DreamBooth
DreamBooth 是一种用于个性化文本到图像模型的方法，类似于 Stable Diffusion，只需要几张（3-5张）主题的图像。它使得模型能够生成主题在不同场景、姿势和视角下的情境化图像。
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Dreambooth的示例来自项目的博客页面。本指南将展示如何使用CompVis/stable-diffusion-v1-4模型在不同GPU尺寸和Flax上对Dreambooth进行微调。如果你有兴趣深入了解并查看其工作原理，所有在本指南中使用的Dreambooth训练脚本都可以在此处找到。
在运行脚本之前，请确保安装了该库的训练依赖项。我们还建议从main GitHub分支安装🧨 Diffusers：
pip install git+[https://github.com/huggingface/diffusers](https://github.com/huggingface/diffusers)
pip install -U -r diffusers/examples/dreambooth/requirements.txt
xFormers不是训练要求的一部分，但是我们建议如果可能的话安装它，因为它可以使训练速度更快，内存占用更少。
在设置好所有依赖项后，使用以下方式初始化🤗 Accelerate环境：
accelerate config
要设置一个默认的🤗 Accelerate环境，无需选择任何配置，请按如下步骤操作：
accelerate config default
如果你的环境不支持交互式Shell（例如笔记本），你可以使用以下方法：
from accelerate.utils import write_basic_config
write_basic_config()
最后，使用以下方法从huggingface.co下载几张狗的图片到DreamBooth：
from huggingface_hub import snapshot_download
local_dir = "./dog"
snapshot_download(
"diffusers/dog-example",
local_dir=local_dir,
repo_type="dataset",
ignore_patterns=".gitattributes",
)
要使用自己的数据集，请查看Create a dataset for training指南。
微调（Finetuning）
DreamBooth的微调对超参数非常敏感，容易过拟合。我们建议您查看我们的深度分析，其中包含针对不同主题的推荐设置，以帮助您选择适当的超参数。
将INSTANCE_DIR环境变量设置为包含狗图片的目录路径。
指定MODEL_NAME环境变量（可以是Hub模型仓库ID或包含模型权重的目录路径），并将其传递给pretrained_model_name_or_path参数。instance_prompt参数是一个文本提示，其中包含唯一标识符，比如sks，以及图像所属的类别，在这个例子中是a photo of a sks dog。
export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export INSTANCE_DIR="./dog"
export OUTPUT_DIR="path_to_saved_model"
然后，您可以使用以下命令启动训练脚本（您可以在此处找到完整的训练脚本）：
accelerate launch train_dreambooth.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir=$INSTANCE_DIR \
--output_dir=$OUTPUT_DIR \
--instance_prompt="a photo of sks dog" \
--resolution=512 \
--train_batch_size=1 \
--gradient_accumulation_steps=1 \
--learning_rate=5e-6 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--max_train_steps=400 \
--push_to_hub
如果您可以使用TPU或希望进行更快的训练，您可以尝试使用Flax训练脚本。Flax训练脚本不支持梯度检查点或梯度累积，因此您需要一块至少拥有30GB内存的GPU。
在运行脚本之前，请确保已安装所需的依赖项：
pip install -U -r requirements.txt
指定MODEL_NAME环境变量（可以是Hub模型仓库ID或包含模型权重的目录路径），并将其传递给pretrained_model_name_or_path参数。instance_prompt参数是一个文本提示，其中包含唯一标识符，比如sks，以及图像所属的类别，在这个例子中是a photo of a sks dog。
现在，您可以使用以下命令启动训练脚本：
export MODEL_NAME="duongna/stable-diffusion-v1-4-flax"
export INSTANCE_DIR="./dog"
export OUTPUT_DIR="path-to-save-model"
python train_dreambooth_flax.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir=$INSTANCE_DIR \
--output_dir=$OUTPUT_DIR \
--instance_prompt="a photo of sks dog" \
--resolution=512 \
--train_batch_size=1 \
--learning_rate=5e-6 \
--max_train_steps=400 \
--push_to_hub
使用保留先验信息的损失进行微调
先验信息保留用于避免过拟合和语言漂移（如果您有兴趣，可以查阅论文了解更多细节）。为了进行先验信息保留，您可以在训练过程中使用同一类别的其他图像。好处在于，您可以使用Stable Diffusion模型本身来生成这些图像！训练脚本将把生成的图像保存到您指定的本地路径。
作者建议为了先验信息保留而生成num_epochs * num_samples张图像。在大多数情况下，生成200-300张图像效果较好。
export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export INSTANCE_DIR="./dog"
export CLASS_DIR="path_to_class_images"
export OUTPUT_DIR="path_to_saved_model"
accelerate launch train_dreambooth.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir=$INSTANCE_DIR \
--class_data_dir=$CLASS_DIR \
--output_dir=$OUTPUT_DIR \
--with_prior_preservation --prior_loss_weight=1.0 \
--instance_prompt="a photo of sks dog" \
--class_prompt="a photo of dog" \
--resolution=512 \
--train_batch_size=1 \
--gradient_accumulation_steps=1 \
--learning_rate=5e-6 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--num_class_images=200 \
--max_train_steps=800 \
--push_to_hub
JAX
export MODEL_NAME="duongna/stable-diffusion-v1-4-flax"
export INSTANCE_DIR="./dog"
export CLASS_DIR="path-to-class-images"
export OUTPUT_DIR="path-to-save-model"
python train_dreambooth_flax.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir=$INSTANCE_DIR \
--class_data_dir=$CLASS_DIR \
--output_dir=$OUTPUT_DIR \
--with_prior_preservation --prior_loss_weight=1.0 \
--instance_prompt="a photo of sks dog" \
--class_prompt="a photo of dog" \
--resolution=512 \
--train_batch_size=1 \
--learning_rate=5e-6 \
--num_class_images=200 \
--max_train_steps=800 \
--push_to_hub
Finetuning the text encoder and UNet
The script also allows you to finetune the text_encoder along with the unet. In our experiments (check out the Training Stable Diffusion with DreamBooth using 🧨 Diffusers post for more details), this yields much better results, especially when generating images of faces.
Training the text encoder requires additional memory and it won’t fit on a 16GB GPU. You’ll need at least 24GB VRAM to use this option.
Pass the --train_text_encoder argument to the training script to enable finetuning the text_encoder and unet:
Pytorch
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export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export INSTANCE_DIR="./dog"
export CLASS_DIR="path_to_class_images"
export OUTPUT_DIR="path_to_saved_model"
accelerate launch train_dreambooth.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--train_text_encoder \
--instance_data_dir=$INSTANCE_DIR \
--class_data_dir=$CLASS_DIR \
--output_dir=$OUTPUT_DIR \
--with_prior_preservation --prior_loss_weight=1.0 \
--instance_prompt="a photo of sks dog" \
--class_prompt="a photo of dog" \
--resolution=512 \
--train_batch_size=1 \
--use_8bit_adam \
--gradient_checkpointing \
--learning_rate=2e-6 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--num_class_images=200 \
--max_train_steps=800 \
--push_to_hub
JAX
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export MODEL_NAME="duongna/stable-diffusion-v1-4-flax"
export INSTANCE_DIR="./dog"
export CLASS_DIR="path-to-class-images"
export OUTPUT_DIR="path-to-save-model"
python train_dreambooth_flax.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--train_text_encoder \
--instance_data_dir=$INSTANCE_DIR \
--class_data_dir=$CLASS_DIR \
--output_dir=$OUTPUT_DIR \
--with_prior_preservation --prior_loss_weight=1.0 \
--instance_prompt="a photo of sks dog" \
--class_prompt="a photo of dog" \
--resolution=512 \
--train_batch_size=1 \
--learning_rate=2e-6 \
--num_class_images=200 \
--max_train_steps=800 \
--push_to_hub
Finetuning with LoRA
You can also use Low-Rank Adaptation of Large Language Models (LoRA), a fine-tuning technique for accelerating training large models, on DreamBooth. For more details, take a look at the LoRA training guide.
Saving checkpoints while training
It’s easy to overfit while training with Dreambooth, so sometimes it’s useful to save regular checkpoints during the training process. One of the intermediate checkpoints might actually work better than the final model! Pass the following argument to the training script to enable saving checkpoints:
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--checkpointing_steps=500
This saves the full training state in subfolders of your output_dir. Subfolder names begin with the prefix checkpoint-, followed by the number of steps performed so far; for example, checkpoint-1500 would be a checkpoint saved after 1500 training steps.
Resume training from a saved checkpoint
If you want to resume training from any of the saved checkpoints, you can pass the argument --resume_from_checkpoint to the script and specify the name of the checkpoint you want to use. You can also use the special string "latest" to resume from the last saved checkpoint (the one with the largest number of steps). For example, the following would resume training from the checkpoint saved after 1500 steps:
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--resume_from_checkpoint="checkpoint-1500"
This is a good opportunity to tweak some of your hyperparameters if you wish.
Inference from a saved checkpoint
Saved checkpoints are stored in a format suitable for resuming training. They not only include the model weights, but also the state of the optimizer, data loaders, and learning rate.
If you have "accelerate>=0.16.0" installed, use the following code to run inference from an intermediate checkpoint.
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from diffusers import DiffusionPipeline, UNet2DConditionModel
from transformers import CLIPTextModel
import torch
# Load the pipeline with the same arguments (model, revision) that were used for training
model_id = "CompVis/stable-diffusion-v1-4"
unet = UNet2DConditionModel.from_pretrained("/sddata/dreambooth/daruma-v2-1/checkpoint-100/unet")
# if you have trained with `--args.train_text_encoder` make sure to also load the text encoder
text_encoder = CLIPTextModel.from_pretrained("/sddata/dreambooth/daruma-v2-1/checkpoint-100/text_encoder")
pipeline = DiffusionPipeline.from_pretrained(model_id, unet=unet, text_encoder=text_encoder, dtype=torch.float16)
pipeline.to("cuda")
# Perform inference, or save, or push to the hub
pipeline.save_pretrained("dreambooth-pipeline")
If you have "accelerate<0.16.0" installed, you need to convert it to an inference pipeline first:
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from accelerate import Accelerator
from diffusers import DiffusionPipeline
# Load the pipeline with the same arguments (model, revision) that were used for training
model_id = "CompVis/stable-diffusion-v1-4"
pipeline = DiffusionPipeline.from_pretrained(model_id)
accelerator = Accelerator()
# Use text_encoder if `--train_text_encoder` was used for the initial training
unet, text_encoder = accelerator.prepare(pipeline.unet, pipeline.text_encoder)
# Restore state from a checkpoint path. You have to use the absolute path here.
accelerator.load_state("/sddata/dreambooth/daruma-v2-1/checkpoint-100")
# Rebuild the pipeline with the unwrapped models (assignment to .unet and .text_encoder should work too)
pipeline = DiffusionPipeline.from_pretrained(
model_id,
unet=accelerator.unwrap_model(unet),
text_encoder=accelerator.unwrap_model(text_encoder),
)
# Perform inference, or save, or push to the hub
pipeline.save_pretrained("dreambooth-pipeline")
Optimizations for different GPU sizes
Depending on your hardware, there are a few different ways to optimize DreamBooth on GPUs from 16GB to just 8GB!
xFormers
xFormers is a toolbox for optimizing Transformers, and it includes a memory-efficient attention mechanism that is used in 🧨 Diffusers. You’ll need to install xFormers and then add the following argument to your training script:
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--enable_xformers_memory_efficient_attention
xFormers is not available in Flax.
Set gradients to none
Another way you can lower your memory footprint is to set the gradients to None instead of zero. However, this may change certain behaviors, so if you run into any issues, try removing this argument. Add the following argument to your training script to set the gradients to None:
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--set_grads_to_none
16GB GPU
With the help of gradient checkpointing and bitsandbytes 8-bit optimizer, it’s possible to train DreamBooth on a 16GB GPU. Make sure you have bitsandbytes installed:
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pip install bitsandbytes
Then pass the --use_8bit_adam option to the training script:
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export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export INSTANCE_DIR="./dog"
export CLASS_DIR="path_to_class_images"
export OUTPUT_DIR="path_to_saved_model"
accelerate launch train_dreambooth.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir=$INSTANCE_DIR \
--class_data_dir=$CLASS_DIR \
--output_dir=$OUTPUT_DIR \
--with_prior_preservation --prior_loss_weight=1.0 \
--instance_prompt="a photo of sks dog" \
--class_prompt="a photo of dog" \
--resolution=512 \
--train_batch_size=1 \
--gradient_accumulation_steps=2 --gradient_checkpointing \
--use_8bit_adam \
--learning_rate=5e-6 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--num_class_images=200 \
--max_train_steps=800 \
--push_to_hub
12GB GPU
To run DreamBooth on a 12GB GPU, you’ll need to enable gradient checkpointing, the 8-bit optimizer, xFormers, and set the gradients to None:
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export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export INSTANCE_DIR="./dog"
export CLASS_DIR="path-to-class-images"
export OUTPUT_DIR="path-to-save-model"
accelerate launch train_dreambooth.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir=$INSTANCE_DIR \
--class_data_dir=$CLASS_DIR \
--output_dir=$OUTPUT_DIR \
--with_prior_preservation --prior_loss_weight=1.0 \
--instance_prompt="a photo of sks dog" \
--class_prompt="a photo of dog" \
--resolution=512 \
--train_batch_size=1 \
--gradient_accumulation_steps=1 --gradient_checkpointing \
--use_8bit_adam \
--enable_xformers_memory_efficient_attention \
--set_grads_to_none \
--learning_rate=2e-6 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--num_class_images=200 \
--max_train_steps=800 \
--push_to_hub
8 GB GPU
For 8GB GPUs, you’ll need the help of DeepSpeed to offload some tensors from the VRAM to either the CPU or NVME, enabling training with less GPU memory.
Run the following command to configure your 🤗 Accelerate environment:
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accelerate config
During configuration, confirm that you want to use DeepSpeed. Now it’s possible to train on under 8GB VRAM by combining DeepSpeed stage 2, fp16 mixed precision, and offloading the model parameters and the optimizer state to the CPU. The drawback is that this requires more system RAM, about 25 GB. See the DeepSpeed documentation for more configuration options.
You should also change the default Adam optimizer to DeepSpeed’s optimized version of Adam deepspeed.ops.adam.DeepSpeedCPUAdam for a substantial speedup. Enabling DeepSpeedCPUAdam requires your system’s CUDA toolchain version to be the same as the one installed with PyTorch.
8-bit optimizers don’t seem to be compatible with DeepSpeed at the moment.
Launch training with the following command:
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export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export INSTANCE_DIR="./dog"
export CLASS_DIR="path_to_class_images"
export OUTPUT_DIR="path_to_saved_model"
accelerate launch train_dreambooth.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir=$INSTANCE_DIR \
--class_data_dir=$CLASS_DIR \
--output_dir=$OUTPUT_DIR \
--with_prior_preservation --prior_loss_weight=1.0 \
--instance_prompt="a photo of sks dog" \
--class_prompt="a photo of dog" \
--resolution=512 \
--train_batch_size=1 \
--sample_batch_size=1 \
--gradient_accumulation_steps=1 --gradient_checkpointing \
--learning_rate=5e-6 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--num_class_images=200 \
--max_train_steps=800 \
--mixed_precision=fp16 \
--push_to_hub
Inference
Once you have trained a model, specify the path to where the model is saved, and use it for inference in the StableDiffusionPipeline. Make sure your prompts include the special identifier used during training (sks in the previous examples).
If you have "accelerate>=0.16.0" installed, you can use the following code to run inference from an intermediate checkpoint:
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from diffusers import DiffusionPipeline
import torch
model_id = "path_to_saved_model"
pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
prompt = "A photo of sks dog in a bucket"
image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]
image.save("dog-bucket.png")
You may also run inference from any of the saved training checkpoints.
IF
You can use the lora and full dreambooth scripts to train the text to image IF model and the stage II upscaler IF model.
Note that IF has a predicted variance, and our finetuning scripts only train the models predicted error, so for finetuned IF models we switch to a fixed variance schedule. The full finetuning scripts will update the scheduler config for the full saved model. However, when loading saved LoRA weights, you must also update the pipeline’s scheduler config.
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from diffusers import DiffusionPipeline
pipe = DiffusionPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0")
pipe.load_lora_weights("<lora weights path>")
# Update scheduler config to fixed variance schedule
pipe.scheduler = pipe.scheduler.__class__.from_config(pipe.scheduler.config, variance_type="fixed_small")
Additionally, a few alternative cli flags are needed for IF.
--resolution=64: IF is a pixel space diffusion model. In order to operate on un-compressed pixels, the input images are of a much smaller resolution.
--pre_compute_text_embeddings: IF uses T5 for its text encoder. In order to save GPU memory, we pre compute all text embeddings and then de-allocate T5.
--tokenizer_max_length=77: T5 has a longer default text length, but the default IF encoding procedure uses a smaller number.
--text_encoder_use_attention_mask: T5 passes the attention mask to the text encoder.
Tips and Tricks
We find LoRA to be sufficient for finetuning the stage I model as the low resolution of the model makes representing finegrained detail hard regardless.For common and/or not-visually complex object concepts, you can get away with not-finetuning the upscaler. Just be sure to adjust the prompt passed to the upscaler to remove the new token from the instance prompt. I.e. if your stage I prompt is “a sks dog”, use “a dog” for your stage II prompt.
For finegrained detail like faces that aren’t present in the original training set, we find that full finetuning of the stage II upscaler is better than LoRA finetuning stage II.
For finegrained detail like faces, we find that lower learning rates along with larger batch sizes work best.
For stage II, we find that lower learning rates are also needed.
We found experimentally that the DDPM scheduler with the default larger number of denoising steps to sometimes work better than the DPM Solver scheduler used in the training scripts.
Stage II additional validation images
The stage II validation requires images to upscale, we can download a downsized version of the training set:
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from huggingface_hub import snapshot_download
local_dir = "./dog_downsized"
snapshot_download(
"diffusers/dog-example-downsized",
local_dir=local_dir,
repo_type="dataset",
ignore_patterns=".gitattributes",
)
IF stage I LoRA Dreambooth
This training configuration requires ~28 GB VRAM.
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export MODEL_NAME="DeepFloyd/IF-I-XL-v1.0"
export INSTANCE_DIR="dog"
export OUTPUT_DIR="dreambooth_dog_lora"
accelerate launch train_dreambooth_lora.py \
--report_to wandb \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir=$INSTANCE_DIR \
--output_dir=$OUTPUT_DIR \
--instance_prompt="a sks dog" \
--resolution=64 \
--train_batch_size=4 \
--gradient_accumulation_steps=1 \
--learning_rate=5e-6 \
--scale_lr \
--max_train_steps=1200 \
--validation_prompt="a sks dog" \
--validation_epochs=25 \
--checkpointing_steps=100 \
--pre_compute_text_embeddings \
--tokenizer_max_length=77 \
--text_encoder_use_attention_mask
IF stage II LoRA Dreambooth
--validation_images: These images are upscaled during validation steps.
--class_labels_conditioning=timesteps: Pass additional conditioning to the UNet needed for stage II.
--learning_rate=1e-6: Lower learning rate than stage I.
--resolution=256: The upscaler expects higher resolution inputs
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export MODEL_NAME="DeepFloyd/IF-II-L-v1.0"
export INSTANCE_DIR="dog"
export OUTPUT_DIR="dreambooth_dog_upscale"
export VALIDATION_IMAGES="dog_downsized/image_1.png dog_downsized/image_2.png dog_downsized/image_3.png dog_downsized/image_4.png"
python train_dreambooth_lora.py \
--report_to wandb \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir=$INSTANCE_DIR \
--output_dir=$OUTPUT_DIR \
--instance_prompt="a sks dog" \
--resolution=256 \
--train_batch_size=4 \
--gradient_accumulation_steps=1 \
--learning_rate=1e-6 \
--max_train_steps=2000 \
--validation_prompt="a sks dog" \
--validation_epochs=100 \
--checkpointing_steps=500 \
--pre_compute_text_embeddings \
--tokenizer_max_length=77 \
--text_encoder_use_attention_mask \
--validation_images $VALIDATION_IMAGES \
--class_labels_conditioning=timesteps
IF Stage I Full Dreambooth
--skip_save_text_encoder: When training the full model, this will skip saving the entire T5 with the finetuned model. You can still load the pipeline with a T5 loaded from the original model.use_8bit_adam: Due to the size of the optimizer states, we recommend training the full XL IF model with 8bit adam.
--learning_rate=1e-7: For full dreambooth, IF requires very low learning rates. With higher learning rates model quality will degrade. Note that it is likely the learning rate can be increased with larger batch sizes.
Using 8bit adam and a batch size of 4, the model can be trained in ~48 GB VRAM.
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export MODEL_NAME="DeepFloyd/IF-I-XL-v1.0"
export INSTANCE_DIR="dog"
export OUTPUT_DIR="dreambooth_if"
accelerate launch train_dreambooth.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir=$INSTANCE_DIR \
--output_dir=$OUTPUT_DIR \
--instance_prompt="a photo of sks dog" \
--resolution=64 \
--train_batch_size=4 \
--gradient_accumulation_steps=1 \
--learning_rate=1e-7 \
--max_train_steps=150 \
--validation_prompt "a photo of sks dog" \
--validation_steps 25 \
--text_encoder_use_attention_mask \
--tokenizer_max_length 77 \
--pre_compute_text_embeddings \
--use_8bit_adam \
--set_grads_to_none \
--skip_save_text_encoder \
--push_to_hub
IF Stage II Full Dreambooth
--learning_rate=5e-6: With a smaller effective batch size of 4, we found that we required learning rates as low as 1e-8.
--resolution=256: The upscaler expects higher resolution inputs
--train_batch_size=2 and --gradient_accumulation_steps=6: We found that full training of stage II particularly with faces required large effective batch sizes.
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export MODEL_NAME="DeepFloyd/IF-II-L-v1.0"
export INSTANCE_DIR="dog"
export OUTPUT_DIR="dreambooth_dog_upscale"
export VALIDATION_IMAGES="dog_downsized/image_1.png dog_downsized/image_2.png dog_downsized/image_3.png dog_downsized/image_4.png"
accelerate launch train_dreambooth.py \
--report_to wandb \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir=$INSTANCE_DIR \
--output_dir=$OUTPUT_DIR \
--instance_prompt="a sks dog" \
--resolution=256 \
--train_batch_size=2 \
--gradient_accumulation_steps=6 \
--learning_rate=5e-6 \
--max_train_steps=2000 \
--validation_prompt="a sks dog" \
--validation_steps=150 \
--checkpointing_steps=500 \
--pre_compute_text_embeddings \
--tokenizer_max_length=77 \
--text_encoder_use_attention_mask \
--validation_images $VALIDATION_IMAGES \
--class_labels_conditioning timesteps \
--push_to_hub
发表于 2023-7-26 20:05:32
发表于 2024-5-13 19:15:53
