init

lora/lora_ckpt/dummy.txt

+lora checkpoints will be saved in this folder

lora/samples/prompts.txt

+# prompts we used when editing the given samples:
+
+cat_dog: a photo of a cat and a dog
+oilpaint1: an oil painting of a mountain besides a lake
+oilpaint2: an oil painting of a mountain and forest
+sculpture: a photo of a sculpture

lora/train_lora.sh

+export SAMPLE_DIR="lora/samples/sculpture"
+export OUTPUT_DIR="lora/lora_ckpt/sculpture_lora"
+
+export MODEL_NAME="botp/stable-diffusion-v1-5"
+export LORA_RANK=16
+
+accelerate launch lora/train_dreambooth_lora.py \
+  --pretrained_model_name_or_path=$MODEL_NAME  \
+  --instance_data_dir=$SAMPLE_DIR \
+  --output_dir=$OUTPUT_DIR \
+  --instance_prompt="a photo of a sculpture" \
+  --resolution=512 \
+  --train_batch_size=1 \
+  --gradient_accumulation_steps=1 \
+  --checkpointing_steps=100 \
+  --learning_rate=2e-4 \
+  --lr_scheduler="constant" \
+  --lr_warmup_steps=0 \
+  --max_train_steps=200 \
+  --lora_rank=$LORA_RANK \
+  --seed="0"

model.properties

+# 模型唯一标识
+modelCode = 1074
+# 模型名称
+modelName=dragdiffusion_pytorch
+# 模型描述
+modelDescription=利用扩散模型进行基于点的交互式图像编辑。
+# 应用场景
+appScenario=AIGC,零售,制造,电商,医疗,教育
+# 框架类型
+frameType=pytorch

release-doc/licenses/LICENSE-lora.txt

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\ No newline at end of file

requirements.txt

+# torch==2.0.0
+# torchvision==0.15.1
+gradio==3.41.1
+pydantic==2.0.2
+albumentations==1.3.0
+opencv-contrib-python
+imageio==2.9.0
+imageio-ffmpeg
+pytorch-lightning
+omegaconf
+test-tube>=0.7.5
+streamlit
+einops==0.6.0
+transformers==4.33.0
+webdataset
+kornia
+open_clip_torch
+invisible-watermark>=0.1.5
+streamlit-drawable-canvas==0.8.0
+torchmetrics==0.6.0
+timm==0.6.12
+addict==2.4.0
+yapf==0.32.0
+prettytable==3.6.0
+safetensors==0.3.1
+#basicsr==1.4.2
+accelerate==0.21.0
+decord==0.6.0
+diffusers==0.24.0
+moviepy==1.0.3
+opencv_python==4.7.0.68
+Pillow==9.4.0
+scikit_image==0.19.3
+scipy==1.10.1
+tensorboardX==2.6
+tqdm>=4.66.3
+numpy==1.26.4
+#tokenizers>=0.19.1
+lpips
+#clip
+openai-clip
\ No newline at end of file