mirror of
https://github.com/finegrain-ai/refiners.git
synced 2024-11-10 07:21:59 +00:00
70 lines
2.5 KiB
Python
70 lines
2.5 KiB
Python
|
from typing import Any
|
||
|
|
from pathlib import Path
|
||
|
|
from warnings import warn
|
||
|
|
import pytest
|
||
|
|
import torch
|
||
|
|
|
||
|
|
from refiners.foundationals.latent_diffusion.sdxl_unet import SDXLUNet
|
||
|
|
from refiners.fluxion.utils import compare_models
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.fixture(scope="module")
|
||
|
|
def stabilityai_sdxl_base_path(test_weights_path: Path) -> Path:
|
||
|
|
r = test_weights_path / "stabilityai" / "stable-diffusion-xl-base-0.9"
|
||
|
|
if not r.is_dir():
|
||
|
|
warn(f"could not find Stability SDXL base weights at {r}, skipping")
|
||
|
|
pytest.skip(allow_module_level=True)
|
||
|
|
return r
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.fixture(scope="module")
|
||
|
|
def diffusers_sdxl(stabilityai_sdxl_base_path: Path) -> Any:
|
||
|
|
from diffusers import DiffusionPipeline # type: ignore
|
||
|
|
|
||
|
|
return DiffusionPipeline.from_pretrained(pretrained_model_name_or_path=stabilityai_sdxl_base_path) # type: ignore
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.fixture(scope="module")
|
||
|
|
def diffusers_sdxl_unet(diffusers_sdxl: Any) -> Any:
|
||
|
|
return diffusers_sdxl.unet
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.fixture(scope="module")
|
||
|
|
def sdxl_unet_weights_std(test_weights_path: Path) -> Path:
|
||
|
|
unet_weights_std = test_weights_path / "sdxl-unet.safetensors"
|
||
|
|
if not unet_weights_std.is_file():
|
||
|
|
warn(message=f"could not find weights at {unet_weights_std}, skipping")
|
||
|
|
pytest.skip(allow_module_level=True)
|
||
|
|
return unet_weights_std
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.fixture(scope="module")
|
||
|
|
def refiners_sdxl_unet(sdxl_unet_weights_std: Path) -> SDXLUNet:
|
||
|
|
unet = SDXLUNet(in_channels=4)
|
||
|
|
unet.load_from_safetensors(tensors_path=sdxl_unet_weights_std)
|
||
|
|
return unet
|
||
|
|
|
||
|
|
|
||
|
|
@torch.no_grad()
|
||
|
|
def test_sdxl_unet(diffusers_sdxl_unet: Any, refiners_sdxl_unet: SDXLUNet) -> None:
|
||
|
|
torch.manual_seed(seed=0) # type: ignore
|
||
|
|
x = torch.randn(1, 4, 32, 32)
|
||
|
|
timestep = torch.tensor(data=[0])
|
||
|
|
clip_text_embeddings = torch.randn(1, 77, 2048)
|
||
|
|
added_cond_kwargs = {"text_embeds": torch.randn(1, 1280), "time_ids": torch.randn(1, 6)}
|
||
|
|
source_args = (x, timestep, clip_text_embeddings, None, None, None, None, added_cond_kwargs)
|
||
|
|
|
||
|
|
refiners_sdxl_unet.set_timestep(timestep=timestep)
|
||
|
|
refiners_sdxl_unet.set_clip_text_embedding(clip_text_embedding=clip_text_embeddings)
|
||
|
|
refiners_sdxl_unet.set_time_ids(time_ids=added_cond_kwargs["time_ids"])
|
||
|
|
refiners_sdxl_unet.set_pooled_text_embedding(pooled_text_embedding=added_cond_kwargs["text_embeds"])
|
||
|
|
target_args = (x,)
|
||
|
|
|
||
|
|
assert compare_models(
|
||
|
|
source_model=diffusers_sdxl_unet,
|
||
|
|
target_model=refiners_sdxl_unet,
|
||
|
|
source_args=source_args,
|
||
|
|
target_args=target_args,
|
||
|
|
threshold=1e-2,
|
||
|
|
)
|