--- license: other license_name: tencent-hunyuan-community license_link: https://huggingface.co/Tencent-Hunyuan/HunyuanDiT/blob/main/LICENSE.txt language: - en --- # HunyuanDiT LoRA Language: **English** ## Instructions The dependencies and installation are basically the same as the [**original model**](https://huggingface.co/Tencent-Hunyuan/HunyuanDiT-v1.2). We provide two types of trained LoRA weights for you to test. Then download the model using the following commands: ```bash 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 --prompt "青花瓷风格，一只猫在追蝴蝶" --no-enhance --load-key ema --lora-ckpt ./ckpts/t2i/lora/porcelain --infer-mode fa ``` ## Training We provide three types of weights for fine-tuning LoRA, `ema`, `module` and `distill`, and you can choose according to the actual effect. By default, we use `ema` weights. Here is an example for LoRA with HunYuanDiT v1.2, we load the `distill` weights into the main model and perform LoRA fine-tuning through the `resume_module_root=./ckpts/t2i/model/pytorch_model_distill.pt` setting. If multiple resolution are used, you need to add the `--multireso` and `--reso-step 64 ` parameter. If you want to train LoRA with HunYuanDiT v1.1, you could add `--use-style-cond`, `--size-cond 1024 1024` and `--beta-end 0.03`. ```bash model='DiT-g/2' # model type task_flag="lora_porcelain_ema_rank64" # task flag resume_module_root=./ckpts/t2i/model/pytorch_model_distill.pt # resume checkpoint index_file=dataset/porcelain/jsons/porcelain.json # the selected data indices results_dir=./log_EXP # save root for results batch_size=1 # training batch size image_size=1024 # training image resolution grad_accu_steps=2 # gradient accumulation steps warmup_num_steps=0 # warm-up steps lr=0.0001 # learning rate ckpt_every=100 # create a ckpt every a few steps. ckpt_latest_every=2000 # create a ckpt named `latest.pt` every a few steps. rank=64 # rank of lora max_training_steps=2000 # Maximum training iteration steps PYTHONPATH=./ deepspeed hydit/train_deepspeed.py \ --task-flag ${task_flag} \ --model ${model} \ --training-parts lora \ --rank ${rank} \ --resume \ --resume-module-root ${resume_module_root} \ --lr ${lr} \ --noise-schedule scaled_linear --beta-start 0.00085 --beta-end 0.018 \ --predict-type v_prediction \ --uncond-p 0 \ --uncond-p-t5 0 \ --index-file ${index_file} \ --random-flip \ --batch-size ${batch_size} \ --image-size ${image_size} \ --global-seed 999 \ --grad-accu-steps ${grad_accu_steps} \ --warmup-num-steps ${warmup_num_steps} \ --use-flash-attn \ --use-fp16 \ --ema-dtype fp32 \ --results-dir ${results_dir} \ --ckpt-every ${ckpt_every} \ --max-training-steps ${max_training_steps}\ --ckpt-latest-every ${ckpt_latest_every} \ --log-every 10 \ --deepspeed \ --deepspeed-optimizer \ --use-zero-stage 2 \ --qk-norm \ --rope-img base512 \ --rope-real \ "$@" ``` Recommended parameter settings | Parameter | Description | Recommended Parameter Value | Note| |:---------------:|:---------:|:---------------------------------------------------:|:--:| | `--batch_size` | Training batch size | 1 | Depends on GPU memory| | `--grad-accu-steps` | Size of gradient accumulation | 2 | - | | `--rank` | Rank of lora | 64 | Choosing from 8-128| | `--max-training-steps` | Training steps | 2000 | Depend on training data size, for reference apply 2000 steps on 100 images| | `--lr` | Learning rate | 0.0001 | - | ## Inference ### Using Gradio Make sure you have activated the conda environment before running the following command. > ⚠️ Important Reminder: > We recommend not using prompt enhance, as it may lead to the disappearance of style words. ```shell # jade style # Using Flash Attention for acceleration. python app/hydit_app.py --infer-mode fa --load-key ema --lora-ckpt ./ckpts/t2i/lora/jade # 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 --infer-mode fa --no-enhance --load-key ema --lora-ckpt ./ckpts/t2i/lora/jade # Start with English UI python app/hydit_app.py --infer-mode fa --lang en --load-key ema --lora-ckpt ./ckpts/t2i/lora/jade # porcelain style # Using Flash Attention for acceleration. python app/hydit_app.py --infer-mode fa --load-key ema --lora-ckpt ./ckpts/t2i/lora/porcelain # 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 --infer-mode fa --no-enhance --load-key ema --lora-ckpt ./ckpts/t2i/lora/porcelain # Start with English UI python app/hydit_app.py --infer-mode fa --lang en --load-key ema --lora-ckpt ./ckpts/t2i/lora/porcelain ``` ### Using Command Line We provide several commands to quick start: ```shell # jade style # Prompt Enhancement + Text-to-Image. Torch mode python sample_t2i.py --infer-mode fa --prompt "玉石绘画风格，一只猫在追蝴蝶" --load-key ema --lora-ckpt ./ckpts/t2i/lora/jade # Only Text-to-Image. Torch mode python sample_t2i.py --infer-mode fa --prompt "玉石绘画风格，一只猫在追蝴蝶" --no-enhance --load-key ema --lora-ckpt ./ckpts/t2i/lora/jade # Generate an image with other image sizes. python sample_t2i.py --infer-mode fa --prompt "玉石绘画风格，一只猫在追蝴蝶" --image-size 1280 768 --load-key ema --lora-ckpt ./ckpts/t2i/lora/jade # porcelain style # Prompt Enhancement + Text-to-Image. Torch mode python sample_t2i.py --infer-mode fa --prompt "青花瓷风格，一只猫在追蝴蝶" --load-key ema --lora-ckpt ./ckpts/t2i/lora/porcelain # Only Text-to-Image. Torch mode 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 # Generate an image with other image sizes. python sample_t2i.py --infer-mode fa --prompt "青花瓷风格，一只猫在追蝴蝶" --image-size 1280 768 --load-key ema --lora-ckpt ./ckpts/t2i/lora/porcelain ``` Regarding how to use the LoRA weights we trained in diffusion, we provide the following script. To ensure compatibility with the diffuser, some modifications are made, which means that LoRA cannot be directly loaded. ```python import torch from diffusers import HunyuanDiTPipeline num_layers = 40 def load_hunyuan_dit_lora(transformer_state_dict, lora_state_dict, lora_scale): for i in range(num_layers): Wqkv = torch.matmul(lora_state_dict[f"blocks.{i}.attn1.Wqkv.lora_B.weight"], lora_state_dict[f"blocks.{i}.attn1.Wqkv.lora_A.weight"]) q, k, v = torch.chunk(Wqkv, 3, dim=0) transformer_state_dict[f"blocks.{i}.attn1.to_q.weight"] += lora_scale * q transformer_state_dict[f"blocks.{i}.attn1.to_k.weight"] += lora_scale * k transformer_state_dict[f"blocks.{i}.attn1.to_v.weight"] += lora_scale * v out_proj = torch.matmul(lora_state_dict[f"blocks.{i}.attn1.out_proj.lora_B.weight"], lora_state_dict[f"blocks.{i}.attn1.out_proj.lora_A.weight"]) transformer_state_dict[f"blocks.{i}.attn1.to_out.0.weight"] += lora_scale * out_proj q_proj = torch.matmul(lora_state_dict[f"blocks.{i}.attn2.q_proj.lora_B.weight"], lora_state_dict[f"blocks.{i}.attn2.q_proj.lora_A.weight"]) transformer_state_dict[f"blocks.{i}.attn2.to_q.weight"] += lora_scale * q_proj kv_proj = torch.matmul(lora_state_dict[f"blocks.{i}.attn2.kv_proj.lora_B.weight"], lora_state_dict[f"blocks.{i}.attn2.kv_proj.lora_A.weight"]) k, v = torch.chunk(kv_proj, 2, dim=0) transformer_state_dict[f"blocks.{i}.attn2.to_k.weight"] += lora_scale * k transformer_state_dict[f"blocks.{i}.attn2.to_v.weight"] += lora_scale * v out_proj = torch.matmul(lora_state_dict[f"blocks.{i}.attn2.out_proj.lora_B.weight"], lora_state_dict[f"blocks.{i}.attn2.out_proj.lora_A.weight"]) transformer_state_dict[f"blocks.{i}.attn2.to_out.0.weight"] += lora_scale * out_proj q_proj = torch.matmul(lora_state_dict["pooler.q_proj.lora_B.weight"], lora_state_dict["pooler.q_proj.lora_A.weight"]) transformer_state_dict["time_extra_emb.pooler.q_proj.weight"] += lora_scale * q_proj return transformer_state_dict pipe = HunyuanDiTPipeline.from_pretrained("Tencent-Hunyuan/HunyuanDiT-v1.2-Diffusers", torch_dtype=torch.float16) pipe.to("cuda") from safetensors import safe_open lora_state_dict = {} with safe_open("./ckpts/t2i/lora/jade/adapter_model.safetensors", framework="pt", device=0) as f: for k in f.keys(): lora_state_dict[k[17:]] = f.get_tensor(k) # remove 'basemodel.model' transformer_state_dict = pipe.transformer.state_dict() transformer_state_dict = load_hunyuan_dit_lora(transformer_state_dict, lora_state_dict, lora_scale=1.0) pipe.transformer.load_state_dict(transformer_state_dict) prompt = "玉石绘画风格，一只猫在追蝴蝶" image = pipe( prompt, num_inference_steps=100, guidance_scale=6.0, ).images[0] image.save('img.png') ``` More example prompts can be found in [example_prompts.txt](example_prompts.txt)