Add v1/models endpoint to diffusion model APIs so that they can be discovered by model gateway (#16425)

python/sglang/multimodal_gen/runtime/entrypoints/http_server.py

@@ -55,9 +55,15 @@ async def health():
     return {"status": "ok"}
 
 
-@health_router.get("/models")
+@health_router.get("/models", deprecated=True)
 async def get_models(request: Request):
-    """Get information about the model served by this server."""
+    """
+    Get information about the model served by this server.
+
+    .. deprecated::
+        Use /v1/models instead for OpenAI-compatible model discovery.
+        This endpoint will be removed in a future version.
+    """
     from sglang.multimodal_gen.registry import get_model_info
 
     server_args: ServerArgs = request.app.state.server_args

python/sglang/multimodal_gen/runtime/entrypoints/openai/common_api.py

@@ -1,7 +1,9 @@
 from typing import Any, Optional
 
 from fastapi import APIRouter, Body, HTTPException
+from fastapi.responses import ORJSONResponse
 
+from sglang.multimodal_gen.registry import get_model_info
 from sglang.multimodal_gen.runtime.entrypoints.openai.utils import (
     MergeLoraWeightsReq,
     SetLoraReq,
@@ -11,11 +13,23 @@ from sglang.multimodal_gen.runtime.pipelines_core.schedule_batch import OutputBa
 from sglang.multimodal_gen.runtime.scheduler_client import async_scheduler_client
 from sglang.multimodal_gen.runtime.server_args import get_global_server_args
 from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
+from sglang.srt.entrypoints.openai.protocol import ModelCard
 
 router = APIRouter(prefix="/v1")
 logger = init_logger(__name__)
 
 
+class DiffusionModelCard(ModelCard):
+    """Extended ModelCard with diffusion-specific fields."""
+
+    num_gpus: Optional[int] = None
+    task_type: Optional[str] = None
+    dit_precision: Optional[str] = None
+    vae_precision: Optional[str] = None
+    pipeline_name: Optional[str] = None
+    pipeline_class: Optional[str] = None
+
+
 async def _handle_lora_request(req: Any, success_msg: str, failure_msg: str):
     try:
         output: OutputBatch = await async_scheduler_client.forward(req)
@@ -117,3 +131,71 @@ async def model_info():
         "model_path": server_args.model_path,
     }
     return result
+
+
+@router.get("/models", response_class=ORJSONResponse)
+async def available_models():
+    """Show available models. OpenAI-compatible endpoint with extended diffusion info."""
+    server_args = get_global_server_args()
+    if not server_args:
+        raise HTTPException(status_code=500, detail="Server args not initialized")
+
+    model_info = get_model_info(server_args.model_path)
+
+    card_kwargs = {
+        "id": server_args.model_path,
+        "root": server_args.model_path,
+        # Extended diffusion-specific fields
+        "num_gpus": server_args.num_gpus,
+        "task_type": server_args.pipeline_config.task_type.name,
+        "dit_precision": server_args.pipeline_config.dit_precision,
+        "vae_precision": server_args.pipeline_config.vae_precision,
+    }
+
+    if model_info:
+        card_kwargs["pipeline_name"] = model_info.pipeline_cls.pipeline_name
+        card_kwargs["pipeline_class"] = model_info.pipeline_cls.__name__
+
+    model_card = DiffusionModelCard(**card_kwargs)
+
+    # Return dict directly to preserve extended fields (ModelList strips them)
+    return {"object": "list", "data": [model_card.model_dump()]}
+
+
+@router.get("/models/{model:path}", response_class=ORJSONResponse)
+async def retrieve_model(model: str):
+    """Retrieve a model instance. OpenAI-compatible endpoint with extended diffusion info."""
+    server_args = get_global_server_args()
+    if not server_args:
+        raise HTTPException(status_code=500, detail="Server args not initialized")
+
+    if model != server_args.model_path:
+        return ORJSONResponse(
+            status_code=404,
+            content={
+                "error": {
+                    "message": f"The model '{model}' does not exist",
+                    "type": "invalid_request_error",
+                    "param": "model",
+                    "code": "model_not_found",
+                }
+            },
+        )
+
+    model_info = get_model_info(server_args.model_path)
+
+    card_kwargs = {
+        "id": model,
+        "root": model,
+        "num_gpus": server_args.num_gpus,
+        "task_type": server_args.pipeline_config.task_type.name,
+        "dit_precision": server_args.pipeline_config.dit_precision,
+        "vae_precision": server_args.pipeline_config.vae_precision,
+    }
+
+    if model_info:
+        card_kwargs["pipeline_name"] = model_info.pipeline_cls.pipeline_name
+        card_kwargs["pipeline_class"] = model_info.pipeline_cls.__name__
+
+    # Return dict to preserve extended fields
+    return DiffusionModelCard(**card_kwargs).model_dump()