoundError: # If the config file is not found, initialize `config` as an empty dictionary to handle missing configurations gracefully. config = {} - log.debug(f"No configuration file found at {scriptdir}/config.toml. Initializing empty configuration.") + log.debug(f"No configuration file found at {config_file_path}. Initializing empty configuration.") return config - def save_config(self, config: dict): + def save_config(self, config: dict, config_file_path: str = "./config.toml"): """ Saves the Kohya SS GUI configuration to a TOML file. @@ -42,7 +42,7 @@ def save_config(self, config: dict): - config (dict): The configuration data to save. """ # Write the configuration data to the TOML file - with open(f"{scriptdir}/config.toml", "w") as f: + with open(f"{config_file_path}", "w") as f: toml.dump(config, f) def get(self, key: str, default=None): From 94c6711c0ab142aba66dd8c379f7120954049465 Mon Sep 17 00:00:00 2001 From: bmaltais Date: Wed, 3 Apr 2024 17:46:45 -0400 Subject: [PATCH 31/42] Update to latest sd-scripts dev branch ommit --- sd-scripts | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sd-scripts b/sd-scripts index 2258a1b75..b748b48db 160000 --- a/sd-scripts +++ b/sd-scripts @@ -1 +1 @@ -Subproject commit 2258a1b753a321ed25b9ae1b7f2ceb1b24ae0736 +Subproject commit b748b48dbbaf6ddd011f032f5ede47aea094a208 From c827268bf39c7ffb70ff554e7c030fb7ab220da1 Mon Sep 17 00:00:00 2001 From: bmaltais Date: Wed, 3 Apr 2024 18:17:59 -0400 Subject: [PATCH 32/42] Cleanup old files and code --- bitsandbytes_windows/cextension.py | 54 ------ bitsandbytes_windows/libbitsandbytes_cpu.dll | Bin 76288 -> 0 bytes .../libbitsandbytes_cuda116.dll | Bin 4724224 -> 0 bytes .../libbitsandbytes_cuda118.dll | Bin 14026752 -> 0 bytes bitsandbytes_windows/main.py | 166 ------------------ kohya_gui/common_gui.py | 54 ------ kohya_gui/convert_model_gui.py | 66 +++---- kohya_gui/dreambooth_gui.py | 8 - kohya_gui/finetune_gui.py | 8 - kohya_gui/textual_inversion_gui.py | 8 - v2_inference/v2-inference-v.yaml | 68 ------- v2_inference/v2-inference.yaml | 67 ------- v2_inference/v2-inpainting-inference.yaml | 158 ----------------- v2_inference/v2-midas-inference.yaml | 74 -------- v2_inference/x4-upscaling.yaml | 76 -------- 15 files changed, 33 insertions(+), 774 deletions(-) delete mode 100644 bitsandbytes_windows/cextension.py delete mode 100644 bitsandbytes_windows/libbitsandbytes_cpu.dll delete mode 100644 bitsandbytes_windows/libbitsandbytes_cuda116.dll delete mode 100644 bitsandbytes_windows/libbitsandbytes_cuda118.dll delete mode 100644 bitsandbytes_windows/main.py delete mode 100644 v2_inference/v2-inference-v.yaml delete mode 100644 v2_inference/v2-inference.yaml delete mode 100644 v2_inference/v2-inpainting-inference.yaml delete mode 100644 v2_inference/v2-midas-inference.yaml delete mode 100644 v2_inference/x4-upscaling.yaml diff --git a/bitsandbytes_windows/cextension.py b/bitsandbytes_windows/cextension.py deleted file mode 100644 index d38684a20..000000000 --- a/bitsandbytes_windows/cextension.py +++ /dev/null @@ -1,54 +0,0 @@ -import ctypes as ct -from pathlib import Path -from warnings import warn - -from .cuda_setup.main import evaluate_cuda_setup - - -class CUDALibrary_Singleton(object): - _instance = None - - def __init__(self): - raise RuntimeError("Call get_instance() instead") - - def initialize(self): - binary_name = evaluate_cuda_setup() - package_dir = Path(__file__).parent - binary_path = package_dir / binary_name - - if not binary_path.exists(): - print(f"CUDA SETUP: TODO: compile library for specific version: {binary_name}") - legacy_binary_name = "libbitsandbytes.so" - print(f"CUDA SETUP: Defaulting to {legacy_binary_name}...") - binary_path = package_dir / legacy_binary_name - if not binary_path.exists(): - print('CUDA SETUP: CUDA detection failed. Either CUDA driver not installed, CUDA not installed, or you have multiple conflicting CUDA libraries!') - print('CUDA SETUP: If you compiled from source, try again with `make CUDA_VERSION=DETECTED_CUDA_VERSION` for example, `make CUDA_VERSION=113`.') - raise Exception('CUDA SETUP: Setup Failed!') - # self.lib = ct.cdll.LoadLibrary(binary_path) - self.lib = ct.cdll.LoadLibrary(str(binary_path)) # $$$ - else: - print(f"CUDA SETUP: Loading binary {binary_path}...") - # self.lib = ct.cdll.LoadLibrary(binary_path) - self.lib = ct.cdll.LoadLibrary(str(binary_path)) # $$$ - - @classmethod - def get_instance(cls): - if cls._instance is None: - cls._instance = cls.__new__(cls) - cls._instance.initialize() - return cls._instance - - -lib = CUDALibrary_Singleton.get_instance().lib -try: - lib.cadam32bit_g32 - lib.get_context.restype = ct.c_void_p - lib.get_cusparse.restype = ct.c_void_p - COMPILED_WITH_CUDA = True -except AttributeError: - warn( - "The installed version of bitsandbytes was compiled without GPU support. " - "8-bit optimizers and GPU quantization are unavailable." - ) - COMPILED_WITH_CUDA = False diff --git a/bitsandbytes_windows/libbitsandbytes_cpu.dll b/bitsandbytes_windows/libbitsandbytes_cpu.dll deleted file mode 100644 index b733af475eb02eb04f5ad8cbf0530a78a58bc758..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 76288 zcmd?Sd3;pW-9J9r$N&j9D1%W^MvWSZ%V1m*BRE46xC0Z31q78%QHsSSg&9G1I*D+- zj@mx9YHM3+7xk&_L-&9S2_YmbLclE+7u@QNqcyldK;i!0@6Wk2nIwQd-|z4B`{&1t z%)R%VbIlhX)f1*p&JIc{?_-!v+B}9 zFP=4N?rjUa)${NA-u#>H@Xo&J&O7f4dvCtgJ3n%#_qIE|x0RhzupmD7rMIN`_n zcTxD(MPcMMt;Y|$%&|M;z@7Z_x?Hm^nt#hp;hS8pqZ5&YI$e|S_X7S>{`t}LMXH#q zr7tpk4oon%)PE>fe^Iq6C}YG}rR~AJ%K0yvzhFKmNMHikxBz?CdB-Yu>+N@;AYmln zXSu>&9SMtq?g`Pxa@pw zs7p7?@|rpe8F~fIt3u{P zU#N6qp%(i&pgm{K9BpWgfL%9FW|fB-GIy~d0rjMqL8`SjK^`rAPzjOG4 z7ISO4nYl(cN4osJv#Jlp9nE`D~3m;3P`>So+*3D@J{-Sye2BX)bn|(7IcclLF zPXw$`_3OI1Nsr}LHUd(IKCRb{{(5YpTaW4Prp|%swkM|nKrtIpgZIYN8g%nol*(F$ 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