Harmon 1 5B

🚀 Harmon: Harmonizing Visual Representations for Unified Multimodal Understanding and Generation

Harmon is a novel unified framework for multimodal understanding and generation. It harmonizes visual presentations via a shared MAR encoder, achieving advanced generation performance and competitive results in relevant tasks. This repo offers inference code for image understanding and text - to - image generation with two model variants.

🚀 Quick Start

Harmon is a unified framework that addresses multimodal understanding and generation. It overcomes the limitations of existing architectures by harmonizing visual presentations through a shared MAR encoder. In this repository, we offer inference code for both image - to - text and text - to - image generation using two model variants: Harmon - 0.5B and Harmon - 1.5B.

✨ Features

• Unified Framework: Harmon provides a single framework for both multimodal understanding and generation, breaking away from the traditional approach of using different encoder models for these two tasks.
• Shared MAR Encoder: The shared MAR encoder harmonizes the visual presentations of understanding and generation, leading to better performance in generation benchmarks and multimodal understanding tasks.
• Multiple Model Variants: Two model variants, Harmon - 0.5B and Harmon - 1.5B, are available, offering flexibility for different application scenarios.

📦 Installation

The provided README does not contain installation steps, so this section is skipped.

💻 Usage Examples

Basic Usage

🖌️ Image - to - text Generation

import torch
import numpy as np
from transformers import AutoTokenizer, AutoModel
from einops import rearrange
from PIL import Image
import requests


PROMPT_TEMPLATE = dict(
    SYSTEM='<|im_start|>system\n{system}<|im_end|>\n',
    INSTRUCTION='<|im_start|>user\n{input}<|im_end|>\n<|im_start|>assistant\n',
    SUFFIX='<|im_end|>',
    SUFFIX_AS_EOS=True,
    SEP='\n',
    STOP_WORDS=['<|im_end|>', '<|endoftext|>'])


def expand2square(pil_img, background_color):
    width, height = pil_img.size
    if width == height:
        return pil_img
    elif width > height:
        result = Image.new(pil_img.mode, (width, width), background_color)
        result.paste(pil_img, (0, (width - height) // 2))
        return result
    else:
        result = Image.new(pil_img.mode, (height, height), background_color)
        result.paste(pil_img, ((height - width) // 2, 0))
        return result


@torch.no_grad()
def question_answer(question,
                    image,
                    model,
                    tokenizer,
                    max_new_tokens=512,
                    image_size=512
                    ):
    assert image_size == 512
    image = expand2square(
        image, (127, 127, 127))
    image = image.resize(size=(image_size, image_size))
    image = torch.from_numpy(np.array(image)).to(dtype=model.dtype, device=model.device)
    image = rearrange(image, 'h w c -> c h w')[None]
    image = 2 * (image / 255) - 1

    prompt = PROMPT_TEMPLATE['INSTRUCTION'].format(input="<image>\n" + question)
    assert '<image>' in prompt
    image_length = (image_size // 16) ** 2 + model.mar.buffer_size
    prompt = prompt.replace('<image>', '<image>'*image_length)
    input_ids = tokenizer.encode(
        prompt, add_special_tokens=True, return_tensors='pt').cuda()
    _, z_enc = model.extract_visual_feature(model.encode(image))
    inputs_embeds = z_enc.new_zeros(*input_ids.shape, model.llm.config.hidden_size)
    inputs_embeds[input_ids == image_token_idx] = z_enc.flatten(0, 1)
    inputs_embeds[input_ids != image_token_idx] = model.llm.get_input_embeddings()(
        input_ids[input_ids != image_token_idx]
    )
    output = model.llm.generate(inputs_embeds=inputs_embeds,
                                use_cache=True,
                                do_sample=False,
                                max_new_tokens=max_new_tokens,
                                eos_token_id=tokenizer.eos_token_id,
                                pad_token_id=tokenizer.pad_token_id
                                if tokenizer.pad_token_id is not None else
                                tokenizer.eos_token_id
                                )
    return tokenizer.decode(output[0])


harmon_tokenizer = AutoTokenizer.from_pretrained("wusize/Harmon-1_5B",
                                                 trust_remote_code=True)
harmon_model = AutoModel.from_pretrained("wusize/Harmon-1_5B",
                                         trust_remote_code=True).eval().cuda().bfloat16()

special_tokens_dict = {'additional_special_tokens': ["<image>", ]}
num_added_toks = harmon_tokenizer.add_special_tokens(special_tokens_dict)
assert num_added_toks == 1

image_token_idx = harmon_tokenizer.encode("<image>", add_special_tokens=False)[-1]
print(f"Image token: {harmon_tokenizer.decode(image_token_idx)}")

image_file = "http://images.cocodataset.org/val2017/000000039769.jpg"
raw_image = Image.open(requests.get(image_file, stream=True).raw).convert('RGB')

output_text = question_answer(question='Describe the image in detail.',
                              image=raw_image,
                              model=harmon_model,
                              tokenizer=harmon_tokenizer,
                              )

print(output_text)

🖼️ Text - to - image Generation

import os
import torch
from transformers import AutoTokenizer, AutoModel
from einops import rearrange
from PIL import Image


PROMPT_TEMPLATE = dict(
    SYSTEM='<|im_start|>system\n{system}<|im_end|>\n',
    INSTRUCTION='<|im_start|>user\n{input}<|im_end|>\n<|im_start|>assistant\n',
    SUFFIX='<|im_end|>',
    SUFFIX_AS_EOS=True,
    SEP='\n',
    STOP_WORDS=['<|im_end|>', '<|endoftext|>'])

GENERATION_TEMPLATE = "Generate an image: {text}"


@torch.no_grad()
def generate_images(prompts,
                    negative_prompt,
                    tokenizer,
                    model,
                    output,
                    grid_size=2,   # will produce 2 x 2 images per prompt
                    num_steps=64, cfg_scale=3.0, temperature=1.0, image_size=512):
    assert image_size == 512
    m = n = image_size // 16

    prompts = [
                  PROMPT_TEMPLATE['INSTRUCTION'].format(input=prompt)
                  for prompt in prompts
              ] * (grid_size ** 2)

    if cfg_scale != 1.0:
        prompts += [PROMPT_TEMPLATE['INSTRUCTION'].format(input=negative_prompt)] * len(prompts)

    inputs = tokenizer(
        prompts, add_special_tokens=True, return_tensors='pt', padding=True).to(model.device)

    images = model.sample(**inputs, num_iter=num_steps, cfg=cfg_scale, cfg_schedule="constant",
                          temperature=temperature, progress=True, image_shape=(m, n))
    images = rearrange(images, '(m n b) c h w -> b (m h) (n w) c', m=grid_size, n=grid_size)

    images = torch.clamp(
        127.5 * images + 128.0, 0, 255).to("cpu", dtype=torch.uint8).numpy()

    os.makedirs(output, exist_ok=True)
    for idx, image in enumerate(images):
        Image.fromarray(image).save(f"{output}/{idx:08d}.jpg")


harmon_tokenizer = AutoTokenizer.from_pretrained("wusize/Harmon-1_5B",
                                                 trust_remote_code=True)
harmon_model = AutoModel.from_pretrained("wusize/Harmon-1_5B",
                                         trust_remote_code=True).cuda().bfloat16().eval()


texts = ['a dog on the left and a cat on the right.',
         'a photo of a pink stop sign.']
pos_prompts = [GENERATION_TEMPLATE.format(text=text) for text in texts]
neg_prompt = 'Generate an image.'   # for classifier-free guidance


generate_images(prompts=pos_prompts,
                negative_prompt=neg_prompt,
                tokenizer=harmon_tokenizer,
                model=harmon_model,
                output='output',)

Advanced Usage

The provided README does not contain advanced usage scenarios, so this part is skipped.

📚 Documentation

The table below shows the details of different model variants:

🔧 Technical Details

The provided README does not contain technical details, so this section is skipped.

📄 License

This project is licensed under NTU S - Lab License 1.0.

📋 Citation

If you find Harmon useful for your research or applications, please cite our paper using the following BibTeX:

@misc{wu2025harmon,
      title={Harmonizing Visual Representations for Unified Multimodal Understanding and Generation}, 
      author={Size Wu and Wenwei Zhang and Lumin Xu and Sheng Jin and Zhonghua Wu and Qingyi Tao and Wentao Liu and Wei Li and Chen Change Loy},
      year={2025},
      eprint={2503.21979},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2503.21979}, 
}