feature: remove training feature
Bryce
10 months ago
committed by
Bryce Drennan
parent
ef0f44646e
commit
db85f0898a
@ -1,85 +0,0 @@
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# Adding a concept to Stable Diffusion
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You can use Imaginairy to teach the model a new concept (a person, thing, style, etc) using the `aimg train-concept`
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command.
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## Requirements
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- Graphics card: 3090 or better
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- Linux
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- A working Imaginairy installation
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- a folder of images of the concept you want to teach the model
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## Background
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To train the model we show it a lot of images of the concept we want to teach it. The problem is the model can easily
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overfit to the images we show it. To prevent this we also show it images of the class of thing that is being trained.
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Imaginairy will generate the images needed for this before running the training job.
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Provided a directory of concept images, a concept token, and a class token, this command will train the model
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to generate images of that concept.
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This happens in a 3-step process:
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1. Cropping and resizing your training images. If --person is set we crop to include the face.
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2. Generating a set of class images to train on. This helps prevent overfitting.
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3. Training the model on the concept and class images.
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The output of this command is a new model weights file that you can use with the --model option.
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## Instructions
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1. Gather a set of images of the concept you want to train on. The images should show the subject from a variety of angles
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and in a variety of situations.
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2. Run `aimg train-concept` to train the model.
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- Concept label: For a person, firstnamelastname should be fine.
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- If all the training images are photos you should add "a photo of" to the beginning of the concept label.
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- Class label: This is the category of the things beings trained on. For people this is typically "person", "man"
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or "woman".
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- If all the training images are photos you should add "a photo of" to the beginning of the class label.
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- CLass images will be generated for you if you do not provide them.
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For example, if you were training on photos of a man named bill hamilton you could run the following:
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```
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aimg train-concept \\
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--person \\
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--concept-label "photo of billhamilton man" \\
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--concept-images-dir ./images/billhamilton \\
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--class-label "photo of a man" \\
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--class-images-dir ./images/man
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```
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3. Stop training before it overfits.
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- The training script will output checkpoint ckpt files into the logs folder of wherever it is run from. You can also
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monitor generated images in the logs/images folder. They will be the ones named "sample"
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- I don't have great advice on when to stop training yet. I stopped mine at epoch 62 at it didn't seem quite good enough, at epoch 111 it
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produced my face correctly 50% of the time but also seemed overfit in some ways (always placing me in the same clothes or background as training photos).
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- You can monitor model training progress in Tensorboard. Run `tensorboard --logdir lightning_logs` and open the link it gives you in your browser.
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4. Prune the model to bring the size from 11gb to ~4gb: `aimg prune-ckpt logs/2023-01-15T05-52-06/checkpoints/epoch\=000049.ckpt`. Copy it somewhere
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and give it a meaninful name.
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## Using the new model
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You can reference the model like this in imaginairy:
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`imagine --model my-models/billhamilton-man-e111.ckpt`
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When you use the model you should prompt with `firstnamelastname classname` (e.g. `billhamilton man`).
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## Disclaimers
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- The settings imaginairy uses to train the model are different than other software projects. As such you cannot follow
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advice you may read from other tutorials regarding learning rate, epochs, steps, batch size. They are not directly
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comparable. In laymans terms the "steps" are much bigger in imaginairy.
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- I consider this training feature experimental and don't currently plan to offer support for it. Any further work will
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be at my leisure. As a result I may close any reported issues related to this feature.
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- You can find a lot more relevant information here: https://github.com/JoePenna/Dreambooth-Stable-Diffusion
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## Todo
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- figure out how to improve consistency of quality from trained model
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- train on the depth guided model instead of SD 1.5 since that will enable more consistent output
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- figure out metric to use for stopping training
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- possibly swap out and randomize backgrounds on training photos so over-fitting does not occur
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@ -1,533 +0,0 @@
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import datetime
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import logging
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import os
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import signal
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import time
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from functools import partial
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import numpy as np
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import pytorch_lightning as pl
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import torch
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import torchvision
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from omegaconf import OmegaConf
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from PIL import Image
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from pytorch_lightning import seed_everything
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from pytorch_lightning.callbacks import Callback, LearningRateMonitor
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try:
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from pytorch_lightning.strategies import DDPStrategy
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except ImportError:
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# let's not break all of imaginairy just because a training import doesn't exist in an older version of PL
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# Use >= 1.6.0 to make this work
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DDPStrategy = None
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import contextlib
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from pytorch_lightning.trainer import Trainer
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from pytorch_lightning.utilities import rank_zero_info
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from pytorch_lightning.utilities.distributed import rank_zero_only
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from torch.utils.data import DataLoader, Dataset
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from imaginairy import config
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from imaginairy.model_manager import get_diffusion_model
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from imaginairy.training_tools.single_concept import SingleConceptDataset
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from imaginairy.utils import get_device, instantiate_from_config
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mod_logger = logging.getLogger(__name__)
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referenced_by_string = [LearningRateMonitor]
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class WrappedDataset(Dataset):
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"""Wraps an arbitrary object with __len__ and __getitem__ into a pytorch dataset."""
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def __init__(self, dataset):
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self.data = dataset
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def __len__(self):
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return len(self.data)
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def __getitem__(self, idx):
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return self.data[idx]
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def worker_init_fn(_):
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worker_info = torch.utils.data.get_worker_info()
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dataset = worker_info.dataset
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worker_id = worker_info.id
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if isinstance(dataset, SingleConceptDataset):
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# split_size = dataset.num_records // worker_info.num_workers
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# reset num_records to the true number to retain reliable length information
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# dataset.sample_ids = dataset.valid_ids[
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# worker_id * split_size : (worker_id + 1) * split_size
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# ]
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current_id = np.random.choice(len(np.random.get_state()[1]), 1)
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return np.random.seed(np.random.get_state()[1][current_id] + worker_id)
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return np.random.seed(np.random.get_state()[1][0] + worker_id)
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class DataModuleFromConfig(pl.LightningDataModule):
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def __init__(
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self,
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batch_size,
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train=None,
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validation=None,
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test=None,
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predict=None,
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wrap=False,
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num_workers=None,
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shuffle_test_loader=False,
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use_worker_init_fn=False,
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shuffle_val_dataloader=False,
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num_val_workers=0,
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):
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super().__init__()
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self.batch_size = batch_size
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self.dataset_configs = {}
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self.num_workers = num_workers if num_workers is not None else batch_size * 2
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if num_val_workers is None:
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self.num_val_workers = self.num_workers
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else:
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self.num_val_workers = num_val_workers
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self.use_worker_init_fn = use_worker_init_fn
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if train is not None:
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self.dataset_configs["train"] = train
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self.train_dataloader = self._train_dataloader
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if validation is not None:
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self.dataset_configs["validation"] = validation
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self.val_dataloader = partial(
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self._val_dataloader, shuffle=shuffle_val_dataloader
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)
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if test is not None:
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self.dataset_configs["test"] = test
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self.test_dataloader = partial(
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self._test_dataloader, shuffle=shuffle_test_loader
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)
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if predict is not None:
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self.dataset_configs["predict"] = predict
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self.predict_dataloader = self._predict_dataloader
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self.wrap = wrap
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self.datasets = None
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def prepare_data(self):
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for data_cfg in self.dataset_configs.values():
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instantiate_from_config(data_cfg)
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def setup(self, stage=None):
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self.datasets = {
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k: instantiate_from_config(c) for k, c in self.dataset_configs.items()
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}
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if self.wrap:
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self.datasets = {k: WrappedDataset(v) for k, v in self.datasets.items()}
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def _train_dataloader(self):
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is_iterable_dataset = isinstance(self.datasets["train"], SingleConceptDataset)
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if is_iterable_dataset or self.use_worker_init_fn:
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pass
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else:
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pass
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return DataLoader(
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self.datasets["train"],
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batch_size=self.batch_size,
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num_workers=self.num_workers,
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shuffle=True,
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worker_init_fn=worker_init_fn,
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)
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def _val_dataloader(self, shuffle=False):
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if (
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isinstance(self.datasets["validation"], SingleConceptDataset)
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or self.use_worker_init_fn
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):
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init_fn = worker_init_fn
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else:
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init_fn = None
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return DataLoader(
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self.datasets["validation"],
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batch_size=self.batch_size,
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num_workers=self.num_val_workers,
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worker_init_fn=init_fn,
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shuffle=shuffle,
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)
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def _test_dataloader(self, shuffle=False):
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is_iterable_dataset = isinstance(self.datasets["train"], SingleConceptDataset)
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if is_iterable_dataset or self.use_worker_init_fn:
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init_fn = worker_init_fn
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else:
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init_fn = None
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is_iterable_dataset = False
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# do not shuffle dataloader for iterable dataset
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shuffle = shuffle and (not is_iterable_dataset)
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return DataLoader(
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self.datasets["test"],
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batch_size=self.batch_size,
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num_workers=self.num_workers,
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worker_init_fn=init_fn,
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shuffle=shuffle,
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)
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def _predict_dataloader(self, shuffle=False):
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if (
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isinstance(self.datasets["predict"], SingleConceptDataset)
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or self.use_worker_init_fn
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):
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init_fn = worker_init_fn
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else:
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init_fn = None
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return DataLoader(
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self.datasets["predict"],
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batch_size=self.batch_size,
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num_workers=self.num_workers,
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worker_init_fn=init_fn,
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)
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class SetupCallback(Callback):
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def __init__(
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self,
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resume,
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now,
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logdir,
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ckptdir,
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cfgdir,
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):
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super().__init__()
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self.resume = resume
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self.now = now
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self.logdir = logdir
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self.ckptdir = ckptdir
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self.cfgdir = cfgdir
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def on_keyboard_interrupt(self, trainer, pl_module):
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if trainer.global_rank == 0:
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mod_logger.info("Stopping execution and saving final checkpoint.")
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ckpt_path = os.path.join(self.ckptdir, "last.ckpt")
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trainer.save_checkpoint(ckpt_path)
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def on_fit_start(self, trainer, pl_module):
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if trainer.global_rank == 0:
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# Create logdirs and save configs
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os.makedirs(self.logdir, exist_ok=True)
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os.makedirs(self.ckptdir, exist_ok=True)
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os.makedirs(self.cfgdir, exist_ok=True)
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else:
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# ModelCheckpoint callback created log directory --- remove it
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if not self.resume and os.path.exists(self.logdir):
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dst, name = os.path.split(self.logdir)
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dst = os.path.join(dst, "child_runs", name)
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os.makedirs(os.path.split(dst)[0], exist_ok=True)
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with contextlib.suppress(FileNotFoundError):
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os.rename(self.logdir, dst)
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class ImageLogger(Callback):
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def __init__(
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self,
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batch_frequency,
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max_images,
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clamp=True,
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increase_log_steps=True,
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rescale=True,
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disabled=False,
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log_on_batch_idx=False,
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log_first_step=False,
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log_images_kwargs=None,
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log_all_val=False,
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concept_label=None,
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):
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super().__init__()
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self.rescale = rescale
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self.batch_freq = batch_frequency
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self.max_images = max_images
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self.logger_log_images = {}
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self.log_steps = [2**n for n in range(int(np.log2(self.batch_freq)) + 1)]
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if not increase_log_steps:
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self.log_steps = [self.batch_freq]
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self.clamp = clamp
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self.disabled = disabled
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self.log_on_batch_idx = log_on_batch_idx
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self.log_images_kwargs = log_images_kwargs if log_images_kwargs else {}
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self.log_first_step = log_first_step
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self.log_all_val = log_all_val
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self.concept_label = concept_label
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@rank_zero_only
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def log_local(self, save_dir, split, images, global_step, current_epoch, batch_idx):
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root = os.path.join(save_dir, "logs", "images", split)
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for k in images:
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grid = torchvision.utils.make_grid(images[k], nrow=4)
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if self.rescale:
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grid = (grid + 1.0) / 2.0 # -1,1 -> 0,1; c,h,w
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grid = grid.transpose(0, 1).transpose(1, 2).squeeze(-1)
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grid = grid.numpy()
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grid = (grid * 255).astype(np.uint8)
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filename = (
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f"{k}_gs-{global_step:06}_e-{current_epoch:06}_b-{batch_idx:06}.png"
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)
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path = os.path.join(root, filename)
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os.makedirs(os.path.split(path)[0], exist_ok=True)
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Image.fromarray(grid).save(path)
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def log_img(self, pl_module, batch, batch_idx, split="train"):
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# always generate the concept label
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batch["txt"][0] = self.concept_label
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check_idx = batch_idx if self.log_on_batch_idx else pl_module.global_step
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if self.log_all_val and split == "val":
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should_log = True
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else:
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should_log = self.check_frequency(check_idx)
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if (
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should_log
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and (batch_idx % self.batch_freq == 0)
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and hasattr(pl_module, "log_images")
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and callable(pl_module.log_images)
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and self.max_images > 0
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):
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logger = type(pl_module.logger)
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is_train = pl_module.training
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if is_train:
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pl_module.eval()
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with torch.no_grad():
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images = pl_module.log_images(
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batch, split=split, **self.log_images_kwargs
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)
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for k in images:
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N = min(images[k].shape[0], self.max_images)
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images[k] = images[k][:N]
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if isinstance(images[k], torch.Tensor):
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images[k] = images[k].detach().cpu()
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if self.clamp:
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images[k] = torch.clamp(images[k], -1.0, 1.0)
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self.log_local(
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pl_module.logger.save_dir,
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split,
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images,
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pl_module.global_step,
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pl_module.current_epoch,
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batch_idx,
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)
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logger_log_images = self.logger_log_images.get(
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logger, lambda *args, **kwargs: None
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)
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logger_log_images(pl_module, images, pl_module.global_step, split)
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if is_train:
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pl_module.train()
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def check_frequency(self, check_idx):
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if (check_idx % self.batch_freq) == 0 and (
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check_idx > 0 or self.log_first_step
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):
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return True
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return False
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def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
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if not self.disabled and (pl_module.global_step > 0 or self.log_first_step):
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self.log_img(pl_module, batch, batch_idx, split="train")
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def on_validation_batch_end(
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self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx
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):
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if not self.disabled and pl_module.global_step > 0:
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self.log_img(pl_module, batch, batch_idx, split="val")
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if (
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hasattr(pl_module, "calibrate_grad_norm")
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and (pl_module.calibrate_grad_norm and batch_idx % 25 == 0)
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and batch_idx > 0
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):
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self.log_gradients(trainer, pl_module, batch_idx=batch_idx)
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class CUDACallback(Callback):
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# see https://github.com/SeanNaren/minGPT/blob/master/mingpt/callback.py
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def on_train_epoch_start(self, trainer, pl_module):
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# Reset the memory use counter
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if "cuda" in get_device():
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torch.cuda.reset_peak_memory_stats(trainer.strategy.root_device.index)
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torch.cuda.synchronize(trainer.strategy.root_device.index)
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self.start_time = time.time()
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def on_train_epoch_end(self, trainer, pl_module):
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if "cuda" in get_device():
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torch.cuda.synchronize(trainer.strategy.root_device.index)
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max_memory = (
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torch.cuda.max_memory_allocated(trainer.strategy.root_device.index)
|
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/ 2**20
|
||||
)
|
||||
epoch_time = time.time() - self.start_time
|
||||
|
||||
try:
|
||||
max_memory = trainer.training_type_plugin.reduce(max_memory)
|
||||
epoch_time = trainer.training_type_plugin.reduce(epoch_time)
|
||||
|
||||
rank_zero_info(f"Average Epoch time: {epoch_time:.2f} seconds")
|
||||
rank_zero_info(f"Average Peak memory {max_memory:.2f}MiB")
|
||||
except AttributeError:
|
||||
pass
|
||||
|
||||
|
||||
def train_diffusion_model(
|
||||
concept_label,
|
||||
concept_images_dir,
|
||||
class_label,
|
||||
class_images_dir,
|
||||
weights_location=config.DEFAULT_MODEL,
|
||||
logdir="logs",
|
||||
learning_rate=1e-6,
|
||||
accumulate_grad_batches=32,
|
||||
resume=None,
|
||||
):
|
||||
"""
|
||||
Train a diffusion model on a single concept.
|
||||
|
||||
accumulate_grad_batches used to simulate a bigger batch size - https://arxiv.org/pdf/1711.00489.pdf
|
||||
"""
|
||||
if DDPStrategy is None:
|
||||
msg = "Please install pytorch-lightning>=1.6.0 to train a model"
|
||||
raise ImportError(msg)
|
||||
|
||||
batch_size = 1
|
||||
seed = 23
|
||||
num_workers = 1
|
||||
num_val_workers = 0
|
||||
now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S") # noqa: DTZ005
|
||||
logdir = os.path.join(logdir, now)
|
||||
|
||||
ckpt_output_dir = os.path.join(logdir, "checkpoints")
|
||||
cfg_output_dir = os.path.join(logdir, "configs")
|
||||
seed_everything(seed)
|
||||
model = get_diffusion_model(
|
||||
weights_location=weights_location, half_mode=False, for_training=True
|
||||
)._model
|
||||
model.learning_rate = learning_rate * accumulate_grad_batches * batch_size
|
||||
|
||||
# add callback which sets up log directory
|
||||
default_callbacks_cfg = {
|
||||
"setup_callback": {
|
||||
"target": "imaginairy.train.SetupCallback",
|
||||
"params": {
|
||||
"resume": False,
|
||||
"now": now,
|
||||
"logdir": logdir,
|
||||
"ckptdir": ckpt_output_dir,
|
||||
"cfgdir": cfg_output_dir,
|
||||
},
|
||||
},
|
||||
"image_logger": {
|
||||
"target": "imaginairy.train.ImageLogger",
|
||||
"params": {
|
||||
"batch_frequency": 10,
|
||||
"max_images": 1,
|
||||
"clamp": True,
|
||||
"increase_log_steps": False,
|
||||
"log_first_step": True,
|
||||
"log_all_val": True,
|
||||
"concept_label": concept_label,
|
||||
"log_images_kwargs": {
|
||||
"use_ema_scope": True,
|
||||
"inpaint": False,
|
||||
"plot_progressive_rows": False,
|
||||
"plot_diffusion_rows": False,
|
||||
"N": 1,
|
||||
"unconditional_guidance_scale:": 7.5,
|
||||
"unconditional_guidance_label": [""],
|
||||
"ddim_steps": 20,
|
||||
},
|
||||
},
|
||||
},
|
||||
"learning_rate_logger": {
|
||||
"target": "imaginairy.train.LearningRateMonitor",
|
||||
"params": {
|
||||
"logging_interval": "step",
|
||||
# "log_momentum": True
|
||||
},
|
||||
},
|
||||
"cuda_callback": {"target": "imaginairy.train.CUDACallback"},
|
||||
}
|
||||
|
||||
default_modelckpt_cfg = {
|
||||
"target": "pytorch_lightning.callbacks.ModelCheckpoint",
|
||||
"params": {
|
||||
"dirpath": ckpt_output_dir,
|
||||
"filename": "{epoch:06}",
|
||||
"verbose": True,
|
||||
"save_last": True,
|
||||
"every_n_train_steps": 50,
|
||||
"save_top_k": -1,
|
||||
"monitor": None,
|
||||
},
|
||||
}
|
||||
|
||||
modelckpt_cfg = OmegaConf.create(default_modelckpt_cfg)
|
||||
default_callbacks_cfg.update({"checkpoint_callback": modelckpt_cfg})
|
||||
|
||||
callbacks_cfg = OmegaConf.create(default_callbacks_cfg)
|
||||
|
||||
dataset_config = {
|
||||
"concept_label": concept_label,
|
||||
"concept_images_dir": concept_images_dir,
|
||||
"class_label": class_label,
|
||||
"class_images_dir": class_images_dir,
|
||||
"image_transforms": [
|
||||
{
|
||||
"target": "torchvision.transforms.Resize",
|
||||
"params": {"size": 512, "interpolation": 3},
|
||||
},
|
||||
{"target": "torchvision.transforms.RandomCrop", "params": {"size": 512}},
|
||||
],
|
||||
}
|
||||
|
||||
data_module_config = {
|
||||
"batch_size": batch_size,
|
||||
"num_workers": num_workers,
|
||||
"num_val_workers": num_val_workers,
|
||||
"train": {
|
||||
"target": "imaginairy.training_tools.single_concept.SingleConceptDataset",
|
||||
"params": dataset_config,
|
||||
},
|
||||
}
|
||||
trainer = Trainer(
|
||||
benchmark=True,
|
||||
num_sanity_val_steps=0,
|
||||
accumulate_grad_batches=accumulate_grad_batches,
|
||||
strategy=DDPStrategy(),
|
||||
callbacks=[instantiate_from_config(callbacks_cfg[k]) for k in callbacks_cfg],
|
||||
gpus=1,
|
||||
default_root_dir=".",
|
||||
)
|
||||
trainer.logdir = logdir
|
||||
|
||||
data = DataModuleFromConfig(**data_module_config)
|
||||
# NOTE according to https://pytorch-lightning.readthedocs.io/en/latest/datamodules.html
|
||||
# calling these ourselves should not be necessary but it is.
|
||||
# lightning still takes care of proper multiprocessing though
|
||||
data.prepare_data()
|
||||
data.setup()
|
||||
|
||||
def melk(*args, **kwargs):
|
||||
if trainer.global_rank == 0:
|
||||
mod_logger.info("Summoning checkpoint.")
|
||||
ckpt_path = os.path.join(ckpt_output_dir, "last.ckpt")
|
||||
trainer.save_checkpoint(ckpt_path)
|
||||
|
||||
signal.signal(signal.SIGUSR1, melk)
|
||||
try:
|
||||
try:
|
||||
trainer.fit(model, data)
|
||||
except Exception:
|
||||
melk()
|
||||
raise
|
||||
finally:
|
||||
mod_logger.info(trainer.profiler.summary())
|
||||
Loading…
Reference in New Issue