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#!/usr/bin/env python | |
# coding=utf-8 | |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
import argparse | |
import logging | |
import math | |
import os | |
import random | |
import shutil | |
from pathlib import Path | |
import accelerate | |
import numpy as np | |
import torch | |
import torch.nn.functional as F | |
import torch.utils.checkpoint | |
import transformers | |
from accelerate import Accelerator | |
from accelerate.logging import get_logger | |
from accelerate.utils import ProjectConfiguration, set_seed | |
from datasets import load_dataset | |
from huggingface_hub import create_repo, upload_folder | |
from packaging import version | |
from PIL import Image | |
from torchvision import transforms | |
from tqdm.auto import tqdm | |
from transformers import AutoTokenizer, PretrainedConfig | |
import diffusers | |
from diffusers import ( | |
AutoencoderKL, | |
ControlNetModel, | |
DDPMScheduler, | |
StableDiffusionControlNetPipeline, | |
UNet2DConditionModel, | |
UniPCMultistepScheduler, | |
) | |
from diffusers.optimization import get_scheduler | |
from diffusers.utils import check_min_version, is_wandb_available | |
from diffusers.utils.import_utils import is_xformers_available | |
if is_wandb_available(): | |
import wandb | |
# Will error if the minimal version of diffusers is not installed. Remove at your own risks. | |
check_min_version("0.24.0.dev0") | |
logger = get_logger(__name__) | |
def image_grid(imgs, rows, cols): | |
assert len(imgs) == rows * cols | |
w, h = imgs[0].size | |
grid = Image.new("RGB", size=(cols * w, rows * h)) | |
for i, img in enumerate(imgs): | |
grid.paste(img, box=(i % cols * w, i // cols * h)) | |
return grid | |
def log_validation(vae, text_encoder, tokenizer, unet, controlnet, args, accelerator, weight_dtype, step): | |
logger.info("Running validation... ") | |
controlnet = accelerator.unwrap_model(controlnet) | |
pipeline = StableDiffusionControlNetPipeline.from_pretrained( | |
args.pretrained_model_name_or_path, | |
vae=vae, | |
text_encoder=text_encoder, | |
tokenizer=tokenizer, | |
unet=unet, | |
controlnet=controlnet, | |
safety_checker=None, | |
revision=args.revision, | |
torch_dtype=weight_dtype, | |
) | |
pipeline.scheduler = UniPCMultistepScheduler.from_config(pipeline.scheduler.config) | |
pipeline = pipeline.to(accelerator.device) | |
pipeline.set_progress_bar_config(disable=True) | |
if args.enable_xformers_memory_efficient_attention: | |
pipeline.enable_xformers_memory_efficient_attention() | |
if args.seed is None: | |
generator = None | |
else: | |
generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) | |
if len(args.validation_image) == len(args.validation_prompt): | |
validation_images = args.validation_image | |
validation_prompts = args.validation_prompt | |
elif len(args.validation_image) == 1: | |
validation_images = args.validation_image * len(args.validation_prompt) | |
validation_prompts = args.validation_prompt | |
elif len(args.validation_prompt) == 1: | |
validation_images = args.validation_image | |
validation_prompts = args.validation_prompt * len(args.validation_image) | |
else: | |
raise ValueError( | |
"number of `args.validation_image` and `args.validation_prompt` should be checked in `parse_args`" | |
) | |
image_logs = [] | |
for validation_prompt, validation_image in zip(validation_prompts, validation_images): | |
validation_image = Image.open(validation_image).convert("RGB") | |
images = [] | |
for _ in range(args.num_validation_images): | |
with torch.autocast("cuda"): | |
image = pipeline( | |
validation_prompt, validation_image, num_inference_steps=20, generator=generator | |
).images[0] | |
images.append(image) | |
image_logs.append( | |
{"validation_image": validation_image, "images": images, "validation_prompt": validation_prompt} | |
) | |
for tracker in accelerator.trackers: | |
if tracker.name == "tensorboard": | |
for log in image_logs: | |
images = log["images"] | |
validation_prompt = log["validation_prompt"] | |
validation_image = log["validation_image"] | |
formatted_images = [] | |
formatted_images.append(np.asarray(validation_image)) | |
for image in images: | |
formatted_images.append(np.asarray(image)) | |
formatted_images = np.stack(formatted_images) | |
tracker.writer.add_images(validation_prompt, formatted_images, step, dataformats="NHWC") | |
elif tracker.name == "wandb": | |
formatted_images = [] | |
for log in image_logs: | |
images = log["images"] | |
validation_prompt = log["validation_prompt"] | |
validation_image = log["validation_image"] | |
formatted_images.append(wandb.Image(validation_image, caption="Controlnet conditioning")) | |
for image in images: | |
image = wandb.Image(image, caption=validation_prompt) | |
formatted_images.append(image) | |
tracker.log({"validation": formatted_images}) | |
else: | |
logger.warn(f"image logging not implemented for {tracker.name}") | |
return image_logs | |
def import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str, revision: str): | |
text_encoder_config = PretrainedConfig.from_pretrained( | |
pretrained_model_name_or_path, | |
subfolder="text_encoder", | |
revision=revision, | |
) | |
model_class = text_encoder_config.architectures[0] | |
if model_class == "CLIPTextModel": | |
from transformers import CLIPTextModel | |
return CLIPTextModel | |
elif model_class == "RobertaSeriesModelWithTransformation": | |
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import RobertaSeriesModelWithTransformation | |
return RobertaSeriesModelWithTransformation | |
else: | |
raise ValueError(f"{model_class} is not supported.") | |
def save_model_card(repo_id: str, image_logs=None, base_model=str, repo_folder=None): | |
img_str = "" | |
if image_logs is not None: | |
img_str = "You can find some example images below.\n" | |
for i, log in enumerate(image_logs): | |
images = log["images"] | |
validation_prompt = log["validation_prompt"] | |
validation_image = log["validation_image"] | |
validation_image.save(os.path.join(repo_folder, "image_control.png")) | |
img_str += f"prompt: {validation_prompt}\n" | |
images = [validation_image] + images | |
image_grid(images, 1, len(images)).save(os.path.join(repo_folder, f"images_{i}.png")) | |
img_str += f"![images_{i})](./images_{i}.png)\n" | |
yaml = f""" | |
--- | |
license: creativeml-openrail-m | |
base_model: {base_model} | |
tags: | |
- stable-diffusion | |
- stable-diffusion-diffusers | |
- text-to-image | |
- diffusers | |
- controlnet | |
inference: true | |
--- | |
""" | |
model_card = f""" | |
# controlnet-{repo_id} | |
These are controlnet weights trained on {base_model} with new type of conditioning. | |
{img_str} | |
""" | |
with open(os.path.join(repo_folder, "README.md"), "w") as f: | |
f.write(yaml + model_card) | |
def parse_args(input_args=None): | |
parser = argparse.ArgumentParser(description="Simple example of a ControlNet training script.") | |
parser.add_argument( | |
"--pretrained_model_name_or_path", | |
type=str, | |
default=None, | |
required=True, | |
help="Path to pretrained model or model identifier from huggingface.co/models.", | |
) | |
parser.add_argument( | |
"--controlnet_model_name_or_path", | |
type=str, | |
default=None, | |
help="Path to pretrained controlnet model or model identifier from huggingface.co/models." | |
" If not specified controlnet weights are initialized from unet.", | |
) | |
parser.add_argument( | |
"--revision", | |
type=str, | |
default=None, | |
required=False, | |
help=( | |
"Revision of pretrained model identifier from huggingface.co/models. Trainable model components should be" | |
" float32 precision." | |
), | |
) | |
parser.add_argument( | |
"--tokenizer_name", | |
type=str, | |
default=None, | |
help="Pretrained tokenizer name or path if not the same as model_name", | |
) | |
parser.add_argument( | |
"--output_dir", | |
type=str, | |
default="controlnet-model", | |
help="The output directory where the model predictions and checkpoints will be written.", | |
) | |
parser.add_argument( | |
"--cache_dir", | |
type=str, | |
default=None, | |
help="The directory where the downloaded models and datasets will be stored.", | |
) | |
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") | |
parser.add_argument( | |
"--resolution", | |
type=int, | |
default=512, | |
help=( | |
"The resolution for input images, all the images in the train/validation dataset will be resized to this" | |
" resolution" | |
), | |
) | |
parser.add_argument( | |
"--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader." | |
) | |
parser.add_argument("--num_train_epochs", type=int, default=1) | |
parser.add_argument( | |
"--max_train_steps", | |
type=int, | |
default=None, | |
help="Total number of training steps to perform. If provided, overrides num_train_epochs.", | |
) | |
parser.add_argument( | |
"--checkpointing_steps", | |
type=int, | |
default=500, | |
help=( | |
"Save a checkpoint of the training state every X updates. Checkpoints can be used for resuming training via `--resume_from_checkpoint`. " | |
"In the case that the checkpoint is better than the final trained model, the checkpoint can also be used for inference." | |
"Using a checkpoint for inference requires separate loading of the original pipeline and the individual checkpointed model components." | |
"See https://huggingface.co/docs/diffusers/main/en/training/dreambooth#performing-inference-using-a-saved-checkpoint for step by step" | |
"instructions." | |
), | |
) | |
parser.add_argument( | |
"--checkpoints_total_limit", | |
type=int, | |
default=None, | |
help=("Max number of checkpoints to store."), | |
) | |
parser.add_argument( | |
"--resume_from_checkpoint", | |
type=str, | |
default=None, | |
help=( | |
"Whether training should be resumed from a previous checkpoint. Use a path saved by" | |
' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.' | |
), | |
) | |
parser.add_argument( | |
"--gradient_accumulation_steps", | |
type=int, | |
default=1, | |
help="Number of updates steps to accumulate before performing a backward/update pass.", | |
) | |
parser.add_argument( | |
"--gradient_checkpointing", | |
action="store_true", | |
help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.", | |
) | |
parser.add_argument( | |
"--learning_rate", | |
type=float, | |
default=5e-6, | |
help="Initial learning rate (after the potential warmup period) to use.", | |
) | |
parser.add_argument( | |
"--scale_lr", | |
action="store_true", | |
default=False, | |
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.", | |
) | |
parser.add_argument( | |
"--lr_scheduler", | |
type=str, | |
default="constant", | |
help=( | |
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' | |
' "constant", "constant_with_warmup"]' | |
), | |
) | |
parser.add_argument( | |
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler." | |
) | |
parser.add_argument( | |
"--lr_num_cycles", | |
type=int, | |
default=1, | |
help="Number of hard resets of the lr in cosine_with_restarts scheduler.", | |
) | |
parser.add_argument("--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.") | |
parser.add_argument( | |
"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes." | |
) | |
parser.add_argument( | |
"--dataloader_num_workers", | |
type=int, | |
default=0, | |
help=( | |
"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process." | |
), | |
) | |
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.") | |
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.") | |
parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.") | |
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer") | |
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") | |
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") | |
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.") | |
parser.add_argument( | |
"--hub_model_id", | |
type=str, | |
default=None, | |
help="The name of the repository to keep in sync with the local `output_dir`.", | |
) | |
parser.add_argument( | |
"--logging_dir", | |
type=str, | |
default="logs", | |
help=( | |
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to" | |
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***." | |
), | |
) | |
parser.add_argument( | |
"--allow_tf32", | |
action="store_true", | |
help=( | |
"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see" | |
" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices" | |
), | |
) | |
parser.add_argument( | |
"--report_to", | |
type=str, | |
default="tensorboard", | |
help=( | |
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`' | |
' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.' | |
), | |
) | |
parser.add_argument( | |
"--mixed_precision", | |
type=str, | |
default=None, | |
choices=["no", "fp16", "bf16"], | |
help=( | |
"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=" | |
" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the" | |
" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config." | |
), | |
) | |
parser.add_argument( | |
"--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers." | |
) | |
parser.add_argument( | |
"--set_grads_to_none", | |
action="store_true", | |
help=( | |
"Save more memory by using setting grads to None instead of zero. Be aware, that this changes certain" | |
" behaviors, so disable this argument if it causes any problems. More info:" | |
" https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html" | |
), | |
) | |
parser.add_argument( | |
"--dataset_name", | |
type=str, | |
default=None, | |
help=( | |
"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private," | |
" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem," | |
" or to a folder containing files that 🤗 Datasets can understand." | |
), | |
) | |
parser.add_argument( | |
"--dataset_config_name", | |
type=str, | |
default=None, | |
help="The config of the Dataset, leave as None if there's only one config.", | |
) | |
parser.add_argument( | |
"--train_data_dir", | |
type=str, | |
default=None, | |
help=( | |
"A folder containing the training data. Folder contents must follow the structure described in" | |
" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file" | |
" must exist to provide the captions for the images. Ignored if `dataset_name` is specified." | |
), | |
) | |
parser.add_argument( | |
"--image_column", type=str, default="image", help="The column of the dataset containing the target image." | |
) | |
parser.add_argument( | |
"--conditioning_image_column", | |
type=str, | |
default="conditioning_image", | |
help="The column of the dataset containing the controlnet conditioning image.", | |
) | |
parser.add_argument( | |
"--caption_column", | |
type=str, | |
default="text", | |
help="The column of the dataset containing a caption or a list of captions.", | |
) | |
parser.add_argument( | |
"--max_train_samples", | |
type=int, | |
default=None, | |
help=( | |
"For debugging purposes or quicker training, truncate the number of training examples to this " | |
"value if set." | |
), | |
) | |
parser.add_argument( | |
"--proportion_empty_prompts", | |
type=float, | |
default=0, | |
help="Proportion of image prompts to be replaced with empty strings. Defaults to 0 (no prompt replacement).", | |
) | |
parser.add_argument( | |
"--validation_prompt", | |
type=str, | |
default=None, | |
nargs="+", | |
help=( | |
"A set of prompts evaluated every `--validation_steps` and logged to `--report_to`." | |
" Provide either a matching number of `--validation_image`s, a single `--validation_image`" | |
" to be used with all prompts, or a single prompt that will be used with all `--validation_image`s." | |
), | |
) | |
parser.add_argument( | |
"--validation_image", | |
type=str, | |
default=None, | |
nargs="+", | |
help=( | |
"A set of paths to the controlnet conditioning image be evaluated every `--validation_steps`" | |
" and logged to `--report_to`. Provide either a matching number of `--validation_prompt`s, a" | |
" a single `--validation_prompt` to be used with all `--validation_image`s, or a single" | |
" `--validation_image` that will be used with all `--validation_prompt`s." | |
), | |
) | |
parser.add_argument( | |
"--num_validation_images", | |
type=int, | |
default=4, | |
help="Number of images to be generated for each `--validation_image`, `--validation_prompt` pair", | |
) | |
parser.add_argument( | |
"--validation_steps", | |
type=int, | |
default=100, | |
help=( | |
"Run validation every X steps. Validation consists of running the prompt" | |
" `args.validation_prompt` multiple times: `args.num_validation_images`" | |
" and logging the images." | |
), | |
) | |
parser.add_argument( | |
"--tracker_project_name", | |
type=str, | |
default="train_controlnet", | |
help=( | |
"The `project_name` argument passed to Accelerator.init_trackers for" | |
" more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator" | |
), | |
) | |
if input_args is not None: | |
args = parser.parse_args(input_args) | |
else: | |
args = parser.parse_args() | |
if args.dataset_name is None and args.train_data_dir is None: | |
raise ValueError("Specify either `--dataset_name` or `--train_data_dir`") | |
if args.dataset_name is not None and args.train_data_dir is not None: | |
raise ValueError("Specify only one of `--dataset_name` or `--train_data_dir`") | |
if args.proportion_empty_prompts < 0 or args.proportion_empty_prompts > 1: | |
raise ValueError("`--proportion_empty_prompts` must be in the range [0, 1].") | |
if args.validation_prompt is not None and args.validation_image is None: | |
raise ValueError("`--validation_image` must be set if `--validation_prompt` is set") | |
if args.validation_prompt is None and args.validation_image is not None: | |
raise ValueError("`--validation_prompt` must be set if `--validation_image` is set") | |
if ( | |
args.validation_image is not None | |
and args.validation_prompt is not None | |
and len(args.validation_image) != 1 | |
and len(args.validation_prompt) != 1 | |
and len(args.validation_image) != len(args.validation_prompt) | |
): | |
raise ValueError( | |
"Must provide either 1 `--validation_image`, 1 `--validation_prompt`," | |
" or the same number of `--validation_prompt`s and `--validation_image`s" | |
) | |
if args.resolution % 8 != 0: | |
raise ValueError( | |
"`--resolution` must be divisible by 8 for consistently sized encoded images between the VAE and the controlnet encoder." | |
) | |
return args | |
def make_train_dataset(args, tokenizer, accelerator): | |
# Get the datasets: you can either provide your own training and evaluation files (see below) | |
# or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub). | |
# In distributed training, the load_dataset function guarantees that only one local process can concurrently | |
# download the dataset. | |
if args.dataset_name is not None: | |
# Downloading and loading a dataset from the hub. | |
dataset = load_dataset( | |
args.dataset_name, | |
args.dataset_config_name, | |
cache_dir=args.cache_dir, | |
) | |
else: | |
if args.train_data_dir is not None: | |
dataset = load_dataset( | |
args.train_data_dir, | |
cache_dir=args.cache_dir, | |
) | |
# See more about loading custom images at | |
# https://huggingface.co/docs/datasets/v2.0.0/en/dataset_script | |
# Preprocessing the datasets. | |
# We need to tokenize inputs and targets. | |
column_names = dataset["train"].column_names | |
# 6. Get the column names for input/target. | |
if args.image_column is None: | |
image_column = column_names[0] | |
logger.info(f"image column defaulting to {image_column}") | |
else: | |
image_column = args.image_column | |
if image_column not in column_names: | |
raise ValueError( | |
f"`--image_column` value '{args.image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}" | |
) | |
if args.caption_column is None: | |
caption_column = column_names[1] | |
logger.info(f"caption column defaulting to {caption_column}") | |
else: | |
caption_column = args.caption_column | |
if caption_column not in column_names: | |
raise ValueError( | |
f"`--caption_column` value '{args.caption_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}" | |
) | |
if args.conditioning_image_column is None: | |
conditioning_image_column = column_names[2] | |
logger.info(f"conditioning image column defaulting to {conditioning_image_column}") | |
else: | |
conditioning_image_column = args.conditioning_image_column | |
if conditioning_image_column not in column_names: | |
raise ValueError( | |
f"`--conditioning_image_column` value '{args.conditioning_image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}" | |
) | |
def tokenize_captions(examples, is_train=True): | |
captions = [] | |
for caption in examples[caption_column]: | |
if random.random() < args.proportion_empty_prompts: | |
captions.append("") | |
elif isinstance(caption, str): | |
captions.append(caption) | |
elif isinstance(caption, (list, np.ndarray)): | |
# take a random caption if there are multiple | |
captions.append(random.choice(caption) if is_train else caption[0]) | |
else: | |
raise ValueError( | |
f"Caption column `{caption_column}` should contain either strings or lists of strings." | |
) | |
inputs = tokenizer( | |
captions, max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt" | |
) | |
return inputs.input_ids | |
image_transforms = transforms.Compose( | |
[ | |
transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR), | |
transforms.CenterCrop(args.resolution), | |
transforms.ToTensor(), | |
transforms.Normalize([0.5], [0.5]), | |
] | |
) | |
conditioning_image_transforms = transforms.Compose( | |
[ | |
transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR), | |
transforms.CenterCrop(args.resolution), | |
transforms.ToTensor(), | |
] | |
) | |
def preprocess_train(examples): | |
images = [image.convert("RGB") for image in examples[image_column]] | |
images = [image_transforms(image) for image in images] | |
conditioning_images = [image.convert("RGB") for image in examples[conditioning_image_column]] | |
conditioning_images = [conditioning_image_transforms(image) for image in conditioning_images] | |
examples["pixel_values"] = images | |
examples["conditioning_pixel_values"] = conditioning_images | |
examples["input_ids"] = tokenize_captions(examples) | |
return examples | |
with accelerator.main_process_first(): | |
if args.max_train_samples is not None: | |
dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples)) | |
# Set the training transforms | |
train_dataset = dataset["train"].with_transform(preprocess_train) | |
return train_dataset | |
def collate_fn(examples): | |
pixel_values = torch.stack([example["pixel_values"] for example in examples]) | |
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float() | |
conditioning_pixel_values = torch.stack([example["conditioning_pixel_values"] for example in examples]) | |
conditioning_pixel_values = conditioning_pixel_values.to(memory_format=torch.contiguous_format).float() | |
input_ids = torch.stack([example["input_ids"] for example in examples]) | |
return { | |
"pixel_values": pixel_values, | |
"conditioning_pixel_values": conditioning_pixel_values, | |
"input_ids": input_ids, | |
} | |
def main(args): | |
logging_dir = Path(args.output_dir, args.logging_dir) | |
accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir) | |
accelerator = Accelerator( | |
gradient_accumulation_steps=args.gradient_accumulation_steps, | |
mixed_precision=args.mixed_precision, | |
log_with=args.report_to, | |
project_config=accelerator_project_config, | |
) | |
# Make one log on every process with the configuration for debugging. | |
logging.basicConfig( | |
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", | |
datefmt="%m/%d/%Y %H:%M:%S", | |
level=logging.INFO, | |
) | |
logger.info(accelerator.state, main_process_only=False) | |
if accelerator.is_local_main_process: | |
transformers.utils.logging.set_verbosity_warning() | |
diffusers.utils.logging.set_verbosity_info() | |
else: | |
transformers.utils.logging.set_verbosity_error() | |
diffusers.utils.logging.set_verbosity_error() | |
# If passed along, set the training seed now. | |
if args.seed is not None: | |
set_seed(args.seed) | |
# Handle the repository creation | |
if accelerator.is_main_process: | |
if args.output_dir is not None: | |
os.makedirs(args.output_dir, exist_ok=True) | |
if args.push_to_hub: | |
repo_id = create_repo( | |
repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token | |
).repo_id | |
# Load the tokenizer | |
if args.tokenizer_name: | |
tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, revision=args.revision, use_fast=False) | |
elif args.pretrained_model_name_or_path: | |
tokenizer = AutoTokenizer.from_pretrained( | |
args.pretrained_model_name_or_path, | |
subfolder="tokenizer", | |
revision=args.revision, | |
use_fast=False, | |
) | |
# import correct text encoder class | |
text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path, args.revision) | |
# Load scheduler and models | |
noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler") | |
text_encoder = text_encoder_cls.from_pretrained( | |
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision | |
) | |
vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision) | |
unet = UNet2DConditionModel.from_pretrained( | |
args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision | |
) | |
if args.controlnet_model_name_or_path: | |
logger.info("Loading existing controlnet weights") | |
controlnet = ControlNetModel.from_pretrained(args.controlnet_model_name_or_path) | |
else: | |
logger.info("Initializing controlnet weights from unet") | |
controlnet = ControlNetModel.from_unet(unet) | |
# `accelerate` 0.16.0 will have better support for customized saving | |
if version.parse(accelerate.__version__) >= version.parse("0.16.0"): | |
# create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format | |
def save_model_hook(models, weights, output_dir): | |
if accelerator.is_main_process: | |
i = len(weights) - 1 | |
while len(weights) > 0: | |
weights.pop() | |
model = models[i] | |
sub_dir = "controlnet" | |
model.save_pretrained(os.path.join(output_dir, sub_dir)) | |
i -= 1 | |
def load_model_hook(models, input_dir): | |
while len(models) > 0: | |
# pop models so that they are not loaded again | |
model = models.pop() | |
# load diffusers style into model | |
load_model = ControlNetModel.from_pretrained(input_dir, subfolder="controlnet") | |
model.register_to_config(**load_model.config) | |
model.load_state_dict(load_model.state_dict()) | |
del load_model | |
accelerator.register_save_state_pre_hook(save_model_hook) | |
accelerator.register_load_state_pre_hook(load_model_hook) | |
vae.requires_grad_(False) | |
unet.requires_grad_(False) | |
text_encoder.requires_grad_(False) | |
controlnet.train() | |
if args.enable_xformers_memory_efficient_attention: | |
if is_xformers_available(): | |
import xformers | |
xformers_version = version.parse(xformers.__version__) | |
if xformers_version == version.parse("0.0.16"): | |
logger.warn( | |
"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details." | |
) | |
unet.enable_xformers_memory_efficient_attention() | |
controlnet.enable_xformers_memory_efficient_attention() | |
else: | |
raise ValueError("xformers is not available. Make sure it is installed correctly") | |
if args.gradient_checkpointing: | |
controlnet.enable_gradient_checkpointing() | |
# Check that all trainable models are in full precision | |
low_precision_error_string = ( | |
" Please make sure to always have all model weights in full float32 precision when starting training - even if" | |
" doing mixed precision training, copy of the weights should still be float32." | |
) | |
if accelerator.unwrap_model(controlnet).dtype != torch.float32: | |
raise ValueError( | |
f"Controlnet loaded as datatype {accelerator.unwrap_model(controlnet).dtype}. {low_precision_error_string}" | |
) | |
# Enable TF32 for faster training on Ampere GPUs, | |
# cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices | |
if args.allow_tf32: | |
torch.backends.cuda.matmul.allow_tf32 = True | |
if args.scale_lr: | |
args.learning_rate = ( | |
args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes | |
) | |
# Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs | |
if args.use_8bit_adam: | |
try: | |
import bitsandbytes as bnb | |
except ImportError: | |
raise ImportError( | |
"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`." | |
) | |
optimizer_class = bnb.optim.AdamW8bit | |
else: | |
optimizer_class = torch.optim.AdamW | |
# Optimizer creation | |
params_to_optimize = controlnet.parameters() | |
optimizer = optimizer_class( | |
params_to_optimize, | |
lr=args.learning_rate, | |
betas=(args.adam_beta1, args.adam_beta2), | |
weight_decay=args.adam_weight_decay, | |
eps=args.adam_epsilon, | |
) | |
train_dataset = make_train_dataset(args, tokenizer, accelerator) | |
train_dataloader = torch.utils.data.DataLoader( | |
train_dataset, | |
shuffle=True, | |
collate_fn=collate_fn, | |
batch_size=args.train_batch_size, | |
num_workers=args.dataloader_num_workers, | |
) | |
# Scheduler and math around the number of training steps. | |
overrode_max_train_steps = False | |
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) | |
if args.max_train_steps is None: | |
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch | |
overrode_max_train_steps = True | |
lr_scheduler = get_scheduler( | |
args.lr_scheduler, | |
optimizer=optimizer, | |
num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes, | |
num_training_steps=args.max_train_steps * accelerator.num_processes, | |
num_cycles=args.lr_num_cycles, | |
power=args.lr_power, | |
) | |
# Prepare everything with our `accelerator`. | |
controlnet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( | |
controlnet, optimizer, train_dataloader, lr_scheduler | |
) | |
# For mixed precision training we cast the text_encoder and vae weights to half-precision | |
# as these models are only used for inference, keeping weights in full precision is not required. | |
weight_dtype = torch.float32 | |
if accelerator.mixed_precision == "fp16": | |
weight_dtype = torch.float16 | |
elif accelerator.mixed_precision == "bf16": | |
weight_dtype = torch.bfloat16 | |
# Move vae, unet and text_encoder to device and cast to weight_dtype | |
vae.to(accelerator.device, dtype=weight_dtype) | |
unet.to(accelerator.device, dtype=weight_dtype) | |
text_encoder.to(accelerator.device, dtype=weight_dtype) | |
# We need to recalculate our total training steps as the size of the training dataloader may have changed. | |
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) | |
if overrode_max_train_steps: | |
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch | |
# Afterwards we recalculate our number of training epochs | |
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) | |
# We need to initialize the trackers we use, and also store our configuration. | |
# The trackers initializes automatically on the main process. | |
if accelerator.is_main_process: | |
tracker_config = dict(vars(args)) | |
# tensorboard cannot handle list types for config | |
tracker_config.pop("validation_prompt") | |
tracker_config.pop("validation_image") | |
accelerator.init_trackers(args.tracker_project_name, config=tracker_config) | |
# Train! | |
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps | |
logger.info("***** Running training *****") | |
logger.info(f" Num examples = {len(train_dataset)}") | |
logger.info(f" Num batches each epoch = {len(train_dataloader)}") | |
logger.info(f" Num Epochs = {args.num_train_epochs}") | |
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}") | |
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") | |
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") | |
logger.info(f" Total optimization steps = {args.max_train_steps}") | |
global_step = 0 | |
first_epoch = 0 | |
# Potentially load in the weights and states from a previous save | |
if args.resume_from_checkpoint: | |
if args.resume_from_checkpoint != "latest": | |
path = os.path.basename(args.resume_from_checkpoint) | |
else: | |
# Get the most recent checkpoint | |
dirs = os.listdir(args.output_dir) | |
dirs = [d for d in dirs if d.startswith("checkpoint")] | |
dirs = sorted(dirs, key=lambda x: int(x.split("-")[1])) | |
path = dirs[-1] if len(dirs) > 0 else None | |
if path is None: | |
accelerator.print( | |
f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run." | |
) | |
args.resume_from_checkpoint = None | |
initial_global_step = 0 | |
else: | |
accelerator.print(f"Resuming from checkpoint {path}") | |
accelerator.load_state(os.path.join(args.output_dir, path)) | |
global_step = int(path.split("-")[1]) | |
initial_global_step = global_step | |
first_epoch = global_step // num_update_steps_per_epoch | |
else: | |
initial_global_step = 0 | |
progress_bar = tqdm( | |
range(0, args.max_train_steps), | |
initial=initial_global_step, | |
desc="Steps", | |
# Only show the progress bar once on each machine. | |
disable=not accelerator.is_local_main_process, | |
) | |
image_logs = None | |
for epoch in range(first_epoch, args.num_train_epochs): | |
for step, batch in enumerate(train_dataloader): | |
with accelerator.accumulate(controlnet): | |
# Convert images to latent space | |
latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample() | |
latents = latents * vae.config.scaling_factor | |
# Sample noise that we'll add to the latents | |
noise = torch.randn_like(latents) | |
bsz = latents.shape[0] | |
# Sample a random timestep for each image | |
timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device) | |
timesteps = timesteps.long() | |
# Add noise to the latents according to the noise magnitude at each timestep | |
# (this is the forward diffusion process) | |
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) | |
# Get the text embedding for conditioning | |
encoder_hidden_states = text_encoder(batch["input_ids"])[0] | |
controlnet_image = batch["conditioning_pixel_values"].to(dtype=weight_dtype) | |
down_block_res_samples, mid_block_res_sample = controlnet( | |
noisy_latents, | |
timesteps, | |
encoder_hidden_states=encoder_hidden_states, | |
controlnet_cond=controlnet_image, | |
return_dict=False, | |
) | |
# Predict the noise residual | |
model_pred = unet( | |
noisy_latents, | |
timesteps, | |
encoder_hidden_states=encoder_hidden_states, | |
down_block_additional_residuals=[ | |
sample.to(dtype=weight_dtype) for sample in down_block_res_samples | |
], | |
mid_block_additional_residual=mid_block_res_sample.to(dtype=weight_dtype), | |
).sample | |
# Get the target for loss depending on the prediction type | |
if noise_scheduler.config.prediction_type == "epsilon": | |
target = noise | |
elif noise_scheduler.config.prediction_type == "v_prediction": | |
target = noise_scheduler.get_velocity(latents, noise, timesteps) | |
else: | |
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}") | |
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") | |
accelerator.backward(loss) | |
if accelerator.sync_gradients: | |
params_to_clip = controlnet.parameters() | |
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm) | |
optimizer.step() | |
lr_scheduler.step() | |
optimizer.zero_grad(set_to_none=args.set_grads_to_none) | |
# Checks if the accelerator has performed an optimization step behind the scenes | |
if accelerator.sync_gradients: | |
progress_bar.update(1) | |
global_step += 1 | |
if accelerator.is_main_process: | |
if global_step % args.checkpointing_steps == 0: | |
# _before_ saving state, check if this save would set us over the `checkpoints_total_limit` | |
if args.checkpoints_total_limit is not None: | |
checkpoints = os.listdir(args.output_dir) | |
checkpoints = [d for d in checkpoints if d.startswith("checkpoint")] | |
checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1])) | |
# before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints | |
if len(checkpoints) >= args.checkpoints_total_limit: | |
num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1 | |
removing_checkpoints = checkpoints[0:num_to_remove] | |
logger.info( | |
f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints" | |
) | |
logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}") | |
for removing_checkpoint in removing_checkpoints: | |
removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint) | |
shutil.rmtree(removing_checkpoint) | |
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}") | |
accelerator.save_state(save_path) | |
logger.info(f"Saved state to {save_path}") | |
if args.validation_prompt is not None and global_step % args.validation_steps == 0: | |
image_logs = log_validation( | |
vae, | |
text_encoder, | |
tokenizer, | |
unet, | |
controlnet, | |
args, | |
accelerator, | |
weight_dtype, | |
global_step, | |
) | |
logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]} | |
progress_bar.set_postfix(**logs) | |
accelerator.log(logs, step=global_step) | |
if global_step >= args.max_train_steps: | |
break | |
# Create the pipeline using using the trained modules and save it. | |
accelerator.wait_for_everyone() | |
if accelerator.is_main_process: | |
controlnet = accelerator.unwrap_model(controlnet) | |
controlnet.save_pretrained(args.output_dir) | |
if args.push_to_hub: | |
save_model_card( | |
repo_id, | |
image_logs=image_logs, | |
base_model=args.pretrained_model_name_or_path, | |
repo_folder=args.output_dir, | |
) | |
upload_folder( | |
repo_id=repo_id, | |
folder_path=args.output_dir, | |
commit_message="End of training", | |
ignore_patterns=["step_*", "epoch_*"], | |
) | |
accelerator.end_training() | |
if __name__ == "__main__": | |
args = parse_args() | |
main(args) | |