[Model] Add Hunyuan Image3 AR Support

examples/offline_inference/hunyuan_image3/image_to_text.py

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+# 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,
+        default="./image.png",
+        help="Path to input image file (PNG, JPG, etc.).",
+    )
+    parser.add_argument(
+        "--prompt",
+        type=str,
+        default="<|startoftext|>You are an assistant that understands images and outputs text.<img>Identify the animal in this image and describe this animal's characteristics in the image.",
+        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)

vllm_omni/model_executor/models/hunyuan_image3/__init__.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from .hunyuan_image3 import HunyuanImage3ForConditionalGeneration
+
+__all__ = ["HunyuanImage3ForConditionalGeneration"]