Thinking-while-Generating
The first Interleaved framework for textual reasoning within the visual generation process
파일 탐색기
최종 버전 다운로드 (.zip)- 3_in_1.py
- __init__.py
- train_vqa_func.py
- utils.py
- bert_config.json
- med_config.json
- vqa.yaml
- __init__.py
- utils.py
- vqa_dataset.py
- __init__.py
- blip.py
- blip_pretrain.py
- blip_vqa.py
- med.py
- vit.py
- randaugment.py
- BLIP_vqa.py
- test.sh
- utils.py
- __init__.py
- bpe_simple_vocab_16e6.txt.gz
- clip.py
- model.py
- simple_tokenizer.py
- CLIP_similarity.py
- 3d_spatial_val.txt
- color_val.txt
- complex_val.txt
- complex_val_action.txt
- complex_val_spatial.txt
- new_objects.txt
- non_spatial_val.txt
- numeracy_val.txt
- shape_val.txt
- spatial_val.txt
- texture_val.txt
- comp.png
- intro.png
- logo.png
- motivation.png
- pipeline.png
- reflect.png
- supp_vis.png
- teaser.gif
- __init__.py
- clip_encoder.py
- image_processing_vlm.py
- modeling_vlm.py
- processing_vlm.py
- siglip_vit.py
- uvit.py
- __init__.py
- __init__.py
- clip_encoder.py
- image_processing_vlm.py
- modeling_vlm.py
- processing_vlm.py
- projector.py
- siglip_vit.py
- vq_model.py
- __init__.py
- conversation.py
- io.py
- __init__.py
- experts.yaml
- __init__.py
- ade_features.pt
- background_features.pt
- caption_dataset.py
- classification_dataset.py
- clip_pca.pkl
- coco_features.pt
- detection_features.pt
- pretrain_dataset.py
- randaugment.py
- utils.py
- vqa_dataset.py
- leaned_mAP_tau30_668.csv
- leaned_mAP_tau30_668.json
- learned_mAP+M.csv
- learned_mAP+M.json
- learned_mAP+M_labelmap_test.json
- learned_mAP.csv
- learned_mAP.json
- manual.csv
- manual.json
- prepare_ade20k_sem_seg.py
- prepare_cocofied_lvis.py
- prepare_for_tests.sh
- prepare_panoptic_fpn.py
- README.md
- base_model.py
- blocks.py
- generate_dataset.py
- models.py
- vit.py
- Base-CRCNN-COCO.yaml
- Unified_learned_OCIM_R50_6x+2x.yaml
- Unified_learned_OCIM_RS200_6x+2x.yaml
- learned_mAP+M.json
- cityscapes_cocoformat.py
- crowdhuman.py
- det_categories.py
- inst_categories.py
- kitti.py
- mapillary.py
- objects365.py
- oid.py
- register_oid.py
- scannet.py
- viper.py
- voc_cocoformat.py
- wilddash.py
- custom_dataset_dataloader.py
- multi_dataset_dataloader.py
- multi_dataset_evaluator.py
- oideval.py
- fpn_p5.py
- resnest.py
- splat.py
- split_rcnn.py
- unified_rcnn.py
- custom_fast_rcnn.py
- custom_roi_heads.py
- multi_dataset_fast_rcnn.py
- split_roi_heads.py
- unified_roi_heads.py
- __init__.py
- config.py
- predictor.py
- generate_dataset.py
- generate_dataset_3d.py
- utils.py
- generate_experts.sh
- generate_objdet.py
- model_bank.py
- model_bank_3d.py
- 2D_spatial_eval.py
- 3D_spatial_eval.py
- numeracy_eval.py
- LICENSE
- README.md
- requirements.txt
- twig.py
- twig.sh
# 설치 가이드
1. 코드 내려받기
git clone https://github.com/ZiyuGuo99/Thinking-while-Generating
깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.
cd Thinking-while-Generating
방금 내려받은 프로젝트 폴더 안으로 이동합니다.
// repository documentation
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