[diffusion] quant: update Wan2.2 modelOpt CI checkpoints (#25483)

docs_new/docs/sglang-diffusion/quantization.mdx

@@ -110,14 +110,15 @@ backend.
 ## Validated ModelOpt Checkpoints
 
 This section is the canonical support matrix for the nine diffusion ModelOpt
-checkpoints currently wired up in SGLang docs and B200 CI coverage.
+checkpoints currently wired up in SGLang docs and validation coverage.
 
 Published checkpoints keep the serialized quantization config as
 `quant_method=modelopt`; the FP8 vs NVFP4 split below is a documentation label
 derived from `quant_algo`.
 
-Eight of the nine repos live under `lmsys/*`. The FLUX.2 NVFP4 entry keeps the
+Six of the nine repos live under `lmsys/*`. The Wan2.2 entries use NVIDIA's
-official `black-forest-labs/FLUX.2-dev-NVFP4` repo.
+official full Diffusers repos, and the FLUX.2 NVFP4 entry keeps the official
+`black-forest-labs/FLUX.2-dev-NVFP4` repo.
 
 <table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
   <colgroup>
@@ -158,10 +159,10 @@ official `black-forest-labs/FLUX.2-dev-NVFP4` repo.
     <tr>
       <td><code>FP8</code></td>
       <td><code>Wan-AI/Wan2.2-T2V-A14B-Diffusers</code></td>
-      <td><code>--transformer-path</code></td>
+      <td><code>--model-path</code></td>
-      <td><code>lmsys/wan22-t2v-a14b-modelopt-fp8-sglang-transformer</code></td>
+      <td><code>nvidia/Wan2.2-T2V-A14B-Diffusers-FP8</code></td>
-      <td>primary <code>transformer</code> quantized, <code>transformer_2</code> kept BF16</td>
+      <td>full Diffusers repo with ModelOpt FP8 Wan2.2 components</td>
-      <td>primary-transformer-only path; keep <code>transformer_2</code> on the base checkpoint, and do not describe this as dual-transformer full-model FP8 unless that path is validated separately</td>
+      <td>validated through direct <code>--model-path</code> loading</td>
     </tr>
     <tr>
       <td><code>FP8</code></td>
@@ -206,24 +207,25 @@ official `black-forest-labs/FLUX.2-dev-NVFP4` repo.
     <tr>
       <td><code>NVFP4</code></td>
       <td><code>Wan-AI/Wan2.2-T2V-A14B-Diffusers</code></td>
-      <td><code>--transformer-path</code></td>
+      <td><code>--model-path</code></td>
-      <td><code>lmsys/wan22-t2v-a14b-modelopt-nvfp4-sglang-transformer</code></td>
+      <td><code>nvidia/Wan2.2-T2V-A14B-Diffusers-NVFP4</code></td>
-      <td>primary <code>transformer</code> quantized with ModelOpt NVFP4, <code>transformer_2</code> kept BF16</td>
+      <td>full Diffusers repo with ModelOpt NVFP4 Wan2.2 components</td>
-      <td>primary-transformer-only path; keep <code>transformer_2</code> on the base checkpoint, and current B200/Blackwell bring-up uses <code>SGLANG_DIFFUSION_FLASHINFER_FP4_GEMM_BACKEND=cudnn</code></td>
+      <td>current B200/Blackwell bring-up uses <code>SGLANG_DIFFUSION_FLASHINFER_FP4_GEMM_BACKEND=trtllm</code></td>
     </tr>
   </tbody>
 </table>
 
-These nine checkpoints are also the intended case set for the B200 diffusion CI
+The FP8 rows run in the regular H100 1-GPU diffusion CI shard; the NVFP4 rows
-job (`multimodal-gen-test-1-b200`).
+run in the B200 diffusion CI shard (`multimodal-gen-test-1-b200`).
 
 ## ModelOpt FP8
 
 ### Usage Examples
 
-Converted ModelOpt FP8 checkpoints should be loaded as transformer component
+Converted ModelOpt FP8 transformer repos should be loaded as transformer
-overrides. If the repo or local directory already contains `config.json`, use
+component overrides. If the repo or local directory already contains
-`--transformer-path`.
+`config.json`, use `--transformer-path`. Full Diffusers repos such as the
+NVIDIA Wan2.2 FP8 checkpoint can be passed directly with `--model-path`.
 
 ```bash
 sglang generate \
@@ -235,8 +237,7 @@ sglang generate \
 
 ```bash
 sglang generate \
-  --model-path Wan-AI/Wan2.2-T2V-A14B-Diffusers \
+  --model-path nvidia/Wan2.2-T2V-A14B-Diffusers-FP8 \
-  --transformer-path lmsys/wan22-t2v-a14b-modelopt-fp8-sglang-transformer \
   --prompt "a fox walking through neon rain" \
   --save-output
 ```
@@ -323,14 +324,12 @@ sglang generate \
   --save-output
 ```
 
-For a dual-transformer Wan2.2 export where only the primary `transformer`
+For Wan2.2 NVFP4:
-was quantized:
 
 ```bash
-SGLANG_DIFFUSION_FLASHINFER_FP4_GEMM_BACKEND=cudnn \
+SGLANG_DIFFUSION_FLASHINFER_FP4_GEMM_BACKEND=trtllm \
 sglang generate \
-  --model-path Wan-AI/Wan2.2-T2V-A14B-Diffusers \
+  --model-path nvidia/Wan2.2-T2V-A14B-Diffusers-NVFP4 \
-  --transformer-path lmsys/wan22-t2v-a14b-modelopt-nvfp4-sglang-transformer \
   --prompt "a fox walking through neon rain" \
   --save-output
 ```
@@ -341,17 +340,16 @@ sglang generate \
   directories that already include `config.json`.
 - Use `--transformer-weights-path` for raw NVFP4 exports, individual
   safetensors files, or repo layouts that should be treated as weights first.
-- For dual-transformer pipelines such as `Wan2.2-T2V-A14B-Diffusers`, the
+- For legacy mixed Wan2.2 transformer overrides, the primary
-  primary `--transformer-path` override targets only `transformer`. Use a
+  `--transformer-path` override targets only `transformer`. Use a per-component
-  per-component override such as `--transformer-2-path` only when you
+  override such as `--transformer-2-path` only when you intentionally want a
-  intentionally want a non-default `transformer_2`.
+  non-default `transformer_2`.
 - On Blackwell, the validated Wan2.2 ModelOpt NVFP4 path currently prefers
   FlashInfer FP4 GEMM via
-  `SGLANG_DIFFUSION_FLASHINFER_FP4_GEMM_BACKEND=cudnn`.
+  `SGLANG_DIFFUSION_FLASHINFER_FP4_GEMM_BACKEND=trtllm`.
-- This environment-variable override is a current workaround for NVFP4 cases
+- This environment-variable override selects the validated Wan2.2 NVFP4
-  where the default sglang JIT/CUTLASS `sm100` path rejects a large-M shape at
+  full-repo path on Blackwell while the other NVFP4 CI cases continue to use
-  `can_implement()`. The intended long-term fix is to add a validated CUTLASS
+  the generic `cudnn` backend.
-  fallback for those shapes rather than rely on the override.
 - Direct `--model-path` loading is a compatibility path for FLUX.2 NVFP4-style
   repos or local directories.
 - If `--transformer-weights-path` is provided explicitly, it takes precedence

python/sglang/multimodal_gen/registry.py

@@ -739,7 +739,10 @@ def _register_configs():
     register_configs(
         sampling_param_cls=Wan2_2_T2V_A14B_SamplingParam,
         pipeline_config_cls=Wan2_2_T2V_A14B_Config,
-        hf_model_paths=["Wan-AI/Wan2.2-T2V-A14B-Diffusers"],
+        hf_model_paths=[
+            "Wan-AI/Wan2.2-T2V-A14B-Diffusers",
+            "nvidia/Wan2.2-T2V-A14B-Diffusers-NVFP4",
+        ],
     )
     register_configs(
         sampling_param_cls=Wan2_2_I2V_A14B_SamplingParam,

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

@@ -202,7 +202,7 @@ class ModelOptFp4Config(ModelOptQuantConfig):
         exclude_modules: List[str] = None,
         packed_modules_mapping: Optional[Dict[str, List[str]]] = None,
         checkpoint_uses_packed_qkv: bool = False,
-        swap_weight_nibbles: bool = True,
+        swap_weight_nibbles: bool = False,
     ) -> None:
         super().__init__(exclude_modules, packed_modules_mapping)
         self.is_checkpoint_nvfp4_serialized = is_checkpoint_nvfp4_serialized
@@ -261,7 +261,7 @@ class ModelOptFp4Config(ModelOptQuantConfig):
     def from_config(cls, config: Dict[str, Any]) -> ModelOptFp4Config:
         group_size = None
         exclude_modules = []
-        swap_weight_nibbles = True
+        swap_weight_nibbles = False
 
         # Flat format (config.json quantization_config)
         quant_method = config.get("quant_algo")
@@ -273,7 +273,10 @@ class ModelOptFp4Config(ModelOptQuantConfig):
                     first_group = next(iter(config_groups.values()), {})
                     group_size = first_group.get("weights", {}).get("group_size")
             exclude_modules = config.get("ignore", [])
-            swap_weight_nibbles = config.get("swap_weight_nibbles", True)
+            swap_weight_nibbles = config.get(
+                "swap_weight_nibbles",
+                config.get("checkpoint_uses_packed_qkv", False),
+            )
         else:
             # Nested format (hf_quant_config.json)
             try:
@@ -283,7 +286,10 @@ class ModelOptFp4Config(ModelOptQuantConfig):
                 exclude_modules = quant_config.get("exclude_modules", [])
                 swap_weight_nibbles = quant_config.get(
                     "swap_weight_nibbles",
-                    config.get("swap_weight_nibbles", True),
+                    config.get(
+                        "swap_weight_nibbles",
+                        config.get("checkpoint_uses_packed_qkv", False),
+                    ),
                 )
             except (ValueError, KeyError):
                 raise ValueError("Cannot find 'quant_algo' in quantization config.")
@@ -494,7 +500,9 @@ class ModelOptFp4LinearMethod(LinearMethodBase):
         w = layer.weight.data
         w_swapped = _prepare_nvfp4_weight_bytes(
             w,
-            swap_weight_nibbles=getattr(self.quant_config, "swap_weight_nibbles", True),
+            swap_weight_nibbles=getattr(
+                self.quant_config, "swap_weight_nibbles", False
+            ),
         )
 
         _, flashinfer_backend = _get_fp4_gemm_op()
@@ -554,8 +562,13 @@ class ModelOptFp4LinearMethod(LinearMethodBase):
         padded_scales[:B, :M, :K] = scales
 
         _, flashinfer_backend = _get_fp4_gemm_op()
-        if flashinfer_backend is None:
+        uses_flux1_scale_layout = not getattr(
-            # CUTLASS (sgl_kernel) path: blockwise interleave to TMA layout
+            self.quant_config, "checkpoint_uses_packed_qkv", False
+        ) and getattr(layer, "prefix", "").startswith(
+            ("transformer_blocks.", "single_transformer_blocks.")
+        )
+        if flashinfer_backend is None or uses_flux1_scale_layout:
+            # CUTLASS and FLUX.1 CUDNN paths need the TMA scale layout.
             padded_scales = padded_scales.reshape(
                 B, M_padded // 128, 4, 32, K_padded // 4, 4
             )

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

@@ -88,12 +88,12 @@ def _merge_modelopt_fp4_configs(
     inferred_config.packed_modules_mapping = getattr(
         existing_config, "packed_modules_mapping", {}
     )
-    inferred_config.swap_weight_nibbles = getattr(
-        existing_config, "swap_weight_nibbles", True
-    )
     inferred_config.checkpoint_uses_packed_qkv = getattr(
         inferred_config, "checkpoint_uses_packed_qkv", False
     ) or getattr(existing_config, "checkpoint_uses_packed_qkv", False)
+    inferred_config.swap_weight_nibbles = getattr(
+        inferred_config, "swap_weight_nibbles", False
+    ) or getattr(existing_config, "swap_weight_nibbles", False)
     if getattr(inferred_config, "group_size", None) is None:
         inferred_config.group_size = getattr(existing_config, "group_size", None)
 

python/sglang/multimodal_gen/test/server/consistency_threshold.json

@@ -13,6 +13,12 @@
             "psnr_threshold": 24.0,
             "mean_abs_diff_threshold": 8.0
         },
+        "flux1_modelopt_fp8_t2i": {
+            "clip_threshold": 0.92,
+            "ssim_threshold": 0.94,
+            "psnr_threshold": 28.0,
+            "mean_abs_diff_threshold": 8.0
+        },
         "flux_2_klein_image_t2i": {
             "clip_threshold": 0.94,
             "ssim_threshold": 0.78,

python/sglang/multimodal_gen/test/server/gpu_cases.py

@@ -11,8 +11,9 @@ from sglang.multimodal_gen.test.server.testcase_configs import (
     MODELOPT_NVFP4_B200_ENV_VARS,
     MODELOPT_QWEN_IMAGE_EDIT_FP8_TRANSFORMER,
     MODELOPT_QWEN_IMAGE_FP8_TRANSFORMER,
-    MODELOPT_WAN22_FP8_TRANSFORMER,
+    MODELOPT_WAN22_FP8_MODEL,
-    MODELOPT_WAN22_NVFP4_TRANSFORMER,
+    MODELOPT_WAN22_NVFP4_B200_ENV_VARS,
+    MODELOPT_WAN22_NVFP4_MODEL,
     T2V_PROMPT,
     DiffusionSamplingParams,
     DiffusionServerArgs,
@@ -419,6 +420,7 @@ else:
             modality="image",
             sampling_params=MODELOPT_T2I_CI_sampling_params,
             extras=["--transformer-path", MODELOPT_FLUX1_FP8_TRANSFORMER],
+            run_consistency_check=True,
         ),
         _make_modelopt_ci_case(
             "flux2_modelopt_fp8_t2i",
@@ -426,13 +428,15 @@ else:
             modality="image",
             sampling_params=MODELOPT_T2I_CI_sampling_params,
             extras=["--transformer-path", MODELOPT_FLUX2_FP8_TRANSFORMER],
+            run_consistency_check=True,
         ),
         _make_modelopt_ci_case(
             "wan22_modelopt_fp8_t2v",
-            model_path=DEFAULT_WAN_2_2_T2V_A14B_MODEL_NAME_FOR_TEST,
+            model_path=MODELOPT_WAN22_FP8_MODEL,
             modality="video",
             sampling_params=MODELOPT_T2V_CI_sampling_params,
-            extras=["--transformer-path", MODELOPT_WAN22_FP8_TRANSFORMER],
+            extras=[],
+            run_consistency_check=True,
         ),
         _make_modelopt_ci_case(
             "hunyuanvideo_modelopt_fp8_t2v",
@@ -469,6 +473,7 @@ else:
             sampling_params=MODELOPT_T2I_CI_sampling_params,
             extras=["--transformer-path", MODELOPT_FLUX1_NVFP4_TRANSFORMER],
             env_vars=MODELOPT_NVFP4_B200_ENV_VARS,
+            run_consistency_check=True,
         ),
         _make_modelopt_ci_case(
             "flux2_modelopt_nvfp4_t2i",
@@ -477,14 +482,16 @@ else:
             sampling_params=MODELOPT_T2I_CI_sampling_params,
             extras=["--transformer-weights-path", MODELOPT_FLUX2_NVFP4_WEIGHTS],
             env_vars=MODELOPT_NVFP4_B200_ENV_VARS,
+            run_consistency_check=True,
         ),
         _make_modelopt_ci_case(
             "wan22_modelopt_nvfp4_t2v",
-            model_path=DEFAULT_WAN_2_2_T2V_A14B_MODEL_NAME_FOR_TEST,
+            model_path=MODELOPT_WAN22_NVFP4_MODEL,
             modality="video",
             sampling_params=MODELOPT_T2V_CI_sampling_params,
-            extras=["--transformer-path", MODELOPT_WAN22_NVFP4_TRANSFORMER],
+            extras=[],
-            env_vars=MODELOPT_NVFP4_B200_ENV_VARS,
+            env_vars=MODELOPT_WAN22_NVFP4_B200_ENV_VARS,
+            run_consistency_check=True,
         ),
     ]
 

python/sglang/multimodal_gen/test/server/testcase_configs.py

@@ -441,7 +441,7 @@ HUNYUAN3D_SHAPE_sampling_params = DiffusionSamplingParams(
 
 MODELOPT_FLUX1_FP8_TRANSFORMER = "lmsys/flux1-dev-modelopt-fp8-sglang-transformer"
 MODELOPT_FLUX2_FP8_TRANSFORMER = "lmsys/flux2-dev-modelopt-fp8-sglang-transformer"
-MODELOPT_WAN22_FP8_TRANSFORMER = "lmsys/wan22-t2v-a14b-modelopt-fp8-sglang-transformer"
+MODELOPT_WAN22_FP8_MODEL = "nvidia/Wan2.2-T2V-A14B-Diffusers-FP8"
 MODELOPT_HUNYUANVIDEO_FP8_TRANSFORMER = (
     "lmsys/hunyuanvideo-modelopt-fp8-sglang-transformer"
 )
@@ -451,10 +451,11 @@ MODELOPT_QWEN_IMAGE_EDIT_FP8_TRANSFORMER = (
 )
 MODELOPT_FLUX1_NVFP4_TRANSFORMER = "lmsys/flux1-dev-modelopt-nvfp4-sglang-transformer"
 MODELOPT_FLUX2_NVFP4_WEIGHTS = "black-forest-labs/FLUX.2-dev-NVFP4"
-MODELOPT_WAN22_NVFP4_TRANSFORMER = (
+MODELOPT_WAN22_NVFP4_MODEL = "nvidia/Wan2.2-T2V-A14B-Diffusers-NVFP4"
-    "lmsys/wan22-t2v-a14b-modelopt-nvfp4-sglang-transformer"
-)
 MODELOPT_NVFP4_B200_ENV_VARS = {"SGLANG_DIFFUSION_FLASHINFER_FP4_GEMM_BACKEND": "cudnn"}
+MODELOPT_WAN22_NVFP4_B200_ENV_VARS = {
+    "SGLANG_DIFFUSION_FLASHINFER_FP4_GEMM_BACKEND": "trtllm"
+}
 
 
 def _make_modelopt_ci_case(
@@ -465,6 +466,7 @@ def _make_modelopt_ci_case(
     sampling_params: DiffusionSamplingParams,
     extras: list[str],
     env_vars: dict[str, str] | None = None,
+    run_consistency_check: bool = False,
 ) -> DiffusionTestCase:
     return DiffusionTestCase(
         case_id,
@@ -477,7 +479,7 @@ def _make_modelopt_ci_case(
         ),
         sampling_params,
         run_perf_check=False,
-        run_consistency_check=False,
+        run_consistency_check=run_consistency_check,
         run_component_accuracy_check=False,
     )
 

python/sglang/multimodal_gen/test/test_utils.py

@@ -33,7 +33,7 @@ if TYPE_CHECKING:
 
 logger = init_logger(__name__)
 
-SGL_TEST_FILES_CI_DATA_REVISION = "c8305f1dd8cc82197f36c17d0f503adc94016cc7"
+SGL_TEST_FILES_CI_DATA_REVISION = "94eab4fcca6d4ddc77cdb3622f13033b61e81002"
 SGL_TEST_FILES_CONSISTENCY_GT_ROOT = (
     "https://raw.githubusercontent.com/"
     f"sgl-project/ci-data/{SGL_TEST_FILES_CI_DATA_REVISION}/"

python/sglang/multimodal_gen/tools/build_modelopt_nvfp4_transformer.py

@@ -218,9 +218,7 @@ def build_modelopt_nvfp4_transformer(
         patterns.extend(keep_bf16_patterns)
 
     resolved_swap_weight_nibbles = (
-        swap_weight_nibbles
+        swap_weight_nibbles if swap_weight_nibbles is not None else False
-        if swap_weight_nibbles is not None
-        else (False if pattern_preset == "flux1-nvfp4" else True)
     )
     output_config = _updated_quant_config(
         _load_config(source_dir),
@@ -373,7 +371,7 @@ def _parse_args() -> argparse.Namespace:
         default=None,
         help=(
             "Whether the runtime should swap packed FP4 nibbles before padding. "
-            "Defaults to false for --pattern-preset flux1-nvfp4 and true otherwise."
+            "Defaults to false."
         ),
     )
     parser.add_argument(