mirror of
https://github.com/finegrain-ai/refiners.git
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84 lines
2.9 KiB
Markdown
84 lines
2.9 KiB
Markdown
---
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icon: material/star-outline
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---
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# Recommended usage
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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.
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Moreover, we recommend using [Rye](https://rye-up.com) which simplifies several things related to Python package management, so start by following the instructions to install it on your system.
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## Installing
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To try Refiners, clone the GitHub repository and install it with all optional features:
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```bash
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git clone "git@github.com:finegrain-ai/refiners.git"
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cd refiners
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rye sync --all-features
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```
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## Converting weights
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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:
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```bash
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python "scripts/conversion/convert_diffusers_autoencoder_kl.py" --to "lda.safetensors"
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```
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If you need to convert weights for all models, check out `script/prepare_test_weights.py`.
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!!! warning
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Using `script/prepare_test_weights.py` requires a GPU with significant VRAM and a lot of disk space.
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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:
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```py
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from PIL import Image
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from refiners.fluxion.utils import no_grad
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from refiners.foundationals.latent_diffusion.stable_diffusion_1.model import SD1Autoencoder
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with no_grad():
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lda = SD1Autoencoder()
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lda.load_from_safetensors("lda.safetensors")
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image = Image.open("input.png")
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latents = lda.image_to_latents(image)
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decoded = lda.latents_to_image(latents)
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decoded.save("output.png")
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```
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Run it:
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```bash
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python ldatest.py
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```
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Inspect `output.png`: it should be similar to `input.png` but have a few differences. Latent Autoencoders are good compressors!
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## Using Refiners in your own project
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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:
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```bash
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rye init --py "3.11" myproject
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cd myproject
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rye add --git "git@github.com:finegrain-ai/refiners.git" --features training refiners
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rye sync
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```
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If you only intend to do inference and no training, you can drop `--features training`.
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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:
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```bash
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rye add --dev --git "git@github.com:finegrain-ai/refiners.git" --features conversion refiners
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```
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!!! note
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You will still need to download the conversion scripts independently if you go that route.
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## What's next?
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We suggest you check out the [guides](/guides/) section to dive into the usage of Refiners, of the [Key Concepts](/concepts/chain/) section for a better understanding of how the framework works.
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