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Hunyuan-DiT : A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding
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Hunyuan-DiT : A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding

             

This repo contains PyTorch model definitions, pre-trained weights and inference/sampling code for our paper exploring Hunyuan-DiT. You can find more visualizations on our project page.

Hunyuan-DiT: A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding

DialogGen: Multi-modal Interactive Dialogue System for Multi-turn Text-to-Image Generation

🔥🔥🔥 News!!

🤖 Try it on the web

Welcome to our web-based Tencent Hunyuan Bot, where you can explore our innovative products! Just input the suggested prompts below or any other imaginative prompts containing drawing-related keywords to activate the Hunyuan text-to-image generation feature. Unleash your creativity and create any picture you desire, all for free!

You can use simple prompts similar to natural language text

画一只穿着西装的猪

draw a pig in a suit

生成一幅画，赛博朋克风，跑车

generate a painting, cyberpunk style, sports car

or multi-turn language interactions to create the picture.

画一个木制的鸟

draw a wooden bird

变成玻璃的

turn into glass

📑 Open-source Plan

Contents

Abstract

We present Hunyuan-DiT, a text-to-image diffusion transformer with fine-grained understanding of both English and Chinese. To construct Hunyuan-DiT, we carefully designed the transformer structure, text encoder, and positional encoding. We also build from scratch a whole data pipeline to update and evaluate data for iterative model optimization. For fine-grained language understanding, we train a Multimodal Large Language Model to refine the captions of the images. Finally, Hunyuan-DiT can perform multi-round multi-modal dialogue with users, generating and refining images according to the context. Through our carefully designed holistic human evaluation protocol with more than 50 professional human evaluators, Hunyuan-DiT sets a new state-of-the-art in Chinese-to-image generation compared with other open-source models.

🎉 Hunyuan-DiT Key Features

Chinese-English Bilingual DiT Architecture

Hunyuan-DiT is a diffusion model in the latent space, as depicted in figure below. Following the Latent Diffusion Model, we use a pre-trained Variational Autoencoder (VAE) to compress the images into low-dimensional latent spaces and train a diffusion model to learn the data distribution with diffusion models. Our diffusion model is parameterized with a transformer. To encode the text prompts, we leverage a combination of pre-trained bilingual (English and Chinese) CLIP and multilingual T5 encoder.

Multi-turn Text2Image Generation

Understanding natural language instructions and performing multi-turn interaction with users are important for a text-to-image system. It can help build a dynamic and iterative creation process that bring the user’s idea into reality step by step. In this section, we will detail how we empower Hunyuan-DiT with the ability to perform multi-round conversations and image generation. We train MLLM to understand the multi-round user dialogue and output the new text prompt for image generation.

📈 Comparisons

In order to comprehensively compare the generation capabilities of HunyuanDiT and other models, we constructed a 4-dimensional test set, including Text-Image Consistency, Excluding AI Artifacts, Subject Clarity, Aesthetic. More than 50 professional evaluators performs the evaluation.

Model Open Source Text-Image Consistency (%) Excluding AI Artifacts (%) Subject Clarity (%) Aesthetics (%) Overall (%)
SDXL ✔ 64.3 60.6 91.1 76.3 42.7
PixArt-α ✔ 68.3 60.9 93.2 77.5 45.5
Playground 2.5 ✔ 71.9 70.8 94.9 83.3 54.3
SD 3 ✘ 77.1 69.3 94.6 82.5 56.7
MidJourney v6✘ 73.5 80.2 93.5 87.2 63.3
DALL-E 3✘ 83.9 80.3 96.5 89.4 71.0
Hunyuan-DiT✔ 74.2 74.3 95.4 86.6 59.0

🎥 Visualization

https://github.com/Tencent/tencent.github.io/assets/27557933/94b4dcc3-104d-44e1-8bb2-dc55108763d1

📜 Requirements

This repo consists of DialogGen (a prompt enhancement model) and Hunyuan-DiT (a text-to-image model).

The following table shows the requirements for running the models (batch size = 1):

Model --load-4bit (DialogGen) GPU Peak Memory GPU
DialogGen + Hunyuan-DiT ✘ 32G A100
DialogGen + Hunyuan-DiT ✔ 22G A100
Hunyuan-DiT - 11G A100
Hunyuan-DiT - 14G RTX3090/RTX4090

🛠️ Dependencies and Installation

Begin by cloning the repository:

git clone https://github.com/tencent/HunyuanDiT
cd HunyuanDiT

Installation Guide for Linux

We provide an environment.yml file for setting up a Conda environment. Conda's installation instructions are available here.

We recommend CUDA versions 11.7 and 12.0+.

# 1. Prepare conda environment
conda env create -f environment.yml

# 2. Activate the environment
conda activate HunyuanDiT

# 3. Install pip dependencies
python -m pip install -r requirements.txt

# 4. Install flash attention v2 for acceleration (requires CUDA 11.6 or above)
python -m pip install git+https://github.com/Dao-AILab/flash-attention.git@v2.1.2.post3

Additionally, you can also use docker to set up the environment.

# 1. Use the following link to download the docker image tar file.
# For CUDA 12
wget https://dit.hunyuan.tencent.com/download/HunyuanDiT/hunyuan_dit_cu12.tar
# For CUDA 11
wget https://dit.hunyuan.tencent.com/download/HunyuanDiT/hunyuan_dit_cu11.tar

# 2. Import the docker tar file and show the image meta information
# For CUDA 12
docker load -i hunyuan_dit_cu12.tar
# For CUDA 11
docker load -i hunyuan_dit_cu11.tar  

docker image ls

# 3. Run the container based on the image
docker run -dit --gpus all --init --net=host --uts=host --ipc=host --name hunyuandit --security-opt=seccomp=unconfined --ulimit=stack=67108864 --ulimit=memlock=-1 --privileged  docker_image_tag

🧱 Download Pretrained Models

To download the model, first install the huggingface-cli. (Detailed instructions are available here.)

python -m pip install "huggingface_hub[cli]"

Then download the model using the following commands:

# Create a directory named 'ckpts' where the model will be saved, fulfilling the prerequisites for running the demo.
mkdir ckpts
# Use the huggingface-cli tool to download the model.
# The download time may vary from 10 minutes to 1 hour depending on network conditions.
huggingface-cli download Tencent-Hunyuan/HunyuanDiT-v1.2 --local-dir ./ckpts
💡Tips for using huggingface-cli (network problem) ##### 1. Using HF-Mirror If you encounter slow download speeds in China, you can try a mirror to speed up the download process. For example, ```shell HF_ENDPOINT=https://hf-mirror.com huggingface-cli download Tencent-Hunyuan/HunyuanDiT-v1.2 --local-dir ./ckpts ``` ##### 2. Resume Download `huggingface-cli` supports resuming downloads. If the download is interrupted, you can just rerun the download command to resume the download process. Note: If an `No such file or directory: 'ckpts/.huggingface/.gitignore.lock'` like error occurs during the download process, you can ignore the error and rerun the download command.

All models will be automatically downloaded. For more information about the model, visit the Hugging Face repository here.

Model #Params Huggingface Download URL Tencent Cloud Download URL
mT5 1.6B mT5 mT5
CLIP 350M CLIP CLIP
Tokenizer - Tokenizer Tokenizer
DialogGen 7.0B DialogGen DialogGen
sdxl-vae-fp16-fix 83M sdxl-vae-fp16-fix sdxl-vae-fp16-fix
Hunyuan-DiT-v1.0 1.5B Hunyuan-DiT Hunyuan-DiT-v1.0
Hunyuan-DiT-v1.1 1.5B Hunyuan-DiT-v1.1 Hunyuan-DiT-v1.1
Hunyuan-DiT-v1.2 1.5B Hunyuan-DiT-v1.2 Hunyuan-DiT-v1.2
Data demo - - Data demo

:truck: Training

Data Preparation

Refer to the commands below to prepare the training data.

  1. Install dependencies

    We offer an efficient data management library, named IndexKits, supporting the management of reading hundreds of millions of data during training, see more in docs.

      # 1 Install dependencies
  cd HunyuanDiT
  pip install -e ./IndexKits

  2. Data download

    Feel free to download the data demo.

     # 2 Data download
 wget -O ./dataset/data_demo.zip https://dit.hunyuan.tencent.com/download/HunyuanDiT/data_demo.zip
 unzip ./dataset/data_demo.zip -d ./dataset
 mkdir ./dataset/porcelain/arrows ./dataset/porcelain/jsons

  3. Data conversion

    Create a CSV file for training data with the fields listed in the table below.

    Fields Required Description Example
    image_path Required image path ./dataset/porcelain/images/0.png
    text_zh Required text 青花瓷风格，一只蓝色的鸟儿站在蓝色的花瓶上，周围点缀着白色花朵，背景是白色
    md5 Optional image md5 (Message Digest Algorithm 5) d41d8cd98f00b204e9800998ecf8427e
    width Optional image width 1024
    height Optional image height 1024

    ⚠️ Optional fields like MD5, width, and height can be omitted. If omitted, the script below will automatically calculate them. This process can be time-consuming when dealing with large-scale training data.

    We utilize Arrow for training data format, offering a standard and efficient in-memory data representation. A conversion script is provided to transform CSV files into Arrow format.

     # 3 Data conversion 
 python ./hydit/data_loader/csv2arrow.py ./dataset/porcelain/csvfile/image_text.csv ./dataset/porcelain/arrows 1

  4. Data Selection and Configuration File Creation

    We configure the training data through YAML files. In these files, you can set up standard data processing strategies for filtering, copying, deduplicating, and more regarding the training data. For more details, see ./IndexKits.

    For a sample file, please refer to file. For a full parameter configuration file, see file.

  5. Create training data index file using YAML file.

      # Single Resolution Data Preparation
  idk base -c dataset/yamls/porcelain.yaml -t dataset/porcelain/jsons/porcelain.json

  # Multi Resolution Data Preparation     
  idk multireso -c dataset/yamls/porcelain_mt.yaml -t dataset/porcelain/jsons/porcelain_mt.json

    The directory structure for porcelain dataset is:

    cd ./dataset

porcelain
  ├──images/  (image files)
  │  ├──0.png
  │  ├──1.png
  │  ├──......
  ├──csvfile/  (csv files containing text-image pairs)
  │  ├──image_text.csv
  ├──arrows/  (arrow files containing all necessary training data)
  │  ├──00000.arrow
  │  ├──00001.arrow
  │  ├──......
  ├──jsons/  (final training data index files which read data from arrow files during training)
  │  ├──porcelain.json
  │  ├──porcelain_mt.json

Full-parameter Training

Requirement:

  1. The minimum requriment is a single GPU with at least 20GB memory, but we recommend to use a GPU with about 30 GB memory to avoid host memory offloading.

  2. Additionally, we encourage users to leverage the multiple GPUs across different nodes to speed up training on large datasets.

    Notice:

  3. Personal users can also use the light-weight Kohya to finetune the model with about 16 GB memory. Currently, we are trying to further reduce the memory usage of our industry-level framework for personal users.

  4. If you have enough GPU memory, please try to remove --cpu-offloading or --gradient-checkpointing for less time costs.

    Specifically for distributed training, you have the flexibility to control single-node / multi-node training by adjusting parameters such as --hostfile and --master_addr. For more details, see link.

    # Single Resolution Training
PYTHONPATH=./ sh hydit/train.sh --index-file dataset/porcelain/jsons/porcelain.json

# Multi Resolution Training
PYTHONPATH=./ sh hydit/train.sh --index-file dataset/porcelain/jsons/porcelain_mt.json --multireso --reso-step 64

# Training with old version of HunyuanDiT (<= v1.1)
PYTHONPATH=./ sh hydit/train_v1.1.sh --index-file dataset/porcelain/jsons/porcelain.json

    After checkpoints are saved, you can use the following command to evaluate the model.

    # Inference
#   You should replace the 'log_EXP/xxx/checkpoints/final.pt' with your actual path.
python sample_t2i.py --infer-mode fa --prompt "青花瓷风格，一只可爱的哈士奇" --no-enhance --dit-weight log_EXP/xxx/checkpoints/final.pt --load-key module

# Old version of HunyuanDiT (<= v1.1)
#   You should replace the 'log_EXP/xxx/checkpoints/final.pt' with your actual path.
python sample_t2i.py --infer-mode fa --prompt "青花瓷风格，一只可爱的哈士奇" --model-root ./HunyuanDiT-v1.1 --use-style-cond --size-cond 1024 1024 --beta-end 0.03 --no-enhance --dit-weight log_EXP/xxx/checkpoints/final.pt --load-key module

LoRA

We provide training and inference scripts for LoRA, detailed in the ./lora. 

  # Training for porcelain LoRA.
  PYTHONPATH=./ sh lora/train_lora.sh --index-file dataset/porcelain/jsons/porcelain.json

  # Inference using trained LORA weights.
  python sample_t2i.py --infer-mode fa --prompt "青花瓷风格，一只小狗"  --no-enhance --lora-ckpt log_EXP/001-lora_porcelain_ema_rank64/checkpoints/0001000.pt

We offer two types of trained LoRA weights for porcelain and jade, see details at links

  cd HunyuanDiT
  # Use the huggingface-cli tool to download the model.
  huggingface-cli download Tencent-Hunyuan/HYDiT-LoRA --local-dir ./ckpts/t2i/lora

  # Quick start
  python sample_t2i.py --infer-mode fa --prompt "青花瓷风格，一只猫在追蝴蝶"  --no-enhance --load-key ema --lora-ckpt ./ckpts/t2i/lora/porcelain
Examples of training data
Image 0 Image 1 Image 2 Image 3
青花瓷风格，一只蓝色的鸟儿站在蓝色的花瓶上，周围点缀着白色花朵，背景是白色 （Porcelain style, a blue bird stands on a blue vase, surrounded by white flowers, with a white background. ） 青花瓷风格，这是一幅蓝白相间的陶瓷盘子，上面描绘着一只狐狸和它的幼崽在森林中漫步，背景是白色 （Porcelain style, this is a blue and white ceramic plate depicting a fox and its cubs strolling in the forest, with a white background.） 青花瓷风格，在黑色背景上，一只蓝色的狼站在蓝白相间的盘子上，周围是树木和月亮 （Porcelain style, on a black background, a blue wolf stands on a blue and white plate, surrounded by trees and the moon.） 青花瓷风格，在蓝色背景上，一只蓝色蝴蝶和白色花朵被放置在中央 （Porcelain style, on a blue background, a blue butterfly and white flowers are placed in the center.）
Examples of inference results
Image 4 Image 5 Image 6 Image 7
青花瓷风格，苏州园林 （Porcelain style, Suzhou Gardens.） 青花瓷风格，一朵荷花 （Porcelain style, a lotus flower.） 青花瓷风格，一只羊（Porcelain style, a sheep.） 青花瓷风格，一个女孩在雨中跳舞（Porcelain style, a girl dancing in the rain.）

🔑 Inference

6GB GPU VRAM Inference

Running HunyuanDiT in under 6GB GPU VRAM is available now based on diffusers. Here we provide instructions and demo for your quick start.

The 6GB version supports Nvidia Ampere architecture series graphics cards such as RTX 3070/3080/4080/4090, A100, and so on.

The only thing you need do is to install the following library:

pip install -U bitsandbytes
pip install git+https://github.com/huggingface/diffusers
pip install torch==2.0.0

Then you can enjoy your HunyuanDiT text-to-image journey under 6GB GPU VRAM directly!

Here is a demo for you.

cd HunyuanDiT

# Quick start
model_id=Tencent-Hunyuan/HunyuanDiT-v1.2-Diffusers-Distilled
prompt=一个宇航员在骑马
infer_steps=50
guidance_scale=6
python3 lite/inference.py ${model_id} ${prompt} ${infer_steps} ${guidance_scale}

More details can be found in ./lite.

Using Gradio

Make sure the conda environment is activated before running the following command.

# By default, we start a Chinese UI. Using Flash Attention for acceleration.
python app/hydit_app.py --infer-mode fa

# You can disable the enhancement model if the GPU memory is insufficient.
# The enhancement will be unavailable until you restart the app without the `--no-enhance` flag. 
python app/hydit_app.py --no-enhance --infer-mode fa

# Start with English UI
python app/hydit_app.py --lang en --infer-mode fa

# Start a multi-turn T2I generation UI. 
# If your GPU memory is less than 32GB, use '--load-4bit' to enable 4-bit quantization, which requires at least 22GB of memory.
python app/multiTurnT2I_app.py --infer-mode fa

Then the demo can be accessed through http://0.0.0.0:443. It should be noted that the 0.0.0.0 here needs to be X.X.X.X with your server IP.

Using 🤗 Diffusers

Please install PyTorch version 2.0 or higher in advance to satisfy the requirements of the specified version of the diffusers library.

Install 🤗 diffusers, ensuring that the version is at least 0.28.1:

pip install git+https://github.com/huggingface/diffusers.git

or

pip install diffusers

You can generate images with both Chinese and English prompts using the following Python script:

import torch
from diffusers import HunyuanDiTPipeline

pipe = HunyuanDiTPipeline.from_pretrained("Tencent-Hunyuan/HunyuanDiT-v1.2-Diffusers", torch_dtype=torch.float16)
pipe.to("cuda")

# You may also use English prompt as HunyuanDiT supports both English and Chinese
# prompt = "An astronaut riding a horse"
prompt = "一个宇航员在骑马"
image = pipe(prompt).images[0]

You can use our distilled model to generate images even faster:

import torch
from diffusers import HunyuanDiTPipeline

pipe = HunyuanDiTPipeline.from_pretrained("Tencent-Hunyuan/HunyuanDiT-v1.2-Diffusers-Distilled", torch_dtype=torch.float16)
pipe.to("cuda")

# You may also use English prompt as HunyuanDiT supports both English and Chinese
# prompt = "An astronaut riding a horse"
prompt = "一个宇航员在骑马"
image = pipe(prompt, num_inference_steps=25).images[0]

More details can be found in HunyuanDiT-v1.2-Diffusers-Distilled

More functions: For other functions like LoRA and ControlNet, please have a look at the README of ./diffusers.

Using Command Line

We provide several commands to quick start: 

# Only Text-to-Image. Flash Attention mode
python sample_t2i.py --infer-mode fa --prompt "渔舟唱晚" --no-enhance

# Generate an image with other image sizes.
python sample_t2i.py --infer-mode fa --prompt "渔舟唱晚" --image-size 1280 768

# Prompt Enhancement + Text-to-Image. DialogGen loads with 4-bit quantization, but it may loss performance.
python sample_t2i.py --infer-mode fa --prompt "渔舟唱晚"  --load-4bit

More example prompts can be found in example_prompts.txt

More Configurations

We list some more useful configurations for easy usage:

Argument Default Description
--prompt None The text prompt for image generation
--image-size 1024 1024 The size of the generated image
--seed 42 The random seed for generating images
--infer-steps 100 The number of steps for sampling
--negative - The negative prompt for image generation
--infer-mode torch The inference mode (torch, fa, or trt)
--sampler ddpm The diffusion sampler (ddpm, ddim, or dpmms)
--no-enhance False Disable the prompt enhancement model
--model-root ckpts The root directory of the model checkpoints
--load-key ema Load the student model or EMA model (ema or module)
--load-4bit Fasle Load DialogGen model with 4bit quantization

Using ComfyUI

Workflow More details can be found in ./comfyui-hydit

Using Kohya

We support custom codes for kohya_ss GUI, and sd-scripts training codes for HunyuanDiT. dreambooth More details can be found in ./kohya_ss-hydit

Using Previous versions

# ============================== v1.1 ==============================
# Download the model
huggingface-cli download Tencent-Hunyuan/HunyuanDiT-v1.1 --local-dir ./HunyuanDiT-v1.1
# Inference with the model
python sample_t2i.py --infer-mode fa --prompt "渔舟唱晚" --model-root ./HunyuanDiT-v1.1 --use-style-cond --size-cond 1024 1024 --beta-end 0.03

# ============================== v1.0 ==============================
# Download the model
huggingface-cli download Tencent-Hunyuan/HunyuanDiT --local-dir ./HunyuanDiT-v1.0
# Inference with the model
python sample_t2i.py --infer-mode fa --prompt "渔舟唱晚" --model-root ./HunyuanDiT-v1.0 --use-style-cond --size-cond 1024 1024 --beta-end 0.03

:building_construction: Adapter

ControlNet

We provide training scripts for ControlNet, detailed in the ./controlnet. 

  # Training for canny ControlNet.
  PYTHONPATH=./ sh hydit/train_controlnet.sh

We offer three types of trained ControlNet weights for canny depth and pose, see details at links

  cd HunyuanDiT
  # Use the huggingface-cli tool to download the model.
  # We recommend using distilled weights as the base model for ControlNet inference, as our provided pretrained weights are trained on them.
  huggingface-cli download Tencent-Hunyuan/HYDiT-ControlNet-v1.2 --local-dir ./ckpts/t2i/controlnet
  huggingface-cli download Tencent-Hunyuan/Distillation-v1.2 ./pytorch_model_distill.pt --local-dir ./ckpts/t2i/model

  # Quick start
  python3 sample_controlnet.py --infer-mode fa --no-enhance --load-key distill --infer-steps 50 --control-type canny --prompt "在夜晚的酒店门前，一座古老的中国风格的狮子雕像矗立着，它的眼睛闪烁着光芒，仿佛在守护着这座建筑。背景是夜晚的酒店前，构图方式是特写，平视，居中构图。这张照片呈现了真实摄影风格，蕴含了中国雕塑文化，同时展现了神秘氛围" --condition-image-path controlnet/asset/input/canny.jpg --control-weight 1.0
Condition Input
Canny ControlNet Depth ControlNet Pose ControlNet
在夜晚的酒店门前，一座古老的中国风格的狮子雕像矗立着，它的眼睛闪烁着光芒，仿佛在守护着这座建筑。背景是夜晚的酒店前，构图方式是特写，平视，居中构图。这张照片呈现了真实摄影风格，蕴含了中国雕塑文化，同时展现了神秘氛围
（At night, an ancient Chinese-style lion statue stands in front of the hotel, its eyes gleaming as if guarding the building. The background is the hotel entrance at night, with a close-up, eye-level, and centered composition. This photo presents a realistic photographic style, embodies Chinese sculpture culture, and reveals a mysterious atmosphere.） 		在茂密的森林中，一只黑白相间的熊猫静静地坐在绿树红花中，周围是山川和海洋。背景是白天的森林，光线充足。照片采用特写、平视和居中构图的方式，呈现出写实的效果
（In the dense forest, a black and white panda sits quietly among the green trees and red flowers, surrounded by mountains and oceans. The background is a daytime forest with ample light. The photo uses a close-up, eye-level, and centered composition to create a realistic effect.） 		在白天的森林中，一位穿着绿色上衣的亚洲女性站在大象旁边。照片采用了中景、平视和居中构图的方式，呈现出写实的效果。这张照片蕴含了人物摄影文化，并展现了宁静的氛围
（In the daytime forest, an Asian woman wearing a green shirt stands beside an elephant. The photo uses a medium shot, eye-level, and centered composition to create a realistic effect. This picture embodies the character photography culture and conveys a serene atmosphere.）
Image 0 Image 1 Image 2
ControlNet Output
Image 3 Image 4 Image 5

:art: Hunyuan-Captioner

Hunyuan-Captioner meets the need of text-to-image techniques by maintaining a high degree of image-text consistency. It can generate high-quality image descriptions from a variety of angles, including object description, objects relationships, background information, image style, etc. Our code is based on LLaVA implementation.

Examples

Image 3

Instructions

a. Install dependencies

The dependencies and installation are basically the same as the base model.

b. Model download

# Use the huggingface-cli tool to download the model.
huggingface-cli download Tencent-Hunyuan/HunyuanCaptioner --local-dir ./ckpts/captioner

Inference

Our model supports three different modes including: directly generating Chinese caption, generating Chinese caption based on specific knowledge, and directly generating English caption. The injected information can be either accurate cues or noisy labels (e.g., raw descriptions crawled from the internet). The model is capable of generating reliable and accurate descriptions based on both the inserted information and the image content.

Mode Prompt Template Description
caption_zh 描述这张图片 Caption in Chinese
insert_content 根据提示词“{}”,描述这张图片 Caption with inserted knowledge
caption_en Please describe the content of this image Caption in English

a. Single picture inference in Chinese

python mllm/caption_demo.py --mode "caption_zh" --image_file "mllm/images/demo1.png" --model_path "./ckpts/captioner"

b. Insert specific knowledge into caption

python mllm/caption_demo.py --mode "insert_content" --content "宫保鸡丁" --image_file "mllm/images/demo2.png" --model_path "./ckpts/captioner"

c. Single picture inference in English

python mllm/caption_demo.py --mode "caption_en" --image_file "mllm/images/demo3.png" --model_path "./ckpts/captioner"

d. Multiple pictures inference in Chinese

### Convert multiple pictures to csv file. 
python mllm/make_csv.py --img_dir "mllm/images" --input_file "mllm/images/demo.csv"

### Multiple pictures inference
python mllm/caption_demo.py --mode "caption_zh" --input_file "mllm/images/demo.csv" --output_file "mllm/images/demo_res.csv" --model_path "./ckpts/captioner"

(Optional) To convert the output csv file to Arrow format, please refer to Data Preparation #3 for detailed instructions.

Gradio

To launch a Gradio demo locally, please run the following commands one by one. For more detailed instructions, please refer to LLaVA. 

cd mllm
python -m llava.serve.controller --host 0.0.0.0 --port 10000

python -m llava.serve.gradio_web_server --controller http://0.0.0.0:10000 --model-list-mode reload --port 443

python -m llava.serve.model_worker --host 0.0.0.0 --controller http://0.0.0.0:10000 --port 40000 --worker http://0.0.0.0:40000 --model-path "../ckpts/captioner" --model-name LlavaMistral

Then the demo can be accessed through http://0.0.0.0:443. It should be noted that the 0.0.0.0 here needs to be X.X.X.X with your server IP.

🚀 Acceleration (for Linux)

🔗 BibTeX

If you find Hunyuan-DiT or DialogGen useful for your research and applications, please cite using this BibTeX:

@misc{li2024hunyuandit,
      title={Hunyuan-DiT: A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding}, 
      author={Zhimin Li and Jianwei Zhang and Qin Lin and Jiangfeng Xiong and Yanxin Long and Xinchi Deng and Yingfang Zhang and Xingchao Liu and Minbin Huang and Zedong Xiao and Dayou Chen and Jiajun He and Jiahao Li and Wenyue Li and Chen Zhang and Rongwei Quan and Jianxiang Lu and Jiabin Huang and Xiaoyan Yuan and Xiaoxiao Zheng and Yixuan Li and Jihong Zhang and Chao Zhang and Meng Chen and Jie Liu and Zheng Fang and Weiyan Wang and Jinbao Xue and Yangyu Tao and Jianchen Zhu and Kai Liu and Sihuan Lin and Yifu Sun and Yun Li and Dongdong Wang and Mingtao Chen and Zhichao Hu and Xiao Xiao and Yan Chen and Yuhong Liu and Wei Liu and Di Wang and Yong Yang and Jie Jiang and Qinglin Lu},
      year={2024},
      eprint={2405.08748},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@article{huang2024dialoggen,
  title={DialogGen: Multi-modal Interactive Dialogue System for Multi-turn Text-to-Image Generation},
  author={Huang, Minbin and Long, Yanxin and Deng, Xinchi and Chu, Ruihang and Xiong, Jiangfeng and Liang, Xiaodan and Cheng, Hong and Lu, Qinglin and Liu, Wei},
  journal={arXiv preprint arXiv:2403.08857},
  year={2024}
}

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