Instructions to use zeyuren2002/EvalMDE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use zeyuren2002/EvalMDE with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("zeyuren2002/EvalMDE", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
File size: 272 Bytes
4b7b610 | 1 2 3 4 5 6 7 8 9 | BASE_DATA_DIR="path/to/basedata"
BASE_CKPT_DIR="path/to/sd2_ckpt"
export CUDA_VISIBLE_DEVICES=3
python train_s1.py --config config/train_s1.yaml \
--base_data_dir $BASE_DATA_DIR \
--base_ckpt_dir $BASE_CKPT_DIR \
--output_dir log/stage1_bs8 \
--no_wandb \ |