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
| accelerate launch --config_file configs/accelerate_config.yaml scripts/train.py \ | |
| --dataset_base_path "/mnt/nfs/workspace/syq/dataset/Hypersim/processed_depth,/mnt/nfs/workspace/syq/dataset/vkitti2" \ | |
| --dataset_metadata_path "./data_split/hypersim_depth/filename_list_train_filtered2.txt,./data_split/vkitti_depth/vkitti_train.txt" \ | |
| --data_file_keys "kontext_images,image" \ | |
| --model_paths "./FLUX.1-Kontext-dev" \ | |
| --learning_rate "1e-5" \ | |
| --num_epochs "8" \ | |
| --remove_prefix_in_ckpt "pipe.dit." \ | |
| --trainable_models "dit" \ | |
| --extra_inputs "kontext_images" \ | |
| --use_gradient_checkpointing \ | |
| --default_caption "Transform to depth map while maintaining original composition" \ | |
| --batch_size "4" \ | |
| --output_path "ckpts/kontext/bs64_sqrt_cons" \ | |
| --eval_file_list "./data_split/nyu_depth/labeled/filename_list_test.txt" \ | |
| --multi_res_noise \ | |
| --save_steps "200" \ | |
| --eval_steps "50" \ | |
| --with_mask \ | |
| --depth_normalization sqrt \ | |
| --dataset_num_workers "16" \ | |
| --extra_loss "cycle_consistency_depth_estimation" \ | |
| --adamw8bit \ | |
| --using_sqrt | |
| # --deterministic_flow | |
| # --extra_loss_start_epoch 0 \ | |
| # --using_sqrt \ | |
| # --resume \ | |