diffusers/v0.18.2/en/training/unconditional_training.html

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<h1 class="relative group"><a id="unconditional-image-generation" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#unconditional-image-generation"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
	<span>Unconditional image generation
	</span></h1>

<p>Unconditional image generation is not conditioned on any text or images, unlike text- or image-to-image models. It only generates images that resemble its training data distribution.</p>
<iframe src="https://stevhliu-ddpm-butterflies-128.hf.space" frameborder="0" width="850" height="550"></iframe>
<p>This guide will show you how to train an unconditional image generation model on existing datasets as well as your own custom dataset. All the training scripts for unconditional image generation can be found <a href="https://github.com/huggingface/diffusers/tree/main/examples/unconditional_image_generation" rel="nofollow">here</a> if you’re interested in learning more about the training details.</p>
<p>Before running the script, make sure you install the library’s training dependencies:</p>

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	<pre><!-- HTML_TAG_START -->pip install diffusers[training] accelerate datasets<!-- HTML_TAG_END --></pre></div>
<p>Next, initialize an 🤗 <a href="https://github.com/huggingface/accelerate/" rel="nofollow">Accelerate</a> environment with:</p>

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	<pre><!-- HTML_TAG_START -->accelerate config<!-- HTML_TAG_END --></pre></div>
<p>To setup a default 🤗 Accelerate environment without choosing any configurations:</p>

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	<pre><!-- HTML_TAG_START -->accelerate config default<!-- HTML_TAG_END --></pre></div>
<p>Or if your environment doesn’t support an interactive shell like a notebook, you can use:</p>

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	<pre><!-- HTML_TAG_START -->from accelerate.utils import write_basic_config

write_basic_config()<!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="upload-model-to-hub" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#upload-model-to-hub"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
	<span>Upload model to Hub
	</span></h2>

<p>You can upload your model on the Hub by adding the following argument to the training script:</p>

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	<pre><!-- HTML_TAG_START -->--push_to_hub<!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="save-and-load-checkpoints" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#save-and-load-checkpoints"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
	<span>Save and load checkpoints
	</span></h2>

<p>It is a good idea to regularly save checkpoints in case anything happens during training. To save a checkpoint, pass the following argument to the training script:</p>

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	<pre><!-- HTML_TAG_START -->--checkpointing_steps=500<!-- HTML_TAG_END --></pre></div>
<p>The full training state is saved in a subfolder in the <code>output_dir</code> every 500 steps, which allows you to load a checkpoint and resume training if you pass the <code>--resume_from_checkpoint</code> argument to the training script:</p>

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	<pre><!-- HTML_TAG_START -->--resume_from_checkpoint=<span class="hljs-string">&quot;checkpoint-1500&quot;</span><!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="finetuning" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#finetuning"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
	<span>Finetuning
	</span></h2>

<p>You’re ready to launch the <a href="https://github.com/huggingface/diffusers/blob/main/examples/unconditional_image_generation/train_unconditional.py" rel="nofollow">training script</a> now! Specify the dataset name to finetune on with the <code>--dataset_name</code> argument and then save it to the path in <code>--output_dir</code>. To use your own dataset, take a look at the <a href="create_dataset">Create a dataset for training</a> guide.</p>
<p>The training script creates and saves a <code>diffusion_pytorch_model.bin</code> file in your repository.</p>


<div class="course-tip  bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>💡 A full training run takes 2 hours on 4xV100 GPUs.</p></div>
<p>For example, to finetune on the <a href="https://huggingface.co/datasets/huggan/flowers-102-categories" rel="nofollow">Oxford Flowers</a> dataset:</p>

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	<pre><!-- HTML_TAG_START -->accelerate launch train_unconditional.py \
  --dataset_name=<span class="hljs-string">&quot;huggan/flowers-102-categories&quot;</span> \
  --resolution=64 \
  --output_dir=<span class="hljs-string">&quot;ddpm-ema-flowers-64&quot;</span> \
  --train_batch_size=16 \
  --num_epochs=100 \
  --gradient_accumulation_steps=1 \
  --learning_rate=1e-4 \
  --lr_warmup_steps=500 \
  --mixed_precision=no \
  --push_to_hub<!-- HTML_TAG_END --></pre></div>
<div class="flex justify-center"><img src="https://user-images.githubusercontent.com/26864830/180248660-a0b143d0-b89a-42c5-8656-2ebf6ece7e52.png"></div>
<p>Or if you want to train your model on the <a href="https://huggingface.co/datasets/huggan/pokemon" rel="nofollow">Pokemon</a> dataset:</p>

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	<pre><!-- HTML_TAG_START -->accelerate launch train_unconditional.py \
  --dataset_name=<span class="hljs-string">&quot;huggan/pokemon&quot;</span> \
  --resolution=64 \
  --output_dir=<span class="hljs-string">&quot;ddpm-ema-pokemon-64&quot;</span> \
  --train_batch_size=16 \
  --num_epochs=100 \
  --gradient_accumulation_steps=1 \
  --learning_rate=1e-4 \
  --lr_warmup_steps=500 \
  --mixed_precision=no \
  --push_to_hub<!-- HTML_TAG_END --></pre></div>
<div class="flex justify-center"><img src="https://user-images.githubusercontent.com/26864830/180248200-928953b4-db38-48db-b0c6-8b740fe6786f.png"></div>
<h3 class="relative group"><a id="training-with-multiple-gpus" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#training-with-multiple-gpus"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
	<span>Training with multiple GPUs
	</span></h3>

<p><code>accelerate</code> allows for seamless multi-GPU training. Follow the instructions <a href="https://huggingface.co/docs/accelerate/basic_tutorials/launch" rel="nofollow">here</a>
for running distributed training with <code>accelerate</code>. Here is an example command:</p>

	<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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	Copied</div></button></div>
	<pre><!-- HTML_TAG_START -->accelerate launch --mixed_precision=<span class="hljs-string">&quot;fp16&quot;</span> --multi_gpu train_unconditional.py \
  --dataset_name=<span class="hljs-string">&quot;huggan/pokemon&quot;</span> \
  --resolution=64 --center_crop --random_flip \
  --output_dir=<span class="hljs-string">&quot;ddpm-ema-pokemon-64&quot;</span> \
  --train_batch_size=16 \
  --num_epochs=100 \
  --gradient_accumulation_steps=1 \
  --use_ema \
  --learning_rate=1e-4 \
  --lr_warmup_steps=500 \
  --mixed_precision=<span class="hljs-string">&quot;fp16&quot;</span> \
  --logger=<span class="hljs-string">&quot;wandb&quot;</span> \
  --push_to_hub<!-- HTML_TAG_END --></pre></div>


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