fde4808723
Signed-off-by: dengyunyang <584797741@qq.com> Signed-off-by: bjf-frz <frz123db@gmail.com> Signed-off-by: Didan Deng <33117903+wtomin@users.noreply.github.com> Co-authored-by: bjf-frz <frz123db@gmail.com> Co-authored-by: Didan Deng <33117903+wtomin@users.noreply.github.com>
90 lines
2.7 KiB
Python
90 lines
2.7 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import argparse
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import os
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from PIL import Image
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from vllm_omni.entrypoints.omni import Omni
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"""
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The tencent/HunyuanImage-3.0-Instruct base model uses the tencent/Hunyuan-A13B-Instruct backbone. It utilizes two tokenizer delimiter templates:
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1) Pretrained template (default for gen_text mode), which concatenates system, image
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tokens, and user question WITHOUT role delimiters:
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"<|startoftext|>{system_prompt}{image_tokens}{user_question}"
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Example (before image token expansion):
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"<|startoftext|>You are an assistant that understands images and outputs text.<img>Describe the content of the picture."
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2) Instruct template, which uses explicit role prefixes and separators.
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"""
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def parse_args() -> argparse.Namespace:
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parser = argparse.ArgumentParser(description="Generate text from image using HunyuanImage-3.0-Instruct.")
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parser.add_argument(
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"--model",
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default="tencent/HunyuanImage-3.0-Instruct",
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help="Model name or local path.",
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)
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parser.add_argument(
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"--image",
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type=str,
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required=True,
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help="Path to input image file (PNG, JPG, etc.).",
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)
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parser.add_argument(
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"--prompt",
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type=str,
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required=True,
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help="Pretrain template prompt: <|startoftext|>{system}<img>{question}",
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)
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parser.add_argument(
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"--enable-diffusion-pipeline-profiler",
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action="store_true",
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help="Enable diffusion pipeline profiler to display stage durations.",
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)
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return parser.parse_args()
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def load_image(image_path: str) -> Image.Image:
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"""Load an image from file path."""
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if not os.path.exists(image_path):
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raise FileNotFoundError(f"Image file not found: {image_path}")
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return Image.open(image_path).convert("RGB")
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def main(args: argparse.Namespace) -> None:
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omni = Omni(
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model=args.model,
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enable_diffusion_pipeline_profiler=args.enable_diffusion_pipeline_profiler,
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)
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prompt_dict = {
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"prompt": args.prompt,
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"modalities": ["text"],
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}
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# Add image input if provided
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if args.image:
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if not os.path.exists(args.image):
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raise FileNotFoundError(f"Input image not found: {args.image}")
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input_image = load_image(args.image)
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prompt_dict["multi_modal_data"] = {"image": input_image}
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prompts = [prompt_dict]
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omni_outputs = omni.generate(prompts=prompts)
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prompt_text = omni_outputs[0].request_output.prompt
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generated_text = omni_outputs[0].request_output.outputs[0].text
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print(f"Prompt: {prompt_text}")
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print(f"Text: {generated_text}")
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if __name__ == "__main__":
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args = parse_args()
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main(args)
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