Instructions to use onethousand/AnimPortrait3D_controlnet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use onethousand/AnimPortrait3D_controlnet with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("onethousand/AnimPortrait3D_controlnet") pipe = StableDiffusionControlNetPipeline.from_pretrained( "SG161222/Realistic_Vision_V5.1_noVAE", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
File size: 586 Bytes
594b244 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | #!/bin/bash
accelerate launch train.py \
--pretrained_model_name_or_path="SG161222/Realistic_Vision_V5.1_noVAE" \
--output_dir="./controlnet-training-runs" \
--train_data_dir=/path/to/dataset \
--cfg_prob=0.1 \
--resolution=512 \
--learning_rate=1e-5 \
--num_validation_images=3 \
--validation_image "./face_normal.png" "./face_seg.png" \
--validation_prompt "a Teen boy, pensive look, dark hair. Preppy sweater, collared shirt, moody room, 80s memorabilia" \
--train_batch_size=4 \
--num_train_epochs=40 \
--validation_steps=500 \
--checkpointing_steps=2000
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