[codex] Align diffusion skills with nightly Nvidia benchmarks (#25842)

python/sglang/multimodal_gen/.claude/skills/sglang-diffusion-benchmark-profile/SKILL.md

@@ -45,7 +45,7 @@ If any benchmark, perf-dump, or `torch.profiler` command prints one of those sig
 - [benchmark-and-profile.md](benchmark-and-profile.md) — canonical denoise benchmark, perf dump, and `torch.profiler` workflow; uses checked-in nightly-aligned presets plus skill-only stress recipes such as `LTX-2.3` one-stage/two-stage, HunyuanVideo, MOVA, Helios, JoyAI/FireRed image edit, and Hunyuan3D shape
 - [existing-fast-paths.md](existing-fast-paths.md) — map bottlenecks to existing fused kernels, packed QKV paths, fused `QK norm + RoPE`, distributed overlap patterns, and open optimization PRs before proposing new code
 - [scripts/diffusion_skill_env.py](scripts/diffusion_skill_env.py) — preflight helper: repo root discovery via `sglang.__file__`, write-access probe, benchmark/profile output directories, idle GPU selection
-- [scripts/bench_diffusion_denoise.py](scripts/bench_diffusion_denoise.py) — end-to-end denoise benchmark preset runner via `sglang generate`; pins `--backend=sglang`, supports `--no-torch-compile`, and saves perf dumps by label for `compare_perf.py`
+- [scripts/bench_diffusion_denoise.py](scripts/bench_diffusion_denoise.py) — end-to-end denoise benchmark preset runner via `sglang generate`; supports `--no-torch-compile`, validates nightly preset drift with `--validate-nightly-alignment`, and saves perf dumps by label for `compare_perf.py`
 
 ## Opportunity Discovery Rule
 

python/sglang/multimodal_gen/.claude/skills/sglang-diffusion-benchmark-profile/benchmark-and-profile.md

@@ -99,8 +99,10 @@ Nightly diffusion comparison is server/API based (`sglang serve` plus requests).
 This skill stays on `sglang generate` for local benchmarking and profiling, but
 the nightly-aligned presets in `bench_diffusion_denoise.py` mirror
 `scripts/ci/utils/diffusion/comparison_configs.json` on model, task, prompt,
-reference image, size, frames, seed, GPU count, serve args, and the request
+reference image, size, frames, seed, GPU count, and SGLang serve args. If
-defaults used by `run_comparison.py` when a case omits steps or guidance.
+`comparison_configs.json` omits sampling params such as steps or guidance, the
+nightly-aligned `sglang generate` preset omits them too and relies on the same
+runtime defaults.
 When in doubt, re-check that JSON before trusting this reference.
 
 List the current preset order:
@@ -109,6 +111,12 @@ List the current preset order:
 PYTHONPATH=python python3 "$BENCH_PY" --list-models
 ```
 
+Check that the nightly presets still match the Nvidia nightly comparison config:
+
+```bash
+PYTHONPATH=python python3 "$BENCH_PY" --validate-nightly-alignment
+```
+
 Run one preset and save a perf dump:
 
 ```bash
@@ -168,18 +176,19 @@ Nightly-aligned presets come first; skill-only presets stay available after them
 
 | Preset | Model | Nightly | Notes |
 | --- | --- | --- | --- |
-| `flux` | `black-forest-labs/FLUX.1-dev` | Yes: `flux1_dev_t2i_1024` | Aligned to nightly prompt plus `--dit-layerwise-offload false` |
+| `flux` | `black-forest-labs/FLUX.1-dev` | Yes: `flux1_dev_t2i_1024` | Prompt, 1024x1024, seed 42, `--dit-layerwise-offload false`; no explicit steps/guidance override |
-| `flux2` | `black-forest-labs/FLUX.2-dev` | Yes: `flux2_dev_t2i_1024` | Aligned to nightly prompt, 50 steps, guidance 4.0 |
+| `flux2` | `black-forest-labs/FLUX.2-dev` | Yes: `flux2_dev_t2i_1024` | Prompt, 1024x1024, seed 42, `--dit-layerwise-offload false`; no explicit steps/guidance override |
-| `qwen` | `Qwen/Qwen-Image-2512` | Yes: `qwen_image_2512_t2i_1024` | Aligned to nightly prompt and steps |
+| `qwen` | `Qwen/Qwen-Image-2512` | Yes: `qwen_image_2512_t2i_1024` | Prompt, 1024x1024, seed 42; no explicit steps/guidance override |
 | `qwen-edit` | `Qwen/Qwen-Image-Edit-2511` | Yes: `qwen_image_edit_2511` | Uses the nightly cat image and edit prompt |
-| `zimage` | `Tongyi-MAI/Z-Image-Turbo` | Yes: `zimage_turbo_t2i_1024` | Aligned to nightly prompt and guidance 4.0 |
+| `zimage` | `Tongyi-MAI/Z-Image-Turbo` | Yes: `zimage_turbo_t2i_1024` | Prompt, 1024x1024, seed 42; no explicit steps/guidance override |
-| `wan-t2v` | `Wan-AI/Wan2.2-T2V-A14B-Diffusers` | Yes: `wan22_t2v_a14b_720p` | Aligned to nightly CFG-parallel 4-GPU launch |
+| `wan-t2v` | `Wan-AI/Wan2.2-T2V-A14B-Diffusers` | Yes: `wan22_t2v_a14b_720p` | 1280x720, 81 frames, 4 GPUs, CFG parallel, Ulysses degree 2, text encoder CPU offload and pinned CPU memory |
-| `wan-ti2v` | `Wan-AI/Wan2.2-TI2V-5B-Diffusers` | Yes: `wan22_ti2v_5b_720p` | Uses the nightly cat image and motion prompt |
+| `wan-ti2v` | `Wan-AI/Wan2.2-TI2V-5B-Diffusers` | Yes: `wan22_ti2v_5b_720p` | Nightly cat image and motion prompt, 1280x720, 81 frames, seed 42 |
-| `ltx2` | `Lightricks/LTX-2` | Yes: `ltx2_twostage_t2v` | Uses `LTX2TwoStagePipeline`; 2 GPUs, CFG parallel, 768x512, 121 frames, seed 42 |
+| `ltx2` | `Lightricks/LTX-2` | Yes: `ltx2_twostage_t2v` | `LTX2TwoStagePipeline`, 2 GPUs, CFG parallel, 768x512, 121 frames, seed 42 |
-| `ltx23-ti2v-two-stage` | `Lightricks/LTX-2.3` | Yes: `ltx2.3_twostage_ti2v_2gpus` | Uses the nightly cat image, motion prompt, `LTX2TwoStagePipeline`, 2 GPUs, 768x512, 121 frames, seed 42 |
+| `ltx23-ti2v-two-stage` | `Lightricks/LTX-2.3` | Yes: `ltx2.3_twostage_ti2v_2gpus` | Nightly cat image, motion prompt, `LTX2TwoStagePipeline`, 2 GPUs, `--cfg-parallel-size 2`, 768x512, 121 frames, seed 42 |
-| `wan-i2v` | `Wan-AI/Wan2.2-I2V-A14B-Diffusers` | Yes: `wan22_i2v_a14b_720p` | Aligned to nightly CFG-parallel 4-GPU launch |
+| `wan-i2v` | `Wan-AI/Wan2.2-I2V-A14B-Diffusers` | Yes: `wan22_i2v_a14b_720p` | Nightly cat image and motion prompt, 1280x720, 81 frames, 4 GPUs, CFG parallel, Ulysses degree 2, text encoder CPU offload and pinned CPU memory |
 | `ltx23-one-stage` | `Lightricks/LTX-2.3` | No | Skill-only extra preset for the native `LTX-2.3` one-stage baseline; 2 GPUs, 768x512, 121 frames, fps 24, 30 steps, guidance 3.0, seed 1234 |
 | `ltx23-two-stage` | `Lightricks/LTX-2.3` | No | Skill-only high-resolution stress preset for the native `LTX-2.3` two-stage path; uses `LTX2TwoStagePipeline`, 2 GPUs, 1536x1024, 121 frames, fps 24, 30 steps, guidance 3.0, seed 1234 |
+| `ltx23-two-stage-cfg-parallel` | `Lightricks/LTX-2.3` | No | Skill-only high-resolution CFG-parallel stress preset matching `ltx23-two-stage` plus `--cfg-parallel-size 2` |
 | `hunyuanvideo` | `hunyuanvideo-community/HunyuanVideo` | No | Skill-only extra preset |
 | `mova-720p` | `OpenMOSS-Team/MOVA-720p` | No | Skill-only extra preset |
 | `helios` | `BestWishYsh/Helios-Base` | No | Skill-only extra preset |
@@ -203,7 +212,6 @@ sglang generate \
   --prompt="A cat and a dog baking a cake together in a kitchen." \
   --width=768 --height=512 \
   --num-frames=121 \
-  --num-inference-steps=50 --guidance-scale=4.0 \
   --seed=42 --num-gpus=2 --enable-cfg-parallel \
   --save-output --enable-torch-compile --warmup
 ```
@@ -221,8 +229,7 @@ sglang generate \
   --image-path="${ASSET_DIR}/cat.png" \
   --width=768 --height=512 \
   --num-frames=121 \
-  --num-inference-steps=50 --guidance-scale=4.0 \
+  --seed=42 --num-gpus=2 --cfg-parallel-size=2 \
-  --seed=42 --num-gpus=2 \
   --save-output --enable-torch-compile --warmup
 ```
 
@@ -331,8 +338,8 @@ sglang generate \
   --model-path=Wan-AI/Wan2.2-I2V-A14B-Diffusers \
   --prompt="The cat starts walking slowly towards the camera." \
   --image-path="${ASSET_DIR}/cat.png" \
-  --720p --num-inference-steps=2 --num-frames=81 \
+  --width=1280 --height=720 --num-frames=81 \
-  --guidance-scale=5.0 --seed=42 --save-output \
+  --seed=42 --save-output \
   --num-gpus=4 --enable-cfg-parallel --ulysses-degree=2 \
   --text-encoder-cpu-offload --pin-cpu-memory \
   --warmup --enable-torch-compile

python/sglang/multimodal_gen/.claude/skills/sglang-diffusion-benchmark-profile/scripts/bench_diffusion_denoise.py

@@ -12,7 +12,7 @@ Usage:
     # Tag the run for later compare_perf.py usage
     python3 python/sglang/multimodal_gen/.claude/skills/sglang-diffusion-benchmark-profile/scripts/bench_diffusion_denoise.py --model flux --label tuned
 
-    # All 19 preset models
+    # All 20 preset models
     python3 python/sglang/multimodal_gen/.claude/skills/sglang-diffusion-benchmark-profile/scripts/bench_diffusion_denoise.py --all
 
     # Show preset order, model path, and nightly mapping
@@ -32,6 +32,7 @@ Input images required for image-guided models:
 import argparse
 import json
 import os
+import shlex
 import subprocess
 import sys
 import time
@@ -52,6 +53,9 @@ from diffusion_skill_env import (  # noqa: E402
 
 REPO_ROOT = get_repo_root()
 ASSET_DIR = ensure_dir(get_assets_dir(REPO_ROOT))
+NIGHTLY_CONFIG_PATH = (
+    REPO_ROOT / "scripts" / "ci" / "utils" / "diffusion" / "comparison_configs.json"
+)
 GATED_MODELS = {"flux", "flux2"}
 DIFFUSERS_FALLBACK_SIGNALS = (
     "falling back to diffusers backend",
@@ -60,6 +64,18 @@ DIFFUSERS_FALLBACK_SIGNALS = (
 )
 CATALOG_TABLE_WIDTH = 105
 RESULTS_TABLE_WIDTH = 105
+NIGHTLY_PRESET_ORDER = (
+    "flux",
+    "flux2",
+    "qwen",
+    "qwen-edit",
+    "zimage",
+    "wan-t2v",
+    "wan-ti2v",
+    "ltx2",
+    "ltx23-ti2v-two-stage",
+    "wan-i2v",
+)
 
 # ---------------------------------------------------------------------------
 # Model configs — kept in exact sync with benchmark-and-profile.md
@@ -76,8 +92,6 @@ MODELS = {
         "extra_args": [
             "--width=1024",
             "--height=1024",
-            "--num-inference-steps=50",
-            "--guidance-scale=4.0",
             "--dit-layerwise-offload",
             "false",
         ],
@@ -90,8 +104,6 @@ MODELS = {
         "extra_args": [
             "--width=1024",
             "--height=1024",
-            "--num-inference-steps=50",
-            "--guidance-scale=4.0",
             "--dit-layerwise-offload",
             "false",
         ],
@@ -104,8 +116,6 @@ MODELS = {
         "extra_args": [
             "--width=1024",
             "--height=1024",
-            "--num-inference-steps=50",
-            "--guidance-scale=4.0",
         ],
     },
     # 4. Nightly: qwen_image_edit_2511
@@ -118,8 +128,6 @@ MODELS = {
         "extra_args": [
             "--width=1024",
             "--height=1024",
-            "--num-inference-steps=50",
-            "--guidance-scale=4.0",
         ],
     },
     # 5. Nightly: zimage_turbo_t2i_1024
@@ -130,8 +138,6 @@ MODELS = {
         "extra_args": [
             "--width=1024",
             "--height=1024",
-            "--num-inference-steps=9",
-            "--guidance-scale=4.0",
         ],
     },
     # 6. Nightly: wan22_t2v_a14b_720p
@@ -140,10 +146,9 @@ MODELS = {
         "path": "Wan-AI/Wan2.2-T2V-A14B-Diffusers",
         "prompt": "A cat and a dog baking a cake together in a kitchen.",
         "extra_args": [
-            "--720p",
+            "--width=1280",
-            "--num-inference-steps=2",
+            "--height=720",
             "--num-frames=81",
-            "--guidance-scale=5.0",
             "--num-gpus=4",
             "--enable-cfg-parallel",
             "--ulysses-degree=2",
@@ -159,10 +164,9 @@ MODELS = {
         "prompt": "The cat starts walking slowly towards the camera.",
         "image_path": str(ASSET_DIR / "cat.png"),
         "extra_args": [
-            "--720p",
+            "--width=1280",
+            "--height=720",
             "--num-frames=81",
-            "--num-inference-steps=50",
-            "--guidance-scale=5.0",
         ],
     },
     # 8. Nightly: ltx2_twostage_t2v
@@ -175,8 +179,6 @@ MODELS = {
             "--width=768",
             "--height=512",
             "--num-frames=121",
-            "--num-inference-steps=50",
-            "--guidance-scale=4.0",
             "--num-gpus=2",
             "--enable-cfg-parallel",
         ],
@@ -193,9 +195,8 @@ MODELS = {
             "--width=768",
             "--height=512",
             "--num-frames=121",
-            "--num-inference-steps=50",
-            "--guidance-scale=4.0",
             "--num-gpus=2",
+            "--cfg-parallel-size=2",
         ],
     },
     # 10. Nightly: wan22_i2v_a14b_720p
@@ -206,10 +207,9 @@ MODELS = {
         "prompt": "The cat starts walking slowly towards the camera.",
         "image_path": str(ASSET_DIR / "cat.png"),
         "extra_args": [
-            "--720p",
+            "--width=1280",
-            "--num-inference-steps=2",
+            "--height=720",
             "--num-frames=81",
-            "--guidance-scale=5.0",
             "--num-gpus=4",
             "--enable-cfg-parallel",
             "--ulysses-degree=2",
@@ -322,6 +322,7 @@ MODELS = {
         "prompt": "Make the cat wear a red hat",
         "image_path": str(ASSET_DIR / "cat.png"),
         "extra_args": [
+            "--backend=sglang",
             "--width=1024",
             "--height=1024",
             "--num-inference-steps=40",
@@ -342,6 +343,7 @@ MODELS = {
         "prompt": "Make the cat wear a red hat",
         "image_path": str(ASSET_DIR / "cat.png"),
         "extra_args": [
+            "--backend=sglang",
             "--width=1024",
             "--height=1024",
             "--num-inference-steps=40",
@@ -362,6 +364,7 @@ MODELS = {
         "prompt": "Make the cat wear a red hat",
         "image_path": str(ASSET_DIR / "cat.png"),
         "extra_args": [
+            "--backend=sglang",
             "--width=1024",
             "--height=1024",
             "--num-inference-steps=40",
@@ -385,6 +388,7 @@ MODELS = {
             "paint_enable": False,
         },
         "extra_args": [
+            "--backend=sglang",
             "--num-inference-steps=50",
             "--guidance-scale=5.0",
             "--dit-layerwise-offload",
@@ -406,6 +410,7 @@ def required_gpus_for_model(model_key: str) -> int:
         "ltx23-ti2v-two-stage",
         "ltx23-one-stage",
         "ltx23-two-stage",
+        "ltx23-two-stage-cfg-parallel",
         "joyai-edit",
         "firered-edit-1.0",
         "firered-edit-1.1",
@@ -418,6 +423,123 @@ def model_nightly_case_id(model_key: str) -> str:
     return MODELS[model_key].get("nightly_case_id", "-")
 
 
+def _parse_cli_args(args: list[str]) -> dict[str, object]:
+    parsed: dict[str, object] = {}
+    i = 0
+    while i < len(args):
+        arg = args[i]
+        if not isinstance(arg, str) or not arg.startswith("--"):
+            i += 1
+            continue
+        if "=" in arg:
+            key, value = arg[2:].split("=", 1)
+            parsed[key] = value
+        elif i + 1 < len(args) and not str(args[i + 1]).startswith("--"):
+            parsed[arg[2:]] = str(args[i + 1])
+            i += 1
+        else:
+            parsed[arg[2:]] = True
+        i += 1
+    return parsed
+
+
+def _normalize_cli_value(value: object) -> str:
+    if isinstance(value, bool):
+        return "true" if value else "false"
+    return str(value)
+
+
+def _expected_nightly_cli_args(case: dict) -> dict[str, str]:
+    expected = {
+        "width": str(case["width"]),
+        "height": str(case["height"]),
+    }
+
+    for key, flag in (
+        ("num_frames", "num-frames"),
+        ("fps", "fps"),
+        ("num_inference_steps", "num-inference-steps"),
+        ("guidance_scale", "guidance-scale"),
+    ):
+        if key in case:
+            expected[flag] = str(case[key])
+
+    if case.get("num_gpus", 1) > 1:
+        expected["num-gpus"] = str(case["num_gpus"])
+
+    serve_args = shlex.split(case["frameworks"]["sglang"].get("serve_args", ""))
+    parsed_serve_args = _parse_cli_args(serve_args)
+    for flag, value in parsed_serve_args.items():
+        if flag in {"enable-torch-compile", "warmup"}:
+            continue
+        expected[flag] = _normalize_cli_value(value)
+
+    return expected
+
+
+def validate_nightly_alignment() -> int:
+    """Validate nightly presets against diffusion comparison_configs.json."""
+    if not NIGHTLY_CONFIG_PATH.exists():
+        print(f"Missing nightly config: {NIGHTLY_CONFIG_PATH}")
+        return 1
+
+    with open(NIGHTLY_CONFIG_PATH) as f:
+        config = json.load(f)
+
+    cases = {case["id"]: case for case in config["cases"]}
+    errors: list[str] = []
+
+    preset_case_ids = [
+        MODELS[model_key].get("nightly_case_id") for model_key in NIGHTLY_PRESET_ORDER
+    ]
+    if preset_case_ids != list(cases):
+        errors.append(
+            "Nightly preset order differs from comparison_configs.json: "
+            f"skill={preset_case_ids}, ci={list(cases)}"
+        )
+
+    for model_key in NIGHTLY_PRESET_ORDER:
+        preset = MODELS[model_key]
+        case_id = preset["nightly_case_id"]
+        case = cases.get(case_id)
+        if case is None:
+            errors.append(f"{model_key}: missing CI case {case_id}")
+            continue
+
+        if preset["path"] != case["model"]:
+            errors.append(f"{model_key}: model path differs")
+        if preset["prompt"] != case["prompt"]:
+            errors.append(f"{model_key}: prompt differs")
+        if bool(preset.get("image_path")) != bool(case.get("reference_image")):
+            errors.append(f"{model_key}: reference image presence differs")
+        if preset.get("seed", 42) != case.get("seed"):
+            errors.append(f"{model_key}: seed differs")
+
+        actual_args = {
+            key: _normalize_cli_value(value)
+            for key, value in _parse_cli_args(preset["extra_args"]).items()
+        }
+        expected_args = _expected_nightly_cli_args(case)
+        if actual_args != expected_args:
+            errors.append(
+                f"{model_key}: CLI args differ\n"
+                f"  skill={actual_args}\n"
+                f"  ci={expected_args}"
+            )
+
+    if errors:
+        print("Nightly alignment check failed:")
+        for error in errors:
+            print(f"- {error}")
+        return 1
+
+    print(
+        "Nightly alignment check passed: presets match "
+        "scripts/ci/utils/diffusion/comparison_configs.json."
+    )
+    return 0
+
+
 def print_model_catalog():
     """Print preset order, model path, and whether each preset maps to nightly."""
     print()
@@ -455,8 +577,6 @@ def build_sglang_cmd(
         "generate",
         f"--model-path={cfg['path']}",
         f"--prompt={cfg['prompt']}",
-        "--backend=sglang",
-        "--log-level=info",
     ]
 
     effective_seed = cfg.get("seed", seed)
@@ -671,12 +791,19 @@ def main():
         choices=list(MODELS.keys()),
         help="Model to benchmark (default: flux)",
     )
-    parser.add_argument("--all", action="store_true", help="Benchmark all 19 models")
+    parser.add_argument(
+        "--all", action="store_true", help=f"Benchmark all {len(MODELS)} models"
+    )
     parser.add_argument(
         "--list-models",
         action="store_true",
         help="List preset order, nightly mapping, and exit",
     )
+    parser.add_argument(
+        "--validate-nightly-alignment",
+        action="store_true",
+        help="Validate nightly presets against scripts/ci/utils/diffusion/comparison_configs.json and exit.",
+    )
     parser.add_argument(
         "--label",
         type=str,
@@ -702,6 +829,9 @@ def main():
         print_model_catalog()
         return
 
+    if args.validate_nightly_alignment:
+        raise SystemExit(validate_nightly_alignment())
+
     output_dir = Path(args.output_dir)
     output_dir.mkdir(parents=True, exist_ok=True)
     warmup = not args.no_warmup

python/sglang/multimodal_gen/.claude/skills/sglang-diffusion-performance/SKILL.md

@@ -89,12 +89,12 @@ sglang generate --model-path Lightricks/LTX-2 \
   --pipeline-class-name LTX2TwoStagePipeline \
   --prompt "A cat and a dog baking a cake together in a kitchen." \
   --width 768 --height 512 \
-  --num-frames 121 --num-inference-steps 50 --guidance-scale 4.0 \
+  --num-frames 121 \
   --seed 42 --num-gpus 2 --enable-cfg-parallel \
   --enable-torch-compile --warmup --save-output
 ```
 
-Note: this generate recipe is aligned with the nightly comparison case `ltx2_twostage_t2v`. `LTX2TwoStagePipeline` is a native path and auto-resolves the spatial upsampler plus distilled LoRA from the same model snapshot unless you override them.
+Note: this generate recipe is aligned with the nightly comparison case `ltx2_twostage_t2v`. The nightly config omits explicit steps and guidance, so this command omits them too and uses runtime defaults. `LTX2TwoStagePipeline` is a native path and auto-resolves the spatial upsampler plus distilled LoRA from the same model snapshot unless you override them.
 
 ### Nightly-aligned model, 2 GPUs: LTX-2.3 TI2V two-stage
 
@@ -104,12 +104,12 @@ sglang generate --model-path Lightricks/LTX-2.3 \
   --prompt "The cat starts walking slowly towards the camera." \
   --image-path "${ASSET_DIR}/cat.png" \
   --width 768 --height 512 \
-  --num-frames 121 --num-inference-steps 50 --guidance-scale 4.0 \
+  --num-frames 121 \
-  --seed 42 --num-gpus 2 \
+  --seed 42 --num-gpus 2 --cfg-parallel-size 2 \
   --enable-torch-compile --warmup --save-output
 ```
 
-Note: this matches the nightly comparison case `ltx2.3_twostage_ti2v_2gpus`. Download `${ASSET_DIR}/cat.png` with the benchmark/profile skill before running it.
+Note: this matches the nightly comparison case `ltx2.3_twostage_ti2v_2gpus`. The nightly config omits explicit steps and guidance, so this command omits them too and uses runtime defaults. Download `${ASSET_DIR}/cat.png` with the benchmark/profile skill before running it.
 
 ### Native baseline, 2 GPUs: LTX-2.3 one-stage
 
@@ -251,12 +251,12 @@ Use these as first commands to benchmark, not as universal winners.
 
 | Model family | First performance shape | Starting flags | Notes |
 |---|---|---|---|
-| FLUX.1 / FLUX.2 image | 1024x1024, 50 steps, 1 GPU | `--enable-torch-compile --warmup --dit-layerwise-offload false` | `black-forest-labs/FLUX.*` repos are gated; for FP8/NVFP4 use validated `--transformer-path` or `--transformer-weights-path` flows from the quant skill. |
+| FLUX.1 / FLUX.2 image | 1024x1024, runtime-default steps/guidance, 1 GPU | `--enable-torch-compile --warmup --dit-layerwise-offload false` | `black-forest-labs/FLUX.*` repos are gated; for FP8/NVFP4 use validated `--transformer-path` or `--transformer-weights-path` flows from the quant skill. |
-| Qwen-Image / Qwen-Image-Edit | 1024x1024, 50 steps, 1 GPU | `--enable-torch-compile --warmup`; optionally native `SGLANG_CACHE_DIT_ENABLED=true` | Cache-DiT is lossy. For edit tasks, keep reference image, seed, and output size fixed. |
+| Qwen-Image / Qwen-Image-Edit | 1024x1024, runtime-default steps/guidance, 1 GPU | `--enable-torch-compile --warmup`; optionally native `SGLANG_CACHE_DIT_ENABLED=true` | Cache-DiT is lossy. For edit tasks, keep reference image, seed, and output size fixed. |
-| Z-Image-Turbo | 1024x1024, 9 steps, guidance 4.0 | `--enable-torch-compile --warmup` | Mainline has Z-Image tanh/gate norm fusions; PR #21912 tracks FP8 plus CUDA Graph work. |
+| Z-Image-Turbo | 1024x1024, runtime-default steps/guidance, 1 GPU | `--enable-torch-compile --warmup` | Mainline has Z-Image tanh/gate norm fusions; PR #21912 tracks FP8 plus CUDA Graph work. |
-| Wan2.2 A14B T2V/I2V | 720p, 81 frames | Nightly: `--num-gpus 4 --enable-cfg-parallel --ulysses-degree 2 --text-encoder-cpu-offload --pin-cpu-memory` | For lowest latency, also benchmark pure Ulysses on the same GPUs. |
+| Wan2.2 A14B T2V/I2V | 1280x720, 81 frames | Nightly: `--num-gpus 4 --enable-cfg-parallel --ulysses-degree 2 --text-encoder-cpu-offload --pin-cpu-memory` | For lowest latency, also benchmark pure Ulysses on the same GPUs. |
-| Wan2.2 TI2V 5B | 720p, 81 frames, 1 GPU | `--enable-torch-compile --warmup` | Keep the input image and motion prompt fixed when comparing sparse attention or Cache-DiT. |
+| Wan2.2 TI2V 5B | 1280x720, 81 frames, 1 GPU | `--enable-torch-compile --warmup` | Keep the input image and motion prompt fixed when comparing sparse attention or Cache-DiT. |
-| LTX-2 / LTX-2.3 | 768x512, 121 frames, 2 GPUs | `--pipeline-class-name LTX2TwoStagePipeline --enable-torch-compile --warmup`; LTX-2 nightly also uses `--enable-cfg-parallel` | Use the benchmark/profile skill presets for exact nightly alignment. PRs #22441, #24025, and #23736 track additional LTX2 perf/parallel work. |
+| LTX-2 / LTX-2.3 | 768x512, 121 frames, runtime-default steps/guidance, 2 GPUs | `--pipeline-class-name LTX2TwoStagePipeline --enable-torch-compile --warmup`; LTX-2 uses `--enable-cfg-parallel`, LTX-2.3 TI2V uses `--cfg-parallel-size 2` | Use the benchmark/profile skill presets for exact nightly alignment. PRs #22441, #24025, and #23736 track additional LTX2 perf/parallel work. |
 | HunyuanVideo | 848x480 or 720p class video | `--text-encoder-cpu-offload --pin-cpu-memory --enable-torch-compile --warmup` | Check VAE decode separately. GroupNorm+SiLU is default-eligible in mainline when wrapper guards pass; use `bench_group_norm_silu.py` when VAE residual blocks are hot. |
 | JoyAI-Image-Edit | 1024-class TI2I, 40 steps, guidance 4.0 | `--backend=sglang --num-gpus 2 --enable-cfg-parallel --ulysses-degree 1 --enable-torch-compile --warmup --dit-layerwise-offload false --dit-cpu-offload false` | Newly supported image-edit path. Keep the input image, prompt, seed, and output size fixed; 2-GPU CFG parallel is the validated H100 starting point. |
 | FireRed-Image-Edit 1.0 / 1.1 | 1024x1024 image edit, 40 steps, guidance 4.0 | `--backend=sglang --num-gpus 2 --enable-cfg-parallel --ulysses-degree 1 --enable-torch-compile --warmup --dit-layerwise-offload false --dit-cpu-offload false` | Uses the native `QwenImageEditPlusPipeline` path. 2-GPU CFG parallel is the validated H100 starting point; benchmark 1.0 and 1.1 separately because checkpoint differences can change denoise latency. |