vllm-omni/examples/offline_inference/hunyuan_image3/end2end.py

"""
HunyuanImage-3.0-Instruct unified end-to-end inference script.

Supports all modalities through a single entry point:
  - text2img:  Text → AR → DiT → Image
  - img2img:   Text+Image → AR → DiT → Edited Image (IT2I)
  - img2text:  Image+Text → AR → Text description (I2T)
  - text2text: Text → AR → Text (comprehension, no image)

Usage:
    python end2end.py --modality text2img --prompts "A cute cat"
    python end2end.py --modality img2img --image-path input.png --prompts "Make it snowy"
    python end2end.py --modality img2text --image-path input.png --prompts "Describe this image"
"""

import argparse
import os

from vllm_omni.diffusion.models.hunyuan_image3.system_prompt import (
    get_system_prompt,
)
from vllm_omni.entrypoints.omni import Omni
from vllm_omni.inputs.data import OmniPromptType

# task → (sys_type, bot_task, trigger_tag)
_TASK_PRESETS: dict[str, tuple[str, str | None, str | None]] = {
    "t2t": ("en_unified", None, None),
    "i2t": ("en_unified", None, None),
    "it2i_think": ("en_unified", "think", "<think>"),
    "it2i_recaption": ("en_unified", "recaption", "<recaption>"),
    "t2i_think": ("en_unified", "think", "<think>"),
    "t2i_recaption": ("en_unified", "recaption", "<recaption>"),
    "t2i_vanilla": ("en_vanilla", "image", None),
}

# Modality → prompt_utils task mapping
_MODALITY_TASK_MAP = {
    "text2img": "t2i_think",
    "img2img": "it2i_think",
    "img2text": "i2t",
    "text2text": "t2t",
}


def build_prompt(
    user_prompt: str,
    task: str = "it2i_think",
    sys_type: str | None = None,
    custom_system_prompt: str | None = None,
) -> str:
    """Build a HunyuanImage-3.0 prompt using pretrain template format."""
    if task not in _TASK_PRESETS:
        raise ValueError(f"Unknown task {task!r}. Choose from: {sorted(_TASK_PRESETS)}")

    preset_sys_type, preset_bot_task, trigger_tag = _TASK_PRESETS[task]
    effective_sys_type = sys_type or preset_sys_type

    system_prompt = get_system_prompt(effective_sys_type, preset_bot_task, custom_system_prompt)
    sys_text = system_prompt.strip() if system_prompt else ""

    has_image_input = task.startswith("i2t") or task.startswith("it2i")

    parts = ["<|startoftext|>"]
    if sys_text:
        parts.append(sys_text)
    if has_image_input:
        parts.append("<img>")
    if trigger_tag:
        parts.append(trigger_tag)
    parts.append(user_prompt)

    return "".join(parts)


# Modality → default stage config
_MODALITY_DEFAULT_CONFIG = {
    "text2img": "hunyuan_image3_t2i.yaml",
    "img2img": "hunyuan_image3_it2i.yaml",
    "img2text": "hunyuan_image3_i2t.yaml",
    "text2text": "hunyuan_image3_t2t.yaml",
}


def parse_args():
    parser = argparse.ArgumentParser(description="HunyuanImage-3.0-Instruct end-to-end inference.")
    parser.add_argument(
        "--model",
        default="tencent/HunyuanImage-3.0-Instruct",
        help="Model name or local path.",
    )
    parser.add_argument(
        "--modality",
        default="text2img",
        choices=["text2img", "img2img", "img2text", "text2text"],
        help="Modality mode to control stage execution.",
    )
    parser.add_argument("--prompts", nargs="+", default=None, help="Input text prompts.")
    parser.add_argument(
        "--image-path",
        type=str,
        default=None,
        help="Path to input image (for img2img/img2text).",
    )
    parser.add_argument(
        "--output",
        type=str,
        default=".",
        help="Output directory to save results.",
    )

    # Generation parameters
    parser.add_argument("--steps", type=int, default=50, help="Number of inference steps.")
    parser.add_argument("--guidance-scale", type=float, default=5.0, help="Classifier-free guidance scale.")
    parser.add_argument("--seed", type=int, default=42, help="Random seed.")
    parser.add_argument("--height", type=int, default=1024, help="Output image height.")
    parser.add_argument("--width", type=int, default=1024, help="Output image width.")

    # Prompt configuration
    parser.add_argument(
        "--bot-task",
        type=str,
        default=None,
        help="Override prompt task (e.g. it2i_think, t2i_recaption). Default: auto from modality.",
    )
    parser.add_argument(
        "--sys-type",
        type=str,
        default=None,
        help="Override system prompt type (e.g. en_unified, en_vanilla).",
    )

    # Omni init args
    parser.add_argument("--stage-configs-path", type=str, default=None, help="Custom stage config YAML path.")
    parser.add_argument("--log-stats", action="store_true", default=False)
    parser.add_argument("--init-timeout", type=int, default=300, help="Initialization timeout in seconds.")
    parser.add_argument("--enforce-eager", action="store_true", help="Disable torch.compile.")

    from vllm_omni.engine.arg_utils import nullify_stage_engine_defaults

    nullify_stage_engine_defaults(parser)
    return parser.parse_args()


def main():
    args = parse_args()
    os.makedirs(args.output, exist_ok=True)

    # Determine task for prompt formatting
    task = args.bot_task or _MODALITY_TASK_MAP[args.modality]

    # Determine stage config
    stage_configs_path = args.stage_configs_path or _MODALITY_DEFAULT_CONFIG[args.modality]

    # Build Omni
    omni_kwargs = {
        "model": args.model,
        "stage_configs_path": stage_configs_path,
        "log_stats": args.log_stats,
        "init_timeout": args.init_timeout,
        "enforce_eager": args.enforce_eager,
    }
    if args.modality in ("text2img", "img2img"):
        omni_kwargs["mode"] = "text-to-image"

    omni = Omni(**omni_kwargs)

    # Prepare prompts
    prompts = args.prompts or ["A cute cat"]
    if not prompts:
        print("[Info] No prompts provided, using default.")
        prompts = ["A cute cat"]

    # Load image if needed
    input_image = None
    if args.modality in ("img2img", "img2text"):
        if not args.image_path or not os.path.exists(args.image_path):
            raise ValueError(f"--image-path required for {args.modality}, got: {args.image_path}")
        from PIL import Image

        input_image = Image.open(args.image_path).convert("RGB")

    # Format prompts
    formatted_prompts: list[OmniPromptType] = []
    for p in prompts:
        formatted_text = build_prompt(p, task=task, sys_type=args.sys_type)

        prompt_dict: dict = {"prompt": formatted_text}

        if args.modality == "text2img":
            prompt_dict["modalities"] = ["image"]
        elif args.modality == "img2img":
            prompt_dict["modalities"] = ["image"]
            prompt_dict["multi_modal_data"] = {"image": input_image}
            prompt_dict["height"] = input_image.height
            prompt_dict["width"] = input_image.width
        elif args.modality == "img2text":
            prompt_dict["modalities"] = ["text"]
            prompt_dict["multi_modal_data"] = {"image": input_image}
        elif args.modality == "text2text":
            prompt_dict["modalities"] = ["text"]

        formatted_prompts.append(prompt_dict)

    # Build sampling params from defaults
    params_list = list(omni.default_sampling_params_list)

    # Override diffusion params if applicable
    from vllm_omni.inputs.data import OmniDiffusionSamplingParams

    for i, sp in enumerate(params_list):
        if isinstance(sp, OmniDiffusionSamplingParams):
            sp.num_inference_steps = args.steps
            sp.guidance_scale = args.guidance_scale
            if args.seed is not None:
                sp.seed = args.seed
            if args.modality in ("text2img",):
                sp.height = args.height
                sp.width = args.width

    # Print configuration
    print(f"\n{'=' * 60}")
    print("HunyuanImage-3.0 Generation Configuration:")
    print(f"  Model: {args.model}")
    print(f"  Modality: {args.modality}")
    print(f"  Stage config: {stage_configs_path}")
    print(f"  Num stages: {omni.num_stages}")
    if args.modality in ("text2img", "img2img"):
        print(f"  Inference steps: {args.steps}")
        print(f"  Guidance scale: {args.guidance_scale}")
        print(f"  Seed: {args.seed}")
    if args.modality == "text2img":
        print(f"  Output size: {args.width}x{args.height}")
    if args.image_path:
        print(f"  Input image: {args.image_path}")
    print(f"  Prompts: {prompts}")
    print(f"{'=' * 60}\n")

    # Generate
    omni_outputs = list(omni.generate(prompts=formatted_prompts, sampling_params_list=params_list))

    # Process outputs
    img_idx = 0
    for req_output in omni_outputs:
        # Text output (AR stage or text-only)
        ro = getattr(req_output, "request_output", None)
        if ro and getattr(ro, "outputs", None):
            txt = "".join(getattr(o, "text", "") or "" for o in ro.outputs)
            if txt:
                print(f"[Output] Text:\n{txt}")

        # Image output (DiT stage)
        images = getattr(req_output, "images", None)
        if not images and ro and hasattr(ro, "images"):
            images = ro.images

        if images:
            for j, img in enumerate(images):
                save_path = os.path.join(args.output, f"output_{img_idx}_{j}.png")
                img.save(save_path)
                print(f"[Output] Saved image to {save_path}")
            img_idx += 1


if __name__ == "__main__":
    main()