455 lines
15 KiB
Python
455 lines
15 KiB
Python
import gradio as gr
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import json
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import math
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import os
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import subprocess
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import pathlib
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import shutil
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from glob import glob
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from os.path import join
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def save_variables(
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file_path,
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pretrained_model_name_or_path,
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v2,
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v_model,
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logging_dir,
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train_data_dir,
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reg_data_dir,
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output_dir,
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max_resolution,
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learning_rate,
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lr_scheduler,
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lr_warmup,
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train_batch_size,
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epoch,
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save_every_n_epochs,
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mixed_precision,
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save_precision,
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seed,
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num_cpu_threads_per_process,
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convert_to_safetensors,
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convert_to_ckpt
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):
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# Return the values of the variables as a dictionary
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variables = {
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"pretrained_model_name_or_path": pretrained_model_name_or_path,
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"v2": v2,
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"v_model": v_model,
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# "model_list": model_list,
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"logging_dir": logging_dir,
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"train_data_dir": train_data_dir,
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"reg_data_dir": reg_data_dir,
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"output_dir": output_dir,
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"max_resolution": max_resolution,
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"learning_rate": learning_rate,
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"lr_scheduler": lr_scheduler,
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"lr_warmup": lr_warmup,
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"train_batch_size": train_batch_size,
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"epoch": epoch,
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"save_every_n_epochs": save_every_n_epochs,
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"mixed_precision": mixed_precision,
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"save_precision": save_precision,
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"seed": seed,
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"num_cpu_threads_per_process": num_cpu_threads_per_process,
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"convert_to_safetensors": convert_to_safetensors,
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"convert_to_ckpt": convert_to_ckpt
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}
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# Save the data to the selected file
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with open(file_path, "w") as file:
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json.dump(variables, file)
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def load_variables(file_path):
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# load variables from JSON file
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with open(file_path, "r") as f:
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my_data = json.load(f)
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# Return the values of the variables as a dictionary
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return (
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my_data.get("pretrained_model_name_or_path", None),
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my_data.get("v2", None),
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my_data.get("v_model", None),
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my_data.get("logging_dir", None),
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# my_data.get("model_list", None),
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my_data.get("train_data_dir", None),
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my_data.get("reg_data_dir", None),
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my_data.get("output_dir", None),
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my_data.get("max_resolution", None),
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my_data.get("learning_rate", None),
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my_data.get("lr_scheduler", None),
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my_data.get("lr_warmup", None),
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my_data.get("train_batch_size", None),
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my_data.get("epoch", None),
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my_data.get("save_every_n_epochs", None),
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my_data.get("mixed_precision", None),
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my_data.get("save_precision", None),
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my_data.get("seed", None),
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my_data.get("num_cpu_threads_per_process", None),
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my_data.get("convert_to_safetensors", None),
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my_data.get("convert_to_ckpt", None)
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)
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def train_model(
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pretrained_model_name_or_path,
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v2,
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v_model,
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logging_dir,
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train_data_dir,
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reg_data_dir,
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output_dir,
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max_resolution,
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learning_rate,
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lr_scheduler,
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lr_warmup,
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train_batch_size,
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epoch,
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save_every_n_epochs,
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mixed_precision,
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save_precision,
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seed,
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num_cpu_threads_per_process,
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convert_to_safetensors,
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convert_to_ckpt,
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cache_latent_input
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):
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def save_inference_file(output_dir, v2, v_model):
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# Copy inference model for v2 if required
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if v2 and v_model:
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print(f"Saving v2-inference-v.yaml as {output_dir}/last.yaml")
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shutil.copy(
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f"./v2_inference/v2-inference-v.yaml",
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f"{output_dir}/last.yaml",
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)
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elif v2:
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print(f"Saving v2-inference.yaml as {output_dir}/last.yaml")
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shutil.copy(
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f"./v2_inference/v2-inference.yaml",
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f"{output_dir}/last.yaml",
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)
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# Get a list of all subfolders in train_data_dir
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subfolders = [f for f in os.listdir(train_data_dir) if os.path.isdir(
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os.path.join(train_data_dir, f))]
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total_steps = 0
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# Loop through each subfolder and extract the number of repeats
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for folder in subfolders:
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# Extract the number of repeats from the folder name
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repeats = int(folder.split("_")[0])
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# Count the number of images in the folder
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num_images = len([f for f in os.listdir(os.path.join(train_data_dir, folder)) if f.endswith(
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".jpg") or f.endswith(".jpeg") or f.endswith(".png") or f.endswith(".webp")])
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# Calculate the total number of steps for this folder
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steps = repeats * num_images
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total_steps += steps
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# Print the result
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print(f"Folder {folder}: {steps} steps")
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# Print the result
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# print(f"{total_steps} total steps")
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# calculate max_train_steps
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max_train_steps = int(
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math.ceil(float(total_steps) / int(train_batch_size) * int(epoch))
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)
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print(f"max_train_steps = {max_train_steps}")
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lr_warmup_steps = round(float(int(lr_warmup) * int(max_train_steps) / 100))
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print(f"lr_warmup_steps = {lr_warmup_steps}")
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run_cmd = f'accelerate launch --num_cpu_threads_per_process={num_cpu_threads_per_process} "train_db_fixed.py"'
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if v2:
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run_cmd += " --v2"
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if v_model:
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run_cmd += " --v_parameterization"
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if cache_latent_input:
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run_cmd += " --cache_latents"
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run_cmd += f" --pretrained_model_name_or_path={pretrained_model_name_or_path}"
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run_cmd += f" --train_data_dir={train_data_dir}"
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run_cmd += f" --reg_data_dir={reg_data_dir}"
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run_cmd += f" --resolution={max_resolution}"
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run_cmd += f" --output_dir={output_dir}"
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run_cmd += f" --train_batch_size={train_batch_size}"
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run_cmd += f" --learning_rate={learning_rate}"
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run_cmd += f" --lr_scheduler={lr_scheduler}"
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run_cmd += f" --lr_warmup_steps={lr_warmup_steps}"
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run_cmd += f" --max_train_steps={max_train_steps}"
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run_cmd += f" --use_8bit_adam"
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run_cmd += f" --xformers"
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run_cmd += f" --mixed_precision={mixed_precision}"
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run_cmd += f" --save_every_n_epochs={save_every_n_epochs}"
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run_cmd += f" --seed={seed}"
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run_cmd += f" --save_precision={save_precision}"
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run_cmd += f" --logging_dir={logging_dir}"
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print(run_cmd)
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# Run the command
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subprocess.run(run_cmd)
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# check if output_dir/last is a directory... therefore it is a diffuser model
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last_dir = pathlib.Path(f"{output_dir}/last")
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print(last_dir)
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if last_dir.is_dir():
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if convert_to_ckpt:
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print(f"Converting diffuser model {last_dir} to {last_dir}.ckpt")
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os.system(
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f"python ./tools/convert_diffusers20_original_sd.py {last_dir} {last_dir}.ckpt --{save_precision}"
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)
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save_inference_file(output_dir, v2, v_model)
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if convert_to_safetensors:
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print(f"Converting diffuser model {last_dir} to {last_dir}.safetensors")
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os.system(
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f"python ./tools/convert_diffusers20_original_sd.py {last_dir} {last_dir}.safetensors --{save_precision}"
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)
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save_inference_file(output_dir, v2, v_model)
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else:
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# Copy inference model for v2 if required
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save_inference_file(output_dir, v2, v_model)
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# Return the values of the variables as a dictionary
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# return
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def set_pretrained_model_name_or_path_input(value, v2, v_model):
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# define a list of substrings to search for
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substrings_v2 = ["stable-diffusion-2-1-base", "stable-diffusion-2-base"]
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# check if $v2 and $v_model are empty and if $pretrained_model_name_or_path contains any of the substrings in the v2 list
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if str(value) in substrings_v2:
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print("SD v2 model detected. Setting --v2 parameter")
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v2 = True
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v_model = False
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value = "stabilityai/{}".format(value)
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return value, v2, v_model
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# define a list of substrings to search for v-objective
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substrings_v_model = ["stable-diffusion-2-1", "stable-diffusion-2"]
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# check if $v2 and $v_model are empty and if $pretrained_model_name_or_path contains any of the substrings in the v_model list
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if str(value) in substrings_v_model:
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print("SD v2 v_model detected. Setting --v2 parameter and --v_parameterization")
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v2 = True
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v_model = True
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value = "stabilityai/{}".format(value)
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return value, v2, v_model
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if value == "custom":
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value = ""
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v2 = False
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v_model = False
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return value, v2, v_model
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# Define the output element
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output = gr.outputs.Textbox(label="Values of variables")
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interface = gr.Blocks()
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with interface:
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gr.Markdown("Enter kohya finetuner parameter using this interface.")
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with gr.Accordion("Configuration File Load/Save", open=False):
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with gr.Row():
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config_file_name = gr.inputs.Textbox(label="Config file name", default="")
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b1 = gr.Button("Load config")
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b2 = gr.Button("Save config")
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with gr.Tab("model"):
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# Define the input elements
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with gr.Row():
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pretrained_model_name_or_path_input = gr.inputs.Textbox(
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label="Pretrained model name or path",
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placeholder="enter the path to custom model or name of pretrained model",
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)
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model_list = gr.Dropdown(
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label="Model Quick Pick",
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choices=[
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"custom",
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"stable-diffusion-2-1-base",
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"stable-diffusion-2-base",
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"stable-diffusion-2-1",
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"stable-diffusion-2",
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],
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value="custom",
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)
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with gr.Row():
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v2_input = gr.inputs.Checkbox(label="v2", default=True)
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v_model_input = gr.inputs.Checkbox(label="v_model", default=False)
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model_list.change(
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set_pretrained_model_name_or_path_input,
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inputs=[model_list, v2_input, v_model_input],
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outputs=[pretrained_model_name_or_path_input, v2_input, v_model_input],
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)
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with gr.Tab("training dataset and output directory"):
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train_data_dir_input = gr.inputs.Textbox(
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label="Image folder", placeholder="directory where the training folders containing the images are located"
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)
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reg_data_dir_input = gr.inputs.Textbox(
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label="Regularisation folder", placeholder="directory where where the regularization folders containing the images are located"
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)
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output_dir_input = gr.inputs.Textbox(
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label="Output directory",
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placeholder="directory to output trained model",
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)
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logging_dir_input = gr.inputs.Textbox(
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label="Logging directory", placeholder="Optional: enable logging and output TensorBoard log to this directory"
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)
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max_resolution_input = gr.inputs.Textbox(
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label="Max resolution", default="512,512"
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)
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with gr.Tab("training parameters"):
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with gr.Row():
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learning_rate_input = gr.inputs.Textbox(label="Learning rate", default=1e-6)
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lr_scheduler_input = gr.Dropdown(
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label="LR Scheduler",
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choices=[
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"constant",
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"constant_with_warmup",
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"cosine",
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"cosine_with_restarts",
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"linear",
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"polynomial",
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],
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value="constant",
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)
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lr_warmup_input = gr.inputs.Textbox(label="LR warmup", default=0)
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with gr.Row():
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train_batch_size_input = gr.inputs.Textbox(
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label="Train batch size", default=1
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)
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epoch_input = gr.inputs.Textbox(label="Epoch", default=1)
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with gr.Row():
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save_every_n_epochs_input = gr.inputs.Textbox(
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label="Save every N epochs", default=1
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)
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mixed_precision_input = gr.Dropdown(
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label="Mixed precision",
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choices=[
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"no",
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"fp16",
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"bf16",
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],
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value="fp16",
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)
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save_precision_input = gr.Dropdown(
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label="Save precision",
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choices=[
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"float",
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"fp16",
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"bf16",
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],
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value="fp16",
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)
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with gr.Row():
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seed_input = gr.inputs.Textbox(label="Seed", default=1234)
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num_cpu_threads_per_process_input = gr.inputs.Textbox(
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label="Number of CPU threads per process", default=4
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)
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cache_latent_input = gr.inputs.Checkbox(
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label="Cache latent", default=True
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)
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with gr.Tab("model conveersion"):
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convert_to_safetensors_input = gr.inputs.Checkbox(
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label="Convert to SafeTensors", default=False
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)
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convert_to_ckpt_input = gr.inputs.Checkbox(
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label="Convert to CKPT", default=False
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)
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b3 = gr.Button("Run")
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b1.click(
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load_variables,
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inputs=[config_file_name],
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outputs=[
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pretrained_model_name_or_path_input,
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v2_input,
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v_model_input,
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logging_dir_input,
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train_data_dir_input,
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reg_data_dir_input,
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output_dir_input,
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max_resolution_input,
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learning_rate_input,
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lr_scheduler_input,
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lr_warmup_input,
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train_batch_size_input,
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epoch_input,
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save_every_n_epochs_input,
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mixed_precision_input,
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save_precision_input,
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seed_input,
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num_cpu_threads_per_process_input,
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convert_to_safetensors_input,
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convert_to_ckpt_input
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]
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)
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b2.click(
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save_variables,
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inputs=[
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config_file_name,
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pretrained_model_name_or_path_input,
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v2_input,
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v_model_input,
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logging_dir_input,
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train_data_dir_input,
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reg_data_dir_input,
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output_dir_input,
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max_resolution_input,
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learning_rate_input,
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lr_scheduler_input,
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lr_warmup_input,
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train_batch_size_input,
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epoch_input,
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save_every_n_epochs_input,
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mixed_precision_input,
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save_precision_input,
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seed_input,
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num_cpu_threads_per_process_input,
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convert_to_safetensors_input,
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convert_to_ckpt_input
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]
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)
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b3.click(
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train_model,
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inputs=[
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pretrained_model_name_or_path_input,
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v2_input,
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v_model_input,
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logging_dir_input,
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train_data_dir_input,
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reg_data_dir_input,
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output_dir_input,
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max_resolution_input,
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learning_rate_input,
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lr_scheduler_input,
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lr_warmup_input,
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train_batch_size_input,
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epoch_input,
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save_every_n_epochs_input,
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mixed_precision_input,
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save_precision_input,
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seed_input,
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num_cpu_threads_per_process_input,
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convert_to_safetensors_input,
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convert_to_ckpt_input,
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cache_latent_input
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]
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)
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# Show the interface
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interface.launch()
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