目录
[3.1 训练代码](#3.1 训练代码)
[3.2 运行命令](#3.2 运行命令)
一、前言
仅供参考。注意：此前局部重绘训练基于sdxl，显存48G可应对，此次FLUX-Fill的局部重绘训练参考项目Sebastian-Zok/FLUX-Fill-LoRa-Training，作者只在NVIDIA A100 GPU (80GB VRAM)上做过测试，代码未经优化。我尝试了一下两张A40(每张48GB VRAM)也可以，其余情况并不清楚。切勿在硬盘资源或者显存资源不足的情况下直接运行项目Sebastian-Zok/FLUX-Fill-LoRa-Training的源代码，可能会导致服务器崩溃。
可以去diffusers官方仓库中搜索"train_"，就可以看到有很多训练的代码。尤其是如果搜索"train_drambooth"，也很多相关的训练代码，比如sdxl的，flux的。
但是搜索跟局部重绘train_inpainting有关的训练特别少，只有一个好像看似是sdxl的局部重绘训练代码。
https://github.com/huggingface/diffusers/blob/main/examples/research_projects/dreambooth_inpaint/train_dreambooth_inpaint.py
https://github.com/huggingface/diffusers/blob/main/examples/research_projects/dreambooth_inpaint/train_dreambooth_inpaint_lora.py
然后我找了一下其他Github仓库，找了一个项目Sebastian-Zok/FLUX-Fill-LoRa-Training
https://github.com/Sebastian-Zok/FLUX-Fill-LoRa-Training/tree/main
二、环境配置和数据集准备
安装一下依赖。还要下载一下基座模型black-forest-labs/FLUX.1-Fill-dev
git clone https://github.com/huggingface/diffusers
cd diffusers
pip install -e .
cd examples/research_projects/dreambooth_inpaint
pip install -r requirements_flux.txt
pip install prodigyopt然后就是要准备一下训练数据集。上次sdxl局部重绘训练数据集是下面这样的（见视觉理解生成 专栏《局部重绘1------训练和推理》）：
draw_data/
├── images/
│ ├── 001.jpg
│ ├── 002.jpg
│ └── ...
└── masks/
├── mask_001.jpg
├── mask_002.jpg
└── ...上次sdxl局部重绘训练数据集是下面这样的（见视觉理解生成 专栏《ControlNet1------训练》），如果你要Controlnet训练的话可以直接去diffusers的官方仓库搜索train_controlnet，上次是直接基于Controlnet源仓库训练的，所以数据集格式可能会跟你去diffusers的官方仓库的有一点不一样。
draw_data/
├── source/
│ ├── 001.png
│ ├── 002.png
│ └── ...
└── target/
│ ├── 001.png
│ ├──002.png
│ └── ...
└──prompt.json
prompt.json每一行的格式示例
{"source": "source/00030_rendered.png", "target": "target/00030_rendered.png", "prompt": "...."}
{"source": "source/00031_rendered.png", "target": "target/00031_rendered.png", "prompt": "...."}
{"source": "source/00032_rendered.png", "target": "target/00032_rendered.png", "prompt": "...."}我们这次训练flux-fill的局部重绘lora训练直接把之前controlnet训练的数据集拿过来改一下名称和格式就可以了,改成下面这样。prompt.json我是直接在vscode里面使用搜索替换的方式把prompt.json里面的内容改了改，你也可以写代码改。
draw_data/
├── images/
│ ├── 001.png
│ ├── 002.png
│ └── ...
└── masks/
│ ├── mask_001.png
│ ├──mask_002.png
│ └── ...
└──prompt.json
prompt.json每一行的格式示例
{"images": "images/00030_rendered.png", "masks": "masks/00030_rendered.png", "prompt": "...."}
{"images": "images/00031_rendered.png", "masks": "masks/00031_rendered.png", "prompt": "...."}
{"images": "images/00032_rendered.png", "masks": "masks/00032_rendered.png", "prompt": "...."}三、训练
原项目运行的代码和命令是下面这样，如果你是跟他一样手里是一张显存80G的A100则按照原项目的Readme进行。
https://github.com/Sebastian-Zok/FLUX-Fill-LoRa-Training/blob/main/examples/research_projects/dreambooth_inpaint/train_dreambooth_inpaint_lora_flux.py
export MODEL_NAME="black-forest-labs/FLUX.1-Fill-dev"export INSTANCE_DIR="dog"
export MASK_DIR="dog_masks"
export OUTPUT_DIR="flux-fill-dog-lora"
accelerate launch train_dreambooth_inpaint_lora_flux.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir=$INSTANCE_DIR \
--mask_data_dir=$MASK_DIR
--output_dir=$OUTPUT_DIR \
--mixed_precision="bf16" \
--instance_prompt="A TOK dog" \
--resolution=512 \
--train_batch_size=1 \
--guidance_scale=1 \
--gradient_accumulation_steps=4 \
--optimizer="prodigy" \
--learning_rate=1. \
--report_to="wandb" \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--checkpointing_steps=50 \
--max_train_steps=500 \
--validation_prompt="A TOK dog" \
--validation_epochs=25 \
--validation_image="./dog/alvan-nee-9M0tSjb-cpA-unsplash.jpeg" \
--validation_mask="./dog_mask/alvan-nee-9M0tSjb-cpA-unsplash.jpeg" \
--seed="0" \
--push_to_hub
但如果你是两张显存为48G的A40，则可以使用下面修改过的代码，有些路径要自己改改。
3.1 训练代码
python
#!/usr/bin/env python
# coding=utf-8
# Copyright 2024 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 copy
import itertools
import logging
import math
import os
import random
import shutil
import warnings
from contextlib import nullcontext
from pathlib import Path
import numpy as np
import torch
import torch.utils.checkpoint
import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import DistributedDataParallelKwargs, ProjectConfiguration, set_seed
from huggingface_hub import create_repo, upload_folder
from huggingface_hub.utils import insecure_hashlib
from peft import LoraConfig, set_peft_model_state_dict
from peft.utils import get_peft_model_state_dict
from PIL import Image, ImageDraw
from PIL.ImageOps import exif_transpose
from torch.utils.data import Dataset
from torchvision import transforms
from torchvision.transforms.functional import crop
from tqdm.auto import tqdm
from transformers import CLIPTokenizer, PretrainedConfig, T5TokenizerFast
import diffusers
from diffusers import (
AutoencoderKL,
FlowMatchEulerDiscreteScheduler,
FluxFillPipeline,
FluxTransformer2DModel,
)
from diffusers.utils import load_image
from diffusers.optimization import get_scheduler
from diffusers.training_utils import (
_set_state_dict_into_text_encoder,
cast_training_params,
compute_density_for_timestep_sampling,
compute_loss_weighting_for_sd3,
free_memory,
)
from diffusers.utils import (
check_min_version,
convert_unet_state_dict_to_peft,
is_wandb_available,
)
from diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card
from diffusers.utils.torch_utils import is_compiled_module
if is_wandb_available():
import wandb
import gc
import psutil
def log_memory_usage(tag=""):
"""打印 CPU 内存与 GPU 显存使用情况"""
cpu_mem = psutil.virtual_memory()
logger.info(f"[{tag}] CPU Memory: {cpu_mem.used / 1024**3:.2f} GB / {cpu_mem.total / 1024**3:.2f} GB ({cpu_mem.percent}%)")
if torch.cuda.is_available():
for i in range(torch.cuda.device_count()):
alloc = torch.cuda.memory_allocated(i) / 1024**3
reserv = torch.cuda.memory_reserved(i) / 1024**3
logger.info(f"[{tag}] GPU{i} Memory: allocated {alloc:.2f} GB, reserved {reserv:.2f} GB")
# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
check_min_version("0.32.0.dev0")
logger = get_logger(__name__)
def save_model_card(
repo_id: str,
images=None,
base_model: str = None,
train_text_encoder=False,
instance_prompt=None,
validation_prompt=None,
repo_folder=None,
):
widget_dict = []
if images is not None:
for i, image in enumerate(images):
image.save(os.path.join(repo_folder, f"image_{i}.png"))
widget_dict.append(
{"text": validation_prompt if validation_prompt else " ", "output": {"url": f"image_{i}.png"}}
)
model_description = f"""
# Flux-Fill DreamBooth LoRA - {repo_id}
<Gallery />
## Model description
These are {repo_id} DreamBooth LoRA weights for {base_model}.
The weights were trained using [DreamBooth](https://dreambooth.github.io/) with a custom [Flux diffusers trainer](https://github.com/Sebastian-Zok/FLUX-Fill-LoRa-Training).
Was LoRA for the text encoder enabled? {train_text_encoder}.
## Trigger words
You should use `{instance_prompt}` to trigger the image generation.
## Download model
[Download the *.safetensors LoRA]({repo_id}/tree/main) in the Files & versions tab.
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to('cuda')
pipeline.load_lora_weights('{repo_id}', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('{validation_prompt if validation_prompt else instance_prompt}').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## License
Please adhere to the licensing terms as described [here](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
"""
model_card = load_or_create_model_card(
repo_id_or_path=repo_id,
from_training=True,
license="other",
base_model=base_model,
prompt=instance_prompt,
model_description=model_description,
widget=widget_dict,
)
tags = [
"text-to-image",
"diffusers-training",
"diffusers",
"lora",
"flux",
"flux-diffusers",
"template:sd-lora",
]
model_card = populate_model_card(model_card, tags=tags)
model_card.save(os.path.join(repo_folder, "README.md"))
def prepare_mask_and_masked_image(image, mask):
image = np.array(image.convert("RGB"))
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
mask = np.array(mask.convert("L"))
mask = mask.astype(np.float32) / 255.0
mask = mask[None, None]
mask[mask < 0.5] = 0
mask[mask >= 0.5] = 1
mask = torch.from_numpy(mask)
masked_image = image * (mask < 0.5)
return mask, masked_image
def get_mask(im_shape, original_image_path, mask_data_path):
# Extract only the file name from the original path
_, filename = os.path.split(original_image_path)
# Construct the mask path (adjust as needed)
mask_path = os.path.join(mask_data_path, filename)
# Load and ensure single-channel
mask = Image.open(mask_path).convert("L")
# Resize the mask to match your desired final image size if needed
# Note: im_shape is typically (width, height)
mask = mask.resize(im_shape, Image.NEAREST)
return mask
def load_text_encoders(class_one, class_two):
text_encoder_one = class_one.from_pretrained(
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant
)
text_encoder_two = class_two.from_pretrained(
args.pretrained_model_name_or_path, subfolder="text_encoder_2", revision=args.revision, variant=args.variant
)
return text_encoder_one, text_encoder_two
def log_validation(
pipeline,
args,
accelerator,
pipeline_args,
epoch,
torch_dtype,
is_final_validation=False,
):
logger.info(
f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
f" {args.validation_prompt}."
)
pipeline = pipeline.to(accelerator.device)
pipeline.set_progress_bar_config(disable=True)
# run inference
generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None
#autocast_ctx = torch.autocast(accelerator.device.type) if not is_final_validation else nullcontext()
autocast_ctx = nullcontext()
with autocast_ctx:
images = [pipeline(**pipeline_args, generator=generator).images[0] for _ in range(args.num_validation_images)]
for tracker in accelerator.trackers:
phase_name = "test" if is_final_validation else "validation"
if tracker.name == "tensorboard":
np_images = np.stack([np.asarray(img) for img in images])
tracker.writer.add_images(phase_name, np_images, epoch, dataformats="NHWC")
if tracker.name == "wandb":
tracker.log(
{
phase_name: [
wandb.Image(image, caption=f"{i}: {args.validation_prompt}") for i, image in enumerate(images)
]
}
)
del pipeline
if torch.cuda.is_available():
torch.cuda.empty_cache()
return images
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
)
model_class = text_encoder_config.architectures[0]
if model_class == "CLIPTextModel":
from transformers import CLIPTextModel
return CLIPTextModel
elif model_class == "T5EncoderModel":
from transformers import T5EncoderModel
return T5EncoderModel
else:
raise ValueError(f"{model_class} is not supported.")
def parse_args(input_args=None):
parser = argparse.ArgumentParser(description="Simple example of a training script.")
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default="/mnt/songwenzheng/FLUX.1-Fill-dev",
# required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--revision",
type=str,
default=None,
required=False,
help="Revision of pretrained model identifier from huggingface.co/models.",
)
parser.add_argument(
"--variant",
type=str,
default=None,
help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16",
)
parser.add_argument(
"--dataset_name",
type=str,
default=None,
help=(
"The name of the Dataset (from the HuggingFace hub) containing the training data of instance images (could be your own, possibly private,"
" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
" or to a folder containing files that 🤗 Datasets can understand."
),
)
parser.add_argument(
"--dataset_config_name",
type=str,
default=None,
help="The config of the Dataset, leave as None if there's only one config.",
)
parser.add_argument(
"--instance_data_dir",
type=str,
default="/mnt/songwenzheng/guanlinyi/FLUX-Fill-LoRa-Training-main/draw_data/images",
help=("A folder containing the training data. "),
)
parser.add_argument(
"--mask_data_dir",
type=str,
default="/mnt/songwenzheng/guanlinyi/FLUX-Fill-LoRa-Training-main/draw_data/masks",
help=("A folder containing the mask data. "),
)
parser.add_argument(
"--cache_dir",
type=str,
default=None,
help="The directory where the downloaded models and datasets will be stored.",
)
parser.add_argument(
"--image_column",
type=str,
default="image",
help="The column of the dataset containing the target image. By "
"default, the standard Image Dataset maps out 'file_name' "
"to 'image'.",
)
parser.add_argument(
"--caption_column",
type=str,
default=None,
help="The column of the dataset containing the instance prompt for each image",
)
parser.add_argument("--repeats", type=int, default=1, help="How many times to repeat the training data.")
parser.add_argument(
"--class_data_dir",
type=str,
default=None,
required=False,
help="A folder containing the training data of class images.",
)
parser.add_argument(
"--instance_prompt",
type=str,
default="a photo of TOK car headlight",
# required=True,
help="The prompt with identifier specifying the instance, e.g. 'photo of a TOK dog', 'in the style of TOK'",
)
parser.add_argument(
"--class_prompt",
type=str,
default=None,
help="The prompt to specify images in the same class as provided instance images.",
)
parser.add_argument(
"--max_sequence_length",
type=int,
default=512,
help="Maximum sequence length to use with with the T5 text encoder",
)
parser.add_argument(
"--validation_prompt",
type=str,
default="a close-up of TOK car headlight, studio lighting",
help="A prompt that is used during validation to verify that the model is learning.",
)
parser.add_argument(
"--validation_image",
type=str,
default="/mnt/songwenzheng/guanlinyi/FLUX-Fill-LoRa-Training-main/input_imgs/testlight.png",
help="A file path to a local or remote (https) image file that is used during validation to verify that the model is learning.",
)
parser.add_argument(
"--validation_mask",
type=str,
default="/mnt/songwenzheng/guanlinyi/FLUX-Fill-LoRa-Training-main/input_imgs/mask_testlight.png",
help="A file path to a local or remote (https) image file that is used as mask for the inpainting task during validation to verify that the model is learning.",
)
parser.add_argument(
"--num_validation_images",
type=int,
# default=4,
default=2,
help="Number of images that should be generated during validation with `validation_prompt`.",
)
parser.add_argument(
"--validation_epochs",
type=int,
default=50,
help=(
"Run dreambooth validation every X epochs. Dreambooth validation consists of running the prompt"
" `args.validation_prompt` multiple times: `args.num_validation_images`."
),
)
parser.add_argument(
"--rank",
type=int,
default=4,
help=("The dimension of the LoRA update matrices."),
)
parser.add_argument(
"--with_prior_preservation",
default=False,
action="store_true",
help="Flag to add prior preservation loss.",
)
parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.")
parser.add_argument(
"--num_class_images",
type=int,
default=100,
help=(
"Minimal class images for prior preservation loss. If there are not enough images already present in"
" class_data_dir, additional images will be sampled with class_prompt."
),
)
parser.add_argument(
"--output_dir",
type=str,
default="flux-dreambooth-lora",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument(
"--resolution",
type=int,
# default=512,
default=1024,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--center_crop",
default=False,
action="store_true",
help=(
"Whether to center crop the input images to the resolution. If not set, the images will be randomly"
" cropped. The images will be resized to the resolution first before cropping."
),
)
parser.add_argument(
"--random_flip",
action="store_true",
help="whether to randomly flip images horizontally",
)
parser.add_argument(
"--train_text_encoder",
action="store_true",
help="Whether to train the text encoder. If set, the text encoder should be float32 precision.",
)
parser.add_argument(
# "--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
"--train_batch_size", type=int, default=1, help="Batch size (per device) for the training dataloader."
)
parser.add_argument(
"--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images."
)
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. These checkpoints can be used both as final"
" checkpoints in case they are better than the last checkpoint, and are also suitable for resuming"
" training using `--resume_from_checkpoint`."
),
)
parser.add_argument(
"--checkpoints_total_limit",
type=int,
default=2,
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,
default=4,
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=1e-4,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--guidance_scale",
type=float,
default=3.5,
help="the FLUX.1 dev variant is a guidance distilled model",
)
parser.add_argument(
"--text_encoder_lr",
type=float,
default=5e-6,
help="Text encoder learning rate 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(
"--dataloader_num_workers",
type=int,
default=0,
help=(
"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
),
)
parser.add_argument(
"--weighting_scheme",
type=str,
default="none",
choices=["sigma_sqrt", "logit_normal", "mode", "cosmap", "none"],
help=('We default to the "none" weighting scheme for uniform sampling and uniform loss'),
)
parser.add_argument(
"--logit_mean", type=float, default=0.0, help="mean to use when using the `'logit_normal'` weighting scheme."
)
parser.add_argument(
"--logit_std", type=float, default=1.0, help="std to use when using the `'logit_normal'` weighting scheme."
)
parser.add_argument(
"--mode_scale",
type=float,
default=1.29,
help="Scale of mode weighting scheme. Only effective when using the `'mode'` as the `weighting_scheme`.",
)
parser.add_argument(
"--optimizer",
type=str,
default="AdamW",
help=('The optimizer type to use. Choose between ["AdamW", "prodigy"]'),
)
parser.add_argument(
"--use_8bit_adam",
action="store_true",
help="Whether or not to use 8-bit Adam from bitsandbytes. Ignored if optimizer is not set to AdamW",
)
parser.add_argument(
"--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam and Prodigy optimizers."
)
parser.add_argument(
"--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam and Prodigy optimizers."
)
parser.add_argument(
"--prodigy_beta3",
type=float,
default=None,
help="coefficients for computing the Prodigy stepsize using running averages. If set to None, "
"uses the value of square root of beta2. Ignored if optimizer is adamW",
)
parser.add_argument("--prodigy_decouple", type=bool, default=True, help="Use AdamW style decoupled weight decay")
parser.add_argument("--adam_weight_decay", type=float, default=1e-04, help="Weight decay to use for unet params")
parser.add_argument(
"--adam_weight_decay_text_encoder", type=float, default=1e-03, help="Weight decay to use for text_encoder"
)
parser.add_argument(
"--lora_layers",
type=str,
default=None,
help=(
'The transformer modules to apply LoRA training on. Please specify the layers in a comma seperated. E.g. - "to_k,to_q,to_v,to_out.0" will result in lora training of attention layers only'
),
)
parser.add_argument(
"--adam_epsilon",
type=float,
default=1e-08,
help="Epsilon value for the Adam optimizer and Prodigy optimizers.",
)
parser.add_argument(
"--prodigy_use_bias_correction",
type=bool,
default=True,
help="Turn on Adam's bias correction. True by default. Ignored if optimizer is adamW",
)
parser.add_argument(
"--prodigy_safeguard_warmup",
type=bool,
default=True,
help="Remove lr from the denominator of D estimate to avoid issues during warm-up stage. True by default. "
"Ignored if optimizer is adamW",
)
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(
"--cache_latents",
action="store_true",
default=False,
help="Cache the VAE latents",
)
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="bf16",
# 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(
"--upcast_before_saving",
action="store_true",
default=False,
help=(
"Whether to upcast the trained transformer layers to float32 before saving (at the end of training). "
"Defaults to precision dtype used for training to save memory"
),
)
parser.add_argument(
"--prior_generation_precision",
type=str,
default=None,
choices=["no", "fp32", "fp16", "bf16"],
help=(
"Choose prior generation precision between fp32, fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU. Default to fp16 if a GPU is available else fp32."
),
)
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
if input_args is not None:
args = parser.parse_args(input_args)
else:
args = parser.parse_args()
if args.dataset_name is None and args.instance_data_dir is None:
raise ValueError("Specify either `--dataset_name` or `--instance_data_dir`")
if args.dataset_name is not None and args.instance_data_dir is not None:
raise ValueError("Specify only one of `--dataset_name` or `--instance_data_dir`")
if args.mask_data_dir is None:
raise ValueError("Specify a --mask_data_dir`")
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
if args.with_prior_preservation:
if args.class_data_dir is None:
raise ValueError("You must specify a data directory for class images.")
if args.class_prompt is None:
raise ValueError("You must specify prompt for class images.")
else:
# logger is not available yet
if args.class_data_dir is not None:
warnings.warn("You need not use --class_data_dir without --with_prior_preservation.")
if args.class_prompt is not None:
warnings.warn("You need not use --class_prompt without --with_prior_preservation.")
return args
class DreamBoothDataset(Dataset):
"""
A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
It pre-processes the images.
"""
def __init__(
self,
instance_data_root,
mask_data_root,
instance_prompt,
class_prompt,
class_data_root=None,
class_num=None,
size=1024,
repeats=1,
center_crop=False,
):
self.size = size
self.center_crop = center_crop
self.instance_prompt = instance_prompt
self.mask_data_root = mask_data_root
self.custom_instance_prompts = None
self.class_prompt = class_prompt
# if --dataset_name is provided or a metadata jsonl file is provided in the local --instance_data directory,
# we load the training data using load_dataset
if args.dataset_name is not None:
try:
from datasets import load_dataset
except ImportError:
raise ImportError(
"You are trying to load your data using the datasets library. If you wish to train using custom "
"captions please install the datasets library: `pip install datasets`. If you wish to load a "
"local folder containing images only, specify --instance_data_dir instead."
)
# Downloading and loading a dataset from the hub.
# See more about loading custom images at
# https://huggingface.co/docs/datasets/v2.0.0/en/dataset_script
dataset = load_dataset(
args.dataset_name,
args.dataset_config_name,
cache_dir=args.cache_dir,
)
# Preprocessing the datasets.
column_names = dataset["train"].column_names
# 6. Get the column names for input/target.
if args.image_column is None:
image_column = column_names[0]
logger.info(f"image column defaulting to {image_column}")
else:
image_column = args.image_column
if image_column not in column_names:
raise ValueError(
f"`--image_column` value '{args.image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
)
instance_images = dataset["train"][image_column]
if args.caption_column is None:
logger.info(
"No caption column provided, defaulting to instance_prompt for all images. If your dataset "
"contains captions/prompts for the images, make sure to specify the "
"column as --caption_column"
)
self.custom_instance_prompts = None
else:
if args.caption_column not in column_names:
raise ValueError(
f"`--caption_column` value '{args.caption_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
)
custom_instance_prompts = dataset["train"][args.caption_column]
# create final list of captions according to --repeats
self.custom_instance_prompts = []
for caption in custom_instance_prompts:
self.custom_instance_prompts.extend(itertools.repeat(caption, repeats))
else:
self.instance_data_root = Path(instance_data_root)
if not self.instance_data_root.exists():
raise ValueError("Instance images root doesn't exists.")
# Kreiere eine Liste mit allen Bildern im Ordner
self.instance_images_path = list(Path(instance_data_root).iterdir()) ## Meine
instance_images = [Image.open(path) for path in list(Path(instance_data_root).iterdir())]
self.custom_instance_prompts = None
self.instance_images = []
for img in instance_images:
self.instance_images.extend(itertools.repeat(img, repeats))
self.pixel_values = []
train_resize = transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR)
train_crop = transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size)
train_flip = transforms.RandomHorizontalFlip(p=1.0)
train_transforms = transforms.Compose(
[
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
for image in self.instance_images:
image = exif_transpose(image)
if not image.mode == "RGB":
image = image.convert("RGB")
image = train_resize(image)
if args.random_flip and random.random() < 0.5:
# flip
image = train_flip(image)
if args.center_crop:
y1 = max(0, int(round((image.height - args.resolution) / 2.0)))
x1 = max(0, int(round((image.width - args.resolution) / 2.0)))
image = train_crop(image)
else:
y1, x1, h, w = train_crop.get_params(image, (args.resolution, args.resolution))
image = crop(image, y1, x1, h, w)
image = train_transforms(image)
self.pixel_values.append(image)
self.num_instance_images = len(self.instance_images)
self._length = self.num_instance_images
if class_data_root is not None:
self.class_data_root = Path(class_data_root)
self.class_data_root.mkdir(parents=True, exist_ok=True)
self.class_images_path = list(self.class_data_root.iterdir())
if class_num is not None:
self.num_class_images = min(len(self.class_images_path), class_num)
else:
self.num_class_images = len(self.class_images_path)
self._length = max(self.num_class_images, self.num_instance_images)
else:
self.class_data_root = None
self.image_transforms = transforms.Compose(
[
transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),
transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
self.image_transforms_resize_and_crop = transforms.Compose(
[
transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),
transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
]
)
def __len__(self):
return self._length
def __getitem__(self, index):
example = {}
pil_image = Image.open(self.instance_images_path[index % self.num_instance_images])
if not pil_image.mode == "RGB":
pil_image = pil_image.convert("RGB")
pil_image = self.image_transforms_resize_and_crop(pil_image)
example["PIL_images"] = pil_image
example["image_path"] = self.instance_images_path[index % self.num_instance_images]
example["mask_data_path"] = self.mask_data_root
instance_image = self.pixel_values[index % self.num_instance_images]
#print(instance_image.shape)
example["instance_images"] = instance_image
if self.custom_instance_prompts:
caption = self.custom_instance_prompts[index % self.num_instance_images]
if caption:
example["instance_prompt"] = caption
else:
example["instance_prompt"] = self.instance_prompt
else: # custom prompts were provided, but length does not match size of image dataset
example["instance_prompt"] = self.instance_prompt
if self.class_data_root:
class_image = Image.open(self.class_images_path[index % self.num_class_images])
class_image = exif_transpose(class_image)
if not class_image.mode == "RGB":
class_image = class_image.convert("RGB")
example["class_images"] = self.image_transforms(class_image)
example["class_prompt"] = self.class_prompt
return example
def collate_fn(examples, with_prior_preservation=False):
pixel_values = [example["instance_images"] for example in examples]
prompts = [example["instance_prompt"] for example in examples]
# Concat class and instance examples for prior preservation.
# We do this to avoid doing two forward passes.
if with_prior_preservation:
pixel_values += [example["class_images"] for example in examples]
prompts += [example["class_prompt"] for example in examples]
masks = []
masked_images = []
for example in examples:
pil_image = example["PIL_images"] # Here maybe PilImages
# Get Mask
mask = get_mask(pil_image.size, example["image_path"], example["mask_data_path"], 1, False)
# prepare mask and masked image
mask, masked_image = prepare_mask_and_masked_image(pil_image, mask)
masks.append(mask)
masked_images.append(masked_image)
pixel_values = torch.stack(pixel_values)
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
masks = torch.stack(masks)
masked_images = torch.stack(masked_images)
batch = {"pixel_values": pixel_values, "prompts": prompts, "masks": masks, "masked_images": masked_images}
return batch
class PromptDataset(Dataset):
"A simple dataset to prepare the prompts to generate class images on multiple GPUs."
def __init__(self, prompt, num_samples):
self.prompt = prompt
self.num_samples = num_samples
def __len__(self):
return self.num_samples
def __getitem__(self, index):
example = {}
example["prompt"] = self.prompt
example["index"] = index
return example
def tokenize_prompt(tokenizer, prompt, max_sequence_length):
text_inputs = tokenizer(
prompt,
padding="max_length",
max_length=max_sequence_length,
truncation=True,
return_length=False,
return_overflowing_tokens=False,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
return text_input_ids
def _encode_prompt_with_t5(
text_encoder,
tokenizer,
max_sequence_length=512,
prompt=None,
num_images_per_prompt=1,
device=None,
text_input_ids=None,
):
prompt = [prompt] if isinstance(prompt, str) else prompt
batch_size = len(prompt)
if tokenizer is not None:
text_inputs = tokenizer(
prompt,
padding="max_length",
max_length=max_sequence_length,
truncation=True,
return_length=False,
return_overflowing_tokens=False,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
else:
if text_input_ids is None:
raise ValueError("text_input_ids must be provided when the tokenizer is not specified")
prompt_embeds = text_encoder(text_input_ids.to(device))[0]
dtype = text_encoder.dtype
prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
_, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
return prompt_embeds
def prepare_mask_latents(
mask,
masked_image_latents,
batch_size,
num_channels_latents,
num_images_per_prompt,
height,
width,
dtype,
device,
vae_scale_factor,
vae_shift_factor
):
""" Prepare mask latents """
# 1. calculate the height and width of the latents
# VAE applies 8x compression on images but we must also account for packing which requires
# latent height and width to be divisible by 2.
height = 2 * (int(height) // (vae_scale_factor * 2))
width = 2 * (int(width) // (vae_scale_factor * 2))
masked_image_latents = (masked_image_latents - vae_shift_factor) * vae_scale_factor #self.vae.config.scaling_factor
masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)
# 3. duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
batch_size = batch_size * num_images_per_prompt
if mask.shape[0] < batch_size:
if not batch_size % mask.shape[0] == 0:
raise ValueError(
"The passed mask and the required batch size don't match. Masks are supposed to be duplicated to"
f" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number"
" of masks that you pass is divisible by the total requested batch size."
)
mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)
if masked_image_latents.shape[0] < batch_size:
if not batch_size % masked_image_latents.shape[0] == 0:
raise ValueError(
"The passed images and the required batch size don't match. Images are supposed to be duplicated"
f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed."
" Make sure the number of images that you pass is divisible by the total requested batch size."
)
masked_image_latents = masked_image_latents.repeat(batch_size // masked_image_latents.shape[0], 1, 1, 1)
# 4. pack the masked_image_latents
# batch_size, num_channels_latents, height, width -> batch_size, height//2 * width//2 , num_channels_latents*4
masked_image_latents = FluxFillPipeline._pack_latents(
masked_image_latents,
batch_size,
num_channels_latents,
height,
width,
)
# 5.resize mask to latents shape we we concatenate the mask to the latents
mask = mask[:, 0, :, :] # batch_size, 8 * height, 8 * width (mask has not been 8x compressed)
mask = mask.view(
batch_size, height, vae_scale_factor, width, vae_scale_factor
) # batch_size, height, 8, width, 8
mask = mask.permute(0, 2, 4, 1, 3) # batch_size, 8, 8, height, width
mask = mask.reshape(
batch_size, vae_scale_factor * vae_scale_factor, height, width
) # batch_size, 8*8, height, width
# 6. pack the mask:
# batch_size, 64, height, width -> batch_size, height//2 * width//2 , 64*2*2
mask = FluxFillPipeline._pack_latents(
mask,
batch_size,
vae_scale_factor * vae_scale_factor,
height,
width,
)
mask = mask.to(device=device, dtype=dtype)
return mask, masked_image_latents
def _encode_prompt_with_clip(
text_encoder,
tokenizer,
prompt: str,
device=None,
text_input_ids=None,
num_images_per_prompt: int = 1,
):
prompt = [prompt] if isinstance(prompt, str) else prompt
batch_size = len(prompt)
if tokenizer is not None:
text_inputs = tokenizer(
prompt,
padding="max_length",
max_length=77,
truncation=True,
return_overflowing_tokens=False,
return_length=False,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
else:
if text_input_ids is None:
raise ValueError("text_input_ids must be provided when the tokenizer is not specified")
prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=False)
# Use pooled output of CLIPTextModel
prompt_embeds = prompt_embeds.pooler_output
prompt_embeds = prompt_embeds.to(dtype=text_encoder.dtype, device=device)
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, -1)
return prompt_embeds
def encode_prompt(
text_encoders,
tokenizers,
prompt: str,
max_sequence_length,
device=None,
num_images_per_prompt: int = 1,
text_input_ids_list=None,
):
prompt = [prompt] if isinstance(prompt, str) else prompt
dtype = text_encoders[0].dtype
pooled_prompt_embeds = _encode_prompt_with_clip(
text_encoder=text_encoders[0],
tokenizer=tokenizers[0],
prompt=prompt,
device=device if device is not None else text_encoders[0].device,
num_images_per_prompt=num_images_per_prompt,
text_input_ids=text_input_ids_list[0] if text_input_ids_list else None,
)
prompt_embeds = _encode_prompt_with_t5(
text_encoder=text_encoders[1],
tokenizer=tokenizers[1],
max_sequence_length=max_sequence_length,
prompt=prompt,
num_images_per_prompt=num_images_per_prompt,
device=device if device is not None else text_encoders[1].device,
text_input_ids=text_input_ids_list[1] if text_input_ids_list else None,
)
text_ids = torch.zeros(prompt_embeds.shape[1], 3).to(device=device, dtype=dtype)
return prompt_embeds, pooled_prompt_embeds, text_ids
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 `huggingface-cli login` to authenticate with the Hub."
)
if torch.backends.mps.is_available() and args.mixed_precision == "bf16":
# due to pytorch#99272, MPS does not yet support bfloat16.
raise ValueError(
"Mixed precision training with bfloat16 is not supported on MPS. Please use fp16 (recommended) or fp32 instead."
)
logging_dir = Path(args.output_dir, args.logging_dir)
accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with=args.report_to,
project_config=accelerator_project_config,
kwargs_handlers=[kwargs],
)
# Disable AMP for MPS.
if torch.backends.mps.is_available():
accelerator.native_amp = False
if args.report_to == "wandb":
if not is_wandb_available():
raise ImportError("Make sure to install wandb if you want to use it for logging during training.")
# 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)
# Generate class images if prior preservation is enabled.
if args.with_prior_preservation:
class_images_dir = Path(args.class_data_dir)
if not class_images_dir.exists():
class_images_dir.mkdir(parents=True)
cur_class_images = len(list(class_images_dir.iterdir()))
if cur_class_images < args.num_class_images:
has_supported_fp16_accelerator = torch.cuda.is_available() or torch.backends.mps.is_available()
torch_dtype = torch.float16 if has_supported_fp16_accelerator else torch.float32
if args.prior_generation_precision == "fp32":
torch_dtype = torch.float32
elif args.prior_generation_precision == "fp16":
torch_dtype = torch.float16
elif args.prior_generation_precision == "bf16":
torch_dtype = torch.bfloat16
pipeline = FluxFillPipeline.from_pretrained(
args.pretrained_model_name_or_path,
torch_dtype=torch_dtype,
revision=args.revision,
variant=args.variant,
)
pipeline.set_progress_bar_config(disable=True)
num_new_images = args.num_class_images - cur_class_images
logger.info(f"Number of class images to sample: {num_new_images}.")
sample_dataset = PromptDataset(args.class_prompt, num_new_images)
sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)
sample_dataloader = accelerator.prepare(sample_dataloader)
pipeline.to(accelerator.device)
for example in tqdm(
sample_dataloader, desc="Generating class images", disable=not accelerator.is_local_main_process
):
images = pipeline(example["prompt"]).images
for i, image in enumerate(images):
hash_image = insecure_hashlib.sha1(image.tobytes()).hexdigest()
image_filename = class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg"
image.save(image_filename)
del pipeline
if torch.cuda.is_available():
torch.cuda.empty_cache()
# 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,
).repo_id
# Load the tokenizers
tokenizer_one = CLIPTokenizer.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="tokenizer",
revision=args.revision,
)
tokenizer_two = T5TokenizerFast.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="tokenizer_2",
revision=args.revision,
)
# 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 = FlowMatchEulerDiscreteScheduler.from_pretrained(
args.pretrained_model_name_or_path, subfolder="scheduler"
)
noise_scheduler_copy = copy.deepcopy(noise_scheduler)
text_encoder_one, text_encoder_two = load_text_encoders(text_encoder_cls_one, text_encoder_cls_two)
vae = AutoencoderKL.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="vae",
revision=args.revision,
variant=args.variant,
)
transformer = FluxTransformer2DModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="transformer", revision=args.revision, variant=args.variant
)
# We only train the additional adapter LoRA layers
transformer.requires_grad_(False)
vae.requires_grad_(False)
text_encoder_one.requires_grad_(False)
text_encoder_two.requires_grad_(False)
# For mixed precision training we cast all non-trainable weights (vae, text_encoder and transformer) to half-precision
# as these weights 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
if torch.backends.mps.is_available() and weight_dtype == torch.bfloat16:
# due to pytorch#99272, MPS does not yet support bfloat16.
raise ValueError(
"Mixed precision training with bfloat16 is not supported on MPS. Please use fp16 (recommended) or fp32 instead."
)
vae.to(accelerator.device, dtype=weight_dtype)
transformer.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.gradient_checkpointing:
transformer.enable_gradient_checkpointing()
if args.train_text_encoder:
text_encoder_one.gradient_checkpointing_enable()
if args.lora_layers is not None:
target_modules = [layer.strip() for layer in args.lora_layers.split(",")]
else:
target_modules = [
"attn.to_k",
"attn.to_q",
"attn.to_v",
"attn.to_out.0",
"attn.add_k_proj",
"attn.add_q_proj",
"attn.add_v_proj",
"attn.to_add_out",
"ff.net.0.proj",
"ff.net.2",
"ff_context.net.0.proj",
"ff_context.net.2",
]
# now we will add new LoRA weights the transformer layers
transformer_lora_config = LoraConfig(
r=args.rank,
lora_alpha=args.rank,
init_lora_weights="gaussian",
target_modules=target_modules,
)
transformer.add_adapter(transformer_lora_config)
if args.train_text_encoder:
text_lora_config = LoraConfig(
r=args.rank,
lora_alpha=args.rank,
init_lora_weights="gaussian",
target_modules=["q_proj", "k_proj", "v_proj", "out_proj"],
)
text_encoder_one.add_adapter(text_lora_config)
def unwrap_model(model):
model = accelerator.unwrap_model(model)
model = model._orig_mod if is_compiled_module(model) else model
return model
# 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:
transformer_lora_layers_to_save = None
text_encoder_one_lora_layers_to_save = None
for model in models:
if isinstance(model, type(unwrap_model(transformer))):
transformer_lora_layers_to_save = get_peft_model_state_dict(model)
elif isinstance(model, type(unwrap_model(text_encoder_one))):
text_encoder_one_lora_layers_to_save = get_peft_model_state_dict(model)
else:
raise ValueError(f"unexpected save model: {model.__class__}")
# make sure to pop weight so that corresponding model is not saved again
weights.pop()
FluxFillPipeline.save_lora_weights(
output_dir,
transformer_lora_layers=transformer_lora_layers_to_save,
text_encoder_lora_layers=text_encoder_one_lora_layers_to_save,
)
def load_model_hook(models, input_dir):
transformer_ = None
text_encoder_one_ = None
while len(models) > 0:
model = models.pop()
if isinstance(model, type(unwrap_model(transformer))):
transformer_ = model
elif isinstance(model, type(unwrap_model(text_encoder_one))):
text_encoder_one_ = model
else:
raise ValueError(f"unexpected save model: {model.__class__}")
lora_state_dict = FluxFillPipeline.lora_state_dict(input_dir)
transformer_state_dict = {
f'{k.replace("transformer.", "")}': v for k, v in lora_state_dict.items() if k.startswith("transformer.")
}
transformer_state_dict = convert_unet_state_dict_to_peft(transformer_state_dict)
incompatible_keys = set_peft_model_state_dict(transformer_, transformer_state_dict, adapter_name="default")
if incompatible_keys is not None:
# check only for unexpected keys
unexpected_keys = getattr(incompatible_keys, "unexpected_keys", None)
if unexpected_keys:
logger.warning(
f"Loading adapter weights from state_dict led to unexpected keys not found in the model: "
f" {unexpected_keys}. "
)
if args.train_text_encoder:
# Do we need to call `scale_lora_layers()` here?
_set_state_dict_into_text_encoder(lora_state_dict, prefix="text_encoder.", text_encoder=text_encoder_one_)
# Make sure the trainable params are in float32. This is again needed since the base models
# are in `weight_dtype`. More details:
# https://github.com/huggingface/diffusers/pull/6514#discussion_r1449796804
if args.mixed_precision == "fp16":
models = [transformer_]
if args.train_text_encoder:
models.extend([text_encoder_one_])
# only upcast trainable parameters (LoRA) into fp32
cast_training_params(models)
accelerator.register_save_state_pre_hook(save_model_hook)
accelerator.register_load_state_pre_hook(load_model_hook)
# 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 and torch.cuda.is_available():
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
)
# Make sure the trainable params are in float32.
if args.mixed_precision == "fp16":
models = [transformer]
if args.train_text_encoder:
models.extend([text_encoder_one])
# only upcast trainable parameters (LoRA) into fp32
cast_training_params(models, dtype=torch.float32)
transformer_lora_parameters = list(filter(lambda p: p.requires_grad, transformer.parameters()))
if args.train_text_encoder:
text_lora_parameters_one = list(filter(lambda p: p.requires_grad, text_encoder_one.parameters()))
# Optimization parameters
transformer_parameters_with_lr = {"params": transformer_lora_parameters, "lr": args.learning_rate}
if args.train_text_encoder:
# different learning rate for text encoder and unet
text_parameters_one_with_lr = {
"params": text_lora_parameters_one,
"weight_decay": args.adam_weight_decay_text_encoder,
"lr": args.text_encoder_lr if args.text_encoder_lr else args.learning_rate,
}
params_to_optimize = [transformer_parameters_with_lr, text_parameters_one_with_lr]
else:
params_to_optimize = [transformer_parameters_with_lr]
# Optimizer creation
if not (args.optimizer.lower() == "prodigy" or args.optimizer.lower() == "adamw"):
logger.warning(
f"Unsupported choice of optimizer: {args.optimizer}.Supported optimizers include [adamW, prodigy]."
"Defaulting to adamW"
)
args.optimizer = "adamw"
if args.use_8bit_adam and not args.optimizer.lower() == "adamw":
logger.warning(
f"use_8bit_adam is ignored when optimizer is not set to 'AdamW'. Optimizer was "
f"set to {args.optimizer.lower()}"
)
if args.optimizer.lower() == "adamw":
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 = optimizer_class(
params_to_optimize,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
if args.optimizer.lower() == "prodigy":
try:
import prodigyopt
except ImportError:
raise ImportError("To use Prodigy, please install the prodigyopt library: `pip install prodigyopt`")
optimizer_class = prodigyopt.Prodigy
if args.learning_rate <= 0.1:
logger.warning(
"Learning rate is too low. When using prodigy, it's generally better to set learning rate around 1.0"
)
if args.train_text_encoder and args.text_encoder_lr:
logger.warning(
f"Learning rates were provided both for the transformer and the text encoder- e.g. text_encoder_lr:"
f" {args.text_encoder_lr} and learning_rate: {args.learning_rate}. "
f"When using prodigy only learning_rate is used as the initial learning rate."
)
# changes the learning rate of text_encoder_parameters_one to be
# --learning_rate
params_to_optimize[1]["lr"] = args.learning_rate
optimizer = optimizer_class(
params_to_optimize,
betas=(args.adam_beta1, args.adam_beta2),
beta3=args.prodigy_beta3,
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
decouple=args.prodigy_decouple,
use_bias_correction=args.prodigy_use_bias_correction,
safeguard_warmup=args.prodigy_safeguard_warmup,
)
# Dataset and DataLoaders creation:
train_dataset = DreamBoothDataset(
instance_data_root=args.instance_data_dir,
mask_data_root=args.mask_data_dir,
instance_prompt=args.instance_prompt,
class_prompt=args.class_prompt,
class_data_root=args.class_data_dir if args.with_prior_preservation else None,
class_num=args.num_class_images,
size=args.resolution,
repeats=args.repeats,
center_crop=args.center_crop,
)
train_dataloader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.train_batch_size,
shuffle=True,
collate_fn=lambda examples: collate_fn(examples, args.with_prior_preservation),
num_workers=args.dataloader_num_workers,
)
if not args.train_text_encoder:
tokenizers = [tokenizer_one, tokenizer_two]
text_encoders = [text_encoder_one, text_encoder_two]
def compute_text_embeddings(prompt, text_encoders, tokenizers):
with torch.no_grad():
prompt_embeds, pooled_prompt_embeds, text_ids = encode_prompt(
text_encoders, tokenizers, prompt, args.max_sequence_length
)
prompt_embeds = prompt_embeds.to(accelerator.device)
pooled_prompt_embeds = pooled_prompt_embeds.to(accelerator.device)
text_ids = text_ids.to(accelerator.device)
return prompt_embeds, pooled_prompt_embeds, text_ids
# If no type of tuning is done on the text_encoder and custom instance prompts are NOT
# provided (i.e. the --instance_prompt is used for all images), we encode the instance prompt once to avoid
# the redundant encoding.
if not args.train_text_encoder and not train_dataset.custom_instance_prompts:
instance_prompt_hidden_states, instance_pooled_prompt_embeds, instance_text_ids = compute_text_embeddings(
args.instance_prompt, text_encoders, tokenizers
)
# Handle class prompt for prior-preservation.
if args.with_prior_preservation:
if not args.train_text_encoder:
class_prompt_hidden_states, class_pooled_prompt_embeds, class_text_ids = compute_text_embeddings(
args.class_prompt, text_encoders, tokenizers
)
# Clear the memory here
if not args.train_text_encoder and not train_dataset.custom_instance_prompts:
del text_encoder_one, text_encoder_two, tokenizer_one, tokenizer_two
free_memory()
# If custom instance prompts are NOT provided (i.e. the instance prompt is used for all images),
# pack the statically computed variables appropriately here. This is so that we don't
# have to pass them to the dataloader.
if not train_dataset.custom_instance_prompts:
if not args.train_text_encoder:
prompt_embeds = instance_prompt_hidden_states
pooled_prompt_embeds = instance_pooled_prompt_embeds
text_ids = instance_text_ids
if args.with_prior_preservation:
prompt_embeds = torch.cat([prompt_embeds, class_prompt_hidden_states], dim=0)
pooled_prompt_embeds = torch.cat([pooled_prompt_embeds, class_pooled_prompt_embeds], dim=0)
text_ids = torch.cat([text_ids, class_text_ids], dim=0)
# if we're optimizing the text encoder (both if instance prompt is used for all images or custom prompts)
# we need to tokenize and encode the batch prompts on all training steps
else:
tokens_one = tokenize_prompt(tokenizer_one, args.instance_prompt, max_sequence_length=77)
tokens_two = tokenize_prompt(
tokenizer_two, args.instance_prompt, max_sequence_length=args.max_sequence_length
)
if args.with_prior_preservation:
class_tokens_one = tokenize_prompt(tokenizer_one, args.class_prompt, max_sequence_length=77)
class_tokens_two = tokenize_prompt(
tokenizer_two, args.class_prompt, max_sequence_length=args.max_sequence_length
)
tokens_one = torch.cat([tokens_one, class_tokens_one], dim=0)
tokens_two = torch.cat([tokens_two, class_tokens_two], dim=0)
vae_config_shift_factor = vae.config.shift_factor
vae_config_scaling_factor = vae.config.scaling_factor
vae_config_block_out_channels = vae.config.block_out_channels
if args.cache_latents:
latents_cache = []
for batch in tqdm(train_dataloader, desc="Caching latents"):
with torch.no_grad():
batch["pixel_values"] = batch["pixel_values"].to(
accelerator.device, non_blocking=True, dtype=weight_dtype
)
latents_cache.append(vae.encode(batch["pixel_values"]).latent_dist)
if args.validation_prompt is None:
del vae
free_memory()
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
overrode_max_train_steps = True
lr_scheduler = get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
num_training_steps=args.max_train_steps * accelerator.num_processes,
num_cycles=args.lr_num_cycles,
power=args.lr_power,
)
# Prepare everything with our `accelerator`.
if args.train_text_encoder:
(
transformer,
text_encoder_one,
optimizer,
train_dataloader,
lr_scheduler,
) = accelerator.prepare(
transformer,
text_encoder_one,
optimizer,
train_dataloader,
lr_scheduler,
)
else:
transformer, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
transformer, optimizer, train_dataloader, 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(len(train_dataloader) / args.gradient_accumulation_steps)
if overrode_max_train_steps:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if accelerator.is_main_process:
tracker_name = "dreambooth-flux-dev-lora"
accelerator.init_trackers(tracker_name, config=vars(args))
# Train!
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num batches each epoch = {len(train_dataloader)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
global_step = 0
first_epoch = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint != "latest":
path = os.path.basename(args.resume_from_checkpoint)
else:
# Get the mos 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,
)
def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
sigmas = noise_scheduler_copy.sigmas.to(device=accelerator.device, dtype=dtype)
schedule_timesteps = noise_scheduler_copy.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
for epoch in range(first_epoch, args.num_train_epochs):
transformer.train()
if args.train_text_encoder:
text_encoder_one.train()
# set top parameter requires_grad = True for gradient checkpointing works
accelerator.unwrap_model(text_encoder_one).text_model.embeddings.requires_grad_(True)
for step, batch in enumerate(train_dataloader):
models_to_accumulate = [transformer]
if args.train_text_encoder:
models_to_accumulate.extend([text_encoder_one])
with accelerator.accumulate(models_to_accumulate):
prompts = batch["prompts"]
# encode batch prompts when custom prompts are provided for each image -
if train_dataset.custom_instance_prompts:
if not args.train_text_encoder:
prompt_embeds, pooled_prompt_embeds, text_ids = compute_text_embeddings(
prompts, text_encoders, tokenizers
)
else:
tokens_one = tokenize_prompt(tokenizer_one, prompts, max_sequence_length=77)
tokens_two = tokenize_prompt(
tokenizer_two, prompts, max_sequence_length=args.max_sequence_length
)
prompt_embeds, pooled_prompt_embeds, text_ids = encode_prompt(
text_encoders=[text_encoder_one, text_encoder_two],
tokenizers=[None, None],
text_input_ids_list=[tokens_one, tokens_two],
max_sequence_length=args.max_sequence_length,
device=accelerator.device,
prompt=prompts,
)
else:
elems_to_repeat = len(prompts)
if args.train_text_encoder:
prompt_embeds, pooled_prompt_embeds, text_ids = encode_prompt(
text_encoders=[text_encoder_one, text_encoder_two],
tokenizers=[None, None],
text_input_ids_list=[
tokens_one.repeat(elems_to_repeat, 1),
tokens_two.repeat(elems_to_repeat, 1),
],
max_sequence_length=args.max_sequence_length,
device=accelerator.device,
prompt=args.instance_prompt,
)
# Convert images to latent space
if args.cache_latents:
model_input = latents_cache[step].sample()
else:
pixel_values = batch["pixel_values"].to(dtype=vae.dtype)
log_memory_usage("before vae encode")
model_input = vae.encode(pixel_values).latent_dist.sample()
log_memory_usage("after vae encode")
model_input = (model_input - vae_config_shift_factor) * vae_config_scaling_factor
model_input = model_input.to(dtype=weight_dtype)
vae_scale_factor = 2 ** (len(vae_config_block_out_channels) - 1)
masked_image_latents = vae.encode(
batch["masked_images"].reshape(batch["pixel_values"].shape).to(dtype=weight_dtype)
).latent_dist.sample()
masked_image_latents = (masked_image_latents - vae.config.shift_factor) * vae.config.scaling_factor
#print("masked image latents", masked_image_latents.shape)
masks = batch["masks"]
#print("masks ", masks.shape)
#print("mask ", masks )
#print("scale", vae_scale_factor)
#print("scale", args.resolution)
#print("scale", model_input.shape[2])
mask = masks
# resize the mask to latents shape as we concatenate the mask to the latents
""" mask = torch.stack(
[
torch.nn.functional.interpolate(mask, size=(args.resolution // 8, args.resolution // 8))
for mask in masks
]
).to(dtype=weight_dtype)
mask = mask.reshape(-1, 1, args.resolution // 8, args.resolution // 8) """
# 5.resize mask to latents shape we we concatenate the mask to the latents
mask = mask[:, 0, :, :] # batch_size, 8 * height, 8 * width (mask has not been 8x compressed)
mask = mask.view(
model_input.shape[0], model_input.shape[2], vae_scale_factor, model_input.shape[3], vae_scale_factor
) # batch_size, height, 8, width, 8
mask = mask.permute(0, 2, 4, 1, 3) # batch_size, 8, 8, height, width
mask = mask.reshape(
model_input.shape[0], vae_scale_factor * vae_scale_factor, model_input.shape[2], model_input.shape[3]
) # ba
#print("mask ", mask.shape)
latent_image_ids = FluxFillPipeline._prepare_latent_image_ids(
model_input.shape[0],
model_input.shape[2] // 2,
model_input.shape[3] // 2,
accelerator.device,
weight_dtype,
)
# Sample noise that we'll add to the latents
noise = torch.randn_like(model_input)
bsz = model_input.shape[0]
# Sample a random timestep for each image
# for weighting schemes where we sample timesteps non-uniformly
u = compute_density_for_timestep_sampling(
weighting_scheme=args.weighting_scheme,
batch_size=bsz,
logit_mean=args.logit_mean,
logit_std=args.logit_std,
mode_scale=args.mode_scale,
)
indices = (u * noise_scheduler_copy.config.num_train_timesteps).long()
timesteps = noise_scheduler_copy.timesteps[indices].to(device=model_input.device)
# Add noise according to flow matching.
# zt = (1 - texp) * x + texp * z1
sigmas = get_sigmas(timesteps, n_dim=model_input.ndim, dtype=model_input.dtype)
noisy_model_input = (1.0 - sigmas) * model_input + sigmas * noise
packed_noisy_model_input = FluxFillPipeline._pack_latents(
noisy_model_input,
batch_size=model_input.shape[0],
num_channels_latents=model_input.shape[1],
height=model_input.shape[2],
width=model_input.shape[3],
)
# handle guidance
if accelerator.unwrap_model(transformer).config.guidance_embeds:
guidance = torch.tensor([args.guidance_scale], device=accelerator.device)
guidance = guidance.expand(model_input.shape[0])
else:
guidance = None
masked_image_latents = FluxFillPipeline._pack_latents(
masked_image_latents,
batch_size=model_input.shape[0],
num_channels_latents=model_input.shape[1],
height=model_input.shape[2],
width=model_input.shape[3],
)
#print("packed masked image latents", masked_image_latents.shape)
#print("model input", model_input)
#print("model inputhshae", model_input.shape)
mask = FluxFillPipeline._pack_latents(
mask,
batch_size=model_input.shape[0],
num_channels_latents=vae_scale_factor*vae_scale_factor,
height=model_input.shape[2],
width=model_input.shape[3],
)
#print("packed mask ", mask.shape)
masked_image_latents = torch.cat((masked_image_latents, mask), dim=-1)
#print("concat masked image latents", masked_image_latents.shape)
transformer_input = torch.cat((packed_noisy_model_input, masked_image_latents), dim=2)
#print(packed_noisy_model_input.shape)
#print("hidden states latents", transformer_input.shape)
# Predict the noise residual
model_pred = transformer(
hidden_states=transformer_input,
# YiYi notes: divide it by 1000 for now because we scale it by 1000 in the transforme rmodel (we should not keep it but I want to keep the inputs same for the model for testing)
timestep=timesteps / 1000,
guidance=guidance,
pooled_projections=pooled_prompt_embeds,
encoder_hidden_states=prompt_embeds,
txt_ids=text_ids,
img_ids=latent_image_ids,
return_dict=False,
)[0]
model_pred = FluxFillPipeline._unpack_latents(
model_pred,
height=model_input.shape[2] * vae_scale_factor,
width=model_input.shape[3] * vae_scale_factor,
vae_scale_factor=vae_scale_factor,
)
# these weighting schemes use a uniform timestep sampling
# and instead post-weight the loss
weighting = compute_loss_weighting_for_sd3(weighting_scheme=args.weighting_scheme, sigmas=sigmas)
# flow matching loss
target = noise - model_input
if args.with_prior_preservation:
# Chunk the noise and model_pred into two parts and compute the loss on each part separately.
model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)
target, target_prior = torch.chunk(target, 2, dim=0)
# Compute prior loss
prior_loss = torch.mean(
(weighting.float() * (model_pred_prior.float() - target_prior.float()) ** 2).reshape(
target_prior.shape[0], -1
),
1,
)
prior_loss = prior_loss.mean()
# Compute regular loss.
loss = torch.mean(
(weighting.float() * (model_pred.float() - target.float()) ** 2).reshape(target.shape[0], -1),
1,
)
loss = loss.mean()
if args.with_prior_preservation:
# Add the prior loss to the instance loss.
loss = loss + args.prior_loss_weight * prior_loss
accelerator.backward(loss)
if accelerator.sync_gradients:
params_to_clip = (
itertools.chain(transformer.parameters(), text_encoder_one.parameters())
if args.train_text_encoder
else transformer.parameters()
)
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# 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}")
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
if accelerator.is_main_process:
if args.validation_prompt is not None and epoch % args.validation_epochs == 0:
# create pipeline
if not args.train_text_encoder:
text_encoder_one, text_encoder_two = load_text_encoders(text_encoder_cls_one, text_encoder_cls_two)
text_encoder_one.to(weight_dtype)
text_encoder_two.to(weight_dtype)
pipeline = FluxFillPipeline.from_pretrained(
args.pretrained_model_name_or_path,
revision=args.revision,
variant=args.variant,
torch_dtype=weight_dtype,
)
val_image = load_image(args.validation_image)
val_mask = load_image(args.validation_mask)
pipeline_args = {"prompt": args.validation_prompt, "image": val_image, "mask_image": val_mask}
images = log_validation(
pipeline=pipeline,
args=args,
accelerator=accelerator,
pipeline_args=pipeline_args,
epoch=epoch,
torch_dtype=weight_dtype
)
if not args.train_text_encoder:
del text_encoder_one, text_encoder_two
free_memory()
images = None
del pipeline
# Save the lora layers
accelerator.wait_for_everyone()
if accelerator.is_main_process:
transformer = unwrap_model(transformer)
if args.upcast_before_saving:
transformer.to(torch.float32)
else:
transformer = transformer.to(weight_dtype)
transformer_lora_layers = get_peft_model_state_dict(transformer)
if args.train_text_encoder:
text_encoder_one = unwrap_model(text_encoder_one)
text_encoder_lora_layers = get_peft_model_state_dict(text_encoder_one.to(torch.float32))
else:
text_encoder_lora_layers = None
FluxFillPipeline.save_lora_weights(
save_directory=args.output_dir,
transformer_lora_layers=transformer_lora_layers,
text_encoder_lora_layers=text_encoder_lora_layers,
)
# Final inference
# Load previous pipeline
pipeline = FluxFillPipeline.from_pretrained(
args.pretrained_model_name_or_path,
revision=args.revision,
variant=args.variant,
torch_dtype=weight_dtype,
)
# load attention processors
pipeline.load_lora_weights(args.output_dir)
# run inference
images = []
if args.validation_prompt and args.num_validation_images > 0:
val_image = load_image(args.validation_image)
val_mask = load_image(args.validation_mask)
pipeline_args = {"prompt": args.validation_prompt, "image": val_image, "mask_image": val_mask}
images = log_validation(
pipeline=pipeline,
args=args,
accelerator=accelerator,
pipeline_args=pipeline_args,
epoch=epoch,
is_final_validation=True,
torch_dtype=weight_dtype,
)
if args.push_to_hub:
save_model_card(
repo_id,
images=images,
base_model=args.pretrained_model_name_or_path,
train_text_encoder=args.train_text_encoder,
instance_prompt=args.instance_prompt,
validation_prompt=args.validation_prompt,
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_*"],
)
images = None
del pipeline
accelerator.end_training()
if __name__ == "__main__":
args = parse_args()
main(args)3.2 运行命令
python
export CUDA_VISIBLE_DEVICES=0,1 # 假设你有第0张和第1张卡
# 更改代码中的defult设置的路径为你自己的路径后，你可以先通过以下命令尝试运行以下代码先训练50epoch看会不会报错
# 模型会输出到flux-dreambooth-lora/checkpoint-50。代码中也有参数可以自己指定输出目录
accelerate launch --num_processes=2 train_flux_fill_lora.py \
--validation_prompt="a close-up of TOK car taillight, red glow, sharp details" \
--resolution=512 \
--train_batch_size=1 \
--gradient_accumulation_steps=1 \
--learning_rate=1e-4 \
--rank=4 \
--max_train_steps=50 \
--checkpointing_steps=25 \
--checkpoints_total_limit=1 \
--mixed_precision="bf16" \
--gradient_checkpointing \
--allow_tf32
# 如果上面的操作成功了且没有显存爆炸，可以尝试将resolution升到1024，max_train_steps升到1500正式进行训练
export CUDA_VISIBLE_DEVICES=0,1
accelerate launch --num_processes=2 train_flux_fill_lora.py \
--validation_prompt="a close-up of TOK car taillight, red glow, sharp details" \
--resolution=1024 \
--train_batch_size=1 \
--gradient_accumulation_steps=4 \
--learning_rate=1e-4 \
--rank=16 \
--max_train_steps=1500 \
--checkpointing_steps=500 \
--checkpoints_total_limit=2 \
--mixed_precision="bf16" \
--gradient_checkpointing \
--allow_tf32
accelerate launch --num_processes=2 train_flux_fill_lora.py \
--validation_prompt="a close-up of TOK car taillight, red glow, sharp details" \
--resolution=1024 \
--train_batch_size=1 \
--gradient_accumulation_steps=4 \
--learning_rate=1e-4 \
--rank=16 \
--num_train_epochs=2 \
--checkpointing_steps=250 \
--checkpoints_total_limit=15 \
--mixed_precision="bf16" \
--gradient_checkpointing \
--allow_tf32
注意：
--num_train_epochs=2 \ # 2 个 epoch，约 4000 steps
--checkpointing_steps=250 \ # 每 250 steps 保存
--checkpoints_total_limit=15 \ # 保留 15 个 ≈ 1GB3.3 问题
验证的时候老是报显存不足，我直接把验证代码注释了
搜索下面的内容，并将这附近这一段if的内容全注释
if args.validation_prompt is not None and epoch % args.validation_epochs == 0:四、推理
python
import torch
from diffusers import FluxFillPipeline
from diffusers.utils import load_image
import PIL
import numpy as np
# 1. 加载 Flux Fill 模型 (使用 bfloat16 以兼顾精度与显存)
# 改成你的模型路径，比如black-forest-labs/FLUX.1-Fill-dev
model_path = "/mnt/songwenzheng/FLUX.1-Fill-dev"
lora_weights_path = input_dir + os.sep + "flux-dreambooth-lora/checkpoint-50"
input_dir = "/mnt/songwenzheng/guanlinyi/diffusers
input_imgs_dir = input_dir + os.sep + "input_imgs"
output_dir ="flux_fill_output"
pipe = FluxFillPipeline.from_pretrained(
# "black-forest-labs/FLUX.1-Fill-dev",
model_path,
torch_dtype=torch.bfloat16
).to("cuda")
pipe.load_lora_weights(lora_weights_path) # 把这行注释掉就变成不加载lora而直接推理了
# 可选：开启内存优化，如果显存有限
# pipe.enable_attention_slicing()
# pipe.enable_model_cpu_offload() # 显存极有限时使用，速度稍慢
# 2. 准备输入图像和掩码 (请替换为你的图片路径或URL)
image_name = "testlight.png"
image_path = input_imgs_dir + os.sep + image_name
image = load_image(image_path)
orig_width, orig_height = image.size
mask_path = input_imgs_dir + os.sep + "mask_" + im
mask = load_image(mask_path)
# 3. 定义提示词
# 改成你的提示词
prompt = "Taillights with premium, high-end design"
# 4. 执行推理
result = pipe(
prompt=prompt,
image=image,
mask_image=mask,
height=orig_height, width=orig_width,
num_inference_steps=50, # Flux Fill 推荐 50 步以获得良好质量[reference:0]
guidance_scale=30, # Flux Fill 使用更高的引导强度，官方示例为 30[reference:1]
generator=torch.Generator("cuda").manual_seed(42) # 设置随机种子以保证结果可复现
).images[0]
# 5. 保存结果
output_path = output_dir + os.sep + f"flux_fill_inpainted_{image_name}"
result.save(output_path)
print(f"图像已保存至{output_path}")五、对比实验
我们写了个批量推理代码，以对比Flux.1-dev和FLUX.1-Fill-dev和FLUX.1-Fill-dev加入自己训练的lora这三种情况输出的推理图，可以通过更改num_images_per_experiment = 10来改变每个实验生成的图片数量。
python
import os
import torch
from diffusers import FluxFillPipeline, FluxInpaintPipeline
from diffusers.utils import load_image
# ========== 用户配置区域 ==========
# 模型路径
model_fill_path = "/mnt/songwenzheng/FLUX.1-Fill-dev" # FLUX.1-Fill-dev 路径
model_dev_path = "/mnt/songwenzheng/FLUX.1-dev" # FLUX.1-dev 路径
# LoRA 权重路径（仅在 FLUX.1-Fill-dev + LoRA 实验中使用）
lora_weights_path = "/mnt/songwenzheng/guanlinyi/diffusers/flux-dreambooth-lora/checkpoint-50"
# 输入数据目录与输出根目录
input_dir = "/mnt/songwenzheng/guanlinyi/diffusers"
input_imgs_dir = os.path.join(input_dir, "input_imgs")
output_base_dir = "flux_fill_output" # 所有实验结果存放于此根目录下
# 待处理的图片文件名（图片和掩码文件需放在 input_imgs_dir 中）
image_name = "testlight.png"
mask_name = f"mask_{image_name}" # 假设掩码文件命名为 mask_testlight.png
# 每个实验生成的图片数量
num_images_per_experiment = 10
# 推理参数（可根据需要调整）
num_inference_steps = 50
guidance_scale = 30
# 定义实验列表
# 每个实验包含: name(实验名称), model_type("fill" 或 "dev"), use_lora(仅 fill 有效), model_path
experiments = [
{
"name": "exp1_flux1_dev",
"model_type": "dev",
"use_lora": False,
"model_path": model_dev_path
},
{
"name": "exp2_flux1_fill_dev",
"model_type": "fill",
"use_lora": False,
"model_path": model_fill_path
},
{
"name": "exp3_flux1_fill_dev_with_lora",
"model_type": "fill",
"use_lora": True,
"model_path": model_fill_path
}
]
# ========== 辅助函数 ==========
def clear_gpu_memory():
"""清理 GPU 显存"""
if torch.cuda.is_available():
torch.cuda.empty_cache()
torch.cuda.synchronize()
def load_pipeline(model_type, model_path, use_lora=False, lora_path=None):
"""根据配置加载对应的 pipeline"""
print(f"加载模型: {model_path}")
if model_type == "fill":
pipe = FluxFillPipeline.from_pretrained(
model_path,
torch_dtype=torch.bfloat16
).to("cuda")
if use_lora and lora_path:
print(f"加载 LoRA 权重: {lora_path}")
pipe.load_lora_weights(lora_path)
elif model_type == "dev":
pipe = FluxInpaintPipeline.from_pretrained(
model_path,
torch_dtype=torch.bfloat16
).to("cuda")
# FLUX.1-dev 不支持 LoRA（此处可根据需要扩展）
else:
raise ValueError(f"未知的 model_type: {model_type}")
# 可选内存优化（根据需要取消注释）
# pipe.enable_attention_slicing()
# pipe.enable_model_cpu_offload()
return pipe
# ========== 主流程 ==========
def main():
# 准备输入图片和掩码
image_path = os.path.join(input_imgs_dir, image_name)
mask_path = os.path.join(input_imgs_dir, mask_name)
if not os.path.exists(image_path):
raise FileNotFoundError(f"图片不存在: {image_path}")
if not os.path.exists(mask_path):
raise FileNotFoundError(f"掩码图片不存在: {mask_path}")
# 加载图片和掩码（只加载一次，所有实验共用）
image = load_image(image_path)
mask = load_image(mask_path)
orig_width, orig_height = image.size
# 循环执行每个实验
for exp in experiments:
print(f"\n========== 开始实验: {exp['name']} ==========")
# 为当前实验创建输出子目录
exp_output_dir = os.path.join(output_base_dir, exp['name'])
os.makedirs(exp_output_dir, exist_ok=True)
# 加载 pipeline（每个实验单独加载，确保模型切换）
pipe = load_pipeline(
model_type=exp['model_type'],
model_path=exp['model_path'],
use_lora=exp['use_lora'],
lora_path=lora_weights_path if exp['use_lora'] else None
)
# 生成多张图片
for i in range(num_images_per_experiment):
seed = i # 使用 0,1,2,... 作为种子，保证可复现
generator = torch.Generator("cuda").manual_seed(seed)
print(f" 生成第 {i+1}/{num_images_per_experiment} 张图片 (seed={seed})")
# 根据 pipeline 类型调用推理（接口基本一致）
result = pipe(
prompt="Taillights with premium, high-end design",
image=image,
mask_image=mask,
height=orig_height,
width=orig_width,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
generator=generator
).images[0]
# 保存结果：文件名包含原图名和种子
out_filename = f"{os.path.splitext(image_name)[0]}_seed{seed:03d}.png"
out_path = os.path.join(exp_output_dir, out_filename)
result.save(out_path)
print(f" 保存至: {out_path}")
# 实验结束，释放 pipeline 并清理显存
del pipe
clear_gpu_memory()
print(f"========== 实验 {exp['name']} 完成 ==========\n")
print("所有实验执行完毕！")
if __name__ == "__main__":
main()六、计划
后续考虑能否在局部重绘推理的时候增加IP-Adapter用于根据参考图进行生成
https://huggingface.co/InstantX/FLUX.1-dev-IP-Adapter/tree/main
一个微调训练的项目