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
| export CUDA=0 | |
| export BASE_TEST_DATA_DIR="datasets/eval/" | |
| export CHECKPOINT_DIR="jingheya/lotus-depth-d-v2-0-disparity" | |
| export OUTPUT_DIR="output/Depth_D_Eval" | |
| export TASK_NAME="depth" | |
| export MODE="regression" | |
| CUDA_VISIBLE_DEVICES=$CUDA python eval.py \ | |
| --pretrained_model_name_or_path=$CHECKPOINT_DIR \ | |
| --prediction_type="sample" \ | |
| --seed=42 \ | |
| --half_precision \ | |
| --base_test_data_dir=$BASE_TEST_DATA_DIR \ | |
| --task_name=$TASK_NAME \ | |
| --mode=$MODE \ | |
| --output_dir=$OUTPUT_DIR \ | |
| --disparity | |
| # The defualt `processing_res` is set in the configuration file of each dataset. | |