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README.md
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## Instructions
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The dependencies and installation are basically the same as the [**
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We provide two types of trained LoRA weights for you to test.
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Then download the model using the following commands:
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```bash
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cd HunyuanDiT
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# Use the huggingface-cli tool to download the model.
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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
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```
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## Training
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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.
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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`.
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```bash
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model='DiT-g/2' # model type
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task_flag="lora_porcelain_ema_rank64" # task flag
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## Instructions
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The dependencies and installation are basically the same as the [**base model**](https://huggingface.co/Tencent-Hunyuan/HunyuanDiT-v1.2).
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We provide two types of trained LoRA weights for you to test.
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Then download the model using the following commands:
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```bash
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cd HunyuanDiT
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# Use the huggingface-cli tool to download the model.
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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
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```
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## Training
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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.
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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`.
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```bash
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model='DiT-g/2' # model type
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task_flag="lora_porcelain_ema_rank64" # task flag
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