update pyright and fix Pillow 10.3 typing issues

2024-11-21 13:48:46 +00:00 · 2024-04-02 17:30:57 +02:00 · 2024-04-02 17:30:57 +02:00 · fd5a15c7e0
parent 328fcb8ed1
commit fd5a15c7e0
16 changed files with 144 additions and 111 deletions
--- a/pyproject.toml
+++ b/pyproject.toml
@ -73,7 +73,7 @@ build-backend = "hatchling.build"
 [tool.rye]
 managed = true
 dev-dependencies = [
-    "pyright==1.1.349",
+    "pyright==1.1.356",
    "docformatter>=1.7.5",
    "pytest>=8.0.0",
    "coverage>=7.4.1",
--- a/requirements.lock
+++ b/requirements.lock
@ -23,9 +23,9 @@ attrs==23.2.0
    # via aiohttp
 babel==2.14.0
    # via mkdocs-material
-bitsandbytes==0.42.0
+bitsandbytes==0.43.0
    # via refiners
-black==24.2.0
+black==24.3.0
    # via refiners
 certifi==2024.2.2
    # via requests
@ -42,14 +42,14 @@ colorama==0.4.6
    # via mkdocs-material
 datasets==2.18.0
    # via refiners
-diffusers==0.26.3
+diffusers==0.27.2
    # via refiners
 dill==0.3.8
    # via datasets
    # via multiprocess
 docker-pycreds==0.4.0
    # via wandb
-filelock==3.13.1
+filelock==3.13.3
    # via datasets
    # via diffusers
    # via huggingface-hub
@ -67,23 +67,24 @@ ghp-import==2.1.0
    # via mkdocs
 gitdb==4.0.11
    # via gitpython
-gitpython==3.1.42
+gitpython==3.1.43
    # via wandb
-griffe==0.41.3
+griffe==0.42.1
    # via mkdocstrings-python
-huggingface-hub==0.21.3
+huggingface-hub==0.22.2
    # via datasets
    # via diffusers
+    # via timm
    # via tokenizers
    # via transformers
 idna==3.6
    # via requests
    # via yarl
-importlib-metadata==7.0.1
+importlib-metadata==7.1.0
    # via diffusers
 invisible-watermark==0.2.0
    # via refiners
-jaxtyping==0.2.25
+jaxtyping==0.2.28
    # via refiners
 jinja2==3.1.3
    # via mkdocs
@ -92,7 +93,7 @@ jinja2==3.1.3
    # via torch
 loguru==0.7.2
    # via refiners
-markdown==3.5.2
+markdown==3.6
    # via mkdocs
    # via mkdocs-autorefs
    # via mkdocs-material
@ -114,7 +115,7 @@ mkdocs-autorefs==1.0.1
    # via mkdocstrings
 mkdocs-literate-nav==0.6.1
    # via refiners
-mkdocs-material==9.5.12
+mkdocs-material==9.5.17
    # via refiners
 mkdocs-material-extensions==1.3.1
    # via mkdocs-material
@ -135,6 +136,7 @@ mypy-extensions==1.0.0
 networkx==3.2.1
    # via torch
 numpy==1.26.4
+    # via bitsandbytes
    # via datasets
    # via diffusers
    # via invisible-watermark
@ -143,7 +145,6 @@ numpy==1.26.4
    # via pandas
    # via pyarrow
    # via pywavelets
-    # via scipy
    # via torchvision
    # via transformers
 nvidia-cublas-cu12==12.1.3.1
@ -169,14 +170,14 @@ nvidia-cusparse-cu12==12.1.0.106
    # via torch
 nvidia-nccl-cu12==2.19.3
    # via torch
-nvidia-nvjitlink-cu12==12.3.101
+nvidia-nvjitlink-cu12==12.4.99
    # via nvidia-cusolver-cu12
    # via nvidia-cusparse-cu12
 nvidia-nvtx-cu12==12.1.105
    # via torch
 opencv-python==4.9.0.80
    # via invisible-watermark
-packaging==23.2
+packaging==24.0
    # via black
    # via datasets
    # via huggingface-hub
@ -190,7 +191,7 @@ pandas==2.2.1
 pathspec==0.12.1
    # via black
    # via mkdocs
-pillow==10.2.0
+pillow==10.3.0
    # via diffusers
    # via invisible-watermark
    # via refiners
@ -207,11 +208,11 @@ protobuf==4.25.3
    # via wandb
 psutil==5.9.8
    # via wandb
-pyarrow==15.0.0
+pyarrow==15.0.2
    # via datasets
 pyarrow-hotfix==0.6
    # via datasets
-pydantic==2.6.3
+pydantic==2.6.4
    # via refiners
 pydantic-core==2.16.3
    # via pydantic
@ -225,7 +226,7 @@ python-dateutil==2.9.0.post0
    # via pandas
 pytz==2024.1
    # via pandas
-pywavelets==1.5.0
+pywavelets==1.6.0
    # via invisible-watermark
 pyyaml==6.0.1
    # via datasets
@ -233,6 +234,7 @@ pyyaml==6.0.1
    # via mkdocs
    # via pymdown-extensions
    # via pyyaml-env-tag
+    # via timm
    # via transformers
    # via wandb
 pyyaml-env-tag==0.1
@ -252,17 +254,17 @@ requests==2.31.0
 safetensors==0.4.2
    # via diffusers
    # via refiners
+    # via timm
    # via transformers
-scipy==1.12.0
-    # via bitsandbytes
 segment-anything-hq==0.3
+    # via refiners
 segment-anything-py==1.0
    # via refiners
-sentry-sdk==1.40.6
+sentry-sdk==1.44.0
    # via wandb
 setproctitle==1.3.3
    # via wandb
-setuptools==69.1.1
+setuptools==69.2.0
    # via wandb
 six==1.16.0
    # via docker-pycreds
@ -272,26 +274,32 @@ smmap==5.0.1
 sympy==1.12
    # via torch
 timm==0.9.16
+    # via refiners
 tokenizers==0.15.2
    # via transformers
 tomli==2.0.1
    # via black
    # via refiners
-torch==2.2.1
+torch==2.2.2
+    # via bitsandbytes
    # via invisible-watermark
    # via refiners
+    # via segment-anything-hq
    # via segment-anything-py
+    # via timm
    # via torchvision
-torchvision==0.17.1
+torchvision==0.17.2
    # via piq
    # via refiners
+    # via segment-anything-hq
    # via segment-anything-py
+    # via timm
 tqdm==4.66.2
    # via datasets
    # via huggingface-hub
    # via refiners
    # via transformers
-transformers==4.38.2
+transformers==4.39.3
    # via refiners
 triton==2.2.0
    # via torch
@ -300,7 +308,6 @@ typeguard==2.13.3
 typing-extensions==4.10.0
    # via black
    # via huggingface-hub
-    # via jaxtyping
    # via pydantic
    # via pydantic-core
    # via torch
@ -309,7 +316,7 @@ tzdata==2024.1
 urllib3==2.2.1
    # via requests
    # via sentry-sdk
-wandb==0.16.3
+wandb==0.16.5
    # via refiners
 watchdog==4.0.0
    # via mkdocs
@ -317,5 +324,5 @@ xxhash==3.4.1
    # via datasets
 yarl==1.9.4
    # via aiohttp
-zipp==3.17.0
+zipp==3.18.1
    # via importlib-metadata
--- a/src/refiners/fluxion/utils.py
+++ b/src/refiners/fluxion/utils.py
@ -145,6 +145,7 @@ def image_to_tensor(image: Image.Image, device: Device | str | None = None, dtyp
    """
    image_tensor = torch.tensor(array(image).astype(float32) / 255.0, device=device, dtype=dtype)

+    assert isinstance(image.mode, str)  # type: ignore
    match image.mode:
        case "L":
            image_tensor = image_tensor.unsqueeze(0)
--- a/src/refiners/foundationals/latent_diffusion/image_prompt.py
+++ b/src/refiners/foundationals/latent_diffusion/image_prompt.py
@ -537,8 +537,9 @@ class IPAdapter(Generic[T], fl.Chain, Adapter[T]):
            mean: The mean to use for normalization.
            std: The standard deviation to use for normalization.
        """
+        resized = image.resize(size)  # type: ignore
        return normalize(
-            image_to_tensor(image.resize(size), device=self.target.device, dtype=self.target.dtype),
+            image_to_tensor(resized, device=self.target.device, dtype=self.target.dtype),
            mean=[0.48145466, 0.4578275, 0.40821073] if mean is None else mean,
            std=[0.26862954, 0.26130258, 0.27577711] if std is None else std,
        )
--- a/src/refiners/foundationals/latent_diffusion/model.py
+++ b/src/refiners/foundationals/latent_diffusion/model.py
@ -52,7 +52,8 @@ class LatentDiffusionModel(fl.Module, ABC):
        if init_image is None:
            x = noise
        else:
-            encoded_image = self.lda.image_to_latents(image=init_image.resize(size=(width, height)))
+            resized = init_image.resize(size=(width, height))  # type: ignore
+            encoded_image = self.lda.image_to_latents(resized)
            x = self.solver.add_noise(
                x=encoded_image,
                noise=noise,
--- a/src/refiners/foundationals/segment_anything/model.py
+++ b/src/refiners/foundationals/segment_anything/model.py
@ -197,8 +197,9 @@ class SegmentAnything(fl.Chain):
        h, w = target_size
        padh = self.image_size - h
        padw = self.image_size - w
+        resized = image.resize((w, h), resample=Image.Resampling.BILINEAR)  # type: ignore
        image_tensor = torch.tensor(
-            np.array(image.resize((w, h), resample=Image.Resampling.BILINEAR)).astype(np.float32).transpose(2, 0, 1),
+            np.array(resized).astype(np.float32).transpose(2, 0, 1),
            device=self.device,
            dtype=self.dtype,
        ).unsqueeze(0)
--- a/src/refiners/training_utils/trainer.py
+++ b/src/refiners/training_utils/trainer.py
@ -1,4 +1,4 @@
-from abc import ABC, abstractmethod, abstractproperty
+from abc import ABC, abstractmethod
 from dataclasses import dataclass
 from functools import cached_property, wraps
 from typing import Any, Callable, Generic, Literal, TypeVar, cast
@ -300,7 +300,8 @@ class Trainer(Generic[ConfigType, Batch], ABC):
        """
        ...

-    @abstractproperty
+    @property
+    @abstractmethod
    def dataset_length(self) -> int:
        """
        Returns the length of the dataset.
--- a/tests/e2e/test_diffusion.py
+++ b/tests/e2e/test_diffusion.py
@ -34,6 +34,10 @@ from refiners.foundationals.latent_diffusion.style_aligned import StyleAlignedAd
 from tests.utils import ensure_similar_images


+def _img_open(path: Path) -> Image.Image:
+    return Image.open(path)  # type: ignore
+
+
@pytest.fixture(autouse=True)
 def ensure_gc():
    # Avoid GPU OOMs
@ -48,112 +52,112 @@ def ref_path(test_e2e_path: Path) -> Path:

@pytest.fixture(scope="module")
 def cutecat_init(ref_path: Path) -> Image.Image:
-    return Image.open(ref_path / "cutecat_init.png").convert("RGB")
+    return _img_open(ref_path / "cutecat_init.png").convert("RGB")


@pytest.fixture(scope="module")
 def kitchen_dog(ref_path: Path) -> Image.Image:
-    return Image.open(ref_path / "kitchen_dog.png").convert("RGB")
+    return _img_open(ref_path / "kitchen_dog.png").convert("RGB")


@pytest.fixture(scope="module")
 def kitchen_dog_mask(ref_path: Path) -> Image.Image:
-    return Image.open(ref_path / "kitchen_dog_mask.png").convert("RGB")
+    return _img_open(ref_path / "kitchen_dog_mask.png").convert("RGB")


@pytest.fixture(scope="module")
 def woman_image(ref_path: Path) -> Image.Image:
-    return Image.open(ref_path / "woman.png").convert("RGB")
+    return _img_open(ref_path / "woman.png").convert("RGB")


@pytest.fixture(scope="module")
 def statue_image(ref_path: Path) -> Image.Image:
-    return Image.open(ref_path / "statue.png").convert("RGB")
+    return _img_open(ref_path / "statue.png").convert("RGB")


@pytest.fixture
 def expected_image_std_random_init(ref_path: Path) -> Image.Image:
-    return Image.open(ref_path / "expected_std_random_init.png").convert("RGB")
+    return _img_open(ref_path / "expected_std_random_init.png").convert("RGB")


@pytest.fixture
 def expected_image_std_random_init_euler(ref_path: Path) -> Image.Image:
-    return Image.open(ref_path / "expected_std_random_init_euler.png").convert("RGB")
+    return _img_open(ref_path / "expected_std_random_init_euler.png").convert("RGB")


@pytest.fixture
 def expected_karras_random_init(ref_path: Path) -> Image.Image:
-    return Image.open(ref_path / "expected_karras_random_init.png").convert("RGB")
+    return _img_open(ref_path / "expected_karras_random_init.png").convert("RGB")


@pytest.fixture
 def expected_image_std_random_init_sag(ref_path: Path) -> Image.Image:
-    return Image.open(ref_path / "expected_std_random_init_sag.png").convert("RGB")
+    return _img_open(ref_path / "expected_std_random_init_sag.png").convert("RGB")


@pytest.fixture
 def expected_image_std_init_image(ref_path: Path) -> Image.Image:
-    return Image.open(ref_path / "expected_std_init_image.png").convert("RGB")
+    return _img_open(ref_path / "expected_std_init_image.png").convert("RGB")


@pytest.fixture
 def expected_image_std_inpainting(ref_path: Path) -> Image.Image:
-    return Image.open(ref_path / "expected_std_inpainting.png").convert("RGB")
+    return _img_open(ref_path / "expected_std_inpainting.png").convert("RGB")


@pytest.fixture
 def expected_image_controlnet_stack(ref_path: Path) -> Image.Image:
-    return Image.open(ref_path / "expected_controlnet_stack.png").convert("RGB")
+    return _img_open(ref_path / "expected_controlnet_stack.png").convert("RGB")


@pytest.fixture
 def expected_image_ip_adapter_woman(ref_path: Path) -> Image.Image:
-    return Image.open(ref_path / "expected_image_ip_adapter_woman.png").convert("RGB")
+    return _img_open(ref_path / "expected_image_ip_adapter_woman.png").convert("RGB")


@pytest.fixture
 def expected_image_ip_adapter_multi(ref_path: Path) -> Image.Image:
-    return Image.open(ref_path / "expected_image_ip_adapter_multi.png").convert("RGB")
+    return _img_open(ref_path / "expected_image_ip_adapter_multi.png").convert("RGB")


@pytest.fixture
 def expected_image_ip_adapter_plus_statue(ref_path: Path) -> Image.Image:
-    return Image.open(ref_path / "expected_image_ip_adapter_plus_statue.png").convert("RGB")
+    return _img_open(ref_path / "expected_image_ip_adapter_plus_statue.png").convert("RGB")


@pytest.fixture
 def expected_image_sdxl_ip_adapter_woman(ref_path: Path) -> Image.Image:
-    return Image.open(ref_path / "expected_image_sdxl_ip_adapter_woman.png").convert("RGB")
+    return _img_open(ref_path / "expected_image_sdxl_ip_adapter_woman.png").convert("RGB")


@pytest.fixture
 def expected_image_sdxl_ip_adapter_plus_woman(ref_path: Path) -> Image.Image:
-    return Image.open(ref_path / "expected_image_sdxl_ip_adapter_plus_woman.png").convert("RGB")
+    return _img_open(ref_path / "expected_image_sdxl_ip_adapter_plus_woman.png").convert("RGB")


@pytest.fixture
 def expected_image_ip_adapter_controlnet(ref_path: Path) -> Image.Image:
-    return Image.open(ref_path / "expected_ip_adapter_controlnet.png").convert("RGB")
+    return _img_open(ref_path / "expected_ip_adapter_controlnet.png").convert("RGB")


@pytest.fixture
 def expected_sdxl_ddim_random_init(ref_path: Path) -> Image.Image:
-    return Image.open(ref_path / "expected_cutecat_sdxl_ddim_random_init.png").convert("RGB")
+    return _img_open(ref_path / "expected_cutecat_sdxl_ddim_random_init.png").convert("RGB")


@pytest.fixture
 def expected_sdxl_ddim_random_init_sag(ref_path: Path) -> Image.Image:
-    return Image.open(ref_path / "expected_cutecat_sdxl_ddim_random_init_sag.png").convert("RGB")
+    return _img_open(ref_path / "expected_cutecat_sdxl_ddim_random_init_sag.png").convert("RGB")


@pytest.fixture
 def expected_sdxl_euler_random_init(ref_path: Path) -> Image.Image:
-    return Image.open(ref_path / "expected_cutecat_sdxl_euler_random_init.png").convert("RGB")
+    return _img_open(ref_path / "expected_cutecat_sdxl_euler_random_init.png").convert("RGB")


@pytest.fixture
 def expected_style_aligned(ref_path: Path) -> Image.Image:
-    return Image.open(fp=ref_path / "expected_style_aligned.png").convert(mode="RGB")
+    return _img_open(ref_path / "expected_style_aligned.png").convert(mode="RGB")


@pytest.fixture(scope="module", params=["canny", "depth", "lineart", "normals", "sam"])
@ -161,8 +165,8 @@ def controlnet_data(
    ref_path: Path, test_weights_path: Path, request: pytest.FixtureRequest
 ) -> Iterator[tuple[str, Image.Image, Image.Image, Path]]:
    cn_name: str = request.param
-    condition_image = Image.open(ref_path / f"cutecat_guide_{cn_name}.png").convert("RGB")
-    expected_image = Image.open(ref_path / f"expected_controlnet_{cn_name}.png").convert("RGB")
+    condition_image = _img_open(ref_path / f"cutecat_guide_{cn_name}.png").convert("RGB")
+    expected_image = _img_open(ref_path / f"expected_controlnet_{cn_name}.png").convert("RGB")
    weights_fn = {
        "depth": "lllyasviel_control_v11f1p_sd15_depth",
        "canny": "lllyasviel_control_v11p_sd15_canny",
@ -178,8 +182,8 @@ def controlnet_data(
@pytest.fixture(scope="module")
 def controlnet_data_canny(ref_path: Path, test_weights_path: Path) -> tuple[str, Image.Image, Image.Image, Path]:
    cn_name = "canny"
-    condition_image = Image.open(ref_path / f"cutecat_guide_{cn_name}.png").convert("RGB")
-    expected_image = Image.open(ref_path / f"expected_controlnet_{cn_name}.png").convert("RGB")
+    condition_image = _img_open(ref_path / f"cutecat_guide_{cn_name}.png").convert("RGB")
+    expected_image = _img_open(ref_path / f"expected_controlnet_{cn_name}.png").convert("RGB")
    weights_path = test_weights_path / "controlnet" / "lllyasviel_control_v11p_sd15_canny.safetensors"
    return cn_name, condition_image, expected_image, weights_path

@ -187,8 +191,8 @@ def controlnet_data_canny(ref_path: Path, test_weights_path: Path) -> tuple[str,
@pytest.fixture(scope="module")
 def controlnet_data_depth(ref_path: Path, test_weights_path: Path) -> tuple[str, Image.Image, Image.Image, Path]:
    cn_name = "depth"
-    condition_image = Image.open(ref_path / f"cutecat_guide_{cn_name}.png").convert("RGB")
-    expected_image = Image.open(ref_path / f"expected_controlnet_{cn_name}.png").convert("RGB")
+    condition_image = _img_open(ref_path / f"cutecat_guide_{cn_name}.png").convert("RGB")
+    expected_image = _img_open(ref_path / f"expected_controlnet_{cn_name}.png").convert("RGB")
    weights_path = test_weights_path / "controlnet" / "lllyasviel_control_v11f1p_sd15_depth.safetensors"
    return cn_name, condition_image, expected_image, weights_path

@ -257,12 +261,12 @@ def controllora_sdxl_config(
 ) -> tuple[Image.Image, dict[str, ControlLoraResolvedConfig]]:
    name: str = request.param[0]
    configs: dict[str, ControlLoraConfig] = request.param[1]
-    expected_image = Image.open(ref_path / name).convert("RGB")
+    expected_image = _img_open(ref_path / name).convert("RGB")

    loaded_configs = {
        config_name: ControlLoraResolvedConfig(
            scale=config.scale,
-            condition_image=Image.open(ref_path / config.condition_path).convert("RGB"),
+            condition_image=_img_open(ref_path / config.condition_path).convert("RGB"),
            weights_path=test_weights_path / "control-loras" / config.weights_path,
        )
        for config_name, config in configs.items()
@ -274,8 +278,8 @@ def controllora_sdxl_config(
@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")
+    condition_image = _img_open(ref_path / f"cutecat_guide_{name}.png").convert("RGB")
+    expected_image = _img_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

@ -283,8 +287,8 @@ def t2i_adapter_data_depth(ref_path: Path, test_weights_path: Path) -> tuple[str
@pytest.fixture(scope="module")
 def t2i_adapter_xl_data_canny(ref_path: Path, test_weights_path: Path) -> tuple[str, Image.Image, Image.Image, Path]:
    name = "canny"
-    condition_image = Image.open(ref_path / f"fairy_guide_{name}.png").convert("RGB")
-    expected_image = Image.open(ref_path / f"expected_t2i_adapter_xl_{name}.png").convert("RGB")
+    condition_image = _img_open(ref_path / f"fairy_guide_{name}.png").convert("RGB")
+    expected_image = _img_open(ref_path / f"expected_t2i_adapter_xl_{name}.png").convert("RGB")
    weights_path = test_weights_path / "T2I-Adapter" / "t2i-adapter-canny-sdxl-1.0.safetensors"

    if not weights_path.is_file():
@ -296,7 +300,7 @@ def t2i_adapter_xl_data_canny(ref_path: Path, test_weights_path: Path) -> tuple[

@pytest.fixture(scope="module")
 def lora_data_pokemon(ref_path: Path, test_weights_path: Path) -> tuple[Image.Image, dict[str, torch.Tensor]]:
-    expected_image = Image.open(ref_path / "expected_lora_pokemon.png").convert("RGB")
+    expected_image = _img_open(ref_path / "expected_lora_pokemon.png").convert("RGB")
    weights_path = test_weights_path / "loras" / "pokemon-lora" / "pytorch_lora_weights.bin"

    if not weights_path.is_file():
@ -309,7 +313,7 @@ def lora_data_pokemon(ref_path: Path, test_weights_path: Path) -> tuple[Image.Im

@pytest.fixture(scope="module")
 def lora_data_dpo(ref_path: Path, test_weights_path: Path) -> tuple[Image.Image, dict[str, torch.Tensor]]:
-    expected_image = Image.open(ref_path / "expected_sdxl_dpo_lora.png").convert("RGB")
+    expected_image = _img_open(ref_path / "expected_sdxl_dpo_lora.png").convert("RGB")
    weights_path = test_weights_path / "loras" / "dpo-lora" / "pytorch_lora_weights.safetensors"

    if not weights_path.is_file():
@ -341,57 +345,57 @@ def lora_sliders(test_weights_path: Path) -> tuple[dict[str, dict[str, torch.Ten

@pytest.fixture
 def scene_image_inpainting_refonly(ref_path: Path) -> Image.Image:
-    return Image.open(ref_path / "inpainting-scene.png").convert("RGB")
+    return _img_open(ref_path / "inpainting-scene.png").convert("RGB")


@pytest.fixture
 def mask_image_inpainting_refonly(ref_path: Path) -> Image.Image:
-    return Image.open(ref_path / "inpainting-mask.png").convert("RGB")
+    return _img_open(ref_path / "inpainting-mask.png").convert("RGB")


@pytest.fixture
 def target_image_inpainting_refonly(ref_path: Path) -> Image.Image:
-    return Image.open(ref_path / "inpainting-target.png").convert("RGB")
+    return _img_open(ref_path / "inpainting-target.png").convert("RGB")


@pytest.fixture
 def expected_image_inpainting_refonly(ref_path: Path) -> Image.Image:
-    return Image.open(ref_path / "expected_inpainting_refonly.png").convert("RGB")
+    return _img_open(ref_path / "expected_inpainting_refonly.png").convert("RGB")


@pytest.fixture
 def expected_image_refonly(ref_path: Path) -> Image.Image:
-    return Image.open(ref_path / "expected_refonly.png").convert("RGB")
+    return _img_open(ref_path / "expected_refonly.png").convert("RGB")


@pytest.fixture
 def condition_image_refonly(ref_path: Path) -> Image.Image:
-    return Image.open(ref_path / "cyberpunk_guide.png").convert("RGB")
+    return _img_open(ref_path / "cyberpunk_guide.png").convert("RGB")


@pytest.fixture
 def expected_image_textual_inversion_random_init(ref_path: Path) -> Image.Image:
-    return Image.open(ref_path / "expected_textual_inversion_random_init.png").convert("RGB")
+    return _img_open(ref_path / "expected_textual_inversion_random_init.png").convert("RGB")


@pytest.fixture
 def expected_multi_diffusion(ref_path: Path) -> Image.Image:
-    return Image.open(fp=ref_path / "expected_multi_diffusion.png").convert(mode="RGB")
+    return _img_open(ref_path / "expected_multi_diffusion.png").convert(mode="RGB")


@pytest.fixture
 def expected_restart(ref_path: Path) -> Image.Image:
-    return Image.open(fp=ref_path / "expected_restart.png").convert(mode="RGB")
+    return _img_open(ref_path / "expected_restart.png").convert(mode="RGB")


@pytest.fixture
 def expected_freeu(ref_path: Path) -> Image.Image:
-    return Image.open(fp=ref_path / "expected_freeu.png").convert(mode="RGB")
+    return _img_open(ref_path / "expected_freeu.png").convert(mode="RGB")


@pytest.fixture
 def expected_sdxl_multi_loras(ref_path: Path) -> Image.Image:
-    return Image.open(fp=ref_path / "expected_sdxl_multi_loras.png").convert(mode="RGB")
+    return _img_open(ref_path / "expected_sdxl_multi_loras.png").convert(mode="RGB")


@pytest.fixture
@ -401,10 +405,10 @@ def hello_world_assets(ref_path: Path) -> tuple[Image.Image, Image.Image, Image.
    image_prompt = assets / "dragon_quest_slime.jpg"
    condition_image = assets / "dropy_canny.png"
    return (
-        Image.open(fp=dropy).convert(mode="RGB"),
-        Image.open(fp=image_prompt).convert(mode="RGB"),
-        Image.open(fp=condition_image).convert(mode="RGB"),
-        Image.open(fp=ref_path / "expected_dropy_slime_9752.png").convert(mode="RGB"),
+        _img_open(dropy).convert(mode="RGB"),
+        _img_open(image_prompt).convert(mode="RGB"),
+        _img_open(condition_image).convert(mode="RGB"),
+        _img_open(ref_path / "expected_dropy_slime_9752.png").convert(mode="RGB"),
    )


@ -2314,7 +2318,7 @@ def test_style_aligned(
    # tile all images horizontally
    merged_image = Image.new("RGB", (1024 * len(predicted_images), 1024))
    for i in range(len(predicted_images)):
-        merged_image.paste(predicted_images[i], (i * 1024, 0))
+        merged_image.paste(predicted_images[i], (i * 1024, 0))  # type: ignore

    # compare against reference image
    ensure_similar_images(merged_image, expected_style_aligned, min_psnr=35, min_ssim=0.99)
--- a/tests/e2e/test_doc_examples.py
+++ b/tests/e2e/test_doc_examples.py
@ -13,6 +13,10 @@ from refiners.foundationals.latent_diffusion.stable_diffusion_xl.model import St
 from tests.utils import ensure_similar_images


+def _img_open(path: Path) -> Image.Image:
+    return Image.open(path)  # type: ignore
+
+
@pytest.fixture(autouse=True)
 def ensure_gc():
    # Avoid GPU OOMs
@ -110,27 +114,27 @@ def sdxl(

@pytest.fixture
 def image_prompt_german_castle(ref_path: Path) -> Image.Image:
-    return Image.open(ref_path / "german-castle.jpg").convert("RGB")
+    return _img_open(ref_path / "german-castle.jpg").convert("RGB")


@pytest.fixture
 def expected_image_guide_adapting_sdxl_vanilla(ref_path: Path) -> Image.Image:
-    return Image.open(ref_path / "expected_image_guide_adapting_sdxl_vanilla.png").convert("RGB")
+    return _img_open(ref_path / "expected_image_guide_adapting_sdxl_vanilla.png").convert("RGB")


@pytest.fixture
 def expected_image_guide_adapting_sdxl_single_lora(ref_path: Path) -> Image.Image:
-    return Image.open(ref_path / "expected_image_guide_adapting_sdxl_single_lora.png").convert("RGB")
+    return _img_open(ref_path / "expected_image_guide_adapting_sdxl_single_lora.png").convert("RGB")


@pytest.fixture
 def expected_image_guide_adapting_sdxl_multiple_loras(ref_path: Path) -> Image.Image:
-    return Image.open(ref_path / "expected_image_guide_adapting_sdxl_multiple_loras.png").convert("RGB")
+    return _img_open(ref_path / "expected_image_guide_adapting_sdxl_multiple_loras.png").convert("RGB")


@pytest.fixture
 def expected_image_guide_adapting_sdxl_loras_ip_adapter(ref_path: Path) -> Image.Image:
-    return Image.open(ref_path / "expected_image_guide_adapting_sdxl_loras_ip_adapter.png").convert("RGB")
+    return _img_open(ref_path / "expected_image_guide_adapting_sdxl_loras_ip_adapter.png").convert("RGB")


@no_grad()
--- a/tests/e2e/test_lcm.py
+++ b/tests/e2e/test_lcm.py
@ -15,6 +15,10 @@ from refiners.foundationals.latent_diffusion.stable_diffusion_xl.model import St
 from tests.utils import ensure_similar_images


+def _img_open(path: Path) -> Image.Image:
+    return Image.open(path)  # type: ignore
+
+
@pytest.fixture(autouse=True)
 def ensure_gc():
    # Avoid GPU OOMs
@ -74,17 +78,17 @@ def ref_path(test_e2e_path: Path) -> Path:

@pytest.fixture
 def expected_lcm_base(ref_path: Path) -> Image.Image:
-    return Image.open(ref_path / "expected_lcm_base.png").convert("RGB")
+    return _img_open(ref_path / "expected_lcm_base.png").convert("RGB")


@pytest.fixture
 def expected_lcm_lora_1_0(ref_path: Path) -> Image.Image:
-    return Image.open(ref_path / "expected_lcm_lora_1_0.png").convert("RGB")
+    return _img_open(ref_path / "expected_lcm_lora_1_0.png").convert("RGB")


@pytest.fixture
 def expected_lcm_lora_1_2(ref_path: Path) -> Image.Image:
-    return Image.open(ref_path / "expected_lcm_lora_1_2.png").convert("RGB")
+    return _img_open(ref_path / "expected_lcm_lora_1_2.png").convert("RGB")


@no_grad()
--- a/tests/e2e/test_lightning.py
+++ b/tests/e2e/test_lightning.py
@ -14,6 +14,10 @@ from refiners.foundationals.latent_diffusion.stable_diffusion_xl.model import St
 from tests.utils import ensure_similar_images


+def _img_open(path: Path) -> Image.Image:
+    return Image.open(path)  # type: ignore
+
+
@pytest.fixture(autouse=True)
 def ensure_gc():
    # Avoid GPU OOMs
@ -82,17 +86,17 @@ def ref_path(test_e2e_path: Path) -> Path:

@pytest.fixture
 def expected_lightning_base_4step(ref_path: Path) -> Image.Image:
-    return Image.open(ref_path / "expected_lightning_base_4step.png").convert("RGB")
+    return _img_open(ref_path / "expected_lightning_base_4step.png").convert("RGB")


@pytest.fixture
 def expected_lightning_base_1step(ref_path: Path) -> Image.Image:
-    return Image.open(ref_path / "expected_lightning_base_1step.png").convert("RGB")
+    return _img_open(ref_path / "expected_lightning_base_1step.png").convert("RGB")


@pytest.fixture
 def expected_lightning_lora_4step(ref_path: Path) -> Image.Image:
-    return Image.open(ref_path / "expected_lightning_lora_4step.png").convert("RGB")
+    return _img_open(ref_path / "expected_lightning_lora_4step.png").convert("RGB")


@no_grad()
--- a/tests/e2e/test_preprocessors.py
+++ b/tests/e2e/test_preprocessors.py
@ -10,6 +10,10 @@ from refiners.foundationals.latent_diffusion.preprocessors.informative_drawings
 from tests.utils import ensure_similar_images


+def _img_open(path: Path) -> Image.Image:
+    return Image.open(path)  # type: ignore
+
+
@pytest.fixture(scope="module")
 def diffusion_ref_path(test_e2e_path: Path) -> Path:
    return test_e2e_path / "test_diffusion_ref"
@ -17,12 +21,12 @@ def diffusion_ref_path(test_e2e_path: Path) -> Path:

@pytest.fixture(scope="module")
 def cutecat_init(diffusion_ref_path: Path) -> Image.Image:
-    return Image.open(diffusion_ref_path / "cutecat_init.png").convert("RGB")
+    return _img_open(diffusion_ref_path / "cutecat_init.png").convert("RGB")


@pytest.fixture
 def expected_image_informative_drawings(diffusion_ref_path: Path) -> Image.Image:
-    return Image.open(diffusion_ref_path / "cutecat_guide_lineart.png").convert("RGB")
+    return _img_open(diffusion_ref_path / "cutecat_guide_lineart.png").convert("RGB")


@pytest.fixture(scope="module")
--- a/tests/fluxion/test_utils.py
+++ b/tests/fluxion/test_utils.py
@ -70,10 +70,10 @@ def test_image_to_tensor() -> None:


 def test_tensor_to_image() -> None:
-    assert tensor_to_image(torch.zeros(1, 3, 512, 512)).mode == "RGB"
-    assert tensor_to_image(torch.zeros(1, 1, 512, 512)).mode == "L"
-    assert tensor_to_image(torch.zeros(1, 4, 512, 512)).mode == "RGBA"
-    assert tensor_to_image(torch.zeros(1, 3, 512, 512, dtype=torch.bfloat16)).mode == "RGB"
+    assert tensor_to_image(torch.zeros(1, 3, 512, 512)).mode == "RGB"  # type: ignore
+    assert tensor_to_image(torch.zeros(1, 1, 512, 512)).mode == "L"  # type: ignore
+    assert tensor_to_image(torch.zeros(1, 4, 512, 512)).mode == "RGBA"  # type: ignore
+    assert tensor_to_image(torch.zeros(1, 3, 512, 512, dtype=torch.bfloat16)).mode == "RGB"  # type: ignore


 def test_summarize_tensor() -> None:
--- a/tests/foundationals/latent_diffusion/test_auto_encoder.py
+++ b/tests/foundationals/latent_diffusion/test_auto_encoder.py
@ -33,7 +33,7 @@ def sample_image(ref_path: Path) -> Image.Image:
    if not test_image.is_file():
        warn(f"could not reference image at {test_image}, skipping")
        pytest.skip(allow_module_level=True)
-    img = Image.open(test_image)
+    img = Image.open(test_image)  # type: ignore
    assert img.size == (512, 512)
    return img

@ -43,7 +43,7 @@ def test_encode_decode_image(encoder: LatentDiffusionAutoencoder, sample_image:
    encoded = encoder.image_to_latents(sample_image)
    decoded = encoder.latents_to_image(encoded)

-    assert decoded.mode == "RGB"
+    assert decoded.mode == "RGB"  # type: ignore

    # Ensure no saturation. The green channel (band = 1) must not max out.
    assert max(iter(decoded.getdata(band=1))) < 255  # type: ignore
--- a/tests/foundationals/segment_anything/test_hq_sam.py
+++ b/tests/foundationals/segment_anything/test_hq_sam.py
@ -33,7 +33,7 @@ def one_prompt() -> SAMPrompt:

@pytest.fixture(scope="module")
 def tennis(ref_path: Path) -> Image.Image:
-    return Image.open(ref_path / "tennis.png").convert("RGB")
+    return Image.open(ref_path / "tennis.png").convert("RGB")  # type: ignore


@pytest.fixture(scope="module")
@ -148,7 +148,7 @@ def test_early_vit_embedding(
 ) -> None:
    HQSAMAdapter(sam_h, weights=load_from_safetensors(hq_adapter_weights)).inject()

-    image_tensor = image_to_tensor(image=tennis.resize(size=(1024, 1024)))
+    image_tensor = image_to_tensor(image=tennis.resize(size=(1024, 1024)))  # type: ignore

    _ = sam_h.image_encoder(image_tensor.to(sam_h.device))
    early_vit_embedding_refiners = sam_h.use_context(context_name="hq_sam")["early_vit_embedding"]
@ -284,7 +284,7 @@ def test_predictor_equal(
    # and to use image_embedding as input

    size = (1024, 1024)
-    resized_tennis = tennis.resize(size)
+    resized_tennis = tennis.resize(size)  # type: ignore

    # Reference
    reference_sam_h_predictor.set_image(np.array(resized_tennis))
--- a/tests/foundationals/segment_anything/test_sam.py
+++ b/tests/foundationals/segment_anything/test_sam.py
@ -91,7 +91,7 @@ def sam_h_single_output(sam_h_weights: Path, test_device: torch.device) -> Segme

@pytest.fixture(scope="module")
 def truck(ref_path: Path) -> Image.Image:
-    return Image.open(ref_path / "truck.jpg").convert("RGB")
+    return Image.open(ref_path / "truck.jpg").convert("RGB")  # type: ignore


@no_grad()
@ -140,7 +140,8 @@ def test_multimask_output_error() -> None:

@no_grad()
 def test_image_encoder(sam_h: SegmentAnythingH, facebook_sam_h: FacebookSAM, truck: Image.Image) -> None:
-    image_tensor = image_to_tensor(image=truck.resize(size=(1024, 1024)), device=facebook_sam_h.device)
+    resized = truck.resize(size=(1024, 1024))  # type: ignore
+    image_tensor = image_to_tensor(image=resized, device=facebook_sam_h.device)
    y_1 = facebook_sam_h.image_encoder(image_tensor)
    y_2 = sam_h.image_encoder(image_tensor)

@ -451,7 +452,7 @@ def test_predictor_resized_single_output(

    predictor = facebook_sam_h_predictor
    size = (1024, 1024)
-    resized_truck = truck.resize(size)
+    resized_truck = truck.resize(size)  # type: ignore
    predictor.set_image(np.array(resized_truck))

    _, _, facebook_low_res_masks = predictor.predict(  # type: ignore