[Chore]: refactor out unused/redundant params in diffusion pipelines

[fhfuih]

Purpose

As is discussed before in #797
many diffusion pipelines have several extra parameters defined in forward function. They have never been used---the forward function has always been called with only one OmniDiffusionRequest object (even before PR 797).

This PR does this refactor. In particular, it also aims to address this discussion: #1196
and ships in companion with #1196---so that the "how to add a new model" documentation teaches developers to follow the correct paradigm.

Test Plan

No new features are added, no logic is changed. Will just run existing tests

Test Result

To be updated

Additional notes

In this refactor

• If a param is NOT present in OmniDiffusionRequest, I'd rather keep them in the function signature. They are never changed by the user (because when calling forward, only req is passed). Only their default values are used
• Otherwise, if the param's default value in the function signature is NOT NONE, I copy the default value in the function body when reading it. Using or instead of if .. is not None, the "default" values are applied when the user-passed values are None or 0. Please see my argument below why this is acceptable.
• If the param's default value in the function signature is NONE, I simply read the value from OmniDiffusionRequest. No fallback default value is used.

Why it is acceptable to apply alternative default values when the user passes 0:

1. Some params defined in OmniDiffusionRequest are not None anyway. This old logic is originally problematic: condition never satisfied.
2. 0 or 0.0 in these values are meaningless (num steps, guidance, etc). Their default values in OmniDiffusionRequest are not 0 either. So it is only possible when the user explicitly makes it 0. In this case, it makes sense to inject other default values in this case: that's the user's intention.
3. I notice that every pipeline has different preferred default values. So I decide to keep them here for documentation purposes.
4. I add default values if and only if it is present in the forward function signature. It means the pipeline authors are intended to adopt these parameters (but could not in the current codebase). So it makes sense to use these values when the user-passed value is invalid on purpose (i.e., explicit 0)

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• The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
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BEFORE SUBMITTING, PLEASE READ https://github.com/vllm-project/vllm-omni/blob/main/CONTRIBUTING.md (anything written below this line will be removed by GitHub Actions)

[chatgpt-codex-connector]

💡 Codex Review

vllm-omni/vllm_omni/diffusion/models/flux/pipeline_flux.py

Lines 632 to 633 in 80edb73

	prompt_embeds=prompt_embeds,
	negative_prompt_embeds=negative_prompt_embeds,

Initialize prompt/negative embeddings before use

When req.prompts contains normal strings and no embeddings (the typical case), prompt_embeds and negative_prompt_embeds are only assigned inside the if any(...) blocks above, so they remain unbound and the subsequent check_inputs/encode_prompt usage raises UnboundLocalError before any generation. Previously these were defaulted to None via the function signature, so this is a regression. Initialize both variables to None before the conditional (the same pattern appears in longcat_image, ovis_image, qwen_image, sd3, stable_audio, and z_image pipelines).

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[Copilot]

Pull request overview

This PR refactors the forward methods in diffusion pipeline implementations to remove unused and redundant parameters. As discussed in PR #797 and #1196, these parameters were never used in practice since the forward function is always called with only an OmniDiffusionRequest object. This cleanup makes the API clearer and teaches developers the correct paradigm for adding new models.

Changes:

• Removed unused function parameters from forward methods (prompt, height, width, num_inference_steps, guidance_scale, generator, latents, prompt_embeds, negative_prompt_embeds, etc.)
• Consolidated parameter extraction to use only req.sampling_params and req.prompts
• Added explicit extraction logic for prompt_embeds and negative_prompt_embeds from request prompts
• Standardized default value fallback patterns across pipelines

Reviewed changes

Copilot reviewed 15 out of 15 changed files in this pull request and generated 12 comments.

Show a summary per file

File	Description
vllm_omni/diffusion/models/z_image/pipeline_z_image.py	Removed 11 unused parameters; consolidated to extract all values from req object
vllm_omni/diffusion/models/wan2_2/pipeline_wan2_2_ti2v.py	Removed 9 unused parameters; added explicit prompt_embeds extraction
vllm_omni/diffusion/models/wan2_2/pipeline_wan2_2_i2v.py	Removed 9 unused parameters; added explicit prompt_embeds extraction
vllm_omni/diffusion/models/wan2_2/pipeline_wan2_2.py	Removed 9 unused parameters; added explicit prompt_embeds extraction
vllm_omni/diffusion/models/stable_audio/pipeline_stable_audio.py	Removed 10 unused parameters; added prompt_embeds extraction logic
vllm_omni/diffusion/models/sd3/pipeline_sd3.py	Removed 10 unused parameters; consolidated parameter extraction from req
vllm_omni/diffusion/models/qwen_image/pipeline_qwen_image_layered.py	Removed 13 unused parameters; added image extraction from multi_modal_data
vllm_omni/diffusion/models/qwen_image/pipeline_qwen_image_edit_plus.py	Removed 13 unused parameters; added image extraction from multi_modal_data
vllm_omni/diffusion/models/qwen_image/pipeline_qwen_image_edit.py	Removed 13 unused parameters; added image extraction from multi_modal_data
vllm_omni/diffusion/models/qwen_image/pipeline_qwen_image.py	Removed 12 unused parameters; consolidated all extraction from req
vllm_omni/diffusion/models/ovis_image/pipeline_ovis_image.py	Removed 12 unused parameters; added prompt_embeds extraction logic
vllm_omni/diffusion/models/longcat_image/pipeline_longcat_image_edit.py	Removed 9 unused parameters; added image extraction from multi_modal_data
vllm_omni/diffusion/models/longcat_image/pipeline_longcat_image.py	Removed 11 unused parameters; reorganized parameter extraction
vllm_omni/diffusion/models/flux2_klein/pipeline_flux2_klein.py	Removed 11 unused parameters; simplified prompt and image extraction
vllm_omni/diffusion/models/flux/pipeline_flux.py	Removed 11 unused parameters; added detailed prompt_embeds extraction

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[Copilot]

If raw_image is None (line 637), then image will be set to None (line 638). However, on line 644, the code attempts to access image[0].size or image.size, which will raise an AttributeError if image is None. This code path should either handle the None case or ensure that image is always set to a valid value before reaching line 644.

Suggested change

	if image is None:
	raise ValueError(
	"No image was provided in 'multi_modal_data' for fallback preprocessing; "
	"an image is required to compute target dimensions."
	)

[fhfuih]

Yeah, many pipelines have strange type annotation that mismatches later type checks. The are confusing and originally conflicting. I am not to fix everything in this PR.

[Copilot]

The condition should check guidance_scale_provided instead of guidance_rescale. This is inconsistent with all other pipelines which use guidance_scale_provided to determine if the user explicitly provided a guidance scale. The current condition checks guidance_rescale (a different parameter), which will likely always evaluate to not None since it has a default value of 0.0, causing the guidance_scale logic to behave incorrectly.

Suggested change

	guidance_scale = req.sampling_params.guidance_scale if req.sampling_params.guidance_rescale is not None else 5.0
	guidance_scale = (
	req.sampling_params.guidance_scale
	if req.sampling_params.guidance_scale_provided
	else 5.0
	)

[fhfuih]

This logic is already there. I don't want to break things.

[Copilot]

Pull request overview

Copilot reviewed 15 out of 15 changed files in this pull request and generated 1 comment.

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