Align diffusion nightly presets and broaden skill discovery (#22099)
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@@ -317,7 +317,14 @@ def _build_sglang_payload(case: dict) -> dict:
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"n": 1,
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"response_format": "b64_json",
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}
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for key in ("num_inference_steps", "guidance_scale", "seed", "num_frames"):
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for key in (
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"num_inference_steps",
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"guidance_scale",
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"seed",
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"num_frames",
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"fps",
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"negative_prompt",
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):
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if key in case:
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payload[key] = case[key]
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return payload
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@@ -447,7 +454,14 @@ def send_image_conditioned_request_sglang(
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"n": "1",
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"response_format": "b64_json",
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}
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for key in ("num_inference_steps", "guidance_scale", "seed", "num_frames"):
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for key in (
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"num_inference_steps",
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"guidance_scale",
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"seed",
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"num_frames",
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"fps",
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"negative_prompt",
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):
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if key in case:
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data[key] = str(case[key])
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if perf_dump_path:
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@@ -521,6 +535,10 @@ def send_request_vllm_omni(base_url: str, case: dict, config: dict) -> float:
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}
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if "num_frames" in case:
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extra_body["num_frames"] = case["num_frames"]
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if "fps" in case:
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extra_body["fps"] = case["fps"]
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if "negative_prompt" in case:
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extra_body["negative_prompt"] = case["negative_prompt"]
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# Build message content (text or text+image)
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content: list[dict] | str = case["prompt"]
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@@ -583,6 +601,10 @@ def send_request_lightx2v(base_url: str, case: dict, config: dict) -> float:
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payload["width"] = case["width"]
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if "guidance_scale" in case:
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payload["guidance_scale"] = case["guidance_scale"]
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if "fps" in case:
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payload["fps"] = case["fps"]
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if "negative_prompt" in case:
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payload["negative_prompt"] = case["negative_prompt"]
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# Image-conditioned: LightX2V accepts image_path (URL or local path)
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if case.get("reference_image"):
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payload["image_path"] = config.get("test_image_url", "")
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