diff --git a/invokeai/app/services/model_install/model_install_default.py b/invokeai/app/services/model_install/model_install_default.py
index 53eb6da1688..4c3e5867021 100644
--- a/invokeai/app/services/model_install/model_install_default.py
+++ b/invokeai/app/services/model_install/model_install_default.py
@@ -1377,9 +1377,13 @@ def _multifile_download(
         # subdirectory within the model folder.
 
         if subfolders and len(subfolders) > 1:
-            # Multiple subfolders: create combined name and keep subfolder structure
+            # Multiple subfolders: create combined name and keep subfolder structure. Entries may
+            # also be explicit files (e.g. "modular_model_index.json" or "transformer/config.json");
+            # use their stems in the combined name so it stays a sane directory name.
             top = Path(remote_files[0].path.parts[0])  # e.g. "Z-Image-Turbo/"
-            subfolder_names = [sf.name.replace("/", "_").replace("\\", "_") for sf in subfolders]
+            subfolder_names = [
+                (sf.stem if sf.suffix else sf.name).replace("/", "_").replace("\\", "_") for sf in subfolders
+            ]
             combined_name = "_".join(subfolder_names)
             path_to_add = Path(f"{top}_{combined_name}")
 
@@ -1390,6 +1394,12 @@ def _multifile_download(
                 file_path = model_file.path
                 new_path: Optional[Path] = None
                 for sf in subfolders:
+                    if file_path == top / sf:
+                        # An explicit file entry: keep its repo-relative path so e.g.
+                        # transformer/config.json stays inside transformer/. (relative_to() below
+                        # would return "." here and flatten the file to the model root.)
+                        new_path = path_to_add / sf
+                        break
                     try:
                         # Try to get relative path from this subfolder
                         relative = file_path.relative_to(top / sf)
diff --git a/invokeai/backend/model_manager/configs/main.py b/invokeai/backend/model_manager/configs/main.py
index 83453062619..825a8ef7edc 100644
--- a/invokeai/backend/model_manager/configs/main.py
+++ b/invokeai/backend/model_manager/configs/main.py
@@ -1487,6 +1487,12 @@ class Main_Diffusers_MiniMaxH3_Config(Diffusers_Config_Base, Main_Config_Base, C
 
     base: Literal[BaseModelType.MiniMaxH3] = Field(BaseModelType.MiniMaxH3)
     variant: MiniMaxH3VariantType = Field()
+    components_only: bool = Field(
+        default=False,
+        description="Whether the folder holds only the shared components (tokenizer, processor, VAEs) "
+        "without transformer weights - a slim install whose transformer and text encoder must be "
+        "supplied as single-file overrides at generation time.",
+    )
 
     @classmethod
     def from_model_on_disk(cls, mod: ModelOnDisk, override_fields: dict[str, Any]) -> Self:
@@ -1515,10 +1521,22 @@ def from_model_on_disk(cls, mod: ModelOnDisk, override_fields: dict[str, Any]) -
 
         repo_variant = override_fields.pop("repo_variant", None) or cls._get_repo_variant_or_raise(mod)
 
+        # A slim ("components-only") install carries the transformer's config.json for variant
+        # identification but no weight shards - the transformer and text encoder come from
+        # single-file installs selected in the model loader instead. Record that here so the UI
+        # can require those selections up front rather than failing mid-generation.
+        components_only = override_fields.pop("components_only", None)
+        if components_only is None:
+            transformer_dir = mod.path / "transformer"
+            components_only = not any(
+                any(transformer_dir.glob(pattern)) for pattern in ("*.safetensors", "*.bin", "*.pth", "*.pt", "*.ckpt")
+            )
+
         return cls(
             **override_fields,
             variant=variant,
             repo_variant=repo_variant,
+            components_only=components_only,
         )
 
     @classmethod
diff --git a/invokeai/backend/model_manager/load/model_loaders/minimax_h3.py b/invokeai/backend/model_manager/load/model_loaders/minimax_h3.py
index 273c7d2bf41..da38598519b 100644
--- a/invokeai/backend/model_manager/load/model_loaders/minimax_h3.py
+++ b/invokeai/backend/model_manager/load/model_loaders/minimax_h3.py
@@ -45,6 +45,22 @@
 from invokeai.backend.util.devices import TorchDevice
 
 
+def _raise_if_no_weight_shards(submodel_path: Path, submodel_label: str) -> None:
+    """Fail with an actionable message when a components-only (slim) install is asked for a
+    submodel whose weights it does not carry.
+
+    A slim MiniMax H3 install ships the shared components (tokenizer, processor, VAEs) plus bare
+    config JSONs, so a generic from_pretrained() here would otherwise die on a cryptic
+    missing-shard error deep inside diffusers/transformers.
+    """
+    if not any(any(submodel_path.glob(pattern)) for pattern in ("*.safetensors", "*.bin", "*.pth", "*.pt", "*.ckpt")):
+        raise ValueError(
+            f"This MiniMax H3 model folder has no {submodel_label} weights - it is a components-only "
+            f"(slim) install. In the MiniMax H3 Model Loader, select a single-file {submodel_label} "
+            f"(for example the Comfy-Org int8 release) to use with it."
+        )
+
+
 @ModelLoaderRegistry.register(base=BaseModelType.MiniMaxH3, type=ModelType.Main, format=ModelFormat.Diffusers)
 class MiniMaxH3DiffusersModel(ModelLoader):
     """Loader for MiniMax H3 diffusers-format models (FL2VA)."""
@@ -67,12 +83,16 @@ def _load_model(
 
         match submodel_type:
             case SubModelType.Transformer:
+                _raise_if_no_weight_shards(submodel_path, "transformer")
+
                 from invokeai.backend.minimax_h3 import MiniMaxH3Transformer3DModel
 
                 return MiniMaxH3Transformer3DModel.from_pretrained(
                     submodel_path, torch_dtype=dtype, local_files_only=True
                 )
             case SubModelType.TextEncoder:
+                _raise_if_no_weight_shards(submodel_path, "text encoder")
+
                 from transformers import AutoConfig, Qwen3VLForConditionalGeneration
 
                 te_config = normalize_qwen3vl_rope_config(
diff --git a/invokeai/backend/model_manager/starter_models.py b/invokeai/backend/model_manager/starter_models.py
index 9d8bd97989c..228134077ad 100644
--- a/invokeai/backend/model_manager/starter_models.py
+++ b/invokeai/backend/model_manager/starter_models.py
@@ -1740,25 +1740,53 @@ def _gemini_3_resolution_presets(
 # endregion
 
 # region MiniMax H3 (local)
-# TODO(minimax-h3): no starter entry yet — the installer cannot express a viable download.
-# MiniMax H3 FL2VA lives in huggingface.co/MiniMaxAI/MiniMax-H3 as a root-level Modular
-# Diffusers layout whose probe requires the root `modular_model_index.json`, but the repo also
-# carries the Ref2VA transformer (`transformer_ref/`) and the original remote-code checkpoints
-# (`FL2VA/`, `Ref2VA/`) as siblings:
-#   - a bare `MiniMaxAI/MiniMax-H3` source downloads ~498 GB (every subtree matches
-#     `filter_files`' weight patterns) for a ~42.5 GB working set;
-#   - a `::transformer+text_encoder+tokenizer+processor+vae+audio_vae` subfolder source skips
-#     the root `modular_model_index.json` (`filter_files` keeps only files INSIDE the listed
-#     subfolders), so identification fails and the install lands as an unknown model.
-# Unblocking this needs either (a) `filter_files` learning to include root config JSONs
-# alongside a subfolder set — an installer-wide behavior change that would also alter what
-# existing `::subfolder` sources (e.g. the Wan T5 encoder) download — or (b) an upstream
-# FL2VA-only diffusers repo. Until then, users install by pointing the Model Manager at a
-# locally assembled root-layout folder (see the "MiniMax H3" default workflows' notes).
-# Any future entry must keep "MiniMax H3" verbatim in its name/description (the MiniMax H3
-# Community License requires prominent attribution) and should note the license's territory
-# restrictions (excludes the US, EU, UK and South Korea, extending to outputs), which is why
-# this region is isolated in its own droppable commit.
+# License note: the MiniMax H3 Community License requires prominent "MiniMax H3" attribution
+# (keep it verbatim in every name/description below) and restricts use by territory (excludes
+# the US, EU, UK and South Korea, extending to outputs). These entries live in their own
+# droppable commit so a release can exclude them without touching anything else.
+#
+# The full huggingface.co/MiniMaxAI/MiniMax-H3 repo is ~498 GB (it also carries the Ref2VA
+# transformer and the original remote-code checkpoints). The slim main below downloads only the
+# shared components (tokenizer, processor, video/audio VAEs) plus the two config JSONs that
+# identification needs (~11 GB); the transformer and text encoder come from Comfy-Org's int8
+# single-file repacks, selected in the MiniMax H3 Model Loader. Total ~59 GB.
+
+minimax_h3_components = StarterModel(
+    name="MiniMax H3 Components",
+    base=BaseModelType.MiniMaxH3,
+    source="MiniMaxAI/MiniMax-H3::modular_model_index.json+transformer/config.json+tokenizer+processor+vae+audio_vae",
+    description="MiniMax H3 shared components: tokenizer, processor and video/audio VAEs, without "
+    "transformer or text-encoder weights (~11 GB). Pair with the MiniMax H3 single-file transformer "
+    "and text encoder. NOTE: This model is distributed under a restrictive license that forbids its "
+    "use in certain territories. Please see https://huggingface.co/MiniMaxAI/MiniMax-H3 for details.",
+    type=ModelType.Main,
+    format=ModelFormat.Diffusers,
+)
+
+minimax_h3_int8_text_encoder = StarterModel(
+    name="MiniMax H3 Text Encoder (int8)",
+    base=BaseModelType.MiniMaxH3,
+    source="Comfy-Org/MiniMax-H3::text_encoders/qwen3vl_32b_minimax_h3_int8_convrot.safetensors",
+    description="Truncated Qwen3-VL-32B conditioning encoder for MiniMax H3, int8 quantized (~27 GB). "
+    "Select it in the MiniMax H3 Model Loader's text encoder field. NOTE: This model is distributed "
+    "under a restrictive license that forbids its use in certain territories. Please see "
+    "https://huggingface.co/MiniMaxAI/MiniMax-H3 for details.",
+    type=ModelType.Qwen3VLEncoder,
+    format=ModelFormat.Checkpoint,
+)
+
+minimax_h3_int8_transformer = StarterModel(
+    name="MiniMax H3 FL2VA Transformer (int8, pruned)",
+    base=BaseModelType.MiniMaxH3,
+    source="Comfy-Org/MiniMax-H3::diffusion_models/minimax_h3_fl2va_pruned_int8_convrot.safetensors",
+    description="MiniMax H3 video+audio generation. AdaLN-pruned int8 single-file transformer (~21 GB); "
+    "select it in the MiniMax H3 Model Loader's transformer field. Total size with dependencies: ~59 GB. "
+    "NOTE: This model is distributed under a restrictive license that forbids its use in certain "
+    "territories. Please see https://huggingface.co/MiniMaxAI/MiniMax-H3 for details.",
+    type=ModelType.Main,
+    format=ModelFormat.Checkpoint,
+    dependencies=[minimax_h3_components, minimax_h3_int8_text_encoder],
+)
 # endregion
 
 alibabacloud_wan26_t2i = StarterModel(
@@ -2261,6 +2289,9 @@ def _gemini_3_resolution_presets(
     wan_22_ti2v_5b_diffusers,
     wan_22_ti2v_5b_gguf_q4_k_m,
     wan_22_ti2v_5b_gguf_q8_0,
+    minimax_h3_int8_transformer,
+    minimax_h3_int8_text_encoder,
+    minimax_h3_components,
     gemini_flash_image,
     gemini_pro_image_preview,
     gemini_3_1_flash_image_preview,
@@ -2432,6 +2463,15 @@ def _gemini_3_resolution_presets(
     ideogram_4_nf4,
 ]
 
+# The minimal working set for MiniMax H3 video+audio generation (~59 GB): shared components from
+# the official repo plus Comfy-Org's int8 single-file transformer and text encoder. See the
+# license note in the MiniMax H3 region above.
+minimax_h3_bundle: list[StarterModel] = [
+    minimax_h3_components,
+    minimax_h3_int8_text_encoder,
+    minimax_h3_int8_transformer,
+]
+
 STARTER_BUNDLES: dict[str, StarterModelBundle] = {
     BaseModelType.StableDiffusion1: StarterModelBundle(name="Stable Diffusion 1.5", models=sd1_bundle),
     BaseModelType.StableDiffusionXL: StarterModelBundle(name="SDXL", models=sdxl_bundle),
@@ -2444,6 +2484,7 @@ def _gemini_3_resolution_presets(
     BaseModelType.Krea2: StarterModelBundle(name="Krea-2", models=krea2_bundle),
     "wan_t2v": StarterModelBundle(name="Wan 2.2 Text-to-Video", models=wan_t2v_bundle),
     "wan_i2v": StarterModelBundle(name="Wan 2.2 Image-to-Video", models=wan_i2v_bundle),
+    BaseModelType.MiniMaxH3: StarterModelBundle(name="MiniMax H3", models=minimax_h3_bundle),
     BaseModelType.Ideogram4: StarterModelBundle(name="Ideogram 4", models=ideogram_bundle),
 }
 
diff --git a/invokeai/backend/model_manager/util/select_hf_files.py b/invokeai/backend/model_manager/util/select_hf_files.py
index a8428f4edcd..d4323cf486a 100644
--- a/invokeai/backend/model_manager/util/select_hf_files.py
+++ b/invokeai/backend/model_manager/util/select_hf_files.py
@@ -32,6 +32,10 @@ def filter_files(
     :param files: List of files relative to the repo root.
     :param subfolder: Filter by the indicated subfolder (deprecated, use subfolders instead).
     :param subfolders: Filter by multiple subfolders. Files from any of these subfolders will be included.
+        An entry that names a repo file exactly (e.g. "modular_model_index.json" or
+        "transformer/config.json") is an explicit file request: it is included verbatim, bypassing
+        the extension prefilter, variant scoring and config-only-folder pruning that apply to
+        subfolder contents.
     :param variant: Filter by files belonging to a particular variant, such as fp16.
 
     The file list can be obtained from the `files` field of HuggingFaceMetadata,
@@ -58,6 +62,17 @@ def filter_files(
         if sf.suffix in [".safetensors", ".bin", ".onnx", ".xml", ".pth", ".pt", ".ckpt", ".msgpack"]:
             return [root / sf]
 
+    # In a multi-entry list, entries that name a repo file exactly are explicit requests for that
+    # file. Split them out so they skip every filter below - a slim install can then combine
+    # component subfolders with the root pipeline index and a bare config.json (whose folder the
+    # config-only pruning at the end would otherwise drop).
+    subfolder_filter_requested = bool(filter_subfolders)
+    explicit_files: List[Path] = []
+    if len(filter_subfolders) > 1:
+        file_set = set(files)
+        explicit_files = [sf for sf in filter_subfolders if (root / sf) in file_set]
+        filter_subfolders = [sf for sf in filter_subfolders if sf not in explicit_files]
+
     # Start by filtering on model file extensions, discarding images, docs, etc
     for file in files:
         if file.name.endswith((".json", ".txt", ".jinja")):  # .jinja for chat templates
@@ -81,13 +96,14 @@ def filter_files(
         elif re.search(r"model.*\.(safetensors|bin|onnx|xml|pth|pt|ckpt|msgpack)$", file.name):
             paths.append(file)
 
-    # limit search to subfolder(s) if requested
-    if filter_subfolders:
+    # limit search to subfolder(s) if requested. When every entry was an explicit file, this
+    # correctly reduces the subfolder-derived selection to nothing rather than the whole repo.
+    if subfolder_filter_requested:
         absolute_subfolders = [root / sf for sf in filter_subfolders]
         paths = [x for x in paths if any(Path(sf) in x.parents for sf in absolute_subfolders)]
 
     # _filter_by_variant uniquifies the paths and returns a set
-    return sorted(_filter_by_variant(paths, variant))
+    return sorted(_filter_by_variant(paths, variant) | {root / sf for sf in explicit_files})
 
 
 @dataclass
diff --git a/invokeai/frontend/web/openapi.json b/invokeai/frontend/web/openapi.json
index 16fc1725ec6..6b3edecc750 100644
--- a/invokeai/frontend/web/openapi.json
+++ b/invokeai/frontend/web/openapi.json
@@ -59080,6 +59080,12 @@
           },
           "variant": {
             "$ref": "#/components/schemas/MiniMaxH3VariantType"
+          },
+          "components_only": {
+            "type": "boolean",
+            "title": "Components Only",
+            "description": "Whether the folder holds only the shared components (tokenizer, processor, VAEs) without transformer weights - a slim install whose transformer and text encoder must be supplied as single-file overrides at generation time.",
+            "default": false
           }
         },
         "type": "object",
@@ -59101,7 +59107,8 @@
           "format",
           "repo_variant",
           "base",
-          "variant"
+          "variant",
+          "components_only"
         ],
         "title": "Main_Diffusers_MiniMaxH3_Config",
         "description": "Model config for MiniMax H3 (Hailuo 3.0) diffusers-format models."
diff --git a/invokeai/frontend/web/public/locales/en.json b/invokeai/frontend/web/public/locales/en.json
index dd977dc5d4e..6d4b4acbcba 100644
--- a/invokeai/frontend/web/public/locales/en.json
+++ b/invokeai/frontend/web/public/locales/en.json
@@ -1788,6 +1788,8 @@
             "noQwenImageComponentSourceSelected": "GGUF Qwen Image models require a Diffusers Component Source for VAE/encoder",
             "noWanComponentSourceSelected": "GGUF Wan 2.2 models require a Diffusers Component Source for VAE/encoder",
             "minimaxH3VideoOnGenerateTab": "MiniMax H3 video generation runs on the Generate tab (switch Output to Image to use MiniMax H3 on canvas)",
+            "noMiniMaxH3TransformerModelSelected": "Components-only MiniMax H3 install: select a single-file Transformer in Advanced settings",
+            "noMiniMaxH3TextEncoderModelSelected": "Components-only MiniMax H3 install: select a single-file Text Encoder in Advanced settings",
             "noZImageVaeSourceSelected": "No VAE source: Select VAE (FLUX) or Qwen3 Source model",
             "noZImageQwen3EncoderSourceSelected": "No Qwen3 Encoder source: Select Qwen3 Encoder or Qwen3 Source model",
             "noKrea2VaeModelSelected": "Non-diffusers Krea-2: select a VAE in Advanced settings",
diff --git a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/modelsLoaded.ts b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/modelsLoaded.ts
index f92317a150a..058097a225a 100644
--- a/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/modelsLoaded.ts
+++ b/invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/modelsLoaded.ts
@@ -7,6 +7,8 @@ import {
   fluxVAESelected,
   krea2Qwen3VlEncoderModelSelected,
   krea2VaeModelSelected,
+  minimaxH3TextEncoderModelSelected,
+  minimaxH3TransformerModelSelected,
   modelChanged,
   refinerModelChanged,
   t5EncoderModelSelected,
@@ -84,6 +86,7 @@ export const addModelsLoadedListener = (startAppListening: AppStartListening) =>
 
       handleMainModels(models, state, dispatch, log);
       handleKrea2Components(models, state, dispatch, log);
+      handleMiniMaxH3Overrides(models, state, dispatch, log);
       handleRefinerModels(models, state, dispatch, log);
       handleVAEModels(models, state, dispatch, log);
       handleLoRAModels(models, state, dispatch, log);
@@ -125,6 +128,19 @@ export const handleKrea2Components: ModelHandler = (models, state, dispatch) =>
   }
 };
 
+export const handleMiniMaxH3Overrides: ModelHandler = (models, state, dispatch) => {
+  // The MiniMax H3 single-file transformer / text-encoder overrides are optional (null = use the
+  // main folder's submodels), so never auto-select - but a selection whose model was uninstalled
+  // must be cleared, or it passes the components-only readiness gate and fails at invoke time.
+  const { minimaxH3TransformerModel, minimaxH3TextEncoderModel } = state.params;
+  if (minimaxH3TransformerModel && !models.some((m) => m.key === minimaxH3TransformerModel.key)) {
+    dispatch(minimaxH3TransformerModelSelected(null));
+  }
+  if (minimaxH3TextEncoderModel && !models.some((m) => m.key === minimaxH3TextEncoderModel.key)) {
+    dispatch(minimaxH3TextEncoderModelSelected(null));
+  }
+};
+
 type ModelHandler = (
   models: AnyModelConfig[],
   state: RootState,
diff --git a/invokeai/frontend/web/src/features/parameters/components/MainModel/mainModelPickerUtils.ts b/invokeai/frontend/web/src/features/parameters/components/MainModel/mainModelPickerUtils.ts
index 3c1d83f4bcd..735dd6e65c3 100644
--- a/invokeai/frontend/web/src/features/parameters/components/MainModel/mainModelPickerUtils.ts
+++ b/invokeai/frontend/web/src/features/parameters/components/MainModel/mainModelPickerUtils.ts
@@ -12,3 +12,22 @@ export const isExternalModelUnsupportedForTab = (model: AnyModelConfigWithExtern
 
   return false;
 };
+
+/**
+ * Some type=main configs are not selectable primary models: they fill a secondary slot in a
+ * family's advanced section, and selecting them as the main model always fails at load time.
+ * Every picker that offers main models must filter with this predicate.
+ */
+export const isSecondarySlotMainModelConfig = (c: AnyModelConfigWithExternal): boolean => {
+  // Low-noise Wan GGUFs belong in the Transformer (Low Noise) slot of the Wan advanced section,
+  // not as a primary main - filter them out so users can't accidentally wire them backwards.
+  if (c.type === 'main' && c.base === 'wan' && c.format === 'gguf_quantized' && 'expert' in c && c.expert === 'low') {
+    return true;
+  }
+  // MiniMax H3 single-file transformers belong in the Transformer (single file) slot of the
+  // MiniMax H3 advanced section - they carry no text encoder or VAEs.
+  if (c.type === 'main' && c.base === 'minimax-h3' && c.format === 'checkpoint') {
+    return true;
+  }
+  return false;
+};
diff --git a/invokeai/frontend/web/src/features/queue/store/readiness.ts b/invokeai/frontend/web/src/features/queue/store/readiness.ts
index fd8a74e2c79..6d12b367ed4 100644
--- a/invokeai/frontend/web/src/features/queue/store/readiness.ts
+++ b/invokeai/frontend/web/src/features/queue/store/readiness.ts
@@ -431,6 +431,18 @@ export const getReasonsWhyCannotEnqueueGenerateTab = (arg: {
     }
   }
 
+  if (model?.base === 'minimax-h3' && model.format === 'diffusers' && model.components_only) {
+    // A slim ("components-only") install ships only the shared components (tokenizer, processor,
+    // VAEs) - the transformer and text encoder must come from single-file installs selected in
+    // Advanced settings, or generation fails at model-load time.
+    if (!params.minimaxH3TransformerModel) {
+      reasons.push({ content: i18n.t('parameters.invoke.noMiniMaxH3TransformerModelSelected') });
+    }
+    if (!params.minimaxH3TextEncoderModel) {
+      reasons.push({ content: i18n.t('parameters.invoke.noMiniMaxH3TextEncoderModelSelected') });
+    }
+  }
+
   if (model) {
     for (const lora of loras.filter(({ isEnabled }) => isEnabled === true)) {
       if (model.base !== lora.model.base) {
@@ -1175,6 +1187,18 @@ export const getReasonsWhyCannotEnqueueCanvasTab = (arg: {
     reasons.push({ content: i18n.t('parameters.invoke.minimaxH3VideoOnGenerateTab') });
   }
 
+  if (model?.base === 'minimax-h3' && model.format === 'diffusers' && model.components_only) {
+    // A slim ("components-only") install ships only the shared components (tokenizer, processor,
+    // VAEs) - the transformer and text encoder must come from single-file installs selected in
+    // Advanced settings, or generation fails at model-load time.
+    if (!params.minimaxH3TransformerModel) {
+      reasons.push({ content: i18n.t('parameters.invoke.noMiniMaxH3TransformerModelSelected') });
+    }
+    if (!params.minimaxH3TextEncoderModel) {
+      reasons.push({ content: i18n.t('parameters.invoke.noMiniMaxH3TextEncoderModelSelected') });
+    }
+  }
+
   if (model) {
     for (const lora of loras.filter(({ isEnabled }) => isEnabled === true)) {
       if (model.base !== lora.model.base) {
diff --git a/invokeai/frontend/web/src/features/settingsAccordions/components/GenerationSettingsAccordion/MainModelPicker.tsx b/invokeai/frontend/web/src/features/settingsAccordions/components/GenerationSettingsAccordion/MainModelPicker.tsx
index 57d7ded9836..7baafd98519 100644
--- a/invokeai/frontend/web/src/features/settingsAccordions/components/GenerationSettingsAccordion/MainModelPicker.tsx
+++ b/invokeai/frontend/web/src/features/settingsAccordions/components/GenerationSettingsAccordion/MainModelPicker.tsx
@@ -1,7 +1,10 @@
 import { Flex, FormLabel, Icon } from '@invoke-ai/ui-library';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { InformationalPopover } from 'common/components/InformationalPopover/InformationalPopover';
-import { isExternalModelUnsupportedForTab } from 'features/parameters/components/MainModel/mainModelPickerUtils';
+import {
+  isExternalModelUnsupportedForTab,
+  isSecondarySlotMainModelConfig,
+} from 'features/parameters/components/MainModel/mainModelPickerUtils';
 import { UseDefaultSettingsButton } from 'features/parameters/components/MainModel/UseDefaultSettingsButton';
 import { ModelPicker } from 'features/parameters/components/ModelPicker';
 import { modelSelected } from 'features/parameters/store/actions';
@@ -18,28 +21,8 @@ export const MainModelPicker = memo(() => {
   const dispatch = useAppDispatch();
   const activeTab = useAppSelector(selectActiveTab);
   const [allModelConfigs] = useMainModels();
-  // Low-noise Wan GGUFs belong in the Transformer (Low Noise) slot of the
-  // Wan advanced section, not as a primary main. Filter them out of the main
-  // model dropdown so users can't accidentally wire them backwards.
   const modelConfigs = useMemo(
-    () =>
-      allModelConfigs.filter((c) => {
-        if (
-          c.type === 'main' &&
-          c.base === 'wan' &&
-          c.format === 'gguf_quantized' &&
-          'expert' in c &&
-          c.expert === 'low'
-        ) {
-          return false;
-        }
-        // MiniMax H3 single-file transformers belong in the Transformer (single file) slot of
-        // the MiniMax H3 advanced section - they carry no text encoder or VAEs.
-        if (c.type === 'main' && c.base === 'minimax-h3' && c.format === 'checkpoint') {
-          return false;
-        }
-        return true;
-      }),
+    () => allModelConfigs.filter((c) => !isSecondarySlotMainModelConfig(c)),
     [allModelConfigs]
   );
   const selectedModelConfig = useSelectedModelConfig();
diff --git a/invokeai/frontend/web/src/features/ui/layouts/InitialStateMainModelPicker.tsx b/invokeai/frontend/web/src/features/ui/layouts/InitialStateMainModelPicker.tsx
index 5c5a304a303..e995e0ecd2d 100644
--- a/invokeai/frontend/web/src/features/ui/layouts/InitialStateMainModelPicker.tsx
+++ b/invokeai/frontend/web/src/features/ui/layouts/InitialStateMainModelPicker.tsx
@@ -1,7 +1,10 @@
 import { Flex, FormControl, FormLabel, Icon } from '@invoke-ai/ui-library';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { InformationalPopover } from 'common/components/InformationalPopover/InformationalPopover';
-import { isExternalModelUnsupportedForTab } from 'features/parameters/components/MainModel/mainModelPickerUtils';
+import {
+  isExternalModelUnsupportedForTab,
+  isSecondarySlotMainModelConfig,
+} from 'features/parameters/components/MainModel/mainModelPickerUtils';
 import { ModelPicker } from 'features/parameters/components/ModelPicker';
 import { modelSelected } from 'features/parameters/store/actions';
 import { selectActiveTab } from 'features/ui/store/uiSelectors';
@@ -16,7 +19,11 @@ export const InitialStateMainModelPicker = memo(() => {
   const { t } = useTranslation();
   const dispatch = useAppDispatch();
   const activeTab = useAppSelector(selectActiveTab);
-  const [modelConfigs] = useMainModels();
+  const [allModelConfigs] = useMainModels();
+  const modelConfigs = useMemo(
+    () => allModelConfigs.filter((c) => !isSecondarySlotMainModelConfig(c)),
+    [allModelConfigs]
+  );
   const selectedModelConfig = useSelectedModelConfig();
   const onChange = useCallback(
     (modelConfig: AnyModelConfigWithExternal) => {
diff --git a/invokeai/frontend/web/src/services/api/schema.ts b/invokeai/frontend/web/src/services/api/schema.ts
index 7f882e8dae4..a7999952b67 100644
--- a/invokeai/frontend/web/src/services/api/schema.ts
+++ b/invokeai/frontend/web/src/services/api/schema.ts
@@ -24519,6 +24519,12 @@ export type components = {
              */
             base: "minimax-h3";
             variant: components["schemas"]["MiniMaxH3VariantType"];
+            /**
+             * Components Only
+             * @description Whether the folder holds only the shared components (tokenizer, processor, VAEs) without transformer weights - a slim install whose transformer and text encoder must be supplied as single-file overrides at generation time.
+             * @default false
+             */
+            components_only: boolean;
         };
         /**
          * Main_Diffusers_QwenImage_Config
diff --git a/tests/app/services/model_install/test_model_install.py b/tests/app/services/model_install/test_model_install.py
index 8d94205237e..62d9648d1e9 100644
--- a/tests/app/services/model_install/test_model_install.py
+++ b/tests/app/services/model_install/test_model_install.py
@@ -1027,3 +1027,33 @@ def test_heuristic_import_with_type(mm2_installer: ModelInstallServiceBase, mode
     mm2_installer.wait_for_job(install_job2, timeout=10)
     assert install_job2.complete
     assert install_job2.config_out if model_params["type"] == "embedding" else not install_job2.config_out
+
+
+def test_multifile_download_layout_with_explicit_files(mm2_installer: ModelInstallServiceBase, tmp_path: Path) -> None:
+    """Explicit file entries in a multi-subfolder source keep their repo-relative paths: the root
+    pipeline index lands at the model root and transformer/config.json stays inside transformer/
+    (naive relative_to() matching would flatten it to the root), while plain subfolder entries keep
+    the pre-existing one-directory-per-subfolder layout."""
+    from invokeai.backend.model_manager.metadata.metadata_base import RemoteModelFile
+
+    remote_files = [
+        RemoteModelFile(url="https://example.com/root_index", path=Path("MiniMax-H3/modular_model_index.json")),
+        RemoteModelFile(url="https://example.com/transformer_config", path=Path("MiniMax-H3/transformer/config.json")),
+        RemoteModelFile(url="https://example.com/vae_config", path=Path("MiniMax-H3/vae/config.json")),
+        RemoteModelFile(
+            url="https://example.com/vae_weights", path=Path("MiniMax-H3/vae/diffusion_pytorch_model.safetensors")
+        ),
+    ]
+    job = mm2_installer._multifile_download(  # pyright: ignore[reportAttributeAccessIssue]
+        remote_files=remote_files,
+        dest=tmp_path,
+        subfolders=[Path("modular_model_index.json"), Path("transformer/config.json"), Path("vae")],
+        submit_job=False,
+    )
+    top = Path("MiniMax-H3_modular_model_index_config_vae")
+    assert {part.dest.relative_to(tmp_path.resolve()) for part in job.download_parts} == {
+        top / "modular_model_index.json",
+        top / "transformer" / "config.json",
+        top / "vae" / "config.json",
+        top / "vae" / "diffusion_pytorch_model.safetensors",
+    }
diff --git a/tests/backend/model_manager/load/test_minimax_h3_loader_guards.py b/tests/backend/model_manager/load/test_minimax_h3_loader_guards.py
new file mode 100644
index 00000000000..ec5ccd2f3e3
--- /dev/null
+++ b/tests/backend/model_manager/load/test_minimax_h3_loader_guards.py
@@ -0,0 +1,65 @@
+"""Boundary tests for the MiniMax H3 diffusers loader's components-only guards.
+
+A slim ("components-only") install carries tokenizer/processor/VAEs plus bare config JSONs, but no
+transformer or text-encoder weights - those come from single-file installs selected in the model
+loader node. Requesting the missing submodels must fail with an actionable message instead of a
+cryptic missing-shard error from inside diffusers/transformers.
+"""
+
+from pathlib import Path
+
+import pytest
+import torch
+
+from invokeai.backend.model_manager.configs.main import Main_Diffusers_MiniMaxH3_Config
+from invokeai.backend.model_manager.load.model_loaders.minimax_h3 import MiniMaxH3DiffusersModel
+from invokeai.backend.model_manager.taxonomy import SubModelType
+
+
+@pytest.fixture
+def slim_model_dir(tmp_path: Path) -> Path:
+    (tmp_path / "transformer").mkdir()
+    (tmp_path / "transformer" / "config.json").write_text("{}")
+    # No text_encoder directory at all - the slim source does not download one.
+    return tmp_path
+
+
+@pytest.fixture
+def loader(monkeypatch) -> MiniMaxH3DiffusersModel:
+    monkeypatch.setattr(
+        "invokeai.backend.model_manager.load.model_loaders.minimax_h3.TorchDevice.choose_torch_device",
+        lambda: torch.device("cpu"),
+    )
+    monkeypatch.setattr(
+        "invokeai.backend.model_manager.load.model_loaders.minimax_h3.TorchDevice.choose_bfloat16_safe_dtype",
+        lambda _device: torch.float32,
+    )
+    return object.__new__(MiniMaxH3DiffusersModel)
+
+
+@pytest.mark.parametrize("submodel_type", [SubModelType.Transformer, SubModelType.TextEncoder])
+def test_components_only_install_raises_actionable_error(
+    loader: MiniMaxH3DiffusersModel, slim_model_dir: Path, submodel_type: SubModelType
+) -> None:
+    config = Main_Diffusers_MiniMaxH3_Config.model_construct(path=str(slim_model_dir), components_only=True)
+
+    with pytest.raises(ValueError, match="components-only"):
+        loader._load_model(config, submodel_type)
+
+
+def test_transformer_with_weight_shards_passes_guard(
+    loader: MiniMaxH3DiffusersModel, slim_model_dir: Path, monkeypatch
+) -> None:
+    (slim_model_dir / "transformer" / "diffusion_pytorch_model-00001-of-00002.safetensors").write_bytes(b"")
+
+    import invokeai.backend.minimax_h3 as minimax_h3_module
+
+    sentinel = object()
+    monkeypatch.setattr(
+        minimax_h3_module.MiniMaxH3Transformer3DModel,
+        "from_pretrained",
+        classmethod(lambda _cls, *_args, **_kwargs: sentinel),
+    )
+
+    config = Main_Diffusers_MiniMaxH3_Config.model_construct(path=str(slim_model_dir))
+    assert loader._load_model(config, SubModelType.Transformer) is sentinel
diff --git a/tests/backend/model_manager/util/test_hf_model_select.py b/tests/backend/model_manager/util/test_hf_model_select.py
index 5530fa0c90e..65ad8d42c57 100644
--- a/tests/backend/model_manager/util/test_hf_model_select.py
+++ b/tests/backend/model_manager/util/test_hf_model_select.py
@@ -403,3 +403,152 @@ def test_select_flux_schnell_files(
 ) -> None:
     filtered_files = filter_files(flux_schnell_test_files, variant)
     assert set(filtered_files) == {Path(f) for f in expected_files}
+
+
+# A subset of huggingface.co/MiniMaxAI/MiniMax-H3: a root-level Modular Diffusers pipeline whose
+# repo also carries a sibling task transformer (transformer_ref) that a slim install must be able
+# to skip while still fetching the root pipeline index and the transformer's bare config.json.
+@pytest.fixture
+def minimax_h3_test_files() -> list[Path]:
+    return [
+        Path(f)
+        for f in [
+            "MiniMax-H3/.gitattributes",
+            "MiniMax-H3/LICENSE",
+            "MiniMax-H3/README.md",
+            "MiniMax-H3/model_index.json",
+            "MiniMax-H3/modular_model_index.json",
+            "MiniMax-H3/audio_scheduler/scheduler_config.json",
+            "MiniMax-H3/audio_vae/config.json",
+            "MiniMax-H3/audio_vae/diffusion_pytorch_model.safetensors",
+            "MiniMax-H3/processor/chat_template.json",
+            "MiniMax-H3/processor/preprocessor_config.json",
+            "MiniMax-H3/processor/tokenizer.json",
+            "MiniMax-H3/scheduler/scheduler_config.json",
+            "MiniMax-H3/text_encoder/config.json",
+            "MiniMax-H3/text_encoder/model-00001-of-00002.safetensors",
+            "MiniMax-H3/text_encoder/model-00002-of-00002.safetensors",
+            "MiniMax-H3/text_encoder/model.safetensors.index.json",
+            "MiniMax-H3/tokenizer/merges.txt",
+            "MiniMax-H3/tokenizer/tokenizer_config.json",
+            "MiniMax-H3/tokenizer/vocab.json",
+            "MiniMax-H3/transformer/config.json",
+            "MiniMax-H3/transformer/diffusion_pytorch_model-00001-of-00002.safetensors",
+            "MiniMax-H3/transformer/diffusion_pytorch_model-00002-of-00002.safetensors",
+            "MiniMax-H3/transformer/diffusion_pytorch_model.safetensors.index.json",
+            "MiniMax-H3/transformer_ref/config.json",
+            "MiniMax-H3/transformer_ref/diffusion_pytorch_model-00001-of-00002.safetensors",
+            "MiniMax-H3/transformer_ref/diffusion_pytorch_model-00002-of-00002.safetensors",
+            "MiniMax-H3/vae/config.json",
+            "MiniMax-H3/vae/diffusion_pytorch_model-00001-of-00003.safetensors",
+            "MiniMax-H3/vae/diffusion_pytorch_model-00002-of-00003.safetensors",
+            "MiniMax-H3/vae/diffusion_pytorch_model-00003-of-00003.safetensors",
+            "MiniMax-H3/vae/diffusion_pytorch_model.safetensors.index.json",
+        ]
+    ]
+
+
+def test_select_subfolders_with_explicit_files(minimax_h3_test_files: list[Path]) -> None:
+    """Explicit file entries ride alongside subfolder entries: the root pipeline index and the
+    transformer's bare config.json are included verbatim (the latter would otherwise be dropped by
+    the config-only-folder pruning), while the transformer weights stay excluded."""
+    filtered_files = filter_files(
+        minimax_h3_test_files,
+        subfolders=[
+            Path("modular_model_index.json"),
+            Path("transformer/config.json"),
+            Path("tokenizer"),
+            Path("processor"),
+            Path("vae"),
+            Path("audio_vae"),
+        ],
+    )
+    assert set(filtered_files) == {
+        Path(f)
+        for f in [
+            "MiniMax-H3/modular_model_index.json",
+            "MiniMax-H3/transformer/config.json",
+            "MiniMax-H3/tokenizer/merges.txt",
+            "MiniMax-H3/tokenizer/tokenizer_config.json",
+            "MiniMax-H3/tokenizer/vocab.json",
+            "MiniMax-H3/processor/chat_template.json",
+            "MiniMax-H3/processor/preprocessor_config.json",
+            "MiniMax-H3/processor/tokenizer.json",
+            "MiniMax-H3/vae/config.json",
+            "MiniMax-H3/vae/diffusion_pytorch_model-00001-of-00003.safetensors",
+            "MiniMax-H3/vae/diffusion_pytorch_model-00002-of-00003.safetensors",
+            "MiniMax-H3/vae/diffusion_pytorch_model-00003-of-00003.safetensors",
+            "MiniMax-H3/vae/diffusion_pytorch_model.safetensors.index.json",
+            "MiniMax-H3/audio_vae/config.json",
+            "MiniMax-H3/audio_vae/diffusion_pytorch_model.safetensors",
+        ]
+    }
+
+
+def test_select_explicit_files_only(minimax_h3_test_files: list[Path]) -> None:
+    """A list made up solely of explicit files selects exactly those files - not the whole repo."""
+    filtered_files = filter_files(
+        minimax_h3_test_files,
+        subfolders=[Path("modular_model_index.json"), Path("transformer/config.json")],
+    )
+    assert set(filtered_files) == {
+        Path("MiniMax-H3/modular_model_index.json"),
+        Path("MiniMax-H3/transformer/config.json"),
+    }
+
+
+def test_select_explicit_weights_file_bypasses_name_prefilter() -> None:
+    """An explicit weights-file entry is included even when its name would fail the 'model'
+    naming-convention prefilter that subfolder contents are subject to."""
+    files = [
+        Path(f)
+        for f in [
+            "Repo/README.md",
+            "Repo/text_encoders/foo_int8_convrot.safetensors",
+            "Repo/vae/config.json",
+            "Repo/vae/diffusion_pytorch_model.safetensors",
+        ]
+    ]
+    filtered_files = filter_files(
+        files,
+        subfolders=[Path("text_encoders/foo_int8_convrot.safetensors"), Path("vae")],
+    )
+    assert set(filtered_files) == {
+        Path("Repo/text_encoders/foo_int8_convrot.safetensors"),
+        Path("Repo/vae/config.json"),
+        Path("Repo/vae/diffusion_pytorch_model.safetensors"),
+    }
+
+
+def test_select_nonexistent_entry_selects_nothing(minimax_h3_test_files: list[Path]) -> None:
+    """An entry matching neither a file nor a folder contributes nothing (and does not disable
+    the subfolder filtering for the remaining entries)."""
+    filtered_files = filter_files(
+        minimax_h3_test_files,
+        subfolders=[Path("no_such_entry.json"), Path("audio_vae")],
+    )
+    assert set(filtered_files) == {
+        Path("MiniMax-H3/audio_vae/config.json"),
+        Path("MiniMax-H3/audio_vae/diffusion_pytorch_model.safetensors"),
+    }
+
+
+def test_select_multiple_plain_subfolders_unchanged(minimax_h3_test_files: list[Path]) -> None:
+    """Regression: a pure-folder multi-subfolder list (the pre-existing '+' syntax) behaves as
+    before - explicit-file handling must not alter it."""
+    filtered_files = filter_files(
+        minimax_h3_test_files,
+        subfolders=[Path("text_encoder"), Path("tokenizer")],
+    )
+    assert set(filtered_files) == {
+        Path(f)
+        for f in [
+            "MiniMax-H3/text_encoder/config.json",
+            "MiniMax-H3/text_encoder/model-00001-of-00002.safetensors",
+            "MiniMax-H3/text_encoder/model-00002-of-00002.safetensors",
+            "MiniMax-H3/text_encoder/model.safetensors.index.json",
+            "MiniMax-H3/tokenizer/merges.txt",
+            "MiniMax-H3/tokenizer/tokenizer_config.json",
+            "MiniMax-H3/tokenizer/vocab.json",
+        ]
+    }
diff --git a/tests/model_identification/stripped_models/6f4fe53b-31e2-45dd-83da-2753fa1b4c08/__test_metadata__.json b/tests/model_identification/stripped_models/6f4fe53b-31e2-45dd-83da-2753fa1b4c08/__test_metadata__.json
index b2fd9454b76..ab6bbe5ad2a 100644
--- a/tests/model_identification/stripped_models/6f4fe53b-31e2-45dd-83da-2753fa1b4c08/__test_metadata__.json
+++ b/tests/model_identification/stripped_models/6f4fe53b-31e2-45dd-83da-2753fa1b4c08/__test_metadata__.json
@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:b2b02183348847b4b2def482b47bae45ebe047748646b1dcce38073ba5c18acd
-size 168
+oid sha256:538453d68843ac126b37da27ffbbbc332ef1d1364af8fb7f2de525dc4cc0d8b9
+size 199
diff --git a/tests/model_identification/stripped_models/e8530bdb-7d05-433f-81ed-e7a2853ad26f/__test_metadata__.json b/tests/model_identification/stripped_models/e8530bdb-7d05-433f-81ed-e7a2853ad26f/__test_metadata__.json
new file mode 100644
index 00000000000..b9d7e02aec2
--- /dev/null
+++ b/tests/model_identification/stripped_models/e8530bdb-7d05-433f-81ed-e7a2853ad26f/__test_metadata__.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b48d92dca1dafc9616d0a2465c7c7866f76db3a8bfa8496bee1ef3a02646d376
+size 394
diff --git a/tests/model_identification/stripped_models/e8530bdb-7d05-433f-81ed-e7a2853ad26f/audio_vae/config.json b/tests/model_identification/stripped_models/e8530bdb-7d05-433f-81ed-e7a2853ad26f/audio_vae/config.json
new file mode 100644
index 00000000000..83b0d085b96
--- /dev/null
+++ b/tests/model_identification/stripped_models/e8530bdb-7d05-433f-81ed-e7a2853ad26f/audio_vae/config.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9a3c645ff892b376c6f5f4c8685964cd75474731af594ff058492a0000caabb6
+size 2271
diff --git a/tests/model_identification/stripped_models/e8530bdb-7d05-433f-81ed-e7a2853ad26f/audio_vae/model.safetensors b/tests/model_identification/stripped_models/e8530bdb-7d05-433f-81ed-e7a2853ad26f/audio_vae/model.safetensors
new file mode 100644
index 00000000000..237b961654c
--- /dev/null
+++ b/tests/model_identification/stripped_models/e8530bdb-7d05-433f-81ed-e7a2853ad26f/audio_vae/model.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fb2e2a2f7686fd2e45fa37dd632d66cdf9f4274d888b842fb6fa7ee01776a819
+size 95
diff --git a/tests/model_identification/stripped_models/e8530bdb-7d05-433f-81ed-e7a2853ad26f/modular_model_index.json b/tests/model_identification/stripped_models/e8530bdb-7d05-433f-81ed-e7a2853ad26f/modular_model_index.json
new file mode 100644
index 00000000000..20c48eddc34
--- /dev/null
+++ b/tests/model_identification/stripped_models/e8530bdb-7d05-433f-81ed-e7a2853ad26f/modular_model_index.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a2b6a210e482ffb78e613b553f570c44e101afce6741bd4ed91429d0559af031
+size 2935
diff --git a/tests/model_identification/stripped_models/e8530bdb-7d05-433f-81ed-e7a2853ad26f/processor/preprocessor_config.json b/tests/model_identification/stripped_models/e8530bdb-7d05-433f-81ed-e7a2853ad26f/processor/preprocessor_config.json
new file mode 100644
index 00000000000..e7a1091ec59
--- /dev/null
+++ b/tests/model_identification/stripped_models/e8530bdb-7d05-433f-81ed-e7a2853ad26f/processor/preprocessor_config.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:27225450ac9c6529872ee1924fcb0962ff5634834f817040f444118116f4e516
+size 390
diff --git a/tests/model_identification/stripped_models/e8530bdb-7d05-433f-81ed-e7a2853ad26f/processor/video_preprocessor_config.json b/tests/model_identification/stripped_models/e8530bdb-7d05-433f-81ed-e7a2853ad26f/processor/video_preprocessor_config.json
new file mode 100644
index 00000000000..32579be08bc
--- /dev/null
+++ b/tests/model_identification/stripped_models/e8530bdb-7d05-433f-81ed-e7a2853ad26f/processor/video_preprocessor_config.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7768af27c1fafa9cc9011c1dc20067e03f8915e03b63504550e11d5066986d13
+size 385
diff --git a/tests/model_identification/stripped_models/e8530bdb-7d05-433f-81ed-e7a2853ad26f/tokenizer/tokenizer_config.json b/tests/model_identification/stripped_models/e8530bdb-7d05-433f-81ed-e7a2853ad26f/tokenizer/tokenizer_config.json
new file mode 100644
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