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
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. See ComfyUI for details.
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.
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
Hunyuan-DiT (Text-to-Image Model)
Inference
Checkpoints
Distillation Version
TensorRT Version
Training
Lora
Controlnet (Pose, Canny, Depth)
6GB GPU VRAM Inference
IP-adapter
Hunyuan-DiT-S checkpoints (0.7B model)
Mllm
Inference
Inference
Hunyuan-Captioner (Re-caption the raw image-text pairs)
Hunyuan-DialogGen (Prompt Enhancement Model)
Web Demo (Gradio)
Multi-turn T2I Demo (Gradio)
Cli Demo
ComfyUI
Diffusers
Kohya
WebUI
Hunyuan-DiT : A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding
Examples
Instructions
Inference
Gradio
ControlNet
6GB GPU VRAM Inference
Using Gradio
Using ? Diffusers
Using Command Line
More Configurations
Using ComfyUI
Using Kohya
Using Previous versions
Data Preparation
Full-parameter Training
LoRA
Installation Guide for Linux
Chinese-English Bilingual DiT Architecture
Multi-turn Text2Image Generation
News!!
Try it on the web
Open-source Plan
Contents
Abstract
Hunyuan-DiT Key Features
Comparisons
Visualization
Requirements
Dependencies and Installation
Download Pretrained Models - 1. Using HF-Mirror - 2. Resume Download
Training
Inference
Adapter
Hunyuan-Captioner
Acceleration (for Linux)
BibTeX
Start History
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 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.
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.
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 |
Chinese Elements
Long Text Input
Multi-turn Text2Image Generation
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
Begin by cloning the repository:
git clone https://github.com/tencent/HunyuanDiTcd HunyuanDiT
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 environmentconda env create -f environment.yml# 2. Activate the environmentconda activate HunyuanDiT# 3. Install pip dependenciespython -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/[email protected]
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 12wget https://dit.hunyuan.tencent.com/download/HunyuanDiT/hunyuan_dit_cu12.tar# For CUDA 11wget 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 12docker load -i hunyuan_dit_cu12.tar# For CUDA 11docker load -i hunyuan_dit_cu11.tar docker image ls# 3. Run the container based on the imagedocker 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
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
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
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 |
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 dependenciescd HunyuanDiT pip install -e ./IndexKits
Data download
Feel free to download the data demo.
# 2 Data downloadwget -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
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 TrainingPYTHONPATH=./ sh hydit/train.sh --index-file dataset/porcelain/jsons/porcelain.json# Multi Resolution TrainingPYTHONPATH=./ 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
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 startpython 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.） |
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 startmodel_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.
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 UIpython 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.
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 torchfrom diffusers import HunyuanDiTPipelinepipe = 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 torchfrom diffusers import HunyuanDiTPipelinepipe = 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.
We provide several commands to quick start:
# Only Text-to-Image. Flash Attention modepython 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
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 |
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 is coming soon.
More details can be found in ./comfyui-hydit
We support custom codes for kohya_ss GUI, and sd-scripts training codes for HunyuanDiT. More details can be found in ./kohya_ss-hydit
Hunyuan-DiT <= v1.1
# ============================== v1.1 ==============================# Download the modelhuggingface-cli download Tencent-Hunyuan/HunyuanDiT-v1.1 --local-dir ./HunyuanDiT-v1.1# Inference with the modelpython 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 modelhuggingface-cli download Tencent-Hunyuan/HunyuanDiT --local-dir ./HunyuanDiT-v1.0# Inference with the modelpython 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
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 startpython3 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 | ||
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.
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
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
