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vllm_omni.diffusion.models.ming_image.pipeline

Diffusion pipeline for Ming-Image Design and Design-Layer checkpoints.

logger module-attribute 

logger = logging.getLogger(__name__)

MingImageDiffusionPipeline

Bases: ZImagePipeline

Ming-Image component adapter around the canonical Z-Image loop.

conditioning instance-attribute 

conditioning = MingImageConditioning(
    model_path, device=self.device, dtype=dtype
)

default_guidance_scale instance-attribute 

default_guidance_scale = (
    2.0 if self.is_layer_decomposition else 1.0
)

default_num_inference_steps instance-attribute 

default_num_inference_steps = 12

device instance-attribute 

device = self._execution_device

image_processor instance-attribute 

image_processor = VaeImageProcessor(
    vae_scale_factor=self.vae_scale_factor * 2,
    do_convert_rgb=False,
)

is_layer_decomposition instance-attribute 

is_layer_decomposition = _validate_variant_config(
    model_index, transformer_config
)

od_config instance-attribute 

od_config = od_config

scheduler instance-attribute 

scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(
    model_path,
    subfolder="scheduler",
    local_files_only=local_files_only,
)

supports_request_batch class-attribute instance-attribute 

supports_request_batch = False

text_encoder instance-attribute 

text_encoder = None

tokenizer instance-attribute 

tokenizer = None

transformer instance-attribute 

transformer = MingImageTransformer2DModel(
    quant_config=od_config.quantization_config,
    **transformer_kwargs,
)

vae instance-attribute 

vae = from_pretrained_with_prefetch(
    DistributedAutoencoderKLQwenImage.from_pretrained,
    model_path,
    subfolder="vae",
    prefetch_list=subfolders,
    local_files_only=local_files_only,
    torch_dtype=dtype,
).to(self.device)

vae_scale_factor instance-attribute 

vae_scale_factor = 2 ** len(
    self.vae.config.temperal_downsample
)

weights_sources instance-attribute 

weights_sources = [
    DiffusersPipelineLoader.ComponentSource(
        model_or_path=model_path,
        subfolder="transformer",
        revision=od_config.revision,
        prefix="transformer.",
        fall_back_to_pt=True,
    )
]

encode_prompt 

encode_prompt(*args, **kwargs)

forward 

forward(req: DiffusionRequestBatch) -> DiffusionOutput

load_weights 

load_weights(
    weights: Iterable[tuple[str, Tensor]],
) -> set[str]

prepare_latents 

prepare_latents(batch_size, *args, **kwargs)

setup_compile 

setup_compile() -> None

get_ming_image_post_process_func 

get_ming_image_post_process_func(
    od_config: OmniDiffusionConfig,
)