usberkeleyandprincepride
84338494d2
Signed-off-by: Bradley <bradley.b.pitt@gmail.com> Signed-off-by: princepride <wangzhipeng628@gmail.com> Signed-off-by: 汪志鹏 <wangzhipeng628@gmail.com> Co-authored-by: princepride <wangzhipeng628@gmail.com>
82 lines
2.5 KiB
Python
82 lines
2.5 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
|
|
|
import argparse
|
|
import os
|
|
|
|
from PIL import Image
|
|
|
|
from vllm_omni.entrypoints.omni import Omni
|
|
|
|
"""
|
|
The tencent/HunyuanImage-3.0-Instruct base model is built on the Hunyuan v1 architecture, specifically the tencent/Hunyuan-A13B-Instruct model. It utilizes two tokenizer delimiter templates:
|
|
|
|
1) Pretrained template (default for gen_text mode), which concatenates system, image
|
|
tokens, and user question WITHOUT role delimiters:
|
|
"<|startoftext|>{system_prompt}{image_tokens}{user_question}"
|
|
|
|
Example (before image token expansion):
|
|
"<|startoftext|>You are an assistant that understands images and outputs text.<img>Describe the content of the picture."
|
|
|
|
2) Instruct template, which uses explicit role prefixes and separators.
|
|
"""
|
|
|
|
|
|
def parse_args() -> argparse.Namespace:
|
|
parser = argparse.ArgumentParser(description="Generate text from image using HunyuanImage-3.0-Instruct.")
|
|
parser.add_argument(
|
|
"--model",
|
|
default="tencent/HunyuanImage-3.0-Instruct",
|
|
help="Model name or local path.",
|
|
)
|
|
parser.add_argument(
|
|
"--image",
|
|
type=str,
|
|
required=True,
|
|
help="Path to input image file (PNG, JPG, etc.).",
|
|
)
|
|
parser.add_argument(
|
|
"--prompt",
|
|
type=str,
|
|
required=True,
|
|
help="Pretrain template prompt: <|startoftext|>{system}<img>{question}",
|
|
)
|
|
return parser.parse_args()
|
|
|
|
|
|
def load_image(image_path: str) -> Image.Image:
|
|
"""Load an image from file path."""
|
|
if not os.path.exists(image_path):
|
|
raise FileNotFoundError(f"Image file not found: {image_path}")
|
|
return Image.open(image_path).convert("RGB")
|
|
|
|
|
|
def main(args: argparse.Namespace) -> None:
|
|
omni = Omni(model=args.model)
|
|
|
|
prompt_dict = {
|
|
"prompt": args.prompt,
|
|
"modalities": ["text"],
|
|
}
|
|
|
|
# Add image input if provided
|
|
if args.image:
|
|
if not os.path.exists(args.image):
|
|
raise FileNotFoundError(f"Input image not found: {args.image}")
|
|
|
|
input_image = load_image(args.image)
|
|
prompt_dict["multi_modal_data"] = {"image": input_image}
|
|
|
|
prompts = [prompt_dict]
|
|
omni_outputs = omni.generate(prompts=prompts)
|
|
|
|
prompt_text = omni_outputs[0].request_output[0].prompt
|
|
generated_text = omni_outputs[0].request_output[0].outputs[0].text
|
|
print(f"Prompt: {prompt_text}")
|
|
print(f"Text: {generated_text}")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
args = parse_args()
|
|
main(args)
|