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1471 lines
58 KiB
Python
1471 lines
58 KiB
Python
#!/usr/bin/env python
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# coding=utf-8
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# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import functools
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import gc
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import itertools
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import json
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import logging
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import math
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import os
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import random
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import shutil
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from pathlib import Path
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from typing import List, Optional, Union
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import accelerate
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import cv2
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import numpy as np
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import torch
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import torch.utils.checkpoint
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import transformers
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import webdataset as wds
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from accelerate import Accelerator
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from accelerate.logging import get_logger
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from accelerate.utils import ProjectConfiguration, set_seed
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from braceexpand import braceexpand
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from huggingface_hub import create_repo, upload_folder
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from packaging import version
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from PIL import Image
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from torch.utils.data import default_collate
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from torchvision import transforms
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from tqdm.auto import tqdm
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from transformers import AutoTokenizer, DPTForDepthEstimation, DPTImageProcessor, PretrainedConfig
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from webdataset.tariterators import (
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base_plus_ext,
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tar_file_expander,
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url_opener,
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valid_sample,
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)
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import diffusers
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from diffusers import (
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AutoencoderKL,
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ControlNetModel,
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EulerDiscreteScheduler,
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StableDiffusionXLControlNetPipeline,
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UNet2DConditionModel,
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)
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from diffusers.optimization import get_scheduler
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from diffusers.utils import check_min_version, is_wandb_available
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from diffusers.utils.import_utils import is_xformers_available
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MAX_SEQ_LENGTH = 77
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if is_wandb_available():
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import wandb
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# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
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check_min_version("0.18.0.dev0")
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logger = get_logger(__name__)
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def filter_keys(key_set):
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def _f(dictionary):
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return {k: v for k, v in dictionary.items() if k in key_set}
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return _f
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def group_by_keys_nothrow(data, keys=base_plus_ext, lcase=True, suffixes=None, handler=None):
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"""Return function over iterator that groups key, value pairs into samples.
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:param keys: function that splits the key into key and extension (base_plus_ext)
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:param lcase: convert suffixes to lower case (Default value = True)
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"""
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current_sample = None
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for filesample in data:
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assert isinstance(filesample, dict)
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fname, value = filesample["fname"], filesample["data"]
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prefix, suffix = keys(fname)
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if prefix is None:
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continue
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if lcase:
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suffix = suffix.lower()
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# FIXME webdataset version throws if suffix in current_sample, but we have a potential for
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# this happening in the current LAION400m dataset if a tar ends with same prefix as the next
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# begins, rare, but can happen since prefix aren't unique across tar files in that dataset
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if current_sample is None or prefix != current_sample["__key__"] or suffix in current_sample:
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if valid_sample(current_sample):
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yield current_sample
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current_sample = {"__key__": prefix, "__url__": filesample["__url__"]}
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if suffixes is None or suffix in suffixes:
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current_sample[suffix] = value
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if valid_sample(current_sample):
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yield current_sample
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def tarfile_to_samples_nothrow(src, handler=wds.warn_and_continue):
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# NOTE this is a re-impl of the webdataset impl with group_by_keys that doesn't throw
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streams = url_opener(src, handler=handler)
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files = tar_file_expander(streams, handler=handler)
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samples = group_by_keys_nothrow(files, handler=handler)
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return samples
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def control_transform(image):
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image = np.array(image)
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low_threshold = 100
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high_threshold = 200
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image = cv2.Canny(image, low_threshold, high_threshold)
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image = image[:, :, None]
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image = np.concatenate([image, image, image], axis=2)
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control_image = Image.fromarray(image)
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return control_image
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def canny_image_transform(example, resolution=1024):
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image = example["image"]
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image = transforms.Resize(resolution, interpolation=transforms.InterpolationMode.BILINEAR)(image)
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# get crop coordinates
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c_top, c_left, _, _ = transforms.RandomCrop.get_params(image, output_size=(resolution, resolution))
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image = transforms.functional.crop(image, c_top, c_left, resolution, resolution)
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control_image = control_transform(image)
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image = transforms.ToTensor()(image)
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image = transforms.Normalize([0.5], [0.5])(image)
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control_image = transforms.ToTensor()(control_image)
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example["image"] = image
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example["control_image"] = control_image
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example["crop_coords"] = (c_top, c_left)
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return example
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def depth_image_transform(example, feature_extractor, resolution=1024):
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image = example["image"]
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image = transforms.Resize(resolution, interpolation=transforms.InterpolationMode.BILINEAR)(image)
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# get crop coordinates
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c_top, c_left, _, _ = transforms.RandomCrop.get_params(image, output_size=(resolution, resolution))
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image = transforms.functional.crop(image, c_top, c_left, resolution, resolution)
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control_image = feature_extractor(images=image, return_tensors="pt").pixel_values.squeeze(0)
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image = transforms.ToTensor()(image)
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image = transforms.Normalize([0.5], [0.5])(image)
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example["image"] = image
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example["control_image"] = control_image
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example["crop_coords"] = (c_top, c_left)
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return example
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class WebdatasetFilter:
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def __init__(self, min_size=1024, max_pwatermark=0.5):
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self.min_size = min_size
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self.max_pwatermark = max_pwatermark
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def __call__(self, x):
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try:
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if "json" in x:
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x_json = json.loads(x["json"])
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filter_size = (x_json.get("original_width", 0.0) or 0.0) >= self.min_size and x_json.get(
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"original_height", 0
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) >= self.min_size
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filter_watermark = (x_json.get("pwatermark", 1.0) or 1.0) <= self.max_pwatermark
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return filter_size and filter_watermark
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else:
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return False
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except Exception:
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return False
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class Text2ImageDataset:
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def __init__(
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self,
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train_shards_path_or_url: Union[str, List[str]],
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eval_shards_path_or_url: Union[str, List[str]],
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num_train_examples: int,
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per_gpu_batch_size: int,
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global_batch_size: int,
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num_workers: int,
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resolution: int = 256,
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center_crop: bool = True,
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random_flip: bool = False,
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shuffle_buffer_size: int = 1000,
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pin_memory: bool = False,
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persistent_workers: bool = False,
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control_type: str = "canny",
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feature_extractor: Optional[DPTImageProcessor] = None,
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):
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if not isinstance(train_shards_path_or_url, str):
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train_shards_path_or_url = [list(braceexpand(urls)) for urls in train_shards_path_or_url]
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# flatten list using itertools
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train_shards_path_or_url = list(itertools.chain.from_iterable(train_shards_path_or_url))
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if not isinstance(eval_shards_path_or_url, str):
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eval_shards_path_or_url = [list(braceexpand(urls)) for urls in eval_shards_path_or_url]
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# flatten list using itertools
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eval_shards_path_or_url = list(itertools.chain.from_iterable(eval_shards_path_or_url))
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def get_orig_size(json):
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return (int(json.get("original_width", 0.0)), int(json.get("original_height", 0.0)))
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if control_type == "canny":
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image_transform = functools.partial(canny_image_transform, resolution=resolution)
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elif control_type == "depth":
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image_transform = functools.partial(
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depth_image_transform, feature_extractor=feature_extractor, resolution=resolution
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)
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processing_pipeline = [
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wds.decode("pil", handler=wds.ignore_and_continue),
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wds.rename(
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image="jpg;png;jpeg;webp",
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control_image="jpg;png;jpeg;webp",
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text="text;txt;caption",
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orig_size="json",
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handler=wds.warn_and_continue,
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),
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wds.map(filter_keys({"image", "control_image", "text", "orig_size"})),
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wds.map_dict(orig_size=get_orig_size),
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wds.map(image_transform),
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wds.to_tuple("image", "control_image", "text", "orig_size", "crop_coords"),
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]
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# Create train dataset and loader
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pipeline = [
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wds.ResampledShards(train_shards_path_or_url),
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tarfile_to_samples_nothrow,
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wds.select(WebdatasetFilter(min_size=512)),
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wds.shuffle(shuffle_buffer_size),
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*processing_pipeline,
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wds.batched(per_gpu_batch_size, partial=False, collation_fn=default_collate),
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]
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num_worker_batches = math.ceil(num_train_examples / (global_batch_size * num_workers)) # per dataloader worker
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num_batches = num_worker_batches * num_workers
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num_samples = num_batches * global_batch_size
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# each worker is iterating over this
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self._train_dataset = wds.DataPipeline(*pipeline).with_epoch(num_worker_batches)
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self._train_dataloader = wds.WebLoader(
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self._train_dataset,
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batch_size=None,
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shuffle=False,
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num_workers=num_workers,
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pin_memory=pin_memory,
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persistent_workers=persistent_workers,
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)
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# add meta-data to dataloader instance for convenience
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self._train_dataloader.num_batches = num_batches
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self._train_dataloader.num_samples = num_samples
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# Create eval dataset and loader
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pipeline = [
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wds.SimpleShardList(eval_shards_path_or_url),
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wds.split_by_worker,
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wds.tarfile_to_samples(handler=wds.ignore_and_continue),
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*processing_pipeline,
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wds.batched(per_gpu_batch_size, partial=False, collation_fn=default_collate),
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]
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self._eval_dataset = wds.DataPipeline(*pipeline)
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self._eval_dataloader = wds.WebLoader(
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self._eval_dataset,
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batch_size=None,
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shuffle=False,
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num_workers=num_workers,
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pin_memory=pin_memory,
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persistent_workers=persistent_workers,
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)
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@property
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def train_dataset(self):
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return self._train_dataset
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@property
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def train_dataloader(self):
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return self._train_dataloader
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@property
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def eval_dataset(self):
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return self._eval_dataset
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@property
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def eval_dataloader(self):
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return self._eval_dataloader
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def image_grid(imgs, rows, cols):
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assert len(imgs) == rows * cols
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w, h = imgs[0].size
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grid = Image.new("RGB", size=(cols * w, rows * h))
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for i, img in enumerate(imgs):
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grid.paste(img, box=(i % cols * w, i // cols * h))
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return grid
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def log_validation(vae, unet, controlnet, args, accelerator, weight_dtype, step):
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logger.info("Running validation... ")
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controlnet = accelerator.unwrap_model(controlnet)
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pipeline = StableDiffusionXLControlNetPipeline.from_pretrained(
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args.pretrained_model_name_or_path,
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vae=vae,
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unet=unet,
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controlnet=controlnet,
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revision=args.revision,
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torch_dtype=weight_dtype,
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)
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# pipeline.scheduler = UniPCMultistepScheduler.from_config(pipeline.scheduler.config)
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pipeline = pipeline.to(accelerator.device)
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pipeline.set_progress_bar_config(disable=True)
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if args.enable_xformers_memory_efficient_attention:
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pipeline.enable_xformers_memory_efficient_attention()
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if args.seed is None:
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generator = None
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else:
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generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
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if len(args.validation_image) == len(args.validation_prompt):
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validation_images = args.validation_image
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validation_prompts = args.validation_prompt
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elif len(args.validation_image) == 1:
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validation_images = args.validation_image * len(args.validation_prompt)
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validation_prompts = args.validation_prompt
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elif len(args.validation_prompt) == 1:
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validation_images = args.validation_image
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validation_prompts = args.validation_prompt * len(args.validation_image)
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else:
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raise ValueError(
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"number of `args.validation_image` and `args.validation_prompt` should be checked in `parse_args`"
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)
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image_logs = []
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for validation_prompt, validation_image in zip(validation_prompts, validation_images):
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validation_image = Image.open(validation_image).convert("RGB")
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validation_image = validation_image.resize((args.resolution, args.resolution))
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images = []
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for _ in range(args.num_validation_images):
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with torch.autocast("cuda"):
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image = pipeline(
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validation_prompt, image=validation_image, num_inference_steps=20, generator=generator
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).images[0]
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images.append(image)
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image_logs.append(
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{"validation_image": validation_image, "images": images, "validation_prompt": validation_prompt}
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)
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for tracker in accelerator.trackers:
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if tracker.name == "tensorboard":
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for log in image_logs:
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images = log["images"]
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validation_prompt = log["validation_prompt"]
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validation_image = log["validation_image"]
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formatted_images = [np.asarray(validation_image)]
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for image in images:
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formatted_images.append(np.asarray(image))
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formatted_images = np.stack(formatted_images)
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tracker.writer.add_images(validation_prompt, formatted_images, step, dataformats="NHWC")
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elif tracker.name == "wandb":
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formatted_images = []
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for log in image_logs:
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images = log["images"]
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validation_prompt = log["validation_prompt"]
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validation_image = log["validation_image"]
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formatted_images.append(wandb.Image(validation_image, caption="Controlnet conditioning"))
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for image in images:
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image = wandb.Image(image, caption=validation_prompt)
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formatted_images.append(image)
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tracker.log({"validation": formatted_images})
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else:
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logger.warning(f"image logging not implemented for {tracker.name}")
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del pipeline
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gc.collect()
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torch.cuda.empty_cache()
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return image_logs
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def import_model_class_from_model_name_or_path(
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pretrained_model_name_or_path: str, revision: str, subfolder: str = "text_encoder"
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):
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text_encoder_config = PretrainedConfig.from_pretrained(
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pretrained_model_name_or_path, subfolder=subfolder, revision=revision
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)
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model_class = text_encoder_config.architectures[0]
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if model_class == "CLIPTextModel":
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from transformers import CLIPTextModel
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return CLIPTextModel
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elif model_class == "CLIPTextModelWithProjection":
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from transformers import CLIPTextModelWithProjection
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return CLIPTextModelWithProjection
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else:
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raise ValueError(f"{model_class} is not supported.")
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def save_model_card(repo_id: str, image_logs=None, base_model=str, repo_folder=None):
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img_str = ""
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if image_logs is not None:
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img_str = "You can find some example images below.\n"
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for i, log in enumerate(image_logs):
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images = log["images"]
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validation_prompt = log["validation_prompt"]
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validation_image = log["validation_image"]
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validation_image.save(os.path.join(repo_folder, "image_control.png"))
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img_str += f"prompt: {validation_prompt}\n"
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images = [validation_image] + images
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image_grid(images, 1, len(images)).save(os.path.join(repo_folder, f"images_{i}.png"))
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img_str += f"\n"
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yaml = f"""
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---
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license: creativeml-openrail-m
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base_model: {base_model}
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tags:
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- stable-diffusion-xl
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- stable-diffusion-xl-diffusers
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- text-to-image
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- diffusers
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- controlnet
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- diffusers-training
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- webdataset
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inference: true
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---
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"""
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model_card = f"""
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# controlnet-{repo_id}
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These are controlnet weights trained on {base_model} with new type of conditioning.
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{img_str}
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"""
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with open(os.path.join(repo_folder, "README.md"), "w") as f:
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f.write(yaml + model_card)
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def parse_args(input_args=None):
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parser = argparse.ArgumentParser(description="Simple example of a ControlNet training script.")
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parser.add_argument(
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"--pretrained_model_name_or_path",
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type=str,
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default=None,
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required=True,
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help="Path to pretrained model or model identifier from huggingface.co/models.",
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)
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parser.add_argument(
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"--pretrained_vae_model_name_or_path",
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type=str,
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default=None,
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help="Path to an improved VAE to stabilize training. For more details check out: https://github.com/huggingface/diffusers/pull/4038.",
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)
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parser.add_argument(
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"--controlnet_model_name_or_path",
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type=str,
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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):
|
|
if args.report_to == "wandb" and args.hub_token is not None:
|
|
raise ValueError(
|
|
"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
|
|
" Please use `hf auth login` to authenticate with the Hub."
|
|
)
|
|
|
|
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,
|
|
)
|
|
|
|
# Disable AMP for MPS.
|
|
if torch.backends.mps.is_available():
|
|
accelerator.native_amp = False
|
|
|
|
# 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
|
|
)
|
|
|
|
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 = DPTImageProcessor.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.warning(
|
|
"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)
|