[Bugfix] Align Offline and Online Inference

vllm_omni/entrypoints/openai/api_server.py

@@ -1533,6 +1533,12 @@ async def generate_images(request: ImageGenerationRequest, raw_request: Request)
                 # Keep /images validation semantics: invalid LoRA should fail with 400.
                 _parse_lora_request(request.lora)
                 extra_body["lora"] = request.lora
+            if request.bot_task is not None:
+                extra_body["bot_task"] = request.bot_task
+            if request.use_system_prompt is not None:
+                extra_body["use_system_prompt"] = request.use_system_prompt
+            if request.system_prompt is not None:
+                extra_body["system_prompt"] = request.system_prompt
 
             generation_result = await chat_handler.generate_diffusion_images(
                 prompt=request.prompt,
@@ -1544,8 +1550,9 @@ async def generate_images(request: ImageGenerationRequest, raw_request: Request)
                     status_code=generation_result.error.code if generation_result.error else 400,
                     content=generation_result.model_dump(),
                 )
-            flat_images, _, _ = generation_result
+            flat_images, _, _, _ = generation_result
             image_data = [ImageData(b64_json=encode_image_base64(img), revised_prompt=None) for img in flat_images]
+
             return ImageGenerationResponse(created=int(time.time()), data=image_data)
 
         # Build params - pass through user values directly
@@ -1558,6 +1565,8 @@ async def generate_images(request: ImageGenerationRequest, raw_request: Request)
             extra_args["use_system_prompt"] = request.use_system_prompt
         if request.system_prompt is not None:
             extra_args["system_prompt"] = request.system_prompt
+        if request.bot_task is not None:
+            extra_args["bot_task"] = request.bot_task
         if extra_args:
             gen_params.extra_args = extra_args
         # Parse per-request LoRA (compatible with chat's extra_body.lora shape).
@@ -1725,6 +1734,7 @@ async def edit_images(
         )
     try:
         # 2. Build prompt & images params
+        cot_output = None
         prompt: OmniTextPrompt = {"prompt": prompt}
         if negative_prompt is not None:
             prompt["negative_prompt"] = negative_prompt
@@ -1935,7 +1945,7 @@ async def edit_images(
                     status_code=generation_result.error.code if generation_result.error else 400,
                     detail=generation_result.message,
                 )
-            images, _, _ = generation_result
+            images, _, _, cot_output = generation_result
         else:
             # Single-stage diffusion: use the direct path.
             result = await _generate_with_async_omni(
@@ -1965,6 +1975,7 @@ async def edit_images(
             data=image_data,
             output_format=output_format,
             size=size_str,
+            cot_output=cot_output,
         )
 
     except (EngineGenerateError, EngineDeadError) as exc:

vllm_omni/entrypoints/openai/protocol/images.py

@@ -32,6 +32,11 @@ class ImageGenerationRequest(BaseModel):
 
     # Required fields
     prompt: str = Field(..., description="Text description of the desired image(s)")
+    bot_task: str | None = Field(
+        None,
+        description="Task mode for the model (e.g., 'cot' enables chain-of-thought generation). "
+        "Only supported by specific diffusion models.",
+    )
 
     # OpenAI standard fields
     model: str | None = Field(
@@ -165,3 +170,8 @@ class ImageGenerationResponse(BaseModel):
     data: list[ImageData] = Field(..., description="Array of generated images")
     output_format: str = Field(None, description="The output format of the image generation")
     size: str = Field(None, description="The size of the image generated")
+    cot_output: str | None = Field(
+        None,
+        description="Chain-of-thought text output from the AR stage. "
+        "Only present for image editing (IT2I) with CoT-enabled models.",
+    )

vllm_omni/entrypoints/openai/serving_chat.py

@@ -2248,7 +2248,7 @@ def _build_multistage_generation_inputs(
         layers = extra_body.get("layers")
         resolution = extra_body.get("resolution")
         bot_task = extra_body.get("bot_task")
-        sys_type = extra_body.get("sys_type")
+        use_system_prompt = extra_body.get("use_system_prompt") or extra_body.get("sys_type")
         custom_system_prompt = extra_body.get("system_prompt")
 
         engine_prompt_data: dict[str, Any] | None = None
@@ -2262,24 +2262,27 @@ def _build_multistage_generation_inputs(
 
         prompt_token_ids: list[int] | None = None
         system_prompt_type: str | None = None
-        if bot_task is not None or sys_type is not None or custom_system_prompt is not None:
+        build_kwargs: dict[str, Any] = {}
+
+        if bot_task is not None or use_system_prompt is not None or custom_system_prompt is not None:
             from vllm_omni.diffusion.models.hunyuan_image3.prompt_utils import (
                 build_prompt,
                 build_prompt_tokens,
             )
 
             build_kwargs: dict[str, Any] = {
                 "task": "it2i" if reference_images else "t2i",
-                "sys_type": sys_type,
+                "sys_type": use_system_prompt,
                 "custom_system_prompt": custom_system_prompt,
                 "num_images": len(reference_images) if reference_images else 1,
             }
+
             if bot_task is not None:
                 build_kwargs["bot_task"] = bot_task
             elif "bot_task" in extra_body:
                 # Explicit None from the caller is plain-mode; omitted lets
                 # each task fall back to its default trigger.
-                build_kwargs["bot_task"] = None
+                build_kwargs["bot_task"] = extra_body["bot_task"]
             if tokenizer is not None:
                 # Feed segment-tokenized prompt_token_ids so AR matches HF
                 # apply_chat_template byte-for-byte (engine BPE would merge
@@ -2515,6 +2518,17 @@ async def generate_diffusion_images(
         images = getattr(result.request_output, "images", [])
         stage_durations = result.stage_durations
         peak_memory_mb = result.peak_memory_mb
+        cot_output = None
+
+        req_out = getattr(result, "request_output", None)
+        if req_out:
+            prompt_obj = getattr(req_out, "prompt", None)
+            if isinstance(prompt_obj, dict):
+                extra = prompt_obj.get("extra", {})
+                if isinstance(extra, dict):
+                    ar_text = extra.get("ar_generated_text")
+                    if isinstance(ar_text, str) and ar_text.strip():
+                        cot_output = ar_text
 
         flat_images: list[Image.Image] = []
         for item in images:
@@ -2523,7 +2537,7 @@ async def generate_diffusion_images(
             else:
                 flat_images.append(item)
 
-        return flat_images, stage_durations, peak_memory_mb
+        return flat_images, stage_durations, peak_memory_mb, cot_output
 
     async def _create_diffusion_chat_completion(
         self,