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: 331 Bytes
4b7b610 | 1 2 3 4 5 6 7 8 9 10 11 12 13 | #!/usr/bin/env bash
set -e
set -x
export CUDA_VISIBLE_DEVICES=5
python evaluate.py \
--base_data_dir path/to/basedata \
--dataset_config config/dataset/data_nyu_test.yaml \
--alignment least_square_sqrt_disp \
--output_dir output/nyu/final1 \
--checkpoint ckpt/eval \
--processing_res 0 \
--seed 1234 \ |