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2dc6cfa27d4b32a85ca96b1c6023acf2.txt_chunk_13	ou may also need to write your own preprocessing function. The script also creates a configuration for the 🤗 PEFT method you’re using, which in this case, is LoRA. The LoraConfig specifies the task type and important parameters such as the dimension of the low-rank matrices, the matrices scaling factor, and the dropo...	2dc6cfa27d4b32a85ca96b1c6023acf2.txt
2dc6cfa27d4b32a85ca96b1c6023acf2.txt_chunk_14	t probability of the LoRA layers. If you want to use a different 🤗 PEFT method, make sure you replace LoraConfig with the appropriate class. Copied def main(): + accelerator = Accelerator() model_name_or_path = "facebook/bart-large" dataset_name = "twitter_complaints" + peft_config = LoraConfig( ...	2dc6cfa27d4b32a85ca96b1c6023acf2.txt
2dc6cfa27d4b32a85ca96b1c6023acf2.txt_chunk_15	task_type=TaskType.SEQ_2_SEQ_LM, inference_mode=False, r=8, lora_alpha=32, lora_dropout=0.1 ) Throughout the script, you’ll see the main_process_first and wait_for_everyone functions which help control and synchronize when processes are executed. The get_peft_model() function takes a base model and the peft_...	2dc6cfa27d4b32a85ca96b1c6023acf2.txt
2dc6cfa27d4b32a85ca96b1c6023acf2.txt_chunk_16	config you prepared earlier to create a PeftModel: Copied model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path) + model = get_peft_model(model, peft_config) Pass all the relevant training objects to 🤗 Accelerate’s prepare which makes sure everything is ready for training: Copied model, train_datalo...	2dc6cfa27d4b32a85ca96b1c6023acf2.txt
2dc6cfa27d4b32a85ca96b1c6023acf2.txt_chunk_17	der, eval_dataloader, test_dataloader, optimizer, lr_scheduler = accelerator.prepare( model, train_dataloader, eval_dataloader, test_dataloader, optimizer, lr_scheduler ) The next bit of code checks whether the DeepSpeed plugin is used in the Accelerator, and if the plugin exists, then the Accelerator uses ZeRO-3 a...	2dc6cfa27d4b32a85ca96b1c6023acf2.txt
2dc6cfa27d4b32a85ca96b1c6023acf2.txt_chunk_18	s specified in the configuration file: Copied is_ds_zero_3 = False if getattr(accelerator.state, "deepspeed_plugin", None): is_ds_zero_3 = accelerator.state.deepspeed_plugin.zero_stage == 3 Inside the training loop, the usual loss.backward() is replaced by 🤗 Accelerate’s backward which uses the correct backward...	2dc6cfa27d4b32a85ca96b1c6023acf2.txt
2dc6cfa27d4b32a85ca96b1c6023acf2.txt_chunk_19	) method based on your configuration: Copied for epoch in range(num_epochs): with TorchTracemalloc() as tracemalloc: model.train() total_loss = 0 for step, batch in enumerate(tqdm(train_dataloader)): outputs = model(**batch) loss = outputs.loss ...	2dc6cfa27d4b32a85ca96b1c6023acf2.txt
2dc6cfa27d4b32a85ca96b1c6023acf2.txt_chunk_20	total_loss += loss.detach().float() + accelerator.backward(loss) optimizer.step() lr_scheduler.step() optimizer.zero_grad() That is all! The rest of the script handles the training loop, evaluation, and even pushes it to the Hub for you. Train Run the fol...	2dc6cfa27d4b32a85ca96b1c6023acf2.txt
2dc6cfa27d4b32a85ca96b1c6023acf2.txt_chunk_21	lowing command to launch the training script. Earlier, you saved the configuration file to ds_zero3_cpu.yaml, so you’ll need to pass the path to the launcher with the --config_file argument like this: Copied accelerate launch --config_file ds_zero3_cpu.yaml examples/peft_lora_seq2seq_accelerate_ds_zero3_offload.py Y...	2dc6cfa27d4b32a85ca96b1c6023acf2.txt
2dc6cfa27d4b32a85ca96b1c6023acf2.txt_chunk_22	ou’ll see some output logs that track memory usage during training, and once it’s completed, the script returns the accuracy and compares the predictions to the labels: Copied GPU Memory before entering the train : 1916 GPU Memory consumed at the end of the train (end-begin): 66 GPU Peak Memory consumed during the t...	2dc6cfa27d4b32a85ca96b1c6023acf2.txt
2dc6cfa27d4b32a85ca96b1c6023acf2.txt_chunk_23	rain (max-begin): 7488 GPU Total Peak Memory consumed during the train (max): 9404 CPU Memory before entering the train : 19411 CPU Memory consumed at the end of the train (end-begin): 0 CPU Peak Memory consumed during the train (max-begin): 0 CPU Total Peak Memory consumed during the train (max): 19411 epoch=4: train_...	2dc6cfa27d4b32a85ca96b1c6023acf2.txt
2dc6cfa27d4b32a85ca96b1c6023acf2.txt_chunk_24	ppl=tensor(1.0705, device='cuda:0') train_epoch_loss=tensor(0.0681, device='cuda:0') 100%\|████████████████████████████████████████████████████████████████████████████████████████████\| 7/7 [00:27<00:00, 3.92s/it] GPU Memory before entering the eval : 1982 GPU Memory consumed at the end of the eval (end-begin): -66 GPU ...	2dc6cfa27d4b32a85ca96b1c6023acf2.txt
2dc6cfa27d4b32a85ca96b1c6023acf2.txt_chunk_25	Peak Memory consumed during the eval (max-begin): 672 GPU Total Peak Memory consumed during the eval (max): 2654 CPU Memory before entering the eval : 19411 CPU Memory consumed at the end of the eval (end-begin): 0 CPU Peak Memory consumed during the eval (max-begin): 0 CPU Total Peak Memory consumed during the eval (m...	2dc6cfa27d4b32a85ca96b1c6023acf2.txt
2dc6cfa27d4b32a85ca96b1c6023acf2.txt_chunk_26	ax): 19411 accuracy=100.0 eval_preds[:10]=['no complaint', 'no complaint', 'complaint', 'complaint', 'no complaint', 'no complaint', 'no complaint', 'complaint', 'complaint', 'no complaint'] dataset['train'][label_column][:10]=['no complaint', 'no complaint', 'complaint', 'complaint', 'no complaint', 'no complaint', 'n...	2dc6cfa27d4b32a85ca96b1c6023acf2.txt
2dc6cfa27d4b32a85ca96b1c6023acf2.txt_chunk_27	o complaint', 'complaint', 'complaint', 'no complaint']	2dc6cfa27d4b32a85ca96b1c6023acf2.txt
d07fa13a3fe1301188b103c336101329.txt_chunk_1	DreamBooth fine-tuning with LoRA This guide demonstrates how to use LoRA, a low-rank approximation technique, to fine-tune DreamBooth with the CompVis/stable-diffusion-v1-4 model. Although LoRA was initially designed as a technique for reducing the number of trainable parameters in large-language models, the techniq...	d07fa13a3fe1301188b103c336101329.txt
d07fa13a3fe1301188b103c336101329.txt_chunk_2	ue can also be applied to diffusion models. Performing a complete model fine-tuning of diffusion models is a time-consuming task, which is why lightweight techniques like DreamBooth or Textual Inversion gained popularity. With the introduction of LoRA, customizing and fine-tuning a model on a specific dataset has becom...	d07fa13a3fe1301188b103c336101329.txt
d07fa13a3fe1301188b103c336101329.txt_chunk_3	e even faster. In this guide we’ll be using a DreamBooth fine-tuning script that is available in PEFT’s GitHub repo. Feel free to explore it and learn how things work. Set up your environment Start by cloning the PEFT repository: Copied git clone https://github.com/huggingface/peft Navigate to the directory cont...	d07fa13a3fe1301188b103c336101329.txt
d07fa13a3fe1301188b103c336101329.txt_chunk_4	aining the training scripts for fine-tuning Dreambooth with LoRA: Copied cd peft/examples/lora_dreambooth Set up your environment: install PEFT, and all the required libraries. At the time of writing this guide we recommend installing PEFT from source. Copied pip install -r requirements.txt pip install git+http...	d07fa13a3fe1301188b103c336101329.txt
d07fa13a3fe1301188b103c336101329.txt_chunk_5	s://github.com/huggingface/peft Fine-tuning DreamBooth Prepare the images that you will use for fine-tuning the model. Set up a few environment variables: Copied export MODEL_NAME="CompVis/stable-diffusion-v1-4" export INSTANCE_DIR="path-to-instance-images" export CLASS_DIR="path-to-class-images" export OUTPUT_...	d07fa13a3fe1301188b103c336101329.txt
d07fa13a3fe1301188b103c336101329.txt_chunk_6	DIR="path-to-save-model" Here: INSTANCE_DIR: The directory containing the images that you intend to use for training your model. CLASS_DIR: The directory containing class-specific images. In this example, we use prior preservation to avoid overfitting and language-drift. For prior preservation, you need other images o...	d07fa13a3fe1301188b103c336101329.txt
d07fa13a3fe1301188b103c336101329.txt_chunk_7	f the same class as part of the training process. However, these images can be generated and the training script will save them to a local path you specify here. OUTPUT_DIR: The destination folder for storing the trained model’s weights. To learn more about DreamBooth fine-tuning with prior-preserving loss, check out t...	d07fa13a3fe1301188b103c336101329.txt
d07fa13a3fe1301188b103c336101329.txt_chunk_8	he Diffusers documentation. Launch the training script with accelerate and pass hyperparameters, as well as LoRa-specific arguments to it such as: use_lora: Enables LoRa in the training script. lora_r: The dimension used by the LoRA update matrices. lora_alpha: Scaling factor. lora_text_encoder_r: LoRA rank for text ...	d07fa13a3fe1301188b103c336101329.txt
d07fa13a3fe1301188b103c336101329.txt_chunk_9	encoder. lora_text_encoder_alpha: LoRA alpha (scaling factor) for text encoder. Here’s what the full set of script arguments may look like: Copied accelerate launch train_dreambooth.py \ --pretrained_model_name_or_path=$MODEL_NAME \ --instance_data_dir=$INSTANCE_DIR \ --class_data_dir=$CLASS_DIR \ --output_...	d07fa13a3fe1301188b103c336101329.txt
d07fa13a3fe1301188b103c336101329.txt_chunk_10	dir=$OUTPUT_DIR \ --train_text_encoder \ --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 \ --lr_scheduler="constant" \ --lr_warmup_steps=0 \ --num_class_images=200 \ --use_lora \ ...	d07fa13a3fe1301188b103c336101329.txt
d07fa13a3fe1301188b103c336101329.txt_chunk_11	--lora_r 16 \ --lora_alpha 27 \ --lora_text_encoder_r 16 \ --lora_text_encoder_alpha 17 \ --learning_rate=1e-4 \ --gradient_accumulation_steps=1 \ --gradient_checkpointing \ --max_train_steps=800 Inference with a single adapter To run inference with the fine-tuned model, first specify the base model wi...	d07fa13a3fe1301188b103c336101329.txt
d07fa13a3fe1301188b103c336101329.txt_chunk_12	th which the fine-tuned LoRA weights will be combined: Copied import os import torch from diffusers import StableDiffusionPipeline from peft import PeftModel, LoraConfig MODEL_NAME = "CompVis/stable-diffusion-v1-4" Next, add a function that will create a Stable Diffusion pipeline for image generation. It will comb...	d07fa13a3fe1301188b103c336101329.txt
d07fa13a3fe1301188b103c336101329.txt_chunk_13	ine the weights of the base model with the fine-tuned LoRA weights using LoraConfig. Copied def get_lora_sd_pipeline( ckpt_dir, base_model_name_or_path=None, dtype=torch.float16, device="cuda", adapter_name="default" ): unet_sub_dir = os.path.join(ckpt_dir, "unet") text_encoder_sub_dir = os.path.join(ckp...	d07fa13a3fe1301188b103c336101329.txt
d07fa13a3fe1301188b103c336101329.txt_chunk_14	t_dir, "text_encoder") if os.path.exists(text_encoder_sub_dir) and base_model_name_or_path is None: config = LoraConfig.from_pretrained(text_encoder_sub_dir) base_model_name_or_path = config.base_model_name_or_path if base_model_name_or_path is None: raise ValueError("Please specify the...	d07fa13a3fe1301188b103c336101329.txt
d07fa13a3fe1301188b103c336101329.txt_chunk_15	base model name or path") pipe = StableDiffusionPipeline.from_pretrained(base_model_name_or_path, torch_dtype=dtype).to(device) pipe.unet = PeftModel.from_pretrained(pipe.unet, unet_sub_dir, adapter_name=adapter_name) if os.path.exists(text_encoder_sub_dir): pipe.text_encoder = PeftModel.from_pre...	d07fa13a3fe1301188b103c336101329.txt
d07fa13a3fe1301188b103c336101329.txt_chunk_16	trained( pipe.text_encoder, text_encoder_sub_dir, adapter_name=adapter_name ) if dtype in (torch.float16, torch.bfloat16): pipe.unet.half() pipe.text_encoder.half() pipe.to(device) return pipe Now you can use the function above to create a Stable Diffusion pipeline usin...	d07fa13a3fe1301188b103c336101329.txt
d07fa13a3fe1301188b103c336101329.txt_chunk_17	g the LoRA weights that you have created during the fine-tuning step. Note, if you’re running inference on the same machine, the path you specify here will be the same as OUTPUT_DIR. Copied pipe = get_lora_sd_pipeline(Path("path-to-saved-model"), adapter_name="dog") Once you have the pipeline with your fine-tuned ...	d07fa13a3fe1301188b103c336101329.txt
d07fa13a3fe1301188b103c336101329.txt_chunk_18	model, you can use it to generate images: Copied prompt = "sks dog playing fetch in the park" negative_prompt = "low quality, blurry, unfinished" image = pipe(prompt, num_inference_steps=50, guidance_scale=7, negative_prompt=negative_prompt).images[0] image.save("DESTINATION_PATH_FOR_THE_IMAGE") Multi-adapter inf...	d07fa13a3fe1301188b103c336101329.txt
d07fa13a3fe1301188b103c336101329.txt_chunk_19	erence With PEFT you can combine multiple adapters for inference. In the previous example you have fine-tuned Stable Diffusion on some dog images. The pipeline created based on these weights got a name - adapter_name="dog. Now, suppose you also fine-tuned this base model on images of a crochet toy. Let’s see how we c...	d07fa13a3fe1301188b103c336101329.txt
d07fa13a3fe1301188b103c336101329.txt_chunk_20	an use both adapters. First, you’ll need to perform all the steps as in the single adapter inference example: Specify the base model. Add a function that creates a Stable Diffusion pipeline for image generation uses LoRA weights. Create a pipe with adapter_name="dog" based on the model fine-tuned on dog images. Next, ...	d07fa13a3fe1301188b103c336101329.txt
d07fa13a3fe1301188b103c336101329.txt_chunk_21	you’re going to need a few more helper functions. To load another adapter, create a load_adapter() function that leverages load_adapter() method of PeftModel (e.g. pipe.unet.load_adapter(peft_model_path, adapter_name)): Copied def load_adapter(pipe, ckpt_dir, adapter_name): unet_sub_dir = os.path.join(ckpt_dir, ...	d07fa13a3fe1301188b103c336101329.txt
d07fa13a3fe1301188b103c336101329.txt_chunk_22	"unet") text_encoder_sub_dir = os.path.join(ckpt_dir, "text_encoder") pipe.unet.load_adapter(unet_sub_dir, adapter_name=adapter_name) if os.path.exists(text_encoder_sub_dir): pipe.text_encoder.load_adapter(text_encoder_sub_dir, adapter_name=adapter_name) To switch between adapters, write a function ...	d07fa13a3fe1301188b103c336101329.txt
d07fa13a3fe1301188b103c336101329.txt_chunk_23	that uses set_adapter() method of PeftModel (see pipe.unet.set_adapter(adapter_name)) Copied def set_adapter(pipe, adapter_name): pipe.unet.set_adapter(adapter_name) if isinstance(pipe.text_encoder, PeftModel): pipe.text_encoder.set_adapter(adapter_name) Finally, add a function to create weighted LoR...	d07fa13a3fe1301188b103c336101329.txt
d07fa13a3fe1301188b103c336101329.txt_chunk_24	A adapter. Copied def create_weighted_lora_adapter(pipe, adapters, weights, adapter_name="default"): pipe.unet.add_weighted_adapter(adapters, weights, adapter_name) if isinstance(pipe.text_encoder, PeftModel): pipe.text_encoder.add_weighted_adapter(adapters, weights, adapter_name) return pipe L...	d07fa13a3fe1301188b103c336101329.txt
d07fa13a3fe1301188b103c336101329.txt_chunk_25	et’s load the second adapter from the model fine-tuned on images of a crochet toy, and give it a unique name: Copied load_adapter(pipe, Path("path-to-the-second-saved-model"), adapter_name="crochet") Create a pipeline using weighted adapters: Copied pipe = create_weighted_lora_adapter(pipe, ["crochet", "dog"], [1...	d07fa13a3fe1301188b103c336101329.txt