mirror of
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76 lines
2.6 KiB
Python
76 lines
2.6 KiB
Python
from typing import Any
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from pathlib import Path
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from warnings import warn
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import pytest
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import torch
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from refiners.fluxion.utils import manual_seed
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from refiners.foundationals.latent_diffusion.stable_diffusion_xl import SDXLUNet
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from refiners.fluxion.model_converter import ModelConverter
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@pytest.fixture(scope="module")
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def stabilityai_sdxl_base_path(test_weights_path: Path) -> Path:
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r = test_weights_path / "stabilityai" / "stable-diffusion-xl-base-0.9"
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if not r.is_dir():
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warn(f"could not find Stability SDXL base 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 diffusers_sdxl(stabilityai_sdxl_base_path: Path) -> Any:
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from diffusers import DiffusionPipeline # type: ignore
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return DiffusionPipeline.from_pretrained(pretrained_model_name_or_path=stabilityai_sdxl_base_path) # type: ignore
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@pytest.fixture(scope="module")
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def diffusers_sdxl_unet(diffusers_sdxl: Any) -> Any:
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return diffusers_sdxl.unet
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@pytest.fixture(scope="module")
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def sdxl_unet_weights_std(test_weights_path: Path) -> Path:
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unet_weights_std = test_weights_path / "sdxl-unet.safetensors"
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if not unet_weights_std.is_file():
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warn(message=f"could not find weights at {unet_weights_std}, skipping")
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pytest.skip(allow_module_level=True)
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return unet_weights_std
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@pytest.fixture(scope="module")
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def refiners_sdxl_unet(sdxl_unet_weights_std: Path) -> SDXLUNet:
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unet = SDXLUNet(in_channels=4)
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unet.load_from_safetensors(tensors_path=sdxl_unet_weights_std)
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return unet
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@torch.no_grad()
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def test_sdxl_unet(diffusers_sdxl_unet: Any, refiners_sdxl_unet: SDXLUNet) -> None:
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source = diffusers_sdxl_unet
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target = refiners_sdxl_unet
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manual_seed(seed=0)
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x = torch.randn(1, 4, 32, 32)
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timestep = torch.tensor(data=[0])
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clip_text_embeddings = torch.randn(1, 77, 2048)
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added_cond_kwargs = {"text_embeds": torch.randn(1, 1280), "time_ids": torch.randn(1, 6)}
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target.set_timestep(timestep=timestep)
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target.set_clip_text_embedding(clip_text_embedding=clip_text_embeddings)
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target.set_time_ids(time_ids=added_cond_kwargs["time_ids"])
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target.set_pooled_text_embedding(pooled_text_embedding=added_cond_kwargs["text_embeds"])
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target_args = (x,)
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source_args = {
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"positional": (x, timestep, clip_text_embeddings),
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"keyword": {"added_cond_kwargs": added_cond_kwargs},
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}
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converter = ModelConverter(source_model=source, target_model=target, verbose=False, threshold=1e-2)
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assert converter.run(
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source_args=source_args,
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target_args=target_args,
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)
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