150 lines
5.2 KiB
Plaintext
150 lines
5.2 KiB
Plaintext
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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-->
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# Unconditional Image-Generation
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In this section, we explain how one can train an unconditional image generation diffusion
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model. "Unconditional" because the model is not conditioned on any context to generate an image - once trained the model will simply generate images that resemble its training data
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distribution.
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## Installing the dependencies
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Before running the scripts, make sure to install the library's training dependencies:
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```bash
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pip install diffusers[training] accelerate datasets
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```
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And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:
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```bash
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accelerate config
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```
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## Unconditional Flowers
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The command to train a DDPM UNet model on the Oxford Flowers dataset:
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```bash
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accelerate launch train_unconditional.py \
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--dataset_name="huggan/flowers-102-categories" \
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--resolution=64 \
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--output_dir="ddpm-ema-flowers-64" \
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--train_batch_size=16 \
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--num_epochs=100 \
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--gradient_accumulation_steps=1 \
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--learning_rate=1e-4 \
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--lr_warmup_steps=500 \
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--mixed_precision=no \
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--push_to_hub
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```
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An example trained model: https://huggingface.co/anton-l/ddpm-ema-flowers-64
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A full training run takes 2 hours on 4xV100 GPUs.
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<img src="https://user-images.githubusercontent.com/26864830/180248660-a0b143d0-b89a-42c5-8656-2ebf6ece7e52.png" width="700" />
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## Unconditional Pokemon
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The command to train a DDPM UNet model on the Pokemon dataset:
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```bash
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accelerate launch train_unconditional.py \
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--dataset_name="huggan/pokemon" \
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--resolution=64 \
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--output_dir="ddpm-ema-pokemon-64" \
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--train_batch_size=16 \
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--num_epochs=100 \
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--gradient_accumulation_steps=1 \
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--learning_rate=1e-4 \
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--lr_warmup_steps=500 \
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--mixed_precision=no \
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--push_to_hub
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```
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An example trained model: https://huggingface.co/anton-l/ddpm-ema-pokemon-64
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A full training run takes 2 hours on 4xV100 GPUs.
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<img src="https://user-images.githubusercontent.com/26864830/180248200-928953b4-db38-48db-b0c6-8b740fe6786f.png" width="700" />
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## Using your own data
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To use your own dataset, there are 2 ways:
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- you can either provide your own folder as `--train_data_dir`
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- or you can upload your dataset to the hub (possibly as a private repo, if you prefer so), and simply pass the `--dataset_name` argument.
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**Note**: If you want to create your own training dataset please have a look at [this document](https://huggingface.co/docs/datasets/image_process#image-datasets).
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Below, we explain both in more detail.
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### Provide the dataset as a folder
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If you provide your own folders with images, the script expects the following directory structure:
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```bash
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data_dir/xxx.png
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data_dir/xxy.png
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data_dir/[...]/xxz.png
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```
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In other words, the script will take care of gathering all images inside the folder. You can then run the script like this:
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```bash
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accelerate launch train_unconditional.py \
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--train_data_dir <path-to-train-directory> \
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<other-arguments>
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```
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Internally, the script will use the [`ImageFolder`](https://huggingface.co/docs/datasets/v2.0.0/en/image_process#imagefolder) feature which will automatically turn the folders into 🤗 Dataset objects.
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### Upload your data to the hub, as a (possibly private) repo
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It's very easy (and convenient) to upload your image dataset to the hub using the [`ImageFolder`](https://huggingface.co/docs/datasets/v2.0.0/en/image_process#imagefolder) feature available in 🤗 Datasets. Simply do the following:
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```python
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from datasets import load_dataset
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# example 1: local folder
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dataset = load_dataset("imagefolder", data_dir="path_to_your_folder")
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# example 2: local files (supported formats are tar, gzip, zip, xz, rar, zstd)
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dataset = load_dataset("imagefolder", data_files="path_to_zip_file")
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# example 3: remote files (supported formats are tar, gzip, zip, xz, rar, zstd)
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dataset = load_dataset(
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"imagefolder",
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data_files="https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip",
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)
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# example 4: providing several splits
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dataset = load_dataset(
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"imagefolder", data_files={"train": ["path/to/file1", "path/to/file2"], "test": ["path/to/file3", "path/to/file4"]}
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)
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```
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`ImageFolder` will create an `image` column containing the PIL-encoded images.
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Next, push it to the hub!
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```python
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# assuming you have ran the huggingface-cli login command in a terminal
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dataset.push_to_hub("name_of_your_dataset")
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# if you want to push to a private repo, simply pass private=True:
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dataset.push_to_hub("name_of_your_dataset", private=True)
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```
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and that's it! You can now train your model by simply setting the `--dataset_name` argument to the name of your dataset on the hub.
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More on this can also be found in [this blog post](https://huggingface.co/blog/image-search-datasets).
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