Buckets:
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| <h1 class="relative group"><a id="texttoimage" 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="#texttoimage"><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>Text-to-image | |
| </span></h1> | |
| <div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"><p>The text-to-image fine-tuning script is experimental. It’s easy to overfit and run into issues like catastrophic forgetting. We recommend you explore different hyperparameters to get the best results on your dataset.</p></div> | |
| <p>Text-to-image models like Stable Diffusion generate an image from a text prompt. This guide will show you how to finetune the <a href="https://huggingface.co/CompVis/stable-diffusion-v1-4" rel="nofollow"><code>CompVis/stable-diffusion-v1-4</code></a> model on your own dataset with PyTorch and Flax. All the training scripts for text-to-image finetuning used in this guide can be found in this <a href="https://github.com/huggingface/diffusers/tree/main/examples/text_to_image" rel="nofollow">repository</a> if you’re interested in taking a closer look.</p> | |
| <p>Before running the scripts, make sure to install the library’s training dependencies:</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 -->pip install git+https://github.com/huggingface/diffusers.git | |
| pip install -U -r requirements.txt<!-- HTML_TAG_END --></pre></div> | |
| <p>And initialize an <a href="https://github.com/huggingface/accelerate/" rel="nofollow">🤗 Accelerate</a> environment with:</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 config<!-- HTML_TAG_END --></pre></div> | |
| <p>If you have already cloned the repo, then you won’t need to go through these steps. Instead, you can pass the path to your local checkout to the training script and it will be loaded from there.</p> | |
| <h2 class="relative group"><a id="hardware-requirements" 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="#hardware-requirements"><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>Hardware requirements | |
| </span></h2> | |
| <p>Using <code>gradient_checkpointing</code> and <code>mixed_precision</code>, it should be possible to finetune the model on a single 24GB GPU. For higher <code>batch_size</code>’s and faster training, it’s better to use GPUs with more than 30GB of GPU memory. You can also use JAX/Flax for fine-tuning on TPUs or GPUs, which will be covered <a href="#flax-jax-finetuning">below</a>.</p> | |
| <p>You can reduce your memory footprint even more by enabling memory efficient attention with xFormers. Make sure you have <a href="./optimization/xformers">xFormers installed</a> and pass the <code>--enable_xformers_memory_efficient_attention</code> flag to the training script.</p> | |
| <p>xFormers is not available for Flax.</p> | |
| <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>Store your model on the Hub by adding the following argument to the training script:</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 --> --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> | |
| <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 --> --checkpointing_steps=500<!-- HTML_TAG_END --></pre></div> | |
| <p>Every 500 steps, the full training state is saved in a subfolder in the <code>output_dir</code>. The checkpoint has the format <code>checkpoint-</code> followed by the number of steps trained so far. For example, <code>checkpoint-1500</code> is a checkpoint saved after 1500 training steps.</p> | |
| <p>To load a checkpoint to resume training, pass the argument <code>--resume_from_checkpoint</code> to the training script and specify the checkpoint you want to resume from. For example, the following argument resumes training from the checkpoint saved after 1500 training steps:</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> | |
| <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> | |
| Copied</div></button></div> | |
| <pre><!-- HTML_TAG_START --> --resume_from_checkpoint=<span class="hljs-string">"checkpoint-1500"</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>Fine-tuning | |
| </span></h2> | |
| <div class="space-y-10 py-6 2xl:py-8 2xl:-mx-4"> | |
| <div class="border border-gray-200 rounded-xl px-4 relative"><div class="flex h-[22px] mt-[-12.5px] justify-between leading-none"><div class="flex px-1 items-center space-x-1 bg-white dark:bg-gray-950"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><defs><clipPath id="a"><rect x="3.05" y="0.5" width="25.73" height="31" fill="none"></rect></clipPath></defs><g clip-path="url(#a)"><path d="M24.94,9.51a12.81,12.81,0,0,1,0,18.16,12.68,12.68,0,0,1-18,0,12.81,12.81,0,0,1,0-18.16l9-9V5l-.84.83-6,6a9.58,9.58,0,1,0,13.55,0ZM20.44,9a1.68,1.68,0,1,1,1.67-1.67A1.68,1.68,0,0,1,20.44,9Z" fill="#ee4c2c"></path></g></svg> | |
| <span>Pytorch</span></div> | |
| <div class="cursor-pointer flex items-center justify-center space-x-1 text-sm px-2 bg-white dark:bg-gray-950 hover:underline leading-none"><svg class="" width="0.9em" height="0.9em" viewBox="0 0 10 9" fill="currentColor" xmlns="http://www.w3.org/2000/svg"><path d="M1.39125 1.9725L0.0883333 0.669997L0.677917 0.0804138L8.9275 8.33041L8.33792 8.91958L6.95875 7.54041C6.22592 8.00523 5.37572 8.25138 4.50792 8.25C2.26125 8.25 0.392083 6.63333 0 4.5C0.179179 3.52946 0.667345 2.64287 1.39167 1.9725H1.39125ZM5.65667 6.23833L5.04667 5.62833C4.81335 5.73996 4.55116 5.77647 4.29622 5.73282C4.04129 5.68918 3.80617 5.56752 3.62328 5.38463C3.44039 5.20175 3.31874 4.96663 3.27509 4.71169C3.23144 4.45676 3.26795 4.19456 3.37958 3.96125L2.76958 3.35125C2.50447 3.75187 2.38595 4.2318 2.4341 4.70978C2.48225 5.18777 2.6941 5.63442 3.0338 5.97411C3.37349 6.31381 3.82015 6.52567 4.29813 6.57382C4.77611 6.62197 5.25605 6.50345 5.65667 6.23833ZM2.83042 1.06666C3.35 0.862497 3.91625 0.749997 4.50792 0.749997C6.75458 0.749997 8.62375 2.36666 9.01583 4.5C8.88816 5.19404 8.60119 5.84899 8.1775 6.41333L6.56917 4.805C6.61694 4.48317 6.58868 4.15463 6.48664 3.84569C6.3846 3.53675 6.21162 3.256 5.98156 3.02594C5.7515 2.79588 5.47075 2.6229 5.16181 2.52086C4.85287 2.41882 4.52433 2.39056 4.2025 2.43833L2.83042 1.06708V1.06666Z" fill="currentColor"></path></svg> | |
| <span>Hide Pytorch content</span></div></div> | |
| <div class="framework-content"> | |
| <p>Launch the <a href="https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py" rel="nofollow">PyTorch training script</a> for a fine-tuning run on the <a href="https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions" rel="nofollow">Pokémon BLIP captions</a> dataset like this.</p> | |
| <p>Specify the <code>MODEL_NAME</code> environment variable (either a Hub model repository id or a path to the directory containing the model weights) and pass it to the <a href="https://huggingface.co/docs/diffusers/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.from_pretrained.pretrained_model_name_or_path" rel="nofollow"><code>pretrained_model_name_or_path</code></a> argument.</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> | |
| <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> | |
| Copied</div></button></div> | |
| <pre><!-- HTML_TAG_START --><span class="hljs-built_in">export</span> MODEL_NAME=<span class="hljs-string">"CompVis/stable-diffusion-v1-4"</span> | |
| <span class="hljs-built_in">export</span> dataset_name=<span class="hljs-string">"lambdalabs/pokemon-blip-captions"</span> | |
| accelerate launch --mixed_precision=<span class="hljs-string">"fp16"</span> train_text_to_image.py \ | |
| --pretrained_model_name_or_path=<span class="hljs-variable">$MODEL_NAME</span> \ | |
| --dataset_name=<span class="hljs-variable">$dataset_name</span> \ | |
| --use_ema \ | |
| --resolution=512 --center_crop --random_flip \ | |
| --train_batch_size=1 \ | |
| --gradient_accumulation_steps=4 \ | |
| --gradient_checkpointing \ | |
| --max_train_steps=15000 \ | |
| --learning_rate=1e-05 \ | |
| --max_grad_norm=1 \ | |
| --lr_scheduler=<span class="hljs-string">"constant"</span> --lr_warmup_steps=0 \ | |
| --output_dir=<span class="hljs-string">"sd-pokemon-model"</span> \ | |
| --push_to_hub<!-- HTML_TAG_END --></pre></div> | |
| <p>To finetune on your own dataset, prepare the dataset according to the format required by 🤗 <a href="https://huggingface.co/docs/datasets/index" rel="nofollow">Datasets</a>. You can <a href="https://huggingface.co/docs/datasets/image_dataset#upload-dataset-to-the-hub" rel="nofollow">upload your dataset to the Hub</a>, or you can <a href="https://huggingface.co/docs/datasets/image_dataset#imagefolder" rel="nofollow">prepare a local folder with your files</a>.</p> | |
| <p>Modify the script if you want to use custom loading logic. We left pointers in the code in the appropriate places to help you. 🤗 The example script below shows how to finetune on a local dataset in <code>TRAIN_DIR</code> and where to save the model to in <code>OUTPUT_DIR</code>:</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> | |
| <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> | |
| Copied</div></button></div> | |
| <pre><!-- HTML_TAG_START --><span class="hljs-built_in">export</span> MODEL_NAME=<span class="hljs-string">"CompVis/stable-diffusion-v1-4"</span> | |
| <span class="hljs-built_in">export</span> TRAIN_DIR=<span class="hljs-string">"path_to_your_dataset"</span> | |
| <span class="hljs-built_in">export</span> OUTPUT_DIR=<span class="hljs-string">"path_to_save_model"</span> | |
| accelerate launch train_text_to_image.py \ | |
| --pretrained_model_name_or_path=<span class="hljs-variable">$MODEL_NAME</span> \ | |
| --train_data_dir=<span class="hljs-variable">$TRAIN_DIR</span> \ | |
| --use_ema \ | |
| --resolution=512 --center_crop --random_flip \ | |
| --train_batch_size=1 \ | |
| --gradient_accumulation_steps=4 \ | |
| --gradient_checkpointing \ | |
| --mixed_precision=<span class="hljs-string">"fp16"</span> \ | |
| --max_train_steps=15000 \ | |
| --learning_rate=1e-05 \ | |
| --max_grad_norm=1 \ | |
| --lr_scheduler=<span class="hljs-string">"constant"</span> | |
| --lr_warmup_steps=0 \ | |
| --output_dir=<span class="hljs-variable">${OUTPUT_DIR}</span> \ | |
| --push_to_hub<!-- HTML_TAG_END --></pre></div> | |
| <h4 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></h4> | |
| <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> | |
| <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> | |
| Copied</div></button></div> | |
| <pre><!-- HTML_TAG_START --><span class="hljs-built_in">export</span> MODEL_NAME=<span class="hljs-string">"CompVis/stable-diffusion-v1-4"</span> | |
| <span class="hljs-built_in">export</span> dataset_name=<span class="hljs-string">"lambdalabs/pokemon-blip-captions"</span> | |
| accelerate launch --mixed_precision=<span class="hljs-string">"fp16"</span> --multi_gpu train_text_to_image.py \ | |
| --pretrained_model_name_or_path=<span class="hljs-variable">$MODEL_NAME</span> \ | |
| --dataset_name=<span class="hljs-variable">$dataset_name</span> \ | |
| --use_ema \ | |
| --resolution=512 --center_crop --random_flip \ | |
| --train_batch_size=1 \ | |
| --gradient_accumulation_steps=4 \ | |
| --gradient_checkpointing \ | |
| --max_train_steps=15000 \ | |
| --learning_rate=1e-05 \ | |
| --max_grad_norm=1 \ | |
| --lr_scheduler=<span class="hljs-string">"constant"</span> \ | |
| --lr_warmup_steps=0 \ | |
| --output_dir=<span class="hljs-string">"sd-pokemon-model"</span> \ | |
| --push_to_hub<!-- HTML_TAG_END --></pre></div></div></div> | |
| <div class="border border-gray-200 rounded-xl px-4 relative"><div class="flex h-[22px] mt-[-12.5px] justify-between leading-none"><div class="flex px-1 items-center space-x-1 bg-white dark:bg-gray-950"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1.73em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 451 260.81"><style>.J { | |
| stroke: #dce0df; | |
| } | |
| .K { | |
| stroke-linejoin: round; | |
| } | |
| </style><g fill="#5e97f6" class="J K"><path d="M50.5 130.4l-25 43.31h50l25-43.31h-50z"></path><path d="M.5 217.01l25-43.3h50l-25 43.3H.5z"></path><path d="M125.5 173.71h-50l-25 43.3h50l25-43.3z"></path><path d="M175.5 173.71h-50l-25 43.3h50l25-43.3z"></path><path d="M150.5 130.4l-25 43.31h50l25-43.31h-50z"></path><path d="M175.5 87.1l-25 43.3h50l25-43.3h-50z"></path><path d="M200.5 43.8l-25 43.3h50l25-43.3h-50z"></path><path d="M225.5.5l-25 43.3h50l25-43.3h-50z"></path></g><g fill="#2a56c6" class="J K"><path d="M.5 217.01l25 43.3h50l-25-43.3H.5z"></path><path d="M125.5 260.31h-50l-25-43.3h50l25 43.3z"></path><path d="M175.5 260.31h-50l-25-43.3h50l25 43.3z"></path></g><g fill="#00796b" class="J K"><path d="M200.5 217.01l-25-43.3-25 43.3 25 43.3 25-43.3zm50-86.61l-25-43.3-25 43.3h50z"></path><path d="M250.5 43.8l-25 43.3 25 43.3 25-43.3-25-43.3z"></path></g><path d="M125.5 173.71l-25-43.31-25 43.31h50z" fill="#3367d6" class="J K"></path><g fill="#26a69a" class="J K"><path d="M250.5 130.4h-50l-25 43.31h50l25-43.31z"></path><path d="M300.5 130.4h-50l-25 43.31h50l25-43.31z"></path></g><g fill="#9c27b0" class="J K"><path d="M350.5 43.8L325.5.5l-25 43.3 25 43.3 25-43.3z"></path><path d="M375.5 87.1l-25-43.3-25 43.3 25 43.3 25-43.3z"></path><path d="M400.5 130.4l-25-43.3-25 43.3 25 43.31 25-43.31z"></path><path d="M425.5 173.71l-25-43.31-25 43.31 25 43.3 25-43.3z"></path><path d="M450.5 217.01l-25-43.3-25 43.3 25 43.3 25-43.3zM425.5.5l-25 43.3 25 43.3 25-43.3-25-43.3z"></path><path d="M375.5 87.1l25-43.3 25 43.3-25 43.3-25-43.3zm-25 43.3l-25 43.31 25 43.3 25-43.3-25-43.31z"></path><path d="M325.5 260.31l-25-43.3 25-43.3 25 43.3-25 43.3z"></path></g><path d="M275.5 260.31l-25-43.3h50l25 43.3h-50z" fill="#6a1b9a" class="J K"></path><g fill="#00695c" class="J K"><path d="M225.5 173.71h-50l25 43.3h50l-25-43.3z"></path><path d="M275.5 173.71h-50l25 43.3 25-43.3zm0-86.61l25 43.3h50l-25-43.3h-50z"></path><path d="M300.5 43.8h-50l25 43.3h50l-25-43.3zm125 216.51l-25-43.3h-50l25 43.3h50z"></path><path d="M375.5 173.71l-25 43.3h50l-25-43.3z"></path></g><g fill="#ea80fc" class="J K"><path d="M325.5.5h-50l-25 43.3h50l25-43.3zm0 173.21h-50l-25 43.3h50l25-43.3z"></path><path d="M350.5 130.4h-50l-25 43.31h50l25-43.31zM425.5.5h-50l-25 43.3h50l25-43.3z"></path><path d="M375.5 87.1l-25-43.3h50l-25 43.3z"></path></g></svg> | |
| <span>JAX</span></div> | |
| <div class="cursor-pointer flex items-center justify-center space-x-1 text-sm px-2 bg-white dark:bg-gray-950 hover:underline leading-none"><svg class="" width="0.9em" height="0.9em" viewBox="0 0 10 9" fill="currentColor" xmlns="http://www.w3.org/2000/svg"><path d="M1.39125 1.9725L0.0883333 0.669997L0.677917 0.0804138L8.9275 8.33041L8.33792 8.91958L6.95875 7.54041C6.22592 8.00523 5.37572 8.25138 4.50792 8.25C2.26125 8.25 0.392083 6.63333 0 4.5C0.179179 3.52946 0.667345 2.64287 1.39167 1.9725H1.39125ZM5.65667 6.23833L5.04667 5.62833C4.81335 5.73996 4.55116 5.77647 4.29622 5.73282C4.04129 5.68918 3.80617 5.56752 3.62328 5.38463C3.44039 5.20175 3.31874 4.96663 3.27509 4.71169C3.23144 4.45676 3.26795 4.19456 3.37958 3.96125L2.76958 3.35125C2.50447 3.75187 2.38595 4.2318 2.4341 4.70978C2.48225 5.18777 2.6941 5.63442 3.0338 5.97411C3.37349 6.31381 3.82015 6.52567 4.29813 6.57382C4.77611 6.62197 5.25605 6.50345 5.65667 6.23833ZM2.83042 1.06666C3.35 0.862497 3.91625 0.749997 4.50792 0.749997C6.75458 0.749997 8.62375 2.36666 9.01583 4.5C8.88816 5.19404 8.60119 5.84899 8.1775 6.41333L6.56917 4.805C6.61694 4.48317 6.58868 4.15463 6.48664 3.84569C6.3846 3.53675 6.21162 3.256 5.98156 3.02594C5.7515 2.79588 5.47075 2.6229 5.16181 2.52086C4.85287 2.41882 4.52433 2.39056 4.2025 2.43833L2.83042 1.06708V1.06666Z" fill="currentColor"></path></svg> | |
| <span>Hide JAX content</span></div></div> | |
| <div class="framework-content"> | |
| <p>With Flax, it’s possible to train a Stable Diffusion model faster on TPUs and GPUs thanks to <a href="https://github.com/duongna21" rel="nofollow">@duongna211</a>. This is very efficient on TPU hardware but works great on GPUs too. The Flax training script doesn’t support features like gradient checkpointing or gradient accumulation yet, so you’ll need a GPU with at least 30GB of memory or a TPU v3.</p> | |
| <p>Before running the script, make sure you have the requirements installed:</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> | |
| <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> | |
| Copied</div></button></div> | |
| <pre><!-- HTML_TAG_START -->pip install -U -r requirements_flax.txt<!-- HTML_TAG_END --></pre></div> | |
| <p>Specify the <code>MODEL_NAME</code> environment variable (either a Hub model repository id or a path to the directory containing the model weights) and pass it to the <a href="https://huggingface.co/docs/diffusers/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.from_pretrained.pretrained_model_name_or_path" rel="nofollow"><code>pretrained_model_name_or_path</code></a> argument.</p> | |
| <p>Now you can launch the <a href="https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_flax.py" rel="nofollow">Flax training script</a> like this:</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> | |
| <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> | |
| Copied</div></button></div> | |
| <pre><!-- HTML_TAG_START --><span class="hljs-built_in">export</span> MODEL_NAME=<span class="hljs-string">"runwayml/stable-diffusion-v1-5"</span> | |
| <span class="hljs-built_in">export</span> dataset_name=<span class="hljs-string">"lambdalabs/pokemon-blip-captions"</span> | |
| python train_text_to_image_flax.py \ | |
| --pretrained_model_name_or_path=<span class="hljs-variable">$MODEL_NAME</span> \ | |
| --dataset_name=<span class="hljs-variable">$dataset_name</span> \ | |
| --resolution=512 --center_crop --random_flip \ | |
| --train_batch_size=1 \ | |
| --max_train_steps=15000 \ | |
| --learning_rate=1e-05 \ | |
| --max_grad_norm=1 \ | |
| --output_dir=<span class="hljs-string">"sd-pokemon-model"</span> \ | |
| --push_to_hub<!-- HTML_TAG_END --></pre></div> | |
| <p>To finetune on your own dataset, prepare the dataset according to the format required by 🤗 <a href="https://huggingface.co/docs/datasets/index" rel="nofollow">Datasets</a>. You can <a href="https://huggingface.co/docs/datasets/image_dataset#upload-dataset-to-the-hub" rel="nofollow">upload your dataset to the Hub</a>, or you can <a href="https://huggingface.co/docs/datasets/image_dataset#imagefolder" rel="nofollow">prepare a local folder with your files</a>.</p> | |
| <p>Modify the script if you want to use custom loading logic. We left pointers in the code in the appropriate places to help you. 🤗 The example script below shows how to finetune on a local dataset in <code>TRAIN_DIR</code>:</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> | |
| <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> | |
| Copied</div></button></div> | |
| <pre><!-- HTML_TAG_START --><span class="hljs-built_in">export</span> MODEL_NAME=<span class="hljs-string">"duongna/stable-diffusion-v1-4-flax"</span> | |
| <span class="hljs-built_in">export</span> TRAIN_DIR=<span class="hljs-string">"path_to_your_dataset"</span> | |
| python train_text_to_image_flax.py \ | |
| --pretrained_model_name_or_path=<span class="hljs-variable">$MODEL_NAME</span> \ | |
| --train_data_dir=<span class="hljs-variable">$TRAIN_DIR</span> \ | |
| --resolution=512 --center_crop --random_flip \ | |
| --train_batch_size=1 \ | |
| --mixed_precision=<span class="hljs-string">"fp16"</span> \ | |
| --max_train_steps=15000 \ | |
| --learning_rate=1e-05 \ | |
| --max_grad_norm=1 \ | |
| --output_dir=<span class="hljs-string">"sd-pokemon-model"</span> \ | |
| --push_to_hub<!-- HTML_TAG_END --></pre></div> | |
| </div></div></div> | |
| <h2 class="relative group"><a id="training-with-minsnr-weighting" 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-minsnr-weighting"><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 Min-SNR weighting | |
| </span></h2> | |
| <p>We support training with the Min-SNR weighting strategy proposed in <a href="https://arxiv.org/abs/2303.09556" rel="nofollow">Efficient Diffusion Training via Min-SNR Weighting Strategy</a> which helps to achieve faster convergence | |
| by rebalancing the loss. In order to use it, one needs to set the <code>--snr_gamma</code> argument. The recommended | |
| value when using it is 5.0. </p> | |
| <p>You can find <a href="https://wandb.ai/sayakpaul/text2image-finetune-minsnr" rel="nofollow">this project on Weights and Biases</a> that compares the loss surfaces of the following setups:</p> | |
| <ul><li>Training without the Min-SNR weighting strategy</li> | |
| <li>Training with the Min-SNR weighting strategy (<code>snr_gamma</code> set to 5.0)</li> | |
| <li>Training with the Min-SNR weighting strategy (<code>snr_gamma</code> set to 1.0)</li></ul> | |
| <p>For our small Pokemons dataset, the effects of Min-SNR weighting strategy might not appear to be pronounced, but for larger datasets, we believe the effects will be more pronounced.</p> | |
| <p>Also, note that in this example, we either predict <code>epsilon</code> (i.e., the noise) or the <code>v_prediction</code>. For both of these cases, the formulation of the Min-SNR weighting strategy that we have used holds. </p> | |
| <div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"><p>Training with Min-SNR weighting strategy is only supported in PyTorch.</p></div> | |
| <h2 class="relative group"><a id="lora" 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="#lora"><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>LoRA | |
| </span></h2> | |
| <p>You can also use Low-Rank Adaptation of Large Language Models (LoRA), a fine-tuning technique for accelerating training large models, for fine-tuning text-to-image models. For more details, take a look at the <a href="lora#text-to-image">LoRA training</a> guide.</p> | |
| <h2 class="relative group"><a id="inference" 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="#inference"><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>Inference | |
| </span></h2> | |
| <p>Now you can load the fine-tuned model for inference by passing the model path or model name on the Hub to the <a href="/docs/diffusers/v0.19.2/en/api/pipelines/stable_diffusion/text2img#diffusers.StableDiffusionPipeline">StableDiffusionPipeline</a>:</p> | |
| <div class="space-y-10 py-6 2xl:py-8 2xl:-mx-4"> | |
| <div class="border border-gray-200 rounded-xl px-4 relative"><div class="flex h-[22px] mt-[-12.5px] justify-between leading-none"><div class="flex px-1 items-center space-x-1 bg-white dark:bg-gray-950"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><defs><clipPath id="a"><rect x="3.05" y="0.5" width="25.73" height="31" fill="none"></rect></clipPath></defs><g clip-path="url(#a)"><path d="M24.94,9.51a12.81,12.81,0,0,1,0,18.16,12.68,12.68,0,0,1-18,0,12.81,12.81,0,0,1,0-18.16l9-9V5l-.84.83-6,6a9.58,9.58,0,1,0,13.55,0ZM20.44,9a1.68,1.68,0,1,1,1.67-1.67A1.68,1.68,0,0,1,20.44,9Z" fill="#ee4c2c"></path></g></svg> | |
| <span>Pytorch</span></div> | |
| <div class="cursor-pointer flex items-center justify-center space-x-1 text-sm px-2 bg-white dark:bg-gray-950 hover:underline leading-none"><svg class="" width="0.9em" height="0.9em" viewBox="0 0 10 9" fill="currentColor" xmlns="http://www.w3.org/2000/svg"><path d="M1.39125 1.9725L0.0883333 0.669997L0.677917 0.0804138L8.9275 8.33041L8.33792 8.91958L6.95875 7.54041C6.22592 8.00523 5.37572 8.25138 4.50792 8.25C2.26125 8.25 0.392083 6.63333 0 4.5C0.179179 3.52946 0.667345 2.64287 1.39167 1.9725H1.39125ZM5.65667 6.23833L5.04667 5.62833C4.81335 5.73996 4.55116 5.77647 4.29622 5.73282C4.04129 5.68918 3.80617 5.56752 3.62328 5.38463C3.44039 5.20175 3.31874 4.96663 3.27509 4.71169C3.23144 4.45676 3.26795 4.19456 3.37958 3.96125L2.76958 3.35125C2.50447 3.75187 2.38595 4.2318 2.4341 4.70978C2.48225 5.18777 2.6941 5.63442 3.0338 5.97411C3.37349 6.31381 3.82015 6.52567 4.29813 6.57382C4.77611 6.62197 5.25605 6.50345 5.65667 6.23833ZM2.83042 1.06666C3.35 0.862497 3.91625 0.749997 4.50792 0.749997C6.75458 0.749997 8.62375 2.36666 9.01583 4.5C8.88816 5.19404 8.60119 5.84899 8.1775 6.41333L6.56917 4.805C6.61694 4.48317 6.58868 4.15463 6.48664 3.84569C6.3846 3.53675 6.21162 3.256 5.98156 3.02594C5.7515 2.79588 5.47075 2.6229 5.16181 2.52086C4.85287 2.41882 4.52433 2.39056 4.2025 2.43833L2.83042 1.06708V1.06666Z" fill="currentColor"></path></svg> | |
| <span>Hide Pytorch content</span></div></div> | |
| <div class="framework-content"> | |
| <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> | |
| <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> | |
| Copied</div></button></div> | |
| <pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionPipeline | |
| model_path = <span class="hljs-string">"path_to_saved_model"</span> | |
| pipe = StableDiffusionPipeline.from_pretrained(model_path, torch_dtype=torch.float16) | |
| pipe.to(<span class="hljs-string">"cuda"</span>) | |
| image = pipe(prompt=<span class="hljs-string">"yoda"</span>).images[<span class="hljs-number">0</span>] | |
| image.save(<span class="hljs-string">"yoda-pokemon.png"</span>)<!-- HTML_TAG_END --></pre></div></div></div> | |
| <div class="border border-gray-200 rounded-xl px-4 relative"><div class="flex h-[22px] mt-[-12.5px] justify-between leading-none"><div class="flex px-1 items-center space-x-1 bg-white dark:bg-gray-950"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1.73em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 451 260.81"><style>.J { | |
| stroke: #dce0df; | |
| } | |
| .K { | |
| stroke-linejoin: round; | |
| } | |
| </style><g fill="#5e97f6" class="J K"><path d="M50.5 130.4l-25 43.31h50l25-43.31h-50z"></path><path d="M.5 217.01l25-43.3h50l-25 43.3H.5z"></path><path d="M125.5 173.71h-50l-25 43.3h50l25-43.3z"></path><path d="M175.5 173.71h-50l-25 43.3h50l25-43.3z"></path><path d="M150.5 130.4l-25 43.31h50l25-43.31h-50z"></path><path d="M175.5 87.1l-25 43.3h50l25-43.3h-50z"></path><path d="M200.5 43.8l-25 43.3h50l25-43.3h-50z"></path><path d="M225.5.5l-25 43.3h50l25-43.3h-50z"></path></g><g fill="#2a56c6" class="J K"><path d="M.5 217.01l25 43.3h50l-25-43.3H.5z"></path><path d="M125.5 260.31h-50l-25-43.3h50l25 43.3z"></path><path d="M175.5 260.31h-50l-25-43.3h50l25 43.3z"></path></g><g fill="#00796b" class="J K"><path d="M200.5 217.01l-25-43.3-25 43.3 25 43.3 25-43.3zm50-86.61l-25-43.3-25 43.3h50z"></path><path d="M250.5 43.8l-25 43.3 25 43.3 25-43.3-25-43.3z"></path></g><path d="M125.5 173.71l-25-43.31-25 43.31h50z" fill="#3367d6" class="J K"></path><g fill="#26a69a" class="J K"><path d="M250.5 130.4h-50l-25 43.31h50l25-43.31z"></path><path d="M300.5 130.4h-50l-25 43.31h50l25-43.31z"></path></g><g fill="#9c27b0" class="J K"><path d="M350.5 43.8L325.5.5l-25 43.3 25 43.3 25-43.3z"></path><path d="M375.5 87.1l-25-43.3-25 43.3 25 43.3 25-43.3z"></path><path d="M400.5 130.4l-25-43.3-25 43.3 25 43.31 25-43.31z"></path><path d="M425.5 173.71l-25-43.31-25 43.31 25 43.3 25-43.3z"></path><path d="M450.5 217.01l-25-43.3-25 43.3 25 43.3 25-43.3zM425.5.5l-25 43.3 25 43.3 25-43.3-25-43.3z"></path><path d="M375.5 87.1l25-43.3 25 43.3-25 43.3-25-43.3zm-25 43.3l-25 43.31 25 43.3 25-43.3-25-43.31z"></path><path d="M325.5 260.31l-25-43.3 25-43.3 25 43.3-25 43.3z"></path></g><path d="M275.5 260.31l-25-43.3h50l25 43.3h-50z" fill="#6a1b9a" class="J K"></path><g fill="#00695c" class="J K"><path d="M225.5 173.71h-50l25 43.3h50l-25-43.3z"></path><path d="M275.5 173.71h-50l25 43.3 25-43.3zm0-86.61l25 43.3h50l-25-43.3h-50z"></path><path d="M300.5 43.8h-50l25 43.3h50l-25-43.3zm125 216.51l-25-43.3h-50l25 43.3h50z"></path><path d="M375.5 173.71l-25 43.3h50l-25-43.3z"></path></g><g fill="#ea80fc" class="J K"><path d="M325.5.5h-50l-25 43.3h50l25-43.3zm0 173.21h-50l-25 43.3h50l25-43.3z"></path><path d="M350.5 130.4h-50l-25 43.31h50l25-43.31zM425.5.5h-50l-25 43.3h50l25-43.3z"></path><path d="M375.5 87.1l-25-43.3h50l-25 43.3z"></path></g></svg> | |
| <span>JAX</span></div> | |
| <div class="cursor-pointer flex items-center justify-center space-x-1 text-sm px-2 bg-white dark:bg-gray-950 hover:underline leading-none"><svg class="" width="0.9em" height="0.9em" viewBox="0 0 10 9" fill="currentColor" xmlns="http://www.w3.org/2000/svg"><path d="M1.39125 1.9725L0.0883333 0.669997L0.677917 0.0804138L8.9275 8.33041L8.33792 8.91958L6.95875 7.54041C6.22592 8.00523 5.37572 8.25138 4.50792 8.25C2.26125 8.25 0.392083 6.63333 0 4.5C0.179179 3.52946 0.667345 2.64287 1.39167 1.9725H1.39125ZM5.65667 6.23833L5.04667 5.62833C4.81335 5.73996 4.55116 5.77647 4.29622 5.73282C4.04129 5.68918 3.80617 5.56752 3.62328 5.38463C3.44039 5.20175 3.31874 4.96663 3.27509 4.71169C3.23144 4.45676 3.26795 4.19456 3.37958 3.96125L2.76958 3.35125C2.50447 3.75187 2.38595 4.2318 2.4341 4.70978C2.48225 5.18777 2.6941 5.63442 3.0338 5.97411C3.37349 6.31381 3.82015 6.52567 4.29813 6.57382C4.77611 6.62197 5.25605 6.50345 5.65667 6.23833ZM2.83042 1.06666C3.35 0.862497 3.91625 0.749997 4.50792 0.749997C6.75458 0.749997 8.62375 2.36666 9.01583 4.5C8.88816 5.19404 8.60119 5.84899 8.1775 6.41333L6.56917 4.805C6.61694 4.48317 6.58868 4.15463 6.48664 3.84569C6.3846 3.53675 6.21162 3.256 5.98156 3.02594C5.7515 2.79588 5.47075 2.6229 5.16181 2.52086C4.85287 2.41882 4.52433 2.39056 4.2025 2.43833L2.83042 1.06708V1.06666Z" fill="currentColor"></path></svg> | |
| <span>Hide JAX content</span></div></div> | |
| <div class="framework-content"> | |
| <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> | |
| <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> | |
| Copied</div></button></div> | |
| <pre><!-- HTML_TAG_START --><span class="hljs-keyword">import</span> jax | |
| <span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np | |
| <span class="hljs-keyword">from</span> flax.jax_utils <span class="hljs-keyword">import</span> replicate | |
| <span class="hljs-keyword">from</span> flax.training.common_utils <span class="hljs-keyword">import</span> shard | |
| <span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> FlaxStableDiffusionPipeline | |
| model_path = <span class="hljs-string">"path_to_saved_model"</span> | |
| pipe, params = FlaxStableDiffusionPipeline.from_pretrained(model_path, dtype=jax.numpy.bfloat16) | |
| prompt = <span class="hljs-string">"yoda pokemon"</span> | |
| prng_seed = jax.random.PRNGKey(<span class="hljs-number">0</span>) | |
| num_inference_steps = <span class="hljs-number">50</span> | |
| num_samples = jax.device_count() | |
| prompt = num_samples * [prompt] | |
| prompt_ids = pipeline.prepare_inputs(prompt) | |
| <span class="hljs-comment"># shard inputs and rng</span> | |
| params = replicate(params) | |
| prng_seed = jax.random.split(prng_seed, jax.device_count()) | |
| prompt_ids = shard(prompt_ids) | |
| images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=<span class="hljs-literal">True</span>).images | |
| images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-<span class="hljs-number">3</span>:]))) | |
| image.save(<span class="hljs-string">"yoda-pokemon.png"</span>)<!-- HTML_TAG_END --></pre></div> | |
| </div></div></div> | |
| <script type="module" data-hydrate="wgozuf"> | |
| import { start } from "/docs/diffusers/v0.19.2/en/_app/start-hf-doc-builder.js"; | |
| start({ | |
| target: document.querySelector('[data-hydrate="wgozuf"]').parentNode, | |
| paths: {"base":"/docs/diffusers/v0.19.2/en","assets":"/docs/diffusers/v0.19.2/en"}, | |
| session: {}, | |
| route: false, | |
| spa: false, | |
| trailing_slash: "never", | |
| hydrate: { | |
| status: 200, | |
| error: null, | |
| nodes: [ | |
| import("/docs/diffusers/v0.19.2/en/_app/pages/__layout.svelte-hf-doc-builder.js"), | |
| import("/docs/diffusers/v0.19.2/en/_app/pages/training/text2image.mdx-hf-doc-builder.js") | |
| ], | |
| params: {} | |
| } | |
| }); | |
| </script> | |
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