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Merge pull request #27 from apolinario/patch-1 

Add diffusers as a way to inference in the model
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Patrick Esser 2022-08-16 23:11:39 +02:00 committed by GitHub
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@ -87,6 +87,25 @@ and sample with
``` ```
python scripts/txt2img.py --prompt "a photograph of an astronaut riding a horse" --plms python scripts/txt2img.py --prompt "a photograph of an astronaut riding a horse" --plms
``` ```
Another way to download and sample Stable Diffusion is by using the [diffusers library](https://github.com/huggingface/diffusers/tree/main#new--stable-diffusion-is-now-fully-compatible-with-diffusers)
```py
# make sure you're logged in with `huggingface-cli login`
from torch import autocast
from diffusers import StableDiffusionPipeline, LMSDiscreteScheduler
pipe = StableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-3-diffusers",
use_auth_token=True
)
prompt = "a photo of an astronaut riding a horse on mars"
with autocast("cuda"):
image = pipe(prompt)["sample"][0]
image.save("astronaut_rides_horse.png")
```
By default, this uses a guidance scale of `--scale 7.5`, [Katherine Crowson's implementation](https://github.com/CompVis/latent-diffusion/pull/51) of the [PLMS](https://arxiv.org/abs/2202.09778) sampler, By default, this uses a guidance scale of `--scale 7.5`, [Katherine Crowson's implementation](https://github.com/CompVis/latent-diffusion/pull/51) of the [PLMS](https://arxiv.org/abs/2202.09778) sampler,
and renders images of size 512x512 (which it was trained on) in 50 steps. All supported arguments are listed below (type `python scripts/txt2img.py --help`). and renders images of size 512x512 (which it was trained on) in 50 steps. All supported arguments are listed below (type `python scripts/txt2img.py --help`).