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🌐 [i18n-KO] Translated docs to Korean (added 7 docs and etc) (huggingface#8804)
* remove unused docs * add ko-18n docs * docs typo, edit etc * reorder list, add `in translation` in toctree * fix minor translation * fix docs minor tone, etc
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Diff for: ‎docs/source/en/using-diffusers/shap-e.md

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).images
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```
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Now use the [`~utils.export_to_gif`] function to turn the list of image frames into a gif of the 3D object.
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이제 [`~utils.export_to_gif`] 함수를 사용해 이미지 프레임 리스트를 3D 오브젝트의 gif로 변환합니다.
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```py
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from diffusers.utils import export_to_gif

Diff for: ‎docs/source/en/using-diffusers/svd.md

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Before you begin, make sure you have the following libraries installed:
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```py
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# Colab에서 필요한 라이브러리를 설치하기 위해 주석을 제외하세요
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!pip install -q -U diffusers transformers accelerate
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```
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Diff for: ‎docs/source/ko/_toctree.yml

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- sections:
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- local: index
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title: "🧨 Diffusers"
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title: 🧨 Diffusers
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- local: quicktour
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title: "훑어보기"
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- local: stable_diffusion
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title: Stable Diffusion
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- local: installation
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title: "설치"
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title: "시작하기"
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title: 설치
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title: 시작하기
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- sections:
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- local: tutorials/tutorial_overview
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title: 개요
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- local: using-diffusers/write_own_pipeline
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title: 모델과 스케줄러 이해하기
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- local: in_translation
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title: AutoPipeline
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- local: in_translation # tutorials/autopipeline
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title: (번역중) AutoPipeline
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- local: tutorials/basic_training
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title: Diffusion 모델 학습하기
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title: Tutorials
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- local: in_translation # tutorials/using_peft_for_inference
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title: (번역중) 추론을 위한 LoRAs 불러오기
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- local: in_translation # tutorials/fast_diffusion
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title: (번역중) Text-to-image diffusion 모델 추론 가속화하기
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- local: in_translation # tutorials/inference_with_big_models
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title: (번역중) 큰 모델로 작업하기
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title: 튜토리얼
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- sections:
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- sections:
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- local: using-diffusers/loading_overview
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title: 개요
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- local: using-diffusers/loading
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title: 파이프라인, 모델, 스케줄러 불러오기
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- local: using-diffusers/schedulers
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title: 다른 스케줄러들을 가져오고 비교하기
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- local: using-diffusers/custom_pipeline_overview
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title: 커뮤니티 파이프라인 불러오기
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- local: using-diffusers/using_safetensors
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title: 세이프텐서 불러오기
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- local: using-diffusers/other-formats
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title: 다른 형식의 Stable Diffusion 불러오기
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- local: in_translation
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title: Hub에 파일 push하기
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title: 불러오기 & 허브
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- sections:
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- local: using-diffusers/pipeline_overview
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title: 개요
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- local: using-diffusers/unconditional_image_generation
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title: Unconditional 이미지 생성
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- local: using-diffusers/conditional_image_generation
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title: Text-to-image 생성
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- local: using-diffusers/img2img
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title: Text-guided image-to-image
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- local: using-diffusers/inpaint
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title: Text-guided 이미지 인페인팅
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- local: using-diffusers/depth2img
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title: Text-guided depth-to-image
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- local: using-diffusers/textual_inversion_inference
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title: Textual inversion
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- local: training/distributed_inference
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title: 여러 GPU를 사용한 분산 추론
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- local: in_translation
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title: Distilled Stable Diffusion 추론
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- local: using-diffusers/reusing_seeds
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title: Deterministic 생성으로 이미지 퀄리티 높이기
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- local: using-diffusers/control_brightness
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title: 이미지 밝기 조정하기
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- local: using-diffusers/reproducibility
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title: 재현 가능한 파이프라인 생성하기
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- local: using-diffusers/custom_pipeline_examples
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title: 커뮤니티 파이프라인들
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- local: using-diffusers/contribute_pipeline
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title: 커뮤티니 파이프라인에 기여하는 방법
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- local: using-diffusers/stable_diffusion_jax_how_to
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title: JAX/Flax에서의 Stable Diffusion
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- local: using-diffusers/weighted_prompts
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title: Weighting Prompts
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title: 추론을 위한 파이프라인
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- sections:
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- local: training/overview
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title: 개요
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- local: training/create_dataset
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title: 학습을 위한 데이터셋 생성하기
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- local: training/adapt_a_model
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title: 새로운 태스크에 모델 적용하기
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- local: using-diffusers/loading
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title: 파이프라인 불러오기
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- local: using-diffusers/custom_pipeline_overview
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title: 커뮤니티 파이프라인과 컴포넌트 불러오기
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- local: using-diffusers/schedulers
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title: 스케줄러와 모델 불러오기
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- local: using-diffusers/other-formats
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title: 모델 파일과 레이아웃
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- local: using-diffusers/loading_adapters
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title: 어댑터 불러오기
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- local: using-diffusers/push_to_hub
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title: 파일들을 Hub로 푸시하기
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title: 파이프라인과 어댑터 불러오기
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- sections:
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- local: using-diffusers/unconditional_image_generation
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title: Unconditional 이미지 생성
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- local: using-diffusers/conditional_image_generation
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title: Text-to-image
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- local: using-diffusers/img2img
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title: Image-to-image
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- local: using-diffusers/inpaint
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title: 인페인팅
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- local: in_translation # using-diffusers/text-img2vid
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title: (번역중) Text 또는 image-to-video
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- local: using-diffusers/depth2img
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title: Depth-to-image
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title: 생성 태스크
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- sections:
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- local: in_translation # using-diffusers/overview_techniques
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title: (번역중) 개요
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- local: training/distributed_inference
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title: 여러 GPU를 사용한 분산 추론
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- local: in_translation # using-diffusers/merge_loras
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title: (번역중) LoRA 병합
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- local: in_translation # using-diffusers/scheduler_features
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title: (번역중) 스케줄러 기능
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- local: in_translation # using-diffusers/callback
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title: (번역중) 파이프라인 콜백
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- local: in_translation # using-diffusers/reusing_seeds
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title: (번역중) 재현 가능한 파이프라인
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- local: in_translation # using-diffusers/image_quality
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title: (번역중) 이미지 퀄리티 조절하기
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- local: using-diffusers/weighted_prompts
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title: 프롬프트 기술
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title: 추론 테크닉
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- sections:
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- local: in_translation # advanced_inference/outpaint
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title: (번역중) Outpainting
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title: 추론 심화
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- sections:
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- local: in_translation # using-diffusers/sdxl
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title: (번역중) Stable Diffusion XL
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- local: using-diffusers/sdxl_turbo
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title: SDXL Turbo
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- local: using-diffusers/kandinsky
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title: Kandinsky
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- local: in_translation # using-diffusers/ip_adapter
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title: (번역중) IP-Adapter
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- local: in_translation # using-diffusers/pag
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title: (번역중) PAG
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- local: in_translation # using-diffusers/controlnet
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title: (번역중) ControlNet
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- local: in_translation # using-diffusers/t2i_adapter
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title: (번역중) T2I-Adapter
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- local: in_translation # using-diffusers/inference_with_lcm
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title: (번역중) Latent Consistency Model
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- local: using-diffusers/textual_inversion_inference
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title: Textual inversion
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- local: using-diffusers/shap-e
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title: Shap-E
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- local: using-diffusers/diffedit
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title: DiffEdit
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- local: in_translation # using-diffusers/inference_with_tcd_lora
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title: (번역중) Trajectory Consistency Distillation-LoRA
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- local: using-diffusers/svd
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title: Stable Video Diffusion
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- local: in_translation # using-diffusers/marigold_usage
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title: (번역중) Marigold 컴퓨터 비전
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title: 특정 파이프라인 예시
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- sections:
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- local: training/overview
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title: 개요
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- local: training/create_dataset
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title: 학습을 위한 데이터셋 생성하기
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- local: training/adapt_a_model
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title: 새로운 태스크에 모델 적용하기
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- isExpanded: false
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sections:
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- local: training/unconditional_training
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title: Unconditional 이미지 생성
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- local: training/text_inversion
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title: Textual Inversion
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- local: training/dreambooth
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title: DreamBooth
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- local: training/text2image
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title: Text-to-image
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- local: training/lora
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title: Low-Rank Adaptation of Large Language Models (LoRA)
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- local: in_translation # training/sdxl
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title: (번역중) Stable Diffusion XL
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- local: in_translation # training/kandinsky
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title: (번역중) Kandinsky 2.2
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- local: in_translation # training/wuerstchen
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title: (번역중) Wuerstchen
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- local: training/controlnet
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title: ControlNet
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- local: in_translation # training/t2i_adapters
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title: (번역중) T2I-Adapters
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- local: training/instructpix2pix
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title: InstructPix2Pix 학습
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title: InstructPix2Pix
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title: 모델
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- isExpanded: false
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sections:
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- local: training/text_inversion
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title: Textual Inversion
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- local: training/dreambooth
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title: DreamBooth
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- local: training/lora
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title: LoRA
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- local: training/custom_diffusion
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title: Custom Diffusion
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title: Training
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title: Diffusers 사용하기
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- local: in_translation # training/lcm_distill
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title: (번역중) Latent Consistency Distillation
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- local: in_translation # training/ddpo
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title: (번역중) DDPO 강화학습 훈련
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title: 메서드
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title: 학습
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- sections:
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- local: optimization/opt_overview
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title: 개요
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- local: optimization/fp16
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title: 메모리와 속도
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title: 추론 스피드업
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- local: in_translation # optimization/memory
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title: (번역중) 메모리 사용량 줄이기
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- local: optimization/torch2.0
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title: Torch2.0 지원
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title: PyTorch 2.0
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- local: optimization/xformers
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title: xFormers
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- local: optimization/onnx
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title: ONNX
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- local: optimization/open_vino
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title: OpenVINO
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- local: optimization/coreml
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title: Core ML
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- local: optimization/mps
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title: MPS
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- local: optimization/habana
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title: Habana Gaudi
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- local: optimization/tome
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title: Token Merging
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title: 최적화/특수 하드웨어
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title: Token merging
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- local: in_translation # optimization/deepcache
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title: (번역중) DeepCache
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- local: in_translation # optimization/tgate
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title: (번역중) TGATE
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- sections:
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- local: using-diffusers/stable_diffusion_jax_how_to
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title: JAX/Flax
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- local: optimization/onnx
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title: ONNX
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- local: optimization/open_vino
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title: OpenVINO
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- local: optimization/coreml
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title: Core ML
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title: 최적화된 모델 형식
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- sections:
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- local: optimization/mps
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title: Metal Performance Shaders (MPS)
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- local: optimization/habana
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title: Habana Gaudi
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title: 최적화된 하드웨어
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title: 추론 가속화와 메모리 줄이기
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- sections:
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- local: conceptual/philosophy
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title: 철학

Diff for: ‎docs/source/ko/index.md

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<p class="text-gray-700">🤗 Diffusers 클래스 및 메서드의 작동 방식에 대한 기술 설명.</p>
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</a>
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</div>
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</div>
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## Supported pipelines
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| Pipeline | Paper/Repository | Tasks |
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|---|---|:---:|
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| [alt_diffusion](./api/pipelines/alt_diffusion) | [AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities](https://arxiv.org/abs/2211.06679) | Image-to-Image Text-Guided Generation |
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| [audio_diffusion](./api/pipelines/audio_diffusion) | [Audio Diffusion](https://github.com/teticio/audio-diffusion.git) | Unconditional Audio Generation |
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| [controlnet](./api/pipelines/stable_diffusion/controlnet) | [Adding Conditional Control to Text-to-Image Diffusion Models](https://arxiv.org/abs/2302.05543) | Image-to-Image Text-Guided Generation |
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| [cycle_diffusion](./api/pipelines/cycle_diffusion) | [Unifying Diffusion Models' Latent Space, with Applications to CycleDiffusion and Guidance](https://arxiv.org/abs/2210.05559) | Image-to-Image Text-Guided Generation |
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| [dance_diffusion](./api/pipelines/dance_diffusion) | [Dance Diffusion](https://github.com/williamberman/diffusers.git) | Unconditional Audio Generation |
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| [ddpm](./api/pipelines/ddpm) | [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239) | Unconditional Image Generation |
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| [ddim](./api/pipelines/ddim) | [Denoising Diffusion Implicit Models](https://arxiv.org/abs/2010.02502) | Unconditional Image Generation |
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| [if](./if) | [**IF**](./api/pipelines/if) | Image Generation |
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| [if_img2img](./if) | [**IF**](./api/pipelines/if) | Image-to-Image Generation |
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| [if_inpainting](./if) | [**IF**](./api/pipelines/if) | Image-to-Image Generation |
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| [latent_diffusion](./api/pipelines/latent_diffusion) | [High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752)| Text-to-Image Generation |
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| [latent_diffusion](./api/pipelines/latent_diffusion) | [High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752)| Super Resolution Image-to-Image |
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| [latent_diffusion_uncond](./api/pipelines/latent_diffusion_uncond) | [High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) | Unconditional Image Generation |
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| [paint_by_example](./api/pipelines/paint_by_example) | [Paint by Example: Exemplar-based Image Editing with Diffusion Models](https://arxiv.org/abs/2211.13227) | Image-Guided Image Inpainting |
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| [pndm](./api/pipelines/pndm) | [Pseudo Numerical Methods for Diffusion Models on Manifolds](https://arxiv.org/abs/2202.09778) | Unconditional Image Generation |
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| [score_sde_ve](./api/pipelines/score_sde_ve) | [Score-Based Generative Modeling through Stochastic Differential Equations](https://openreview.net/forum?id=PxTIG12RRHS) | Unconditional Image Generation |
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| [score_sde_vp](./api/pipelines/score_sde_vp) | [Score-Based Generative Modeling through Stochastic Differential Equations](https://openreview.net/forum?id=PxTIG12RRHS) | Unconditional Image Generation |
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| [semantic_stable_diffusion](./api/pipelines/semantic_stable_diffusion) | [Semantic Guidance](https://arxiv.org/abs/2301.12247) | Text-Guided Generation |
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| [stable_diffusion_text2img](./api/pipelines/stable_diffusion/text2img) | [Stable Diffusion](https://stability.ai/blog/stable-diffusion-public-release) | Text-to-Image Generation |
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| [stable_diffusion_img2img](./api/pipelines/stable_diffusion/img2img) | [Stable Diffusion](https://stability.ai/blog/stable-diffusion-public-release) | Image-to-Image Text-Guided Generation |
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| [stable_diffusion_inpaint](./api/pipelines/stable_diffusion/inpaint) | [Stable Diffusion](https://stability.ai/blog/stable-diffusion-public-release) | Text-Guided Image Inpainting |
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| [stable_diffusion_panorama](./api/pipelines/stable_diffusion/panorama) | [MultiDiffusion](https://multidiffusion.github.io/) | Text-to-Panorama Generation |
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| [stable_diffusion_pix2pix](./api/pipelines/stable_diffusion/pix2pix) | [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://arxiv.org/abs/2211.09800) | Text-Guided Image Editing|
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| [stable_diffusion_pix2pix_zero](./api/pipelines/stable_diffusion/pix2pix_zero) | [Zero-shot Image-to-Image Translation](https://pix2pixzero.github.io/) | Text-Guided Image Editing |
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| [stable_diffusion_attend_and_excite](./api/pipelines/stable_diffusion/attend_and_excite) | [Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models](https://arxiv.org/abs/2301.13826) | Text-to-Image Generation |
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| [stable_diffusion_self_attention_guidance](./api/pipelines/stable_diffusion/self_attention_guidance) | [Improving Sample Quality of Diffusion Models Using Self-Attention Guidance](https://arxiv.org/abs/2210.00939) | Text-to-Image Generation Unconditional Image Generation |
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| [stable_diffusion_image_variation](./stable_diffusion/image_variation) | [Stable Diffusion Image Variations](https://github.com/LambdaLabsML/lambda-diffusers#stable-diffusion-image-variations) | Image-to-Image Generation |
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| [stable_diffusion_latent_upscale](./stable_diffusion/latent_upscale) | [Stable Diffusion Latent Upscaler](https://twitter.com/StabilityAI/status/1590531958815064065) | Text-Guided Super Resolution Image-to-Image |
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| [stable_diffusion_model_editing](./api/pipelines/stable_diffusion/model_editing) | [Editing Implicit Assumptions in Text-to-Image Diffusion Models](https://time-diffusion.github.io/) | Text-to-Image Model Editing |
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| [stable_diffusion_2](./api/pipelines/stable_diffusion_2) | [Stable Diffusion 2](https://stability.ai/blog/stable-diffusion-v2-release) | Text-to-Image Generation |
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| [stable_diffusion_2](./api/pipelines/stable_diffusion_2) | [Stable Diffusion 2](https://stability.ai/blog/stable-diffusion-v2-release) | Text-Guided Image Inpainting |
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| [stable_diffusion_2](./api/pipelines/stable_diffusion_2) | [Depth-Conditional Stable Diffusion](https://github.com/Stability-AI/stablediffusion#depth-conditional-stable-diffusion) | Depth-to-Image Generation |
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| [stable_diffusion_2](./api/pipelines/stable_diffusion_2) | [Stable Diffusion 2](https://stability.ai/blog/stable-diffusion-v2-release) | Text-Guided Super Resolution Image-to-Image |
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| [stable_diffusion_safe](./api/pipelines/stable_diffusion_safe) | [Safe Stable Diffusion](https://arxiv.org/abs/2211.05105) | Text-Guided Generation |
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| [stable_unclip](./stable_unclip) | Stable unCLIP | Text-to-Image Generation |
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| [stable_unclip](./stable_unclip) | Stable unCLIP | Image-to-Image Text-Guided Generation |
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| [stochastic_karras_ve](./api/pipelines/stochastic_karras_ve) | [Elucidating the Design Space of Diffusion-Based Generative Models](https://arxiv.org/abs/2206.00364) | Unconditional Image Generation |
92-
| [text_to_video_sd](./api/pipelines/text_to_video) | [Modelscope's Text-to-video-synthesis Model in Open Domain](https://modelscope.cn/models/damo/text-to-video-synthesis/summary) | Text-to-Video Generation |
93-
| [unclip](./api/pipelines/unclip) | [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://arxiv.org/abs/2204.06125)(implementation by [kakaobrain](https://github.com/kakaobrain/karlo)) | Text-to-Image Generation |
94-
| [versatile_diffusion](./api/pipelines/versatile_diffusion) | [Versatile Diffusion: Text, Images and Variations All in One Diffusion Model](https://arxiv.org/abs/2211.08332) | Text-to-Image Generation |
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| [versatile_diffusion](./api/pipelines/versatile_diffusion) | [Versatile Diffusion: Text, Images and Variations All in One Diffusion Model](https://arxiv.org/abs/2211.08332) | Image Variations Generation |
96-
| [versatile_diffusion](./api/pipelines/versatile_diffusion) | [Versatile Diffusion: Text, Images and Variations All in One Diffusion Model](https://arxiv.org/abs/2211.08332) | Dual Image and Text Guided Generation |
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| [vq_diffusion](./api/pipelines/vq_diffusion) | [Vector Quantized Diffusion Model for Text-to-Image Synthesis](https://arxiv.org/abs/2111.14822) | Text-to-Image Generation |
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</div>

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