diff --git a/docs/src/content/docs/features/hidiffusion.mdx b/docs/src/content/docs/features/hidiffusion.mdx
index 8158f4980a1..1a8a35b276b 100644
--- a/docs/src/content/docs/features/hidiffusion.mdx
+++ b/docs/src/content/docs/features/hidiffusion.mdx
@@ -17,6 +17,7 @@ Learn more: https://github.com/megvii-research/HiDiffusion
 3. In the **Advanced** grid, enable **HiDiffusion** and optionally adjust the two sub‑toggles and ratios:
    - **HiDiffusion: RAU‑Net**
    - **HiDiffusion: Window Attention**
+   - **HiDiffusion: Automatic Ratios**
    - **HiDiffusion: T1 Ratio**
    - **HiDiffusion: T2 Ratio**
 
@@ -28,9 +29,11 @@ Learn more: https://github.com/megvii-research/HiDiffusion
 
 - **HiDiffusion: Window Attention**: Enables windowed attention blocks. This can boost local texture/detail, but may slightly affect global coherence in some prompts.
 
-- **HiDiffusion: T1 Ratio**: Controls when HiDiffusion switches into its mid‑stage behavior. Lower values switch earlier; higher values preserve global structure longer.
+- **HiDiffusion: Automatic Ratios**: Uses HiDiffusion's model- and resolution-specific T1/T2 presets. Disable it to enter manual overrides.
 
-- **HiDiffusion: T2 Ratio**: Controls when HiDiffusion switches into its late‑stage behavior. Higher values keep window attention active longer and can sharpen local detail.
+- **HiDiffusion: T1 Ratio**: Controls the first RAU-Net switching threshold. Lower values switch earlier; higher values keep the first resolution-aware stage active longer.
+
+- **HiDiffusion: T2 Ratio**: Controls the second RAU-Net switching threshold used for extreme-resolution generation. It does not control window attention. Higher values keep the second resolution-aware stage active longer.
 
 ## Tips
 
diff --git a/invokeai/app/invocations/denoise_latents.py b/invokeai/app/invocations/denoise_latents.py
index 2d48dd87607..e00e48cbbb4 100644
--- a/invokeai/app/invocations/denoise_latents.py
+++ b/invokeai/app/invocations/denoise_latents.py
@@ -133,7 +133,7 @@ def get_scheduler(
     title="Denoise - SD1.5, SDXL",
     tags=["latents", "denoise", "txt2img", "t2i", "t2l", "img2img", "i2i", "l2l"],
     category="latents",
-    version="1.6.0",
+    version="1.7.0",
 )
 class DenoiseLatentsInvocation(BaseInvocation):
     """Denoises noisy latents to decodable images"""
@@ -209,15 +209,15 @@ class DenoiseLatentsInvocation(BaseInvocation):
         description=FieldDescriptions.hidiffusion_window_attn,
         title="HiDiffusion: Window Attention",
     )
-    hidiffusion_t1_ratio: float = InputField(
-        default=0.4,
+    hidiffusion_t1_ratio: Optional[float] = InputField(
+        default=None,
         ge=0,
         le=1,
         description=FieldDescriptions.hidiffusion_t1_ratio,
         title="HiDiffusion: T1 Ratio",
     )
-    hidiffusion_t2_ratio: float = InputField(
-        default=0.0,
+    hidiffusion_t2_ratio: Optional[float] = InputField(
+        default=None,
         ge=0,
         le=1,
         description=FieldDescriptions.hidiffusion_t2_ratio,
@@ -926,6 +926,10 @@ def step_callback(state: PipelineIntermediateState) -> None:
                     t1_ratio=self.hidiffusion_t1_ratio,
                     t2_ratio=self.hidiffusion_t2_ratio,
                     generator=torch.Generator(device="cpu").manual_seed(seed),
+                    is_inpainting_task=self.denoise_mask is not None,
+                    use_aggressive_raunet=False,
+                    denoising_start=self.denoising_start,
+                    denoising_end=self.denoising_end,
                 )
             )
 
@@ -1157,6 +1161,10 @@ def _lora_loader() -> Iterator[PatchSpec]:
                     t1_ratio=self.hidiffusion_t1_ratio,
                     t2_ratio=self.hidiffusion_t2_ratio,
                     generator=torch.Generator(device="cpu").manual_seed(seed),
+                    is_inpainting_task=self.denoise_mask is not None,
+                    use_aggressive_raunet=False,
+                    denoising_start=self.denoising_start,
+                    denoising_end=self.denoising_end,
                 )
                 if self.hidiffusion
                 else nullcontext()
diff --git a/invokeai/app/invocations/metadata_linked.py b/invokeai/app/invocations/metadata_linked.py
index 5f8c18ecc98..be23ab6d85e 100644
--- a/invokeai/app/invocations/metadata_linked.py
+++ b/invokeai/app/invocations/metadata_linked.py
@@ -624,7 +624,7 @@ class LatentsMetaOutput(LatentsOutput, MetadataOutput):
     title=f"{DenoiseLatentsInvocation.UIConfig.title} + Metadata",
     tags=["latents", "denoise", "txt2img", "t2i", "t2l", "img2img", "i2i", "l2l"],
     category="metadata",
-    version="1.2.0",
+    version="1.3.0",
 )
 class DenoiseLatentsMetaInvocation(DenoiseLatentsInvocation, WithMetadata):
     def invoke(self, context: InvocationContext) -> LatentsMetaOutput:
diff --git a/invokeai/backend/hidiffusion/hidiffusion.py b/invokeai/backend/hidiffusion/hidiffusion.py
index 33437853214..6bd8fec67b0 100644
--- a/invokeai/backend/hidiffusion/hidiffusion.py
+++ b/invokeai/backend/hidiffusion/hidiffusion.py
@@ -136,6 +136,32 @@ def _get_max_timesteps(info_dict: dict) -> int:
         return len(pipeline.scheduler.timesteps)
 
 
+def _get_switching_threshold_ratio(module: torch.nn.Module, presets: dict, preset_key: str) -> float:
+    """Resolve a threshold ratio for the executed part of the denoising schedule."""
+    override = module.info["switching_threshold_overrides"].get(module.switching_threshold_ratio)
+    full_schedule_ratio = override if override is not None else presets[preset_key][module.switching_threshold_ratio]
+
+    denoising_start = module.info.get("denoising_start", 0.0)
+    denoising_end = module.info.get("denoising_end", 1.0)
+    if denoising_end <= denoising_start:
+        return 0.0
+
+    executed_schedule_ratio = (full_schedule_ratio - denoising_start) / (denoising_end - denoising_start)
+    return max(0.0, min(1.0, executed_schedule_ratio))
+
+
+def _should_use_aggressive_raunet(module: torch.nn.Module) -> bool:
+    """Resolve whether RAU-Net should be activated after denoising has already started."""
+    override = module.info.get("use_aggressive_raunet")
+    if override is not None:
+        return override
+    if module.info["is_inpainting_task"]:
+        return inpainting_is_aggressive_raunet
+    if module.info["is_playground"]:
+        return playground_is_aggressive_raunet
+    return is_aggressive_raunet
+
+
 def make_diffusers_sdxl_controlnet_ppl(block_class):
     class sdxl_controlnet_ppl(block_class):
         # Save for unpatching later
@@ -1559,32 +1585,29 @@ def forward(
             ori_H, ori_W = self.info["size"]
             if self.model == "sd15":
                 if ori_H < 256 or ori_W < 256:
-                    self.T1_ratio = switching_threshold_ratio_dict["sd15_1024"][self.switching_threshold_ratio]
+                    self.T1_ratio = _get_switching_threshold_ratio(self, switching_threshold_ratio_dict, "sd15_1024")
                 else:
-                    self.T1_ratio = switching_threshold_ratio_dict["sd15_2048"][self.switching_threshold_ratio]
+                    self.T1_ratio = _get_switching_threshold_ratio(self, switching_threshold_ratio_dict, "sd15_2048")
             elif self.model == "sdxl":
                 if ori_H < 512 or ori_W < 512:
                     if self.info["text_to_img_controlnet"]:
-                        self.T1_ratio = text_to_img_controlnet_switching_threshold_ratio_dict["sdxl_2048"][
-                            self.switching_threshold_ratio
-                        ]
+                        self.T1_ratio = _get_switching_threshold_ratio(
+                            self, text_to_img_controlnet_switching_threshold_ratio_dict, "sdxl_2048"
+                        )
                     else:
-                        self.T1_ratio = switching_threshold_ratio_dict["sdxl_2048"][self.switching_threshold_ratio]
+                        self.T1_ratio = _get_switching_threshold_ratio(
+                            self, switching_threshold_ratio_dict, "sdxl_2048"
+                        )
 
-                    if self.info["is_inpainting_task"]:
-                        self.aggressive_raunet = inpainting_is_aggressive_raunet
-                    elif self.info["is_playground"]:
-                        self.aggressive_raunet = playground_is_aggressive_raunet
-                    else:
-                        self.aggressive_raunet = is_aggressive_raunet
+                    self.aggressive_raunet = _should_use_aggressive_raunet(self)
                 else:
-                    self.T1_ratio = switching_threshold_ratio_dict["sdxl_4096"][self.switching_threshold_ratio]
+                    self.T1_ratio = _get_switching_threshold_ratio(self, switching_threshold_ratio_dict, "sdxl_4096")
             elif self.model == "sdxl_turbo":
-                self.T1_ratio = switching_threshold_ratio_dict["sdxl_turbo_1024"][self.switching_threshold_ratio]
+                self.T1_ratio = _get_switching_threshold_ratio(self, switching_threshold_ratio_dict, "sdxl_turbo_1024")
             else:
                 raise Exception("Error model. HiDiffusion now only supports sd15, sd21, sdxl, sdxl-turbo.")
 
-            if self.aggressive_raunet:
+            if self.aggressive_raunet and self.switching_threshold_ratio == "T1_ratio":
                 # self.T1_start = min(int(self.max_timestep * self.T1_ratio * 0.4), int(8/50 * self.max_timestep))
                 self.T1_start = int(aggressive_step / 50 * self.max_timestep)
                 self.T1_end = int(self.max_timestep * self.T1_ratio)
@@ -1693,33 +1716,30 @@ def forward(
             ori_H, ori_W = self.info["size"]
             if self.model == "sd15":
                 if ori_H < 256 or ori_W < 256:
-                    self.T1_ratio = switching_threshold_ratio_dict["sd15_1024"][self.switching_threshold_ratio]
+                    self.T1_ratio = _get_switching_threshold_ratio(self, switching_threshold_ratio_dict, "sd15_1024")
                 else:
-                    self.T1_ratio = switching_threshold_ratio_dict["sd15_2048"][self.switching_threshold_ratio]
+                    self.T1_ratio = _get_switching_threshold_ratio(self, switching_threshold_ratio_dict, "sd15_2048")
             elif self.model == "sdxl":
                 if ori_H < 512 or ori_W < 512:
                     if self.info["text_to_img_controlnet"]:
-                        self.T1_ratio = text_to_img_controlnet_switching_threshold_ratio_dict["sdxl_2048"][
-                            self.switching_threshold_ratio
-                        ]
+                        self.T1_ratio = _get_switching_threshold_ratio(
+                            self, text_to_img_controlnet_switching_threshold_ratio_dict, "sdxl_2048"
+                        )
                     else:
-                        self.T1_ratio = switching_threshold_ratio_dict["sdxl_2048"][self.switching_threshold_ratio]
+                        self.T1_ratio = _get_switching_threshold_ratio(
+                            self, switching_threshold_ratio_dict, "sdxl_2048"
+                        )
 
-                    if self.info["is_inpainting_task"]:
-                        self.aggressive_raunet = inpainting_is_aggressive_raunet
-                    elif self.info["is_playground"]:
-                        self.aggressive_raunet = playground_is_aggressive_raunet
-                    else:
-                        self.aggressive_raunet = is_aggressive_raunet
+                    self.aggressive_raunet = _should_use_aggressive_raunet(self)
 
                 else:
-                    self.T1_ratio = switching_threshold_ratio_dict["sdxl_4096"][self.switching_threshold_ratio]
+                    self.T1_ratio = _get_switching_threshold_ratio(self, switching_threshold_ratio_dict, "sdxl_4096")
             elif self.model == "sdxl_turbo":
-                self.T1_ratio = switching_threshold_ratio_dict["sdxl_turbo_1024"][self.switching_threshold_ratio]
+                self.T1_ratio = _get_switching_threshold_ratio(self, switching_threshold_ratio_dict, "sdxl_turbo_1024")
             else:
                 raise Exception("Error model. HiDiffusion now only supports sd15, sd21, sdxl, sdxl-turbo.")
 
-            if self.aggressive_raunet:
+            if self.aggressive_raunet and self.switching_threshold_ratio == "T1_ratio":
                 # self.T1_start = min(int(self.max_timestep * self.T1_ratio * 0.4), int(8/50 * self.max_timestep))
                 self.T1_start = int(aggressive_step / 50 * self.max_timestep)
                 self.T1_end = int(self.max_timestep * self.T1_ratio)
@@ -1830,32 +1850,29 @@ def forward(self, hidden_states: torch.Tensor, scale=1.0) -> torch.Tensor:
             ori_H, ori_W = self.info["size"]
             if self.model == "sd15":
                 if ori_H < 256 or ori_W < 256:
-                    self.T1_ratio = switching_threshold_ratio_dict["sd15_1024"][self.switching_threshold_ratio]
+                    self.T1_ratio = _get_switching_threshold_ratio(self, switching_threshold_ratio_dict, "sd15_1024")
                 else:
-                    self.T1_ratio = switching_threshold_ratio_dict["sd15_2048"][self.switching_threshold_ratio]
+                    self.T1_ratio = _get_switching_threshold_ratio(self, switching_threshold_ratio_dict, "sd15_2048")
             elif self.model == "sdxl":
                 if ori_H < 512 or ori_W < 512:
                     if self.info["text_to_img_controlnet"]:
-                        self.T1_ratio = text_to_img_controlnet_switching_threshold_ratio_dict["sdxl_2048"][
-                            self.switching_threshold_ratio
-                        ]
+                        self.T1_ratio = _get_switching_threshold_ratio(
+                            self, text_to_img_controlnet_switching_threshold_ratio_dict, "sdxl_2048"
+                        )
                     else:
-                        self.T1_ratio = switching_threshold_ratio_dict["sdxl_2048"][self.switching_threshold_ratio]
+                        self.T1_ratio = _get_switching_threshold_ratio(
+                            self, switching_threshold_ratio_dict, "sdxl_2048"
+                        )
 
-                    if self.info["is_inpainting_task"]:
-                        self.aggressive_raunet = inpainting_is_aggressive_raunet
-                    elif self.info["is_playground"]:
-                        self.aggressive_raunet = playground_is_aggressive_raunet
-                    else:
-                        self.aggressive_raunet = is_aggressive_raunet
+                    self.aggressive_raunet = _should_use_aggressive_raunet(self)
                 else:
-                    self.T1_ratio = switching_threshold_ratio_dict["sdxl_4096"][self.switching_threshold_ratio]
+                    self.T1_ratio = _get_switching_threshold_ratio(self, switching_threshold_ratio_dict, "sdxl_4096")
             elif self.model == "sdxl_turbo":
-                self.T1_ratio = switching_threshold_ratio_dict["sdxl_turbo_1024"][self.switching_threshold_ratio]
+                self.T1_ratio = _get_switching_threshold_ratio(self, switching_threshold_ratio_dict, "sdxl_turbo_1024")
             else:
                 raise Exception("Error model. HiDiffusion now only supports sd15, sd21, sdxl, sdxl-turbo.")
 
-            if self.aggressive_raunet:
+            if self.aggressive_raunet and self.switching_threshold_ratio == "T1_ratio":
                 # self.T1 = min(int(self.max_timestep * self.T1_ratio), int(8/50 * self.max_timestep))
                 self.T1 = int(aggressive_step / 50 * self.max_timestep)
             else:
@@ -1911,32 +1928,29 @@ def forward(self, hidden_states: torch.Tensor, scale=1.0) -> torch.Tensor:
             ori_H, ori_W = self.info["size"]
             if self.model == "sd15":
                 if ori_H < 256 or ori_W < 256:
-                    self.T1_ratio = switching_threshold_ratio_dict["sd15_1024"][self.switching_threshold_ratio]
+                    self.T1_ratio = _get_switching_threshold_ratio(self, switching_threshold_ratio_dict, "sd15_1024")
                 else:
-                    self.T1_ratio = switching_threshold_ratio_dict["sd15_2048"][self.switching_threshold_ratio]
+                    self.T1_ratio = _get_switching_threshold_ratio(self, switching_threshold_ratio_dict, "sd15_2048")
             elif self.model == "sdxl":
                 if ori_H < 512 or ori_W < 512:
                     if self.info["text_to_img_controlnet"]:
-                        self.T1_ratio = text_to_img_controlnet_switching_threshold_ratio_dict["sdxl_2048"][
-                            self.switching_threshold_ratio
-                        ]
+                        self.T1_ratio = _get_switching_threshold_ratio(
+                            self, text_to_img_controlnet_switching_threshold_ratio_dict, "sdxl_2048"
+                        )
                     else:
-                        self.T1_ratio = switching_threshold_ratio_dict["sdxl_2048"][self.switching_threshold_ratio]
+                        self.T1_ratio = _get_switching_threshold_ratio(
+                            self, switching_threshold_ratio_dict, "sdxl_2048"
+                        )
 
-                    if self.info["is_inpainting_task"]:
-                        self.aggressive_raunet = inpainting_is_aggressive_raunet
-                    elif self.info["is_playground"]:
-                        self.aggressive_raunet = playground_is_aggressive_raunet
-                    else:
-                        self.aggressive_raunet = is_aggressive_raunet
+                    self.aggressive_raunet = _should_use_aggressive_raunet(self)
                 else:
-                    self.T1_ratio = switching_threshold_ratio_dict["sdxl_4096"][self.switching_threshold_ratio]
+                    self.T1_ratio = _get_switching_threshold_ratio(self, switching_threshold_ratio_dict, "sdxl_4096")
             elif self.model == "sdxl_turbo":
-                self.T1_ratio = switching_threshold_ratio_dict["sdxl_turbo_1024"][self.switching_threshold_ratio]
+                self.T1_ratio = _get_switching_threshold_ratio(self, switching_threshold_ratio_dict, "sdxl_turbo_1024")
             else:
                 raise Exception("Error model. HiDiffusion now only supports sd15, sd21, sdxl, sdxl-turbo.")
 
-            if self.aggressive_raunet:
+            if self.aggressive_raunet and self.switching_threshold_ratio == "T1_ratio":
                 # self.T1 = min(int(self.max_timestep * self.T1_ratio), int(8/50 * self.max_timestep))
                 self.T1 = int(aggressive_step / 50 * self.max_timestep)
             else:
@@ -2045,6 +2059,12 @@ def apply_hidiffusion(
     generator: torch.Generator | None = None,
     has_controlnet: bool = False,
     is_controlnet_text_to_image: bool = False,
+    t1_ratio: float | None = None,
+    t2_ratio: float | None = None,
+    is_inpainting_task: bool | None = None,
+    use_aggressive_raunet: bool | None = None,
+    denoising_start: float = 0.0,
+    denoising_end: float = 1.0,
 ):
     """
     model: diffusers model. We support SD 1.5, 2.1, XL, XL Turbo.
@@ -2120,14 +2140,19 @@ def apply_hidiffusion(
         elif set(sdxl_module_key) < set(diffusion_model_module_key):
             name_or_path = "stabilityai/stable-diffusion-xl-base-1.0"
 
+    detected_inpainting_task = model.__class__ in auto_pipeline.AUTO_INPAINT_PIPELINES_MAPPING.values()
     diffusion_model.info = {
         "size": None,
         "upsample_size": None,
         "hooks": [],
         "text_to_img_controlnet": has_controlnet and is_controlnet_text_to_image,
-        "is_inpainting_task": model.__class__ in auto_pipeline.AUTO_INPAINT_PIPELINES_MAPPING.values(),
+        "is_inpainting_task": detected_inpainting_task if is_inpainting_task is None else is_inpainting_task,
         "is_playground": is_playground,
+        "use_aggressive_raunet": use_aggressive_raunet,
+        "denoising_start": denoising_start,
+        "denoising_end": denoising_end,
         "pipeline": model,
+        "switching_threshold_overrides": {"T1_ratio": t1_ratio, "T2_ratio": t2_ratio},
     }
     model.info = diffusion_model.info
     hook_diffusion_model(diffusion_model)
diff --git a/invokeai/backend/stable_diffusion/diffusion/regional_ip_data.py b/invokeai/backend/stable_diffusion/diffusion/regional_ip_data.py
index 792c97114da..eb2a9eea56b 100644
--- a/invokeai/backend/stable_diffusion/diffusion/regional_ip_data.py
+++ b/invokeai/backend/stable_diffusion/diffusion/regional_ip_data.py
@@ -11,9 +11,12 @@ def __init__(
         masks: list[torch.Tensor],
         dtype: torch.dtype,
         device: torch.device,
-        max_downscale_factor: int = 8,
+        max_downscale_factor: int = 16,
     ):
-        """Initialize a `IPAdapterConditioningData` object."""
+        """Initialize an `IPAdapterConditioningData` object.
+
+        HiDiffusion's RAU-Net requires one mask level beyond the standard UNet's 8x downscale.
+        """
         assert len(image_prompt_embeds) == len(scales) == len(masks)
 
         # The image prompt embeddings.
diff --git a/invokeai/backend/stable_diffusion/diffusion/regional_prompt_data.py b/invokeai/backend/stable_diffusion/diffusion/regional_prompt_data.py
index eddd31f0c42..bebe00b35b1 100644
--- a/invokeai/backend/stable_diffusion/diffusion/regional_prompt_data.py
+++ b/invokeai/backend/stable_diffusion/diffusion/regional_prompt_data.py
@@ -19,7 +19,7 @@ def __init__(
         regions: list[TextConditioningRegions],
         device: torch.device,
         dtype: torch.dtype,
-        max_downscale_factor: int = 8,
+        max_downscale_factor: int = 16,
     ):
         """Initialize a `RegionalPromptData` object.
         Args:
@@ -28,7 +28,7 @@ def __init__(
             device (torch.device): The device to use for the attention masks.
             dtype (torch.dtype): The data type to use for the attention masks.
             max_downscale_factor: Spatial masks will be prepared for downscale factors from 1 to max_downscale_factor
-                in steps of 2x.
+                in steps of 2x. HiDiffusion's RAU-Net requires one level beyond the standard UNet's 8x downscale.
         """
         self._regions = regions
         self._device = device
@@ -41,7 +41,7 @@ def __init__(
         self._negative_cross_attn_mask_score = -10000.0
 
     def _prepare_spatial_masks(
-        self, regions: list[TextConditioningRegions], max_downscale_factor: int = 8
+        self, regions: list[TextConditioningRegions], max_downscale_factor: int = 16
     ) -> list[dict[int, torch.Tensor]]:
         """Prepare the spatial masks for all downscaling factors."""
         # batch_masks_by_seq_len[b][s] contains the spatial masks for the b'th batch sample with a query sequence length
diff --git a/invokeai/backend/stable_diffusion/extensions/hidiffusion.py b/invokeai/backend/stable_diffusion/extensions/hidiffusion.py
index 13a1763f35e..464e6a1d197 100644
--- a/invokeai/backend/stable_diffusion/extensions/hidiffusion.py
+++ b/invokeai/backend/stable_diffusion/extensions/hidiffusion.py
@@ -22,6 +22,10 @@ def __init__(
         generator: torch.Generator | None = None,
         has_controlnet: bool = False,
         is_controlnet_text_to_image: bool = False,
+        is_inpainting_task: bool | None = None,
+        use_aggressive_raunet: bool | None = None,
+        denoising_start: float = 0.0,
+        denoising_end: float = 1.0,
     ):
         super().__init__()
         self._name_or_path = name_or_path
@@ -29,6 +33,10 @@ def __init__(
         self._apply_window_attn = apply_window_attn
         self._has_controlnet = has_controlnet
         self._is_controlnet_text_to_image = is_controlnet_text_to_image
+        self._is_inpainting_task = is_inpainting_task
+        self._use_aggressive_raunet = use_aggressive_raunet
+        self._denoising_start = denoising_start
+        self._denoising_end = denoising_end
         self._t1_ratio = t1_ratio
         self._t2_ratio = t2_ratio
         self._generator = generator
@@ -45,5 +53,9 @@ def patch_unet(self, unet: UNet2DConditionModel, original_weights: OriginalWeigh
             t1_ratio=self._t1_ratio,
             t2_ratio=self._t2_ratio,
             generator=self._generator,
+            is_inpainting_task=self._is_inpainting_task,
+            use_aggressive_raunet=self._use_aggressive_raunet,
+            denoising_start=self._denoising_start,
+            denoising_end=self._denoising_end,
         ):
             yield None
diff --git a/invokeai/backend/stable_diffusion/hidiffusion_utils.py b/invokeai/backend/stable_diffusion/hidiffusion_utils.py
index f6e6e1681b3..371da5ca6bd 100644
--- a/invokeai/backend/stable_diffusion/hidiffusion_utils.py
+++ b/invokeai/backend/stable_diffusion/hidiffusion_utils.py
@@ -1,19 +1,11 @@
 from __future__ import annotations
 
-import copy
 import sys
 from contextlib import contextmanager
 from typing import Any, Optional
 
 import torch
 
-from invokeai.backend.hidiffusion.hidiffusion import (
-    switching_threshold_ratio_dict as _switching_threshold_ratio_dict,
-)
-from invokeai.backend.hidiffusion.hidiffusion import (
-    text_to_img_controlnet_switching_threshold_ratio_dict as _text_to_img_controlnet_switching_threshold_ratio_dict,
-)
-
 
 @contextmanager
 def hidiffusion_patch(
@@ -26,6 +18,10 @@ def hidiffusion_patch(
     generator: torch.Generator | None = None,
     has_controlnet: bool = False,
     is_controlnet_text_to_image: bool = False,
+    is_inpainting_task: bool | None = None,
+    use_aggressive_raunet: bool | None = None,
+    denoising_start: float = 0.0,
+    denoising_end: float = 1.0,
 ):
     """Context manager that applies HiDiffusion and restores the model on exit."""
     from invokeai.backend.hidiffusion.hidiffusion import apply_hidiffusion, remove_hidiffusion
@@ -70,14 +66,6 @@ def _set_name_or_path_on_config(cfg, value: str) -> bool:
 
     original_num_upsamplers = getattr(target, "num_upsamplers", None)
 
-    ratio_overrides = None
-    ratio_dicts = None
-    if t1_ratio is not None or t2_ratio is not None:
-        ratio_dicts = (
-            _switching_threshold_ratio_dict,
-            _text_to_img_controlnet_switching_threshold_ratio_dict,
-        )
-
     set_model_name_or_path = False
     set_config_name_or_path = False
     try:
@@ -103,26 +91,19 @@ def _set_name_or_path_on_config(cfg, value: str) -> bool:
         except Exception:
             pass
 
-        if ratio_dicts is not None:
-            ratio_overrides = (copy.deepcopy(ratio_dicts[0]), copy.deepcopy(ratio_dicts[1]))
-
-            def _apply_ratio_overrides(ratio_dict: dict) -> None:
-                for _, entry in ratio_dict.items():
-                    if t1_ratio is not None:
-                        entry["T1_ratio"] = t1_ratio
-                    if t2_ratio is not None and "T2_ratio" in entry:
-                        entry["T2_ratio"] = t2_ratio
-
-            _apply_ratio_overrides(ratio_dicts[0])
-            _apply_ratio_overrides(ratio_dicts[1])
-
         apply_hidiffusion(
             model,
             apply_raunet=apply_raunet,
             apply_window_attn=apply_window_attn,
+            t1_ratio=t1_ratio,
+            t2_ratio=t2_ratio,
             has_controlnet=has_controlnet,
             is_controlnet_text_to_image=is_controlnet_text_to_image,
             generator=generator,
+            is_inpainting_task=is_inpainting_task,
+            use_aggressive_raunet=use_aggressive_raunet,
+            denoising_start=denoising_start,
+            denoising_end=denoising_end,
         )
         yield
     finally:
@@ -133,11 +114,6 @@ def _apply_ratio_overrides(ratio_dict: dict) -> None:
         except Exception as error:
             if not had_active_exception:
                 teardown_error = error
-        if ratio_overrides is not None and ratio_dicts is not None:
-            ratio_dicts[0].clear()
-            ratio_dicts[0].update(ratio_overrides[0])
-            ratio_dicts[1].clear()
-            ratio_dicts[1].update(ratio_overrides[1])
         if original_num_upsamplers is not None:
             target.num_upsamplers = original_num_upsamplers
         if set_model_name_or_path:
diff --git a/invokeai/frontend/web/openapi.json b/invokeai/frontend/web/openapi.json
index ccd0f318694..199f5f89b6c 100644
--- a/invokeai/frontend/web/openapi.json
+++ b/invokeai/frontend/web/openapi.json
@@ -24636,28 +24636,42 @@
             "type": "boolean"
           },
           "hidiffusion_t1_ratio": {
-            "default": 0.4,
+            "anyOf": [
+              {
+                "maximum": 1,
+                "minimum": 0,
+                "type": "number"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
             "description": "Override HiDiffusion early switch threshold (T1 ratio)",
             "field_kind": "input",
             "input": "any",
-            "maximum": 1,
-            "minimum": 0,
-            "orig_default": 0.4,
+            "orig_default": null,
             "orig_required": false,
-            "title": "HiDiffusion: T1 Ratio",
-            "type": "number"
+            "title": "HiDiffusion: T1 Ratio"
           },
           "hidiffusion_t2_ratio": {
-            "default": 0.0,
+            "anyOf": [
+              {
+                "maximum": 1,
+                "minimum": 0,
+                "type": "number"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
             "description": "Override HiDiffusion late switch threshold (T2 ratio)",
             "field_kind": "input",
             "input": "any",
-            "maximum": 1,
-            "minimum": 0,
-            "orig_default": 0.0,
+            "orig_default": null,
             "orig_required": false,
-            "title": "HiDiffusion: T2 Ratio",
-            "type": "number"
+            "title": "HiDiffusion: T2 Ratio"
           },
           "latents": {
             "anyOf": [
@@ -24705,7 +24719,7 @@
         "tags": ["latents", "denoise", "txt2img", "t2i", "t2l", "img2img", "i2i", "l2l"],
         "title": "Denoise - SD1.5, SDXL",
         "type": "object",
-        "version": "1.6.0",
+        "version": "1.7.0",
         "output": {
           "$ref": "#/components/schemas/LatentsOutput"
         }
@@ -25049,28 +25063,42 @@
             "type": "boolean"
           },
           "hidiffusion_t1_ratio": {
-            "default": 0.4,
+            "anyOf": [
+              {
+                "maximum": 1,
+                "minimum": 0,
+                "type": "number"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
             "description": "Override HiDiffusion early switch threshold (T1 ratio)",
             "field_kind": "input",
             "input": "any",
-            "maximum": 1,
-            "minimum": 0,
-            "orig_default": 0.4,
+            "orig_default": null,
             "orig_required": false,
-            "title": "HiDiffusion: T1 Ratio",
-            "type": "number"
+            "title": "HiDiffusion: T1 Ratio"
           },
           "hidiffusion_t2_ratio": {
-            "default": 0.0,
+            "anyOf": [
+              {
+                "maximum": 1,
+                "minimum": 0,
+                "type": "number"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
             "description": "Override HiDiffusion late switch threshold (T2 ratio)",
             "field_kind": "input",
             "input": "any",
-            "maximum": 1,
-            "minimum": 0,
-            "orig_default": 0.0,
+            "orig_default": null,
             "orig_required": false,
-            "title": "HiDiffusion: T2 Ratio",
-            "type": "number"
+            "title": "HiDiffusion: T2 Ratio"
           },
           "latents": {
             "anyOf": [
@@ -25118,7 +25146,7 @@
         "tags": ["latents", "denoise", "txt2img", "t2i", "t2l", "img2img", "i2i", "l2l"],
         "title": "Denoise - SD1.5, SDXL + Metadata",
         "type": "object",
-        "version": "1.2.0",
+        "version": "1.3.0",
         "output": {
           "$ref": "#/components/schemas/LatentsMetaOutput"
         }
diff --git a/invokeai/frontend/web/public/locales/en.json b/invokeai/frontend/web/public/locales/en.json
index c2a4853cc20..a63e37c9b89 100644
--- a/invokeai/frontend/web/public/locales/en.json
+++ b/invokeai/frontend/web/public/locales/en.json
@@ -1734,6 +1734,7 @@
         "hiDiffusion": "HiDiffusion",
         "hiDiffusionRauNet": "HiDiffusion: RAU-Net",
         "hiDiffusionWindowAttn": "HiDiffusion: Window Attention",
+        "hiDiffusionRatiosAuto": "HiDiffusion: Automatic Ratios",
         "hiDiffusionT1Ratio": "HiDiffusion: T1 Ratio",
         "hiDiffusionT2Ratio": "HiDiffusion: T2 Ratio",
         "coherenceMode": "Mode",
@@ -2183,6 +2184,13 @@
                 "Can boost local detail, but may affect global coherence."
             ]
         },
+        "hidiffusionRatiosAuto": {
+            "heading": "HiDiffusion: Automatic Ratios",
+            "paragraphs": [
+                "Uses the model- and resolution-specific T1 and T2 ratios recommended by HiDiffusion.",
+                "Disable this to set explicit ratio overrides below."
+            ]
+        },
         "hidiffusionT1Ratio": {
             "heading": "HiDiffusion: T1 Ratio",
             "paragraphs": [
@@ -2194,7 +2202,7 @@
             "heading": "HiDiffusion: T2 Ratio",
             "paragraphs": [
                 "Controls the late switch point for HiDiffusion (T2).",
-                "Higher values keep window attention active longer."
+                "Higher values keep the second RAU-Net resolution-aware stage active longer."
             ]
         },
         "clipSkip": {
diff --git a/invokeai/frontend/web/src/common/components/InformationalPopover/constants.ts b/invokeai/frontend/web/src/common/components/InformationalPopover/constants.ts
index d91077f6607..edbe78ddab2 100644
--- a/invokeai/frontend/web/src/common/components/InformationalPopover/constants.ts
+++ b/invokeai/frontend/web/src/common/components/InformationalPopover/constants.ts
@@ -78,6 +78,7 @@ export type Feature =
   | 'hidiffusion'
   | 'hidiffusionRauNet'
   | 'hidiffusionWindowAttn'
+  | 'hidiffusionRatiosAuto'
   | 'hidiffusionT1Ratio'
   | 'hidiffusionT2Ratio'
   | 'colorCompensation'
@@ -256,6 +257,7 @@ export const POPOVER_DATA: { [key in Feature]?: PopoverData } = {
   },
   hidiffusionRauNet: {},
   hidiffusionWindowAttn: {},
+  hidiffusionRatiosAuto: {},
   hidiffusionT1Ratio: {},
   hidiffusionT2Ratio: {},
 } as const;
diff --git a/invokeai/frontend/web/src/features/controlLayers/store/paramsSlice.test.ts b/invokeai/frontend/web/src/features/controlLayers/store/paramsSlice.test.ts
index e2a2713aa41..30c2690c72a 100644
--- a/invokeai/frontend/web/src/features/controlLayers/store/paramsSlice.test.ts
+++ b/invokeai/frontend/web/src/features/controlLayers/store/paramsSlice.test.ts
@@ -172,8 +172,9 @@ describe('paramsSlice selectors for external models', () => {
  *     `animaT5EncoderModel`, since removed from the schema.
  *   - v4: the narrowest v4 blob is not a release at all — it is the one written by the build that did
  *     the bump, `1aeb05bbf0` (97 keys). Releases writing v4 start at v6.14.0-rc1.
- *   - v5: the current version, reached by the FLUX.2 [dev] merge `f10d2a4f5a`, which is also the
- *     build that wrote the narrowest v5 blob. Pinning the fixture at the bump commit is what keeps
+ *   - v5: reached by the FLUX.2 [dev] merge `f10d2a4f5a`, which is also the build that wrote the
+ *     narrowest v5 blob. The current v6 migration converts legacy HiDiffusion defaults to Auto.
+ *     Pinning the fixture at the v5 bump commit is what keeps
  *     the invariant below meaningful for the current tier: the version steps can never cover it (a
  *     v5 blob matches no branch), so every key added since the bump has to carry a zod default, and
  *     this entry is what proves it does.
@@ -900,14 +901,14 @@ describe('paramsSliceConfig persisted state migration', () => {
 
     // v2 migrates all the way through the current chain (v2 -> v3 adds Qwen fields,
     // v3 -> v4 adds Krea-2 and PiD fields).
-    expect(result._version).toBe(5);
+    expect(result._version).toBe(6);
     expect(result.qwenImageVaeModel).toBeNull();
     expect(result.qwenImageQwenVLEncoderModel).toBeNull();
     expect(result.hiDiffusionEnabled).toBe(false);
     expect(result.hiDiffusionRauNetEnabled).toBe(true);
     expect(result.hiDiffusionWindowAttnEnabled).toBe(true);
-    expect(result.hiDiffusionT1Ratio).toBe(0.4);
-    expect(result.hiDiffusionT2Ratio).toBe(0.0);
+    expect(result.hiDiffusionT1Ratio).toBeNull();
+    expect(result.hiDiffusionT2Ratio).toBeNull();
     // Existing params should be preserved
     expect(result.positivePrompt).toBe('a fluffy cat');
     expect(result.seed).toBe(42);
@@ -916,6 +917,29 @@ describe('paramsSliceConfig persisted state migration', () => {
     expect(result.dimensions.height).toBe(768);
   });
 
+  it('migrates old HiDiffusion defaults to automatic ratios and preserves custom overrides', () => {
+    expect(migrate).toBeDefined();
+    const initial = getInitialParamsState();
+
+    const oldDefaults = migrate?.({
+      ...initial,
+      _version: 5,
+      hiDiffusionT1Ratio: 0.4,
+      hiDiffusionT2Ratio: 0.0,
+    }) as ReturnType<typeof getInitialParamsState>;
+    const customOverrides = migrate?.({
+      ...initial,
+      _version: 5,
+      hiDiffusionT1Ratio: 0.65,
+      hiDiffusionT2Ratio: 0.25,
+    }) as ReturnType<typeof getInitialParamsState>;
+
+    expect(oldDefaults.hiDiffusionT1Ratio).toBeNull();
+    expect(oldDefaults.hiDiffusionT2Ratio).toBeNull();
+    expect(customOverrides.hiDiffusionT1Ratio).toBe(0.65);
+    expect(customOverrides.hiDiffusionT2Ratio).toBe(0.25);
+  });
+
   it('merges the separate Klein / dev VAE slots into flux2VaeModel when migrating from v3', () => {
     expect(migrate).toBeDefined();
 
@@ -938,7 +962,7 @@ describe('paramsSliceConfig persisted state migration', () => {
 
     const result = migrate?.(v3State) as ReturnType<typeof getInitialParamsState> & Record<string, unknown>;
 
-    expect(result._version).toBe(5);
+    expect(result._version).toBe(6);
     expect((result.flux2VaeModel as { key: string } | null)?.key).toBe('klein-vae');
     // The new standalone dev Mistral encoder slot must be seeded, not left undefined.
     expect(result.flux2DevMistralEncoderModel).toBeNull();
@@ -972,7 +996,7 @@ describe('paramsSliceConfig persisted state migration', () => {
 
     const result = migrate?.(v3State) as ReturnType<typeof getInitialParamsState>;
 
-    expect(result._version).toBe(5);
+    expect(result._version).toBe(6);
     expect(result.krea2VaeModel).toBeNull();
     expect(result.krea2Qwen3VlEncoderModel).toBeNull();
     expect(result.krea2SeedVarianceEnabled).toBe(false);
@@ -1006,7 +1030,7 @@ describe('paramsSliceConfig persisted state migration', () => {
 
     const result = migrate?.(mainV4State) as ReturnType<typeof getInitialParamsState> & Record<string, unknown>;
 
-    expect(result._version).toBe(5);
+    expect(result._version).toBe(6);
     expect((result.flux2VaeModel as { key: string } | null)?.key).toBe('klein-vae');
     expect(result.flux2DevMistralEncoderModel).toBeNull();
     // main's own v4 values must survive untouched.
@@ -1034,7 +1058,7 @@ describe('paramsSliceConfig persisted state migration', () => {
 
     const result = migrate?.(devV4State) as ReturnType<typeof getInitialParamsState> & Record<string, unknown>;
 
-    expect(result._version).toBe(5);
+    expect(result._version).toBe(6);
     // The branch's own v4 values must survive untouched.
     expect((result.flux2VaeModel as { key: string } | null)?.key).toBe('flux2-vae');
     expect(result.pidMode).toBe('off');
@@ -1091,7 +1115,7 @@ describe('paramsSliceConfig persisted state migration', () => {
 
       const result = migrate?.(blob) as ReturnType<typeof getInitialParamsState>;
 
-      expect(result._version).toBe(5);
+      expect(result._version).toBe(6);
       expect(result.positivePrompt).toBe('a fluffy cat');
       expect(result.seed).toBe(42);
       expect(result.shouldRandomizeSeed).toBe(false);
@@ -1139,7 +1163,7 @@ describe('paramsSliceConfig persisted state migration', () => {
 
       expect(
         backfilled,
-        version === getInitialParamsState()._version
+        Number(version) === getInitialParamsState()._version
           ? `Keys missing from a blob written at ${release}, the commit that bumped _version to ${version}. ` +
               `A blob already at the current version matches no branch in the migration chain, so no step can ` +
               `seed these — each needs a zod default, or upgrading throws in zParamsState.parse() and wipes ` +
@@ -1257,7 +1281,7 @@ describe('paramsSliceConfig persisted state migration', () => {
 
     const result = migrate?.(blob) as ReturnType<typeof getInitialParamsState>;
 
-    expect(result._version).toBe(5);
+    expect(result._version).toBe(6);
     expect(result.dimensions).toEqual(getInitialParamsState().dimensions);
     expect(result.positivePrompt).toBe('a fluffy cat');
     expect(result.seed).toBe(7);
@@ -1274,7 +1298,7 @@ describe('paramsSliceConfig persisted state migration', () => {
 
     const result = migrate?.(blob) as ReturnType<typeof getInitialParamsState>;
 
-    expect(result._version).toBe(5);
+    expect(result._version).toBe(6);
     expect(result.positivePromptHistory).toEqual([]);
     expect(result.qwenImageVaeModel).toBeNull();
     expect(result.wanVaeModel).toBeNull();
@@ -1284,18 +1308,18 @@ describe('paramsSliceConfig persisted state migration', () => {
   it('never repairs _version, so version detection cannot be bypassed', () => {
     // `_version` is the input to the version steps, so the net must leave it alone. If it repaired
     // it, any blob whose version is not the current literal — including one written by a *newer*
-    // build — would be silently stamped v5 having run no step, and its stale field values would be
+    // build — would be silently stamped with the current version having run no step, and its stale field values would be
     // accepted as current. Deliberately not routed through migrate(): the version steps normalise
     // `_version` before the net ever sees it, so only calling the net directly tests the guard.
     // The blob is otherwise complete (the current tier's key set), so `_version` is the only thing
     // the parse below can object to.
-    const blob = buildReleaseBlob('f10d2a4f5a', { _version: 6, positivePrompt: 'a fluffy cat' });
+    const blob = buildReleaseBlob('f10d2a4f5a', { _version: 7, positivePrompt: 'a fluffy cat' });
 
     const { backfilled, reset } = repairParamsState(blob);
 
     expect(backfilled).toEqual([]);
     expect(reset).toEqual([]);
-    expect(blob._version).toBe(6);
+    expect(blob._version).toBe(7);
     // Still fatal, which is the correct outcome for a downgrade: that slice really was written by a
     // schema this build does not know.
     expect(() => zParamsState.parse(blob)).toThrow();
@@ -1311,7 +1335,7 @@ describe('paramsSliceConfig persisted state migration', () => {
 
     const result = migrate?.(blob) as ReturnType<typeof getInitialParamsState>;
 
-    expect(result._version).toBe(5);
+    expect(result._version).toBe(6);
     expect(result.positivePrompt).toBe('a fluffy cat');
     expect(result.seed).toBe(7);
     expect(result.dimensions).toBeDefined();
@@ -1328,7 +1352,7 @@ describe('paramsSliceConfig persisted state migration', () => {
 
     const result = migrate?.(v3State) as ReturnType<typeof getInitialParamsState>;
 
-    expect(result._version).toBe(5);
+    expect(result._version).toBe(6);
     expect(result.wanTransformerLowNoise).toBeNull();
     expect(result.wanComponentSource).toBeNull();
     expect(result.wanVaeModel).toBeNull();
@@ -1350,7 +1374,7 @@ describe('paramsSliceConfig persisted state migration', () => {
 
     const result = migrate?.(v2State) as ReturnType<typeof getInitialParamsState>;
 
-    expect(result._version).toBe(5);
+    expect(result._version).toBe(6);
     expect(result.fluxScheduler).toBe('euler');
     expect(result.zImageScheduler).toBe('euler');
     expect(result.colorCompensation).toBe(false);
@@ -1410,7 +1434,7 @@ describe('paramsSliceConfig persisted state migration', () => {
     expect('hiDiffusionEnabled' in blob).toBe(false);
 
     applyParamsVersionMigrations(blob);
-    expect(blob._version).toBe(5);
+    expect(blob._version).toBe(6);
 
     // The value assertions below cannot, on their own, prove the defaults exist: three mechanisms
     // produce the identical values, so any two can hide the third being reverted. Parsing directly
@@ -1435,8 +1459,8 @@ describe('paramsSliceConfig persisted state migration', () => {
     expect(result.hiDiffusionEnabled).toBe(false);
     expect(result.hiDiffusionRauNetEnabled).toBe(true);
     expect(result.hiDiffusionWindowAttnEnabled).toBe(true);
-    expect(result.hiDiffusionT1Ratio).toBe(0.4);
-    expect(result.hiDiffusionT2Ratio).toBe(0.0);
+    expect(result.hiDiffusionT1Ratio).toBeNull();
+    expect(result.hiDiffusionT2Ratio).toBeNull();
     expect(result.positivePrompt).toBe('a fluffy cat');
     expect(result.seed).toBe(42);
   });
diff --git a/invokeai/frontend/web/src/features/controlLayers/store/paramsSlice.ts b/invokeai/frontend/web/src/features/controlLayers/store/paramsSlice.ts
index f7c8420dade..3db8e6b356d 100644
--- a/invokeai/frontend/web/src/features/controlLayers/store/paramsSlice.ts
+++ b/invokeai/frontend/web/src/features/controlLayers/store/paramsSlice.ts
@@ -182,10 +182,10 @@ const slice = createSlice({
     setHiDiffusionWindowAttnEnabled: (state, action: PayloadAction<boolean>) => {
       state.hiDiffusionWindowAttnEnabled = action.payload;
     },
-    setHiDiffusionT1Ratio: (state, action: PayloadAction<number>) => {
+    setHiDiffusionT1Ratio: (state, action: PayloadAction<number | null>) => {
       state.hiDiffusionT1Ratio = action.payload;
     },
-    setHiDiffusionT2Ratio: (state, action: PayloadAction<number>) => {
+    setHiDiffusionT2Ratio: (state, action: PayloadAction<number | null>) => {
       state.hiDiffusionT2Ratio = action.payload;
     },
     setSeamlessXAxis: (state, action: PayloadAction<boolean>) => {
@@ -1162,6 +1162,15 @@ export const applyParamsVersionMigrations = (state: any): void => {
     state.gemma2EncoderModel = state.gemma2EncoderModel ?? null;
     state.pidSteps = state.pidSteps ?? 4;
   }
+
+  if (state._version === 5) {
+    // v5 -> v6: numeric HiDiffusion defaults unintentionally overrode the library's
+    // resolution-aware presets. Treat the old default values as automatic thresholds while
+    // preserving values that users explicitly changed.
+    state._version = 6;
+    state.hiDiffusionT1Ratio = state.hiDiffusionT1Ratio === 0.4 ? null : state.hiDiffusionT1Ratio;
+    state.hiDiffusionT2Ratio = state.hiDiffusionT2Ratio === 0.0 ? null : state.hiDiffusionT2Ratio;
+  }
 };
 
 export const paramsSliceConfig: SliceConfig<typeof slice> = {
diff --git a/invokeai/frontend/web/src/features/controlLayers/store/types.ts b/invokeai/frontend/web/src/features/controlLayers/store/types.ts
index 2143e58f997..b6fff1580f7 100644
--- a/invokeai/frontend/web/src/features/controlLayers/store/types.ts
+++ b/invokeai/frontend/web/src/features/controlLayers/store/types.ts
@@ -817,7 +817,7 @@ const zPidMode = z.enum(['off', 'fit', 'native']);
 export type PidMode = z.infer<typeof zPidMode>;
 
 export const zParamsState = z.object({
-  _version: z.literal(5),
+  _version: z.literal(6),
   maskBlur: z.number(),
   maskBlurMethod: zParameterMaskBlurMethod,
   canvasCoherenceMode: zParameterCanvasCoherenceMode,
@@ -839,8 +839,8 @@ export const zParamsState = z.object({
   hiDiffusionEnabled: z.boolean().default(false),
   hiDiffusionRauNetEnabled: z.boolean().default(true),
   hiDiffusionWindowAttnEnabled: z.boolean().default(true),
-  hiDiffusionT1Ratio: z.number().default(0.4),
-  hiDiffusionT2Ratio: z.number().default(0.0),
+  hiDiffusionT1Ratio: z.number().nullable().default(null),
+  hiDiffusionT2Ratio: z.number().nullable().default(null),
   iterations: z.number(),
   scheduler: zParameterScheduler,
   fluxScheduler: zParameterFluxScheduler,
@@ -965,7 +965,7 @@ export const zParamsState = z.object({
 });
 export type ParamsState = z.infer<typeof zParamsState>;
 export const getInitialParamsState = (): ParamsState => ({
-  _version: 5,
+  _version: 6,
   maskBlur: 16,
   maskBlurMethod: 'box',
   canvasCoherenceMode: 'Gaussian Blur',
@@ -983,8 +983,8 @@ export const getInitialParamsState = (): ParamsState => ({
   hiDiffusionEnabled: false,
   hiDiffusionRauNetEnabled: true,
   hiDiffusionWindowAttnEnabled: true,
-  hiDiffusionT1Ratio: 0.4,
-  hiDiffusionT2Ratio: 0.0,
+  hiDiffusionT1Ratio: null,
+  hiDiffusionT2Ratio: null,
   iterations: 1,
   scheduler: 'dpmpp_3m_k',
   fluxScheduler: 'euler',
diff --git a/invokeai/frontend/web/src/features/metadata/parsing.test.ts b/invokeai/frontend/web/src/features/metadata/parsing.test.ts
index 6072aa1c10f..12c8a96fa9e 100644
--- a/invokeai/frontend/web/src/features/metadata/parsing.test.ts
+++ b/invokeai/frontend/web/src/features/metadata/parsing.test.ts
@@ -1,5 +1,9 @@
 import type { AppStore } from 'app/store/store';
-import { setHiDiffusionEnabled } from 'features/controlLayers/store/paramsSlice';
+import {
+  setHiDiffusionEnabled,
+  setHiDiffusionT1Ratio,
+  setHiDiffusionT2Ratio,
+} from 'features/controlLayers/store/paramsSlice';
 import { describe, expect, it, vi } from 'vitest';
 
 import { ImageMetadataHandlers, MetadataUtils, parseMetadataHandler } from './parsing';
@@ -163,12 +167,31 @@ describe('Qwen metadata parsing', () => {
 });
 
 describe('HiDiffusion metadata parsing', () => {
+  it('recalls null ratios as automatic thresholds', async () => {
+    const store = createStore();
+    const metadata = { hidiffusion_t1_ratio: null, hidiffusion_t2_ratio: null };
+
+    const t1 = await parseMetadataHandler(metadata, ImageMetadataHandlers.HiDiffusionT1Ratio, store);
+    const t2 = await parseMetadataHandler(metadata, ImageMetadataHandlers.HiDiffusionT2Ratio, store);
+    ImageMetadataHandlers.HiDiffusionT1Ratio.recall(t1, store);
+    ImageMetadataHandlers.HiDiffusionT2Ratio.recall(t2, store);
+
+    expect(store.dispatch).toHaveBeenCalledWith(setHiDiffusionT1Ratio(null));
+    expect(store.dispatch).toHaveBeenCalledWith(setHiDiffusionT2Ratio(null));
+  });
+
   it('disables HiDiffusion when recalling all metadata from an older image', async () => {
     let hiDiffusionEnabled = true;
+    let hiDiffusionT1Ratio: number | null = 0.8;
+    let hiDiffusionT2Ratio: number | null = 0.6;
     const store = {
       dispatch: vi.fn((action) => {
         if (action.type === setHiDiffusionEnabled.type) {
           hiDiffusionEnabled = action.payload;
+        } else if (action.type === setHiDiffusionT1Ratio.type) {
+          hiDiffusionT1Ratio = action.payload;
+        } else if (action.type === setHiDiffusionT2Ratio.type) {
+          hiDiffusionT2Ratio = action.payload;
         }
         return action;
       }),
@@ -193,5 +216,7 @@ describe('HiDiffusion metadata parsing', () => {
 
     expect(store.dispatch).toHaveBeenCalledWith(setHiDiffusionEnabled(false));
     expect(hiDiffusionEnabled).toBe(false);
+    expect(hiDiffusionT1Ratio).toBeNull();
+    expect(hiDiffusionT2Ratio).toBeNull();
   });
 });
diff --git a/invokeai/frontend/web/src/features/metadata/parsing.tsx b/invokeai/frontend/web/src/features/metadata/parsing.tsx
index a653afc10b8..0c57a4058d9 100644
--- a/invokeai/frontend/web/src/features/metadata/parsing.tsx
+++ b/invokeai/frontend/web/src/features/metadata/parsing.tsx
@@ -787,12 +787,17 @@ const HiDiffusionWindowAttn: SingleMetadataHandler<boolean> = {
 //#endregion HiDiffusionWindowAttn
 
 //#region HiDiffusionT1Ratio
-const HiDiffusionT1Ratio: SingleMetadataHandler<number> = {
+const HiDiffusionRatioValue = ({ value }: SingleMetadataValueProps<number | null>) => {
+  const { t } = useTranslation();
+  return <MetadataPrimitiveValue value={value ?? t('common.auto')} />;
+};
+
+const HiDiffusionT1Ratio: SingleMetadataHandler<number | null> = {
   [SingleMetadataKey]: true,
   type: 'HiDiffusionT1Ratio',
   parse: (metadata, _store) => {
     const raw = getProperty(metadata, 'hidiffusion_t1_ratio');
-    const parsed = z.number().parse(raw);
+    const parsed = raw === undefined ? null : z.number().nullable().parse(raw);
     return Promise.resolve(parsed);
   },
   recall: (value, store) => {
@@ -800,17 +805,17 @@ const HiDiffusionT1Ratio: SingleMetadataHandler<number> = {
   },
   i18nKey: 'metadata.hiDiffusionT1Ratio',
   LabelComponent: MetadataLabel,
-  ValueComponent: ({ value }: SingleMetadataValueProps<number>) => <MetadataPrimitiveValue value={value} />,
+  ValueComponent: HiDiffusionRatioValue,
 };
 //#endregion HiDiffusionT1Ratio
 
 //#region HiDiffusionT2Ratio
-const HiDiffusionT2Ratio: SingleMetadataHandler<number> = {
+const HiDiffusionT2Ratio: SingleMetadataHandler<number | null> = {
   [SingleMetadataKey]: true,
   type: 'HiDiffusionT2Ratio',
   parse: (metadata, _store) => {
     const raw = getProperty(metadata, 'hidiffusion_t2_ratio');
-    const parsed = z.number().parse(raw);
+    const parsed = raw === undefined ? null : z.number().nullable().parse(raw);
     return Promise.resolve(parsed);
   },
   recall: (value, store) => {
@@ -818,7 +823,7 @@ const HiDiffusionT2Ratio: SingleMetadataHandler<number> = {
   },
   i18nKey: 'metadata.hiDiffusionT2Ratio',
   LabelComponent: MetadataLabel,
-  ValueComponent: ({ value }: SingleMetadataValueProps<number>) => <MetadataPrimitiveValue value={value} />,
+  ValueComponent: HiDiffusionRatioValue,
 };
 //#endregion HiDiffusionT2Ratio
 
diff --git a/invokeai/frontend/web/src/features/nodes/util/graph/generation/addSDXLRefiner.ts b/invokeai/frontend/web/src/features/nodes/util/graph/generation/addSDXLRefiner.ts
index 11a5333c7d2..2f755377df9 100644
--- a/invokeai/frontend/web/src/features/nodes/util/graph/generation/addSDXLRefiner.ts
+++ b/invokeai/frontend/web/src/features/nodes/util/graph/generation/addSDXLRefiner.ts
@@ -65,8 +65,8 @@ export const addSDXLRefiner = async (
     hidiffusion: hiDiffusionEnabled,
     hidiffusion_raunet: hiDiffusionRauNetEnabled,
     hidiffusion_window_attn: hiDiffusionWindowAttnEnabled,
-    hidiffusion_t1_ratio: hiDiffusionEnabled ? hiDiffusionT1Ratio : undefined,
-    hidiffusion_t2_ratio: hiDiffusionEnabled ? hiDiffusionT2Ratio : undefined,
+    hidiffusion_t1_ratio: hiDiffusionEnabled ? (hiDiffusionT1Ratio ?? undefined) : undefined,
+    hidiffusion_t2_ratio: hiDiffusionEnabled ? (hiDiffusionT2Ratio ?? undefined) : undefined,
     denoising_start: refinerStart,
     denoising_end: 1,
   });
diff --git a/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildHiDiffusionGraph.test.ts b/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildHiDiffusionGraph.test.ts
index 8853af97baa..6ed9b077a82 100644
--- a/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildHiDiffusionGraph.test.ts
+++ b/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildHiDiffusionGraph.test.ts
@@ -32,8 +32,8 @@ const defaultParams = {
   cfgRescaleMultiplier: 0,
   hiDiffusionEnabled: false,
   hiDiffusionRauNetEnabled: false,
-  hiDiffusionT1Ratio: 0.25,
-  hiDiffusionT2Ratio: 0.1,
+  hiDiffusionT1Ratio: 0.25 as number | null,
+  hiDiffusionT2Ratio: 0.1 as number | null,
   hiDiffusionWindowAttnEnabled: false,
   scheduler: 'euler',
   steps: 20,
@@ -193,4 +193,21 @@ describe('HiDiffusion graph metadata', () => {
     expect(metadata.hidiffusion_t1_ratio).toBe(0.25);
     expect(metadata.hidiffusion_t2_ratio).toBe(0.1);
   });
+
+  it('omits automatic ratio overrides from the SDXL denoise and metadata nodes', async () => {
+    currentModel = sdxlModel;
+    params.hiDiffusionEnabled = true;
+    params.hiDiffusionRauNetEnabled = true;
+    params.hiDiffusionT1Ratio = null;
+    params.hiDiffusionT2Ratio = null;
+
+    const { g } = await buildSDXLGraph(buildGraphArg());
+    const denoise = g.getNodes().find((node) => node.type === 'denoise_latents');
+    const metadata = getMetadata(g);
+
+    expect(denoise?.hidiffusion_t1_ratio).toBeUndefined();
+    expect(denoise?.hidiffusion_t2_ratio).toBeUndefined();
+    expect(metadata.hidiffusion_t1_ratio).toBeNull();
+    expect(metadata.hidiffusion_t2_ratio).toBeNull();
+  });
 });
diff --git a/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildSD1Graph.ts b/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildSD1Graph.ts
index 2a8d15b1e75..8ac62a08885 100644
--- a/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildSD1Graph.ts
+++ b/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildSD1Graph.ts
@@ -109,8 +109,8 @@ export const buildSD1Graph = async (arg: GraphBuilderArg): Promise<GraphBuilderR
     hidiffusion: hiDiffusionEnabled,
     hidiffusion_raunet: hiDiffusionRauNetEnabled,
     hidiffusion_window_attn: hiDiffusionWindowAttnEnabled,
-    hidiffusion_t1_ratio: hiDiffusionEnabled ? hiDiffusionT1Ratio : undefined,
-    hidiffusion_t2_ratio: hiDiffusionEnabled ? hiDiffusionT2Ratio : undefined,
+    hidiffusion_t1_ratio: hiDiffusionEnabled ? (hiDiffusionT1Ratio ?? undefined) : undefined,
+    hidiffusion_t2_ratio: hiDiffusionEnabled ? (hiDiffusionT2Ratio ?? undefined) : undefined,
     scheduler,
     steps,
     denoising_start: 0,
diff --git a/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildSDXLGraph.ts b/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildSDXLGraph.ts
index e5173f91e52..6c238ed9726 100644
--- a/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildSDXLGraph.ts
+++ b/invokeai/frontend/web/src/features/nodes/util/graph/generation/buildSDXLGraph.ts
@@ -122,8 +122,8 @@ export const buildSDXLGraph = async (arg: GraphBuilderArg): Promise<GraphBuilder
     hidiffusion: hiDiffusionEnabled,
     hidiffusion_raunet: hiDiffusionRauNetEnabled,
     hidiffusion_window_attn: hiDiffusionWindowAttnEnabled,
-    hidiffusion_t1_ratio: hiDiffusionEnabled ? hiDiffusionT1Ratio : undefined,
-    hidiffusion_t2_ratio: hiDiffusionEnabled ? hiDiffusionT2Ratio : undefined,
+    hidiffusion_t1_ratio: hiDiffusionEnabled ? (hiDiffusionT1Ratio ?? undefined) : undefined,
+    hidiffusion_t2_ratio: hiDiffusionEnabled ? (hiDiffusionT2Ratio ?? undefined) : undefined,
     scheduler,
     steps,
   });
diff --git a/invokeai/frontend/web/src/features/parameters/components/Advanced/ParamHiDiffusionToggle.tsx b/invokeai/frontend/web/src/features/parameters/components/Advanced/ParamHiDiffusionToggle.tsx
index 90ab0c6d454..fee69a63977 100644
--- a/invokeai/frontend/web/src/features/parameters/components/Advanced/ParamHiDiffusionToggle.tsx
+++ b/invokeai/frontend/web/src/features/parameters/components/Advanced/ParamHiDiffusionToggle.tsx
@@ -118,23 +118,70 @@ const RATIO_CONSTRAINTS = {
   },
 } as const;
 
+export const ParamHiDiffusionAutoRatiosToggle = memo(() => {
+  const hiDiffusionEnabled = useAppSelector(selectHiDiffusionEnabled);
+  const hiDiffusionRauNetEnabled = useAppSelector(selectHiDiffusionRauNetEnabled);
+  const hiDiffusionT1Ratio = useAppSelector(selectHiDiffusionT1Ratio);
+  const hiDiffusionT2Ratio = useAppSelector(selectHiDiffusionT2Ratio);
+  const dispatch = useAppDispatch();
+  const { t } = useTranslation();
+  const isAutomatic = hiDiffusionT1Ratio === null && hiDiffusionT2Ratio === null;
+
+  const onChange = useCallback(
+    (event: ChangeEvent<HTMLInputElement>) => {
+      if (event.target.checked) {
+        dispatch(setHiDiffusionT1Ratio(null));
+        dispatch(setHiDiffusionT2Ratio(null));
+      } else {
+        dispatch(setHiDiffusionT1Ratio(RATIO_CONSTRAINTS.t1.initial));
+        dispatch(setHiDiffusionT2Ratio(RATIO_CONSTRAINTS.t2.initial));
+      }
+    },
+    [dispatch]
+  );
+
+  return (
+    <FormControl>
+      <InformationalPopover feature="hidiffusionRatiosAuto">
+        <FormLabel maxW="100%" whiteSpace="nowrap" overflow="hidden" textOverflow="ellipsis">
+          {t('parameters.hiDiffusionRatiosAuto')}
+        </FormLabel>
+      </InformationalPopover>
+      <Switch
+        isChecked={isAutomatic}
+        isDisabled={!hiDiffusionEnabled || !hiDiffusionRauNetEnabled}
+        onChange={onChange}
+      />
+    </FormControl>
+  );
+});
+
+ParamHiDiffusionAutoRatiosToggle.displayName = 'ParamHiDiffusionAutoRatiosToggle';
+
 export const ParamHiDiffusionT1Ratio = memo(() => {
   const hiDiffusionEnabled = useAppSelector(selectHiDiffusionEnabled);
+  const hiDiffusionRauNetEnabled = useAppSelector(selectHiDiffusionRauNetEnabled);
   const hiDiffusionT1Ratio = useAppSelector(selectHiDiffusionT1Ratio);
+  const hiDiffusionT2Ratio = useAppSelector(selectHiDiffusionT2Ratio);
   const dispatch = useAppDispatch();
   const { t } = useTranslation();
 
   const onChange = useCallback((value: number) => dispatch(setHiDiffusionT1Ratio(value)), [dispatch]);
 
   return (
-    <FormControl isDisabled={!hiDiffusionEnabled} gridColumn="1 / -1">
+    <FormControl
+      isDisabled={
+        !hiDiffusionEnabled || !hiDiffusionRauNetEnabled || (hiDiffusionT1Ratio === null && hiDiffusionT2Ratio === null)
+      }
+      gridColumn="1 / -1"
+    >
       <InformationalPopover feature="hidiffusionT1Ratio">
         <FormLabel minW="9rem" maxW="100%" whiteSpace="nowrap" overflow="hidden" textOverflow="ellipsis">
           {t('parameters.hiDiffusionT1Ratio')}
         </FormLabel>
       </InformationalPopover>
       <CompositeSlider
-        value={hiDiffusionT1Ratio}
+        value={hiDiffusionT1Ratio ?? RATIO_CONSTRAINTS.t1.initial}
         defaultValue={RATIO_CONSTRAINTS.t1.initial}
         min={RATIO_CONSTRAINTS.t1.sliderMin}
         max={RATIO_CONSTRAINTS.t1.sliderMax}
@@ -144,7 +191,7 @@ export const ParamHiDiffusionT1Ratio = memo(() => {
         marks
       />
       <CompositeNumberInput
-        value={hiDiffusionT1Ratio}
+        value={hiDiffusionT1Ratio ?? RATIO_CONSTRAINTS.t1.initial}
         defaultValue={RATIO_CONSTRAINTS.t1.initial}
         min={RATIO_CONSTRAINTS.t1.numberInputMin}
         max={RATIO_CONSTRAINTS.t1.numberInputMax}
@@ -160,6 +207,8 @@ ParamHiDiffusionT1Ratio.displayName = 'ParamHiDiffusionT1Ratio';
 
 export const ParamHiDiffusionT2Ratio = memo(() => {
   const hiDiffusionEnabled = useAppSelector(selectHiDiffusionEnabled);
+  const hiDiffusionRauNetEnabled = useAppSelector(selectHiDiffusionRauNetEnabled);
+  const hiDiffusionT1Ratio = useAppSelector(selectHiDiffusionT1Ratio);
   const hiDiffusionT2Ratio = useAppSelector(selectHiDiffusionT2Ratio);
   const dispatch = useAppDispatch();
   const { t } = useTranslation();
@@ -167,14 +216,19 @@ export const ParamHiDiffusionT2Ratio = memo(() => {
   const onChange = useCallback((value: number) => dispatch(setHiDiffusionT2Ratio(value)), [dispatch]);
 
   return (
-    <FormControl isDisabled={!hiDiffusionEnabled} gridColumn="1 / -1">
+    <FormControl
+      isDisabled={
+        !hiDiffusionEnabled || !hiDiffusionRauNetEnabled || (hiDiffusionT1Ratio === null && hiDiffusionT2Ratio === null)
+      }
+      gridColumn="1 / -1"
+    >
       <InformationalPopover feature="hidiffusionT2Ratio">
         <FormLabel minW="9rem" maxW="100%" whiteSpace="nowrap" overflow="hidden" textOverflow="ellipsis">
           {t('parameters.hiDiffusionT2Ratio')}
         </FormLabel>
       </InformationalPopover>
       <CompositeSlider
-        value={hiDiffusionT2Ratio}
+        value={hiDiffusionT2Ratio ?? RATIO_CONSTRAINTS.t2.initial}
         defaultValue={RATIO_CONSTRAINTS.t2.initial}
         min={RATIO_CONSTRAINTS.t2.sliderMin}
         max={RATIO_CONSTRAINTS.t2.sliderMax}
@@ -184,7 +238,7 @@ export const ParamHiDiffusionT2Ratio = memo(() => {
         marks
       />
       <CompositeNumberInput
-        value={hiDiffusionT2Ratio}
+        value={hiDiffusionT2Ratio ?? RATIO_CONSTRAINTS.t2.initial}
         defaultValue={RATIO_CONSTRAINTS.t2.initial}
         min={RATIO_CONSTRAINTS.t2.numberInputMin}
         max={RATIO_CONSTRAINTS.t2.numberInputMax}
diff --git a/invokeai/frontend/web/src/features/settingsAccordions/components/AdvancedSettingsAccordion/AdvancedSettingsAccordion.tsx b/invokeai/frontend/web/src/features/settingsAccordions/components/AdvancedSettingsAccordion/AdvancedSettingsAccordion.tsx
index 374822d4058..4635a4618eb 100644
--- a/invokeai/frontend/web/src/features/settingsAccordions/components/AdvancedSettingsAccordion/AdvancedSettingsAccordion.tsx
+++ b/invokeai/frontend/web/src/features/settingsAccordions/components/AdvancedSettingsAccordion/AdvancedSettingsAccordion.tsx
@@ -28,6 +28,7 @@ import ParamClipSkip from 'features/parameters/components/Advanced/ParamClipSkip
 import ParamFlux2DevModelSelect from 'features/parameters/components/Advanced/ParamFlux2DevModelSelect';
 import ParamFlux2KleinModelSelect from 'features/parameters/components/Advanced/ParamFlux2KleinModelSelect';
 import {
+  ParamHiDiffusionAutoRatiosToggle,
   ParamHiDiffusionRauNetToggle,
   ParamHiDiffusionT1Ratio,
   ParamHiDiffusionT2Ratio,
@@ -170,6 +171,7 @@ export const AdvancedSettingsAccordion = memo(() => {
                     <ParamColorCompensation />
                     <ParamHiDiffusionRauNetToggle />
                     <ParamHiDiffusionWindowAttnToggle />
+                    <ParamHiDiffusionAutoRatiosToggle />
                     <ParamHiDiffusionT1Ratio />
                     <ParamHiDiffusionT2Ratio />
                   </SimpleGrid>
diff --git a/invokeai/frontend/web/src/services/api/schema.ts b/invokeai/frontend/web/src/services/api/schema.ts
index c19832d09a1..d2b880fb4ec 100644
--- a/invokeai/frontend/web/src/services/api/schema.ts
+++ b/invokeai/frontend/web/src/services/api/schema.ts
@@ -9338,15 +9338,15 @@ export type components = {
             /**
              * HiDiffusion: T1 Ratio
              * @description Override HiDiffusion early switch threshold (T1 ratio)
-             * @default 0.4
+             * @default null
              */
-            hidiffusion_t1_ratio?: number;
+            hidiffusion_t1_ratio?: number | null;
             /**
              * HiDiffusion: T2 Ratio
              * @description Override HiDiffusion late switch threshold (T2 ratio)
-             * @default 0
+             * @default null
              */
-            hidiffusion_t2_ratio?: number;
+            hidiffusion_t2_ratio?: number | null;
             /**
              * @description Latents tensor
              * @default null
@@ -9486,15 +9486,15 @@ export type components = {
             /**
              * HiDiffusion: T1 Ratio
              * @description Override HiDiffusion early switch threshold (T1 ratio)
-             * @default 0.4
+             * @default null
              */
-            hidiffusion_t1_ratio?: number;
+            hidiffusion_t1_ratio?: number | null;
             /**
              * HiDiffusion: T2 Ratio
              * @description Override HiDiffusion late switch threshold (T2 ratio)
-             * @default 0
+             * @default null
              */
-            hidiffusion_t2_ratio?: number;
+            hidiffusion_t2_ratio?: number | null;
             /**
              * @description Latents tensor
              * @default null
diff --git a/tests/backend/stable_diffusion/test_hidiffusion_utils.py b/tests/backend/stable_diffusion/test_hidiffusion_utils.py
index 5f8619a4882..b7e9bad9130 100644
--- a/tests/backend/stable_diffusion/test_hidiffusion_utils.py
+++ b/tests/backend/stable_diffusion/test_hidiffusion_utils.py
@@ -6,7 +6,10 @@
 import torch
 
 from invokeai.backend.hidiffusion.hidiffusion import (
+    _get_switching_threshold_ratio,
     _resize_controlnet_residual,
+    _should_use_aggressive_raunet,
+    make_diffusers_downsampler_block,
     switching_threshold_ratio_dict,
     text_to_img_controlnet_switching_threshold_ratio_dict,
 )
@@ -256,16 +259,14 @@ def test_hidiffusion_patch_restores_state_when_apply_hidiffusion_raises():
     )
     hook = MagicMock()
 
-    def fake_apply_hidiffusion(patched_model, **_kwargs):
+    def fake_apply_hidiffusion(patched_model, **kwargs):
         assert patched_model._name_or_path == "patched-model-name"
         assert patched_model.config._name_or_path == "patched-model-name"
 
-        first_switching_entry = next(iter(switching_threshold_ratio_dict.values()))
-        first_controlnet_entry = next(iter(text_to_img_controlnet_switching_threshold_ratio_dict.values()))
-        assert first_switching_entry["T1_ratio"] == 0.25
-        assert first_switching_entry["T2_ratio"] == 0.1
-        assert first_controlnet_entry["T1_ratio"] == 0.25
-        assert first_controlnet_entry["T2_ratio"] == 0.1
+        assert kwargs["t1_ratio"] == 0.25
+        assert kwargs["t2_ratio"] == 0.1
+        assert switching_threshold_ratio_dict == original_switching
+        assert text_to_img_controlnet_switching_threshold_ratio_dict == original_controlnet
 
         patched_model.unet.num_upsamplers = 99
         patched_model.unet.layer.info = {"hooks": [hook]}
@@ -369,3 +370,144 @@ def __getattr__(self, name):
             assert config._internal_dict["_name_or_path"] == "patched-model-name"
 
     assert "_name_or_path" not in config._internal_dict
+
+
+def test_hidiffusion_ratio_overrides_are_isolated_between_overlapping_patches():
+    original_switching = copy.deepcopy(switching_threshold_ratio_dict)
+    original_controlnet = copy.deepcopy(text_to_img_controlnet_switching_threshold_ratio_dict)
+    first_model = SimpleNamespace(unet=DummyUNet())
+    second_model = SimpleNamespace(unet=DummyUNet())
+    applied_overrides: list[tuple[object, float | None, float | None]] = []
+
+    def fake_apply_hidiffusion(model, **kwargs):
+        applied_overrides.append((model, kwargs["t1_ratio"], kwargs["t2_ratio"]))
+
+    with (
+        patch("invokeai.backend.hidiffusion.hidiffusion.apply_hidiffusion", side_effect=fake_apply_hidiffusion),
+        patch("invokeai.backend.hidiffusion.hidiffusion.remove_hidiffusion"),
+    ):
+        first_patch = hidiffusion_patch(first_model, name_or_path="first", t1_ratio=0.2, t2_ratio=0.1)
+        second_patch = hidiffusion_patch(second_model, name_or_path="second", t1_ratio=0.8, t2_ratio=0.9)
+        first_patch.__enter__()
+        second_patch.__enter__()
+        first_patch.__exit__(None, None, None)
+        second_patch.__exit__(None, None, None)
+
+    assert applied_overrides == [(first_model, 0.2, 0.1), (second_model, 0.8, 0.9)]
+    assert switching_threshold_ratio_dict == original_switching
+    assert text_to_img_controlnet_switching_threshold_ratio_dict == original_controlnet
+
+
+def test_hidiffusion_patch_forwards_generation_context():
+    model = SimpleNamespace(unet=DummyUNet())
+
+    with (
+        patch("invokeai.backend.hidiffusion.hidiffusion.apply_hidiffusion") as mock_apply_hidiffusion,
+        patch("invokeai.backend.hidiffusion.hidiffusion.remove_hidiffusion"),
+    ):
+        with hidiffusion_patch(
+            model,
+            name_or_path="stabilityai/stable-diffusion-xl-base-1.0",
+            is_inpainting_task=True,
+            use_aggressive_raunet=False,
+            denoising_start=0.6,
+            denoising_end=1.0,
+        ):
+            pass
+
+    kwargs = mock_apply_hidiffusion.call_args.kwargs
+    assert kwargs["is_inpainting_task"] is True
+    assert kwargs["use_aggressive_raunet"] is False
+    assert kwargs["denoising_start"] == 0.6
+    assert kwargs["denoising_end"] == 1.0
+
+
+@pytest.mark.parametrize(
+    ("denoising_start", "denoising_end", "expected_ratio"),
+    [
+        (0.0, 1.0, 0.4),
+        (0.6, 1.0, 0.0),
+        (0.0, 0.2, 1.0),
+        (0.2, 0.6, 0.5),
+    ],
+)
+def test_hidiffusion_ratios_are_mapped_to_the_executed_denoising_range(
+    denoising_start: float, denoising_end: float, expected_ratio: float
+):
+    module = SimpleNamespace(
+        switching_threshold_ratio="T1_ratio",
+        info={
+            "switching_threshold_overrides": {"T1_ratio": None, "T2_ratio": None},
+            "denoising_start": denoising_start,
+            "denoising_end": denoising_end,
+        },
+    )
+
+    ratio = _get_switching_threshold_ratio(module, switching_threshold_ratio_dict, "sdxl_2048")
+
+    assert ratio == pytest.approx(expected_ratio)
+
+
+@pytest.mark.parametrize(
+    ("is_inpainting_task", "override", "expected"),
+    [
+        (False, False, False),
+        (True, True, True),
+        (True, None, False),
+        (False, None, True),
+    ],
+)
+def test_explicit_aggressive_raunet_setting_takes_precedence(
+    is_inpainting_task: bool, override: bool | None, expected: bool
+):
+    module = SimpleNamespace(
+        info={
+            "is_inpainting_task": is_inpainting_task,
+            "is_playground": False,
+            "use_aggressive_raunet": override,
+        }
+    )
+
+    assert _should_use_aggressive_raunet(module) is expected
+
+
+def test_sdxl_t2_override_controls_downsampler_at_2048_resolution():
+    patched_conv = make_diffusers_downsampler_block(torch.nn.Conv2d)
+    hidden_states = torch.arange(64, dtype=torch.float32).reshape(1, 1, 8, 8)
+
+    def run(t2_ratio: float) -> torch.Tensor:
+        module = patched_conv(1, 1, kernel_size=3, stride=2, padding=1, bias=False)
+        torch.nn.init.constant_(module.weight, 1.0)
+        module.info = {
+            "size": (256, 256),
+            "pipeline": SimpleNamespace(_num_timesteps=30),
+            "text_to_img_controlnet": False,
+            "is_inpainting_task": False,
+            "is_playground": False,
+            "switching_threshold_overrides": {"T1_ratio": None, "T2_ratio": t2_ratio},
+        }
+        module.model = "sdxl"
+        module.switching_threshold_ratio = "T2_ratio"
+        return module(hidden_states)
+
+    assert not torch.equal(run(0.0), run(1.0))
+
+
+def test_sdxl_automatic_ratios_preserve_extreme_resolution_preset():
+    patched_conv = make_diffusers_downsampler_block(torch.nn.Conv2d)
+    module = patched_conv(1, 1, kernel_size=3, stride=2, padding=1, bias=False)
+    module.info = {
+        "size": (512, 512),
+        "pipeline": SimpleNamespace(_num_timesteps=30),
+        "text_to_img_controlnet": False,
+        "is_inpainting_task": False,
+        "is_playground": False,
+        "switching_threshold_overrides": {"T1_ratio": None, "T2_ratio": None},
+    }
+    module.model = "sdxl"
+    module.switching_threshold_ratio = "T2_ratio"
+
+    module(torch.ones(1, 1, 8, 8))
+
+    assert module.T1_ratio == 0.3
+    assert module.T1 == 9
diff --git a/tests/backend/stable_diffusion/test_regional_ip_data.py b/tests/backend/stable_diffusion/test_regional_ip_data.py
new file mode 100644
index 00000000000..734fac305e3
--- /dev/null
+++ b/tests/backend/stable_diffusion/test_regional_ip_data.py
@@ -0,0 +1,18 @@
+import torch
+
+from invokeai.backend.stable_diffusion.diffusion.regional_ip_data import RegionalIPData
+
+
+def test_regional_ip_data_supports_hidiffusion_raunet_downscale() -> None:
+    regional_ip_data = RegionalIPData(
+        image_prompt_embeds=[torch.zeros((1, 1, 4, 8))],
+        scales=[1.0],
+        masks=[torch.ones((1, 1, 192, 192))],
+        dtype=torch.float32,
+        device=torch.device("cpu"),
+    )
+
+    masks = regional_ip_data.get_masks(query_seq_len=12 * 12)
+
+    assert masks.shape == (1, 1, 12 * 12, 1)
+    assert torch.count_nonzero(masks) == masks.numel()
diff --git a/tests/backend/stable_diffusion/test_regional_prompt_data.py b/tests/backend/stable_diffusion/test_regional_prompt_data.py
new file mode 100644
index 00000000000..e1990838c2c
--- /dev/null
+++ b/tests/backend/stable_diffusion/test_regional_prompt_data.py
@@ -0,0 +1,21 @@
+import torch
+
+from invokeai.backend.stable_diffusion.diffusion.conditioning_data import Range, TextConditioningRegions
+from invokeai.backend.stable_diffusion.diffusion.regional_prompt_data import RegionalPromptData
+
+
+def test_regional_prompt_data_supports_hidiffusion_raunet_downscale() -> None:
+    regions = TextConditioningRegions(
+        masks=torch.ones((1, 1, 192, 192), dtype=torch.bool),
+        ranges=[Range(start=0, end=4)],
+    )
+    regional_prompt_data = RegionalPromptData(
+        regions=[regions],
+        device=torch.device("cpu"),
+        dtype=torch.float32,
+    )
+
+    attention_mask = regional_prompt_data.get_cross_attn_mask(query_seq_len=12 * 12, key_seq_len=4)
+
+    assert attention_mask.shape == (1, 12 * 12, 4)
+    assert torch.count_nonzero(attention_mask) == 0