add end-to-end test for euler scheduler

Reference image generated with diffusers [1]

[1]: tests/e2e/test_diffusion_ref/README.md#expected-outputs

tests/e2e/test_diffusion.py

@@ -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