#!/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"\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):
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_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)