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Cédric Deltheil 17046e0717 docs/getting-started: tweak toc and update snippet

`encode_image` has been deprecated part of #213

2024-02-02 14:05:06 +01:00

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Recommended usage

Refiners is still a young project and development is active, so to use the latest and greatest version of the framework we recommend you use the main branch from our development repository.

Moreover, we recommend using Rye which simplifies several things related to Python package management, so start by following the instructions to install it on your system.

Installing

To try Refiners, clone the GitHub repository and install it with all optional features:

git clone "git@github.com:finegrain-ai/refiners.git"
cd refiners
rye sync --all-features

Converting weights

The format of state dicts used by Refiners is custom and we do not redistribute model weights, but we provide conversion tools and working scripts for popular models. For instance, let us convert the autoencoder from Stable Diffusion 1.5:

python "scripts/conversion/convert_diffusers_autoencoder_kl.py" --to "lda.safetensors"

If you need to convert weights for all models, check out script/prepare_test_weights.py.

!!! warning Using script/prepare_test_weights.py requires a GPU with significant VRAM and a lot of disk space.

Now to check that it works copy your favorite 512x512 picture in the current directory as input.png and create ldatest.py with this content:

from PIL import Image
from refiners.fluxion.utils import no_grad
from refiners.foundationals.latent_diffusion.stable_diffusion_1.model import SD1Autoencoder

with no_grad():
    lda = SD1Autoencoder()
    lda.load_from_safetensors("lda.safetensors")

    image = Image.open("input.png")
    latents = lda.image_to_latents(image)
    decoded = lda.latents_to_image(latents)
    decoded.save("output.png")

Run it:

python ldatest.py

Inspect output.png: it should be similar to input.png but have a few differences. Latent Autoencoders are good compressors!

Using Refiners in your own project

So far you used Refiners as a standalone package, but if you want to create your own project using it as a dependency here is how you can proceed:

rye init --py "3.11" myproject
cd myproject
rye add --git "git@github.com:finegrain-ai/refiners.git" --features training refiners
rye sync

If you only intend to do inference and no training, you can drop --features training.

To convert weights, you can either use a copy of the refiners repository as described above or add the conversion feature as a development dependency:

rye add --dev --git "git@github.com:finegrain-ai/refiners.git" --features conversion refiners

!!! note You will still need to download the conversion scripts independently if you go that route.

What's next?

We suggest you check out the guides section to dive into the usage of Refiners, of the Key Concepts section for a better understanding of how the framework works.

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