Janus-FocusDiff-7B

<h2 align="center" style="line-height: 25px;"> FocusDiff: Advancing Fine-Grained Text-Image Alignment for Autoregressive Visual Generation through RL </h2> <p align="center"> <a href="https://arxiv.org/abs/2506.05501" style="display: inline-block; margin: 0 5px;"> <img src="https://img.shields.io/badge/Paper-red?style=flat&logo=arxiv" style="height: 15px;"> </a> <a href="https://focusdiff.github.io/" style="display: inline-block; margin: 0 5px;"> <img src="https://img.shields.io/badge/Project Page-white?style=flat&logo=google-docs" style="height: 15px;"> </a> <a href="https://github.com/wendell0218/FocusDiff" style="display: inline-block; margin: 0 5px;"> <img src="https://img.shields.io/badge/Code-black?style=flat&logo=github" style="height: 15px;"> </a> <a href="https://huggingface.co/wendell0218/Janus-FocusDiff-7B" style="display: inline-block; margin: 0 5px;"> <img src="https://img.shields.io/badge/-%F0%9F%A4%97%20Checkpoint-orange?style=flat" style="height: 15px;"/> </a> </p> <div align="center"> <span style="font-size: smaller;"> Kaihang Pan^1*, Wendong Bu^1*, Yuruo Wu^1*, Yang Wu², Kai Shen¹, Yunfei Li², <br>Hang Zhao², Juncheng Li^1&dagger;, Siliang Tang¹, Yueting Zhuang¹ <br>¹Zhejiang University, ²Ant Group <br>*Equal Contribution, ^&dagger;Corresponding Authors </span> </div>

🚀 Overview

FocusDiff is a new method for improving fine-grained text-image alignment in autoregressive text-to-image models. By introducing the FocusDiff-Data dataset and a novel Pair-GRPO reinforcement learning framework, we help models learn subtle semantic differences between similar text-image pairs. Based on paired data in FocusDiff-Data, we further introduce the PairComp Benchmark, which focuses on subtle semantic differences.

Key Contributions:

1. PairComp Benchmark: A new benchmark focusing on fine-grained differences in text prompts.

   <img src="https://raw.githubusercontent.com/wendell0218/FocusDiff/refs/heads/main/assets/benchmark.png" width="100%"/>

2. FocusDiff Approach: A method using paired data and reinforcement learning to enhance fine-grained text-image alignment.

   <div style="text-align: center;"> <img src="https://raw.githubusercontent.com/wendell0218/FocusDiff/refs/heads/main/assets/grpo.png" width="100%" /> </div>

3. SOTA Results: Our model is evaluated with the top performance on multiple benchmarks including GenEval, T2I-CompBench, DPG-Bench, and our newly proposed PairComp benchmark.

✨️ Quickstart

1. Prepare Environment

We recommend using Python 3.10 and setting up a virtual environment:

# clone our repo
git clone https://github.com/wendell0218/FocusDiff.git
cd FocusDiff

# prepare python environment
conda create -n focus-diff python=3.10
conda activate focus-diff
pip install -r requirements.txt

2. Prepare Pretrained Model

FocusDiff utilizes Janus-Pro-7B as the pretrained model for subsequent supervised fine-tuning. You can download the corresponding model using the following command:

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/deepseek-ai/Janus-Pro-7B
cd Janus-Pro-7B
git lfs pull

3. Start Generating!

import os
import torch
import PIL.Image
import numpy as np
from transformers import AutoModelForCausalLM
from janus.models import MultiModalityCausalLM, VLChatProcessor

@torch.inference_mode()
def generate(
    mmgpt: MultiModalityCausalLM,
    vl_chat_processor: VLChatProcessor,
    prompt: str,
    temperature: float = 1.0,
    parallel_size: int = 4,
    cfg_weight: float = 5.0,
    image_token_num_per_image: int = 576,
    img_size: int = 384,
    patch_size: int = 16,
    img_top_k: int = 1,
    img_top_p: float = 1.0,
):
    images = []
    input_ids = vl_chat_processor.tokenizer.encode(prompt)
    input_ids = torch.LongTensor(input_ids)
    tokens = torch.zeros((parallel_size*2, len(input_ids)), dtype=torch.int).cuda()
    for i in range(parallel_size*2):
        tokens[i, :] = input_ids
        if i % 2 != 0:
            tokens[i, 1:-1] = vl_chat_processor.pad_id
    inputs_embeds = mmgpt.language_model.get_input_embeddings()(tokens) 
    generated_tokens = torch.zeros((parallel_size, image_token_num_per_image), dtype=torch.int).cuda()
    for i in range(image_token_num_per_image):
        outputs = mmgpt.language_model.model(inputs_embeds=inputs_embeds, use_cache=True, past_key_values=outputs.past_key_values if i != 0 else None)
        hidden_states = outputs.last_hidden_state
        logits = mmgpt.gen_head(hidden_states[:, -1, :])
        logit_cond = logits[0::2, :]
        logit_uncond = logits[1::2, :]
        logits = logit_uncond + cfg_weight * (logit_cond-logit_uncond)
        if img_top_k:
            v, _ = torch.topk(logits, min(img_top_k, logits.size(-1)))
            logits[logits < v[:, [-1]]] = float("-inf")
        probs = torch.softmax(logits / temperature, dim=-1)
        if img_top_p:
            probs_sort, probs_idx = torch.sort(probs,
                                            dim=-1,
                                            descending=True)
            probs_sum = torch.cumsum(probs_sort, dim=-1)
            mask = probs_sum - probs_sort > img_top_p
            probs_sort[mask] = 0.0
            probs_sort.div_(probs_sort.sum(dim=-1, keepdim=True))
            next_token = torch.multinomial(probs_sort, num_samples=1)
            next_token = torch.gather(probs_idx, -1, next_token)
        else:
            next_token = torch.multinomial(probs, num_samples=1)
        generated_tokens[:, i] = next_token.squeeze(dim=-1)
        next_token = torch.cat([next_token.unsqueeze(dim=1), next_token.unsqueeze(dim=1)], dim=1).view(-1)
        img_embeds = mmgpt.prepare_gen_img_embeds(next_token)
        inputs_embeds = img_embeds.unsqueeze(dim=1)
    dec = mmgpt.gen_vision_model.decode_code(generated_tokens.to(dtype=torch.int), shape=[parallel_size, 8, img_size//patch_size, img_size//patch_size])
    dec = dec.to(torch.float32).cpu().numpy().transpose(0, 2, 3, 1)
    dec = np.clip((dec + 1) / 2 * 255, 0, 255)
    visual_img = np.zeros((parallel_size, img_size, img_size, 3), dtype=np.uint8)
    visual_img[:, :, :] = dec
    for i in range(parallel_size):
        images.append(PIL.Image.fromarray(visual_img[i]))

    return images
  

if __name__ == "__main__":
    import argparse
    parser = argparse.ArgumentParser()

    parser.add_argument("--model_path", type=str, default="deepseek-ai/Janus-Pro-7B")
    parser.add_argument("--ckpt_path", type=str, default=None)
    parser.add_argument("--caption", type=str, default="a brown giraffe and a white stop sign")
    parser.add_argument("--gen_path", type=str, default='results/samples')
    parser.add_argument("--cfg", type=float, default=5.0)
    parser.add_argument("--parallel_size", type=int, default=4)

    args = parser.parse_args()
    vl_chat_processor: VLChatProcessor = VLChatProcessor.from_pretrained(args.model_path)
    vl_gpt: MultiModalityCausalLM = AutoModelForCausalLM.from_pretrained(args.model_path, trust_remote_code=True)
    if args.ckpt_path is not None:
        state_dict = torch.load(f"{args.ckpt_path}", map_location="cpu")
        vl_gpt.load_state_dict(state_dict)
    vl_gpt = vl_gpt.to(torch.bfloat16).cuda().eval()
    prompt = f'<|User|>: {args.caption}\n\n<|Assistant|>:<begin_of_image>'
    images = generate(
        vl_gpt,
        vl_chat_processor,
        prompt,
        parallel_size = args.parallel_size,
        cfg_weight = args.cfg, 
    )
    if not os.path.exists(args.gen_path):
        os.makedirs(args.gen_path, exist_ok=True)
    for i in range(args.parallel_size):
        img_name = str(i).zfill(4)+".png"
        save_path = os.path.join(args.gen_path, img_name)
        images[i].save(save_path)

🤝 Acknowledgment

Our project is developed based on the following repositories:

• Janus-Series: Unified Multimodal Understanding and Generation Models
• Open-R1: Fully open reproduction of DeepSeek-R1

📜 Citation

If you find this work useful for your research, please cite our paper and star our git repo:

@article{pan2025focusdiff,
  title={FocusDiff: Advancing Fine-Grained Text-Image Alignment for Autoregressive Visual Generation through RL},
  author={Pan, Kaihang and Bu, Wendong and Wu, Yuruo and Wu, Yang and Shen, Kai and Li, Yunfei and Zhao, Hang and Li, Juncheng and Tang, Siliang and Zhuang, Yueting},
  journal={arXiv preprint arXiv:2506.05501},
  year={2025}
}