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!!

Dec 17, 2024: 🎉 Optimize Lora training with refined grad checkpoint and low-bit optimizer. Just use --lowbit-opt to get started.
Sep 13, 2024: 🎉 IPAdapter is officially supported by HunYuanDiT. Document for it: ./ipadapter. And scaled attention is utilized to replace flash attention on V100 GPUs.
Aug 26, 2024, 🎉 HunYuanDIT Controlnet and LoRA are officially supported by ComfyUI. Document for it: ./comfyui
Jul 15, 2024: 🚀 HunYuanDiT and Shakker.Ai have jointly launched a fine-tuning event based on the HunYuanDiT 1.2 model. By publishing a lora or fine-tuned model based on HunYuanDiT, you can earn up to $230 bonus from Shakker.Ai. See Shakker.Ai for more details.
Jul 15, 2024: 🎉 Update ComfyUI to support standardized workflows and compatibility with weights from t2i module and Lora training for versions 1.1/1.2, as well as those trained by Kohya or the official script.
Jul 15, 2024: ⚡ We offer Docker environments for CUDA 11/12, allowing you to bypass complex installations and play with a single click! See dockers for details.
Jul 08, 2024: 🎉 HYDiT-v1.2 version is released. Please check HunyuanDiT-v1.2 and Distillation-v1.2 for more details.
Jul 03, 2024: 🎉 Kohya-hydit version now available for v1.1 and v1.2 models, with GUI for inference. Official Kohya version is under review. See kohya for details.
Jun 27, 2024: 🎨 Hunyuan-Captioner is released, providing fine-grained caption for training data. See mllm for details.
Jun 27, 2024: 🎉 Support LoRa and ControlNet in diffusers. See diffusers for details.
Jun 27, 2024: 🎉 6GB GPU VRAM Inference scripts are released. See lite for details.
Jun 19, 2024: 🎉 ControlNet is released, supporting canny, pose and depth control. See training/inference codes for details.
Jun 13, 2024: ⚡ HYDiT-v1.1 version is released, which mitigates the issue of image oversaturation and alleviates the watermark issue. Please check HunyuanDiT-v1.1 and Distillation-v1.1 for more details.
Jun 13, 2024: 🚚 The training code is released, offering full-parameter training and LoRA training.
Jun 06, 2024: 🎉 Hunyuan-DiT is now available in ComfyUI. Please check ComfyUI for more details.
Jun 06, 2024: 🚀 We introduce Distillation version for Hunyuan-DiT acceleration, which achieves 50% acceleration on NVIDIA GPUs. Please check Distillation for more details.
Jun 05, 2024: 🤗 Hunyuan-DiT is now available in 🤗 Diffusers! Please check the example below.
Jun 04, 2024: 🌐 Support Tencent Cloud links to download the pretrained models! Please check the links below.
May 22, 2024: 🚀 We introduce TensorRT version for Hunyuan-DiT acceleration, which achieves 47% acceleration on NVIDIA GPUs. Please check TensorRT-libs for instructions.
May 22, 2024: 💬 We support demo running multi-turn text2image generation now. Please check the script below.

🤖 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

🤗 Community Contribution Leaderboard

By @TTPlanetPig
- HunyuanDIT_v1.2 ControlNet models
  - Inpaint controlnet: https://huggingface.co/TTPlanet/HunyuanDiT_Controlnet_inpainting
  - Tile controlnet: https://huggingface.co/TTPlanet/HunyuanDiT_Controlnet_tile
  - Lineart controlnet: https://huggingface.co/TTPlanet/HunyuanDiT_Controlnet_lineart
- HunyuanDIT_v1.2 ComfyUI nodes
  - Comfyui_TTP_CN_Preprocessor: https://github.com/TTPlanetPig/Comfyui_TTP_CN_Preprocessor
  - Comfyui_TTP_Toolset: https://github.com/TTPlanetPig/Comfyui_TTP_Toolset
By @sdbds (bilibili up 青龙圣者)
- Kohya_ss-hydit train tools: https://github.com/zml-ai/HunyuanDIT-PRE/tree/main/kohya_ss-hydit
By @CrazyBoyM (bilibili up 飞鸟白菜)
- ComfyUI support for HunyuanDIT_v1.2 Controlnet: comfyanonymous/ComfyUI#4245
By @L_A_X
- HunyuanDIT_v1.2 base model for anime
  - Original hf: https://huggingface.co/Laxhar/Freeway_Animation_HunYuan_Demo
  - Converted ComfyUI model: https://huggingface.co/comfyanonymous/Freeway_Animation_Hunyuan_Demo_ComfyUI_Converted

📑 Open-source Plan

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 (%)
Model	Open Source	SDXL	✔	64.3	Aesthetics (%)	Overall (%)	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

Chinese Elements

Long Text Input

Multi-turn Text2Image Generation

Hunyuan_MultiTurn_T2I_Demo.mp4

📜 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

An NVIDIA GPU with CUDA support is required.
- We have tested V100 and A100 GPUs.
- Minimum: The minimum GPU memory required is 11GB.
- Recommended: We recommend using a GPU with 32GB of memory for better generation quality.
Tested operating system: Linux

🛠️ 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,

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

🚚 Training

Data Preparation

Refer to the commands below to prepare the training data.

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
```

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

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

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.

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:

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

Notice:

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.
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_with_fa.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

If you can't install flash_attn, use code:

# 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 torch --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

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

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

# Using special port and host
python app/hydit_app.py --infer-mode fa --server_name 0.0.0.0 --server_port 443 --load-key distill

# 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

Support two workflows: Standard ComfyUI and Diffusers Wrapper, with the former being recommended.
Support HunyuanDiT-v1.1 and v1.2.
Support module, lora and clip lora models trained by Kohya.
Support module, lora models trained by HunyunDiT official training scripts.
ControlNet support.

More details can be found in ./comfyui

Using Kohya

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

Using Previous versions

Hunyuan-DiT <= v1.1

# ============================== 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

🏗️ 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.）

ControlNet Output

IP-Adapter

We provide training scripts for IP-Adapter, detailed in the ./ipadapter.

# Training for IP-Adapter.
PYTHONPATH=./ sh hydit/train_ipadapter.sh

We offer trained IP-Adapter weights, see details at links

cd HunyuanDiT
# Use the huggingface-cli tool to download the model.
# We recommend using module weights as the base model for IP-Adapter inference, as our provided pretrained weights are trained on them.
huggingface-cli download Tencent-Hunyuan/IP-Adapter ipa.pt --local-dir ./ckpts/t2i/model
huggingface-cli download Tencent-Hunyuan/IP-Adapter clip_img_encoder.pt  --local-dir ./ckpts/t2i/model/clip_img_encoder

# Quick start
python3 sample_ipadapter.py  --infer-mode fa --ref-image-path ipadapter/asset/input/tiger.png --i-scale 1.0 --prompt 一只老虎在海洋中游泳，背景是海洋。构图方式是居中构图，呈现了动漫风格和文化，营造了平静的氛围。 --infer-steps 100 --is-ipa True --load-key distill

Examples of ref input and IP-Adapter results are as follows:

Ref Input

IP-Adapter Output
一只老虎在奔跑。（A tiger running.）	一个卡通美女，抱着一只小猪。（A cartoon beauty holding a little pig.）	一片紫色薰衣草地。（A purple lavender field.）

一只老虎在看书。（A tiger is reading a book.）	一个卡通美女，穿着绿色衣服。（A cartoon beauty wearing green clothes.）	一片紫色薰衣草地，有一只可爱的小狗。（A purple lavender field with a cute puppy.）

一只老虎在咆哮。（A tiger is roaring.）	一个卡通美女，戴着墨镜。（A cartoon beauty wearing sunglasses.）	水墨风格,一片紫色薰衣草地。（Ink style. A purple lavender field.）

🎨 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

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)

We provide TensorRT version of HunyuanDiT for inference acceleration (faster than flash attention). See Tencent-Hunyuan/TensorRT-libs for more details.
We provide Distillation version of HunyuanDiT for inference acceleration. See Tencent-Hunyuan/Distillation for more details.

🔗 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}
}

Ref Input

IP-Adapter Output
一只老虎在奔跑。（A tiger running.）	一个卡通美女，抱着一只小猪。（A cartoon beauty holding a little pig.）	一片紫色薰衣草地。（A purple lavender field.）

一只老虎在看书。（A tiger is reading a book.）	一个卡通美女，穿着绿色衣服。（A cartoon beauty wearing green clothes.）	一片紫色薰衣草地，有一只可爱的小狗。（A purple lavender field with a cute puppy.）

一只老虎在咆哮。（A tiger is roaring.）	一个卡通美女，戴着墨镜。（A cartoon beauty wearing sunglasses.）	水墨风格,一片紫色薰衣草地。（Ink style. A purple lavender field.）

Name		Name	Last commit message	Last commit date
Latest commit History 42 Commits
.github/ISSUE_TEMPLATE		.github/ISSUE_TEMPLATE
IndexKits		IndexKits
app		app
asset		asset
comfyui		comfyui
controlnet		controlnet
dataset/yamls		dataset/yamls
diffusers		diffusers
hydit		hydit
ipadapter		ipadapter
kohya_ss-hydit		kohya_ss-hydit
lite		lite
lora		lora
mllm		mllm
tests		tests
trt		trt
utils		utils
.gitignore		.gitignore
.gitmodules		.gitmodules
LICENSE.txt		LICENSE.txt
Notice		Notice
README.md		README.md
environment.yml		environment.yml
example_prompts.txt		example_prompts.txt
requirements.txt		requirements.txt
sample_controlnet.py		sample_controlnet.py
sample_ipadapter.py		sample_ipadapter.py
sample_t2i.py		sample_t2i.py

License

Tencent/HunyuanDiT

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