[diffusion] feat: generalize layerwise offload residency mixin to all components (#24593)

docs/diffusion/api/cli.md

@@ -160,6 +160,21 @@ HTTP server-only arguments are ignored by `sglang generate`.
 
 For diffusers pipelines, Cache-DiT can be enabled with `SGLANG_CACHE_DIT_ENABLED=true` or `--cache-dit-config`. See [Cache-DiT](../performance/cache/cache_dit.md).
 
+### Layerwise Offload
+
+Use layerwise offload when a large component does not fit comfortably in GPU memory. By default, `--dit-layerwise-offload` only applies to legacy DiT components. Use `--layerwise-offload-components` to select pipeline component names explicitly (`--layerwise-offload-modules` is accepted as an alias):
+
+```bash
+sglang generate \
+  --model-path Wan-AI/Wan2.2-T2V-A14B-Diffusers \
+  --dit-layerwise-offload \
+  --layerwise-offload-components transformer text_encoder \
+  --dit-offload-prefetch-size 0 \
+  --prompt "A quiet city street after rain"
+```
+
+The values must match keys in the selected pipeline's `pipeline.modules`, such as `transformer`, `text_encoder`, `image_encoder`, `vae`, `condition_image_encoder`, `spatial_upsampler`, or `vocoder`. Use `all` to select every layerwise-offloadable component. Prefer the smallest component set that solves the memory issue because layerwise offload can increase latency.
+
 ## Serve
 
 `sglang serve` starts the HTTP server and keeps the model loaded for repeated requests.

python/sglang/multimodal_gen/configs/pipeline_configs/base.py

@@ -230,6 +230,7 @@ class PipelineConfig:
     # Image encoder configuration
     image_encoder_config: EncoderConfig = field(default_factory=EncoderConfig)
     image_encoder_precision: str = "fp32"
+    image_encoder_extra_args: dict = field(default_factory=lambda: {})
 
     # Text encoder configuration
     DEFAULT_TEXT_ENCODER_PRECISIONS = ("fp32",)
@@ -240,9 +241,6 @@ class PipelineConfig:
     text_encoder_precisions: tuple[str, ...] = field(default_factory=lambda: ("fp32",))
     text_encoder_extra_args: list[dict] = field(default_factory=lambda: [{}])
 
-    # image encoding
-    image_encoder_extra_args: dict = field(default_factory=lambda: {})
-
     def get_model_deployment_config(self) -> ModelDeploymentConfig:
         return ModelDeploymentConfig()
 
@@ -266,6 +264,10 @@ class PipelineConfig:
     # DMD parameters
     dmd_denoising_steps: list[int] | None = field(default=None)
 
+    def get_model_deployment_config(self) -> ModelDeploymentConfig:
+        # return the model-specific config for optimal deployment setting
+        return ModelDeploymentConfig()
+
     # Wan2.2 TI2V parameters
     boundary_ratio: float | None = None
 

python/sglang/multimodal_gen/configs/pipeline_configs/wan.py

@@ -88,8 +88,6 @@ class WanT2V480PConfig(PipelineConfig):
     vae_precision: str = "fp32"
     text_encoder_precisions: tuple[str, ...] = field(default_factory=lambda: ("fp32",))
 
-    # WanConfig-specific added parameters
-
     def __post_init__(self):
         self.vae_config.load_encoder = False
         self.vae_config.load_decoder = True

python/sglang/multimodal_gen/runtime/loader/component_loaders/component_loader.py

@@ -25,6 +25,15 @@ from sglang.multimodal_gen.runtime.loader.utils import (
     component_name_to_loader_cls,
     get_memory_usage_of_component,
 )
+from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload import (
+    configure_layerwise_offload_modules,
+    is_layerwise_offloaded_module,
+)
+from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload_components import (
+    LAYERWISE_OFFLOAD_ALL_COMPONENTS,
+    LAYERWISE_OFFLOAD_DEFAULT_COMPONENTS,
+    layerwise_component_matches_selection,
+)
 from sglang.multimodal_gen.runtime.platforms import current_platform
 from sglang.multimodal_gen.runtime.server_args import ServerArgs
 from sglang.multimodal_gen.runtime.utils.hf_diffusers_utils import (
@@ -96,6 +105,66 @@ class ComponentLoader(ABC):
         else:
             return get_local_torch_device()
 
+    def customized_load_kwargs_for_component(
+        self, _server_args: ServerArgs, _component_name: str
+    ) -> dict[str, Any]:
+        return {}
+
+    @staticmethod
+    def _is_component_set_as_layerwise_load(
+        server_args: ServerArgs, component_name: str
+    ) -> bool:
+        """if a component should be loaded in a layerwise-fashion"""
+        selected_component_names = server_args.layerwise_offload_components
+        if selected_component_names is None:
+            return False
+        selected_component_names = set(selected_component_names)
+        if LAYERWISE_OFFLOAD_ALL_COMPONENTS in selected_component_names:
+            return True
+        explicit_component_names = selected_component_names - {
+            LAYERWISE_OFFLOAD_DEFAULT_COMPONENTS
+        }
+        return any(
+            layerwise_component_matches_selection(component_name, selected_component)
+            for selected_component in explicit_component_names
+        )
+
+    def _maybe_configure_layerwise_after_startup_cpu_staging(
+        self,
+        component: AutoModel,
+        server_args: ServerArgs,
+        component_name: str,
+        load_kwargs: dict[str, Any],
+    ) -> AutoModel:
+        if not load_kwargs.get("cpu_offload_flag"):
+            return component
+        if not isinstance(component, nn.Module):
+            return component
+
+        # try to configure layerwise-offload with the component
+        configured_components = configure_layerwise_offload_modules(
+            {component_name: component},
+            server_args,
+            component_names=server_args.layerwise_offload_components,
+            warn_missing=False,
+        )
+        if is_layerwise_offloaded_module(component):
+            logger.info(
+                "Configured layerwise offload for %s immediately after startup CPU staging",
+                component_name,
+            )
+            return component
+
+        logger.warning(
+            "Layerwise startup CPU staging was requested for %s, but the loaded "
+            "module did not enable layerwise offload. Moving it to GPU.",
+            component_name,
+        )
+        # ensures the module is on GPU
+        if component_name in configured_components:
+            return component
+        return component.to(get_local_torch_device())
+
     def load(
         self,
         component_model_path: str,
@@ -135,8 +204,15 @@ class ComponentLoader(ABC):
             with component_attn_backend_context_manager(
                 attn_backend, component_name=component_attn_name
             ):
+                load_kwargs = self.customized_load_kwargs_for_component(
+                    server_args, component_name
+                )
                 component = self.load_customized(
-                    component_model_path, server_args, component_name
+                    component_model_path, server_args, component_name, **load_kwargs
+                )
+                # configure layerwise to make enough VRAM headroom
+                component = self._maybe_configure_layerwise_after_startup_cpu_staging(
+                    component, server_args, component_name, load_kwargs
                 )
             source = "sgl-diffusion"
         except Exception as e:

python/sglang/multimodal_gen/runtime/loader/component_loaders/image_encoder_loader.py

@@ -30,7 +30,11 @@ class ImageEncoderLoader(TextEncoderLoader):
         return use_cpu_offload
 
     def load_customized(
-        self, component_model_path: str, server_args: ServerArgs, *args
+        self,
+        component_model_path: str,
+        server_args: ServerArgs,
+        component_name: str = "image_encoder",
+        cpu_offload_flag: bool | None = None,
     ):
         """Load the text encoders based on the model path, and inference args."""
         # model_config: PretrainedConfig = get_hf_config(
@@ -53,5 +57,9 @@ class ImageEncoderLoader(TextEncoderLoader):
             encoder_config,
             server_args,
             server_args.pipeline_config.image_encoder_precision,
-            cpu_offload_flag=server_args.image_encoder_cpu_offload,
+            cpu_offload_flag=(
+                cpu_offload_flag
+                if cpu_offload_flag is not None
+                else server_args.image_encoder_cpu_offload
+            ),
         )

python/sglang/multimodal_gen/runtime/loader/component_loaders/text_encoder_loader.py

@@ -81,6 +81,19 @@ class TextEncoderLoader(ComponentLoader):
         use_cpu_offload = should_offload and len(fsdp_shard_conditions) > 0
         return use_cpu_offload
 
+    def customized_load_kwargs_for_component(
+        self, server_args: ServerArgs, component_name: str
+    ) -> dict[str, bool]:
+        if ComponentLoader._is_component_set_as_layerwise_load(
+            server_args, component_name
+        ):
+            logger.info(
+                "Loading %s on CPU first because it is selected for layerwise offload",
+                component_name,
+            )
+            return {"cpu_offload_flag": True}
+        return {}
+
     def load_native(
         self,
         component_model_path: str,

python/sglang/multimodal_gen/runtime/loader/weights_updater.py

@@ -53,7 +53,9 @@ from sglang.multimodal_gen.runtime.loader.utils import (
 from sglang.multimodal_gen.runtime.loader.weight_utils import (
     safetensors_weights_iterator,
 )
-from sglang.multimodal_gen.runtime.managers.layerwise_offload import OffloadableDiTMixin
+from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload import (
+    is_layerwise_offloaded_module,
+)
 from sglang.multimodal_gen.runtime.pipelines.diffusers_pipeline import DiffusersPipeline
 from sglang.multimodal_gen.runtime.utils.hf_diffusers_utils import maybe_download_model
 from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
@@ -114,7 +116,7 @@ def _load_weights_into_module(module: torch.nn.Module, weights_iter) -> None:
     update_cpu_weights(); non-offloaded parameters use in-place copy.
     """
     offload_managers: list = []
-    if isinstance(module, OffloadableDiTMixin) and module.layerwise_offload_managers:
+    if is_layerwise_offloaded_module(module):
         offload_managers = [m for m in module.layerwise_offload_managers if m.enabled]
 
     if offload_managers:

python/sglang/multimodal_gen/runtime/managers/dynamic_batch_admission.py

@@ -160,7 +160,7 @@ class BatchAdmissionController:
         self._mode = getattr(server_args, "batching_mode", "dynamic")
         self._user_max_batch_size = max(1, int(server_args.batching_max_size))
         self._model_path = server_args.model_path
-        self._offload = bool(server_args.dit_layerwise_offload)
+        self._offload = bool(server_args.layerwise_offload_components)
         self._device_memory_gb = self._get_device_memory_gb(gpu_id)
         self._rules = load_batching_config(server_args.batching_config)
         self._pipeline_config = server_args.pipeline_config

python/sglang/multimodal_gen/runtime/managers/gpu_worker.py

@@ -41,8 +41,8 @@ from sglang.multimodal_gen.runtime.loader.weights_updater import (
     WeightsUpdater,
     get_updatable_modules,
 )
-from sglang.multimodal_gen.runtime.managers.layerwise_offload import (
+from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload import (
-    OffloadableDiTMixin,
+    configure_layerwise_offload_modules,
     iter_materialized_weights,
 )
 from sglang.multimodal_gen.runtime.pipelines_core import (
@@ -165,23 +165,12 @@ class GPUWorker:
 
         # apply layerwise offload after lora is applied while building LoRAPipeline
         # otherwise empty offloaded weights could fail lora converting
-        if self.server_args.dit_layerwise_offload:
+        if self.server_args.layerwise_offload_components:
-            # enable layerwise offload if possible
+            configure_layerwise_offload_modules(
-            for module_name in [
+                self.pipeline.modules,
-                "transformer",
+                self.server_args,
-                "transformer_2",
+                component_names=self.server_args.layerwise_offload_components,
-                "video_dit",
+            )
-                "video_dit_2",
-                "audio_dit",
-            ]:
-                dit = self.pipeline.get_module(module_name)
-                if dit:
-                    if isinstance(dit, OffloadableDiTMixin):
-                        dit.configure_layerwise_offload(self.server_args)
-                    else:
-                        logger.info(
-                            f"Module {type(dit).__name__} does not support layerwise offload. Skipping."
-                        )
 
         logger.info(
             f"Worker {self.rank}: Initialized device, model, and distributed environment."
@@ -234,7 +223,7 @@ class GPUWorker:
             elif component in ("text_encoder", "text_encoder_2"):
                 arg = "--text-encoder-cpu-offload"
             elif component == "transformer":
-                if self.server_args.dit_layerwise_offload:
+                if self.server_args.is_dit_layerwise_offload_selected:
                     arg = "--dit-layerwise-offload"
                 elif self.server_args.dit_cpu_offload:
                     arg = "--dit-cpu-offload"
@@ -740,7 +729,7 @@ class GPUWorker:
         # If the flag is True, it is currently offloaded, so it is a candidate to stay resident.
         offload_flags = {
             "transformer": self.server_args.dit_cpu_offload
-            or self.server_args.dit_layerwise_offload,
+            or self.server_args.is_dit_layerwise_offload_selected,
             "vae": self.server_args.vae_cpu_offload,
             "text_encoder": self.server_args.text_encoder_cpu_offload,
             "text_encoder_2": self.server_args.text_encoder_cpu_offload,

python/sglang/multimodal_gen/runtime/managers/memory_managers/component_manager.py

@@ -7,13 +7,21 @@ from typing import Mapping, MutableMapping, Protocol, Sequence, TypeVar
 import torch
 import torch.nn as nn
 
-from sglang.multimodal_gen.runtime.managers.component_resident_strategies import (
+from sglang.multimodal_gen.runtime.managers.memory_managers.component_resident_strategies import (
     ComponentResidencyStrategy,
     LayerwiseOffloadStrategy,
     ResidentStrategy,
     VanillaD2HStrategy,
 )
-from sglang.multimodal_gen.runtime.managers.layerwise_offload import OffloadableDiTMixin
+from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload import (
+    is_layerwise_offloaded_module,
+)
+from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload_components import (
+    is_dit_component_name,
+    is_image_encoder_component_name,
+    is_text_encoder_component_name,
+    is_vae_component_name,
+)
 from sglang.multimodal_gen.runtime.server_args import ServerArgs
 from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
 
@@ -85,70 +93,35 @@ class ComponentResidencyPipeline(Protocol):
     component_residency_strategies: MutableMapping[str, "ComponentResidencyStrategy"]
 
 
-def build_dit_residency_strategy(
-    module: nn.Module,
-    server_args: ServerArgs,
-) -> ComponentResidencyStrategy:
-    if (
-        isinstance(module, OffloadableDiTMixin)
-        and module.layerwise_offload_managers
-        and any(manager.enabled for manager in module.layerwise_offload_managers)
-    ):
-        # only if dit_layerwise_offload is enabled
-        return LayerwiseOffloadStrategy()
-    if server_args.dit_cpu_offload and not server_args.use_fsdp_inference:
-        # handles offload by vanalla D2H
-        return VanillaD2HStrategy()
-    return ResidentStrategy()
-
-
 def is_fsdp_managed_module(module: nn.Module) -> bool:
     return module.__class__.__name__.startswith("FSDP")
 
 
+def should_cpu_offload_component(
+    component_name: str, module: nn.Module, server_args: ServerArgs
+) -> bool:
+    if server_args.use_fsdp_inference or is_fsdp_managed_module(module):
+        return False
+    if is_dit_component_name(component_name):
+        return bool(server_args.dit_cpu_offload)
+    if is_text_encoder_component_name(component_name):
+        return bool(server_args.text_encoder_cpu_offload)
+    if is_image_encoder_component_name(component_name):
+        return bool(server_args.image_encoder_cpu_offload)
+    if is_vae_component_name(component_name):
+        return bool(server_args.vae_cpu_offload)
+    return False
+
+
 def build_component_residency_strategy(
     component_name: str,
     module: nn.Module,
     server_args: ServerArgs,
 ) -> ComponentResidencyStrategy:
-    if component_name in {
+    if is_layerwise_offloaded_module(module):
-        "transformer",
+        return LayerwiseOffloadStrategy()
-        "transformer_2",
+    if should_cpu_offload_component(component_name, module, server_args):
-        "video_dit",
+        return VanillaD2HStrategy()
-        "video_dit_2",
-        "audio_dit",
-        "dual_tower_bridge",
-    }:
-        return build_dit_residency_strategy(module, server_args)
-
-    if component_name.startswith("text_encoder") or component_name.endswith(
-        "text_encoder"
-    ):
-        if (
-            server_args.text_encoder_cpu_offload
-            and not server_args.use_fsdp_inference
-            and not is_fsdp_managed_module(module)
-        ):
-            return VanillaD2HStrategy()
-        return ResidentStrategy()
-
-    if component_name == "image_encoder":
-        if server_args.image_encoder_cpu_offload and not server_args.use_fsdp_inference:
-            return VanillaD2HStrategy()
-        return ResidentStrategy()
-
-    if component_name in {
-        "vae",
-        "video_vae",
-        "audio_vae",
-        "vocoder",
-        "spatial_upsampler",
-        "condition_image_encoder",
-    }:
-        if server_args.vae_cpu_offload and not server_args.use_fsdp_inference:
-            return VanillaD2HStrategy()
-        return ResidentStrategy()
-
     return ResidentStrategy()
 
 

python/sglang/multimodal_gen/runtime/managers/memory_managers/component_resident_strategies.py

@@ -10,12 +10,14 @@ import torch
 import torch.nn as nn
 
 from sglang.multimodal_gen.runtime.distributed import get_local_torch_device
-from sglang.multimodal_gen.runtime.managers.layerwise_offload import OffloadableDiTMixin
+from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload import (
+    LayerwiseOffloadableModuleMixin,
+)
 from sglang.multimodal_gen.runtime.platforms import current_platform
 from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
 
 if TYPE_CHECKING:
-    from sglang.multimodal_gen.runtime.managers.component_manager import (
+    from sglang.multimodal_gen.runtime.managers.memory_managers.component_manager import (
         ComponentUse,
         ResidencyState,
     )
@@ -484,11 +486,11 @@ class LayerwiseOffloadStrategy(ComponentResidencyStrategy):
     name = "layerwise"
 
     def enter(self, module: nn.Module) -> None:
-        if isinstance(module, OffloadableDiTMixin):
+        if isinstance(module, LayerwiseOffloadableModuleMixin):
             module.prepare_for_next_req()
 
     def exit(self, module: nn.Module, next_module: nn.Module | None = None) -> None:
-        if not isinstance(module, OffloadableDiTMixin):
+        if not isinstance(module, LayerwiseOffloadableModuleMixin):
             return
         for manager in module.layerwise_offload_managers:
             manager.release_all()

python/sglang/multimodal_gen/runtime/managers/memory_managers/layerwise_offload.py

@@ -1,9 +1,14 @@
 import re
-from itertools import chain
+from collections.abc import Mapping, Sequence
 from typing import Any, Dict, List, Set, Tuple
 
 import torch
+from torch.distributed.tensor import DTensor
 
+from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload_components import (
+    LAYERWISE_OFFLOAD_DEFAULT_COMPONENTS,
+    layerwise_component_matches_selection,
+)
 from sglang.multimodal_gen.runtime.platforms import current_platform
 from sglang.multimodal_gen.runtime.server_args import ServerArgs
 from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
@@ -70,6 +75,7 @@ class LayerwiseOffloadManager:
         self._named_parameters: Dict[str, torch.nn.Parameter] = {}
         self._named_buffers: Dict[str, torch.Tensor] = {}
         self._offload_placeholders: Dict[torch.dtype, torch.Tensor] = {}
+        self._has_dtensor_weights = False
         # Store forward hooks for removal
         self._forward_hooks: List[Any] = []
 
@@ -91,6 +97,26 @@ class LayerwiseOffloadManager:
             self._offload_placeholders[dtype] = placeholder
         return placeholder
 
+    @staticmethod
+    def _to_local_tensor(tensor: torch.Tensor) -> torch.Tensor:
+        if isinstance(tensor, DTensor):
+            return tensor.to_local()
+        return tensor
+
+    def _wrap_for_target(
+        self, target: torch.Tensor, local_tensor: torch.Tensor
+    ) -> torch.Tensor:
+        if isinstance(target, DTensor):
+            return DTensor.from_local(
+                local_tensor, target.device_mesh, target.placements
+            )
+        return local_tensor
+
+    def _get_shared_empty_tensor_for_target(
+        self, target: torch.Tensor, dtype: torch.dtype
+    ) -> torch.Tensor:
+        return self._wrap_for_target(target, self._get_shared_empty_tensor(dtype))
+
     @staticmethod
     def _get_alignment_numel(dtype: torch.dtype, alignment_bytes: int = 32) -> int:
         element_size = torch.empty((), dtype=dtype).element_size()
@@ -114,16 +140,20 @@ class LayerwiseOffloadManager:
         self._named_parameters = dict(self.model.named_parameters())
         self._named_buffers = dict(self.model.named_buffers())
 
-        # 1. collect and group tensors by layer and dtype
+        # 1. collect and group layer parameters by dtype. Keep buffers resident:
+        # shared buffers such as RoPE caches may be referenced by many layers.
         layer_groups: Dict[int, Dict[torch.dtype, List[Tuple[str, torch.Tensor]]]] = {}
-        all_tensors = chain(self._named_parameters.items(), self._named_buffers.items())
+        for name, tensor in self._named_parameters.items():
-        for name, tensor in all_tensors:
             layer_idx = self._match_layer_idx(name)
             if layer_idx is None or layer_idx >= self.num_layers:
                 continue
-            layer_groups.setdefault(layer_idx, {}).setdefault(tensor.dtype, []).append(
+            self._has_dtensor_weights = self._has_dtensor_weights or isinstance(
-                (name, tensor)
+                tensor, DTensor
             )
+            local_tensor = self._to_local_tensor(tensor)
+            layer_groups.setdefault(layer_idx, {}).setdefault(
+                local_tensor.dtype, []
+            ).append((name, tensor))
 
         # 2. concat and offload (in pinned memory)
         for layer_idx, dtype_to_params in layer_groups.items():
@@ -132,43 +162,46 @@ class LayerwiseOffloadManager:
             self._weight_metadata[layer_idx] = {}
 
             for dtype, weights in dtype_to_params.items():
-                contiguous_weights: List[Tuple[str, torch.Tensor]] = []
+                contiguous_weights: List[Tuple[str, torch.Tensor, torch.Tensor]] = []
                 for name, weight in weights:
-                    if weight.is_contiguous():
+                    local_weight = self._to_local_tensor(weight)
-                        contiguous_weights.append((name, weight))
+                    if local_weight.is_contiguous():
+                        contiguous_weights.append((name, weight, local_weight))
                         continue
 
                     # Preserve non-contiguous layouts such as the transposed FP8
                     # weight views expected by CUTLASS kernels.
                     cpu_tensor = torch.empty_strided(
-                        size=weight.shape,
+                        size=local_weight.shape,
-                        stride=weight.stride(),
+                        stride=local_weight.stride(),
                         dtype=dtype,
                         pin_memory=self.pin_cpu_memory,
                     )
-                    cpu_tensor.copy_(weight)
+                    cpu_tensor.copy_(local_weight)
                     self._strided_cpu_weights[layer_idx][name] = cpu_tensor
                     self._weight_metadata[layer_idx][name] = {
                         "dtype": dtype,
-                        "shape": weight.shape,
+                        "shape": local_weight.shape,
-                        "stride": weight.stride(),
+                        "stride": local_weight.stride(),
                         "preserve_strides": True,
                     }
-                    weight.data = self._get_shared_empty_tensor(dtype)
+                    weight.data = self._get_shared_empty_tensor_for_target(
+                        weight, dtype
+                    )
 
                 if not contiguous_weights:
                     continue
 
                 current_offset = 0
                 aligned_offsets: Dict[str, int] = {}
-                for name, weight in contiguous_weights:
+                for name, weight, local_weight in contiguous_weights:
                     # Some fused diffusion kernels require tensor base pointers to
                     # satisfy a 32-byte alignment contract. Reusing one flat buffer
                     # is still fine, but each logical tensor slice must start on an
                     # aligned offset inside that buffer.
                     current_offset = self._align_numel_offset(current_offset, dtype)
                     aligned_offsets[name] = current_offset
-                    current_offset += weight.numel()
+                    current_offset += local_weight.numel()
 
                 total_numel = current_offset
 
@@ -178,22 +211,24 @@ class LayerwiseOffloadManager:
                 )
 
                 # offload weights to the buffer
-                for name, weight in contiguous_weights:
+                for name, weight, local_weight in contiguous_weights:
                     current_offset = aligned_offsets[name]
-                    numel = weight.numel()
+                    numel = local_weight.numel()
                     cpu_buffer[current_offset : current_offset + numel].copy_(
-                        weight.flatten()
+                        local_weight.flatten()
                     )
                     self._weight_metadata[layer_idx][name] = {
                         "dtype": dtype,
                         "offset": current_offset,
                         "numel": numel,
-                        "shape": weight.shape,
+                        "shape": local_weight.shape,
-                        "stride": weight.stride(),
+                        "stride": local_weight.stride(),
                         "preserve_strides": False,
                     }
 
-                    weight.data = self._get_shared_empty_tensor(dtype)
+                    weight.data = self._get_shared_empty_tensor_for_target(
+                        weight, dtype
+                    )
 
                     current_offset += numel
 
@@ -202,7 +237,8 @@ class LayerwiseOffloadManager:
         # Keep non-layer parameters resident on GPU. Layer tensors have already
         # been replaced by tiny device placeholders, so this does not reload the
         # offloaded layer weights.
-        self.model.to(self.device)
+        if not self._has_dtensor_weights:
+            self.model.to(self.device)
 
         # prefetch the first layer for warm-up
         self.prepare_for_next_req(non_blocking=False)
@@ -271,16 +307,17 @@ class LayerwiseOffloadManager:
                         device=self.device,
                     )
                     gpu_tensor.copy_(cpu_tensor, non_blocking=non_blocking)
-                    target.data = gpu_tensor
+                    target.data = self._wrap_for_target(target, gpu_tensor)
                     continue
 
                 dtype = meta["dtype"]
                 gpu_buffer = gpu_buffers[dtype]
 
                 # map the parameter's data to the correct slice of the GPU buffer
-                target.data = gpu_buffer[
+                local_tensor = gpu_buffer[
                     meta["offset"] : meta["offset"] + meta["numel"]
                 ].view(meta["shape"])
+                target.data = self._wrap_for_target(target, local_tensor)
 
         # record the prefetch event of this layer after all copies are enqueued
         event = torch.get_device_module().Event()
@@ -307,7 +344,9 @@ class LayerwiseOffloadManager:
         for name, meta in self._weight_metadata.get(layer_idx, {}).items():
             target = self.get_target_with_name(name)
             # Wraparound prefetch will reload the layer when it is needed again
-            target.data = self._get_shared_empty_tensor(meta["dtype"])
+            target.data = self._get_shared_empty_tensor_for_target(
+                target, meta["dtype"]
+            )
 
         self._gpu_layers.discard(layer_idx)
 
@@ -347,11 +386,12 @@ class LayerwiseOffloadManager:
         # Collect current GPU weights and write back to CPU buffer
         for name, meta in self._weight_metadata.get(layer_idx, {}).items():
             target = self.get_target_with_name(name)
+            target_local = self._to_local_tensor(target)
             if meta.get("preserve_strides", False):
-                self._strided_cpu_weights[layer_idx][name].copy_(target.data.cpu())
+                self._strided_cpu_weights[layer_idx][name].copy_(target_local.cpu())
                 continue
 
-            gpu_weight = target.data.flatten().cpu()
+            gpu_weight = target_local.flatten().cpu()
 
             dtype = meta["dtype"]
             cpu_buffer = self._consolidated_cpu_weights[layer_idx][dtype]
@@ -408,30 +448,32 @@ class LayerwiseOffloadManager:
                 continue
 
             meta = meta_layer[name]
-            if tuple(meta["shape"]) != tuple(loaded_weight.shape):
+            local_loaded_weight = self._to_local_tensor(loaded_weight)
+            if tuple(meta["shape"]) != tuple(local_loaded_weight.shape):
                 raise ValueError(
                     f"Shape mismatch for {name}: "
                     f"expected={tuple(meta['shape'])}, "
-                    f"loaded={tuple(loaded_weight.shape)}"
+                    f"loaded={tuple(local_loaded_weight.shape)}"
                 )
 
             dtype = meta["dtype"]
             if meta.get("preserve_strides", False):
                 self._strided_cpu_weights[layer_idx][name].copy_(
-                    loaded_weight.to(dtype=dtype)
+                    local_loaded_weight.to(dtype=dtype)
                 )
             else:
                 offset = meta["offset"]
                 numel = meta["numel"]
                 cpu_buffer = self._consolidated_cpu_weights[layer_idx][dtype]
                 cpu_buffer[offset : offset + numel].copy_(
-                    loaded_weight.to(dtype=dtype).flatten()
+                    local_loaded_weight.to(dtype=dtype).flatten()
                 )
 
             # If this layer is currently on GPU, update the live parameter.
             if layer_idx in self._gpu_layers:
                 target = self.get_target_with_name(name)
-                target.data.copy_(loaded_weight.to(dtype=target.dtype))
+                target_local = self._to_local_tensor(target)
+                target_local.copy_(local_loaded_weight.to(dtype=target_local.dtype))
 
             updated_names.add(name)
 
@@ -467,7 +509,7 @@ class LayerwiseOffloadManager:
         if not self.enabled:
             return
 
-        layers = getattr(self.model, self.layers_attr_str)
+        layers = dict(self.model.named_modules())[self.layers_attr_str]
 
         def make_pre_hook(i):
             def hook(module, input):
@@ -509,21 +551,24 @@ class LayerwiseOffloadManager:
         self._forward_hooks.clear()
 
 
-class OffloadableDiTMixin:
+class LayerwiseOffloadableModuleMixin:
-    """
+    """A mixin that registers forward hooks to enable layerwise offload."""
-    A mixin that registers forward hooks for a DiT to enable layerwise offload
-    """
 
-    # the list of names of a DiT's layers/blocks
+    # Legacy --dit-layerwise-offload configures these modules when no component is named.
-    layer_names: List[str]
+    layerwise_offload_default_enabled: bool = True
+    # The list of names of this module's layer/block ModuleList or Sequential attributes.
+    layer_names: List[str] = []
     layerwise_offload_managers: list[LayerwiseOffloadManager] = []
 
     def configure_layerwise_offload(self, server_args: ServerArgs):
         self.layerwise_offload_managers = []
+        named_modules = dict(self.named_modules())
+        configured_layer_names = []
         for layer_name in self.layer_names:
-            # a manager per layer-list
+            module_list = named_modules.get(layer_name)
-            module_list = getattr(self, layer_name, None)
+            if not isinstance(module_list, (torch.nn.ModuleList, torch.nn.Sequential)):
-            if module_list is None or not isinstance(module_list, torch.nn.ModuleList):
+                continue
+            if len(module_list) == 0:
                 continue
 
             num_layers = len(module_list)
@@ -543,10 +588,20 @@ class OffloadableDiTMixin:
                 prefetch_size=prefetch_size,
             )
             self.layerwise_offload_managers.append(manager)
+            configured_layer_names.append(layer_name)
 
-        logger.info(
+        if configured_layer_names:
-            f"Enabled layerwise offload for {self.__class__.__name__} on modules: {self.layer_names}"
+            logger.info(
-        )
+                "Enabled layerwise offload for %s on modules: %s",
+                self.__class__.__name__,
+                configured_layer_names,
+            )
+        else:
+            logger.info(
+                "No layerwise-offloadable ModuleList found for %s. Candidates: %s",
+                self.__class__.__name__,
+                self.layer_names,
+            )
 
     def prepare_for_next_req(self):
         if self.layerwise_offload_managers is None:
@@ -583,7 +638,7 @@ def iter_materialized_weights(module: torch.nn.Module):
     the non-offloaded parameters.
     """
     offload_managers: list = []
-    if isinstance(module, OffloadableDiTMixin) and module.layerwise_offload_managers:
+    if is_layerwise_offloaded_module(module):
         offload_managers = [m for m in module.layerwise_offload_managers if m.enabled]
 
     if not offload_managers:
@@ -601,3 +656,117 @@ def iter_materialized_weights(module: torch.nn.Module):
     for name, param in module.named_parameters():
         if name not in offloaded_names:
             yield name, param
+
+
+def is_layerwise_offloaded_module(module: torch.nn.Module) -> bool:
+    return isinstance(module, LayerwiseOffloadableModuleMixin) and any(
+        manager.enabled for manager in module.layerwise_offload_managers
+    )
+
+
+def configure_layerwise_offload_modules(
+    modules: Mapping[str, object],
+    server_args: ServerArgs,
+    component_names: Sequence[str] | None = None,
+    warn_missing: bool = True,
+) -> list[str]:
+    """Configure layerwise offload for the given modules, from the given component_names
+
+    Args:
+        modules: the dict of {component_name: component}, containing the components to be chosen from
+        component_names: list of component names. component with names not in this list shouldn't be configured
+
+    Returns a list of component names of modules configured to be layerwise-offload
+    """
+
+    # components which has already been configured to be layerwise-offload
+    configured_component_names: list[str] = []
+    configured_module_ids: set[int] = set()
+    selected_component_names = (
+        set(component_names) if component_names is not None else None
+    )
+    select_all = (
+        selected_component_names is not None and "all" in selected_component_names
+    )
+    select_default = (
+        selected_component_names is not None
+        and LAYERWISE_OFFLOAD_DEFAULT_COMPONENTS in selected_component_names
+    )
+
+    if warn_missing and selected_component_names is not None and not select_all:
+        explicit_component_names = selected_component_names - {
+            LAYERWISE_OFFLOAD_DEFAULT_COMPONENTS
+        }
+        missing_component_names = [
+            selected_component_name
+            for selected_component_name in explicit_component_names
+            if not any(
+                layerwise_component_matches_selection(
+                    component_name, selected_component_name
+                )
+                for component_name in modules
+            )
+        ]
+        if missing_component_names:
+            logger.warning(
+                "Layerwise offload components are not currently loaded: %s. "
+                "Available pipeline components: %s",
+                sorted(missing_component_names),
+                sorted(modules),
+            )
+
+        unsupported_component_names = [
+            component_name
+            for component_name in modules
+            if any(
+                layerwise_component_matches_selection(
+                    component_name, selected_component_name
+                )
+                for selected_component_name in explicit_component_names
+            )
+            if not isinstance(modules[component_name], LayerwiseOffloadableModuleMixin)
+        ]
+        if unsupported_component_names:
+            logger.warning(
+                "Layerwise offload components do not support layerwise offload: %s",
+                sorted(unsupported_component_names),
+            )
+
+    for component_name, module in modules.items():
+        if not isinstance(module, LayerwiseOffloadableModuleMixin):
+            continue
+        if selected_component_names is None:
+            if not module.layerwise_offload_default_enabled:
+                continue
+        elif (
+            not select_all
+            and not any(
+                layerwise_component_matches_selection(
+                    component_name, selected_component_name
+                )
+                for selected_component_name in selected_component_names
+            )
+            and not (select_default and module.layerwise_offload_default_enabled)
+        ):
+            # if the current component is not selected to be layerwise-offload, skip
+            continue
+        module_id = id(module)
+        if module_id in configured_module_ids:
+            # avoid multiple configures on a same module
+            continue
+
+        configured_module_ids.add(module_id)
+
+        if not is_layerwise_offloaded_module(module):
+            module.configure_layerwise_offload(server_args)
+        if is_layerwise_offloaded_module(module):
+            configured_component_names.append(component_name)
+
+    if configured_component_names:
+        logger.info(
+            "Enabled layerwise offload for pipeline components: %s",
+            configured_component_names,
+        )
+    else:
+        logger.info("No pipeline component supports layerwise offload.")
+    return configured_component_names

python/sglang/multimodal_gen/runtime/managers/memory_managers/layerwise_offload_components.py

@@ -0,0 +1,117 @@
+from collections.abc import Sequence
+
+LAYERWISE_OFFLOAD_ALL_COMPONENTS = "all"
+LAYERWISE_OFFLOAD_DEFAULT_COMPONENTS = "default"
+DIT_COMPONENT_NAMES = frozenset(
+    {
+        "transformer",
+        "transformer_2",
+        "video_dit",
+        "video_dit_2",
+        "audio_dit",
+        "dual_tower_bridge",
+    }
+)
+VAE_COMPONENT_NAMES = frozenset(
+    {
+        "vae",
+        "video_vae",
+        "audio_vae",
+        "vocoder",
+        "spatial_upsampler",
+        "condition_image_encoder",
+    }
+)
+CPU_OFFLOAD_FLAG_NAMES = (
+    "dit_cpu_offload",
+    "text_encoder_cpu_offload",
+    "image_encoder_cpu_offload",
+    "vae_cpu_offload",
+)
+
+
+def is_dit_component_name(component_name: str) -> bool:
+    return component_name in DIT_COMPONENT_NAMES
+
+
+def is_text_encoder_component_name(component_name: str) -> bool:
+    return component_name.startswith("text_encoder") or component_name.endswith(
+        "text_encoder"
+    )
+
+
+def is_image_encoder_component_name(component_name: str) -> bool:
+    return component_name == "image_encoder"
+
+
+def is_vae_component_name(component_name: str) -> bool:
+    return component_name in VAE_COMPONENT_NAMES
+
+
+def layerwise_component_matches_selection(
+    component_name: str,
+    selected_component_name: str,
+) -> bool:
+    """if the provided component_name (unnormalized, e.g., text_encoder_2)  matches with the selected_component_name (normalized)"""
+    if selected_component_name == "text_encoder":
+        return is_text_encoder_component_name(component_name)
+    if selected_component_name == "vae":
+        return is_vae_component_name(component_name)
+    return component_name == selected_component_name
+
+
+def cpu_offload_flags_for_layerwise_components(
+    component_names: Sequence[str],
+) -> tuple[str, ...]:
+    if LAYERWISE_OFFLOAD_ALL_COMPONENTS in component_names:
+        return CPU_OFFLOAD_FLAG_NAMES
+
+    flag_names: list[str] = []
+    if LAYERWISE_OFFLOAD_DEFAULT_COMPONENTS in component_names:
+        flag_names.append("dit_cpu_offload")
+
+    for component_name in component_names:
+        if component_name == LAYERWISE_OFFLOAD_DEFAULT_COMPONENTS:
+            continue
+        if is_dit_component_name(component_name):
+            flag_name = "dit_cpu_offload"
+        elif is_text_encoder_component_name(component_name):
+            flag_name = "text_encoder_cpu_offload"
+        elif is_image_encoder_component_name(component_name):
+            flag_name = "image_encoder_cpu_offload"
+        elif is_vae_component_name(component_name):
+            flag_name = "vae_cpu_offload"
+        else:
+            continue
+
+        if flag_name not in flag_names:
+            flag_names.append(flag_name)
+
+    return tuple(flag_names)
+
+
+def normalize_layerwise_offload_components(
+    component_names: str | Sequence[str] | None,
+) -> list[str] | None:
+    if component_names is None:
+        return None
+
+    raw_components = (
+        [component_names] if isinstance(component_names, str) else component_names
+    )
+    normalized_components: list[str] = []
+    for raw_component in raw_components:
+        if not isinstance(raw_component, str):
+            raise ValueError(
+                f"Invalid layerwise offload component name: {raw_component}."
+            )
+        for component_name in raw_component.split(","):
+            component_name = component_name.strip().replace("-", "_").lower()
+            if not component_name:
+                continue
+            if component_name == LAYERWISE_OFFLOAD_ALL_COMPONENTS:
+                return [LAYERWISE_OFFLOAD_ALL_COMPONENTS]
+            if component_name not in normalized_components:
+                normalized_components.append(component_name)
+
+    return normalized_components or None

python/sglang/multimodal_gen/runtime/models/bridges/mova_dual_tower.py

@@ -26,7 +26,9 @@ from sglang.multimodal_gen.runtime.layers.linear import (
 from sglang.multimodal_gen.runtime.layers.rotary_embedding import (
     apply_flashinfer_rope_qk_inplace,
 )
-from sglang.multimodal_gen.runtime.managers.layerwise_offload import OffloadableDiTMixin
+from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload import (
+    LayerwiseOffloadableModuleMixin,
+)
 from sglang.multimodal_gen.runtime.models.dits.base import CachableDiT
 from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
 
@@ -397,7 +399,7 @@ class ConditionalCrossAttentionBlock(nn.Module):
 
 class DualTowerConditionalBridge(
     CachableDiT,
-    OffloadableDiTMixin,
+    LayerwiseOffloadableModuleMixin,
 ):
     """Dual-tower conditional bridge module v2 (SGLang optimized version).
 
@@ -407,6 +409,8 @@ class DualTowerConditionalBridge(
     3. Cross-attention interaction between the hidden states of the two DiTs.
     """
 
+    layerwise_offload_default_enabled = False
+
     _fsdp_shard_conditions = MOVADualTowerConfig()._fsdp_shard_conditions
     _compile_conditions = MOVADualTowerConfig()._compile_conditions
     _supported_attention_backends = MOVADualTowerConfig()._supported_attention_backends

python/sglang/multimodal_gen/runtime/models/dits/causal_wanvideo.py

@@ -13,7 +13,9 @@ from torch.nn.attention.flex_attention import (
     flex_attention,
 )
 
-from sglang.multimodal_gen.runtime.managers.layerwise_offload import OffloadableDiTMixin
+from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload import (
+    LayerwiseOffloadableModuleMixin,
+)
 
 # wan 1.3B model has a weird channel / head configurations and require max-autotune to work with flexattention
 # see https://github.com/pytorch/pytorch/issues/133254
@@ -58,7 +60,6 @@ logger = init_logger(__name__)
 
 
 class CausalWanSelfAttention(nn.Module):
-
     def __init__(
         self,
         dim: int,
@@ -251,7 +252,6 @@ class CausalWanSelfAttention(nn.Module):
 
 
 class CausalWanTransformerBlock(nn.Module):
-
     def __init__(
         self,
         dim: int,
@@ -429,7 +429,7 @@ class CausalWanTransformerBlock(nn.Module):
         return hidden_states
 
 
-class CausalWanTransformer3DModel(BaseDiT, OffloadableDiTMixin):
+class CausalWanTransformer3DModel(BaseDiT, LayerwiseOffloadableModuleMixin):
     _fsdp_shard_conditions = WanVideoConfig()._fsdp_shard_conditions
     _compile_conditions = WanVideoConfig()._compile_conditions
     _supported_attention_backends = WanVideoConfig()._supported_attention_backends
@@ -660,10 +660,13 @@ class CausalWanTransformer3DModel(BaseDiT, OffloadableDiTMixin):
         hidden_states = self.patch_embedding(hidden_states)
         hidden_states = hidden_states.flatten(2).transpose(1, 2)
 
-        temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image = (
+        (
-            self.condition_embedder(
+            temb,
-                timestep.flatten(), encoder_hidden_states, encoder_hidden_states_image
+            timestep_proj,
-            )
+            encoder_hidden_states,
+            encoder_hidden_states_image,
+        ) = self.condition_embedder(
+            timestep.flatten(), encoder_hidden_states, encoder_hidden_states_image
         )
         timestep_proj = timestep_proj.unflatten(1, (6, self.hidden_size)).unflatten(
             dim=0, sizes=timestep.shape
@@ -802,10 +805,13 @@ class CausalWanTransformer3DModel(BaseDiT, OffloadableDiTMixin):
         hidden_states = self.patch_embedding(hidden_states)
         hidden_states = hidden_states.flatten(2).transpose(1, 2)
 
-        temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image = (
+        (
-            self.condition_embedder(
+            temb,
-                timestep.flatten(), encoder_hidden_states, encoder_hidden_states_image
+            timestep_proj,
-            )
+            encoder_hidden_states,
+            encoder_hidden_states_image,
+        ) = self.condition_embedder(
+            timestep.flatten(), encoder_hidden_states, encoder_hidden_states_image
         )
         timestep_proj = timestep_proj.unflatten(1, (6, self.hidden_size)).unflatten(
             dim=0, sizes=timestep.shape

python/sglang/multimodal_gen/runtime/models/dits/ernie_image.py

@@ -33,7 +33,9 @@ from sglang.multimodal_gen.runtime.layers.linear import (
     RowParallelLinear,
 )
 from sglang.multimodal_gen.runtime.layers.quantization import QuantizationConfig
-from sglang.multimodal_gen.runtime.managers.layerwise_offload import OffloadableDiTMixin
+from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload import (
+    LayerwiseOffloadableModuleMixin,
+)
 from sglang.multimodal_gen.runtime.models.dits.base import CachableDiT
 
 
@@ -172,7 +174,6 @@ class ErnieImageSelfAttention(nn.Module):
 
 
 class ErnieImageMLP(nn.Module):
-
     def __init__(
         self,
         hidden_size: int,
@@ -266,7 +267,7 @@ def _apply_rotary_bshd(x: torch.Tensor, freqs: torch.Tensor) -> torch.Tensor:
     return torch.cat((x_rot, x_pass), dim=-1)
 
 
-class ErnieImageTransformer2DModel(CachableDiT, OffloadableDiTMixin):
+class ErnieImageTransformer2DModel(CachableDiT, LayerwiseOffloadableModuleMixin):
     """ErnieImage DiT: Single-stream transformer with Shared AdaLN."""
 
     _supports_gradient_checkpointing = True

python/sglang/multimodal_gen/runtime/models/dits/flux.py

@@ -52,7 +52,9 @@ from sglang.multimodal_gen.runtime.layers.visual_embedding import (
     CombinedTimestepGuidanceTextProjEmbeddings,
     CombinedTimestepTextProjEmbeddings,
 )
-from sglang.multimodal_gen.runtime.managers.layerwise_offload import OffloadableDiTMixin
+from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload import (
+    LayerwiseOffloadableModuleMixin,
+)
 from sglang.multimodal_gen.runtime.models.dits.base import CachableDiT
 from sglang.multimodal_gen.runtime.platforms import current_platform
 from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
@@ -355,9 +357,14 @@ class FluxAttention(torch.nn.Module, AttentionModuleMixin):
         freqs_cis=None,
         num_replicated_prefix: int = 0,
     ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
-        query, key, value, encoder_query, encoder_key, encoder_value = (
+        (
-            _get_qkv_projections(self, x, encoder_hidden_states)
+            query,
-        )
+            key,
+            value,
+            encoder_query,
+            encoder_key,
+            encoder_value,
+        ) = _get_qkv_projections(self, x, encoder_hidden_states)
 
         query = query.unflatten(-1, (self.heads, -1))
         key = key.unflatten(-1, (self.heads, -1))
@@ -658,9 +665,13 @@ class FluxTransformerBlock(nn.Module):
             hidden_states, emb=temb
         )
 
-        norm_encoder_hidden_states, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = (
+        (
-            self.norm1_context(encoder_hidden_states, emb=temb)
+            norm_encoder_hidden_states,
-        )
+            c_gate_msa,
+            c_shift_mlp,
+            c_scale_mlp,
+            c_gate_mlp,
+        ) = self.norm1_context(encoder_hidden_states, emb=temb)
 
         joint_attention_kwargs = joint_attention_kwargs or {}
         # Attention.
@@ -745,7 +756,7 @@ class FluxPosEmbed(nn.Module):
         return freqs_cos.contiguous().float(), freqs_sin.contiguous().float()
 
 
-class FluxTransformer2DModel(CachableDiT, OffloadableDiTMixin):
+class FluxTransformer2DModel(CachableDiT, LayerwiseOffloadableModuleMixin):
     """
     The Transformer model introduced in Flux.
 

python/sglang/multimodal_gen/runtime/models/dits/flux_2.py

@@ -42,7 +42,9 @@ from sglang.multimodal_gen.runtime.layers.rotary_embedding import (
     NDRotaryEmbedding,
     apply_flashinfer_rope_qk_inplace,
 )
-from sglang.multimodal_gen.runtime.managers.layerwise_offload import OffloadableDiTMixin
+from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload import (
+    LayerwiseOffloadableModuleMixin,
+)
 from sglang.multimodal_gen.runtime.models.dits.base import CachableDiT
 from sglang.multimodal_gen.runtime.platforms import (
     AttentionBackendEnum,
@@ -294,9 +296,14 @@ class Flux2Attention(torch.nn.Module, AttentionModuleMixin):
         encoder_hidden_states: Optional[torch.Tensor] = None,
         freqs_cis: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
     ) -> torch.Tensor:
-        query, key, value, encoder_query, encoder_key, encoder_value = (
+        (
-            _get_qkv_projections(self, hidden_states, encoder_hidden_states)
+            query,
-        )
+            key,
+            value,
+            encoder_query,
+            encoder_key,
+            encoder_value,
+        ) = _get_qkv_projections(self, hidden_states, encoder_hidden_states)
 
         query = query.unflatten(-1, (self.local_heads, -1))
         key = key.unflatten(-1, (self.local_heads, -1))
@@ -850,7 +857,7 @@ class Flux2PosEmbed(nn.Module):
         return freqs_cos.contiguous().float(), freqs_sin.contiguous().float()
 
 
-class Flux2Transformer2DModel(CachableDiT, OffloadableDiTMixin):
+class Flux2Transformer2DModel(CachableDiT, LayerwiseOffloadableModuleMixin):
     """
     The Transformer model introduced in Flux 2.
 

python/sglang/multimodal_gen/runtime/models/dits/glm_image.py

@@ -37,7 +37,9 @@ from sglang.multimodal_gen.runtime.layers.rotary_embedding import (
     apply_flashinfer_rope_qk_inplace,
 )
 from sglang.multimodal_gen.runtime.layers.visual_embedding import Timesteps
-from sglang.multimodal_gen.runtime.managers.layerwise_offload import OffloadableDiTMixin
+from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload import (
+    LayerwiseOffloadableModuleMixin,
+)
 from sglang.multimodal_gen.runtime.models.dits.base import CachableDiT
 from sglang.multimodal_gen.runtime.platforms import (
     AttentionBackendEnum,
@@ -661,7 +663,7 @@ class GlmImageAdaLayerNormContinuous(nn.Module):
         return x
 
 
-class GlmImageTransformer2DModel(CachableDiT, OffloadableDiTMixin):
+class GlmImageTransformer2DModel(CachableDiT, LayerwiseOffloadableModuleMixin):
     r"""
     Args:
         patch_size (`int`, defaults to `2`):

python/sglang/multimodal_gen/runtime/models/dits/helios.py

@@ -47,7 +47,9 @@ from sglang.multimodal_gen.runtime.layers.visual_embedding import (
     TimestepEmbedder,
 )
 from sglang.multimodal_gen.runtime.managers.forward_context import get_forward_context
-from sglang.multimodal_gen.runtime.managers.layerwise_offload import OffloadableDiTMixin
+from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload import (
+    LayerwiseOffloadableModuleMixin,
+)
 from sglang.multimodal_gen.runtime.models.dits.base import CachableDiT
 from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
 
@@ -525,7 +527,7 @@ class HeliosTransformerBlock(nn.Module):
 # ---------------------------------------------------------------------------
 
 
-class HeliosTransformer3DModel(CachableDiT, OffloadableDiTMixin):
+class HeliosTransformer3DModel(CachableDiT, LayerwiseOffloadableModuleMixin):
     """
     Helios Transformer 3D model for video generation.
 
@@ -671,9 +673,13 @@ class HeliosTransformer3DModel(CachableDiT, OffloadableDiTMixin):
 
         # 1. Patch embed the noisy latents
         hidden_states = self.patch_embedding(hidden_states)
-        _, _, post_patch_num_frames, post_patch_height, post_patch_width = (
+        (
-            hidden_states.shape
+            _,
-        )
+            _,
+            post_patch_num_frames,
+            post_patch_height,
+            post_patch_width,
+        ) = hidden_states.shape
 
         if indices_hidden_states is None:
             indices_hidden_states = (

python/sglang/multimodal_gen/runtime/models/dits/hunyuan3d.py

@@ -27,7 +27,9 @@ from sglang.multimodal_gen.runtime.layers.linear import (
     RowParallelLinear,
 )
 from sglang.multimodal_gen.runtime.layers.mlp import MLP
-from sglang.multimodal_gen.runtime.managers.layerwise_offload import OffloadableDiTMixin
+from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload import (
+    LayerwiseOffloadableModuleMixin,
+)
 from sglang.multimodal_gen.runtime.models.dits.base import CachableDiT
 from sglang.multimodal_gen.runtime.platforms import AttentionBackendEnum
 from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
@@ -453,7 +455,7 @@ class _FluxLastLayer(nn.Module):
         return x
 
 
-class Hunyuan3D2DiT(CachableDiT, OffloadableDiTMixin):
+class Hunyuan3D2DiT(CachableDiT, LayerwiseOffloadableModuleMixin):
     """Hunyuan3D DiT model (Flux-style architecture for Hunyuan3D-2.0)."""
 
     _aliases = ["hy3dgen.shapegen.models.Hunyuan3DDiT"]
@@ -560,7 +562,7 @@ class Hunyuan3D2DiT(CachableDiT, OffloadableDiTMixin):
 
         self.final_layer = _FluxLastLayer(self.hidden_size, 1, self.out_channels)
 
-        # OffloadableDiTMixin
+        # LayerwiseOffloadableModuleMixin
         self.layer_names = ["double_blocks", "single_blocks"]
 
     def forward(

python/sglang/multimodal_gen/runtime/models/dits/hunyuanvideo.py

@@ -37,7 +37,9 @@ from sglang.multimodal_gen.runtime.layers.visual_embedding import (
     unpatchify,
 )
 from sglang.multimodal_gen.runtime.managers.forward_context import get_forward_context
-from sglang.multimodal_gen.runtime.managers.layerwise_offload import OffloadableDiTMixin
+from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload import (
+    LayerwiseOffloadableModuleMixin,
+)
 from sglang.multimodal_gen.runtime.models.dits.base import CachableDiT
 from sglang.multimodal_gen.runtime.models.utils import modulate
 from sglang.multimodal_gen.runtime.platforms import (
@@ -418,7 +420,7 @@ class MMSingleStreamBlock(nn.Module):
         return self.output_residual(output, mod_gate, x)
 
 
-class HunyuanVideoTransformer3DModel(CachableDiT, OffloadableDiTMixin):
+class HunyuanVideoTransformer3DModel(CachableDiT, LayerwiseOffloadableModuleMixin):
     """
     HunyuanVideo Transformer backbone adapted for distributed training.
 

python/sglang/multimodal_gen/runtime/models/dits/joy_image.py

@@ -27,7 +27,9 @@ from sglang.multimodal_gen.runtime.layers.quantization.configs.base_config impor
 )
 from sglang.multimodal_gen.runtime.layers.rotary_embedding import NDRotaryEmbedding
 from sglang.multimodal_gen.runtime.managers.forward_context import get_forward_context
-from sglang.multimodal_gen.runtime.managers.layerwise_offload import OffloadableDiTMixin
+from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload import (
+    LayerwiseOffloadableModuleMixin,
+)
 from sglang.multimodal_gen.runtime.models.dits.base import CachableDiT
 from sglang.multimodal_gen.runtime.models.dits.wanvideo import WanTimeTextImageEmbedding
 from sglang.multimodal_gen.runtime.models.utils import set_weight_attrs
@@ -89,7 +91,6 @@ class ModulateWan(nn.Module):
 
 
 class MMDoubleStreamBlock(nn.Module):
-
     def __init__(
         self,
         hidden_size: int,
@@ -328,7 +329,7 @@ class MMDoubleStreamBlock(nn.Module):
         return img, txt
 
 
-class JoyTransformer3DModel(CachableDiT, OffloadableDiTMixin):
+class JoyTransformer3DModel(CachableDiT, LayerwiseOffloadableModuleMixin):
     """
     JoyImage Transformer 3D Model for image generation.
 

python/sglang/multimodal_gen/runtime/models/dits/ltx_2.py

@@ -31,7 +31,9 @@ from sglang.multimodal_gen.runtime.layers.quantization.configs.base_config impor
     QuantizationConfig,
 )
 from sglang.multimodal_gen.runtime.layers.visual_embedding import timestep_embedding
-from sglang.multimodal_gen.runtime.managers.layerwise_offload import OffloadableDiTMixin
+from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload import (
+    LayerwiseOffloadableModuleMixin,
+)
 from sglang.multimodal_gen.runtime.models.dits.base import CachableDiT
 from sglang.multimodal_gen.runtime.platforms import AttentionBackendEnum
 from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
@@ -1235,7 +1237,7 @@ class LTX2TransformerBlock(nn.Module):
         return hidden_states, audio_hidden_states
 
 
-class LTX2VideoTransformer3DModel(CachableDiT, OffloadableDiTMixin):
+class LTX2VideoTransformer3DModel(CachableDiT, LayerwiseOffloadableModuleMixin):
     _fsdp_shard_conditions = LTX2ArchConfig()._fsdp_shard_conditions
     _compile_conditions = LTX2ArchConfig()._compile_conditions
     _supported_attention_backends = LTX2ArchConfig()._supported_attention_backends

python/sglang/multimodal_gen/runtime/models/dits/mova_audio_dit.py

@@ -18,7 +18,9 @@ from sglang.multimodal_gen.runtime.layers.mlp import MLP
 from sglang.multimodal_gen.runtime.layers.quantization.configs.base_config import (
     QuantizationConfig,
 )
-from sglang.multimodal_gen.runtime.managers.layerwise_offload import OffloadableDiTMixin
+from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload import (
+    LayerwiseOffloadableModuleMixin,
+)
 from sglang.multimodal_gen.runtime.models.dits.base import CachableDiT
 
 # Reuse common functions and classes from mova_video_dit
@@ -101,7 +103,7 @@ class Conv1dLocalIsland(nn.Conv1d):
             return super().forward(input)
 
 
-class WanAudioModel(CachableDiT, OffloadableDiTMixin):
+class WanAudioModel(CachableDiT, LayerwiseOffloadableModuleMixin):
     _fsdp_shard_conditions = MOVAAudioConfig()._fsdp_shard_conditions
     _compile_conditions = MOVAAudioConfig()._compile_conditions
     _supported_attention_backends = MOVAAudioConfig()._supported_attention_backends

python/sglang/multimodal_gen/runtime/models/dits/mova_video_dit.py

@@ -33,7 +33,9 @@ from sglang.multimodal_gen.runtime.layers.mlp import MLP
 from sglang.multimodal_gen.runtime.layers.quantization.configs.base_config import (
     QuantizationConfig,
 )
-from sglang.multimodal_gen.runtime.managers.layerwise_offload import OffloadableDiTMixin
+from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload import (
+    LayerwiseOffloadableModuleMixin,
+)
 from sglang.multimodal_gen.runtime.models.dits.base import CachableDiT
 from sglang.multimodal_gen.runtime.platforms import current_platform
 from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
@@ -419,7 +421,7 @@ class Conv3dLocalIsland(nn.Conv3d):
             return super().forward(input)
 
 
-class WanModel(CachableDiT, OffloadableDiTMixin):
+class WanModel(CachableDiT, LayerwiseOffloadableModuleMixin):
     _fsdp_shard_conditions = MOVAVideoConfig()._fsdp_shard_conditions
     _compile_conditions = MOVAVideoConfig()._compile_conditions
     _supported_attention_backends = MOVAVideoConfig()._supported_attention_backends

python/sglang/multimodal_gen/runtime/models/dits/qwen_image.py

@@ -45,7 +45,9 @@ from sglang.multimodal_gen.runtime.layers.quantization.configs.nunchaku_config i
 from sglang.multimodal_gen.runtime.layers.rotary_embedding import (
     apply_flashinfer_rope_qk_inplace,
 )
-from sglang.multimodal_gen.runtime.managers.layerwise_offload import OffloadableDiTMixin
+from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload import (
+    LayerwiseOffloadableModuleMixin,
+)
 from sglang.multimodal_gen.runtime.models.dits.base import CachableDiT
 from sglang.multimodal_gen.runtime.platforms import AttentionBackendEnum
 from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
@@ -652,9 +654,14 @@ class QwenImageCrossAttention(nn.Module):
             "encoder_hidden_states_mask"
         )
 
-        img_query, img_key, img_value, txt_query, txt_key, txt_value = (
+        (
-            _get_qkv_projections(self, hidden_states, encoder_hidden_states)
+            img_query,
-        )
+            img_key,
+            img_value,
+            txt_query,
+            txt_key,
+            txt_value,
+        ) = _get_qkv_projections(self, hidden_states, encoder_hidden_states)
 
         # Reshape for multi-head attention
         img_query = img_query.unflatten(-1, (self.num_heads, -1))
@@ -1118,7 +1125,7 @@ def to_hashable(obj):
     return obj
 
 
-class QwenImageTransformer2DModel(CachableDiT, OffloadableDiTMixin):
+class QwenImageTransformer2DModel(CachableDiT, LayerwiseOffloadableModuleMixin):
     """
     The Transformer model introduced in Qwen.
 

python/sglang/multimodal_gen/runtime/models/dits/sana.py

@@ -8,7 +8,9 @@ from diffusers.models.embeddings import PixArtAlphaTextProjection, TimestepEmbed
 from sglang.multimodal_gen.configs.models.dits.sana import SanaConfig
 from sglang.multimodal_gen.runtime.layers.layernorm import RMSNorm
 from sglang.multimodal_gen.runtime.layers.visual_embedding import Timesteps
-from sglang.multimodal_gen.runtime.managers.layerwise_offload import OffloadableDiTMixin
+from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload import (
+    LayerwiseOffloadableModuleMixin,
+)
 from sglang.multimodal_gen.runtime.models.dits.base import CachableDiT
 from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
 
@@ -239,7 +241,7 @@ class SanaTransformerBlock(nn.Module):
         return hidden_states
 
 
-class SanaTransformer2DModel(CachableDiT, OffloadableDiTMixin):
+class SanaTransformer2DModel(CachableDiT, LayerwiseOffloadableModuleMixin):
 
     _fsdp_shard_conditions = [
         lambda n, m: isinstance(m, SanaTransformerBlock),

python/sglang/multimodal_gen/runtime/models/dits/stablediffusion3.py

@@ -17,16 +17,20 @@ from diffusers.models.normalization import AdaLayerNormContinuous
 from sglang.multimodal_gen.configs.models.dits.stablediffusion3 import (
     StableDiffusion3TransformerConfig,
 )
+from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload import (
+    LayerwiseOffloadableModuleMixin,
+)
 from sglang.multimodal_gen.runtime.models.dits.base import CachableDiT
 from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
 
 logger = init_logger(__name__)
 
 
-class SD3Transformer2DModel(CachableDiT):
+class SD3Transformer2DModel(CachableDiT, LayerwiseOffloadableModuleMixin):
     _supports_gradient_checkpointing = True
     _no_split_modules = ["JointTransformerBlock"]
     _skip_layerwise_casting_patterns = ["pos_embed", "norm"]
+    layer_names = ["transformer_blocks"]
 
     def __init__(
         self,

python/sglang/multimodal_gen/runtime/models/dits/wanvideo.py

@@ -49,7 +49,9 @@ from sglang.multimodal_gen.runtime.layers.visual_embedding import (
     TimestepEmbedder,
 )
 from sglang.multimodal_gen.runtime.managers.forward_context import get_forward_context
-from sglang.multimodal_gen.runtime.managers.layerwise_offload import OffloadableDiTMixin
+from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload import (
+    LayerwiseOffloadableModuleMixin,
+)
 from sglang.multimodal_gen.runtime.models.dits.base import CachableDiT
 from sglang.multimodal_gen.runtime.models.utils import (
     _use_aiter,
@@ -70,7 +72,6 @@ if _use_aiter:
 
 
 class WanImageEmbedding(torch.nn.Module):
-
     def __init__(self, in_features: int, out_features: int):
         super().__init__()
 
@@ -87,7 +88,6 @@ class WanImageEmbedding(torch.nn.Module):
 
 
 class WanTimeTextImageEmbedding(nn.Module):
-
     def __init__(
         self,
         dim: int,
@@ -130,7 +130,6 @@ class WanTimeTextImageEmbedding(nn.Module):
 
 
 class WanSelfAttention(nn.Module):
-
     def __init__(
         self,
         dim: int,
@@ -247,7 +246,6 @@ class WanT2VCrossAttention(WanSelfAttention):
 
 
 class WanI2VCrossAttention(WanSelfAttention):
-
     def __init__(
         self,
         dim: int,
@@ -335,7 +333,6 @@ class WanI2VCrossAttention(WanSelfAttention):
 
 
 class WanTransformerBlock(nn.Module):
-
     def __init__(
         self,
         dim: int,
@@ -514,9 +511,14 @@ class WanTransformerBlock(nn.Module):
         else:
             # temb: batch_size, 6, inner_dim (wan2.1/wan2.2 14B)
             e = self.scale_shift_table + temb.float()
-            shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = (
+            (
-                e.chunk(6, dim=1)
+                shift_msa,
-            )
+                scale_msa,
+                gate_msa,
+                c_shift_msa,
+                c_scale_msa,
+                c_gate_msa,
+            ) = e.chunk(6, dim=1)
 
         assert shift_msa.dtype == torch.float32
 
@@ -611,7 +613,6 @@ class WanTransformerBlock(nn.Module):
 
 
 class WanTransformerBlock_VSA(nn.Module):
-
     def __init__(
         self,
         dim: int,
@@ -855,7 +856,7 @@ class WanTransformerBlock_VSA(nn.Module):
         return hidden_states
 
 
-class WanTransformer3DModel(CachableDiT, OffloadableDiTMixin):
+class WanTransformer3DModel(CachableDiT, LayerwiseOffloadableModuleMixin):
     _fsdp_shard_conditions = WanVideoConfig()._fsdp_shard_conditions
     _compile_conditions = WanVideoConfig()._compile_conditions
     _supported_attention_backends = WanVideoConfig()._supported_attention_backends
@@ -1088,13 +1089,16 @@ class WanTransformer3DModel(CachableDiT, OffloadableDiTMixin):
         else:
             ts_seq_len = None
 
-        temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image = (
+        (
-            self.condition_embedder(
+            temb,
-                timestep,
+            timestep_proj,
-                encoder_hidden_states,
+            encoder_hidden_states,
-                encoder_hidden_states_image,
+            encoder_hidden_states_image,
-                timestep_seq_len=ts_seq_len,
+        ) = self.condition_embedder(
-            )
+            timestep,
+            encoder_hidden_states,
+            encoder_hidden_states_image,
+            timestep_seq_len=ts_seq_len,
         )
         if ts_seq_len is not None:
             # batch_size, seq_len, 6, inner_dim

python/sglang/multimodal_gen/runtime/models/dits/zimage.py

@@ -41,7 +41,9 @@ from sglang.multimodal_gen.runtime.layers.rotary_embedding import (
     _apply_rotary_emb,
     apply_flashinfer_rope_qk_inplace,
 )
-from sglang.multimodal_gen.runtime.managers.layerwise_offload import OffloadableDiTMixin
+from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload import (
+    LayerwiseOffloadableModuleMixin,
+)
 from sglang.multimodal_gen.runtime.models.dits.base import CachableDiT
 from sglang.multimodal_gen.runtime.platforms import current_platform
 from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
@@ -617,7 +619,7 @@ class RopeEmbedder:
         return torch.cat(cos_out, dim=-1), torch.cat(sin_out, dim=-1)
 
 
-class ZImageTransformer2DModel(CachableDiT, OffloadableDiTMixin):
+class ZImageTransformer2DModel(CachableDiT, LayerwiseOffloadableModuleMixin):
     _supports_gradient_checkpointing = True
     _no_split_modules = ["ZImageTransformerBlock"]
     _fsdp_shard_conditions = ZImageDitConfig().arch_config._fsdp_shard_conditions

python/sglang/multimodal_gen/runtime/models/encoders/base.py

@@ -12,10 +12,20 @@ from sglang.multimodal_gen.configs.models.encoders import (
     ImageEncoderConfig,
     TextEncoderConfig,
 )
+from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload import (
+    LayerwiseOffloadableModuleMixin,
+)
 from sglang.multimodal_gen.runtime.platforms import AttentionBackendEnum
 
 
-class TextEncoder(nn.Module, ABC):
+class TextEncoder(nn.Module, ABC, LayerwiseOffloadableModuleMixin):
+    layerwise_offload_default_enabled = False
+    layer_names = [
+        "layers",
+        "encoder.block",
+        "text_model.encoder.layers",
+        "model.language_model.layers",
+    ]
     _fsdp_shard_conditions: list = field(default_factory=lambda: [])
     _stacked_params_mapping: list[tuple[str, str, str]] = field(default_factory=list)
     _supported_attention_backends: set[AttentionBackendEnum] = (
@@ -49,7 +59,13 @@ class TextEncoder(nn.Module, ABC):
         return self._supported_attention_backends
 
 
-class ImageEncoder(nn.Module, ABC):
+class ImageEncoder(nn.Module, ABC, LayerwiseOffloadableModuleMixin):
+    layerwise_offload_default_enabled = False
+    layer_names = [
+        "layers",
+        "vision_model.encoder.layers",
+        "model.visual.blocks",
+    ]
     _supported_attention_backends: set[AttentionBackendEnum] = (
         ImageEncoderConfig()._supported_attention_backends
     )

python/sglang/multimodal_gen/runtime/models/encoders/gemma2.py

@@ -35,6 +35,9 @@ from sglang.multimodal_gen.runtime.layers.vocab_parallel_embedding import (
     VocabParallelEmbedding,
 )
 from sglang.multimodal_gen.runtime.loader.weight_utils import default_weight_loader
+from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload import (
+    LayerwiseOffloadableModuleMixin,
+)
 
 logger = logging.getLogger(__name__)
 
@@ -280,10 +283,12 @@ class Gemma2DecoderLayer(nn.Module):
         return hidden_states
 
 
-class Gemma2Model(nn.Module):
+class Gemma2Model(nn.Module, LayerwiseOffloadableModuleMixin):
     """Gemma2 text encoder model for SANA pipeline."""
 
     _fsdp_shard_conditions = []
+    layerwise_offload_default_enabled = False
+    layer_names = ["layers"]
 
     def __init__(self, config: Gemma2Config, **kwargs):
         super().__init__()

python/sglang/multimodal_gen/runtime/models/encoders/gemma_3.py

@@ -24,6 +24,9 @@ from sglang.multimodal_gen.runtime.layers.linear import (
 from sglang.multimodal_gen.runtime.layers.quantization import QuantizationConfig
 from sglang.multimodal_gen.runtime.layers.rotary_embedding import get_rope
 from sglang.multimodal_gen.runtime.loader.weight_utils import default_weight_loader
+from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload import (
+    LayerwiseOffloadableModuleMixin,
+)
 from sglang.multimodal_gen.runtime.utils.common import add_prefix
 
 logger = logging.getLogger(__name__)
@@ -934,10 +937,13 @@ class Gemma3TextModel(nn.Module):
         return loaded_params
 
 
-class Gemma3ForConditionalGeneration(nn.Module):
+class Gemma3ForConditionalGeneration(nn.Module, LayerwiseOffloadableModuleMixin):
     # transformers 5.6.0 flattened SiglipVisionModel, dropping the
     # `vision_model` intermediate wrapper. Our reimpl keeps it, so remap
     # HF source keys back into our nested namespace when transferring weights.
+    layerwise_offload_default_enabled = False
+    layer_names = ["language_model.layers"]
+
     param_names_mapping = {
         r"^(vision_tower\.)(embeddings|encoder|post_layernorm|head)\.": r"\1vision_model.\2.",
     }

python/sglang/multimodal_gen/runtime/models/encoders/hunyuan3d.py

@@ -11,6 +11,10 @@ from transformers import (
     Dinov2Model,
 )
 
+from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload import (
+    LayerwiseOffloadableModuleMixin,
+)
+
 
 def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
 
@@ -28,7 +32,12 @@ def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
     return np.concatenate([emb_sin, emb_cos], axis=1)
 
 
-class ImageEncoder(nn.Module):
+class ImageEncoder(nn.Module, LayerwiseOffloadableModuleMixin):
+    layerwise_offload_default_enabled = False
+    layer_names = [
+        "model.encoder.layer",
+        "model.vision_model.encoder.layers",
+    ]
     MODEL_CLASS = None
     MODEL_CONFIG_CLASS = None
     mean = []
@@ -203,7 +212,15 @@ def build_image_encoder(config):
         raise ValueError(f'Unknown image encoder type: {config["type"]}')
 
 
-class DualImageEncoder(nn.Module):
+class DualImageEncoder(nn.Module, LayerwiseOffloadableModuleMixin):
+    layerwise_offload_default_enabled = False
+    layer_names = [
+        "main_image_encoder.model.encoder.layer",
+        "main_image_encoder.model.vision_model.encoder.layers",
+        "additional_image_encoder.model.encoder.layer",
+        "additional_image_encoder.model.vision_model.encoder.layers",
+    ]
+
     def __init__(
         self,
         main_image_encoder,
@@ -232,7 +249,13 @@ class DualImageEncoder(nn.Module):
         return outputs
 
 
-class SingleImageEncoder(nn.Module):
+class SingleImageEncoder(nn.Module, LayerwiseOffloadableModuleMixin):
+    layerwise_offload_default_enabled = False
+    layer_names = [
+        "main_image_encoder.model.encoder.layer",
+        "main_image_encoder.model.vision_model.encoder.layers",
+    ]
+
     def __init__(
         self,
         main_image_encoder,

python/sglang/multimodal_gen/runtime/models/encoders/mistral_3.py

@@ -41,6 +41,9 @@ from transformers.models.mistral.modeling_mistral import (
 )
 
 from sglang.multimodal_gen.runtime.loader.weight_utils import default_weight_loader
+from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload import (
+    LayerwiseOffloadableModuleMixin,
+)
 from sglang.multimodal_gen.runtime.platforms import current_platform
 from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
 
@@ -370,7 +373,7 @@ class Mistral3Model(nn.Module):
         )
 
 
-class Mistral3ForConditionalGeneration(nn.Module):
+class Mistral3ForConditionalGeneration(nn.Module, LayerwiseOffloadableModuleMixin):
     _checkpoint_conversion_mapping = {
         "^language_model.model": "model.language_model",
         "^multi_modal_projector": "model.multi_modal_projector",
@@ -378,6 +381,8 @@ class Mistral3ForConditionalGeneration(nn.Module):
     }
     _tied_weights_keys = ["lm_head.weight"]
     uses_sglang_forward_context = False
+    layerwise_offload_default_enabled = False
+    layer_names = ["model.language_model.layers"]
 
     def __init__(self, config: LlavaConfig):
         super().__init__()

python/sglang/multimodal_gen/runtime/models/upsampler/latent_upsampler.py

@@ -8,6 +8,10 @@ import torch
 import torch.nn.functional as F
 from einops import rearrange
 
+from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload import (
+    LayerwiseOffloadableModuleMixin,
+)
+
 
 class BlurDownsample(torch.nn.Module):
     """Anti-aliased spatial downsampling by integer stride using a fixed separable binomial kernel."""
@@ -146,7 +150,7 @@ class SpatialRationalResampler(torch.nn.Module):
         return x
 
 
-class LatentUpsampler(torch.nn.Module):
+class LatentUpsampler(torch.nn.Module, LayerwiseOffloadableModuleMixin):
     """
     Upsample VAE latents spatially and/or temporally.
 
@@ -161,6 +165,9 @@ class LatentUpsampler(torch.nn.Module):
         rational_resampler: Whether to use rational resampler for spatial upsampling.
     """
 
+    layerwise_offload_default_enabled = False
+    layer_names = ["res_blocks", "post_upsample_res_blocks"]
+
     def __init__(
         self,
         in_channels: int = 128,

python/sglang/multimodal_gen/runtime/models/vaes/autoencoder.py

@@ -22,9 +22,12 @@ from diffusers.models.modeling_outputs import AutoencoderKLOutput
 from torch import nn
 
 from sglang.multimodal_gen.configs.models.vaes.flux import FluxVAEConfig
+from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload import (
+    LayerwiseOffloadableModuleMixin,
+)
 
 
-class AutoencoderKL(nn.Module):
+class AutoencoderKL(nn.Module, LayerwiseOffloadableModuleMixin):
     r"""
     A VAE model with KL loss for encoding images into latents and decoding latent representations into images.
 
@@ -59,8 +62,10 @@ class AutoencoderKL(nn.Module):
             mid_block will only have resnet blocks
     """
 
+    layerwise_offload_default_enabled = False
     _supports_gradient_checkpointing = True
     _no_split_modules = ["BasicTransformerBlock", "ResnetBlock2D"]
+    layer_names = ["encoder.down_blocks", "decoder.up_blocks"]
 
     def __init__(
         self,

python/sglang/multimodal_gen/runtime/models/vaes/autoencoder_dc.py

@@ -6,14 +6,20 @@ import torch
 from torch import nn
 
 from sglang.multimodal_gen.configs.models.vaes.sana import SanaVAEConfig
+from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload import (
+    LayerwiseOffloadableModuleMixin,
+)
 from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
 
 logger = init_logger(__name__)
 
 
-class AutoencoderDC(nn.Module):
+class AutoencoderDC(nn.Module, LayerwiseOffloadableModuleMixin):
     """Deep Compression Autoencoder wrapper with 32x spatial compression."""
 
+    layerwise_offload_default_enabled = False
+    layer_names = ["_inner_model.encoder.down_blocks", "_inner_model.decoder.up_blocks"]
+
     def __init__(self, config: SanaVAEConfig = None, **kwargs):
         super().__init__()
         self._config = config

python/sglang/multimodal_gen/runtime/models/vaes/common.py

@@ -18,9 +18,19 @@ from sglang.multimodal_gen.runtime.distributed import (
     get_sp_parallel_rank,
     get_sp_world_size,
 )
+from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload import (
+    LayerwiseOffloadableModuleMixin,
+)
 
 
-class ParallelTiledVAE(ABC, nn.Module):
+class ParallelTiledVAE(ABC, nn.Module, LayerwiseOffloadableModuleMixin):
+    layerwise_offload_default_enabled = False
+    layer_names = [
+        "encoder.down_blocks",
+        "decoder.up_blocks",
+        "encoder.down",
+        "decoder.up",
+    ]
     tile_sample_min_height: int
     tile_sample_min_width: int
     tile_sample_min_num_frames: int

python/sglang/multimodal_gen/runtime/models/vaes/dac.py

@@ -12,6 +12,9 @@ from einops import rearrange
 from torch import nn
 
 from sglang.multimodal_gen.configs.models.vaes.dac import DacVAEConfig
+from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload import (
+    LayerwiseOffloadableModuleMixin,
+)
 from sglang.multimodal_gen.runtime.models.vaes.common import (
     DiagonalGaussianDistribution,
 )
@@ -413,7 +416,10 @@ class Decoder(nn.Module):
         return self.model(x)
 
 
-class DAC(nn.Module):
+class DAC(nn.Module, LayerwiseOffloadableModuleMixin):
+    layerwise_offload_default_enabled = False
+    layer_names = ["encoder.block", "decoder.model"]
+
     def __init__(
         self,
         config: DacVAEConfig,

python/sglang/multimodal_gen/runtime/models/vaes/hunyuan3d_vae.py

@@ -12,6 +12,9 @@ import torch.nn.functional as F
 from einops import rearrange, repeat
 from tqdm import tqdm
 
+from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload import (
+    LayerwiseOffloadableModuleMixin,
+)
 from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
 
 logger = init_logger(__name__)
@@ -1099,9 +1102,12 @@ SurfaceExtractors = {
 }
 
 
-class VectsetVAE(nn.Module):
+class VectsetVAE(nn.Module, LayerwiseOffloadableModuleMixin):
     """Base VAE class for vector set encoding."""
 
+    layerwise_offload_default_enabled = False
+    layer_names = ["transformer.resblocks"]
+
     def __init__(self, volume_decoder=None, surface_extractor=None):
         super().__init__()
         if volume_decoder is None:

python/sglang/multimodal_gen/runtime/models/vaes/ltx_2_3_condition_encoder.py

@@ -3,6 +3,9 @@ from typing import Any
 import torch
 import torch.nn as nn
 
+from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload import (
+    LayerwiseOffloadableModuleMixin,
+)
 from sglang.multimodal_gen.runtime.models.vaes.ltx_2_vae import (
     LTX2VideoCausalConv3d,
     LTX2VideoResnetBlock3d,
@@ -110,7 +113,10 @@ def _make_ltx23_encoder_block(
     )
 
 
-class LTX23VideoConditionEncoder(nn.Module):
+class LTX23VideoConditionEncoder(nn.Module, LayerwiseOffloadableModuleMixin):
+    layerwise_offload_default_enabled = False
+    layer_names = ["down_blocks"]
+
     def __init__(self, config: dict[str, Any]) -> None:
         super().__init__()
 

python/sglang/multimodal_gen/runtime/models/vocoder/ltx_2_vocoder.py

@@ -9,6 +9,9 @@ import torch.nn as nn
 import torch.nn.functional as F
 
 from sglang.multimodal_gen.configs.models.vocoder.ltx_vocoder import LTXVocoderConfig
+from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload import (
+    LayerwiseOffloadableModuleMixin,
+)
 
 LRELU_SLOPE = 0.1
 
@@ -531,11 +534,21 @@ class LTX23VocoderCore(nn.Module):
         return x
 
 
-class LTX2Vocoder(ABC, nn.Module):
+class LTX2Vocoder(ABC, nn.Module, LayerwiseOffloadableModuleMixin):
     r"""
     LTX 2.0 vocoder for converting generated mel spectrograms back to audio waveforms.
     """
 
+    layerwise_offload_default_enabled = False
+    layer_names = [
+        "upsamplers",
+        "resnets",
+        "vocoder.ups",
+        "vocoder.resblocks",
+        "bwe_generator.ups",
+        "bwe_generator.resblocks",
+    ]
+
     def __init__(
         self,
         config: LTXVocoderConfig,

python/sglang/multimodal_gen/runtime/pipelines/diffusers_pipeline.py

@@ -20,7 +20,7 @@ from PIL import Image
 
 from sglang.multimodal_gen.configs.pipeline_configs.base import PipelineConfig
 from sglang.multimodal_gen.runtime.distributed import get_local_torch_device
-from sglang.multimodal_gen.runtime.managers.component_manager import (
+from sglang.multimodal_gen.runtime.managers.memory_managers.component_manager import (
     ComponentResidencyStrategy,
     get_global_component_residency_manager,
 )

python/sglang/multimodal_gen/runtime/pipelines/ltx_2_pipeline.py

@@ -16,12 +16,12 @@ from sglang.multimodal_gen.runtime.loader.component_loaders.component_loader imp
     PipelineComponentLoader,
 )
 from sglang.multimodal_gen.runtime.loader.utils import BYTES_PER_GB
-from sglang.multimodal_gen.runtime.managers.component_manager import (
+from sglang.multimodal_gen.runtime.managers.memory_managers.component_manager import (
     ComponentResidencyStrategy,
     ComponentUse,
     ResidencyState,
 )
-from sglang.multimodal_gen.runtime.managers.component_resident_strategies import (
+from sglang.multimodal_gen.runtime.managers.memory_managers.component_resident_strategies import (
     SnapshotModuleResidency,
     SnapshotStrategy,
 )

python/sglang/multimodal_gen/runtime/pipelines_core/composed_pipeline_base.py

@@ -24,7 +24,7 @@ from sglang.multimodal_gen.runtime.layers.attention.selector import (
 from sglang.multimodal_gen.runtime.loader.component_loaders.component_loader import (
     PipelineComponentLoader,
 )
-from sglang.multimodal_gen.runtime.managers.component_manager import (
+from sglang.multimodal_gen.runtime.managers.memory_managers.component_manager import (
     ComponentResidencyManager,
     ComponentResidencyStrategy,
     get_global_component_residency_manager,

python/sglang/multimodal_gen/runtime/pipelines_core/lora_pipeline.py

@@ -170,8 +170,8 @@ class LoRAPipeline(ComposedPipelineBase):
         Yields:
             List of modules that had offload disabled.
         """
-        from sglang.multimodal_gen.runtime.managers.layerwise_offload import (
+        from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload import (
-            OffloadableDiTMixin,
+            is_layerwise_offloaded_module,
         )
 
         module_names = []
@@ -204,10 +204,9 @@ class LoRAPipeline(ComposedPipelineBase):
         offload_disabled_modules = []
         for module_name in module_names:
             module = self.modules.get(module_name)
-            if module is not None and isinstance(module, OffloadableDiTMixin):
+            if module is not None and is_layerwise_offloaded_module(module):
-                if module.layerwise_offload_managers is not None:
+                module.disable_offload()
-                    module.disable_offload()
+                offload_disabled_modules.append(module)
-                    offload_disabled_modules.append(module)
 
         try:
             yield offload_disabled_modules

python/sglang/multimodal_gen/runtime/pipelines_core/stages/base.py

@@ -17,7 +17,9 @@ from enum import Enum, auto
 import torch
 
 from sglang.multimodal_gen.runtime.disaggregation.roles import RoleType
-from sglang.multimodal_gen.runtime.managers.component_manager import ComponentUse
+from sglang.multimodal_gen.runtime.managers.memory_managers.component_manager import (
+    ComponentUse,
+)
 from sglang.multimodal_gen.runtime.pipelines_core.schedule_batch import Req
 from sglang.multimodal_gen.runtime.pipelines_core.stages.dedup import StageDedupMixin
 from sglang.multimodal_gen.runtime.pipelines_core.stages.validators import (

python/sglang/multimodal_gen/runtime/pipelines_core/stages/decoding.py

@@ -11,7 +11,9 @@ import torch
 
 from sglang.multimodal_gen.runtime.distributed import get_local_torch_device
 from sglang.multimodal_gen.runtime.loader.component_loaders.vae_loader import VAELoader
-from sglang.multimodal_gen.runtime.managers.component_manager import ComponentUse
+from sglang.multimodal_gen.runtime.managers.memory_managers.component_manager import (
+    ComponentUse,
+)
 from sglang.multimodal_gen.runtime.models.vaes.common import ParallelTiledVAE
 from sglang.multimodal_gen.runtime.pipelines_core.schedule_batch import OutputBatch, Req
 from sglang.multimodal_gen.runtime.pipelines_core.stages.base import (

python/sglang/multimodal_gen/runtime/pipelines_core/stages/decoding_av.py

@@ -1,7 +1,9 @@
 import torch
 
 from sglang.multimodal_gen.runtime.distributed import get_local_torch_device
-from sglang.multimodal_gen.runtime.managers.component_manager import ComponentUse
+from sglang.multimodal_gen.runtime.managers.memory_managers.component_manager import (
+    ComponentUse,
+)
 from sglang.multimodal_gen.runtime.pipelines_core.schedule_batch import OutputBatch, Req
 from sglang.multimodal_gen.runtime.pipelines_core.stages.decoding import DecodingStage
 from sglang.multimodal_gen.runtime.platforms import current_platform

python/sglang/multimodal_gen/runtime/pipelines_core/stages/denoising.py

@@ -67,8 +67,10 @@ from sglang.multimodal_gen.runtime.layers.attention.STA_configuration import (
 from sglang.multimodal_gen.runtime.loader.component_loaders.transformer_loader import (
     TransformerLoader,
 )
-from sglang.multimodal_gen.runtime.managers.component_manager import ComponentUse
 from sglang.multimodal_gen.runtime.managers.forward_context import set_forward_context
+from sglang.multimodal_gen.runtime.managers.memory_managers.component_manager import (
+    ComponentUse,
+)
 from sglang.multimodal_gen.runtime.pipelines_core.schedule_batch import Req
 from sglang.multimodal_gen.runtime.pipelines_core.stages.base import (
     PipelineStage,

python/sglang/multimodal_gen/runtime/pipelines_core/stages/denoising_av.py

@@ -2,7 +2,9 @@ import torch
 from diffusers.utils.torch_utils import randn_tensor
 
 from sglang.multimodal_gen.configs.pipeline_configs.ltx_2 import is_ltx23_native_variant
-from sglang.multimodal_gen.runtime.managers.component_manager import ComponentUse
+from sglang.multimodal_gen.runtime.managers.memory_managers.component_manager import (
+    ComponentUse,
+)
 from sglang.multimodal_gen.runtime.pipelines_core.diffusion_scheduler_utils import (
     clone_scheduler_runtime,
 )

python/sglang/multimodal_gen/runtime/pipelines_core/stages/encoding.py

@@ -8,7 +8,9 @@ Encoding stage for diffusion pipelines.
 import torch
 
 from sglang.multimodal_gen.runtime.distributed import get_local_torch_device
-from sglang.multimodal_gen.runtime.managers.component_manager import ComponentUse
+from sglang.multimodal_gen.runtime.managers.memory_managers.component_manager import (
+    ComponentUse,
+)
 from sglang.multimodal_gen.runtime.models.vaes.common import ParallelTiledVAE
 from sglang.multimodal_gen.runtime.pipelines_core.schedule_batch import Req
 from sglang.multimodal_gen.runtime.pipelines_core.stages.base import PipelineStage

python/sglang/multimodal_gen/runtime/pipelines_core/stages/image_encoding.py

@@ -20,8 +20,13 @@ from diffusers.models.modeling_outputs import AutoencoderKLOutput
 
 from sglang.multimodal_gen.configs.pipeline_configs.base import TextConditioningOutput
 from sglang.multimodal_gen.runtime.distributed import get_local_torch_device
-from sglang.multimodal_gen.runtime.managers.component_manager import ComponentUse
 from sglang.multimodal_gen.runtime.managers.forward_context import set_forward_context
+from sglang.multimodal_gen.runtime.managers.memory_managers.component_manager import (
+    ComponentUse,
+)
+from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload import (
+    configure_layerwise_offload_modules,
+)
 from sglang.multimodal_gen.runtime.models.vaes.common import ParallelTiledVAE
 from sglang.multimodal_gen.runtime.models.vision_utils import (
     normalize,
@@ -485,6 +490,14 @@ class LTX2ImageEncodingStage(PipelineStage):
             safetensors_load_file(weights_path), strict=True
         )
         self._condition_image_encoder_dir = encoder_dir
+        if server_args.should_configure_layerwise_offload_for_lazy_component():
+            modules = {"condition_image_encoder": self._condition_image_encoder}
+            configure_layerwise_offload_modules(
+                modules,
+                server_args,
+                component_names=server_args.layerwise_offload_components,
+                warn_missing=False,
+            )
         return True
 
     # -- image preprocessing ---------------------------------------------

python/sglang/multimodal_gen/runtime/pipelines_core/stages/model_specific_stages/helios_denoising.py

@@ -13,8 +13,10 @@ import numpy as np
 import torch
 import torch.nn.functional as F
 
-from sglang.multimodal_gen.runtime.managers.component_manager import ComponentUse
 from sglang.multimodal_gen.runtime.managers.forward_context import set_forward_context
+from sglang.multimodal_gen.runtime.managers.memory_managers.component_manager import (
+    ComponentUse,
+)
 from sglang.multimodal_gen.runtime.pipelines_core.diffusion_scheduler_utils import (
     get_or_create_request_scheduler,
 )

python/sglang/multimodal_gen/runtime/pipelines_core/stages/model_specific_stages/mova.py

@@ -46,7 +46,9 @@ from sglang.multimodal_gen.runtime.models.dits.mova_video_dit import (
 # Create aliases for backward compatibility
 video_sinusoidal_embedding_1d = sinusoidal_embedding_1d
 audio_sinusoidal_embedding_1d = sinusoidal_embedding_1d
-from sglang.multimodal_gen.runtime.managers.component_manager import ComponentUse
+from sglang.multimodal_gen.runtime.managers.memory_managers.component_manager import (
+    ComponentUse,
+)
 from sglang.multimodal_gen.runtime.pipelines_core.schedule_batch import OutputBatch, Req
 from sglang.multimodal_gen.runtime.pipelines_core.stages.base import (
     PipelineStage,

python/sglang/multimodal_gen/runtime/pipelines_core/stages/model_specific_stages/qwen_image_layered.py

@@ -8,8 +8,10 @@ from diffusers.image_processor import VaeImageProcessor
 from diffusers.utils.torch_utils import randn_tensor
 
 from sglang.multimodal_gen.runtime.distributed import get_local_torch_device
-from sglang.multimodal_gen.runtime.managers.component_manager import ComponentUse
 from sglang.multimodal_gen.runtime.managers.forward_context import set_forward_context
+from sglang.multimodal_gen.runtime.managers.memory_managers.component_manager import (
+    ComponentUse,
+)
 from sglang.multimodal_gen.runtime.models.vision_utils import load_image
 from sglang.multimodal_gen.runtime.pipelines_core.schedule_batch import Req
 from sglang.multimodal_gen.runtime.pipelines_core.stages.base import PipelineStage

python/sglang/multimodal_gen/runtime/pipelines_core/stages/text_encoding.py

@@ -16,8 +16,10 @@ import torch
 from sglang.multimodal_gen.configs.models.encoders import BaseEncoderOutput
 from sglang.multimodal_gen.configs.pipeline_configs.base import TextConditioningOutput
 from sglang.multimodal_gen.runtime.distributed import get_local_torch_device
-from sglang.multimodal_gen.runtime.managers.component_manager import ComponentUse
 from sglang.multimodal_gen.runtime.managers.forward_context import set_forward_context
+from sglang.multimodal_gen.runtime.managers.memory_managers.component_manager import (
+    ComponentUse,
+)
 from sglang.multimodal_gen.runtime.pipelines_core.schedule_batch import Req
 from sglang.multimodal_gen.runtime.pipelines_core.stages.base import PipelineStage
 from sglang.multimodal_gen.runtime.pipelines_core.stages.validators import (

python/sglang/multimodal_gen/runtime/pipelines_core/stages/upsampling.py

@@ -1,7 +1,9 @@
 import torch
 
 from sglang.multimodal_gen.runtime.distributed import get_local_torch_device
-from sglang.multimodal_gen.runtime.managers.component_manager import ComponentUse
+from sglang.multimodal_gen.runtime.managers.memory_managers.component_manager import (
+    ComponentUse,
+)
 from sglang.multimodal_gen.runtime.pipelines_core.schedule_batch import Req
 from sglang.multimodal_gen.runtime.pipelines_core.stages.base import PipelineStage
 from sglang.multimodal_gen.runtime.server_args import ServerArgs

python/sglang/multimodal_gen/runtime/postprocess/realesrgan_upscaler.py

@@ -26,6 +26,9 @@ logger = init_logger(__name__)
 # Default HuggingFace repo and filename for Real-ESRGAN weights
 _DEFAULT_REALESRGAN_HF_REPO = "ai-forever/Real-ESRGAN"
 _DEFAULT_REALESRGAN_FILENAME = "RealESRGAN_x4.pth"
+_LOW_MEMORY_TILED_UPSCALE_FREE_BYTES = 2 * 1024**3
+_REALESRGAN_TILE_SIZE = 256
+_REALESRGAN_TILE_PAD = 32
 
 # Module-level cache: model_path -> UpscalerModel instance
 _MODEL_CACHE: dict[str, "UpscalerModel"] = {}
@@ -263,6 +266,60 @@ class UpscalerModel:
     def device(self) -> torch.device:
         return next(self.net.parameters()).device
 
+    @property
+    def dtype(self) -> torch.dtype:
+        return next(self.net.parameters()).dtype
+
+    def _should_use_tiled_upscale(self, h: int, w: int) -> bool:
+        if self.device.type != "cuda":
+            return False
+        free_bytes, _ = torch.cuda.mem_get_info(self.device)
+        output_bytes = h * w * self.scale * self.scale * 3 * 4
+        required_free_bytes = max(
+            _LOW_MEMORY_TILED_UPSCALE_FREE_BYTES,
+            output_bytes * 4,
+        )
+        return free_bytes < required_free_bytes
+
+    def _upscale_tiled_to_cpu(
+        self,
+        img_t: torch.Tensor,
+        tile_size: int = _REALESRGAN_TILE_SIZE,
+        tile_pad: int = _REALESRGAN_TILE_PAD,
+    ) -> torch.Tensor:
+        _, channels, h, w = img_t.shape
+        scale = self.scale
+        output = torch.empty(
+            (1, channels, h * scale, w * scale),
+            dtype=torch.float32,
+            device="cpu",
+        )
+
+        for y in range(0, h, tile_size):
+            tile_h = min(tile_size, h - y)
+            in_y0 = max(y - tile_pad, 0)
+            in_y1 = min(y + tile_h + tile_pad, h)
+            out_y0 = y * scale
+            out_y1 = (y + tile_h) * scale
+            crop_y0 = (y - in_y0) * scale
+            crop_y1 = crop_y0 + tile_h * scale
+
+            for x in range(0, w, tile_size):
+                tile_w = min(tile_size, w - x)
+                in_x0 = max(x - tile_pad, 0)
+                in_x1 = min(x + tile_w + tile_pad, w)
+                out_x0 = x * scale
+                out_x1 = (x + tile_w) * scale
+                crop_x0 = (x - in_x0) * scale
+                crop_x1 = crop_x0 + tile_w * scale
+
+                tile = img_t[..., in_y0:in_y1, in_x0:in_x1]
+                out_tile = self.net(tile)
+                out_tile = out_tile[..., crop_y0:crop_y1, crop_x0:crop_x1].float()
+                output[..., out_y0:out_y1, out_x0:out_x1].copy_(out_tile.cpu())
+
+        return output
+
     def upscale(self, frame: np.ndarray, outscale: float | None = None) -> np.ndarray:
         """Upscale a single HWC uint8 frame → HWC uint8 frame.
 
@@ -276,9 +333,34 @@ class UpscalerModel:
         """
         h, w = frame.shape[:2]
         img = frame.astype(np.float32) / 255.0
-        img_t = torch.from_numpy(img).permute(2, 0, 1).unsqueeze(0).to(self.device)
+        img_t = (
+            torch.from_numpy(img)
+            .permute(2, 0, 1)
+            .unsqueeze(0)
+            .to(device=self.device, dtype=self.dtype)
+        )
         with torch.no_grad():
-            out = self.net(img_t)
+            if self._should_use_tiled_upscale(h, w):
+                logger.info(
+                    "Using tiled Real-ESRGAN upscale for low GPU memory: "
+                    "frame=%dx%d, tile_size=%d, tile_pad=%d",
+                    w,
+                    h,
+                    _REALESRGAN_TILE_SIZE,
+                    _REALESRGAN_TILE_PAD,
+                )
+                out = self._upscale_tiled_to_cpu(img_t)
+            else:
+                try:
+                    out = self.net(img_t)
+                except torch.cuda.OutOfMemoryError:
+                    if self.device.type != "cuda":
+                        raise
+                    torch.cuda.empty_cache()
+                    logger.warning(
+                        "Real-ESRGAN full-frame upscale OOM; retrying with tiled upscale"
+                    )
+                    out = self._upscale_tiled_to_cpu(img_t)
 
         # If the desired outscale differs from the model's native scale,
         # resize to (h * outscale, w * outscale).

python/sglang/multimodal_gen/runtime/server_args.py

@@ -37,6 +37,11 @@ from sglang.multimodal_gen.runtime.layers.quantization.configs.nunchaku_config i
     NunchakuConfig,
 )
 from sglang.multimodal_gen.runtime.loader.utils import BYTES_PER_GB
+from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload_components import (
+    LAYERWISE_OFFLOAD_DEFAULT_COMPONENTS,
+    cpu_offload_flags_for_layerwise_components,
+    normalize_layerwise_offload_components,
+)
 from sglang.multimodal_gen.runtime.platforms import (
     AttentionBackendEnum,
     current_platform,
@@ -194,7 +199,9 @@ class ServerArgs(DisaggArgsMixin):
 
     # CPU offload parameters
     dit_cpu_offload: bool | None = None
+    # if true, add the legacy default DiT components
     dit_layerwise_offload: bool | None = None
+    layerwise_offload_components: list[str] | None = None
     dit_offload_prefetch_size: float = 0.0
     text_encoder_cpu_offload: bool | None = None
     image_encoder_cpu_offload: bool | None = None
@@ -323,14 +330,15 @@ class ServerArgs(DisaggArgsMixin):
     def _adjust_parameters(self):
         """set defaults and normalize values."""
         auto_tuner = ServerArgsAutoTuner(self)
-        auto_tuner.adjust()
+        auto_tuner.adjust_based_on_performance_mode()
         if auto_tuner.could_override_server_args():
             self._adjust_offload()
-            auto_tuner.maybe_adjust_auto_dit_layerwise_offload()
+            auto_tuner.maybe_adjust_auto_default_layerwise_offload()
         self._adjust_ltx2_two_stage_device_mode()
         if auto_tuner.could_override_server_args():
             auto_tuner.maybe_adjust_auto_component_residency_after_offload()
             auto_tuner.maybe_adjust_auto_fsdp_with_offload_enabled()
+            auto_tuner.maybe_replace_cpu_offloaded_components_with_layerwise()
         self._adjust_path()
         self._adjust_quant_config()
         self._adjust_warmup()
@@ -339,6 +347,7 @@ class ServerArgs(DisaggArgsMixin):
         self._adjust_parallelism()
         self._adjust_attention_backend()
         self._adjust_platform_specific()
+        self._adjust_layerwise_offload_components()
         self._adjust_autocast()
         auto_tuner.finalize_auto_flags()
         self.adjust_pipeline_config()
@@ -802,6 +811,82 @@ class ServerArgs(DisaggArgsMixin):
         if current_platform.is_mps():
             self.use_fsdp_inference = False
             self.dit_layerwise_offload = False
+            self.layerwise_offload_components = None
+
+    def should_configure_layerwise_offload_for_lazy_component(self) -> bool:
+        """Return whether a lazy-loaded component should try layerwise offload.
+
+        Lazy components are loaded after the normal pipeline-wide configuration
+        pass, so they should only attempt layerwise configuration when a
+        component scope is present.
+        """
+        return bool(self.layerwise_offload_components)
+
+    @property
+    def is_dit_layerwise_offload_selected(self) -> bool:
+        """returns if dit is selected to be layerwise-offload"""
+        component_names = self.layerwise_offload_components
+        return bool(
+            component_names
+            and "dit_cpu_offload"
+            in cpu_offload_flags_for_layerwise_components(component_names)
+        )
+
+    def _adjust_layerwise_offload_components(self):
+        explicitly_set_component_names = normalize_layerwise_offload_components(
+            self.layerwise_offload_components
+        )
+        if self.dit_layerwise_offload:
+            if explicitly_set_component_names is None:
+                explicitly_set_component_names = [LAYERWISE_OFFLOAD_DEFAULT_COMPONENTS]
+            elif (
+                LAYERWISE_OFFLOAD_DEFAULT_COMPONENTS
+                not in explicitly_set_component_names
+            ):
+                explicitly_set_component_names = [
+                    LAYERWISE_OFFLOAD_DEFAULT_COMPONENTS,
+                    *explicitly_set_component_names,
+                ]
+
+        if explicitly_set_component_names is not None:
+            self.layerwise_offload_components = explicitly_set_component_names
+            self._disable_cpu_offload_for_layerwise_components(
+                explicitly_set_component_names
+            )
+            return
+
+    def _disable_cpu_offload_for_layerwise_components(
+        self, component_names: list[str]
+    ) -> None:
+        # Layerwise offload owns H2D/D2H for selected component weights.
+        flag_names = cpu_offload_flags_for_layerwise_components(component_names)
+        disabled_flag_names: list[str] = []
+
+        if "dit_cpu_offload" in flag_names and self.dit_cpu_offload is not False:
+            self.dit_cpu_offload = False
+            disabled_flag_names.append("dit_cpu_offload")
+        if (
+            "text_encoder_cpu_offload" in flag_names
+            and self.text_encoder_cpu_offload is not False
+        ):
+            self.text_encoder_cpu_offload = False
+            disabled_flag_names.append("text_encoder_cpu_offload")
+        if (
+            "image_encoder_cpu_offload" in flag_names
+            and self.image_encoder_cpu_offload is not False
+        ):
+            self.image_encoder_cpu_offload = False
+            disabled_flag_names.append("image_encoder_cpu_offload")
+        if "vae_cpu_offload" in flag_names and self.vae_cpu_offload is not False:
+            self.vae_cpu_offload = False
+            disabled_flag_names.append("vae_cpu_offload")
+
+        if disabled_flag_names:
+            logger.info(
+                "Disabling %s because the selected layerwise offload components "
+                "manage the same weights.",
+                ", ".join(disabled_flag_names),
+            )
 
     def _adjust_autocast(self):
         if self.disable_autocast is None:
@@ -944,13 +1029,6 @@ class ServerArgs(DisaggArgsMixin):
             help="The specific model version to use (can be a branch name, tag name, or commit id)",
         )
 
-        # Parallelism
-        parser.add_argument(
-            "--num-gpus",
-            type=int,
-            default=ServerArgs.num_gpus,
-            help="The number of GPUs to use.",
-        )
         parser.add_argument(
             "--performance-mode",
             "--mode",
@@ -959,7 +1037,7 @@ class ServerArgs(DisaggArgsMixin):
             default=ServerArgs.performance_mode,
             help=(
                 "Preset for performance and memory defaults. "
-                "'manual' keeps performance-related server args under explicit user control; "
+                "'manual' keeps performance-related server args under explicit user control, no adjustment is made; "
                 "'auto' keeps safe defaults and applies high-confidence FSDP/CFG improvements; "
                 "'speed' favors GPU-resident execution for lower latency and higher throughput, and may OOM; "
                 "'memory' favors lower GPU memory usage; "
@@ -967,6 +1045,13 @@ class ServerArgs(DisaggArgsMixin):
             ),
         )
 
+        # Parallelism
+        parser.add_argument(
+            "--num-gpus",
+            type=int,
+            default=ServerArgs.num_gpus,
+            help="The number of GPUs to use.",
+        )
         parser.add_argument(
             "--tp-size",
             type=int,
@@ -1093,8 +1178,21 @@ class ServerArgs(DisaggArgsMixin):
             "--dit-layerwise-offload",
             action=StoreBoolean,
             default=ServerArgs.dit_layerwise_offload,
-            help="Enable layerwise CPU offload with async H2D prefetch overlap for supported DiT models (e.g., Wan, MOVA). "
+            help="Enable layerwise CPU offload with async H2D prefetch overlap for DiTs. "
-            "Cannot be used together with cache-dit (SGLANG_CACHE_DIT_ENABLED), dit_cpu_offload, or use_fsdp_inference.",
+            "It only selects the legacy default DiT components. Cannot be used together with cache-dit "
+            "(SGLANG_CACHE_DIT_ENABLED), dit_cpu_offload, or use_fsdp_inference.",
+        )
+        parser.add_argument(
+            "--layerwise-offload-components",
+            "--layerwise-offload-modules",
+            type=str,
+            nargs="+",
+            default=ServerArgs.layerwise_offload_components,
+            help="Select pipeline components for layerwise offload. "
+            "Use default to select the legacy default DiT components, "
+            "or all to select every layerwise-offloadable component. "
+            "This option does not imply --dit-layerwise-offload. Example: "
+            "--layerwise-offload-components text_encoder image_encoder.",
         )
         parser.add_argument(
             "--dit-offload-prefetch-size",
@@ -1520,7 +1618,7 @@ class ServerArgs(DisaggArgsMixin):
     @classmethod
     def from_dict(cls, kwargs: dict[str, Any]) -> "ServerArgs":
         """Create a ServerArgs object from a dictionary."""
-        attrs = [attr.name for attr in dataclasses.fields(cls)]
+        attrs = [attr.name for attr in dataclasses.fields(cls) if attr.init]
         server_args_kwargs: dict[str, Any] = {}
 
         component_paths = dict(kwargs.get("component_paths") or {})
@@ -1623,34 +1721,36 @@ class ServerArgs(DisaggArgsMixin):
                 "We do not recommend --dit-offload-prefetch-size to be between 0.5 and 1.0"
             )
 
-        # validate dit_layerwise_offload conflicts
+        # validate layerwise offload conflicts
-        if self.dit_layerwise_offload:
+        if self.layerwise_offload_components:
             if self.dit_offload_prefetch_size < 0.0:
                 raise ValueError("dit_offload_prefetch_size must be non-negative")
 
             if self.use_fsdp_inference:
                 logger.warning(
-                    "dit_layerwise_offload is enabled, automatically disabling use_fsdp_inference."
+                    "layerwise offload components are selected, automatically disabling use_fsdp_inference."
                 )
                 self.use_fsdp_inference = False
 
-            if self.dit_cpu_offload is None:
+            should_disable_dit_cpu_offload = self.is_dit_layerwise_offload_selected
+            if should_disable_dit_cpu_offload and self.dit_cpu_offload is not False:
                 logger.warning(
-                    "dit_layerwise_offload is enabled, automatically disabling dit_cpu_offload."
+                    "layerwise offload is selected for DiT components, automatically disabling dit_cpu_offload."
                 )
                 self.dit_cpu_offload = False
 
-            if envs.SGLANG_CACHE_DIT_ENABLED:
+            if envs.SGLANG_CACHE_DIT_ENABLED and should_disable_dit_cpu_offload:
                 raise ValueError(
-                    "dit_layerwise_offload cannot be enabled together with cache-dit. "
+                    "DiT layerwise offload cannot be enabled together with cache-dit. "
                     "cache-dit may reuse skipped blocks whose weights have been released by layerwise offload, "
                     "causing shape mismatch errors. "
-                    "Please disable either --dit-layerwise-offload or SGLANG_CACHE_DIT_ENABLED."
+                    "Please disable --dit-layerwise-offload, remove DiT from --layerwise-offload-components, "
+                    "or disable SGLANG_CACHE_DIT_ENABLED."
                 )
 
             logger.warning(
-                "dit_layerwise_offload is enabled: %slower GPU memory usage%s, but %smay reduce throughput or increase latency%s. "
+                "layerwise offload components are selected: %slower GPU memory usage%s, but %smay reduce throughput or increase latency%s. "
-                "%sIf you are using multi-GPU deployment and already have enough memory headroom, prefer keeping dit_layerwise_offload disabled.%s "
+                "%sIf you are using multi-GPU deployment and already have enough memory headroom, prefer keeping layerwise offload disabled.%s "
                 "Please tune this based on your memory headroom and performance target.",
                 GREEN,
                 RESET,

python/sglang/multimodal_gen/runtime/server_args_auto_tune.py

@@ -10,6 +10,9 @@ from sglang.multimodal_gen import envs
 from sglang.multimodal_gen.configs.pipeline_configs.model_deployment_config import (
     ModelDeploymentConfig,
 )
+from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload_components import (
+    LAYERWISE_OFFLOAD_DEFAULT_COMPONENTS,
+)
 from sglang.multimodal_gen.runtime.platforms import current_platform
 from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
 
@@ -27,11 +30,14 @@ class ServerArgsAutoTuner:
     def __init__(self, server_args: "ServerArgs"):
         self.server_args = server_args
         self._explicit_memory_policy = self._has_explicit_memory_policy()
+        self._explicit_layerwise_replacement_policy = (
+            self._has_explicit_layerwise_replacement_policy()
+        )
 
     def _deployment_config(self) -> ModelDeploymentConfig:
         return self.server_args.pipeline_config.get_model_deployment_config()
 
-    def adjust(self) -> None:
+    def adjust_based_on_performance_mode(self) -> None:
         """Adjust the server args based on the performance mode"""
         args = self.server_args
         args.performance_mode = self._normalize_performance_mode()
@@ -56,8 +62,8 @@ class ServerArgsAutoTuner:
                 self._set_gpu_resident_defaults(use_fsdp=True)
                 return
             args.use_fsdp_inference = False
-            if self._can_apply_dit_layerwise_offload_policy():
+            if self._can_apply_default_layerwise_offload_policy():
-                # apply dit layerwise offload to save VRAM during denoising stage
+                # apply default layerwise offload to save VRAM during denoising stage
                 self._set_layerwise_offload_defaults()
             else:
                 self._set_component_offload_defaults()
@@ -127,7 +133,8 @@ class ServerArgsAutoTuner:
                 args.dit_layerwise_offload = False
             self._enable_cfg_parallel_if_supported()
 
-    def maybe_adjust_auto_dit_layerwise_offload(self) -> None:
+    def maybe_adjust_auto_default_layerwise_offload(self) -> None:
+        """adjust the default layerwise offload policy"""
         args = self.server_args
         if not self.could_override_server_args():
             return
@@ -178,6 +185,43 @@ class ServerArgsAutoTuner:
             args.dit_layerwise_offload = True
             args.dit_cpu_offload = False
 
+    def maybe_replace_cpu_offloaded_components_with_layerwise(self) -> None:
+        args = self.server_args
+        if (
+            not self.could_override_server_args()
+            or self._explicit_layerwise_replacement_policy
+            or current_platform.is_cpu()
+            or not current_platform.is_cuda()
+            or envs.SGLANG_CACHE_DIT_ENABLED
+            or args.use_fsdp_inference
+            or args.layerwise_offload_components is not None
+        ):
+            return
+
+        layerwise_components: list[str] = []
+        if args.dit_layerwise_offload:
+            layerwise_components.append(LAYERWISE_OFFLOAD_DEFAULT_COMPONENTS)
+
+        changed: list[str] = []
+        if args.text_encoder_cpu_offload:
+            layerwise_components.append("text_encoder")
+            changed.append("text_encoder")
+        if args.image_encoder_cpu_offload:
+            layerwise_components.append("image_encoder")
+            changed.append("image_encoder")
+        if args.vae_cpu_offload:
+            layerwise_components.append("vae")
+            changed.append("vae")
+
+        if not changed:
+            return
+
+        args.layerwise_offload_components = layerwise_components
+        logger.info(
+            "Automatically replacing CPU offload with layerwise offload for components: %s",
+            ", ".join(changed),
+        )
+
     def finalize_auto_flags(self) -> None:
         """if some args are unset after all the adjustment, set them to defaults"""
         if not self.could_override_server_args():
@@ -266,7 +310,7 @@ class ServerArgsAutoTuner:
         if args.image_encoder_cpu_offload is None:
             args.image_encoder_cpu_offload = True
 
-    def _can_apply_dit_layerwise_offload_policy(self) -> bool:
+    def _can_apply_default_layerwise_offload_policy(self) -> bool:
         return (
             self._deployment_config().auto_dit_layerwise_offload
             and not envs.SGLANG_CACHE_DIT_ENABLED
@@ -299,10 +343,21 @@ class ServerArgsAutoTuner:
             args.use_fsdp_inference is not None
             or args.dit_cpu_offload is not None
             or args.dit_layerwise_offload is not None
+            or args.layerwise_offload_components is not None
             or args.text_encoder_cpu_offload is not None
             or args.image_encoder_cpu_offload is not None
         )
 
+    def _has_explicit_layerwise_replacement_policy(self) -> bool:
+        args = self.server_args
+        return (
+            args.dit_layerwise_offload is not None
+            or args.layerwise_offload_components is not None
+            or args.text_encoder_cpu_offload is not None
+            or args.image_encoder_cpu_offload is not None
+            or args.vae_cpu_offload is True
+        )
+
     def _has_explicit_parallel_policy(self) -> bool:
         args = self.server_args
         return (

python/sglang/multimodal_gen/test/test_utils.py

@@ -801,7 +801,10 @@ def get_clip_model() -> tuple[Any, Any]:
             )
         model = CLIPModel.from_pretrained(CLIP_MODEL_NAME)
 
-        device = "cuda" if torch.cuda.is_available() else "cpu"
+        # ci server tests keep the generation server alive while consistency runs
+        device = (
+            "cpu" if is_in_ci() else ("cuda" if torch.cuda.is_available() else "cpu")
+        )
         model = model.to(device)
         model.eval()
 

python/sglang/multimodal_gen/test/unit/test_layerwise_offload.py

@@ -9,11 +9,22 @@ from sglang.multimodal_gen.runtime.layers.quantization.modelopt_quant import (
 from sglang.multimodal_gen.runtime.loader.transformer_load_utils import (
     _ModelOptFp8OffloadAdapter,
 )
-from sglang.multimodal_gen.runtime.managers import (
+from sglang.multimodal_gen.runtime.managers.memory_managers import (
     layerwise_offload as layerwise_offload_mod,
 )
-from sglang.multimodal_gen.runtime.managers.layerwise_offload import (
+from sglang.multimodal_gen.runtime.managers.memory_managers.component_manager import (
+    build_component_residency_strategy,
+)
+from sglang.multimodal_gen.runtime.managers.memory_managers.component_resident_strategies import (
+    LayerwiseOffloadStrategy,
+    ResidentStrategy,
+    VanillaD2HStrategy,
+)
+from sglang.multimodal_gen.runtime.managers.memory_managers.layerwise_offload import (
+    LayerwiseOffloadableModuleMixin,
     LayerwiseOffloadManager,
+    configure_layerwise_offload_modules,
+    is_layerwise_offloaded_module,
 )
 
 
@@ -65,6 +76,65 @@ class _DummyModel(torch.nn.Module):
         self.blocks = torch.nn.ModuleList([_DummyBlock()])
 
 
+class _NestedDummyModel(torch.nn.Module, LayerwiseOffloadableModuleMixin):
+    layer_names = ["encoder.blocks"]
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.encoder = _DummyModel()
+
+
+class _SharedBuffer(torch.nn.Module):
+    def __init__(self) -> None:
+        super().__init__()
+        self.register_buffer(
+            "cache", torch.arange(12, dtype=torch.float32).reshape(6, 2)
+        )
+
+
+class _SharedBufferLayer(torch.nn.Module):
+    def __init__(self, shared: _SharedBuffer) -> None:
+        super().__init__()
+        self.shared = shared
+        self.weight = torch.nn.Parameter(torch.ones(2, 2, dtype=torch.float32))
+
+
+class _SharedBufferModel(torch.nn.Module):
+    def __init__(self) -> None:
+        super().__init__()
+        shared = _SharedBuffer()
+        self.blocks = torch.nn.ModuleList(
+            [_SharedBufferLayer(shared), _SharedBufferLayer(shared)]
+        )
+
+
+class _NestedEncoderDummyModel(_NestedDummyModel):
+    layerwise_offload_default_enabled = False
+
+
+class _LayerwiseComponent(torch.nn.Module, LayerwiseOffloadableModuleMixin):
+    layer_names = ["blocks"]
+
+    def __init__(self, enabled: bool) -> None:
+        super().__init__()
+        self.blocks = torch.nn.ModuleList([_DummyBlock()])
+        self.layerwise_offload_managers = [SimpleNamespace(enabled=enabled)]
+
+
+def _server_args(**kwargs):
+    defaults = dict(
+        use_fsdp_inference=False,
+        dit_cpu_offload=False,
+        text_encoder_cpu_offload=False,
+        image_encoder_cpu_offload=False,
+        vae_cpu_offload=False,
+        dit_offload_prefetch_size=1,
+        pin_cpu_memory=False,
+    )
+    defaults.update(kwargs)
+    return SimpleNamespace(**defaults)
+
+
 def test_layerwise_offload_preserves_non_contiguous_stride(monkeypatch):
     monkeypatch.setattr(
         layerwise_offload_mod.torch, "get_device_module", lambda: _FakeDeviceModule
@@ -103,6 +173,36 @@ def test_layerwise_offload_preserves_non_contiguous_stride(monkeypatch):
     assert torch.equal(reloaded_weight, original_weight)
 
 
+def test_layerwise_offload_keeps_shared_buffers_resident(monkeypatch):
+    monkeypatch.setattr(
+        layerwise_offload_mod.torch, "get_device_module", lambda: _FakeDeviceModule
+    )
+    monkeypatch.setattr(layerwise_offload_mod.current_platform, "device_type", "cpu")
+
+    model = _SharedBufferModel()
+    original_cache = model.blocks[0].shared.cache.detach().clone()
+
+    manager = LayerwiseOffloadManager(
+        model=model,
+        layers_attr_str="blocks",
+        num_layers=2,
+        enabled=True,
+        pin_cpu_memory=False,
+        prefetch_size=1,
+    )
+
+    assert not any(
+        "cache" in name
+        for metadata in manager._weight_metadata.values()
+        for name in metadata
+    )
+    manager.release_layer(0)
+
+    cache = model.blocks[1].shared.cache
+    assert torch.equal(cache, original_cache)
+    assert torch.equal(cache.index_select(0, torch.tensor([2])), original_cache[2:3])
+
+
 def test_modelopt_fp8_adapter_keeps_layerwise_offload_enabled():
     server_args = SimpleNamespace(
         dit_cpu_offload=True,
@@ -119,6 +219,133 @@ def test_modelopt_fp8_adapter_keeps_layerwise_offload_enabled():
     assert server_args.dit_layerwise_offload is True
 
 
+def test_layerwise_capability_selects_layerwise_strategy_for_any_component():
+    module = _LayerwiseComponent(enabled=True)
+
+    assert is_layerwise_offloaded_module(module)
+    strategy = build_component_residency_strategy(
+        "text_encoder", module, _server_args(text_encoder_cpu_offload=True)
+    )
+
+    assert isinstance(strategy, LayerwiseOffloadStrategy)
+
+
+def test_layerwise_configuration_uses_legacy_default_components(monkeypatch):
+    monkeypatch.setattr(
+        layerwise_offload_mod.torch, "get_device_module", lambda: _FakeDeviceModule
+    )
+    monkeypatch.setattr(layerwise_offload_mod.current_platform, "device_type", "cpu")
+    layerwise_module = _NestedDummyModel()
+    modules = {
+        "text_encoder": layerwise_module,
+        "text_encoder_alias": layerwise_module,
+        "scheduler": object(),
+    }
+
+    configured = configure_layerwise_offload_modules(modules, _server_args())
+
+    assert configured == ["text_encoder"]
+    assert is_layerwise_offloaded_module(layerwise_module)
+
+
+def test_layerwise_configuration_filters_by_component_name(monkeypatch):
+    monkeypatch.setattr(
+        layerwise_offload_mod.torch, "get_device_module", lambda: _FakeDeviceModule
+    )
+    monkeypatch.setattr(layerwise_offload_mod.current_platform, "device_type", "cpu")
+    text_encoder = _NestedEncoderDummyModel()
+    transformer = _NestedDummyModel()
+    vae = _NestedDummyModel()
+    modules = {
+        "custom_encoder_name": text_encoder,
+        "custom_transformer_name": transformer,
+        "custom_vae_name": vae,
+    }
+
+    configured = configure_layerwise_offload_modules(
+        modules, _server_args(), component_names=["custom_encoder_name"]
+    )
+
+    assert configured == ["custom_encoder_name"]
+    assert is_layerwise_offloaded_module(text_encoder)
+    assert not is_layerwise_offloaded_module(transformer)
+    assert not is_layerwise_offloaded_module(vae)
+
+
+def test_layerwise_configuration_default_marker_extends_legacy_defaults(monkeypatch):
+    monkeypatch.setattr(
+        layerwise_offload_mod.torch, "get_device_module", lambda: _FakeDeviceModule
+    )
+    monkeypatch.setattr(layerwise_offload_mod.current_platform, "device_type", "cpu")
+    text_encoder = _NestedEncoderDummyModel()
+    text_encoder_2 = _NestedEncoderDummyModel()
+    transformer = _NestedDummyModel()
+    vae = _NestedEncoderDummyModel()
+    audio_vae = _NestedEncoderDummyModel()
+    condition_image_encoder = _NestedEncoderDummyModel()
+    modules = {
+        "text_encoder": text_encoder,
+        "text_encoder_2": text_encoder_2,
+        "transformer": transformer,
+        "vae": vae,
+        "audio_vae": audio_vae,
+        "condition_image_encoder": condition_image_encoder,
+    }
+
+    configured = configure_layerwise_offload_modules(
+        modules, _server_args(), component_names=["default", "text_encoder", "vae"]
+    )
+
+    assert configured == [
+        "text_encoder",
+        "text_encoder_2",
+        "transformer",
+        "vae",
+        "audio_vae",
+        "condition_image_encoder",
+    ]
+    assert is_layerwise_offloaded_module(text_encoder)
+    assert is_layerwise_offloaded_module(text_encoder_2)
+    assert is_layerwise_offloaded_module(transformer)
+    assert is_layerwise_offloaded_module(vae)
+    assert is_layerwise_offloaded_module(audio_vae)
+    assert is_layerwise_offloaded_module(condition_image_encoder)
+
+
+def test_layerwise_configuration_all_selects_every_capable_component(monkeypatch):
+    monkeypatch.setattr(
+        layerwise_offload_mod.torch, "get_device_module", lambda: _FakeDeviceModule
+    )
+    monkeypatch.setattr(layerwise_offload_mod.current_platform, "device_type", "cpu")
+    text_encoder = _NestedEncoderDummyModel()
+    transformer = _NestedDummyModel()
+    modules = {
+        "custom_encoder_name": text_encoder,
+        "custom_transformer_name": transformer,
+        "scheduler": object(),
+    }
+
+    configured = configure_layerwise_offload_modules(
+        modules, _server_args(), component_names=["all"]
+    )
+
+    assert configured == ["custom_encoder_name", "custom_transformer_name"]
+    assert is_layerwise_offloaded_module(text_encoder)
+    assert is_layerwise_offloaded_module(transformer)
+
+
+def test_component_cpu_offload_strategy_remains_flag_driven():
+    strategy = build_component_residency_strategy(
+        "text_encoder", _DummyModel(), _server_args(text_encoder_cpu_offload=True)
+    )
+    assert isinstance(strategy, VanillaD2HStrategy)
+
+    strategy = build_component_residency_strategy(
+        "unknown_component", _DummyModel(), _server_args(text_encoder_cpu_offload=True)
+    )
+    assert isinstance(strategy, ResidentStrategy)
+
+
 def test_layerwise_offload_aligns_contiguous_tensor_offsets(monkeypatch):
     monkeypatch.setattr(
         layerwise_offload_mod.torch, "get_device_module", lambda: _FakeDeviceModule

python/sglang/multimodal_gen/test/unit/test_server_args.py

@@ -147,6 +147,106 @@ class TestServerArgsPathExpansion(unittest.TestCase):
             server_args.component_attention_backends, {"text_encoder": "torch_sdpa"}
         )
 
+    def test_layerwise_offload_components_imply_layerwise(self):
+        args = self._from_dict_without_model_resolution(
+            {
+                "model_path": "/data/my-model",
+                "performance_mode": "manual",
+            }
+        )
+        args.layerwise_offload_components = ["text_encoder", "transformer"]
+        args._adjust_layerwise_offload_components()
+
+        self.assertTrue(args.layerwise_offload_components)
+        self.assertEqual(
+            args.layerwise_offload_components, ["text_encoder", "transformer"]
+        )
+
+    def test_dit_layerwise_offload_extends_default_components(self):
+        args = self._from_dict_without_model_resolution(
+            {
+                "model_path": "/data/my-model",
+                "performance_mode": "manual",
+                "dit_layerwise_offload": True,
+            }
+        )
+
+        self.assertTrue(args.layerwise_offload_components)
+        self.assertEqual(args.layerwise_offload_components, ["default"])
+
+    def test_dit_layerwise_offload_from_kwargs(self):
+        with patch.object(
+            PipelineConfig, "from_kwargs", return_value=QwenImagePipelineConfig()
+        ):
+            args = ServerArgs.from_kwargs(
+                model_path="/data/my-model",
+                performance_mode="manual",
+                dit_layerwise_offload=True,
+            )
+
+        self.assertTrue(args.layerwise_offload_components)
+        self.assertEqual(args.layerwise_offload_components, ["default"])
+
+    def test_layerwise_offload_components_normalize_commas(self):
+        args = self._from_dict_without_model_resolution(
+            {
+                "model_path": "/data/my-model",
+                "performance_mode": "manual",
+            }
+        )
+        args.layerwise_offload_components = ["text-encoder,transformer"]
+        args._adjust_layerwise_offload_components()
+
+        self.assertEqual(
+            args.layerwise_offload_components, ["text_encoder", "transformer"]
+        )
+
+    def test_dit_layerwise_offload_cli_arg(self):
+        parser = FlexibleArgumentParser()
+        ServerArgs.add_cli_args(parser)
+        argv = [
+            "--model-path",
+            "/fake",
+            "--performance-mode",
+            "manual",
+            "--dit-layerwise-offload",
+            "true",
+        ]
+
+        with patch.object(sys, "argv", ["sglang"] + argv):
+            args, unknown_args = parser.parse_known_args(argv)
+            with patch.object(
+                PipelineConfig, "from_kwargs", return_value=QwenImagePipelineConfig()
+            ):
+                server_args = ServerArgs.from_cli_args(args, unknown_args)
+
+        self.assertTrue(server_args.layerwise_offload_components)
+        self.assertEqual(server_args.layerwise_offload_components, ["default"])
+
+    def test_layerwise_offload_components_cli_args(self):
+        parser = FlexibleArgumentParser()
+        ServerArgs.add_cli_args(parser)
+        argv = [
+            "--model-path",
+            "/fake",
+            "--performance-mode",
+            "manual",
+            "--layerwise-offload-components",
+            "transformer",
+            "text_encoder",
+        ]
+
+        with patch.object(sys, "argv", ["sglang"] + argv):
+            args, unknown_args = parser.parse_known_args(argv)
+            with patch.object(
+                PipelineConfig, "from_kwargs", return_value=QwenImagePipelineConfig()
+            ):
+                server_args = ServerArgs.from_cli_args(args, unknown_args)
+
+        self.assertEqual(
+            server_args.layerwise_offload_components, ["transformer", "text_encoder"]
+        )
+
 
 class TestOffloadDefaults(unittest.TestCase):
     def _from_dict_with_pipeline_config(
@@ -208,6 +308,10 @@ class TestOffloadDefaults(unittest.TestCase):
                 "sglang.multimodal_gen.runtime.server_args.current_platform.is_cpu",
                 return_value=False,
             ),
+            patch(
+                "sglang.multimodal_gen.runtime.server_args.current_platform.is_cuda",
+                return_value=True,
+            ),
             patch(
                 "sglang.multimodal_gen.runtime.server_args.current_platform.get_device_total_memory",
                 return_value=memory_gb * 1024**3,
@@ -233,16 +337,48 @@ class TestOffloadDefaults(unittest.TestCase):
 
         self.assertFalse(args.vae_cpu_offload)
         self.assertTrue(args.dit_cpu_offload)
-        self.assertTrue(args.text_encoder_cpu_offload)
+        self.assertFalse(args.text_encoder_cpu_offload)
-        self.assertTrue(args.image_encoder_cpu_offload)
+        self.assertFalse(args.image_encoder_cpu_offload)
+        self.assertEqual(
+            args.layerwise_offload_components, ["text_encoder", "image_encoder"]
+        )
 
-    def test_explicit_vae_cpu_offload_true_is_preserved(self):
+    def test_explicit_vae_cpu_offload_true_is_preserved_without_component_selection(
+        self,
+    ):
         args = self._from_dict_with_task_type(
             ModelTaskType.T2V,
             kwargs={"vae_cpu_offload": True},
         )
 
         self.assertTrue(args.vae_cpu_offload)
+        self.assertFalse(args.layerwise_offload_components)
+
+    def test_layerwise_components_disable_matching_cpu_offloads(self):
+        args = self._from_dict_with_task_type(
+            ModelTaskType.T2V,
+            memory_gb=16,
+            kwargs={
+                "performance_mode": "manual",
+                "dit_cpu_offload": True,
+                "text_encoder_cpu_offload": True,
+                "image_encoder_cpu_offload": True,
+                "vae_cpu_offload": True,
+            },
+        )
+        args.layerwise_offload_components = [
+            "text_encoder",
+            "image_encoder",
+            "video_dit",
+            "vae",
+        ]
+        args._adjust_layerwise_offload_components()
+
+        self.assertTrue(args.layerwise_offload_components)
+        self.assertFalse(args.dit_cpu_offload)
+        self.assertFalse(args.text_encoder_cpu_offload)
+        self.assertFalse(args.image_encoder_cpu_offload)
+        self.assertFalse(args.vae_cpu_offload)
 
     def test_pipeline_configs_declare_auto_tune_hints(self):
         qwen_deployment = QwenImagePipelineConfig().get_model_deployment_config()
@@ -285,11 +421,12 @@ class TestOffloadDefaults(unittest.TestCase):
         self.assertIsNone(args.use_fsdp_inference)
         self.assertIsNone(args.dit_cpu_offload)
         self.assertIsNone(args.dit_layerwise_offload)
+        self.assertIsNone(args.layerwise_offload_components)
         self.assertIsNone(args.text_encoder_cpu_offload)
         self.assertIsNone(args.image_encoder_cpu_offload)
         self.assertFalse(args.enable_cfg_parallel)
 
-    def test_default_auto_keeps_legacy_single_gpu_offload_defaults(self):
+    def test_default_auto_replaces_text_encoder_cpu_offload_with_layerwise(self):
         args = self._from_dict_with_pipeline_config(
             QwenImagePipelineConfig(),
             kwargs={"model_path": "Qwen/Qwen-Image"},
@@ -298,11 +435,14 @@ class TestOffloadDefaults(unittest.TestCase):
         self.assertEqual(args.performance_mode, "auto")
         self.assertFalse(args.use_fsdp_inference)
         self.assertTrue(args.dit_cpu_offload)
-        self.assertFalse(args.dit_layerwise_offload)
+        self.assertTrue(args.layerwise_offload_components)
-        self.assertTrue(args.text_encoder_cpu_offload)
+        self.assertFalse(args.text_encoder_cpu_offload)
         self.assertFalse(args.image_encoder_cpu_offload)
+        self.assertEqual(args.layerwise_offload_components, ["text_encoder"])
 
-    def test_auto_ltx_snapshot_keeps_dit_offload_with_headroom(self):
+    def test_auto_ltx_snapshot_keeps_dit_offload_and_replaces_encoder_cpu_offload(
+        self,
+    ):
         args = self._from_dict_with_pipeline_config(
             LTX2PipelineConfig(),
             available_memory_gb=76,
@@ -316,8 +456,12 @@ class TestOffloadDefaults(unittest.TestCase):
 
         self.assertEqual(args.ltx2_two_stage_device_mode, "snapshot")
         self.assertTrue(args.dit_cpu_offload)
-        self.assertTrue(args.text_encoder_cpu_offload)
+        self.assertTrue(args.layerwise_offload_components)
-        self.assertTrue(args.image_encoder_cpu_offload)
+        self.assertFalse(args.text_encoder_cpu_offload)
+        self.assertFalse(args.image_encoder_cpu_offload)
+        self.assertEqual(
+            args.layerwise_offload_components, ["text_encoder", "image_encoder"]
+        )
 
     def test_auto_wan_layerwise_offload_is_enabled_without_fsdp(self):
         args = self._from_dict_with_pipeline_config(
@@ -325,8 +469,14 @@ class TestOffloadDefaults(unittest.TestCase):
             kwargs={"performance_mode": "auto"},
         )
 
-        self.assertTrue(args.dit_layerwise_offload)
+        self.assertTrue(args.layerwise_offload_components)
         self.assertFalse(args.use_fsdp_inference)
+        self.assertFalse(args.text_encoder_cpu_offload)
+        self.assertFalse(args.image_encoder_cpu_offload)
+        self.assertEqual(
+            args.layerwise_offload_components,
+            ["default", "text_encoder", "image_encoder"],
+        )
 
     def test_memory_wan_layerwise_offload_is_enabled_without_fsdp(self):
         args = self._from_dict_with_pipeline_config(
@@ -334,8 +484,14 @@ class TestOffloadDefaults(unittest.TestCase):
             kwargs={"performance_mode": "memory"},
         )
 
-        self.assertTrue(args.dit_layerwise_offload)
+        self.assertTrue(args.layerwise_offload_components)
         self.assertFalse(args.use_fsdp_inference)
+        self.assertFalse(args.text_encoder_cpu_offload)
+        self.assertFalse(args.image_encoder_cpu_offload)
+        self.assertEqual(
+            args.layerwise_offload_components,
+            ["default", "text_encoder", "image_encoder"],
+        )
 
     def test_auto_wan_layerwise_offload_does_not_disable_explicit_fsdp(self):
         args = self._from_dict_with_pipeline_config(
@@ -348,7 +504,7 @@ class TestOffloadDefaults(unittest.TestCase):
             },
         )
 
-        self.assertFalse(args.dit_layerwise_offload)
+        self.assertFalse(args.layerwise_offload_components)
         self.assertTrue(args.use_fsdp_inference)
 
     def test_auto_multi_gpu_wan_uses_layerwise_offload_without_cfg(self):
@@ -365,9 +521,52 @@ class TestOffloadDefaults(unittest.TestCase):
         self.assertFalse(args.use_fsdp_inference)
         self.assertFalse(args.enable_cfg_parallel)
         self.assertFalse(args.dit_cpu_offload)
-        self.assertTrue(args.dit_layerwise_offload)
+        self.assertTrue(args.layerwise_offload_components)
+        self.assertFalse(args.text_encoder_cpu_offload)
+        self.assertFalse(args.image_encoder_cpu_offload)
+        self.assertEqual(
+            args.layerwise_offload_components,
+            ["default", "text_encoder", "image_encoder"],
+        )
 
-    def test_auto_multi_gpu_qwen_keeps_legacy_offload_with_cfg(self):
+    def test_explicit_multi_gpu_dit_layerwise_only_selects_default_component(self):
+        args = self._from_dict_with_pipeline_config(
+            MOVAPipelineConfig(),
+            kwargs={
+                "model_path": "OpenMOSS-Team/MOVA-360p",
+                "num_gpus": 2,
+                "dit_layerwise_offload": True,
+            },
+        )
+
+        self.assertFalse(args.use_fsdp_inference)
+        self.assertFalse(args.dit_cpu_offload)
+        self.assertTrue(args.layerwise_offload_components)
+        self.assertTrue(args.text_encoder_cpu_offload)
+        self.assertTrue(args.image_encoder_cpu_offload)
+        self.assertEqual(args.layerwise_offload_components, ["default"])
+
+    def test_auto_multi_gpu_ltx_replaces_component_cpu_offload_with_resident_dit(self):
+        args = self._from_dict_with_pipeline_config(
+            LTX2PipelineConfig(),
+            available_memory_gb=76,
+            kwargs={
+                "model_path": "Lightricks/LTX-2",
+                "num_gpus": 2,
+                "pipeline_class_name": "LTX2TwoStagePipeline",
+            },
+        )
+
+        self.assertFalse(args.use_fsdp_inference)
+        self.assertFalse(args.dit_cpu_offload)
+        self.assertTrue(args.layerwise_offload_components)
+        self.assertFalse(args.text_encoder_cpu_offload)
+        self.assertFalse(args.image_encoder_cpu_offload)
+        self.assertEqual(
+            args.layerwise_offload_components, ["text_encoder", "image_encoder"]
+        )
+
+    def test_auto_multi_gpu_qwen_replaces_text_encoder_offload_with_cfg(self):
         args = self._from_dict_with_pipeline_config(
             QwenImagePipelineConfig(),
             kwargs={
@@ -380,9 +579,10 @@ class TestOffloadDefaults(unittest.TestCase):
         self.assertFalse(args.use_fsdp_inference)
         self.assertTrue(args.enable_cfg_parallel)
         self.assertTrue(args.dit_cpu_offload)
-        self.assertFalse(args.dit_layerwise_offload)
+        self.assertTrue(args.layerwise_offload_components)
-        self.assertTrue(args.text_encoder_cpu_offload)
+        self.assertFalse(args.text_encoder_cpu_offload)
         self.assertFalse(args.image_encoder_cpu_offload)
+        self.assertEqual(args.layerwise_offload_components, ["text_encoder"])
 
     def test_auto_multi_gpu_zimage_base_prefers_fsdp(self):
         args = self._from_dict_with_pipeline_config(
@@ -424,8 +624,9 @@ class TestOffloadDefaults(unittest.TestCase):
         self.assertFalse(args.use_fsdp_inference)
         self.assertTrue(args.enable_cfg_parallel)
         self.assertTrue(args.dit_cpu_offload)
-        self.assertTrue(args.text_encoder_cpu_offload)
+        self.assertFalse(args.text_encoder_cpu_offload)
         self.assertFalse(args.image_encoder_cpu_offload)
+        self.assertEqual(args.layerwise_offload_components, ["text_encoder"])
 
     def test_auto_multi_gpu_qwen_skips_fsdp_when_available_memory_is_low(self):
         args = self._from_dict_with_pipeline_config(
@@ -441,8 +642,9 @@ class TestOffloadDefaults(unittest.TestCase):
         self.assertFalse(args.use_fsdp_inference)
         self.assertTrue(args.enable_cfg_parallel)
         self.assertTrue(args.dit_cpu_offload)
-        self.assertTrue(args.text_encoder_cpu_offload)
+        self.assertFalse(args.text_encoder_cpu_offload)
         self.assertFalse(args.image_encoder_cpu_offload)
+        self.assertEqual(args.layerwise_offload_components, ["text_encoder"])
 
     def test_auto_multi_gpu_qwen_uses_selected_gpu_min_available_memory(self):
         args = self._from_dict_with_pipeline_config(
@@ -459,7 +661,7 @@ class TestOffloadDefaults(unittest.TestCase):
         self.assertFalse(args.use_fsdp_inference)
         self.assertTrue(args.enable_cfg_parallel)
 
-    def test_auto_multi_gpu_qwen_keeps_legacy_offload_with_headroom(self):
+    def test_auto_multi_gpu_qwen_replaces_text_encoder_offload_with_headroom(self):
         args = self._from_dict_with_pipeline_config(
             QwenImagePipelineConfig(),
             available_memory_gb={1: 72, 2: 80},
@@ -474,8 +676,9 @@ class TestOffloadDefaults(unittest.TestCase):
         self.assertFalse(args.use_fsdp_inference)
         self.assertTrue(args.enable_cfg_parallel)
         self.assertTrue(args.dit_cpu_offload)
-        self.assertTrue(args.text_encoder_cpu_offload)
+        self.assertFalse(args.text_encoder_cpu_offload)
         self.assertFalse(args.image_encoder_cpu_offload)
+        self.assertEqual(args.layerwise_offload_components, ["text_encoder"])
 
     def test_speed_mode_single_gpu_disables_offload(self):
         args = self._from_dict_with_pipeline_config(
@@ -489,7 +692,7 @@ class TestOffloadDefaults(unittest.TestCase):
         self.assertEqual(args.performance_mode, "speed")
         self.assertFalse(args.use_fsdp_inference)
         self.assertFalse(args.dit_cpu_offload)
-        self.assertFalse(args.dit_layerwise_offload)
+        self.assertFalse(args.layerwise_offload_components)
         self.assertFalse(args.text_encoder_cpu_offload)
         self.assertFalse(args.image_encoder_cpu_offload)
 
@@ -518,10 +721,14 @@ class TestOffloadDefaults(unittest.TestCase):
         )
 
         self.assertFalse(args.use_fsdp_inference)
-        self.assertTrue(args.dit_layerwise_offload)
+        self.assertTrue(args.layerwise_offload_components)
         self.assertFalse(args.dit_cpu_offload)
-        self.assertTrue(args.text_encoder_cpu_offload)
+        self.assertFalse(args.text_encoder_cpu_offload)
-        self.assertTrue(args.image_encoder_cpu_offload)
+        self.assertFalse(args.image_encoder_cpu_offload)
+        self.assertEqual(
+            args.layerwise_offload_components,
+            ["default", "text_encoder", "image_encoder"],
+        )
 
     def test_memory_mode_preserves_explicit_fsdp(self):
         args = self._from_dict_with_pipeline_config(
@@ -535,7 +742,7 @@ class TestOffloadDefaults(unittest.TestCase):
         )
 
         self.assertTrue(args.use_fsdp_inference)
-        self.assertFalse(args.dit_layerwise_offload)
+        self.assertFalse(args.layerwise_offload_components)
         self.assertFalse(args.dit_cpu_offload)
 
     def test_invalid_performance_mode_raises(self):