mirror of
https://github.com/finegrain-ai/refiners.git
synced 2024-11-23 14:48:45 +00:00
4a6146bb6c
* text_encoder([str1, str2]) * lda decode_latents/encode_image image_to_latent/latent_to_image * images_to_tensor, tensor_to_images --------- Co-authored-by: Cédric Deltheil <355031+deltheil@users.noreply.github.com>
61 lines
2.1 KiB
Python
61 lines
2.1 KiB
Python
from pathlib import Path
|
|
from warnings import warn
|
|
|
|
import pytest
|
|
import torch
|
|
from PIL import Image
|
|
from tests.utils import ensure_similar_images
|
|
|
|
from refiners.fluxion.utils import load_from_safetensors, no_grad
|
|
from refiners.foundationals.latent_diffusion.auto_encoder import LatentDiffusionAutoencoder
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def ref_path() -> Path:
|
|
return Path(__file__).parent / "test_auto_encoder_ref"
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def encoder(test_weights_path: Path, test_device: torch.device) -> LatentDiffusionAutoencoder:
|
|
lda_weights = test_weights_path / "lda.safetensors"
|
|
if not lda_weights.is_file():
|
|
warn(f"could not find weights at {lda_weights}, skipping")
|
|
pytest.skip(allow_module_level=True)
|
|
encoder = LatentDiffusionAutoencoder(device=test_device)
|
|
tensors = load_from_safetensors(lda_weights)
|
|
encoder.load_state_dict(tensors)
|
|
return encoder
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def sample_image(ref_path: Path) -> Image.Image:
|
|
test_image = ref_path / "macaw.png"
|
|
if not test_image.is_file():
|
|
warn(f"could not reference image at {test_image}, skipping")
|
|
pytest.skip(allow_module_level=True)
|
|
img = Image.open(test_image)
|
|
assert img.size == (512, 512)
|
|
return img
|
|
|
|
|
|
@no_grad()
|
|
def test_encode_decode_image(encoder: LatentDiffusionAutoencoder, sample_image: Image.Image):
|
|
encoded = encoder.image_to_latents(sample_image)
|
|
decoded = encoder.latents_to_image(encoded)
|
|
|
|
assert decoded.mode == "RGB"
|
|
|
|
# Ensure no saturation. The green channel (band = 1) must not max out.
|
|
assert max(iter(decoded.getdata(band=1))) < 255 # type: ignore
|
|
|
|
ensure_similar_images(sample_image, decoded, min_psnr=20, min_ssim=0.9)
|
|
|
|
|
|
@no_grad()
|
|
def test_encode_decode_images(encoder: LatentDiffusionAutoencoder, sample_image: Image.Image):
|
|
encoded = encoder.images_to_latents([sample_image, sample_image])
|
|
images = encoder.latents_to_images(encoded)
|
|
assert isinstance(images, list)
|
|
assert len(images) == 2
|
|
ensure_similar_images(sample_image, images[1], min_psnr=20, min_ssim=0.9)
|