diff --git a/modular_diffusion_nodes_library/artifact_utils/latent_artifact.py b/modular_diffusion_nodes_library/artifact_utils/latent_artifact.py
index 17e8a35..8b510d6 100644
--- a/modular_diffusion_nodes_library/artifact_utils/latent_artifact.py
+++ b/modular_diffusion_nodes_library/artifact_utils/latent_artifact.py
@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 import pickle
-from typing import Any
+from typing import Any, NoReturn
 
 import torch  # type: ignore[reportMissingImports]
 from griptape.artifacts.base_artifact import BaseArtifact
@@ -106,7 +106,7 @@ def to_torch(
     def to_text(self) -> str:
         return repr(self)
 
-    def to_dict(self) -> dict[str, Any]:
+    def to_dict(self) -> dict[str, Any]:  # type: ignore[reportIncompatibleMethodOverride]
         tensor = self.to_torch().detach().cpu()
         if tensor.numel() == 0:
             return {
@@ -137,7 +137,7 @@ def to_dict(self) -> dict[str, Any]:
             "sample": sample,
         }
 
-    def __reduce__(self) -> None:
+    def __reduce__(self) -> NoReturn:
         raise pickle.PicklingError("LatentArtifact is an in-process-only type and should not be serialized.")
 
     def __bool__(self) -> bool:
diff --git a/modular_diffusion_nodes_library/artifact_utils/pipeline_artifact.py b/modular_diffusion_nodes_library/artifact_utils/pipeline_artifact.py
index 3cfcf84..05af840 100644
--- a/modular_diffusion_nodes_library/artifact_utils/pipeline_artifact.py
+++ b/modular_diffusion_nodes_library/artifact_utils/pipeline_artifact.py
@@ -107,6 +107,7 @@ def __init__(
         self._is_prequantized = is_prequantized
         self._supports_layerwise_casting = supports_layerwise_casting
         self._requires_device_map = requires_device_map
+        self._extra_runtime_adapter_steps: list[PipelineRuntimeAdapterStep] = []
 
     @property
     def builder_module(self) -> str | None:
@@ -174,7 +175,7 @@ def get_or_build_pipeline(self, log_params: Any | None = None) -> ModularPipelin
 
     def runtime_adapter_steps(self) -> list[PipelineRuntimeAdapterStep]:
         """Per-generation context-managed transformations applied around each generation call."""
-        return list(self._extra_runtime_adapter_steps) if hasattr(self, "_extra_runtime_adapter_steps") else []
+        return list(self._extra_runtime_adapter_steps)
 
     def with_additional_runtime_adapter_steps(
         self, steps: list[PipelineRuntimeAdapterStep]
diff --git a/modular_diffusion_nodes_library/artifact_utils/pipeline_build_steps.py b/modular_diffusion_nodes_library/artifact_utils/pipeline_build_steps.py
index 3aa144a..753c0b6 100644
--- a/modular_diffusion_nodes_library/artifact_utils/pipeline_build_steps.py
+++ b/modular_diffusion_nodes_library/artifact_utils/pipeline_build_steps.py
@@ -147,7 +147,7 @@ def apply(self, pipe: Pipe | None, *, log_params: Any | None = None) -> Pipe:
             raise RuntimeError("ApplyOptimizationStep requires a pipe.")
         with _profile(log_params, "Applying optimizations"):
             optimize_diffusion_pipeline(
-                pipe=pipe,
+                pipe=pipe,  # type: ignore[reportArgumentType]
                 is_prequantized=self._is_prequantized,
                 supports_layerwise_casting=self._supports_layerwise_casting,
                 requires_device_map=self._requires_device_map,
diff --git a/modular_diffusion_nodes_library/latent_pipeline_drivers/base_driver.py b/modular_diffusion_nodes_library/latent_pipeline_drivers/base_driver.py
index 2586c18..08563e8 100644
--- a/modular_diffusion_nodes_library/latent_pipeline_drivers/base_driver.py
+++ b/modular_diffusion_nodes_library/latent_pipeline_drivers/base_driver.py
@@ -149,7 +149,7 @@ def _call_block(self, block: ModularPipelineBlocks, **kwargs: Any) -> dict[str,
         for param in block.inputs:
             if param.name in kwargs:
                 state.set(param.name, kwargs[param.name], param.kwargs_type)
-        _, state = block(self.modular_pipe, state)
+        _, state = block(self.modular_pipe, state)  # type: ignore[reportOperatorIssue]
         return state.values
 
     @staticmethod
@@ -261,7 +261,13 @@ def encode_masked_image(
     ) -> LatentArtifact:
         """Encode the source image with the masked region zeroed out."""
         image_processor = self.modular_pipe.image_processor
-        source_t = image_processor.preprocess(image.image, height=image.image.height, width=image.image.width)
+        if isinstance(image.image, torch.Tensor):
+            height = image.image.shape[-2]
+            width = image.image.shape[-1]
+        else:
+            height = image.image.height
+            width = image.image.width
+        source_t = image_processor.preprocess(image.image, height=height, width=width)
         mask_t = torch.from_numpy(np.array(mask.mask, dtype="float32") / 255.0)[None, None]
         masked_t = source_t * (mask_t < 0.5)
         return self.encode_media(ImageMedia(image=masked_t, source_shape=image.source_shape), generator_state)
@@ -436,10 +442,11 @@ def _get_inpaint_kwargs(self, artifact: InpaintMaskArtifact) -> dict[str, Any]:
         """Map an InpaintMaskArtifact to pipeline call kwargs."""
         device, dtype = self._get_device_and_type()
         result: dict[str, Any] = {
-            "image": artifact.source_latent.to(device=device, dtype=dtype),
             "mask_image": artifact.mask_image,
             "strength": artifact.strength,
         }
+        if artifact.source_latent is not None:
+            result["image"] = artifact.source_latent.to(device=device, dtype=dtype)
         if artifact.masked_latent is not None:
             result["masked_image_latents"] = artifact.masked_latent.to(device=device, dtype=dtype)
         return result
diff --git a/modular_diffusion_nodes_library/latent_pipeline_drivers/flux.py b/modular_diffusion_nodes_library/latent_pipeline_drivers/flux.py
index e34711f..a0d6905 100644
--- a/modular_diffusion_nodes_library/latent_pipeline_drivers/flux.py
+++ b/modular_diffusion_nodes_library/latent_pipeline_drivers/flux.py
@@ -2,13 +2,8 @@
 from typing import Any, ClassVar, override
 
 import torch  # type: ignore[reportMissingImports]
-from diffusers import (  # type: ignore[reportMissingImports]
-    FluxControlNetInpaintPipeline,
-    FluxControlNetModel,
-    FluxControlNetPipeline,
-    FluxInpaintPipeline,
-)
 from diffusers.models import FluxTransformer2DModel  # type: ignore[reportMissingImports]
+from diffusers.models.controlnets.controlnet_flux import FluxControlNetModel  # type: ignore[reportMissingImports]
 from diffusers.modular_pipelines.flux.before_denoise import (  # type: ignore[reportMissingImports]
     FluxImg2ImgPrepareLatentsStep,
     FluxImg2ImgSetTimestepsStep,
@@ -25,6 +20,13 @@
 )
 from diffusers.modular_pipelines.modular_pipeline_utils import ComponentSpec  # type: ignore[reportMissingImports]
 from diffusers.pipelines.flux.pipeline_flux import FluxPipeline  # type: ignore[reportMissingImports]
+from diffusers.pipelines.flux.pipeline_flux_controlnet import (
+    FluxControlNetPipeline,  # type: ignore[reportMissingImports]
+)
+from diffusers.pipelines.flux.pipeline_flux_controlnet_inpainting import (
+    FluxControlNetInpaintPipeline,  # type: ignore[reportMissingImports]
+)
+from diffusers.pipelines.flux.pipeline_flux_inpaint import FluxInpaintPipeline  # type: ignore[reportMissingImports]
 from diffusers.pipelines.pipeline_utils import DiffusionPipeline  # type: ignore[reportMissingImports]
 from PIL.Image import Image
 
@@ -165,7 +167,7 @@ def create_noise_latent(self, source_shape: tuple[int, ...], generator_state: Ge
             batch_size=1,
             dtype=dtype,
         )
-        latents = output_state.get("latents")
+        latents = self._get_required(output_state, "latents", torch.Tensor)
         latents = latents.to(device)
         latents = self.unpack_latents(latents, height, width)
         return self._make_latent_artifact(
@@ -213,7 +215,7 @@ def add_noise_to_latent(
             image_latents=packed_image_latents,
         )
 
-        noisy_latents = output_state.get("latents")
+        noisy_latents = self._get_required(output_state, "latents", torch.Tensor)
         unpacked = self.unpack_latents(noisy_latents, height, width)
         return self._make_latent_artifact(
             unpacked,
@@ -233,7 +235,8 @@ def decode_latent(self, latent: LatentArtifact) -> Image:
 
         decode_pipeline = self.modular_pipe.blocks.sub_blocks["decode"]
         output_state = self._call_block(decode_pipeline, latents=packed, output_type="pil", width=width, height=height)
-        return output_state.get("images")[0]
+        images = self._get_required(output_state, "images", list)
+        return images[0]
 
     @override
     def encode_media(self, media: ImageMedia | VideoMedia, generator_state: GeneratorState) -> LatentArtifact:
@@ -249,7 +252,7 @@ def encode_media(self, media: ImageMedia | VideoMedia, generator_state: Generato
         generator = generator_state.to_generator()
         encode_pipeline = self.modular_pipe.blocks.sub_blocks["vae_encoder"]
         output_state = self._call_block(encode_pipeline, image=image, height=height, width=width, generator=generator)
-        latents = output_state.get("image_latents")
+        latents = self._get_required(output_state, "image_latents", torch.Tensor)
         return self._make_latent_artifact(latents, source_shape=media.source_shape)
 
     @override
diff --git a/modular_diffusion_nodes_library/latent_pipeline_drivers/flux2_base.py b/modular_diffusion_nodes_library/latent_pipeline_drivers/flux2_base.py
index e71276e..b6fcf1f 100644
--- a/modular_diffusion_nodes_library/latent_pipeline_drivers/flux2_base.py
+++ b/modular_diffusion_nodes_library/latent_pipeline_drivers/flux2_base.py
@@ -62,7 +62,7 @@ def create_noise_latent(self, source_shape: tuple[int, ...], generator_state: Ge
 
         unpack_latents = Flux2UnpackLatentsStep()
         latents_state = self._call_block(unpack_latents, latents=packed_latents, latent_ids=latent_ids)
-        latents = latents_state.get("latents")
+        latents = self._get_required(latents_state, "latents", torch.Tensor)
         latents = latents.to(device)
         return self._make_latent_artifact(
             latents,
@@ -78,7 +78,7 @@ def decode_latent(self, latent: LatentArtifact) -> Image:
         decode_block = self.modular_pipe.blocks.sub_blocks["decode"]
         output_state = self._call_block(decode_block, latents=latents, output_type="pil")
 
-        return output_state.get("images")[0]
+        return self._get_required(output_state, "images", list)[0]
 
     def _unpack_latents(self, latents: torch.Tensor, height: int, width: int) -> torch.Tensor:
         """Convert packed Flux2 latents ``[B, seq_len, C]`` to ``[B, C, H/vae/2, W/vae/2]``."""
@@ -97,7 +97,7 @@ def _unpack_latents(self, latents: torch.Tensor, height: int, width: int) -> tor
         latent_ids = id_state.get("latent_ids")
 
         unpack_state = self._call_block(Flux2UnpackLatentsStep(), latents=latents, latent_ids=latent_ids)
-        return unpack_state.get("latents")
+        return self._get_required(unpack_state, "latents", torch.Tensor)
 
     @override
     def encode_media(self, media: ImageMedia | VideoMedia, generator_state: GeneratorState) -> LatentArtifact:
@@ -113,7 +113,9 @@ def encode_media(self, media: ImageMedia | VideoMedia, generator_state: Generato
         output_state = self._call_block(
             encode_block, image=image, height=image.height, width=image.width, generator=generator
         )
-        return self._make_latent_artifact(output_state.get("image_latents")[0], source_shape=media.source_shape)
+        return self._make_latent_artifact(
+            self._get_required(output_state, "image_latents", list)[0], source_shape=media.source_shape
+        )
 
     @override
     def encode_masked_image(
@@ -126,17 +128,19 @@ def encode_masked_image(
 
         # PIL -> aligned, normalized tensor (multiple-of-32 alignment + <=1024^2 cap).
         pre = self._call_block(Flux2ProcessImagesInputStep(), image=pil_image)
-        source_t = pre.get("condition_images")[0]
+        source_t = self._get_required(pre, "condition_images", list)[0]
 
         # Resize mask to match the (possibly aligned/cropped) tensor dims.
         aligned_h, aligned_w = source_t.shape[-2:]
-        mask_resized = mask.mask.convert("L").resize((aligned_w, aligned_h), PIL.Image.NEAREST)
+        mask_resized = mask.mask.convert("L").resize((aligned_w, aligned_h), PIL.Image.Resampling.NEAREST)
         mask_t = TF.to_tensor(mask_resized)[None]  # (1, 1, H, W), float32 in [0, 1]
 
         masked_t = source_t * (mask_t < 0.5)
 
         out = self._call_block(Flux2VaeEncoderStep(), condition_images=[masked_t])
-        return self._make_latent_artifact(out.get("image_latents")[0], source_shape=image.source_shape)
+        return self._make_latent_artifact(
+            self._get_required(out, "image_latents", list)[0], source_shape=image.source_shape
+        )
 
     @override
     def add_noise_to_latent(
diff --git a/modular_diffusion_nodes_library/latent_pipeline_drivers/flux2_klein.py b/modular_diffusion_nodes_library/latent_pipeline_drivers/flux2_klein.py
index 3207c3d..f56b229 100644
--- a/modular_diffusion_nodes_library/latent_pipeline_drivers/flux2_klein.py
+++ b/modular_diffusion_nodes_library/latent_pipeline_drivers/flux2_klein.py
@@ -55,7 +55,7 @@ def _get_inpaint_kwargs(self, artifact: InpaintMaskArtifact) -> dict[str, Any]:
         The pipeline will BN-normalize image latent internally, so we must denormalize first.
         """
         device, dtype = self._get_device_and_type()
-        source_latent = self._denormalize_latent(artifact.source_latent).to(device=device, dtype=dtype)
+        source_latent = self._denormalize_latent(artifact.source_latent).to(device=device, dtype=dtype)  # type: ignore[reportArgumentType]
         return {
             "image": source_latent,
             "mask_image": artifact.mask_image,
diff --git a/modular_diffusion_nodes_library/latent_pipeline_drivers/ltx.py b/modular_diffusion_nodes_library/latent_pipeline_drivers/ltx.py
index 3d166a7..145ae62 100644
--- a/modular_diffusion_nodes_library/latent_pipeline_drivers/ltx.py
+++ b/modular_diffusion_nodes_library/latent_pipeline_drivers/ltx.py
@@ -1,5 +1,5 @@
 import logging
-from typing import Any, ClassVar, override
+from typing import Any, ClassVar, cast, override
 
 import torch  # type: ignore[reportMissingImports]
 from diffusers import LTXConditionPipeline  # type: ignore[reportMissingImports]
@@ -13,14 +13,16 @@
     LTXAutoVaeEncoderStep,
 )
 from diffusers.modular_pipelines.modular_pipeline import (  # type: ignore[reportMissingImports]
-    ComponentSpec,
-    InputParam,
     ModularPipeline,
     ModularPipelineBlocks,
-    OutputParam,
     PipelineState,
     SequentialPipelineBlocks,
 )
+from diffusers.modular_pipelines.modular_pipeline_utils import (  # type: ignore[reportMissingImports]
+    ComponentSpec,
+    InputParam,
+    OutputParam,
+)
 from diffusers.pipelines.ltx.pipeline_ltx_condition import LTXVideoCondition  # type: ignore[reportMissingImports]
 from diffusers.pipelines.pipeline_utils import DiffusionPipeline  # type: ignore[reportMissingImports]
 from diffusers.schedulers import FlowMatchEulerDiscreteScheduler  # type: ignore[reportMissingImports]
@@ -86,7 +88,7 @@ def intermediate_outputs(self) -> list[OutputParam]:
 
     @torch.no_grad()
     def __call__(self, components, state: PipelineState):
-        block_state = self.get_block_state(state)
+        block_state = cast(Any, self.get_block_state(state))
 
         init_timestep = min(
             block_state.num_inference_steps * block_state.strength,
@@ -131,7 +133,7 @@ def intermediate_outputs(self) -> list[OutputParam]:
 
     @torch.no_grad()
     def __call__(self, components, state: PipelineState):
-        block_state = self.get_block_state(state)
+        block_state = cast(Any, self.get_block_state(state))
 
         latent_timestep = block_state.timesteps[:1].repeat(block_state.latents.shape[0])
         block_state.latents = components.scheduler.scale_noise(
@@ -150,7 +152,7 @@ class LTXAddNoiseStep(SequentialPipelineBlocks):
     then strength slicing, then scale_noise.
     """
 
-    model_name = "ltx"
+    model_name = "ltx"  # type: ignore[reportIncompatibleMethodOverride]
     block_classes = [
         LTXSetTimestepsStep,
         LTXSetTimestepsWithStrengthStep,
@@ -236,7 +238,7 @@ def create_noise_latent(self, source_shape: tuple[int, ...], generator_state: Ge
             num_videos_per_prompt=1,
             generator=generator,
         )
-        packed_latents = output_state.get("latents")
+        packed_latents = self._get_required(output_state, "latents", torch.Tensor)
         latents = self._unpack_latents(packed_latents, height, width, num_frames)
         return self._make_latent_artifact(
             latents,
@@ -278,7 +280,7 @@ def add_noise_to_latent(
         )
         noise_generator_state = GeneratorState.from_artifact(noise_artifact) or generator_state
         return self._make_latent_artifact(
-            output_state.get("latents"),
+            self._get_required(output_state, "latents", torch.Tensor),
             source_shape=source_shape,
             upstream=latent,
             meta=noise_generator_state.as_meta(),
@@ -312,8 +314,7 @@ def decode_latent(self, latent: LatentArtifact) -> DecodeResult:
             output_type="pil",
             decode_timestep=0.0,
         )
-        videos = output_state.get("videos")
-        return videos[0]
+        return self._get_required(output_state, "videos", list)[0]
 
     @override
     def encode_media(self, media: ImageMedia | VideoMedia, generator_state: GeneratorState) -> LatentArtifact:
@@ -321,7 +322,9 @@ def encode_media(self, media: ImageMedia | VideoMedia, generator_state: Generato
         generator = generator_state.to_generator()
         if isinstance(media, ImageMedia):
             output_state = self._call_block(LTXAutoVaeEncoderStep(), image=media.image, generator=generator)
-            return self._make_latent_artifact(output_state.get("image_latents"), source_shape=media.source_shape)
+            return self._make_latent_artifact(
+                self._get_required(output_state, "image_latents", torch.Tensor), source_shape=media.source_shape
+            )
 
         device, dtype = self._get_device_and_type()
         vae = self.modular_pipe.vae
diff --git a/modular_diffusion_nodes_library/latent_pipeline_drivers/ltx2.py b/modular_diffusion_nodes_library/latent_pipeline_drivers/ltx2.py
index c4aaa34..983c8b1 100644
--- a/modular_diffusion_nodes_library/latent_pipeline_drivers/ltx2.py
+++ b/modular_diffusion_nodes_library/latent_pipeline_drivers/ltx2.py
@@ -283,7 +283,7 @@ def _extract_latents_from_output(self, pipe_output: Any) -> torch.Tensor:
         """LTX2 pipelines return video frames under ``.frames`` instead of ``.images``."""
         return pipe_output.frames
 
-    def _decode_hdr_to_linear_np(self, video: torch.Tensor) -> np.ndarray:
+    def _decode_hdr_to_linear_np(self, video: torch.Tensor) -> torch.Tensor | np.ndarray:
         """Convert decoded LogC3-compressed VAE output to linear HDR np.ndarray.
 
         Returns shape ``(B, F, H, W, 3)`` float32 with linear HDR values in ``[0, ∞)``.
@@ -328,7 +328,7 @@ def decode_latent(self, latent: LatentArtifact) -> list[Image] | np.ndarray:
 
         if self._latent_was_produced_for_hdr(latent):
             # HDR IC-LoRA path: return raw linear HDR for encode_hdr_tensor_to_mp4 in vae_decoder.
-            return self._decode_hdr_to_linear_np(video)
+            return self._decode_hdr_to_linear_np(video)  # type: ignore[reportReturnType]
 
         frames = self.pipe.video_processor.postprocess_video(video, output_type="pil")[0]
         return frames
diff --git a/modular_diffusion_nodes_library/latent_pipeline_drivers/stable_diffusion_3.py b/modular_diffusion_nodes_library/latent_pipeline_drivers/stable_diffusion_3.py
index e1532ff..9d54ece 100644
--- a/modular_diffusion_nodes_library/latent_pipeline_drivers/stable_diffusion_3.py
+++ b/modular_diffusion_nodes_library/latent_pipeline_drivers/stable_diffusion_3.py
@@ -1,11 +1,7 @@
 import logging
 from typing import Any, ClassVar, override
 
-from diffusers import (  # type: ignore[reportMissingImports]
-    StableDiffusion3ControlNetInpaintingPipeline,
-    StableDiffusion3ControlNetPipeline,
-    StableDiffusion3InpaintPipeline,
-)
+import torch  # type: ignore[reportMissingImports]
 from diffusers.models.controlnets.controlnet_sd3 import SD3ControlNetModel  # type: ignore[reportMissingImports]
 from diffusers.modular_pipelines.modular_pipeline import (  # type: ignore[reportMissingImports]
     ModularPipeline,
@@ -20,7 +16,16 @@
 from diffusers.modular_pipelines.stable_diffusion_3.modular_blocks_stable_diffusion_3 import (  # type: ignore[reportMissingImports]
     StableDiffusion3AutoBlocks,
 )
+from diffusers.pipelines.controlnet_sd3.pipeline_stable_diffusion_3_controlnet import (  # type: ignore[reportMissingImports]
+    StableDiffusion3ControlNetPipeline,
+)
+from diffusers.pipelines.controlnet_sd3.pipeline_stable_diffusion_3_controlnet_inpainting import (  # type: ignore[reportMissingImports]
+    StableDiffusion3ControlNetInpaintingPipeline,
+)
 from diffusers.pipelines.pipeline_utils import DiffusionPipeline  # type: ignore[reportMissingImports]
+from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3_inpaint import (  # type: ignore[reportMissingImports]
+    StableDiffusion3InpaintPipeline,
+)
 
 from modular_diffusion_nodes_library.artifact_utils.inpaint_mask_artifact import InpaintMaskArtifact
 from modular_diffusion_nodes_library.artifact_utils.latent_artifact import LatentArtifact
@@ -43,7 +48,7 @@
 class _SD3PrepareNoiseLatentStep(SequentialPipelineBlocks):
     """``set_timesteps`` → ``prepare_latents`` so ``init_noise_sigma`` is valid."""
 
-    model_name = "stable-diffusion-3"
+    model_name = "stable-diffusion-3"  # type: ignore[reportIncompatibleMethodOverride]
     block_classes = [StableDiffusion3SetTimestepsStep, StableDiffusion3PrepareLatentsStep]
     block_names = ["set_timesteps", "prepare_latents"]
 
@@ -51,7 +56,7 @@ class _SD3PrepareNoiseLatentStep(SequentialPipelineBlocks):
 class _SD3AddNoiseStep(SequentialPipelineBlocks):
     """``Img2ImgSetTimesteps`` → ``Img2ImgPrepareLatents`` for img2img noise."""
 
-    model_name = "stable-diffusion-3"
+    model_name = "stable-diffusion-3"  # type: ignore[reportIncompatibleMethodOverride]
     block_classes = [
         StableDiffusion3Img2ImgSetTimestepsStep,
         StableDiffusion3Img2ImgPrepareLatentsStep,
@@ -126,7 +131,7 @@ def create_noise_latent(
             num_inference_steps=self._DEFAULT_NUM_INFERENCE_STEPS,
             generator=generator,
         )
-        latents = output_state.get("latents")
+        latents = self._get_required(output_state, "latents", torch.Tensor)
         meta = GeneratorState.from_generator(generator).as_meta()
         return self._make_latent_artifact(latents, source_shape=source_shape, meta=meta)
 
@@ -159,7 +164,7 @@ def add_noise_to_latent(
             height=latent.source_shape[-2],
             width=latent.source_shape[-1],
         )
-        noised = output_state.get("latents")
+        noised = self._get_required(output_state, "latents", torch.Tensor)
 
         noise_state = GeneratorState.from_artifact(noise_artifact) or generator_state
         return self._make_latent_artifact(
@@ -209,8 +214,7 @@ def decode_latent(self, latent: LatentArtifact) -> DecodeResult:
         latents = latent.to_torch(device=device, dtype=dtype)
         decode_block = self.modular_pipe.blocks.sub_blocks["decode"]
         output_state = self._call_block(decode_block, latents=latents, output_type="pil")
-        images = output_state.get("images")
-        return images[0]
+        return self._get_required(output_state, "images", list)[0]
 
     @override
     def _get_inpaint_kwargs(self, artifact: InpaintMaskArtifact) -> dict[str, Any]:
@@ -240,5 +244,5 @@ def encode_media(self, media: ImageMedia | VideoMedia, generator_state: Generato
             raise NotImplementedError(f"'{self.pipe.__class__.__name__}' does not support video.")
         encode_block = self.modular_pipe.blocks.sub_blocks["vae_encoder"]
         output_state = self._call_block(encode_block, image=media.image, generator=generator_state.to_generator())
-        result = output_state.get("image_latents")
+        result = self._get_required(output_state, "image_latents", torch.Tensor)
         return self._make_latent_artifact(result, source_shape=media.source_shape)
diff --git a/modular_diffusion_nodes_library/latent_pipeline_drivers/stable_diffusion_xl.py b/modular_diffusion_nodes_library/latent_pipeline_drivers/stable_diffusion_xl.py
index e429bac..712a378 100644
--- a/modular_diffusion_nodes_library/latent_pipeline_drivers/stable_diffusion_xl.py
+++ b/modular_diffusion_nodes_library/latent_pipeline_drivers/stable_diffusion_xl.py
@@ -1,12 +1,8 @@
 import logging
 from typing import Any, ClassVar, override
 
-from diffusers import (  # type: ignore[reportMissingImports]
-    ControlNetModel,
-    StableDiffusionXLControlNetImg2ImgPipeline,
-    StableDiffusionXLControlNetInpaintPipeline,
-    StableDiffusionXLInpaintPipeline,
-)
+import torch  # type: ignore[reportMissingImports]
+from diffusers.models.controlnets.controlnet import ControlNetModel  # type: ignore[reportMissingImports]
 from diffusers.modular_pipelines.modular_pipeline import (  # type: ignore[reportMissingImports]
     ModularPipeline,
     SequentialPipelineBlocks,
@@ -20,7 +16,16 @@
 from diffusers.modular_pipelines.stable_diffusion_xl.modular_blocks_stable_diffusion_xl import (  # type: ignore[reportMissingImports]
     StableDiffusionXLAutoBlocks,
 )
+from diffusers.pipelines.controlnet.pipeline_controlnet_inpaint_sd_xl import (  # type: ignore[reportMissingImports]
+    StableDiffusionXLControlNetInpaintPipeline,
+)
+from diffusers.pipelines.controlnet.pipeline_controlnet_sd_xl_img2img import (  # type: ignore[reportMissingImports]
+    StableDiffusionXLControlNetImg2ImgPipeline,
+)
 from diffusers.pipelines.pipeline_utils import DiffusionPipeline  # type: ignore[reportMissingImports]
+from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_inpaint import (  # type: ignore[reportMissingImports]
+    StableDiffusionXLInpaintPipeline,
+)
 from PIL.Image import Image
 
 from modular_diffusion_nodes_library.artifact_utils.inpaint_mask_artifact import InpaintMaskArtifact
@@ -53,7 +58,7 @@
 class _SDXLPrepareNoiseLatentStep(SequentialPipelineBlocks):
     """``set_timesteps`` → ``prepare_latents`` so ``init_noise_sigma`` is valid."""
 
-    model_name = "stable-diffusion-xl"
+    model_name = "stable-diffusion-xl"  # type: ignore[reportIncompatibleMethodOverride]
     block_classes = [StableDiffusionXLSetTimestepsStep, StableDiffusionXLPrepareLatentsStep]
     block_names = ["set_timesteps", "prepare_latents"]
 
@@ -61,7 +66,7 @@ class _SDXLPrepareNoiseLatentStep(SequentialPipelineBlocks):
 class _SDXLAddNoiseStep(SequentialPipelineBlocks):
     """``Img2ImgSetTimesteps`` → ``Img2ImgPrepareLatents`` for img2img noise."""
 
-    model_name = "stable-diffusion-xl"
+    model_name = "stable-diffusion-xl"  # type: ignore[reportIncompatibleMethodOverride]
     block_classes = [
         StableDiffusionXLImg2ImgSetTimestepsStep,
         StableDiffusionXLImg2ImgPrepareLatentsStep,
@@ -188,7 +193,7 @@ def add_noise_to_latent(
             generator=generator,
             dtype=dtype,
         )
-        noised = output_state.get("latents")
+        noised = self._get_required(output_state, "latents", torch.Tensor)
         generator_meta = GeneratorState.from_generator(generator).as_meta()
 
         kind = read_driver_meta(latent, _KIND_META_KEY, self.driver_namespace, "")
@@ -266,8 +271,7 @@ def decode_latent(self, latent: LatentArtifact) -> Image:
         latents = latent.to_torch(device=device, dtype=dtype)
         decode_block = self.modular_pipe.blocks.sub_blocks["decode"]
         output_state = self._call_block(decode_block, latents=latents, output_type="pil")
-        images = output_state.get("images")
-        return images[0]
+        return self._get_required(output_state, "images", list)[0]
 
     @override
     def _get_inpaint_kwargs(self, artifact: InpaintMaskArtifact) -> dict[str, Any]:
@@ -292,6 +296,6 @@ def encode_media(self, media: ImageMedia | VideoMedia, generator_state: Generato
         generator = generator_state.to_generator()
         encode_block = self.modular_pipe.blocks.sub_blocks["vae_encoder"]
         output_state = self._call_block(encode_block, image=media.image, generator=generator)
-        result = output_state.get("image_latents")
+        result = self._get_required(output_state, "image_latents", torch.Tensor)
         meta = {_KIND_META_KEY: "image_latents"}
         return self._make_latent_artifact(result, source_shape=media.source_shape, meta=meta)
diff --git a/modular_diffusion_nodes_library/latent_pipeline_drivers/wan.py b/modular_diffusion_nodes_library/latent_pipeline_drivers/wan.py
index bbf5b55..0303517 100644
--- a/modular_diffusion_nodes_library/latent_pipeline_drivers/wan.py
+++ b/modular_diffusion_nodes_library/latent_pipeline_drivers/wan.py
@@ -1,5 +1,5 @@
 import logging
-from typing import Any, ClassVar, override
+from typing import Any, ClassVar, cast, override
 
 import torch  # type: ignore[reportMissingImports]
 from diffusers.modular_pipelines.modular_pipeline import (  # type: ignore[reportMissingImports]
@@ -58,7 +58,7 @@ def intermediate_outputs(self) -> list[OutputParam]:
     def __call__(
         self, components: WanModularPipeline, state: PipelineState
     ) -> tuple[WanModularPipeline, PipelineState]:
-        block_state = self.get_block_state(state)
+        block_state = cast(Any, self.get_block_state(state))
 
         device = components._execution_device
         vae_dtype = components.vae.dtype
@@ -114,7 +114,7 @@ def create_noise_latent(self, source_shape: tuple[int, ...], generator_state: Ge
             batch_size=1,
             dtype=dtype,
         )
-        latents = output_state.get("latents")
+        latents = self._get_required(output_state, "latents", torch.Tensor)
         return self._make_latent_artifact(
             latents,
             source_shape=source_shape,
@@ -137,7 +137,7 @@ def decode_latent(self, latent: LatentArtifact) -> DecodeResult:
             latents=latents,
             output_type="pil",
         )
-        video_frames = output_state.get("videos")[0]
+        video_frames = self._get_required(output_state, "videos", list)[0]
         return video_frames
 
     @override
diff --git a/modular_diffusion_nodes_library/latent_pipeline_drivers/wan_i2v.py b/modular_diffusion_nodes_library/latent_pipeline_drivers/wan_i2v.py
index 4e0e90a..4e8b520 100644
--- a/modular_diffusion_nodes_library/latent_pipeline_drivers/wan_i2v.py
+++ b/modular_diffusion_nodes_library/latent_pipeline_drivers/wan_i2v.py
@@ -56,7 +56,7 @@ def create_noise_latent(self, source_shape: tuple[int, ...], generator_state: Ge
     def decode_latent(self, latent: LatentArtifact) -> DecodeResult:
         frames = super().decode_latent(latent)
         source_shape = latent.source_shape
-        output_frames = [frame.resize((source_shape[-1], source_shape[-2]), Resampling.LANCZOS) for frame in frames]
+        output_frames = [frame.resize((source_shape[-1], source_shape[-2]), Resampling.LANCZOS) for frame in frames]  # type: ignore[reportGeneralTypeIssues]
         return output_frames
 
     @override
@@ -70,7 +70,7 @@ def encode_media(self, media: ImageMedia | VideoMedia, generator_state: Generato
         return super().encode_media(preprocessed, generator_state)
 
     @override
-    def denoise_latent(
+    def denoise_latent(  # type: ignore[reportIncompatibleMethodOverride]
         self,
         latent: LatentArtifact,
         num_inference_steps: int,
@@ -137,7 +137,7 @@ def denoise_latent(
 
     def get_resize_dimensions(self, width: int, height: int) -> tuple[int, int]:
         """Calculate the resize dimensions for a given width and height."""
-        repo_id = self.pipe.config._name_or_path
+        repo_id = self.pipe.config._name_or_path  # type: ignore[reportAttributeAccessIssue]
         match repo_id:
             case "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers":
                 max_area = 832 * 480  # I2V 480P model
diff --git a/modular_diffusion_nodes_library/latent_pipeline_drivers/wan_vace.py b/modular_diffusion_nodes_library/latent_pipeline_drivers/wan_vace.py
index b13d7c7..64c9454 100644
--- a/modular_diffusion_nodes_library/latent_pipeline_drivers/wan_vace.py
+++ b/modular_diffusion_nodes_library/latent_pipeline_drivers/wan_vace.py
@@ -84,7 +84,7 @@ def _derive_mask_from_source_media(
     num_frames: int,
     height: int,
     width: int,
-) -> list[PIL.Image.Image]:
+) -> list[PIL.Image.Image] | None:
     """Auto-derive a binary mask from the source video payload.
 
     VIDEO payload: black (preserve) for the covered frame range, white (generate) elsewhere.
diff --git a/modular_diffusion_nodes_library/latent_pipeline_drivers/z_image.py b/modular_diffusion_nodes_library/latent_pipeline_drivers/z_image.py
index 524c1ca..83e73be 100644
--- a/modular_diffusion_nodes_library/latent_pipeline_drivers/z_image.py
+++ b/modular_diffusion_nodes_library/latent_pipeline_drivers/z_image.py
@@ -1,12 +1,8 @@
 import logging
 from typing import Any, ClassVar, override
 
-from diffusers import (  # type: ignore[reportMissingImports]
-    ZImageControlNetInpaintPipeline,
-    ZImageControlNetModel,
-    ZImageControlNetPipeline,
-    ZImageInpaintPipeline,
-)
+import torch  # type: ignore[reportMissingImports]
+from diffusers.models.controlnets.controlnet_z_image import ZImageControlNetModel  # type: ignore[reportMissingImports]
 from diffusers.modular_pipelines.modular_pipeline import (  # type: ignore[reportMissingImports]
     ModularPipeline,
     SequentialPipelineBlocks,
@@ -21,6 +17,15 @@
     ZImageAutoBlocks,  # type: ignore[reportMissingImports]
 )
 from diffusers.pipelines.pipeline_utils import DiffusionPipeline  # type: ignore[reportMissingImports]
+from diffusers.pipelines.z_image.pipeline_z_image_controlnet import (  # type: ignore[reportMissingImports]
+    ZImageControlNetPipeline,
+)
+from diffusers.pipelines.z_image.pipeline_z_image_controlnet_inpaint import (  # type: ignore[reportMissingImports]
+    ZImageControlNetInpaintPipeline,
+)
+from diffusers.pipelines.z_image.pipeline_z_image_inpaint import (  # type: ignore[reportMissingImports]
+    ZImageInpaintPipeline,
+)
 from huggingface_hub import hf_hub_download  # type: ignore[reportMissingImports]
 from PIL.Image import Image
 
@@ -50,7 +55,7 @@ class _ZImageAddNoiseStep(SequentialPipelineBlocks):
     calls scheduler.scale_noise internally.
     """
 
-    model_name = "z-image"
+    model_name = "z-image"  # type: ignore[reportIncompatibleMethodOverride]
     block_classes = [
         ZImageSetTimestepsStep,
         ZImageSetTimestepsWithStrengthStep,
@@ -94,8 +99,7 @@ def control_pipe_from_standard(
     ):
         if not control_net_model_lists:
             return pipe
-
-        offload_method = detect_offload_method(pipe)
+        offload_method = detect_offload_method(pipe)  # type: ignore[reportArgumentType]
         if offload_method is not None:
             raise RuntimeError(
                 f"Failed to build Z-Image ControlNet pipeline. "
@@ -153,7 +157,7 @@ def create_noise_latent(self, source_shape: tuple[int, ...], generator_state: Ge
             num_images_per_prompt=1,
             generator=generator,
         )
-        latents = output_state.get("latents")
+        latents = self._get_required(output_state, "latents", torch.Tensor)
         return self._make_latent_artifact(
             latents,
             source_shape=source_shape,
@@ -183,7 +187,7 @@ def add_noise_to_latent(
             num_inference_steps=num_inference_steps,
             strength=strength,
         )
-        result = output_state.get("latents")
+        result = self._get_required(output_state, "latents", torch.Tensor)
         return self._make_latent_artifact(
             result,
             source_shape=source_shape,
@@ -197,8 +201,7 @@ def decode_latent(self, latent: LatentArtifact) -> Image:
         latents = latent.to_torch(device=device, dtype=dtype)
         decode_block = self.modular_pipe.blocks.sub_blocks["decode"]
         output_state = self._call_block(decode_block, latents=latents, output_type="pil")
-        images = output_state.get("images")
-        return images[0]
+        return self._get_required(output_state, "images", list)[0]
 
     @override
     def encode_media(self, media: ImageMedia | VideoMedia, generator_state: GeneratorState) -> LatentArtifact:
@@ -207,7 +210,7 @@ def encode_media(self, media: ImageMedia | VideoMedia, generator_state: Generato
         generator = generator_state.to_generator()
         encode_block = self.modular_pipe.blocks.sub_blocks["vae_encoder"]
         output_state = self._call_block(encode_block, image=media.image, generator=generator)
-        result = output_state.get("image_latents")
+        result = self._get_required(output_state, "image_latents", torch.Tensor)
         return self._make_latent_artifact(result, source_shape=media.source_shape)
 
     @override
diff --git a/modular_diffusion_nodes_library/misc/partial_denoise.py b/modular_diffusion_nodes_library/misc/partial_denoise.py
index 80523e1..c4d1dfe 100644
--- a/modular_diffusion_nodes_library/misc/partial_denoise.py
+++ b/modular_diffusion_nodes_library/misc/partial_denoise.py
@@ -104,6 +104,8 @@ def set_timesteps(
             scheduler.set_timesteps(num_inference_steps=num_inference_steps, device=device, **kwargs)
 
         timesteps = scheduler.timesteps
+        if timesteps is None:
+            return
         n = len(timesteps)
 
         if n == 0:
@@ -119,13 +121,13 @@ def set_timesteps(
         if hasattr(scheduler, "sigmas") and scheduler.sigmas is not None:
             sigmas = scheduler.sigmas
             # Slice from begin to end+1 (to include the sigma for the next step boundary)
-            sigmas_slice = sigmas[begin : min(end + 1, len(sigmas))]
+            sigmas_slice = sigmas[begin : min(end + 1, len(sigmas))]  # type: ignore[reportOptionalSubscript, reportArgumentType]
 
         # Optionally append the final timestep/sigma to ensure a fully denoised output, even when denoise_end < 1.0.
         if return_fully_denoised and timesteps_slice[-1] != timesteps[-1]:
-            timesteps_slice = torch.cat([timesteps_slice, timesteps[-1:]])
+            timesteps_slice = torch.cat([timesteps_slice, timesteps[-1:]])  # type: ignore[reportArgumentType]
             if sigmas_slice is not None:
-                sigmas_slice = torch.cat([sigmas_slice, sigmas[-1:]])
+                sigmas_slice = torch.cat([sigmas_slice, sigmas[-1:]])  # type: ignore[reportArgumentType, reportOptionalSubscript]
 
         m = len(timesteps_slice)
         logger.debug(
diff --git a/modular_diffusion_nodes_library/nodes/elementwise_latent_math.py b/modular_diffusion_nodes_library/nodes/elementwise_latent_math.py
index 24631b2..dffc9f5 100644
--- a/modular_diffusion_nodes_library/nodes/elementwise_latent_math.py
+++ b/modular_diffusion_nodes_library/nodes/elementwise_latent_math.py
@@ -81,7 +81,7 @@ def validate_before_node_run(self) -> list[Exception] | None:
             return exceptions
 
         try:
-            self._apply_operation(left_latent, right_latent)
+            self._apply_operation(left_latent, right_latent)  # type: ignore[reportArgumentType]
         except (TypeError, ValueError, RuntimeError) as error:
             return [error]
 
diff --git a/modular_diffusion_nodes_library/nodes/latent_composite_mask_node.py b/modular_diffusion_nodes_library/nodes/latent_composite_mask_node.py
index 63aa115..8037164 100644
--- a/modular_diffusion_nodes_library/nodes/latent_composite_mask_node.py
+++ b/modular_diffusion_nodes_library/nodes/latent_composite_mask_node.py
@@ -144,7 +144,7 @@ def __init__(self, **kwargs) -> None:
         self._initializing = False
         self._create_status_parameters()
 
-    def add_parameter(self, parameter: Parameter) -> None:
+    def add_parameter(self, parameter: Parameter) -> None:  # type: ignore[reportIncompatibleMethodOverride]
         if not self._initializing:
             return
         super().add_parameter(parameter)
diff --git a/modular_diffusion_nodes_library/nodes/vae_mask_encoder.py b/modular_diffusion_nodes_library/nodes/vae_mask_encoder.py
index 0aef48e..fec955d 100644
--- a/modular_diffusion_nodes_library/nodes/vae_mask_encoder.py
+++ b/modular_diffusion_nodes_library/nodes/vae_mask_encoder.py
@@ -161,7 +161,7 @@ def _encode(self) -> None:
 
         # Ensure mask is same size as image
         if mask_pil.size != image_pil.size:
-            mask_pil = mask_pil.resize(image_pil.size, PILImage.NEAREST)
+            mask_pil = mask_pil.resize(image_pil.size, PILImage.Resampling.NEAREST)
 
         source_shape = (1, 3, image_pil.height, image_pil.width)
         image = ImageMedia(image=image_pil, source_shape=source_shape)
diff --git a/modular_diffusion_nodes_library/parameters/generate_latent_parameters.py b/modular_diffusion_nodes_library/parameters/generate_latent_parameters.py
index 96685e1..d8bd111 100644
--- a/modular_diffusion_nodes_library/parameters/generate_latent_parameters.py
+++ b/modular_diffusion_nodes_library/parameters/generate_latent_parameters.py
@@ -296,7 +296,7 @@ def callback_on_step_end(
                 generator_state=self._resolve_generator_state(input_latent_for_denoise),
                 **pipe_kwargs,
             )
-        self.publish_output_latent(output_latent_artifact)
+        self.publish_output_latent(output_latent_artifact)  # type: ignore[reportArgumentType]
         self._node.log_params.append_to_logs("Done.\n")  # type: ignore[reportAttributeAccessIssue]
 
     def prepare_input_latent(
diff --git a/modular_diffusion_nodes_library/parameters/pipeline_builder_parameters.py b/modular_diffusion_nodes_library/parameters/pipeline_builder_parameters.py
index fa01580..5f9f842 100644
--- a/modular_diffusion_nodes_library/parameters/pipeline_builder_parameters.py
+++ b/modular_diffusion_nodes_library/parameters/pipeline_builder_parameters.py
@@ -73,7 +73,7 @@ def set_pipeline_type_parameters(self, provider: str) -> None:
             logger.error(msg)
             raise ValueError(msg)
 
-        provider_class = MODULAR_PIPELINE_TYPE_PROVIDER_MAP[provider]
+        provider_class = MODULAR_PIPELINE_TYPE_PROVIDER_MAP[provider]  # type: ignore[reportArgumentType]
         self._pipeline_type_parameters = provider_class(self._node)
 
     def before_value_set(self, parameter: Parameter, value: Any) -> None:
diff --git a/modular_diffusion_nodes_library/parameters/scheduler_parameters.py b/modular_diffusion_nodes_library/parameters/scheduler_parameters.py
index 9782a45..899585f 100644
--- a/modular_diffusion_nodes_library/parameters/scheduler_parameters.py
+++ b/modular_diffusion_nodes_library/parameters/scheduler_parameters.py
@@ -10,7 +10,7 @@
 
 
 class SchedulerParameters:
-    def __init__(self, node: BaseNode, scheduler_types: list[type[diffusers.SchedulerMixin]]):
+    def __init__(self, node: BaseNode, scheduler_types: list[type[diffusers.SchedulerMixin]]):  # type: ignore[reportPrivateImportUsage]
         self._node = node
         self._scheduler_type_parameter_name = "scheduler_type"
         self._scheduler_config_parameter_name = "scheduler_config"
@@ -71,7 +71,7 @@ def get_config_kwargs(self) -> dict:
             ),
         }
 
-    def get_scheduler_class(self) -> type[diffusers.SchedulerMixin]:
+    def get_scheduler_class(self) -> type[diffusers.SchedulerMixin]:  # type: ignore[reportPrivateImportUsage]
         scheduler_type_name = self._node.get_parameter_value(self._scheduler_type_parameter_name)
         return self._scheduler_types_by_name[scheduler_type_name]
 
@@ -91,7 +91,7 @@ def get_scheduler_config(self) -> dict:
             msg = f"Invalid {self._scheduler_config_parameter_name} provided. Must be json, str, or dict"
             raise TypeError(msg)
 
-    def get_scheduler(self) -> diffusers.SchedulerMixin:
+    def get_scheduler(self) -> diffusers.SchedulerMixin:  # type: ignore[reportPrivateImportUsage]
         scheduler_class = self.get_scheduler_class()
         scheduler_config = self.get_scheduler_config()
-        return scheduler_class.from_config(scheduler_config)
+        return scheduler_class.from_config(scheduler_config)  # type: ignore[reportAttributeAccessIssue]
diff --git a/modular_diffusion_nodes_library/parameters/upsampler_parameter_type.py b/modular_diffusion_nodes_library/parameters/upsampler_parameter_type.py
index 5b7739f..3a5e9e2 100644
--- a/modular_diffusion_nodes_library/parameters/upsampler_parameter_type.py
+++ b/modular_diffusion_nodes_library/parameters/upsampler_parameter_type.py
@@ -118,7 +118,7 @@ def _upsample(
             )[0]
 
             upsampled_normalized = LTX2Pipeline._normalize_latents(
-                upsampled_raw,
+                upsampled_raw,  # type: ignore[reportArgumentType]
                 vae.latents_mean,
                 vae.latents_std,
                 vae.config.scaling_factor,  # type: ignore[reportUndefinedVariable]
diff --git a/modular_diffusion_nodes_library/runtime_parameters/conditioning_runtime_parameter.py b/modular_diffusion_nodes_library/runtime_parameters/conditioning_runtime_parameter.py
index 272dae6..9ba1e7c 100644
--- a/modular_diffusion_nodes_library/runtime_parameters/conditioning_runtime_parameter.py
+++ b/modular_diffusion_nodes_library/runtime_parameters/conditioning_runtime_parameter.py
@@ -111,14 +111,12 @@ def remove_input_parameters(self) -> None:
     def show(self) -> None:
         if self._multiple:
             assert self._fixed_size_list is not None
-            self._fixed_size_list.show()
         else:
             self._node.show_parameter_by_name(self._param_name)
 
     def hide(self) -> None:
         if self._multiple:
             assert self._fixed_size_list is not None
-            self._fixed_size_list.hide()
         else:
             self._node.hide_parameter_by_name(self._param_name)
 
diff --git a/modular_diffusion_nodes_library/utils/conditioning_utils.py b/modular_diffusion_nodes_library/utils/conditioning_utils.py
index 58e1a3a..c44c40c 100644
--- a/modular_diffusion_nodes_library/utils/conditioning_utils.py
+++ b/modular_diffusion_nodes_library/utils/conditioning_utils.py
@@ -146,7 +146,7 @@ def resize_frames_scale_to_fill(
     top = (new_h - target_height) // 2
     resized_frames = []
     for frame in frames:
-        resized = frame.resize((new_w, new_h), PILImage.LANCZOS)
+        resized = frame.resize((new_w, new_h), PILImage.Resampling.LANCZOS)
         cropped = resized.crop((left, top, left + target_width, top + target_height))
         resized_frames.append(cropped)
     return resized_frames
diff --git a/modular_diffusion_nodes_library/utils/pipeline_utils.py b/modular_diffusion_nodes_library/utils/pipeline_utils.py
index 3c926eb..b5bc3fa 100644
--- a/modular_diffusion_nodes_library/utils/pipeline_utils.py
+++ b/modular_diffusion_nodes_library/utils/pipeline_utils.py
@@ -50,9 +50,9 @@ def build_scheduler_with_overrides(
         subfolder=subfolder,
         local_files_only=True,
     )
-    merged_config = dict(base_scheduler.config)
+    merged_config = dict(base_scheduler.config)  # type: ignore[reportAttributeAccessIssue]
     merged_config.update(config_overrides or {})
-    return scheduler_class.from_config(merged_config)
+    return scheduler_class.from_config(merged_config)  # type: ignore[reportAttributeAccessIssue]
 
 
 def detect_offload_method(pipe: DiffusionPipeline) -> str | None:
diff --git a/modular_diffusion_nodes_library/utils/torch_utils.py b/modular_diffusion_nodes_library/utils/torch_utils.py
index af9f5dc..42b7c36 100644
--- a/modular_diffusion_nodes_library/utils/torch_utils.py
+++ b/modular_diffusion_nodes_library/utils/torch_utils.py
@@ -197,3 +197,5 @@ def should_enable_attention_slicing(device: torch.device) -> bool:  # noqa: PLR0
             return True
         logger.info("CUDA device has %s memory, attention slicing not needed.", to_human_readable_size(total_mem))
         return False
+
+    return False
diff --git a/modular_diffusion_nodes_library/utils/video_utils.py b/modular_diffusion_nodes_library/utils/video_utils.py
index 0b09935..71fa6d5 100644
--- a/modular_diffusion_nodes_library/utils/video_utils.py
+++ b/modular_diffusion_nodes_library/utils/video_utils.py
@@ -54,7 +54,7 @@ def load_video_frames_from_url_artifact(video_url_artifact: VideoUrlArtifact) ->
 
     temp_path = download_video_to_temp_file(video_url_artifact)
     try:
-        return diffusers.utils.load_video(str(temp_path))
+        return diffusers.utils.load_video(str(temp_path))  # type: ignore[reportPrivateImportUsage]
     finally:
         temp_path.unlink(missing_ok=True)
 
diff --git a/pyproject.toml b/pyproject.toml
index 94685ab..75e6b78 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -101,6 +101,9 @@ indent-style = "space"
 skip-magic-trailing-comma = false
 line-ending = "auto"
 
+[tool.pyright]
+exclude = ["workflows/", ".venv/", "**/__pycache__"]
+
 [dependency-groups]
 dev = [
     "pyright>=1.1.396",
diff --git a/tests/workflows/test_workflows.py b/tests/workflows/test_workflows.py
index 828a02b..6ac66ad 100644
--- a/tests/workflows/test_workflows.py
+++ b/tests/workflows/test_workflows.py
@@ -4,7 +4,7 @@
 from typing import Any
 
 import pytest
-import pytest_asyncio
+import pytest_asyncio  # type: ignore[reportMissingImports]
 from dotenv import load_dotenv
 from griptape_nodes.bootstrap.workflow_executors.local_workflow_executor import LocalWorkflowExecutor
 from griptape_nodes.retained_mode.events.object_events import ClearAllObjectStateRequest
diff --git a/uv.lock b/uv.lock
index 981cd53..2f6c0d9 100644
--- a/uv.lock
+++ b/uv.lock
@@ -3,7 +3,9 @@ revision = 3
 requires-python = ">=3.12, <4"
 resolution-markers = [
     "sys_platform == 'win32'",
-    "sys_platform != 'win32'",
+    "(python_full_version >= '3.14' and sys_platform == 'linux') or (platform_machine != 'aarch64' and sys_platform == 'linux') or (platform_python_implementation != 'CPython' and sys_platform == 'linux')",
+    "python_full_version < '3.14' and platform_machine == 'aarch64' and platform_python_implementation == 'CPython' and sys_platform == 'linux'",
+    "sys_platform != 'linux' and sys_platform != 'win32'",
 ]
 
 [[package]]
@@ -17,7 +19,8 @@ dependencies = [
     { name = "psutil" },
     { name = "pyyaml" },
     { name = "safetensors" },
-    { name = "torch" },
+    { name = "torch", version = "2.7.0", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform != 'linux' and sys_platform != 'win32'" },
+    { name = "torch", version = "2.7.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/ca/14/787e5498cd062640f0f3d92ef4ae4063174f76f9afd29d13fc52a319daae/accelerate-1.13.0.tar.gz", hash = "sha256:d631b4e0f5b3de4aff2d7e9e6857d164810dfc3237d54d017f075122d057b236", size = 402835, upload-time = "2026-03-04T19:34:12.359Z" }
 wheels = [
@@ -158,9 +161,9 @@ name = "bitsandbytes"
 version = "0.49.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "numpy" },
-    { name = "packaging" },
-    { name = "torch" },
+    { name = "numpy", marker = "sys_platform == 'win32'" },
+    { name = "packaging", marker = "sys_platform == 'win32'" },
+    { name = "torch", version = "2.7.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "sys_platform == 'win32'" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/b6/d4/501655842ad6771fb077f576d78cbedb5445d15b1c3c91343ed58ca46f0e/bitsandbytes-0.49.2-py3-none-win_amd64.whl", hash = "sha256:2e0ddd09cd778155388023cbe81f00afbb7c000c214caef3ce83386e7144df7d", size = 55372289, upload-time = "2026-02-16T21:26:16.267Z" },
@@ -493,8 +496,11 @@ dependencies = [
     { name = "scikit-image" },
     { name = "scipy" },
     { name = "timm" },
-    { name = "torch" },
-    { name = "torchvision" },
+    { name = "torch", version = "2.7.0", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform != 'linux' and sys_platform != 'win32'" },
+    { name = "torch", version = "2.7.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
+    { name = "torchvision", version = "0.22.0", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "python_full_version < '3.14' and platform_machine == 'aarch64' and platform_python_implementation == 'CPython' and sys_platform == 'linux'" },
+    { name = "torchvision", version = "0.22.0", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform != 'linux' and sys_platform != 'win32'" },
+    { name = "torchvision", version = "0.22.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "(python_full_version >= '3.14' and sys_platform == 'linux') or (platform_machine != 'aarch64' and sys_platform == 'linux') or (platform_python_implementation != 'CPython' and sys_platform == 'linux') or sys_platform == 'win32'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/14/ad/2eb8cd9a8e17e35b9e5d39ad29afdde8fe810bda85e6e59117050519955d/controlnet_aux-0.0.10.tar.gz", hash = "sha256:31dc265a54448bdcee033a130b47423c80587fa35ccac752113af1b4d48f5183", size = 215016, upload-time = "2025-05-08T10:38:30.845Z" }
 wheels = [
@@ -938,7 +944,7 @@ wheels = [
 
 [[package]]
 name = "griptape-nodes-library-modular-diffusion"
-version = "0.66.8"
+version = "0.3.0"
 source = { editable = "." }
 dependencies = [
     { name = "accelerate" },
@@ -970,9 +976,14 @@ dependencies = [
     { name = "static-ffmpeg" },
     { name = "supervision" },
     { name = "tokenizers" },
-    { name = "torch" },
-    { name = "torchaudio" },
-    { name = "torchvision" },
+    { name = "torch", version = "2.7.0", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform != 'linux' and sys_platform != 'win32'" },
+    { name = "torch", version = "2.7.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
+    { name = "torchaudio", version = "2.7.0", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "python_full_version < '3.14' and platform_machine == 'aarch64' and platform_python_implementation == 'CPython' and sys_platform == 'linux'" },
+    { name = "torchaudio", version = "2.7.0", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform != 'linux' and sys_platform != 'win32'" },
+    { name = "torchaudio", version = "2.7.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "(python_full_version >= '3.14' and sys_platform == 'linux') or (platform_machine != 'aarch64' and sys_platform == 'linux') or (platform_python_implementation != 'CPython' and sys_platform == 'linux') or sys_platform == 'win32'" },
+    { name = "torchvision", version = "0.22.0", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "python_full_version < '3.14' and platform_machine == 'aarch64' and platform_python_implementation == 'CPython' and sys_platform == 'linux'" },
+    { name = "torchvision", version = "0.22.0", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform != 'linux' and sys_platform != 'win32'" },
+    { name = "torchvision", version = "0.22.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "(python_full_version >= '3.14' and sys_platform == 'linux') or (platform_machine != 'aarch64' and sys_platform == 'linux') or (platform_python_implementation != 'CPython' and sys_platform == 'linux') or sys_platform == 'win32'" },
     { name = "tqdm" },
     { name = "transformers" },
     { name = "ultralytics" },
@@ -980,6 +991,7 @@ dependencies = [
 
 [package.dev-dependencies]
 dev = [
+    { name = "pyright" },
     { name = "pytest" },
     { name = "ruff" },
 ]
@@ -1015,9 +1027,12 @@ requires-dist = [
     { name = "static-ffmpeg", specifier = ">=2.8" },
     { name = "supervision", specifier = ">=0.27.0" },
     { name = "tokenizers", specifier = ">=0.22.0,<0.23" },
-    { name = "torch", specifier = "==2.7.0" },
-    { name = "torchaudio", specifier = "==2.7.0" },
-    { name = "torchvision", specifier = "==0.22.0" },
+    { name = "torch", marker = "sys_platform != 'linux' and sys_platform != 'win32'", specifier = "==2.7.0" },
+    { name = "torch", marker = "sys_platform == 'linux' or sys_platform == 'win32'", specifier = "==2.7.0", index = "https://download.pytorch.org/whl/cu128" },
+    { name = "torchaudio", marker = "sys_platform != 'linux' and sys_platform != 'win32'", specifier = "==2.7.0" },
+    { name = "torchaudio", marker = "sys_platform == 'linux' or sys_platform == 'win32'", specifier = "==2.7.0", index = "https://download.pytorch.org/whl/cu128" },
+    { name = "torchvision", marker = "sys_platform != 'linux' and sys_platform != 'win32'", specifier = "==0.22.0" },
+    { name = "torchvision", marker = "sys_platform == 'linux' or sys_platform == 'win32'", specifier = "==0.22.0", index = "https://download.pytorch.org/whl/cu128" },
     { name = "tqdm", specifier = ">=4.67.1" },
     { name = "transformers", url = "https://github.com/huggingface/transformers/archive/136c621c00b2536d1f608ed3c6de59b9897afbb8.zip" },
     { name = "ultralytics", specifier = ">=8.0.0" },
@@ -1025,6 +1040,7 @@ requires-dist = [
 
 [package.metadata.requires-dev]
 dev = [
+    { name = "pyright", specifier = ">=1.1.396" },
     { name = "pytest" },
     { name = "ruff", specifier = ">=0.8.0" },
 ]
@@ -1930,6 +1946,15 @@ wheels = [
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 ]
 
+[[package]]
+name = "nodeenv"
+version = "1.10.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/24/bf/d1bda4f6168e0b2e9e5958945e01910052158313224ada5ce1fb2e1113b8/nodeenv-1.10.0.tar.gz", hash = "sha256:996c191ad80897d076bdfba80a41994c2b47c68e224c542b48feba42ba00f8bb", size = 55611, upload-time = "2025-12-20T14:08:54.006Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/88/b2/d0896bdcdc8d28a7fc5717c305f1a861c26e18c05047949fb371034d98bd/nodeenv-1.10.0-py2.py3-none-any.whl", hash = "sha256:5bb13e3eed2923615535339b3c620e76779af4cb4c6a90deccc9e36b274d3827", size = 23438, upload-time = "2025-12-20T14:08:52.782Z" },
+]
+
 [[package]]
 name = "numpy"
 version = "2.4.6"
@@ -1993,102 +2018,96 @@ wheels = [
 
 [[package]]
 name = "nvidia-cublas-cu12"
-version = "12.6.4.1"
+version = "12.8.3.14"
 source = { registry = "https://pypi.org/simple" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/af/eb/ff4b8c503fa1f1796679dce648854d58751982426e4e4b37d6fce49d259c/nvidia_cublas_cu12-12.6.4.1-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:08ed2686e9875d01b58e3cb379c6896df8e76c75e0d4a7f7dace3d7b6d9ef8eb", size = 393138322, upload-time = "2024-11-20T17:40:25.65Z" },
+    { url = "https://files.pythonhosted.org/packages/82/df/4b01f10069e23c641f116c62fc31e31e8dc361a153175d81561d15c8143b/nvidia_cublas_cu12-12.8.3.14-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:3f0e05e7293598cf61933258b73e66a160c27d59c4422670bf0b79348c04be44", size = 609620630, upload-time = "2025-01-23T17:55:00.753Z" },
 ]
 
 [[package]]
 name = "nvidia-cuda-cupti-cu12"
-version = "12.6.80"
+version = "12.8.57"
 source = { registry = "https://pypi.org/simple" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/49/60/7b6497946d74bcf1de852a21824d63baad12cd417db4195fc1bfe59db953/nvidia_cuda_cupti_cu12-12.6.80-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:6768bad6cab4f19e8292125e5f1ac8aa7d1718704012a0e3272a6f61c4bce132", size = 8917980, upload-time = "2024-11-20T17:36:04.019Z" },
-    { url = "https://files.pythonhosted.org/packages/a5/24/120ee57b218d9952c379d1e026c4479c9ece9997a4fb46303611ee48f038/nvidia_cuda_cupti_cu12-12.6.80-py3-none-manylinux2014_x86_64.whl", hash = "sha256:a3eff6cdfcc6a4c35db968a06fcadb061cbc7d6dde548609a941ff8701b98b73", size = 8917972, upload-time = "2024-10-01T16:58:06.036Z" },
+    { url = "https://files.pythonhosted.org/packages/39/6f/3683ecf4e38931971946777d231c2df00dd5c1c4c2c914c42ad8f9f4dca6/nvidia_cuda_cupti_cu12-12.8.57-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:8e0b2eb847de260739bee4a3f66fac31378f4ff49538ff527a38a01a9a39f950", size = 10237547, upload-time = "2025-01-23T17:47:56.863Z" },
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 [[package]]
 name = "nvidia-cuda-nvrtc-cu12"
-version = "12.6.77"
+version = "12.8.61"
 source = { registry = "https://pypi.org/simple" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/d4/22/32029d4583f7b19cfe75c84399cbcfd23f2aaf41c66fc8db4da460104fff/nvidia_cuda_nvrtc_cu12-12.8.61-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:a0fa9c2a21583105550ebd871bd76e2037205d56f33f128e69f6d2a55e0af9ed", size = 88024585, upload-time = "2025-01-23T17:50:10.722Z" },
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 [[package]]
 name = "nvidia-cuda-runtime-cu12"
-version = "12.6.77"
+version = "12.8.57"
 source = { registry = "https://pypi.org/simple" }
 wheels = [
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 [[package]]
 name = "nvidia-cudnn-cu12"
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+version = "9.7.1.26"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "nvidia-cublas-cu12" },
+    { name = "nvidia-cublas-cu12", marker = "sys_platform == 'linux'" },
 ]
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/2a/78/4535c9c7f859a64781e43c969a3a7e84c54634e319a996d43ef32ce46f83/nvidia_cudnn_cu12-9.5.1.17-py3-none-manylinux_2_28_x86_64.whl", hash = "sha256:30ac3869f6db17d170e0e556dd6cc5eee02647abc31ca856634d5a40f82c15b2", size = 570988386, upload-time = "2024-10-25T19:54:26.39Z" },
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 [[package]]
 name = "nvidia-cufft-cu12"
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+version = "11.3.3.41"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "nvidia-nvjitlink-cu12" },
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 ]
 wheels = [
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 [[package]]
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