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