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: 1,316 Bytes
7f921f4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | export TORCH_NCCL_TIMEOUT=1800
accelerate launch --config_file configs/accelerate_config.yaml scripts/train.py \
--dataset_base_path "/mnt/nfs/workspace/syq/dataset/Hypersim/processed_normal,/mnt/nfs/workspace/syq/dataset/InteriorVerse/processed_normal,/mnt/nfs/workspace/syq/dataset/sintel" \
--dataset_metadata_path "./data_split/hypersim_normals/hypersim_filtered_all_checked.txt,./data_split/interiorverse_normals/interiorverse_filtered_all.txt,./data_split/sintel_normals/sintel_filtered.txt" \
--data_file_keys "kontext_images,image" \
--model_paths "./FLUX.1-Kontext-dev" \
--learning_rate "1e-4" \
--num_epochs "8" \
--remove_prefix_in_ckpt "pipe.dit." \
--trainable_models "dit" \
--extra_inputs "kontext_images" \
--use_gradient_checkpointing \
--default_caption "Transform to normal map while maintaining original composition" \
--batch_size "16" \
--output_path "ckpts/kontext_normal/bs16_deter" \
--eval_file_list "./data_split/nyu_normals/nyuv2_test2.txt" \
--multi_res_noise \
--save_steps "200" \
--eval_steps "50" \
--with_mask \
--resume \
--height 512 \
--width 768
# --extra_loss "cycle_consistency_normal_estimation" \
# --deterministic_flow
# --using_log
# --using_log \
# --adamw8bit \
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