mirror of
https://github.com/finegrain-ai/refiners.git
synced 2024-11-23 14:48:45 +00:00
32 lines
1.1 KiB
Python
32 lines
1.1 KiB
Python
import torch
|
|
from PIL import Image
|
|
|
|
from refiners.fluxion.utils import manual_seed, no_grad
|
|
from refiners.foundationals.latent_diffusion import StableDiffusion_1_Inpainting
|
|
from refiners.foundationals.latent_diffusion.model import LatentDiffusionModel
|
|
|
|
|
|
@no_grad()
|
|
def test_sample_noise():
|
|
manual_seed(2)
|
|
latents_0 = LatentDiffusionModel.sample_noise(size=(1, 4, 64, 64))
|
|
manual_seed(2)
|
|
latents_1 = LatentDiffusionModel.sample_noise(size=(1, 4, 64, 64), offset_noise=0.0)
|
|
|
|
assert torch.allclose(latents_0, latents_1, atol=1e-6, rtol=0)
|
|
|
|
|
|
@no_grad()
|
|
def test_sd1_inpainting(test_device: torch.device) -> None:
|
|
sd = StableDiffusion_1_Inpainting(device=test_device)
|
|
|
|
latent_noise = torch.randn(1, 4, 64, 64, device=test_device)
|
|
target_image = Image.new("RGB", (512, 512))
|
|
mask = Image.new("L", (512, 512))
|
|
|
|
sd.set_inpainting_conditions(target_image=target_image, mask=mask)
|
|
text_embedding = sd.compute_clip_text_embedding("")
|
|
output = sd(latent_noise, step=0, clip_text_embedding=text_embedding)
|
|
|
|
assert output.shape == (1, 4, 64, 64)
|