add e2e test for T2I-Adapter depth

Expected output generated with diffusers' StableDiffusionAdapterPipeline

tests/e2e/test_diffusion.py

@@ -14,6 +14,7 @@ from refiners.foundationals.latent_diffusion import (
     SD1UNet,
     SD1ControlnetAdapter,
     SD1IPAdapter,
+    SD1T2IAdapter,
     SDXLIPAdapter,
 )
 from refiners.foundationals.latent_diffusion.lora import SD1LoraAdapter
@@ -128,6 +129,14 @@ def controlnet_data_depth(ref_path: Path, test_weights_path: Path) -> tuple[str,
     weights_path = test_weights_path / "controlnet" / "lllyasviel_control_v11f1p_sd15_depth.safetensors"
     return cn_name, condition_image, expected_image, weights_path
 
+@pytest.fixture(scope="module")
+def t2i_adapter_data_depth(ref_path: Path, test_weights_path: Path) -> tuple[str, Image.Image, Image.Image, Path]:
+    name = "depth"
+    condition_image = Image.open(ref_path / f"cutecat_guide_{name}.png").convert("RGB")
+    expected_image = Image.open(ref_path / f"expected_t2i_adapter_{name}.png").convert("RGB")
+    weights_path = test_weights_path / "T2I-Adapter" / "t2iadapter_depth_sd15v2.safetensors"
+    return name, condition_image, expected_image, weights_path
+
 
 @pytest.fixture(scope="module")
 def lora_data_pokemon(ref_path: Path, test_weights_path: Path) -> tuple[Image.Image, Path]:
@@ -1233,3 +1242,44 @@ def test_multi_diffusion(sd15_ddim: StableDiffusion_1, expected_multi_diffusion:
         )
     result = sd.lda.decode_latents(x=x)
     ensure_similar_images(img_1=result, img_2=expected_multi_diffusion, min_psnr=35, min_ssim=0.98)
+
+
+@torch.no_grad()
+def test_t2i_adapter_depth(
+    sd15_std: StableDiffusion_1,
+    t2i_adapter_data_depth: tuple[str, Image.Image, Image.Image, Path],
+    test_device: torch.device,
+):
+    sd15 = sd15_std
+    n_steps = 30
+
+    name, condition_image, expected_image, weights_path = t2i_adapter_data_depth
+
+    if not weights_path.is_file():
+        warn(f"could not find weights at {weights_path}, skipping")
+        pytest.skip(allow_module_level=True)
+
+    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)
+
+    t2i_adapter = SD1T2IAdapter(target=sd15.unet, name=name, weights=load_from_safetensors(weights_path)).inject()
+
+    condition = image_to_tensor(condition_image.convert("RGB"), device=test_device)
+    t2i_adapter.set_condition_features(features=t2i_adapter.compute_condition_features(condition))
+
+    manual_seed(2)
+    x = torch.randn(1, 4, 64, 64, device=test_device)
+
+    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)