[diffusion] quant: support pruned safetensors checkpoints for minimax-h3 (#35418)

docs/cookbook/diffusion/MiniMax/MiniMax-H3.mdx

@@ -248,6 +248,59 @@ server environment.
 
 For MiniMax-H3, `--performance-mode speed` deliberately keeps the DiT eager. The current `torch.compile` path changes the model's numerical output, so it is not enabled implicitly by any recommended lossless preset. An explicit `--enable-torch-compile true` remains available for controlled experiments, but it should not be used to generate consistency ground truth.
 
+### AdaLN-pruned safetensors transformers
+
+[Comfy-Org/MiniMax-H3](https://huggingface.co/Comfy-Org/MiniMax-H3/tree/main/diffusion_models)
+publishes smaller DiT-only checkpoints that replace the original AdaLN branches
+with an interpolated curve table. Select one file explicitly; the base model
+still supplies the text encoder and VAEs.
+
+```bash Command
+sglang serve \
+  --model-path MiniMaxAI/MiniMax-H3 \
+  --model-variant fl2va \
+  --transformer-weights-path \
+    Comfy-Org/MiniMax-H3/diffusion_models/minimax_h3_fl2va_pruned_bf16.safetensors \
+  --num-gpus 4 \
+  --tp-size 2 \
+  --ulysses-degree 2 \
+  --performance-mode speed \
+  --port 30010
+```
+
+The pruned checkpoint is approximate and is therefore rejected by
+`quality="high"`, which remains limited to the audited official BF16 DiT.
+
+The Comfy `pruned_fp8_scaled` FL2VA and Ref2VA files are also supported. Their
+per-layer markers are detected automatically; do not add `--quantization`:
+
+```bash Command
+sglang serve \
+  --model-path MiniMaxAI/MiniMax-H3 \
+  --model-variant fl2va \
+  --transformer-weights-path \
+    Comfy-Org/MiniMax-H3/diffusion_models/minimax_h3_fl2va_pruned_fp8_scaled.safetensors \
+  --num-gpus 4 \
+  --tp-size 2 \
+  --ulysses-degree 2 \
+  --performance-mode speed \
+  --port 30010
+```
+
+SGLang uses its native static-activation FP8 linear path for attention and
+`fc1`. The checkpoint marks `fc2` for full-precision matrix multiplication, so
+SGLang retains its FP8 storage but materializes and scales one compute-dtype
+`fc2` matrix for each call. This preserves the checkpoint's mixed execution
+contract and low resident weight memory, but that part is slower than a fully
+quantized FP8 GEMM. TP, Ulysses/Ring sequence parallelism, and
+component/layerwise offload are supported; FSDP inference is rejected.
+
+The `pruned_int8_convrot` files are detected but remain unsupported. They
+require online regular-Hadamard ConvRot, dynamic INT8 activation quantization,
+and a matching W8A8 GEMM. SGLang fails before loading them instead of silently
+treating their stored INT8 values as ordinary weights. A native ConvRot kernel
+path should be added and benchmarked separately before these files are accepted.
+
 ### Advanced: precomputed AdaLN cache
 
 The [model card](https://huggingface.co/MiniMaxAI/MiniMax-H3) notes that about

docs/docs/sglang-diffusion/quantization.mdx

@@ -15,7 +15,9 @@ Use these paths:
 
 - `--model-path`: the base or original model
 - `--transformer-path`: a quantized transformers-style transformer component directory that already contains its own `config.json`
-- `--transformer-weights-path`: quantized transformer weights provided as a single safetensors file, a sharded safetensors directory, a local path, or a Hugging Face repo ID
+- `--transformer-weights-path`: replacement transformer weights in safetensors
+  format (file, directory, or Hub repository/file) or a supported GGUF file
+  (local or Hub)
 - `--quantization`: apply online quantization to unquantized models at load time (activations are quantized dynamically)
 - `--quantization-ignored-layers` layer name patterns to keep unquantized (e.g. `attention.to_`)
 - `--component-paths.text_encoder`: replace a native text encoder with a checkpoint whose `quantization_config` is auto-detected
@@ -45,6 +47,12 @@ directory directly as `--model-path`, but that is a compatibility path. If a
 repo contains multiple candidate checkpoints, pass
 `--transformer-weights-path` explicitly.
 
+MiniMax-H3 auto-detects the per-layer metadata in Comfy's
+`pruned_fp8_scaled` safetensors. Pass one selected FL2VA or Ref2VA file by local
+path, `owner/repo/path/file.safetensors`, or direct Hugging Face file URL; do
+not combine it with `--quantization`. MiniMax-H3 GGUF usage is documented in
+the [MiniMax-H3 cookbook](/cookbook/diffusion/MiniMax/MiniMax-H3#pre-quantized-gguf-transformer).
+
 ## Quant Families
 
 Here, `quant_family` means a checkpoint and loading family with shared CLI
@@ -111,6 +119,22 @@ backend.
       <td>None</td>
       <td>Mixed override repos keep the base model separate; full Qwen Image exports can be loaded directly as <code>--model-path</code>; raw exports such as <code>black-forest-labs/FLUX.2-dev-NVFP4</code> still use the weights-path flow</td>
     </tr>
+    <tr>
+      <td><code>gguf</code></td>
+      <td>One selected GGUF DiT file</td>
+      <td><code>--transformer-weights-path</code></td>
+      <td>MiniMax-H3 original or pruned FL2VA / Ref2VA DiTs</td>
+      <td>None</td>
+      <td>CUDA only; auto-detected; supports standard and K-quant GGML types; FSDP and the separate Qwen3-VL text-encoder GGUF files are not supported</td>
+    </tr>
+    <tr>
+      <td><code>comfy-fp8</code></td>
+      <td>One selected Comfy safetensors file with per-layer <code>comfy_quant</code> metadata</td>
+      <td><code>--transformer-weights-path</code></td>
+      <td>MiniMax-H3 pruned FL2VA / Ref2VA DiTs</td>
+      <td>None</td>
+      <td>CUDA; auto-detected; TP, sequence parallelism, and component/layerwise offload are supported, while FSDP is not. Checkpoint-marked <code>fc2</code> layers retain FP8 storage and use compute-dtype matmul.</td>
+    </tr>
     <tr>
       <td><code>qvg-kv</code></td>
       <td>Unquantized model with runtime causal KV-cache compression</td>

python/sglang/multimodal_gen/runtime/layers/quantization/comfy_fp8.py

@@ -0,0 +1,145 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Comfy per-layer FP8 checkpoint support."""
+
+from __future__ import annotations
+
+from typing import Any
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from sglang.multimodal_gen.runtime.layers.linear import (
+    LinearBase,
+    LinearMethodBase,
+    UnquantizedLinearMethod,
+)
+from sglang.multimodal_gen.runtime.layers.quantization.configs.base_config import (
+    QuantizationConfig,
+    QuantizeMethodBase,
+)
+from sglang.multimodal_gen.runtime.layers.quantization.fp8 import (
+    Fp8Config,
+    Fp8LinearMethod,
+)
+from sglang.multimodal_gen.runtime.models.parameter import (
+    ModelWeightParameter,
+    PerTensorScaleParameter,
+)
+
+
+class ComfyFullPrecisionFp8LinearMethod(LinearMethodBase):
+    """Keep FP8 storage but honor Comfy's full-precision matmul marker."""
+
+    def create_weights(
+        self,
+        layer: nn.Module,
+        input_size_per_partition: int,
+        output_partition_sizes: list[int],
+        input_size: int,
+        output_size: int,
+        params_dtype: torch.dtype,
+        **extra_weight_attrs: Any,
+    ) -> None:
+        if len(output_partition_sizes) != 1:
+            raise ValueError(
+                "Comfy full_precision_matrix_mult does not support fused linears"
+            )
+        weight_loader = extra_weight_attrs.get("weight_loader")
+        layer.logical_widths = output_partition_sizes
+        layer.input_size_per_partition = input_size_per_partition
+        layer.output_size_per_partition = output_partition_sizes[0]
+        layer.orig_dtype = params_dtype
+        weight = ModelWeightParameter(
+            data=torch.empty(
+                output_partition_sizes[0],
+                input_size_per_partition,
+                dtype=torch.float8_e4m3fn,
+            ),
+            input_dim=1,
+            output_dim=0,
+            weight_loader=weight_loader,
+        )
+        layer.register_parameter("weight", weight)
+        weight_scale = PerTensorScaleParameter(
+            data=torch.empty(1, dtype=torch.float32),
+            weight_loader=weight_loader,
+        )
+        layer.register_parameter("weight_scale", weight_scale)
+
+    def process_weights_after_loading(self, layer: nn.Module) -> None:
+        layer.weight = nn.Parameter(layer.weight.data, requires_grad=False)
+        layer.weight_scale = nn.Parameter(layer.weight_scale.data, requires_grad=False)
+
+    def apply(
+        self,
+        layer: nn.Module,
+        x: torch.Tensor,
+        bias: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        # Comfy's marker disables quantized GEMM for this layer. Materialize one
+        # compute-dtype matrix per call so the checkpoint's FP8 residency benefit
+        # is retained instead of permanently expanding every fc2 weight to BF16.
+        weight = layer.weight.to(dtype=x.dtype)
+        weight.mul_(layer.weight_scale[0].to(dtype=x.dtype))
+        return F.linear(x, weight, bias)
+
+
+class ComfyFp8Config(QuantizationConfig):
+    """Dispatch each Linear according to its serialized ``comfy_quant`` marker."""
+
+    def __init__(self, layer_markers: dict[str, dict[str, Any]]) -> None:
+        super().__init__()
+        self.layer_markers = layer_markers
+        self._fp8_config = Fp8Config(
+            is_checkpoint_fp8_serialized=True,
+            activation_scheme="static",
+        )
+
+        unsupported = {
+            prefix: marker.get("format")
+            for prefix, marker in layer_markers.items()
+            if marker.get("format") != "float8_e4m3fn"
+        }
+        if unsupported:
+            raise ValueError(f"Unsupported Comfy FP8 layer formats: {unsupported}")
+
+    @classmethod
+    def get_name(cls) -> str:
+        return "comfy_fp8"
+
+    @classmethod
+    def get_supported_act_dtypes(cls) -> list[torch.dtype]:
+        return Fp8Config.get_supported_act_dtypes()
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return Fp8Config.get_min_capability()
+
+    @staticmethod
+    def get_config_filenames() -> list[str]:
+        return []
+
+    @classmethod
+    def from_config(cls, config: dict[str, Any]) -> ComfyFp8Config:
+        raise ValueError(
+            "ComfyFp8Config must be constructed from safetensors layer markers"
+        )
+
+    def get_quant_method(
+        self, layer: nn.Module, prefix: str
+    ) -> QuantizeMethodBase | None:
+        if not isinstance(layer, LinearBase):
+            return None
+        marker = self.layer_markers.get(prefix)
+        if marker is None:
+            return UnquantizedLinearMethod()
+        if marker.get("full_precision_matrix_mult", False):
+            return ComfyFullPrecisionFp8LinearMethod()
+        return Fp8LinearMethod(self._fp8_config)
+
+
+__all__ = [
+    "ComfyFp8Config",
+    "ComfyFullPrecisionFp8LinearMethod",
+]

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

@@ -17,6 +17,12 @@ from sglang.multimodal_gen.runtime.loader.component_loaders.component_loader imp
 )
 from sglang.multimodal_gen.runtime.loader.fsdp_load import maybe_load_fsdp_model
 from sglang.multimodal_gen.runtime.loader.gguf_weights import gguf_weights_iterator
+from sglang.multimodal_gen.runtime.loader.minimax_h3_weights import (
+    comfy_quant_key_filter,
+    inspect_minimax_h3_safetensors,
+    resolve_minimax_h3_checkpoint_quantization,
+    validate_minimax_h3_checkpoint_variant,
+)
 from sglang.multimodal_gen.runtime.loader.transformer_load_utils import (
     TransformerQuantLoadSpec,
     resolve_transformer_gguf_to_load,
@@ -214,6 +220,35 @@ class TransformerLoader(ComponentLoader):
         cls_name = config.pop("_class_name")
         model_cls, _ = ModelRegistry.resolve_model_cls(cls_name)
 
+        checkpoint_quant_config = None
+        if cls_name == "MiniMaxH3DiTModel":
+            selected_variant = str(component_server_args.model_variant or "fl2va")
+            if gguf_file is not None:
+                validate_minimax_h3_checkpoint_variant([gguf_file], selected_variant)
+            elif component_server_args.transformer_weights_path is not None:
+                validate_minimax_h3_checkpoint_variant(
+                    safetensors_list, selected_variant
+                )
+                adaln_curve_shape, layer_markers = inspect_minimax_h3_safetensors(
+                    safetensors_list
+                )
+                checkpoint_quant_config = resolve_minimax_h3_checkpoint_quantization(
+                    layer_markers
+                )
+                if adaln_curve_shape is not None:
+                    (
+                        dit_config.arch_config.adaln_curve_grid,
+                        dit_config.arch_config.time_embed_dim,
+                    ) = adaln_curve_shape
+                    if (
+                        component_server_args.minimax_h3_adaln_cache_path is not None
+                        or component_server_args.minimax_h3_adaln_online
+                    ):
+                        raise ValueError(
+                            "MiniMax-H3 pruned curve checkpoints cannot use a "
+                            "separate AdaLN cache or online AdaLN rebuild"
+                        )
+
         quant_spec = resolve_transformer_quant_load_spec(
             hf_config=config,
             server_args=component_server_args,
@@ -223,6 +258,7 @@ class TransformerLoader(ComponentLoader):
             cls_name=cls_name,
             component_name=component_name,
             gguf_file=gguf_file,
+            checkpoint_quant_config=checkpoint_quant_config,
         )
         if quant_spec.gguf_file is not None and cls_name == "MiniMaxH3DiTModel":
             assert quant_spec.quant_config is not None
@@ -244,6 +280,11 @@ class TransformerLoader(ComponentLoader):
             or cpu_offload_flag
         )
         use_fsdp = server_args.should_use_fsdp_for_component(component_name)
+        if quant_spec.is_comfy_fp8 and use_fsdp:
+            raise ValueError(
+                "MiniMax-H3 Comfy FP8 does not support FSDP inference; use TP "
+                "and/or sequence parallelism instead"
+            )
 
         if quant_spec.gguf_file is not None:
             logger.info(
@@ -266,7 +307,9 @@ class TransformerLoader(ComponentLoader):
             "hf_config": config,
             "quant_config": quant_spec.runtime_quant_config,
         }
-        checkpoint_key_filter: Callable[[str], bool] | None = None
+        checkpoint_key_filter: Callable[[str], bool] | None = (
+            comfy_quant_key_filter if quant_spec.is_comfy_fp8 else None
+        )
         adaln_cache_path = component_server_args.minimax_h3_adaln_cache_path
         if adaln_cache_path is not None:
             if cls_name != "MiniMaxH3DiTModel":
@@ -304,8 +347,9 @@ class TransformerLoader(ComponentLoader):
             init_params["quant_config"] is None
             and component_server_args.transformer_weights_path is not None
         ):
-            logger.warning(
-                "transformer_weights_path provided, but quantization config not resolved, which is unexpected and likely to cause errors"
+            logger.info(
+                "Using an unquantized transformer weight override from %s",
+                component_server_args.transformer_weights_path,
             )
         else:
             logger.debug("quantization config: %s", init_params["quant_config"])

python/sglang/multimodal_gen/runtime/loader/minimax_h3_weights.py

@@ -0,0 +1,139 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Checkpoint inspection for MiniMax-H3 transformer overrides."""
+
+import json
+from typing import Any
+
+from safetensors import safe_open
+
+from sglang.multimodal_gen.runtime.layers.quantization.comfy_fp8 import ComfyFp8Config
+from sglang.multimodal_gen.runtime.layers.quantization.configs.base_config import (
+    QuantizationConfig,
+)
+
+
+def comfy_quant_key_filter(name: str) -> bool:
+    return not name.endswith(".comfy_quant")
+
+
+def inspect_minimax_h3_safetensors(
+    safetensors_list: list[str],
+) -> tuple[tuple[int, int] | None, dict[str, dict[str, Any]]]:
+    """Read H3 architecture metadata and Comfy per-layer format markers."""
+    adaln_curve_shape = None
+    layer_markers: dict[str, dict[str, Any]] = {}
+    checkpoint_keys: set[str] = set()
+    fp8_weight_prefixes: set[str] = set()
+
+    for path in safetensors_list:
+        with safe_open(path, framework="pt", device="cpu") as checkpoint:
+            keys = checkpoint.keys()
+            checkpoint_keys.update(keys)
+            if "adaln_t_table" in keys:
+                shape = tuple(checkpoint.get_slice("adaln_t_table").get_shape())
+                if len(shape) != 2 or shape[0] < 2:
+                    raise ValueError(
+                        "MiniMax-H3 adaln_t_table must have shape [N, D] with "
+                        f"N >= 2, got {shape} in {path}"
+                    )
+                if adaln_curve_shape is not None and adaln_curve_shape != shape:
+                    raise ValueError(
+                        "MiniMax-H3 checkpoint shards disagree on adaln_t_table "
+                        f"shape: {adaln_curve_shape} vs {shape}"
+                    )
+                adaln_curve_shape = shape
+
+            for key in keys:
+                if (
+                    key.endswith(".weight")
+                    and checkpoint.get_slice(key).get_dtype() == "F8_E4M3"
+                ):
+                    fp8_weight_prefixes.add(key.removesuffix(".weight"))
+                if not key.endswith(".comfy_quant"):
+                    continue
+                try:
+                    marker = json.loads(checkpoint.get_tensor(key).numpy().tobytes())
+                except (json.JSONDecodeError, UnicodeDecodeError) as exc:
+                    raise ValueError(
+                        f"Invalid Comfy quantization marker {key!r} in {path}"
+                    ) from exc
+                if not isinstance(marker, dict):
+                    raise ValueError(
+                        f"Comfy quantization marker {key!r} must contain a JSON object"
+                    )
+                prefix = key.removesuffix(".comfy_quant")
+                previous = layer_markers.get(prefix)
+                if previous is not None and previous != marker:
+                    raise ValueError(
+                        f"Conflicting Comfy quantization markers for {prefix!r}"
+                    )
+                layer_markers[prefix] = marker
+
+    if layer_markers:
+        missing_markers = fp8_weight_prefixes - layer_markers.keys()
+        if missing_markers:
+            raise ValueError(
+                "MiniMax-H3 FP8 weights are missing comfy_quant metadata: "
+                f"{sorted(missing_markers)[:5]}"
+            )
+
+    for prefix, marker in layer_markers.items():
+        if marker.get("format") != "float8_e4m3fn":
+            continue
+        required = {f"{prefix}.weight", f"{prefix}.weight_scale"}
+        if not marker.get("full_precision_matrix_mult", False):
+            required.add(f"{prefix}.input_scale")
+        missing = required - checkpoint_keys
+        if missing:
+            raise ValueError(
+                f"MiniMax-H3 Comfy FP8 layer {prefix!r} is missing checkpoint "
+                f"tensors: {sorted(missing)}"
+            )
+
+    return adaln_curve_shape, layer_markers
+
+
+def resolve_minimax_h3_checkpoint_quantization(
+    layer_markers: dict[str, dict[str, Any]],
+) -> QuantizationConfig | None:
+    if not layer_markers:
+        return None
+
+    formats = sorted({str(marker.get("format")) for marker in layer_markers.values()})
+    if "int8_tensorwise" in formats:
+        raise NotImplementedError(
+            "MiniMax-H3 pruned_int8_convrot is not supported yet. Its "
+            "int8_tensorwise weights require an online regular-Hadamard ConvRot "
+            "and dynamic INT8 activation quantization kernel; loading them as "
+            "ordinary INT8/BF16 weights would produce incorrect output."
+        )
+    if formats == ["float8_e4m3fn"]:
+        return ComfyFp8Config(layer_markers)
+    raise NotImplementedError(
+        "Unsupported MiniMax-H3 Comfy quantization format(s): " + ", ".join(formats)
+    )
+
+
+def validate_minimax_h3_checkpoint_variant(
+    checkpoint_paths: list[str], selected_variant: str
+) -> None:
+    names = " ".join(path.lower() for path in checkpoint_paths)
+    checkpoint_variant = next(
+        (variant for variant in ("fl2va", "ref2va") if variant in names), None
+    )
+    if (
+        checkpoint_variant is not None
+        and checkpoint_variant != selected_variant.lower()
+    ):
+        raise ValueError(
+            f"MiniMax-H3 checkpoint variant {checkpoint_variant!r} does not match "
+            f"--model-variant {selected_variant!r}"
+        )
+
+
+__all__ = [
+    "comfy_quant_key_filter",
+    "inspect_minimax_h3_safetensors",
+    "resolve_minimax_h3_checkpoint_quantization",
+    "validate_minimax_h3_checkpoint_variant",
+]

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

@@ -37,6 +37,7 @@ from sglang.multimodal_gen.runtime.managers.memory_managers.component_residency
 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 (
+    hf_hub_download,
     maybe_download_model,
     snapshot_download,
 )
@@ -61,6 +62,11 @@ _PRECISION_VARIANT_SUFFIX_RE = re.compile(
     r"^(?P<stem>.+?)(?P<precision>\.(?:fp16|bf16|fp32))(?P<shard>-\d+-of-\d+)?(?P<ext>\.safetensors)$"
 )
 _MIXED_SAFETENSORS_RE = re.compile(r".*-mixed(?:-\d+-of-\d+)?\.safetensors$")
+_HF_SAFETENSORS_URL_RE = re.compile(
+    r"https?://huggingface\.co/(?P<repo>[^/]+/[^/]+)/"
+    r"(?:blob|resolve)/(?P<revision>[^/]+)/(?P<filename>.+\.safetensors)$",
+    re.IGNORECASE,
+)
 
 
 def _get_quant_config_name(config: Optional[QuantizationConfig]) -> Optional[str]:
@@ -154,6 +160,10 @@ class TransformerQuantLoadSpec:
     def is_modelopt_fp4(self) -> bool:
         return _get_quant_config_name(self.quant_config) == "modelopt_fp4"
 
+    @property
+    def is_comfy_fp8(self) -> bool:
+        return _get_quant_config_name(self.quant_config) == "comfy_fp8"
+
 
 class _TransformerQuantAdapter:
     def prepare(self) -> None:
@@ -567,7 +577,31 @@ def resolve_transformer_safetensors_to_load(
 
     if quantized_path:
         original_quantized_path = quantized_path
-        quantized_path = maybe_download_model(original_quantized_path)
+        direct_url = _HF_SAFETENSORS_URL_RE.fullmatch(original_quantized_path)
+        if direct_url is not None:
+            quantized_path = hf_hub_download(
+                repo_id=direct_url.group("repo"),
+                filename=direct_url.group("filename"),
+                revision=direct_url.group("revision"),
+            )
+        else:
+            parts = original_quantized_path.strip("/").split("/")
+            is_hub_file = (
+                not os.path.exists(original_quantized_path)
+                and not os.path.isabs(original_quantized_path)
+                and not original_quantized_path.startswith((".", "~"))
+                and len(parts) > 2
+                and original_quantized_path.endswith(".safetensors")
+            )
+            quantized_path = (
+                hf_hub_download(
+                    repo_id="/".join(parts[:2]),
+                    filename="/".join(parts[2:]),
+                    revision=server_args.revision,
+                )
+                if is_hub_file
+                else maybe_download_model(original_quantized_path)
+            )
         logger.info("using quantized transformer weights from: %s", quantized_path)
         if os.path.isfile(quantized_path) and quantized_path.endswith(".safetensors"):
             safetensors_list = [quantized_path]
@@ -694,8 +728,11 @@ def resolve_transformer_quant_load_spec(
     cls_name: str,
     component_name: str | None = None,
     gguf_file: str | None = None,
+    checkpoint_quant_config: QuantizationConfig | None = None,
 ) -> TransformerQuantLoadSpec:
     if gguf_file is not None:
+        if checkpoint_quant_config is not None:
+            raise ValueError("GGUF and safetensors quantization metadata conflict")
         return _resolve_gguf_quant_load_spec(
             gguf_file=gguf_file,
             server_args=server_args,
@@ -703,7 +740,19 @@ def resolve_transformer_quant_load_spec(
             component_name=component_name,
         )
 
-    if getattr(model_cls, "handles_checkpoint_quantization", False):
+    if checkpoint_quant_config is not None:
+        if server_args.quantization is not None:
+            raise ValueError(
+                "Checkpoint quantization is encoded in per-layer metadata; do not "
+                "also set --quantization"
+            )
+        if server_args.nunchaku_config is not None:
+            raise ValueError(
+                "Per-layer checkpoint quantization and Nunchaku are mutually "
+                "exclusive"
+            )
+        quant_config = checkpoint_quant_config
+    elif getattr(model_cls, "handles_checkpoint_quantization", False):
         quant_config = None
     else:
         quant_config = _resolve_quant_config(
@@ -793,7 +842,7 @@ def _needs_device_weight_postprocess(
 ) -> bool:
     """Return whether post-load weight processing needs CUDA/NPU tensors."""
     quant_name = _get_quant_config_name(quant_config)
-    if quant_name == "modelopt_fp8":
+    if quant_name in ("modelopt_fp8", "comfy_fp8"):
         return True
 
     serialized_flag_by_quant_name = {

python/sglang/multimodal_gen/runtime/models/dits/minimax_h3.py

@@ -193,8 +193,8 @@ def _copy_grouped_qkv_tp_shard(
         or getattr(param, "output_dim", None) != 0
         or getattr(param, "is_sharded_weight", False)
         or getattr(param, "packed_dim", None) is not None
-        or param.dtype != _BF16_DTYPE
-        or loaded_weight.dtype != _BF16_DTYPE
+        or param.dtype != loaded_weight.dtype
+        or param.dtype not in (_BF16_DTYPE, torch.float8_e4m3fn)
         or not param.is_contiguous()
         or not loaded_weight.is_contiguous()
     ):

python/sglang/multimodal_gen/test/unit/test_minimax_h3_dit_contract.py

@@ -97,36 +97,35 @@ def test_native_weight_names_and_grouped_qkv_reorder():
     ).reshape(12, 1)
     torch.testing.assert_close(actual, expected)
 
-    grouped = torch.arange(48, dtype=torch.int16).reshape(24, 2).view(torch.bfloat16)
-    reordered = _reorder_grouped_qkv_to_qkv(
-        grouped,
-        num_query_groups=4,
-        heads_per_group=1,
-        head_dim=2,
-    )
-    for tp_size in (1, 2, 4):
-        local_rows = 8 // tp_size
-        for tp_rank in range(tp_size):
-            start = tp_rank * local_rows
-            param = torch.nn.Parameter(
-                torch.empty(3 * local_rows, 2, dtype=torch.bfloat16),
-                requires_grad=False,
-            )
-            param.output_dim = 0
-            assert _copy_grouped_qkv_tp_shard(
-                param,
-                grouped,
-                num_query_groups=4,
-                head_dim=2,
-                tp_rank=tp_rank,
-                tp_size=tp_size,
-            )
-            expected_shard = reordered.view(3, 8, 2)[
-                :, start : start + local_rows
-            ].reshape(-1, 2)
-            assert torch.equal(
-                param.view(torch.int16), expected_shard.view(torch.int16)
-            )
+    for dtype in (torch.bfloat16, torch.float8_e4m3fn):
+        grouped = torch.arange(48, dtype=torch.float32).reshape(24, 2).to(dtype)
+        reordered = _reorder_grouped_qkv_to_qkv(
+            grouped,
+            num_query_groups=4,
+            heads_per_group=1,
+            head_dim=2,
+        )
+        for tp_size in (1, 2, 4):
+            local_rows = 8 // tp_size
+            for tp_rank in range(tp_size):
+                start = tp_rank * local_rows
+                param = torch.nn.Parameter(
+                    torch.empty(3 * local_rows, 2, dtype=dtype),
+                    requires_grad=False,
+                )
+                param.output_dim = 0
+                assert _copy_grouped_qkv_tp_shard(
+                    param,
+                    grouped,
+                    num_query_groups=4,
+                    head_dim=2,
+                    tp_rank=tp_rank,
+                    tp_size=tp_size,
+                )
+                expected_shard = reordered.view(3, 8, 2)[
+                    :, start : start + local_rows
+                ].reshape(-1, 2)
+                assert torch.equal(param, expected_shard)
 
 
 def test_pruned_adaln_curve_interpolates_without_timestep_mlp():

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

@@ -45,11 +45,21 @@ sys.modules.setdefault(
 )
 sys.modules.setdefault("partial_json_parser.core.options", partial_json_parser_options)
 
-from sglang.multimodal_gen.runtime.layers.linear import UnquantizedLinearMethod
+from sglang.multimodal_gen.runtime.layers.linear import (
+    LinearBase,
+    UnquantizedLinearMethod,
+)
+from sglang.multimodal_gen.runtime.layers.quantization.comfy_fp8 import (
+    ComfyFp8Config,
+    ComfyFullPrecisionFp8LinearMethod,
+)
 from sglang.multimodal_gen.runtime.layers.quantization.configs.nunchaku_config import (
     NunchakuConfig,
 )
-from sglang.multimodal_gen.runtime.layers.quantization.fp8 import Fp8Config
+from sglang.multimodal_gen.runtime.layers.quantization.fp8 import (
+    Fp8Config,
+    Fp8LinearMethod,
+)
 from sglang.multimodal_gen.runtime.layers.quantization.modelopt_quant import (
     ModelOptFp4Config,
     ModelOptFp8Config,
@@ -61,6 +71,10 @@ from sglang.multimodal_gen.runtime.loader.component_loaders.transformer_loader i
     _resolve_checkpoint_load_device,
     _warn_if_expected_param_dtype_missing,
 )
+from sglang.multimodal_gen.runtime.loader.minimax_h3_weights import (
+    inspect_minimax_h3_safetensors,
+    resolve_minimax_h3_checkpoint_quantization,
+)
 from sglang.multimodal_gen.runtime.loader.transformer_load_utils import (
     TransformerQuantLoadSpec,
     _filter_duplicate_precision_variant_safetensors,
@@ -119,6 +133,7 @@ class TestTransformerQuantHelpers(unittest.TestCase):
             nunchaku_config=None,
             quantization=None,
             quantization_ignored_layers=None,
+            revision="test-revision",
             tp_size=1,
             dit_cpu_offload=False,
             direct_gpu_weight_loading=False,
@@ -170,6 +185,164 @@ class TestTransformerQuantHelpers(unittest.TestCase):
 
         self.assertEqual(resolved, [f.name])
 
+    @patch(
+        "sglang.multimodal_gen.runtime.loader.transformer_load_utils.hf_hub_download",
+        return_value="/cache/model.safetensors",
+    )
+    def test_resolve_transformer_safetensors_to_load_uses_hf_file_reference(
+        self, mock_download
+    ):
+        filename = "diffusion_models/minimax_h3_fl2va_pruned_bf16.safetensors"
+        references = (
+            (
+                f"https://huggingface.co/Comfy-Org/MiniMax-H3/resolve/main/{filename}",
+                "main",
+            ),
+            (f"Comfy-Org/MiniMax-H3/{filename}", "test-revision"),
+        )
+
+        for reference, revision in references:
+            with self.subTest(reference=reference):
+                server_args = self._make_server_args(transformer_weights_path=reference)
+                with patch(
+                    "os.path.isfile",
+                    side_effect=lambda path: path == "/cache/model.safetensors",
+                ):
+                    self.assertEqual(
+                        resolve_transformer_safetensors_to_load(
+                            server_args, "/unused/component/path"
+                        ),
+                        ["/cache/model.safetensors"],
+                    )
+                mock_download.assert_called_once_with(
+                    repo_id="Comfy-Org/MiniMax-H3",
+                    filename=filename,
+                    revision=revision,
+                )
+                mock_download.reset_mock()
+
+    def test_inspect_minimax_h3_safetensors_detects_curve_and_comfy_format(self):
+        marker = json.dumps({"format": "int8_tensorwise", "convrot": True}).encode()
+        with tempfile.NamedTemporaryFile(suffix=".safetensors") as f:
+            save_file(
+                {
+                    "adaln_t_table": torch.zeros((1025, 8)),
+                    "blocks.0.mlp.fc1.comfy_quant": torch.tensor(
+                        list(marker), dtype=torch.uint8
+                    ),
+                },
+                f.name,
+            )
+
+            curve_shape, comfy_quant = inspect_minimax_h3_safetensors([f.name])
+
+        self.assertEqual(curve_shape, (1025, 8))
+        self.assertEqual(comfy_quant["blocks.0.mlp.fc1"]["format"], "int8_tensorwise")
+
+    def test_inspect_minimax_h3_fp8_validates_required_scales(self):
+        marker = torch.tensor(list(b'{"format":"float8_e4m3fn"}'), dtype=torch.uint8)
+        with tempfile.NamedTemporaryFile(suffix=".safetensors") as f:
+            save_file(
+                {
+                    "blocks.0.mlp.fc1.weight": torch.ones(
+                        (2, 2), dtype=torch.float8_e4m3fn
+                    ),
+                    "blocks.0.mlp.fc1.weight_scale": torch.tensor(0.5),
+                    "blocks.0.mlp.fc1.input_scale": torch.tensor(0.25),
+                    "blocks.0.mlp.fc1.comfy_quant": marker,
+                },
+                f.name,
+            )
+
+            _, layer_markers = inspect_minimax_h3_safetensors([f.name])
+
+        self.assertEqual(layer_markers["blocks.0.mlp.fc1"], {"format": "float8_e4m3fn"})
+
+    def test_minimax_h3_comfy_int8_fails_before_weight_loading(self):
+        with self.assertRaisesRegex(NotImplementedError, "regular-Hadamard"):
+            resolve_minimax_h3_checkpoint_quantization(
+                {
+                    "blocks.0.mlp.fc1": {
+                        "format": "int8_tensorwise",
+                        "convrot": True,
+                    }
+                }
+            )
+
+    def test_minimax_h3_comfy_fp8_resolves_per_layer_dispatch(self):
+        config = resolve_minimax_h3_checkpoint_quantization(
+            {
+                "blocks.0.attn.qkv_proj": {"format": "float8_e4m3fn"},
+                "blocks.0.mlp.fc2": {
+                    "format": "float8_e4m3fn",
+                    "full_precision_matrix_mult": True,
+                },
+            }
+        )
+
+        self.assertIsInstance(config, ComfyFp8Config)
+        layer = LinearBase(input_size=1, output_size=1)
+        self.assertIsInstance(
+            config.get_quant_method(layer, "blocks.0.mlp.fc2"),
+            ComfyFullPrecisionFp8LinearMethod,
+        )
+        self.assertIsInstance(
+            config.get_quant_method(layer, "blocks.0.attn.qkv_proj"),
+            Fp8LinearMethod,
+        )
+        self.assertIsInstance(
+            config.get_quant_method(layer, "unmarked"),
+            UnquantizedLinearMethod,
+        )
+
+    def test_comfy_full_precision_fp8_dequantizes_before_linear(self):
+        layer = torch.nn.Module()
+        layer.weight = torch.nn.Parameter(
+            torch.tensor([[2.0, -4.0]], dtype=torch.float8_e4m3fn),
+            requires_grad=False,
+        )
+        layer.weight_scale = torch.nn.Parameter(
+            torch.tensor([0.5]), requires_grad=False
+        )
+
+        output = ComfyFullPrecisionFp8LinearMethod().apply(
+            layer, torch.tensor([[3.0, 1.0]])
+        )
+
+        torch.testing.assert_close(output, torch.tensor([[1.0]]))
+
+    def test_checkpoint_quantization_metadata_drives_load_spec(self):
+        config = ComfyFp8Config({})
+        server_args = self._make_server_args()
+
+        spec = resolve_transformer_quant_load_spec(
+            hf_config={},
+            server_args=server_args,
+            safetensors_list=["model.safetensors"],
+            component_model_path="/unused/component/path",
+            model_cls=_FakeFluxTransformer,
+            cls_name=_FakeFluxTransformer.__name__,
+            checkpoint_quant_config=config,
+        )
+
+        self.assertIs(spec.quant_config, config)
+        self.assertTrue(spec.is_comfy_fp8)
+        self.assertTrue(spec.needs_device_weight_postprocess)
+
+    def test_checkpoint_quantization_rejects_explicit_quantization(self):
+        server_args = self._make_server_args(quantization="fp8")
+
+        with self.assertRaisesRegex(ValueError, "per-layer metadata"):
+            resolve_transformer_quant_load_spec(
+                hf_config={},
+                server_args=server_args,
+                safetensors_list=["model.safetensors"],
+                component_model_path="/unused/component/path",
+                model_cls=_FakeFluxTransformer,
+                cls_name=_FakeFluxTransformer.__name__,
+                checkpoint_quant_config=ComfyFp8Config({}),
+            )
+
     @patch(
         "sglang.multimodal_gen.runtime.loader.transformer_load_utils.maybe_download_model",
         side_effect=lambda path, **kw: path,
@@ -355,6 +528,11 @@ class TestTransformerQuantHelpers(unittest.TestCase):
             )
         )
 
+    def test_comfy_fp8_needs_device_weight_postprocess(self):
+        self.assertTrue(
+            _needs_device_weight_postprocess(_make_quant_config("comfy_fp8"))
+        )
+
     def test_online_fp8_receives_cli_ignored_layer_patterns(self):
         ignored_layers = ["blocks.0.attn.out_proj", "condition_proj"]
         server_args = self._make_server_args(