add end-to-end test for euler scheduler

Reference image generated with diffusers [1]

[1]: tests/e2e/test_diffusion_ref/README.md#expected-outputs
This commit is contained in:
Cédric Deltheil 2024-01-10 12:26:47 +01:00 committed by Cédric Deltheil
parent 4bf1f27031
commit 6dbaec3e56
2 changed files with 55 additions and 1 deletions

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@ -23,7 +23,7 @@ from refiners.foundationals.latent_diffusion.lora import SD1LoraAdapter
from refiners.foundationals.latent_diffusion.multi_diffusion import DiffusionTarget
from refiners.foundationals.latent_diffusion.reference_only_control import ReferenceOnlyControlAdapter
from refiners.foundationals.latent_diffusion.restart import Restart
from refiners.foundationals.latent_diffusion.schedulers import DDIM
from refiners.foundationals.latent_diffusion.schedulers import DDIM, EulerScheduler
from refiners.foundationals.latent_diffusion.schedulers.scheduler import NoiseSchedule
from refiners.foundationals.latent_diffusion.stable_diffusion_1.multi_diffusion import SD1MultiDiffusion
from refiners.foundationals.latent_diffusion.stable_diffusion_xl.model import StableDiffusion_XL
@ -65,6 +65,11 @@ def expected_image_std_random_init(ref_path: Path) -> Image.Image:
return Image.open(ref_path / "expected_std_random_init.png").convert("RGB")
@pytest.fixture
def expected_image_std_random_init_euler(ref_path: Path) -> Image.Image:
return Image.open(ref_path / "expected_std_random_init_euler.png").convert("RGB")
@pytest.fixture
def expected_karras_random_init(ref_path: Path) -> Image.Image:
return Image.open(ref_path / "expected_karras_random_init.png").convert("RGB")
@ -438,6 +443,24 @@ def sd15_ddim_karras(
return sd15
@pytest.fixture
def sd15_euler(
text_encoder_weights: Path, lda_weights: Path, unet_weights_std: Path, test_device: torch.device
) -> StableDiffusion_1:
if test_device.type == "cpu":
warn("not running on CPU, skipping")
pytest.skip()
euler_scheduler = EulerScheduler(num_inference_steps=30)
sd15 = StableDiffusion_1(scheduler=euler_scheduler, device=test_device)
sd15.clip_text_encoder.load_from_safetensors(text_encoder_weights)
sd15.lda.load_from_safetensors(lda_weights)
sd15.unet.load_from_safetensors(unet_weights_std)
return sd15
@pytest.fixture
def sd15_ddim_lda_ft_mse(
text_encoder_weights: Path, lda_ft_mse_weights: Path, unet_weights_std: Path, test_device: torch.device
@ -529,6 +552,37 @@ def test_diffusion_std_random_init(
ensure_similar_images(predicted_image, expected_image_std_random_init)
@no_grad()
def test_diffusion_std_random_init_euler(
sd15_euler: StableDiffusion_1, expected_image_std_random_init_euler: Image.Image, test_device: torch.device
):
sd15 = sd15_euler
euler_scheduler = sd15_euler.scheduler
assert isinstance(euler_scheduler, EulerScheduler)
n_steps = 30
prompt = "a cute cat, detailed high-quality professional image"
negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
sd15.set_num_inference_steps(n_steps)
manual_seed(2)
x = torch.randn(1, 4, 64, 64, device=test_device)
x = x * euler_scheduler.init_noise_sigma
for step in sd15.steps:
x = sd15(
x,
step=step,
clip_text_embedding=clip_text_embedding,
condition_scale=7.5,
)
predicted_image = sd15.lda.decode_latents(x)
ensure_similar_images(predicted_image, expected_image_std_random_init_euler)
@no_grad()
def test_diffusion_karras_random_init(
sd15_ddim_karras: StableDiffusion_1, expected_karras_random_init: Image.Image, test_device: torch.device

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