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# Hunyuan DiT | |
Hunyuan DiT is an image generation model based on DiT. We provide training and inference support for Hunyuan DiT. | |
## Download models | |
Four files will be used for constructing Hunyuan DiT. You can download them from [huggingface](https://huggingface.co/Tencent-Hunyuan/HunyuanDiT) or [modelscope](https://www.modelscope.cn/models/modelscope/HunyuanDiT/summary). | |
``` | |
models/HunyuanDiT/ | |
βββ Put Hunyuan DiT checkpoints here.txt | |
βββ t2i | |
βββ clip_text_encoder | |
β βββ pytorch_model.bin | |
βββ model | |
β βββ pytorch_model_ema.pt | |
βββ mt5 | |
β βββ pytorch_model.bin | |
βββ sdxl-vae-fp16-fix | |
βββ diffusion_pytorch_model.bin | |
``` | |
You can use the following code to download these files: | |
```python | |
from diffsynth import download_models | |
download_models(["HunyuanDiT"]) | |
``` | |
## Train | |
### Install training dependency | |
``` | |
pip install peft lightning pandas torchvision | |
``` | |
### Prepare your dataset | |
We provide an example dataset [here](https://modelscope.cn/datasets/buptwq/lora-stable-diffusion-finetune/files). You need to manage the training images as follows: | |
``` | |
data/dog/ | |
βββ train | |
βββ 00.jpg | |
βββ 01.jpg | |
βββ 02.jpg | |
βββ 03.jpg | |
βββ 04.jpg | |
βββ metadata.csv | |
``` | |
`metadata.csv`: | |
``` | |
file_name,text | |
00.jpg,δΈεͺε°η | |
01.jpg,δΈεͺε°η | |
02.jpg,δΈεͺε°η | |
03.jpg,δΈεͺε°η | |
04.jpg,δΈεͺε°η | |
``` | |
### Train a LoRA model | |
We provide a training script `train_hunyuan_dit_lora.py`. Before you run this training script, please copy it to the root directory of this project. | |
If GPU memory >= 24GB, we recommmand to use the following settings. | |
``` | |
CUDA_VISIBLE_DEVICES="0" python train_hunyuan_dit_lora.py \ | |
--pretrained_path models/HunyuanDiT/t2i \ | |
--dataset_path data/dog \ | |
--output_path ./models \ | |
--max_epochs 1 \ | |
--center_crop | |
``` | |
If 12GB <= GPU memory <= 24GB, we recommand to enable gradient checkpointing. | |
``` | |
CUDA_VISIBLE_DEVICES="0" python train_hunyuan_dit_lora.py \ | |
--pretrained_path models/HunyuanDiT/t2i \ | |
--dataset_path data/dog \ | |
--output_path ./models \ | |
--max_epochs 1 \ | |
--center_crop \ | |
--use_gradient_checkpointing | |
``` | |
Optional arguments: | |
``` | |
-h, --help show this help message and exit | |
--pretrained_path PRETRAINED_PATH | |
Path to pretrained model. For example, `./HunyuanDiT/t2i`. | |
--dataset_path DATASET_PATH | |
The path of the Dataset. | |
--output_path OUTPUT_PATH | |
Path to save the model. | |
--steps_per_epoch STEPS_PER_EPOCH | |
Number of steps per epoch. | |
--height HEIGHT Image height. | |
--width WIDTH Image width. | |
--center_crop Whether to center crop the input images to the resolution. If not set, the images will be randomly cropped. The images will be resized to the resolution first before cropping. | |
--random_flip Whether to randomly flip images horizontally | |
--batch_size BATCH_SIZE | |
Batch size (per device) for the training dataloader. | |
--dataloader_num_workers DATALOADER_NUM_WORKERS | |
Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process. | |
--precision {32,16,16-mixed} | |
Training precision | |
--learning_rate LEARNING_RATE | |
Learning rate. | |
--lora_rank LORA_RANK | |
The dimension of the LoRA update matrices. | |
--lora_alpha LORA_ALPHA | |
The weight of the LoRA update matrices. | |
--use_gradient_checkpointing | |
Whether to use gradient checkpointing. | |
--accumulate_grad_batches ACCUMULATE_GRAD_BATCHES | |
The number of batches in gradient accumulation. | |
--training_strategy {auto,deepspeed_stage_1,deepspeed_stage_2,deepspeed_stage_3} | |
Training strategy | |
--max_epochs MAX_EPOCHS | |
Number of epochs. | |
``` | |
### Inference with your own LoRA model | |
After training, you can use your own LoRA model to generate new images. Here are some examples. | |
```python | |
from diffsynth import ModelManager, HunyuanDiTImagePipeline | |
from peft import LoraConfig, inject_adapter_in_model | |
import torch | |
def load_lora(dit, lora_rank, lora_alpha, lora_path): | |
lora_config = LoraConfig( | |
r=lora_rank, | |
lora_alpha=lora_alpha, | |
init_lora_weights="gaussian", | |
target_modules=["to_q", "to_k", "to_v", "to_out"], | |
) | |
dit = inject_adapter_in_model(lora_config, dit) | |
state_dict = torch.load(lora_path, map_location="cpu") | |
dit.load_state_dict(state_dict, strict=False) | |
return dit | |
# Load models | |
model_manager = ModelManager(torch_dtype=torch.float16, device="cuda") | |
model_manager.load_models([ | |
"models/HunyuanDiT/t2i/clip_text_encoder/pytorch_model.bin", | |
"models/HunyuanDiT/t2i/mt5/pytorch_model.bin", | |
"models/HunyuanDiT/t2i/model/pytorch_model_ema.pt", | |
"models/HunyuanDiT/t2i/sdxl-vae-fp16-fix/diffusion_pytorch_model.bin" | |
]) | |
pipe = HunyuanDiTImagePipeline.from_model_manager(model_manager) | |
# Generate an image with lora | |
pipe.dit = load_lora( | |
pipe.dit, | |
lora_rank=4, lora_alpha=4.0, # The two parameters should be consistent with those in your training script. | |
lora_path="path/to/your/lora/model/lightning_logs/version_x/checkpoints/epoch=x-step=xxx.ckpt" | |
) | |
torch.manual_seed(0) | |
image = pipe( | |
prompt="δΈεͺε°ηθΉ¦θΉ¦θ·³θ·³οΌε¨ε΄ζ―ε§Ήη΄«ε«£ηΊ’ηι²θ±οΌθΏε€ζ―ε±±θ", | |
negative_prompt="", | |
num_inference_steps=50, height=1024, width=1024, | |
) | |
image.save("image_with_lora.png") | |
``` | |
Prompt: δΈεͺε°ηθΉ¦θΉ¦θ·³θ·³οΌε¨ε΄ζ―ε§Ήη΄«ε«£ηΊ’ηι²θ±οΌθΏε€ζ―ε±±θ | |
|Without LoRA|With LoRA| | |
|-|-| | |
|![image_without_lora](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/1aa21de5-a992-4b66-b14f-caa44e08876e)|![image_with_lora](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/83a0a41a-691f-4610-8e7b-d8e17c50a282)| | |