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[Chore]: refactor out unused/redundant params in diffusion pipelines #1235

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80edb73
[Chore]: refactor out unused and redundant params in diffusion pipeli…
fhfuih Feb 5, 2026
eea1994
add docstring for `req` if it is missing
fhfuih Feb 6, 2026
b1cb15e
bugfix: typos
fhfuih Feb 6, 2026
3577ed3
fix: redundance OR in if-condition
fhfuih Feb 6, 2026
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50 changes: 26 additions & 24 deletions vllm_omni/diffusion/models/flux/pipeline_flux.py
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Original file line number Diff line number Diff line change
Expand Up @@ -573,51 +573,53 @@ def check_cfg_parallel_validity(self, true_cfg_scale: float, has_neg_prompt: boo
def forward(
self,
req: OmniDiffusionRequest,
prompt: str | list[str] | None = None,
prompt_2: str | list[str] | None = None,
negative_prompt: str | list[str] | None = None,
negative_prompt_2: str | list[str] | None = None,
true_cfg_scale: float = 1.0,
height: int | None = None,
width: int | None = None,
num_inference_steps: int = 28,
sigmas: list[float] | None = None,
guidance_scale: float = 3.5,
num_images_per_prompt: int = 1,
generator: torch.Generator | list[torch.Generator] | None = None,
latents: torch.FloatTensor | None = None,
prompt_embeds: torch.FloatTensor | None = None,
pooled_prompt_embeds: torch.FloatTensor | None = None,
negative_prompt_embeds: torch.FloatTensor | None = None,
negative_pooled_prompt_embeds: torch.FloatTensor | None = None,
output_type: str | None = "pil",
return_dict: bool = True,
joint_attention_kwargs: dict[str, Any] | None = None,
callback_on_step_end_tensor_inputs: list[str] = ["latents"],
max_sequence_length: int = 512,
):
"""Forward pass for flux."""
# TODO: In online mode, sometimes it receives [{"negative_prompt": None}, {...}], so cannot use .get("...", "")
# TODO: May be some data formatting operations on the API side. Hack for now.
prompt = [p if isinstance(p, str) else (p.get("prompt") or "") for p in req.prompts] or prompt
prompt = [p if isinstance(p, str) else (p.get("prompt") or "") for p in req.prompts]

# For negative prompt, make it None if ALL are None---making it falsy and skipping CFG
# If only some of them are not None, only set those to empty strings---because we cannot skip CFG anyway.
if all(isinstance(p, str) or p.get("negative_prompt") is None for p in req.prompts):
negative_prompt = None
elif req.prompts:
negative_prompt = ["" if isinstance(p, str) else (p.get("negative_prompt") or "") for p in req.prompts]

req_prompt_embeds = [p.get("prompt_embeds") if not isinstance(p, str) else None for p in req.prompts]
if any(p is not None for p in req_prompt_embeds):
# If at list one prompt is provided as an embedding,
# Then assume that the user wants to provide embeddings for all prompts, and enter this if block
# If the user in fact provides mixed input format, req_prompt_embeds will have some None's
# And `torch.stack` automatically raises an exception for us
prompt_embeds = torch.stack(req_prompt_embeds) # type: ignore # intentionally expect TypeError

req_negative_prompt_embeds = [
p.get("negative_prompt_embeds") if not isinstance(p, str) else None for p in req.prompts
]
if any(p is not None for p in req_negative_prompt_embeds):
negative_prompt_embeds = torch.stack(req_negative_prompt_embeds) # type: ignore # intentionally expect TypeError

height = req.sampling_params.height or self.default_sample_size * self.vae_scale_factor
width = req.sampling_params.width or self.default_sample_size * self.vae_scale_factor
num_inference_steps = req.sampling_params.num_inference_steps or num_inference_steps
sigmas = req.sampling_params.sigmas or sigmas
guidance_scale = (
req.sampling_params.guidance_scale if req.sampling_params.guidance_scale is not None else guidance_scale
)
generator = req.sampling_params.generator or generator
true_cfg_scale = req.sampling_params.true_cfg_scale or 1.0
num_inference_steps = req.sampling_params.num_inference_steps or 28
sigmas = req.sampling_params.sigmas
max_sequence_length = req.sampling_params.max_sequence_length or 512
guidance_scale = req.sampling_params.guidance_scale or 3.5
generator = req.sampling_params.generator
num_images_per_prompt = (
req.sampling_params.num_outputs_per_prompt
if req.sampling_params.num_outputs_per_prompt > 0
else num_images_per_prompt
req.sampling_params.num_outputs_per_prompt if req.sampling_params.num_outputs_per_prompt > 0 else 1
)
latents = req.sampling_params.latents

# 1. Check inputs. Raise error if not correct
self.check_inputs(
Expand Down
96 changes: 13 additions & 83 deletions vllm_omni/diffusion/models/flux2_klein/pipeline_flux2_klein.py
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Original file line number Diff line number Diff line change
Expand Up @@ -648,72 +648,17 @@ def interrupt(self):
def forward(
self,
req: OmniDiffusionRequest,
image: PIL.Image.Image | list[PIL.Image.Image] | None = None,
prompt: str | list[str] | None = None,
height: int | None = None,
width: int | None = None,
num_inference_steps: int = 50,
sigmas: list[float] | None = None,
guidance_scale: float | None = 4.0,
num_images_per_prompt: int = 1,
generator: torch.Generator | list[torch.Generator] | None = None,
latents: torch.Tensor | None = None,
prompt_embeds: torch.Tensor | None = None,
negative_prompt_embeds: torch.Tensor | None = None,
output_type: str | None = "pil",
return_dict: bool = True,
attention_kwargs: dict[str, Any] | None = None,
callback_on_step_end: Callable[[int, int, dict], None] | None = None,
callback_on_step_end_tensor_inputs: list[str] = ["latents"],
max_sequence_length: int = 512,
text_encoder_out_layers: tuple[int, ...] = (9, 18, 27),
) -> DiffusionOutput:
r"""
Function invoked when calling the pipeline for generation.

Args:
image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, or list of these):
`Image`, numpy array or tensor representing an image batch to be used as the starting point. For both
numpy array and pytorch tensor, the expected value range is between `[0, 1]` If it's a tensor or a list
or tensors, the expected shape should be `(B, C, H, W)` or `(C, H, W)`. If it is a numpy array or a
list of arrays, the expected shape should be `(B, H, W, C)` or `(H, W, C)` It can also accept image
latents as `image`, but if passing latents directly it is not encoded again.
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
instead.
guidance_scale (`float`, *optional*, defaults to 4.0):
Guidance scale as defined in [Classifier-Free Diffusion
Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2.
of [Imagen Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting
`guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to
the text `prompt`, usually at the expense of lower image quality. For step-wise distilled models,
`guidance_scale` is ignored.
height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
The height in pixels of the generated image. This is set to 1024 by default for the best results.
width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
The width in pixels of the generated image. This is set to 1024 by default for the best results.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
sigmas (`List[float]`, *optional*):
Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
will be used.
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
latents (`torch.Tensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will be generated by sampling using the supplied random `generator`.
prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative text embeddings. Note that "" is used as the negative prompt in this pipeline.
If not provided, will be generated from "".
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
Expand All @@ -732,7 +677,6 @@ def forward(
The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
`._callback_tensor_inputs` attribute of your pipeline class.
max_sequence_length (`int` defaults to 512): Maximum sequence length to use with the `prompt`.
text_encoder_out_layers (`Tuple[int]`):
Layer indices to use in the `text_encoder` to derive the final prompt embeddings.

Expand All @@ -751,46 +695,32 @@ def forward(
first_prompt = req.prompts[0]
prompt = first_prompt if isinstance(first_prompt, str) else (first_prompt.get("prompt") or "")

prompt_embeds = None if isinstance(first_prompt, str) else first_prompt.get("prompt_embeds")
negative_prompt_embeds = None if isinstance(first_prompt, str) else first_prompt.get("negative_prompt_embeds")

if (
raw_image := None
if isinstance(first_prompt, str)
else first_prompt.get("multi_modal_data", {}).get("image")
) is None:
pass # use image from param list
image = None
elif isinstance(raw_image, list):
image = [PIL.Image.open(im) if isinstance(im, str) else cast(PIL.Image.Image, im) for im in raw_image]
else:
image = PIL.Image.open(raw_image) if isinstance(raw_image, str) else cast(PIL.Image.Image, raw_image)

height = req.sampling_params.height or height
width = req.sampling_params.width or width
num_inference_steps = req.sampling_params.num_inference_steps or num_inference_steps
sigmas = req.sampling_params.sigmas or sigmas
guidance_scale = (
req.sampling_params.guidance_scale if req.sampling_params.guidance_scale is not None else guidance_scale
)
generator = req.sampling_params.generator or generator
height = req.sampling_params.height
width = req.sampling_params.width
num_inference_steps = req.sampling_params.num_inference_steps or 50
sigmas = req.sampling_params.sigmas
guidance_scale = req.sampling_params.guidance_scale or 4.0
generator = req.sampling_params.generator
num_images_per_prompt = (
req.sampling_params.num_outputs_per_prompt
if req.sampling_params.num_outputs_per_prompt > 0
else num_images_per_prompt
req.sampling_params.num_outputs_per_prompt if req.sampling_params.num_outputs_per_prompt > 0 else 1
)
max_sequence_length = req.sampling_params.max_sequence_length or max_sequence_length
max_sequence_length = req.sampling_params.max_sequence_length or 512
text_encoder_out_layers = req.sampling_params.extra_args.get("text_encoder_out_layers", text_encoder_out_layers)

req_prompt_embeds = [p.get("prompt_embeds") if not isinstance(p, str) else None for p in req.prompts]
if any(p is not None for p in req_prompt_embeds):
# If at list one prompt is provided as an embedding,
# Then assume that the user wants to provide embeddings for all prompts, and enter this if block
# If the user in fact provides mixed input format, req_prompt_embeds will have some None's
# And `torch.stack` automatically raises an exception for us
prompt_embeds = torch.stack(req_prompt_embeds) # type: ignore # intentionally expect TypeError

req_negative_prompt_embeds = [
p.get("negative_prompt_embeds") if not isinstance(p, str) else None for p in req.prompts
]
if any(p is not None for p in req_negative_prompt_embeds):
negative_prompt_embeds = torch.stack(req_negative_prompt_embeds) # type: ignore # intentionally expect TypeError
latents = req.sampling_params.latents

# 1. Check inputs. Raise error if not correct
self.check_inputs(
Expand Down
45 changes: 15 additions & 30 deletions vllm_omni/diffusion/models/longcat_image/pipeline_longcat_image.py
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -473,18 +473,6 @@ def cfg_normalize_function(self, noise_pred, comb_pred, cfg_renorm_min=0.0):
def forward(
self,
req: OmniDiffusionRequest,
prompt: str | list[str] | None = None,
negative_prompt: str | list[str] | None = None,
height: int | None = None,
width: int | None = None,
num_inference_steps: int = 50,
sigmas: list[float] | None = None,
guidance_scale: float = 4.5,
num_images_per_prompt: int = 1,
generator: torch.Generator | list[torch.Generator] | None = None,
latents: torch.FloatTensor | None = None,
prompt_embeds: torch.Tensor | None = None,
negative_prompt_embeds: torch.Tensor | None = None,
output_type: str | None = "pil",
return_dict: bool = True,
joint_attention_kwargs: dict[str, Any] | None = None,
Expand All @@ -494,29 +482,12 @@ def forward(
) -> DiffusionOutput:
# TODO: In online mode, sometimes it receives [{"negative_prompt": None}, {...}], so cannot use .get("...", "")
# TODO: May be some data formatting operations on the API side. Hack for now.
prompt = [p if isinstance(p, str) else (p.get("prompt") or "") for p in req.prompts] or prompt
prompt = [p if isinstance(p, str) else (p.get("prompt") or "") for p in req.prompts]
if all(isinstance(p, str) or p.get("negative_prompt") is None for p in req.prompts):
negative_prompt = None
elif req.prompts:
negative_prompt = ["" if isinstance(p, str) else (p.get("negative_prompt") or "") for p in req.prompts]

height = req.sampling_params.height or height or self.default_sample_size * self.vae_scale_factor
width = req.sampling_params.width or width or self.default_sample_size * self.vae_scale_factor
num_inference_steps = req.sampling_params.num_inference_steps or num_inference_steps
sigmas = req.sampling_params.sigmas or sigmas
generator = req.sampling_params.generator or generator
guidance_scale = (
req.sampling_params.guidance_scale if req.sampling_params.guidance_scale is not None else guidance_scale
)
num_images_per_prompt = (
req.sampling_params.num_outputs_per_prompt
if req.sampling_params.num_outputs_per_prompt is not None
else num_images_per_prompt
)
enable_prompt_rewrite = req.sampling_params.extra_args.get("enable_prompt_rewrite", enable_prompt_rewrite)
enable_cfg_renorm = req.sampling_params.extra_args.get("enable_cfg_renorm", enable_cfg_renorm)
cfg_renorm_min = req.sampling_params.extra_args.get("cfg_renorm_min", cfg_renorm_min)

req_prompt_embeds = [p.get("prompt_embeds") if not isinstance(p, str) else None for p in req.prompts]
if any(p is not None for p in req_prompt_embeds):
# If at list one prompt is provided as an embedding,
Expand All @@ -531,6 +502,20 @@ def forward(
if any(p is not None for p in req_negative_prompt_embeds):
negative_prompt_embeds = torch.stack(req_negative_prompt_embeds) # type: ignore # intentionally expect TypeError

height = req.sampling_params.height or self.default_sample_size * self.vae_scale_factor
width = req.sampling_params.width or self.default_sample_size * self.vae_scale_factor
num_inference_steps = req.sampling_params.num_inference_steps or 50
sigmas = req.sampling_params.sigmas
generator = req.sampling_params.generator
guidance_scale = req.sampling_params.guidance_scale or 4.5
num_images_per_prompt = (
req.sampling_params.num_outputs_per_prompt if req.sampling_params.num_outputs_per_prompt > 0 else 1
)
latents = req.sampling_params.latents
enable_prompt_rewrite = req.sampling_params.extra_args.get("enable_prompt_rewrite", enable_prompt_rewrite)
enable_cfg_renorm = req.sampling_params.extra_args.get("enable_cfg_renorm", enable_cfg_renorm)
cfg_renorm_min = req.sampling_params.extra_args.get("cfg_renorm_min", cfg_renorm_min)

self.check_inputs(
prompt,
height,
Expand Down
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