Fix: Enable /v1/models endpoint for pure diffusion mode

[majiayu000]

Fixes #751. Initializes OpenAIServingModels in pure diffusion mode to ensure the /v1/models endpoint is correctly populated.

[gcanlin]

I think it's better to move the original OpenAIServingModels up.

vllm-omni/vllm_omni/entrypoints/openai/api_server.py

Lines 418 to 422 in 8a9ea75

	state.openai_serving_models = OpenAIServingModels(
	engine_client=engine_client,
	base_model_paths=base_model_paths,
	lora_modules=lora_modules,
	)

[tzhouam]

Please add test plan and results. Thanks

[majiayu000]

Updated the PR to address reviewer feedback:

1. @gcanlin's suggestion: Consolidated initialization to eliminate code duplication. The initialization is now shared between pure diffusion mode and LLM mode, improving code maintainability.

2. @tzhouam's request for test plan: Added a comprehensive test plan in the commit message. Manual testing is required with a diffusion model to verify the /v1/models endpoint returns the model information correctly.

Changes Made

• Moved OpenAIServingModels initialization before the is_pure_diffusion check
• Both diffusion and LLM modes now use the same initialization code path
• Properly handle lora_modules processing for both modes
• DCO signoff included

The implementation properly fixes issue #751 by ensuring the /v1/models endpoint works in pure diffusion mode.

[hsliuustc0106]

@fake0fan PTAL

[Copilot]

Pull request overview

This PR fixes issue #751 by enabling the /v1/models endpoint for pure diffusion mode. Previously, OpenAIServingModels was only initialized in LLM mode, causing the /v1/models endpoint to be unavailable when running pure diffusion models like Qwen-Image.

Changes:

• Initialize OpenAIServingModels before the pure diffusion mode check to ensure it's available for both diffusion and LLM modes
• Add state.args assignment for potential future use
• Refactor lora_modules handling to support early initialization

---

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[Copilot]

In LLM mode with default_mm_loras, OpenAIServingModels is initialized twice - once at lines 286-290 and again at lines 374-378. This creates an unnecessary object that will be immediately discarded. Consider initializing OpenAIServingModels only once after determining the final lora_modules value. For example, move the first initialization to after the lora_modules processing logic (after line 378) and use conditional logic to determine whether to use args.lora_modules or the merged lora_modules.

Suggested change

	# Initialize OpenAIServingModels (shared by both diffusion and LLM modes)
	state.openai_serving_models = OpenAIServingModels(
	engine_client=engine_client,
	base_model_paths=base_model_paths,
	lora_modules=lora_modules,
	)
	# Initialize OpenAIServingModels early only for pure diffusion mode.
	# In LLM/multi-stage modes, OpenAIServingModels will be initialized later
	# after any lora_modules processing/merging is complete.
	if is_pure_diffusion:
	state.openai_serving_models = OpenAIServingModels(
	engine_client=engine_client,
	base_model_paths=base_model_paths,
	lora_modules=lora_modules,
	)

[Copilot]

The assignment state.args = args appears to be unused. There are no other references to state.args in the codebase. If this is intended for future use or debugging purposes, consider adding a comment explaining why it's being stored. Otherwise, this line can be removed.

Suggested change

state.args = args

[Copilot]

The variable lora_modules is assigned to args.lora_modules at line 283, but in LLM mode it's reassigned to the same value at line 359. The assignment at line 283 is used for pure diffusion mode (line 289) and the assignment at line 359 is used for LLM mode. Consider refactoring to avoid this redundancy - for example, by only setting lora_modules once before the diffusion mode check, or by using a different variable name for the LLM-specific processing.

Suggested change

	# Process lora_modules early for OpenAIServingModels initialization
	# In pure diffusion mode, vllm_config will be None, so we use args.lora_modules directly
	lora_modules = args.lora_modules
	# Initialize OpenAIServingModels (shared by both diffusion and LLM modes)
	state.openai_serving_models = OpenAIServingModels(
	engine_client=engine_client,
	base_model_paths=base_model_paths,
	lora_modules=lora_modules,
	# Initialize OpenAIServingModels (shared by both diffusion and LLM modes)
	# In pure diffusion mode, vllm_config will be None, so we pass args.lora_modules directly
	state.openai_serving_models = OpenAIServingModels(
	engine_client=engine_client,
	base_model_paths=base_model_paths,
	lora_modules=args.lora_modules,

[fake0fan]

#454 has already been merged, and I see that it also contains /v1/models/ API tests for diffusion models. What is the relationship between that and this? Could you explain it simply?

[fake0fan]

I don't understand why we need to reinitialize OpenAIServingModels with merged lora_modules? Can we solve this problem by moving lora_modules up as well?

[majiayu000]

To clarify, this is not a re-initialization. Since we use omni_run_server_worker as the entry point, the upstream vLLM init_app_state is never called.

[lishunyang12]

@majiayu000 Hey, this fixes #751 and it's only 20 lines — initializing OpenAIServingModels in pure diffusion mode so /v1/models works. Seems like a straightforward fix. Is there anything blocking this?

[Gaohan123]

Please fix precommit. Thanks

[majiayu000]

Thanks for the PR. I've formatted the code, resolved the conflicts with PR #454, and restored and adapted the tests for the unified OpenAIServingModels in pure diffusion mode. The changes have been pushed to this branch.

[Gaohan123]

Please resolve CI failure

[Gaohan123]

Please fix DCO

[majiayu000]

Fixed