tests: add test for clip image encoder

This covers a CLIPImageEncoderH model (Stable Diffusion v2-1-unclip)
specifically

tests/foundationals/clip/test_image_encoder.py

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+import torch
+import pytest
+
+from warnings import warn
+from pathlib import Path
+
+from transformers import CLIPVisionModelWithProjection  # type: ignore
+
+from refiners.foundationals.clip.image_encoder import CLIPImageEncoderH
+from refiners.fluxion.utils import load_from_safetensors
+
+
+@pytest.fixture(scope="module")
+def our_encoder(test_weights_path: Path, test_device: torch.device) -> CLIPImageEncoderH:
+    weights = test_weights_path / "CLIPImageEncoderH.safetensors"
+    if not weights.is_file():
+        warn(f"could not find weights at {weights}, skipping")
+        pytest.skip(allow_module_level=True)
+    encoder = CLIPImageEncoderH(device=test_device)
+    tensors = load_from_safetensors(weights)
+    encoder.load_state_dict(tensors)
+    return encoder
+
+
+@pytest.fixture(scope="module")
+def stabilityai_unclip_weights_path(test_weights_path: Path):
+    r = test_weights_path / "stabilityai" / "stable-diffusion-2-1-unclip"
+    if not r.is_dir():
+        warn(f"could not find Stability AI weights at {r}, skipping")
+        pytest.skip(allow_module_level=True)
+    return r
+
+
+@pytest.fixture(scope="module")
+def ref_encoder(stabilityai_unclip_weights_path: Path, test_device: torch.device) -> CLIPVisionModelWithProjection:
+    return CLIPVisionModelWithProjection.from_pretrained(stabilityai_unclip_weights_path, subfolder="image_encoder").to(test_device)  # type: ignore
+
+
+def test_encoder(
+    ref_encoder: CLIPVisionModelWithProjection,
+    our_encoder: CLIPImageEncoderH,
+    test_device: torch.device,
+):
+    x = torch.randn(1, 3, 224, 224).to(test_device)
+
+    with torch.no_grad():
+        ref_embeddings = ref_encoder(x).image_embeds
+        our_embeddings = our_encoder(x)
+
+    assert ref_embeddings.shape == (1, 1024)
+    assert our_embeddings.shape == (1, 1024)
+
+    assert (our_embeddings - ref_embeddings).abs().max() < 0.01