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Follow the installation instructions below for the deep learning library you are using:",C,x,y='<li><a href="https://pytorch.org/get-started/locally/" rel="nofollow">PyTorch</a> installation instructions</li> <li><a href="https://flax.readthedocs.io/en/latest/" rel="nofollow">Flax</a> installation instructions</li>',j,z,R,N,Q=`You should install 🤗 Diffusers in a <a href="https://docs.python.org/3/library/venv.html" rel="nofollow">virtual environment</a>.
If you’re unfamiliar with Python virtual environments, take a look at this <a href="https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/" rel="nofollow">guide</a>.
A virtual environment makes it easier to manage different projects and avoid compatibility issues between dependencies.`,T,P,V="Start by creating a virtual environment in your project directory:",D,k,U,F,Z="Activate the virtual environment:",X,ee,oe,te,we="You should also install 🤗 Transformers because 🤗 Diffusers relies on its models:",fe,K,re,A,ce,le,_e="After activating your virtual environment, with <code>conda</code> (maintained by the community):",ue,B,he,ne,pe,W,G="Before installing 🤗 Diffusers from source, make sure you have PyTorch and 🤗 Accelerate installed.",Me,Y,Ee="To install 🤗 Accelerate:",ve,ie,se,ae,xe="Then install 🤗 Diffusers from source:",at,Te,rt,ze,Ut=`This command installs the bleeding edge <code>main</code> version rather than the latest <code>stable</code> version.
The <code>main</code> version is useful for staying up-to-date with the latest developments.
For instance, if a bug has been fixed since the last official release but a new release hasn’t been rolled out yet.
However, this means the <code>main</code> version may not always be stable.
We strive to keep the <code>main</code> version operational, and most issues are usually resolved within a few hours or a day.
If you run into a problem, please open an <a href="https://github.com/huggingface/diffusers/issues/new/choose" rel="nofollow">Issue</a> so we can fix it even sooner!`,it,Le,ot,He,St="You will need an editable install if you’d like to:",ft,Ie,Zt="<li>Use the <code>main</code> version of the source code.</li> <li>Contribute to 🤗 Diffusers and need to test changes in the code.</li>",ct,Je,Nt="Clone the repository and install 🤗 Diffusers with the following commands:",ut,je,ht,be,pt,Pe,Ft=`These commands will link the folder you cloned the repository to and your Python library paths.
Python will now look inside the folder you cloned to in addition to the normal library paths.
For example, if your Python packages are typically installed in <code>~/anaconda3/envs/main/lib/python3.8/site-packages/</code>, Python will also search the <code>~/diffusers/</code> folder you cloned to.`,mt,ye,dt,Ue,Rt="Now you can easily update your clone to the latest version of 🤗 Diffusers with the following command:",$t,Se,gt,Ze,Vt="Your Python environment will find the <code>main</code> version of 🤗 Diffusers on the next run.",wt,Ne,_t,Fe,At='Model weights and files are downloaded from the Hub to a cache which is usually your home directory. You can change the cache location by specifying the <code>HF_HOME</code> or <code>HUGGINFACE_HUB_CACHE</code> environment variables or configuring the <code>cache_dir</code> parameter in methods like <a href="/docs/diffusers/v0.26.0/en/api/pipelines/overview#diffusers.DiffusionPipeline.from_pretrained">from_pretrained()</a>.',vt,Re,Gt="Cached files allow you to run 🤗 Diffusers offline. To prevent 🤗 Diffusers from connecting to the internet, set the <code>HF_HUB_OFFLINE</code> environment variable to <code>True</code> and 🤗 Diffusers will only load previously downloaded files in the cache.",bt,Ve,yt,Ae,Dt='For more details about managing and cleaning the cache, take a look at the <a href="https://huggingface.co/docs/huggingface_hub/guides/manage-cache" rel="nofollow">caching</a> guide.',Ct,Ge,kt,De,Bt=`Our library gathers telemetry information during <a href="/docs/diffusers/v0.26.0/en/api/pipelines/overview#diffusers.DiffusionPipeline.from_pretrained">from_pretrained()</a> requests.
The data gathered includes the version of 🤗 Diffusers and PyTorch/Flax, the requested model or pipeline class,
and the path to a pretrained checkpoint if it is hosted on the Hugging Face Hub.
This usage data helps us debug issues and prioritize new features.
Telemetry is only sent when loading models and pipelines from the Hub,
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