From 7b93c2a882d8e12209fbaeffa51ee2b599ab5349 Mon Sep 17 00:00:00 2001 From: Suraj Patil Date: Wed, 16 Aug 2023 14:57:08 +0200 Subject: [PATCH] [research_projects] SDXL controlnet script (#4633) add controlent script, --- .../controlnet/train_controlnet_webdataset.py | 1459 +++++++++++++++++ 1 file changed, 1459 insertions(+) create mode 100644 examples/research_projects/controlnet/train_controlnet_webdataset.py diff --git a/examples/research_projects/controlnet/train_controlnet_webdataset.py b/examples/research_projects/controlnet/train_controlnet_webdataset.py new file mode 100644 index 0000000000..023a3e3d2a --- /dev/null +++ b/examples/research_projects/controlnet/train_controlnet_webdataset.py @@ -0,0 +1,1459 @@ +#!/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 functools +import gc +import itertools +import json +import logging +import math +import os +import random +import shutil +from pathlib import Path +from typing import List, Optional, Union + +import accelerate +import cv2 +import numpy as np +import torch +import torch.utils.checkpoint +import transformers +import webdataset as wds +from accelerate import Accelerator +from accelerate.logging import get_logger +from accelerate.utils import ProjectConfiguration, set_seed +from braceexpand import braceexpand +from huggingface_hub import create_repo, upload_folder +from packaging import version +from PIL import Image +from torch.utils.data import default_collate +from torchvision import transforms +from tqdm.auto import tqdm +from transformers import AutoTokenizer, DPTFeatureExtractor, DPTForDepthEstimation, PretrainedConfig +from webdataset.tariterators import ( + base_plus_ext, + tar_file_expander, + url_opener, + valid_sample, +) + +import diffusers +from diffusers import ( + AutoencoderKL, + ControlNetModel, + EulerDiscreteScheduler, + StableDiffusionXLControlNetPipeline, + UNet2DConditionModel, +) +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 + + +MAX_SEQ_LENGTH = 77 + +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("0.18.0.dev0") + +logger = get_logger(__name__) + + +def filter_keys(key_set): + def _f(dictionary): + return {k: v for k, v in dictionary.items() if k in key_set} + + return _f + + +def group_by_keys_nothrow(data, keys=base_plus_ext, lcase=True, suffixes=None, handler=None): + """Return function over iterator that groups key, value pairs into samples. + + :param keys: function that splits the key into key and extension (base_plus_ext) + :param lcase: convert suffixes to lower case (Default value = True) + """ + current_sample = None + for filesample in data: + assert isinstance(filesample, dict) + fname, value = filesample["fname"], filesample["data"] + prefix, suffix = keys(fname) + if prefix is None: + continue + if lcase: + suffix = suffix.lower() + # FIXME webdataset version throws if suffix in current_sample, but we have a potential for + # this happening in the current LAION400m dataset if a tar ends with same prefix as the next + # begins, rare, but can happen since prefix aren't unique across tar files in that dataset + if current_sample is None or prefix != current_sample["__key__"] or suffix in current_sample: + if valid_sample(current_sample): + yield current_sample + current_sample = {"__key__": prefix, "__url__": filesample["__url__"]} + if suffixes is None or suffix in suffixes: + current_sample[suffix] = value + if valid_sample(current_sample): + yield current_sample + + +def tarfile_to_samples_nothrow(src, handler=wds.warn_and_continue): + # NOTE this is a re-impl of the webdataset impl with group_by_keys that doesn't throw + streams = url_opener(src, handler=handler) + files = tar_file_expander(streams, handler=handler) + samples = group_by_keys_nothrow(files, handler=handler) + return samples + + +def control_transform(image): + image = np.array(image) + + low_threshold = 100 + high_threshold = 200 + + image = cv2.Canny(image, low_threshold, high_threshold) + image = image[:, :, None] + image = np.concatenate([image, image, image], axis=2) + control_image = Image.fromarray(image) + return control_image + + +def canny_image_transform(example, resolution=1024): + image = example["image"] + image = transforms.Resize(resolution, interpolation=transforms.InterpolationMode.BILINEAR)(image) + # get crop coordinates + c_top, c_left, _, _ = transforms.RandomCrop.get_params(image, output_size=(resolution, resolution)) + image = transforms.functional.crop(image, c_top, c_left, resolution, resolution) + control_image = control_transform(image) + + image = transforms.ToTensor()(image) + image = transforms.Normalize([0.5], [0.5])(image) + control_image = transforms.ToTensor()(control_image) + + example["image"] = image + example["control_image"] = control_image + example["crop_coords"] = (c_top, c_left) + + return example + + +def depth_image_transform(example, feature_extractor, resolution=1024): + image = example["image"] + image = transforms.Resize(resolution, interpolation=transforms.InterpolationMode.BILINEAR)(image) + # get crop coordinates + c_top, c_left, _, _ = transforms.RandomCrop.get_params(image, output_size=(resolution, resolution)) + image = transforms.functional.crop(image, c_top, c_left, resolution, resolution) + + control_image = feature_extractor(images=image, return_tensors="pt").pixel_values.squeeze(0) + + image = transforms.ToTensor()(image) + image = transforms.Normalize([0.5], [0.5])(image) + + example["image"] = image + example["control_image"] = control_image + example["crop_coords"] = (c_top, c_left) + + return example + + +class WebdatasetFilter: + def __init__(self, min_size=1024, max_pwatermark=0.5): + self.min_size = min_size + self.max_pwatermark = max_pwatermark + + def __call__(self, x): + try: + if "json" in x: + x_json = json.loads(x["json"]) + filter_size = (x_json.get("original_width", 0.0) or 0.0) >= self.min_size and x_json.get( + "original_height", 0 + ) >= self.min_size + filter_watermark = (x_json.get("pwatermark", 1.0) or 1.0) <= self.max_pwatermark + return filter_size and filter_watermark + else: + return False + except Exception: + return False + + +class Text2ImageDataset: + def __init__( + self, + train_shards_path_or_url: Union[str, List[str]], + eval_shards_path_or_url: Union[str, List[str]], + num_train_examples: int, + per_gpu_batch_size: int, + global_batch_size: int, + num_workers: int, + resolution: int = 256, + center_crop: bool = True, + random_flip: bool = False, + shuffle_buffer_size: int = 1000, + pin_memory: bool = False, + persistent_workers: bool = False, + control_type: str = "canny", + feature_extractor: Optional[DPTFeatureExtractor] = None, + ): + if not isinstance(train_shards_path_or_url, str): + train_shards_path_or_url = [list(braceexpand(urls)) for urls in train_shards_path_or_url] + # flatten list using itertools + train_shards_path_or_url = list(itertools.chain.from_iterable(train_shards_path_or_url)) + + if not isinstance(eval_shards_path_or_url, str): + eval_shards_path_or_url = [list(braceexpand(urls)) for urls in eval_shards_path_or_url] + # flatten list using itertools + eval_shards_path_or_url = list(itertools.chain.from_iterable(eval_shards_path_or_url)) + + def get_orig_size(json): + return (int(json.get("original_width", 0.0)), int(json.get("original_height", 0.0))) + + if control_type == "canny": + image_transform = functools.partial(canny_image_transform, resolution=resolution) + elif control_type == "depth": + image_transform = functools.partial( + depth_image_transform, feature_extractor=feature_extractor, resolution=resolution + ) + + processing_pipeline = [ + wds.decode("pil", handler=wds.ignore_and_continue), + wds.rename( + image="jpg;png;jpeg;webp", + control_image="jpg;png;jpeg;webp", + text="text;txt;caption", + orig_size="json", + handler=wds.warn_and_continue, + ), + wds.map(filter_keys({"image", "control_image", "text", "orig_size"})), + wds.map_dict(orig_size=get_orig_size), + wds.map(image_transform), + wds.to_tuple("image", "control_image", "text", "orig_size", "crop_coords"), + ] + + # Create train dataset and loader + pipeline = [ + wds.ResampledShards(train_shards_path_or_url), + tarfile_to_samples_nothrow, + wds.select(WebdatasetFilter(min_size=512)), + wds.shuffle(shuffle_buffer_size), + *processing_pipeline, + wds.batched(per_gpu_batch_size, partial=False, collation_fn=default_collate), + ] + + num_worker_batches = math.ceil(num_train_examples / (global_batch_size * num_workers)) # per dataloader worker + num_batches = num_worker_batches * num_workers + num_samples = num_batches * global_batch_size + + # each worker is iterating over this + self._train_dataset = wds.DataPipeline(*pipeline).with_epoch(num_worker_batches) + self._train_dataloader = wds.WebLoader( + self._train_dataset, + batch_size=None, + shuffle=False, + num_workers=num_workers, + pin_memory=pin_memory, + persistent_workers=persistent_workers, + ) + # add meta-data to dataloader instance for convenience + self._train_dataloader.num_batches = num_batches + self._train_dataloader.num_samples = num_samples + + # Create eval dataset and loader + pipeline = [ + wds.SimpleShardList(eval_shards_path_or_url), + wds.split_by_worker, + wds.tarfile_to_samples(handler=wds.ignore_and_continue), + *processing_pipeline, + wds.batched(per_gpu_batch_size, partial=False, collation_fn=default_collate), + ] + self._eval_dataset = wds.DataPipeline(*pipeline) + self._eval_dataloader = wds.WebLoader( + self._eval_dataset, + batch_size=None, + shuffle=False, + num_workers=num_workers, + pin_memory=pin_memory, + persistent_workers=persistent_workers, + ) + + @property + def train_dataset(self): + return self._train_dataset + + @property + def train_dataloader(self): + return self._train_dataloader + + @property + def eval_dataset(self): + return self._eval_dataset + + @property + def eval_dataloader(self): + return self._eval_dataloader + + +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, unet, controlnet, args, accelerator, weight_dtype, step): + logger.info("Running validation... ") + + controlnet = accelerator.unwrap_model(controlnet) + + pipeline = StableDiffusionXLControlNetPipeline.from_pretrained( + args.pretrained_model_name_or_path, + vae=vae, + unet=unet, + controlnet=controlnet, + 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") + validation_image = validation_image.resize((args.resolution, args.resolution)) + + images = [] + + for _ in range(args.num_validation_images): + with torch.autocast("cuda"): + image = pipeline( + validation_prompt, image=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}") + + del pipeline + gc.collect() + torch.cuda.empty_cache() + + return image_logs + + +def import_model_class_from_model_name_or_path( + pretrained_model_name_or_path: str, revision: str, subfolder: str = "text_encoder" +): + text_encoder_config = PretrainedConfig.from_pretrained( + pretrained_model_name_or_path, subfolder=subfolder, revision=revision, use_auth_token=True + ) + model_class = text_encoder_config.architectures[0] + + if model_class == "CLIPTextModel": + from transformers import CLIPTextModel + + return CLIPTextModel + elif model_class == "CLIPTextModelWithProjection": + from transformers import CLIPTextModelWithProjection + + return CLIPTextModelWithProjection + 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-xl +- stable-diffusion-xl-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( + "--pretrained_vae_model_name_or_path", + type=str, + default=None, + help="Path to an improved VAE to stabilize training. For more details check out: https://github.com/huggingface/diffusers/pull/4038.", + ) + 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( + "--crops_coords_top_left_h", + type=int, + default=0, + help=("Coordinate for (the height) to be included in the crop coordinate embeddings needed by SDXL UNet."), + ) + parser.add_argument( + "--crops_coords_top_left_w", + type=int, + default=0, + help=("Coordinate for (the height) to be included in the crop coordinate embeddings needed by SDXL UNet."), + ) + 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=3, + 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=1, + help=("Number of subprocesses to use for data loading."), + ) + 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( + "--train_shards_path_or_url", + 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( + "--eval_shards_path_or_url", + 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="sd_xl_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" + ), + ) + parser.add_argument( + "--control_type", + type=str, + default="canny", + help=("The type of controlnet conditioning image to use. One of `canny`, `depth`" " Defaults to `canny`."), + ) + parser.add_argument( + "--transformer_layers_per_block", + type=str, + default=None, + help=("The number of layers per block in the transformer. If None, defaults to" " `args.transformer_layers`."), + ) + parser.add_argument( + "--old_style_controlnet", + action="store_true", + default=False, + help=( + "Use the old style controlnet, which is a single transformer layer with" + " a single head. Defaults to False." + ), + ) + + if input_args is not None: + args = parser.parse_args(input_args) + else: + args = parser.parse_args() + + 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 + + +# Adapted from pipelines.StableDiffusionXLPipeline.encode_prompt +def encode_prompt(prompt_batch, text_encoders, tokenizers, proportion_empty_prompts, is_train=True): + prompt_embeds_list = [] + + captions = [] + for caption in prompt_batch: + if random.random() < 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]) + + with torch.no_grad(): + for tokenizer, text_encoder in zip(tokenizers, text_encoders): + text_inputs = tokenizer( + captions, + padding="max_length", + max_length=tokenizer.model_max_length, + truncation=True, + return_tensors="pt", + ) + text_input_ids = text_inputs.input_ids + prompt_embeds = text_encoder( + text_input_ids.to(text_encoder.device), + output_hidden_states=True, + ) + + # We are only ALWAYS interested in the pooled output of the final text encoder + pooled_prompt_embeds = prompt_embeds[0] + prompt_embeds = prompt_embeds.hidden_states[-2] + bs_embed, seq_len, _ = prompt_embeds.shape + prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1) + prompt_embeds_list.append(prompt_embeds) + + prompt_embeds = torch.concat(prompt_embeds_list, dim=-1) + pooled_prompt_embeds = pooled_prompt_embeds.view(bs_embed, -1) + return prompt_embeds, pooled_prompt_embeds + + +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, + private=True, + ).repo_id + + # Load the tokenizers + tokenizer_one = AutoTokenizer.from_pretrained( + args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision, use_fast=False + ) + tokenizer_two = AutoTokenizer.from_pretrained( + args.pretrained_model_name_or_path, subfolder="tokenizer_2", revision=args.revision, use_fast=False + ) + + # import correct text encoder classes + text_encoder_cls_one = import_model_class_from_model_name_or_path( + args.pretrained_model_name_or_path, args.revision + ) + text_encoder_cls_two = import_model_class_from_model_name_or_path( + args.pretrained_model_name_or_path, args.revision, subfolder="text_encoder_2" + ) + + # Load scheduler and models + # noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler") + noise_scheduler = EulerDiscreteScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler") + text_encoder_one = text_encoder_cls_one.from_pretrained( + args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision + ) + text_encoder_two = text_encoder_cls_two.from_pretrained( + args.pretrained_model_name_or_path, subfolder="text_encoder_2", revision=args.revision + ) + vae_path = ( + args.pretrained_model_name_or_path + if args.pretrained_vae_model_name_or_path is None + else args.pretrained_vae_model_name_or_path + ) + vae = AutoencoderKL.from_pretrained( + vae_path, + subfolder="vae" if args.pretrained_vae_model_name_or_path is None else None, + revision=args.revision, + ) + unet = UNet2DConditionModel.from_pretrained( + args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, use_auth_token=True + ) + + if args.controlnet_model_name_or_path: + logger.info("Loading existing controlnet weights") + pre_controlnet = ControlNetModel.from_pretrained(args.controlnet_model_name_or_path) + else: + logger.info("Initializing controlnet weights from unet") + pre_controlnet = ControlNetModel.from_unet(unet) + + if args.transformer_layers_per_block is not None: + transformer_layers_per_block = [int(x) for x in args.transformer_layers_per_block.split(",")] + down_block_types = ["DownBlock2D" if l == 0 else "CrossAttnDownBlock2D" for l in transformer_layers_per_block] + controlnet = ControlNetModel.from_config( + pre_controlnet.config, + down_block_types=down_block_types, + transformer_layers_per_block=transformer_layers_per_block, + ) + controlnet.load_state_dict(pre_controlnet.state_dict(), strict=False) + del pre_controlnet + else: + controlnet = pre_controlnet + + if args.control_type == "depth": + feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-hybrid-midas") + depth_model = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas") + depth_model.requires_grad_(False) + else: + feature_extractor = None + depth_model = None + + # `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): + 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_one.requires_grad_(False) + text_encoder_two.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, + ) + + # 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 + # The VAE is in float32 to avoid NaN losses. + if args.pretrained_vae_model_name_or_path is not None: + vae.to(accelerator.device, dtype=weight_dtype) + else: + vae.to(accelerator.device, dtype=torch.float32) + unet.to(accelerator.device, dtype=weight_dtype) + text_encoder_one.to(accelerator.device, dtype=weight_dtype) + text_encoder_two.to(accelerator.device, dtype=weight_dtype) + if args.control_type == "depth": + depth_model.to(accelerator.device, dtype=weight_dtype) + + # Here, we compute not just the text embeddings but also the additional embeddings + # needed for the SD XL UNet to operate. + def compute_embeddings( + prompt_batch, original_sizes, crop_coords, proportion_empty_prompts, text_encoders, tokenizers, is_train=True + ): + target_size = (args.resolution, args.resolution) + original_sizes = list(map(list, zip(*original_sizes))) + crops_coords_top_left = list(map(list, zip(*crop_coords))) + + original_sizes = torch.tensor(original_sizes, dtype=torch.long) + crops_coords_top_left = torch.tensor(crops_coords_top_left, dtype=torch.long) + + # crops_coords_top_left = (args.crops_coords_top_left_h, args.crops_coords_top_left_w) + prompt_embeds, pooled_prompt_embeds = encode_prompt( + prompt_batch, text_encoders, tokenizers, proportion_empty_prompts, is_train + ) + add_text_embeds = pooled_prompt_embeds + + # Adapted from pipeline.StableDiffusionXLPipeline._get_add_time_ids + # add_time_ids = list(crops_coords_top_left + target_size) + add_time_ids = list(target_size) + add_time_ids = torch.tensor([add_time_ids]) + add_time_ids = add_time_ids.repeat(len(prompt_batch), 1) + # add_time_ids = torch.cat([torch.tensor(original_sizes, dtype=torch.long), add_time_ids], dim=-1) + add_time_ids = torch.cat([original_sizes, crops_coords_top_left, add_time_ids], dim=-1) + add_time_ids = add_time_ids.to(accelerator.device, dtype=prompt_embeds.dtype) + + prompt_embeds = prompt_embeds.to(accelerator.device) + add_text_embeds = add_text_embeds.to(accelerator.device) + unet_added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids} + + return {"prompt_embeds": prompt_embeds, **unet_added_cond_kwargs} + + def get_sigmas(timesteps, n_dim=4, dtype=torch.float32): + sigmas = noise_scheduler.sigmas.to(device=accelerator.device, dtype=dtype) + schedule_timesteps = noise_scheduler.timesteps.to(accelerator.device) + timesteps = timesteps.to(accelerator.device) + + step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps] + + sigma = sigmas[step_indices].flatten() + while len(sigma.shape) < n_dim: + sigma = sigma.unsqueeze(-1) + return sigma + + dataset = Text2ImageDataset( + train_shards_path_or_url=args.train_shards_path_or_url, + eval_shards_path_or_url=args.eval_shards_path_or_url, + num_train_examples=args.max_train_samples, + per_gpu_batch_size=args.train_batch_size, + global_batch_size=args.train_batch_size * accelerator.num_processes, + num_workers=args.dataloader_num_workers, + resolution=args.resolution, + center_crop=False, + random_flip=False, + shuffle_buffer_size=1000, + pin_memory=True, + persistent_workers=True, + control_type=args.control_type, + feature_extractor=feature_extractor, + ) + train_dataloader = dataset.train_dataloader + + # Let's first compute all the embeddings so that we can free up the text encoders + # from memory. + text_encoders = [text_encoder_one, text_encoder_two] + tokenizers = [tokenizer_one, tokenizer_two] + + compute_embeddings_fn = functools.partial( + compute_embeddings, + proportion_empty_prompts=args.proportion_empty_prompts, + text_encoders=text_encoders, + tokenizers=tokenizers, + ) + + # Scheduler and math around the number of training steps. + overrode_max_train_steps = False + num_update_steps_per_epoch = math.ceil(train_dataloader.num_batches / 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, lr_scheduler = accelerator.prepare(controlnet, optimizer, lr_scheduler) + + # 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(train_dataloader.num_batches / 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 batches each epoch = {train_dataloader.num_batches}") + 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): + image, control_image, text, orig_size, crop_coords = batch + + encoded_text = compute_embeddings_fn(text, orig_size, crop_coords) + image = image.to(accelerator.device, non_blocking=True) + control_image = control_image.to(accelerator.device, non_blocking=True) + + if args.pretrained_vae_model_name_or_path is not None: + pixel_values = image.to(dtype=weight_dtype) + if vae.dtype != weight_dtype: + vae.to(dtype=weight_dtype) + else: + pixel_values = image + + # latents = vae.encode(pixel_values).latent_dist.sample() + # encode pixel values with batch size of at most 8 + latents = [] + for i in range(0, pixel_values.shape[0], 8): + latents.append(vae.encode(pixel_values[i : i + 8]).latent_dist.sample()) + latents = torch.cat(latents, dim=0) + + latents = latents * vae.config.scaling_factor + if args.pretrained_vae_model_name_or_path is None: + latents = latents.to(weight_dtype) + + if args.control_type == "depth": + control_image = control_image.to(weight_dtype) + with torch.autocast("cuda"): + depth_map = depth_model(control_image).predicted_depth + depth_map = torch.nn.functional.interpolate( + depth_map.unsqueeze(1), + size=image.shape[2:], + mode="bicubic", + align_corners=False, + ) + depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True) + depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True) + depth_map = (depth_map - depth_min) / (depth_max - depth_min) + control_image = (depth_map * 255.0).to(torch.uint8).float() / 255.0 # hack to match inference + control_image = torch.cat([control_image] * 3, dim=1) + + # 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) + sigmas = get_sigmas(timesteps, len(noisy_latents.shape), noisy_latents.dtype) + inp_noisy_latents = noisy_latents / ((sigmas**2 + 1) ** 0.5) + + # ControlNet conditioning. + controlnet_image = control_image.to(dtype=weight_dtype) + prompt_embeds = encoded_text.pop("prompt_embeds") + down_block_res_samples, mid_block_res_sample = controlnet( + inp_noisy_latents, + timesteps, + encoder_hidden_states=prompt_embeds, + added_cond_kwargs=encoded_text, + controlnet_cond=controlnet_image, + return_dict=False, + ) + + # Predict the noise residual + model_pred = unet( + inp_noisy_latents, + timesteps, + encoder_hidden_states=prompt_embeds, + added_cond_kwargs=encoded_text, + 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 + + model_pred = model_pred * (-sigmas) + noisy_latents + weighing = sigmas**-2.0 + + # Get the target for loss depending on the prediction type + if noise_scheduler.config.prediction_type == "epsilon": + target = latents # compute loss against the denoised latents + 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 = torch.mean( + (weighing.float() * (model_pred.float() - target.float()) ** 2).reshape(target.shape[0], -1), 1 + ) + loss = loss.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, 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)