[diffusion] Add Qwen-Image ModelOpt NVFP4 support (#28928)

Co-authored-by: jingyu-ml <jingyux@nvidia.com>
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

docs_new/cookbook/diffusion/Qwen-Image/Qwen-Image.mdx

@@ -30,6 +30,28 @@ Qwen-Image is a text-to-image model. The recommended launch configurations vary
 
 <QwenImageDeployment />
 
+For the validated ModelOpt NVFP4 checkpoint on Blackwell, load the published
+Qwen-Image-2512 NVFP4 repo directly:
+
+```bash Command
+sglang serve \
+  --model-path lmsys/qwen-image-2512-modelopt-nvfp4-sglang \
+  --ulysses-degree=1 \
+  --ring-degree=1
+```
+
+For high-resolution B200 generations, the FlashInfer CUTLASS FP4 GEMM backend
+can be faster than the default TensorRT-LLM FP4 GEMM backend:
+
+```bash Command
+SGLANG_DIFFUSION_FLASHINFER_FP4_GEMM_BACKEND=cutlass \
+sglang generate \
+  --model-path lmsys/qwen-image-2512-modelopt-nvfp4-sglang \
+  --width 2048 --height 2048 \
+  --prompt "A tiny astronaut reading a book under a glass greenhouse" \
+  --save-output
+```
+
 ### 3.2 Configuration Tips
 
 Currently supported optimizations are listed [here](/docs/sglang-diffusion/compatibility_matrix).

docs_new/docs/sglang-diffusion/quantization.mdx

@@ -93,11 +93,11 @@ backend.
     </tr>
     <tr>
       <td><code>modelopt-nvfp4</code></td>
-      <td>Mixed transformer directory/repo with <code>config.json</code>, or raw NVFP4 safetensors export/repo</td>
+      <td>Mixed transformer directory/repo with <code>config.json</code>, raw NVFP4 safetensors export/repo, or full ModelOpt Diffusers repo</td>
-      <td><code>--transformer-path</code> for mixed overrides; <code>--transformer-weights-path</code> for raw exports</td>
+      <td><code>--transformer-path</code> for mixed overrides; <code>--transformer-weights-path</code> for raw exports; <code>--model-path</code> for full repos</td>
-      <td>FLUX.1, FLUX.2, Wan2.2</td>
+      <td>FLUX.1, FLUX.2, Wan2.2, Qwen Image, Qwen Image 2512, Qwen Image Edit, Qwen Image Edit 2511</td>
       <td>None</td>
-      <td>Mixed override repos keep the base model separate; raw exports such as <code>black-forest-labs/FLUX.2-dev-NVFP4</code> still use the weights-path flow</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>nunchaku-svdq</code></td>
@@ -172,16 +172,16 @@ Each pattern is matched against the full layer prefix (e.g. `layers.0.attention.
 
 ## Validated ModelOpt Checkpoints
 
-This section is the canonical support matrix for the nine diffusion ModelOpt
+This section is the canonical support matrix for the thirteen published
-checkpoints currently wired up in SGLang docs and validation coverage.
+diffusion ModelOpt 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`.
 
-Six of the nine repos live under `lmsys/*`. The Wan2.2 entries use NVIDIA's
+Twelve of the thirteen repos live under `lmsys/*`. The FLUX.2 NVFP4 entry keeps
-official full Diffusers repos, and the FLUX.2 NVFP4 entry keeps the official
+the official `black-forest-labs/FLUX.2-dev-NVFP4` repo.
-`black-forest-labs/FLUX.2-dev-NVFP4` repo.
 
 <table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
   <colgroup>
@@ -222,10 +222,10 @@ official full Diffusers repos, and the FLUX.2 NVFP4 entry keeps the official
     <tr>
       <td><code>FP8</code></td>
       <td><code>Wan-AI/Wan2.2-T2V-A14B-Diffusers</code></td>
-      <td><code>--model-path</code></td>
+      <td><code>--transformer-path</code></td>
-      <td><code>nvidia/Wan2.2-T2V-A14B-Diffusers-FP8</code></td>
+      <td><code>lmsys/wan22-t2v-a14b-modelopt-fp8-sglang-transformer</code></td>
-      <td>full Diffusers repo with ModelOpt FP8 Wan2.2 components</td>
+      <td>primary <code>transformer</code> quantized, <code>transformer_2</code> kept BF16</td>
-      <td>validated through direct <code>--model-path</code> loading</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>
     </tr>
     <tr>
       <td><code>FP8</code></td>
@@ -270,16 +270,50 @@ official full Diffusers repos, and the FLUX.2 NVFP4 entry keeps the official
     <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>lmsys/wan22-t2v-a14b-modelopt-nvfp4-sglang-transformer</code></td>
+      <td>primary <code>transformer</code> quantized with ModelOpt NVFP4, <code>transformer_2</code> kept BF16</td>
+      <td>primary-transformer-only path; keep <code>transformer_2</code> on the base checkpoint; the default FP4 GEMM backend is <code>flashinfer_trtllm</code></td>
+    </tr>
+    <tr>
+      <td><code>NVFP4</code></td>
+      <td><code>Qwen/Qwen-Image</code></td>
       <td><code>--model-path</code></td>
-      <td><code>nvidia/Wan2.2-T2V-A14B-Diffusers-NVFP4</code></td>
+      <td><code>lmsys/qwen-image-modelopt-nvfp4-sglang</code></td>
-      <td>full Diffusers repo with ModelOpt NVFP4 Wan2.2 components</td>
+      <td>full ModelOpt NVFP4 Diffusers repo, BF16-vs-NVFP4 B200 image comparison</td>
-      <td>default FP4 GEMM backend is <code>flashinfer_trtllm</code></td>
+      <td>full repo loaded directly; exported with ModelOpt PR #1706 SVDQuant NVFP4 (<code>--format fp4</code>, max calibration, block size 16) and BF16 fallbacks for attention-sensitive modules plus first/last transformer blocks</td>
+    </tr>
+    <tr>
+      <td><code>NVFP4</code></td>
+      <td><code>Qwen/Qwen-Image-2512</code></td>
+      <td><code>--model-path</code></td>
+      <td><code>lmsys/qwen-image-2512-modelopt-nvfp4-sglang</code></td>
+      <td>full ModelOpt NVFP4 Diffusers repo, BF16-vs-NVFP4 B200 image comparison, B200 CI case</td>
+      <td>same full-repo loader path as Qwen Image; this is the Qwen Image NVFP4 representative in <code>multimodal-gen-test-1-b200</code></td>
+    </tr>
+    <tr>
+      <td><code>NVFP4</code></td>
+      <td><code>Qwen/Qwen-Image-Edit</code></td>
+      <td><code>--model-path</code></td>
+      <td><code>lmsys/qwen-image-edit-modelopt-nvfp4-sglang</code></td>
+      <td>TI2I edit full ModelOpt NVFP4 Diffusers repo, BF16-vs-NVFP4 B200 image comparison</td>
+      <td>full repo loaded directly with normal image-edit inputs; exported with the same ModelOpt PR #1706 NVFP4 recipe</td>
+    </tr>
+    <tr>
+      <td><code>NVFP4</code></td>
+      <td><code>Qwen/Qwen-Image-Edit-2511</code></td>
+      <td><code>--model-path</code></td>
+      <td><code>lmsys/qwen-image-edit-2511-modelopt-nvfp4-sglang</code></td>
+      <td>TI2I edit full ModelOpt NVFP4 Diffusers repo, BF16-vs-NVFP4 B200 image comparison</td>
+      <td>full repo loaded directly with normal image-edit inputs; exported with the same ModelOpt PR #1706 NVFP4 recipe</td>
     </tr>
   </tbody>
 </table>
 
-The FP8 rows run in the regular H100 1-GPU diffusion CI shard; the NVFP4 rows
+These thirteen checkpoints are the intended ModelOpt documentation support
-run in the B200 diffusion CI shard (`multimodal-gen-test-1-b200`).
+set. The B200 diffusion CI job (`multimodal-gen-test-1-b200`) uses a
+representative NVFP4 subset and includes
+`lmsys/qwen-image-2512-modelopt-nvfp4-sglang` for Qwen Image coverage.
 
 ## ModelOpt FP8
 
@@ -300,7 +334,8 @@ sglang generate \
 
 ```bash
 sglang generate \
-  --model-path nvidia/Wan2.2-T2V-A14B-Diffusers-FP8 \
+  --model-path Wan-AI/Wan2.2-T2V-A14B-Diffusers \
+  --transformer-path lmsys/wan22-t2v-a14b-modelopt-fp8-sglang-transformer \
   --prompt "a fox walking through neon rain" \
   --save-output
 ```
@@ -387,15 +422,38 @@ sglang generate \
   --save-output
 ```
 
-For Wan2.2 NVFP4:
+For a dual-transformer Wan2.2 export where only the primary `transformer`
+was quantized:
 
 ```bash
 sglang generate \
-  --model-path nvidia/Wan2.2-T2V-A14B-Diffusers-NVFP4 \
+  --model-path Wan-AI/Wan2.2-T2V-A14B-Diffusers \
+  --transformer-path lmsys/wan22-t2v-a14b-modelopt-nvfp4-sglang-transformer \
   --prompt "a fox walking through neon rain" \
   --save-output
 ```
 
+For full Qwen Image NVFP4 exports, load the published repo directly:
+
+```bash
+sglang generate \
+  --model-path lmsys/qwen-image-2512-modelopt-nvfp4-sglang \
+  --prompt "A tiny astronaut reading a book under a glass greenhouse" \
+  --save-output
+```
+
+For high-resolution Qwen-Image-family generations on B200, the FlashInfer
+CUTLASS FP4 GEMM backend can be faster than the default TensorRT-LLM backend:
+
+```bash
+SGLANG_DIFFUSION_FLASHINFER_FP4_GEMM_BACKEND=cutlass \
+sglang generate \
+  --model-path lmsys/qwen-image-2512-modelopt-nvfp4-sglang \
+  --width 2048 --height 2048 \
+  --prompt "A tiny astronaut reading a book under a glass greenhouse" \
+  --save-output
+```
+
 ### Notes
 
 - Use `--transformer-path` for mixed ModelOpt NVFP4 transformer repos or local
@@ -408,8 +466,15 @@ sglang generate \
   intentionally want a non-default `transformer_2`.
 - On Blackwell, the diffusion ModelOpt NVFP4 path defaults to FlashInfer
   TensorRT-LLM FP4 GEMM (`flashinfer_trtllm`).
-- Direct `--model-path` loading is a compatibility path for FLUX.2 NVFP4-style
+- The published Qwen Image NVFP4 exports keep the `img_mod`/`txt_mod`
-  repos or local directories.
+  modulation projections and first/last transformer blocks in BF16.
+- Qwen-Image NVFP4 does not always improve latency at 1024x1024. On B200, the
+  validated ModelOpt exports were faster than BF16 at 2048x2048 with
+  `SGLANG_DIFFUSION_FLASHINFER_FP4_GEMM_BACKEND=cutlass`, while 1024x1024
+  remained BF16-faster.
+- Direct `--model-path` loading is the canonical path for full Qwen Image
+  ModelOpt NVFP4 repos and a compatibility path for FLUX.2 NVFP4-style repos
+  or local directories.
 - If `--transformer-weights-path` is provided explicitly, it takes precedence
   over the compatibility `--model-path` flow.
 - For local directories, SGLang first looks for `*-mixed.safetensors`, then

docs_new/src/snippets/diffusion/qwen-image-deployment.jsx

@@ -7,16 +7,40 @@ export const QwenImageDeployment = () => {
         name: 'hardware',
         title: 'Hardware Platform',
         items: [
-          { id: 'mi300x', label: 'MI300X', default: true },
+          { id: 'b200', label: 'B200', default: true },
+          { id: 'b300', label: 'B300', default: false },
+          { id: 'h200', label: 'H200', default: false },
+          { id: 'h100', label: 'H100', default: false },
+          { id: 'mi300x', label: 'MI300X', default: false },
           { id: 'mi325x', label: 'MI325X', default: false },
           { id: 'mi355x', label: 'MI355X', default: false }
         ]
+      },
+      precision: {
+        name: 'precision',
+        title: 'Precision',
+        items: [
+          { id: 'bf16', label: 'BF16', default: true },
+          {
+            id: 'nvfp4',
+            label: 'NVFP4',
+            default: false,
+            disabledWhen: (values) => !['b200', 'b300'].includes(values.hardware),
+            disabledReason: 'ModelOpt NVFP4 requires Blackwell hardware such as B200 or B300'
+          }
+        ]
       }
     },
 
     generateCommand: function(values) {
+      const isBlackwell = ['b200', 'b300'].includes(values.hardware);
+      const isNvfp4 = values.precision === 'nvfp4' && isBlackwell;
+      const modelPath = isNvfp4
+        ? 'lmsys/qwen-image-2512-modelopt-nvfp4-sglang'
+        : 'Qwen/Qwen-Image';
+
       return `sglang serve \\
-  --model-path Qwen/Qwen-Image \\
+  --model-path ${modelPath} \\
   --ulysses-degree=1 \\
   --ring-degree=1`;
     }
@@ -89,7 +113,17 @@ export const QwenImageDeployment = () => {
   }, []);
 
   const handleRadioChange = (optionName, value) => {
-    setValues((prev) => ({ ...prev, [optionName]: value }));
+    setValues((prev) => {
+      const next = { ...prev, [optionName]: value };
+      if (
+        optionName === 'hardware' &&
+        !['b200', 'b300'].includes(value) &&
+        next.precision === 'nvfp4'
+      ) {
+        next.precision = 'bf16';
+      }
+      return next;
+    });
   };
 
   const handleCheckboxChange = (optionName, itemId, isChecked) => {
@@ -266,7 +300,9 @@ export const QwenImageDeployment = () => {
               ) : (
                 items.map((item) => {
                   const isChecked = values[option.name] === item.id;
-                  const isDisabled = Boolean(item.disabled);
+                  const isDisabled =
+                    item.disabled ||
+                    (typeof item.disabledWhen === 'function' && item.disabledWhen(values));
 
                   return (
                     <label

python/sglang/multimodal_gen/registry.py

@@ -859,7 +859,7 @@ def _register_configs():
     register_configs(
         sampling_param_cls=QwenImageSamplingParams,
         pipeline_config_cls=QwenImagePipelineConfig,
-        hf_model_paths=["Qwen/Qwen-Image"],
+        hf_model_paths=["Qwen/Qwen-Image", "nvidia/Qwen-Image-NVFP4"],
         model_detectors=[
             lambda hf_id: (
                 "qwen-image" in hf_id.lower()

python/sglang/multimodal_gen/runtime/utils/quantization_utils.py

@@ -301,6 +301,22 @@ def get_metadata_from_safetensors_file(file_path: str):
         logger.warning(e)
 
 
+def _canonicalize_modulation_exclude(module_name: str) -> str:
+    """Map a serialized modulation weight's parent to the runtime linear prefix.
+
+    Qwen-Image wraps the modulation projection in ``nn.Sequential(SiLU, Linear)``,
+    so its weights serialize as ``...img_mod.1.weight`` while the runtime
+    ReplicatedLinear advertises ``...img_mod`` as its quant/exclusion prefix.
+    Strip the trailing Sequential index so a safetensors-inferred BF16 exclude
+    entry actually matches the linear (mirrors the ModelOpt FP8 converter, which
+    canonicalizes ``.img_mod.1``/``.txt_mod.1`` to ``.img_mod``/``.txt_mod``).
+    No-op for any other module name.
+    """
+    if module_name.endswith((".img_mod.1", ".txt_mod.1")):
+        return module_name.removesuffix(".1")
+    return module_name
+
+
 def _build_nvfp4_config_from_safetensors_files(
     file_paths: list[str],
     param_names_mapping_dict: Optional[dict] = None,
@@ -470,7 +486,9 @@ def _build_nvfp4_config_from_safetensors_files(
 
         exclude_modules.append(module_bfl)
 
-    exclude_modules = sorted(set(exclude_modules))
+    exclude_modules = sorted(
+        {_canonicalize_modulation_exclude(m) for m in exclude_modules}
+    )
 
     try:
         quant_cls = get_quantization_config("modelopt_fp4")

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

@@ -25,6 +25,12 @@
             "psnr_threshold": 18.0,
             "mean_abs_diff_threshold": 18.0
         },
+        "qwen_image_2512_modelopt_nvfp4_t2i": {
+            "clip_threshold": 0.95,
+            "ssim_threshold": 0.88,
+            "psnr_threshold": 23.0,
+            "mean_abs_diff_threshold": 9.0
+        },
         "flux_2_klein_image_t2i": {
             "clip_threshold": 0.94,
             "ssim_threshold": 0.78,

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

@@ -9,6 +9,7 @@ from sglang.multimodal_gen.test.server.testcase_configs import (
     MODELOPT_FLUX2_NVFP4_WEIGHTS,
     MODELOPT_HUNYUANVIDEO_FP8_TRANSFORMER,
     MODELOPT_NVFP4_B200_ENV_VARS,
+    MODELOPT_QWEN_IMAGE_2512_NVFP4_MODEL,
     MODELOPT_QWEN_IMAGE_EDIT_FP8_TRANSFORMER,
     MODELOPT_QWEN_IMAGE_FP8_TRANSFORMER,
     MODELOPT_WAN22_FP8_MODEL,
@@ -20,6 +21,7 @@ from sglang.multimodal_gen.test.server.testcase_configs import (
     DiffusionTestCase,
     IDEOGRAM4_CI_sampling_params,
     LINGBOT_WORLD_REALTIME_sampling_params,
+    MODELOPT_QWEN_IMAGE_2512_NVFP4_CI_sampling_params,
     MODELOPT_T2I_CI_sampling_params,
     MODELOPT_T2V_CI_sampling_params,
     MODELOPT_TI2I_CI_sampling_params,
@@ -550,6 +552,15 @@ else:
             env_vars=MODELOPT_NVFP4_B200_ENV_VARS,
             run_consistency_check=True,
         ),
+        _make_modelopt_ci_case(
+            "qwen_image_2512_modelopt_nvfp4_t2i",
+            model_path=MODELOPT_QWEN_IMAGE_2512_NVFP4_MODEL,
+            modality="image",
+            sampling_params=MODELOPT_QWEN_IMAGE_2512_NVFP4_CI_sampling_params,
+            extras=[],
+            env_vars=MODELOPT_NVFP4_B200_ENV_VARS,
+            run_consistency_check=True,
+        ),
         _make_modelopt_ci_case(
             "wan22_modelopt_nvfp4_t2v",
             model_path=MODELOPT_WAN22_NVFP4_MODEL,

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

@@ -2846,6 +2846,14 @@
             "expected_median_denoise_ms": 0.0,
             "estimated_full_test_time_s": 592.3
         },
+        "qwen_image_2512_modelopt_nvfp4_t2i": {
+            "stages_ms": {},
+            "denoise_step_ms": {},
+            "expected_e2e_ms": 0.0,
+            "expected_avg_denoise_ms": 0.0,
+            "expected_median_denoise_ms": 0.0,
+            "estimated_full_test_time_s": 120.0
+        },
         "wan22_modelopt_nvfp4_t2v": {
             "stages_ms": {},
             "denoise_step_ms": {},

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

@@ -488,6 +488,11 @@ MODELOPT_T2I_CI_sampling_params = DiffusionSamplingParams(
     extras={"num_inference_steps": 12, "seed": 0},
 )
 
+MODELOPT_QWEN_IMAGE_2512_NVFP4_CI_sampling_params = replace(
+    MODELOPT_T2I_CI_sampling_params,
+    extras={"num_inference_steps": 50, "seed": 0},
+)
+
 MODELOPT_TI2I_CI_sampling_params = DiffusionSamplingParams(
     prompt="Convert 2D style to 3D style",
     image_path="https://github.com/lm-sys/lm-sys.github.io/releases/download/test/TI2I_Qwen_Image_Edit_Input.jpg",
@@ -636,6 +641,7 @@ 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_QWEN_IMAGE_2512_NVFP4_MODEL = "lmsys/qwen-image-2512-modelopt-nvfp4-sglang"
 MODELOPT_WAN22_NVFP4_MODEL = "nvidia/Wan2.2-T2V-A14B-Diffusers-NVFP4"
 MODELOPT_NVFP4_B200_ENV_VARS = {}
 MODELOPT_WAN22_NVFP4_B200_ENV_VARS = {}

python/sglang/multimodal_gen/test/test_utils.py

@@ -34,7 +34,7 @@ if TYPE_CHECKING:
 
 logger = init_logger(__name__)
 
-SGL_TEST_FILES_CI_DATA_REVISION = "14a963c21b96bcc4bb3811be02076977e8f8439b"
+SGL_TEST_FILES_CI_DATA_REVISION = "3c6e06ae99001d93f7901bc9b7fdf19ec6c2ce4e"
 
 if current_platform.is_npu():
     SGL_TEST_FILES_CI_DATA_REVISION = "670d66a8a290b62c0c3c077b3e9b0f4a4d9a44e7"