update pyright and fix Pillow 10.3 typing issues

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",

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

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)

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,
         )

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,

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)

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.

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")
+    condition_image = _img_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")
+    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")
+    condition_image = _img_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")
+    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")
+    condition_image = _img_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")
+    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")
+    condition_image = _img_open(ref_path / f"cutecat_guide_{name}.png").convert("RGB")
-    expected_image = Image.open(ref_path / f"expected_t2i_adapter_{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")
+    condition_image = _img_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")
+    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"),
+        _img_open(dropy).convert(mode="RGB"),
-        Image.open(fp=image_prompt).convert(mode="RGB"),
+        _img_open(image_prompt).convert(mode="RGB"),
-        Image.open(fp=condition_image).convert(mode="RGB"),
+        _img_open(condition_image).convert(mode="RGB"),
-        Image.open(fp=ref_path / "expected_dropy_slime_9752.png").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)

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()

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()

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()

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")

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

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

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))

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