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* zh translated docs update

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docs/source/zh/_toctree.yml
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- local: quicktour
title: 快速入门
- local: stable_diffusion
title: Stable Diffusion
title: Effective and efficient diffusion
- local: installation
title: 安装
title: 开始
- sections:
- local: tutorials/tutorial_overview
title: Overview
- local: using-diffusers/write_own_pipeline
title: Understanding models and schedulers
- local: tutorials/basic_training
title: Train a diffusion model
title: Tutorials
- sections:
- sections:
- local: using-diffusers/loading_overview
title: Overview
- local: using-diffusers/loading
title: Loading Pipelines, Models, and Schedulers
title: Load pipelines, models, and schedulers
- local: using-diffusers/schedulers
title: Using different Schedulers
- local: using-diffusers/configuration
title: Configuring Pipelines, Models, and Schedulers
title: Load and compare different schedulers
- local: using-diffusers/custom_pipeline_overview
title: Loading and Adding Custom Pipelines
title: Load community pipelines
- local: using-diffusers/kerascv
title: Using KerasCV Stable Diffusion Checkpoints in Diffusers
title: Load KerasCV Stable Diffusion checkpoints
title: Loading & Hub
- sections:
- local: using-diffusers/pipeline_overview
title: Overview
- local: using-diffusers/unconditional_image_generation
title: Unconditional Image Generation
title: Unconditional image generation
- local: using-diffusers/conditional_image_generation
title: Text-to-Image Generation
title: Text-to-image generation
- local: using-diffusers/img2img
title: Text-Guided Image-to-Image
title: Text-guided image-to-image
- local: using-diffusers/inpaint
title: Text-Guided Image-Inpainting
title: Text-guided image-inpainting
- local: using-diffusers/depth2img
title: Text-Guided Depth-to-Image
- local: using-diffusers/controlling_generation
title: Controlling generation
title: Text-guided depth-to-image
- local: using-diffusers/reusing_seeds
title: Reusing seeds for deterministic generation
title: Improve image quality with deterministic generation
- local: using-diffusers/reproducibility
title: Reproducibility
title: Create reproducible pipelines
- local: using-diffusers/custom_pipeline_examples
title: Community Pipelines
title: Community pipelines
- local: using-diffusers/contribute_pipeline
title: How to contribute a Pipeline
title: How to contribute a community pipeline
- local: using-diffusers/using_safetensors
title: Using safetensors
- local: using-diffusers/stable_diffusion_jax_how_to
title: Stable Diffusion in JAX/Flax
- local: using-diffusers/weighted_prompts
title: Weighting Prompts
title: Pipelines for Inference
- sections:
- local: training/overview
title: Overview
- local: training/unconditional_training
title: Unconditional image generation
- local: training/text_inversion
title: Textual Inversion
- local: training/dreambooth
title: DreamBooth
- local: training/text2image
title: Text-to-image
- local: training/lora
title: Low-Rank Adaptation of Large Language Models (LoRA)
- local: training/controlnet
title: ControlNet
- local: training/instructpix2pix
title: InstructPix2Pix Training
- local: training/custom_diffusion
title: Custom Diffusion
title: Training
- sections:
- local: using-diffusers/rl
title: Reinforcement Learning
......@@ -59,6 +87,8 @@
title: Taking Diffusers Beyond Images
title: Using Diffusers
- sections:
- local: optimization/opt_overview
title: Overview
- local: optimization/fp16
title: Memory and Speed
- local: optimization/torch2.0
......@@ -69,32 +99,26 @@
title: ONNX
- local: optimization/open_vino
title: OpenVINO
- local: optimization/coreml
title: Core ML
- local: optimization/mps
title: MPS
- local: optimization/habana
title: Habana Gaudi
- local: optimization/tome
title: Token Merging
title: Optimization/Special Hardware
- sections:
- local: training/overview
title: Overview
- local: training/unconditional_training
title: Unconditional Image Generation
- local: training/text_inversion
title: Textual Inversion
- local: training/dreambooth
title: DreamBooth
- local: training/text2image
title: Text-to-image
- local: training/lora
title: Low-Rank Adaptation of Large Language Models (LoRA)
title: Training
- sections:
- local: conceptual/philosophy
title: Philosophy
- local: using-diffusers/controlling_generation
title: Controlled generation
- local: conceptual/contribution
title: How to contribute?
- local: conceptual/ethical_guidelines
title: Diffusers' Ethical Guidelines
- local: conceptual/evaluation
title: Evaluating Diffusion Models
title: Conceptual Guides
- sections:
- sections:
......@@ -118,6 +142,8 @@
title: AltDiffusion
- local: api/pipelines/audio_diffusion
title: Audio Diffusion
- local: api/pipelines/audioldm
title: AudioLDM
- local: api/pipelines/cycle_diffusion
title: Cycle Diffusion
- local: api/pipelines/dance_diffusion
......@@ -128,6 +154,8 @@
title: DDPM
- local: api/pipelines/dit
title: DiT
- local: api/pipelines/if
title: IF
- local: api/pipelines/latent_diffusion
title: Latent Diffusion
- local: api/pipelines/paint_by_example
......@@ -142,6 +170,8 @@
title: Score SDE VE
- local: api/pipelines/semantic_stable_diffusion
title: Semantic Guidance
- local: api/pipelines/spectrogram_diffusion
title: "Spectrogram Diffusion"
- sections:
- local: api/pipelines/stable_diffusion/overview
title: Overview
......@@ -171,6 +201,8 @@
title: MultiDiffusion Panorama
- local: api/pipelines/stable_diffusion/controlnet
title: Text-to-Image Generation with ControlNet Conditioning
- local: api/pipelines/stable_diffusion/model_editing
title: Text-to-Image Model Editing
title: Stable Diffusion
- local: api/pipelines/stable_diffusion_2
title: Stable Diffusion 2
......@@ -178,6 +210,10 @@
title: Stable unCLIP
- local: api/pipelines/stochastic_karras_ve
title: Stochastic Karras VE
- local: api/pipelines/text_to_video
title: Text-to-Video
- local: api/pipelines/text_to_video_zero
title: Text-to-Video Zero
- local: api/pipelines/unclip
title: UnCLIP
- local: api/pipelines/latent_diffusion_uncond
......@@ -235,4 +271,4 @@
- local: api/experimental/rl
title: RL Planning
title: Experimental Features
title: API
title: API
\ No newline at end of file
docs/source/zh/index.mdx
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......@@ -18,61 +18,84 @@ specific language governing permissions and limitations under the License.
# 🧨 Diffusers
🤗Diffusers提供了预训练好的视觉和音频扩散模型，并可以作为推理和训练的模块化工具箱。
🤗 Diffusers 是一个值得首选用于生成图像、音频甚至 3D 分子结构的，最先进的预训练扩散模型库。
无论您是在寻找简单的推理解决方案，还是想训练自己的扩散模型，🤗 Diffusers 这一模块化工具箱都能对其提供支持。
本库的设计更偏重于[可用而非高性能](conceptual/philosophy#usability-over-performance)、[简明而非简单](conceptual/philosophy#simple-over-easy)以及[易用而非抽象](conceptual/philosophy#tweakable-contributorfriendly-over-abstraction)。
更准确地说，🤗Diffusers提供了：
- 最先进的扩散管道，可以在推理中仅用几行代码运行(详情看[**Using Diffusers**](./using-diffusers/conditional_image_generation))或看[**管道**](#pipelines) 以获取所有支持的管道及其对应的论文的概述。
- 可以在推理中交替使用的各种噪声调度程序，以便在推理过程中权衡如何选择速度和质量。有关更多信息，可以看[**Schedulers**](./api/schedulers/overview)。
- 多种类型的模型，如U-Net，可用作端到端扩散系统中的构建模块。有关更多详细信息，可以看 [**Models**](./api/models) 。
- 训练示例，展示如何训练最流行的扩散模型任务。更多相关信息，可以看[**Training**](./training/overview)。
本库包含三个主要组件：
- 最先进的扩散管道 [diffusion pipelines](api/pipelines/overview)，只需几行代码即可进行推理。
- 可交替使用的各种噪声调度器 [noise schedulers](api/schedulers/overview)，用于平衡生成速度和质量。
- 预训练模型 [models](api/models)，可作为构建模块，并与调度程序结合使用，来创建您自己的端到端扩散系统。
## 🧨 Diffusers pipelines
下表总结了所有官方支持的pipelines及其对应的论文，部分提供了colab，可以直接尝试一下。
<div class="mt-10">
<div class="w-full flex flex-col space-y-4 md:space-y-0 md:grid md:grid-cols-2 md:gap-y-4 md:gap-x-5">
<a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="./tutorials/tutorial_overview"
><div class="w-full text-center bg-gradient-to-br from-blue-400 to-blue-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">Tutorials</div>
<p class="text-gray-700">Learn the fundamental skills you need to start generating outputs, build your own diffusion system, and train a diffusion model. We recommend starting here if you're using 🤗 Diffusers for the first time!</p>
</a>
<a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="./using-diffusers/loading_overview"
><div class="w-full text-center bg-gradient-to-br from-indigo-400 to-indigo-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">How-to guides</div>
<p class="text-gray-700">Practical guides for helping you load pipelines, models, and schedulers. You'll also learn how to use pipelines for specific tasks, control how outputs are generated, optimize for inference speed, and different training techniques.</p>
</a>
<a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="./conceptual/philosophy"
><div class="w-full text-center bg-gradient-to-br from-pink-400 to-pink-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">Conceptual guides</div>
<p class="text-gray-700">Understand why the library was designed the way it was, and learn more about the ethical guidelines and safety implementations for using the library.</p>
</a>
<a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="./api/models"
><div class="w-full text-center bg-gradient-to-br from-purple-400 to-purple-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">Reference</div>
<p class="text-gray-700">Technical descriptions of how 🤗 Diffusers classes and methods work.</p>
</a>
</div>
</div>
## 🧨 Diffusers pipelines
| 管道 | 论文 | 任务 | Colab
|---|---|:---:|:---:|
| [alt_diffusion](./api/pipelines/alt_diffusion) | [**AltDiffusion**](https://arxiv.org/abs/2211.06679) | Image-to-Image Text-Guided Generation |
| [audio_diffusion](./api/pipelines/audio_diffusion) | [**Audio Diffusion**](https://github.com/teticio/audio-diffusion.git) | Unconditional Audio Generation | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/teticio/audio-diffusion/blob/master/notebooks/audio_diffusion_pipeline.ipynb)
| [controlnet](./api/pipelines/stable_diffusion/controlnet) | [**ControlNet with Stable Diffusion**](https://arxiv.org/abs/2302.05543) | Image-to-Image Text-Guided Generation | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/controlnet.ipynb)
| [cycle_diffusion](./api/pipelines/cycle_diffusion) | [**Cycle Diffusion**](https://arxiv.org/abs/2210.05559) | Image-to-Image Text-Guided Generation |
| [dance_diffusion](./api/pipelines/dance_diffusion) | [**Dance Diffusion**](https://github.com/williamberman/diffusers.git) | Unconditional Audio Generation |
| [ddpm](./api/pipelines/ddpm) | [**Denoising Diffusion Probabilistic Models**](https://arxiv.org/abs/2006.11239) | Unconditional Image Generation |
| [ddim](./api/pipelines/ddim) | [**Denoising Diffusion Implicit Models**](https://arxiv.org/abs/2010.02502) | Unconditional Image Generation |
| [latent_diffusion](./api/pipelines/latent_diffusion) | [**High-Resolution Image Synthesis with Latent Diffusion Models**](https://arxiv.org/abs/2112.10752)| Text-to-Image Generation |
| [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 |
| [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 |
| [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 |
| [pndm](./api/pipelines/pndm) | [**Pseudo Numerical Methods for Diffusion Models on Manifolds**](https://arxiv.org/abs/2202.09778) | Unconditional Image Generation |
| [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 |
| [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 |
| [semantic_stable_diffusion](./api/pipelines/semantic_stable_diffusion) | [**Semantic Guidance**](https://arxiv.org/abs/2301.12247) | Text-Guided Generation | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ml-research/semantic-image-editing/blob/main/examples/SemanticGuidance.ipynb)
| [stable_diffusion_text2img](./api/pipelines/stable_diffusion/text2img) | [**Stable Diffusion**](https://stability.ai/blog/stable-diffusion-public-release) | Text-to-Image Generation | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb)
| [stable_diffusion_img2img](./api/pipelines/stable_diffusion/img2img) | [**Stable Diffusion**](https://stability.ai/blog/stable-diffusion-public-release) | Image-to-Image Text-Guided Generation | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/image_2_image_using_diffusers.ipynb)
| [stable_diffusion_inpaint](./api/pipelines/stable_diffusion/inpaint) | [**Stable Diffusion**](https://stability.ai/blog/stable-diffusion-public-release) | Text-Guided Image Inpainting | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/in_painting_with_stable_diffusion_using_diffusers.ipynb)
| [stable_diffusion_panorama](./api/pipelines/stable_diffusion/panorama) | [**MultiDiffusion**](https://multidiffusion.github.io/) | Text-to-Panorama Generation |
| [stable_diffusion_pix2pix](./api/pipelines/stable_diffusion/pix2pix) | [**InstructPix2Pix**](https://github.com/timothybrooks/instruct-pix2pix) | Text-Guided Image Editing|
| [stable_diffusion_pix2pix_zero](./api/pipelines/stable_diffusion/pix2pix_zero) | [**Zero-shot Image-to-Image Translation**](https://pix2pixzero.github.io/) | Text-Guided Image Editing |
| [stable_diffusion_attend_and_excite](./api/pipelines/stable_diffusion/attend_and_excite) | [**Attend and Excite for Stable Diffusion**](https://attendandexcite.github.io/Attend-and-Excite/) | Text-to-Image Generation |
| [stable_diffusion_self_attention_guidance](./api/pipelines/stable_diffusion/self_attention_guidance) | [**Self-Attention Guidance**](https://ku-cvlab.github.io/Self-Attention-Guidance) | Text-to-Image Generation |
| [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 |
| [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 |
| [stable_diffusion_2](./api/pipelines/stable_diffusion_2) | [**Stable Diffusion 2**](https://stability.ai/blog/stable-diffusion-v2-release) | Text-to-Image Generation |
| [stable_diffusion_2](./api/pipelines/stable_diffusion_2) | [**Stable Diffusion 2**](https://stability.ai/blog/stable-diffusion-v2-release) | Text-Guided Image Inpainting |
| [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 |
| [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 |
| [stable_diffusion_safe](./api/pipelines/stable_diffusion_safe) | [**Safe Stable Diffusion**](https://arxiv.org/abs/2211.05105) | Text-Guided Generation | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ml-research/safe-latent-diffusion/blob/main/examples/Safe%20Latent%20Diffusion.ipynb)
| [stable_unclip](./stable_unclip) | **Stable unCLIP** | Text-to-Image Generation |
| [stable_unclip](./stable_unclip) | **Stable unCLIP** | Image-to-Image Text-Guided Generation |
| [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 |
| [unclip](./api/pipelines/unclip) | [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://arxiv.org/abs/2204.06125) | Text-to-Image Generation |
| [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 |
| [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 |
| [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 |
| [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 |
下表汇总了当前所有官方支持的pipelines及其对应的论文.
**注意**: 管道是如何使用相应论文中提出的扩散模型的简单示例。
\ No newline at end of file
| 管道 | 论文/仓库 | 任务 |
|---|---|:---:|
| [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 |
| [audio_diffusion](./api/pipelines/audio_diffusion) | [Audio Diffusion](https://github.com/teticio/audio-diffusion.git) | Unconditional Audio Generation |
| [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 |
| [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 |
| [dance_diffusion](./api/pipelines/dance_diffusion) | [Dance Diffusion](https://github.com/williamberman/diffusers.git) | Unconditional Audio Generation |
| [ddpm](./api/pipelines/ddpm) | [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239) | Unconditional Image Generation |
| [ddim](./api/pipelines/ddim) | [Denoising Diffusion Implicit Models](https://arxiv.org/abs/2010.02502) | Unconditional Image Generation |
| [if](./if) | [**IF**](./api/pipelines/if) | Image Generation |
| [if_img2img](./if) | [**IF**](./api/pipelines/if) | Image-to-Image Generation |
| [if_inpainting](./if) | [**IF**](./api/pipelines/if) | Image-to-Image Generation |
| [latent_diffusion](./api/pipelines/latent_diffusion) | [High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752)| Text-to-Image Generation |
| [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 |
| [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 |
| [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 |
| [pndm](./api/pipelines/pndm) | [Pseudo Numerical Methods for Diffusion Models on Manifolds](https://arxiv.org/abs/2202.09778) | Unconditional Image Generation |
| [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 |
| [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 |
| [semantic_stable_diffusion](./api/pipelines/semantic_stable_diffusion) | [Semantic Guidance](https://arxiv.org/abs/2301.12247) | Text-Guided Generation |
| [stable_diffusion_text2img](./api/pipelines/stable_diffusion/text2img) | [Stable Diffusion](https://stability.ai/blog/stable-diffusion-public-release) | Text-to-Image Generation |
| [stable_diffusion_img2img](./api/pipelines/stable_diffusion/img2img) | [Stable Diffusion](https://stability.ai/blog/stable-diffusion-public-release) | Image-to-Image Text-Guided Generation |
| [stable_diffusion_inpaint](./api/pipelines/stable_diffusion/inpaint) | [Stable Diffusion](https://stability.ai/blog/stable-diffusion-public-release) | Text-Guided Image Inpainting |
| [stable_diffusion_panorama](./api/pipelines/stable_diffusion/panorama) | [MultiDiffusion](https://multidiffusion.github.io/) | Text-to-Panorama Generation |
| [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|
| [stable_diffusion_pix2pix_zero](./api/pipelines/stable_diffusion/pix2pix_zero) | [Zero-shot Image-to-Image Translation](https://pix2pixzero.github.io/) | Text-Guided Image Editing |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [stable_diffusion_2](./api/pipelines/stable_diffusion_2) | [Stable Diffusion 2](https://stability.ai/blog/stable-diffusion-v2-release) | Text-to-Image Generation |
| [stable_diffusion_2](./api/pipelines/stable_diffusion_2) | [Stable Diffusion 2](https://stability.ai/blog/stable-diffusion-v2-release) | Text-Guided Image Inpainting |
| [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 |
| [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 |
| [stable_diffusion_safe](./api/pipelines/stable_diffusion_safe) | [Safe Stable Diffusion](https://arxiv.org/abs/2211.05105) | Text-Guided Generation |
| [stable_unclip](./stable_unclip) | Stable unCLIP | Text-to-Image Generation |
| [stable_unclip](./stable_unclip) | Stable unCLIP | Image-to-Image Text-Guided Generation |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
docs/source/zh/installation.mdx
View file @ 0614fd20
......@@ -12,7 +12,7 @@ specific language governing permissions and limitations under the License.
# 安装
安装🤗 Diffusers 到你正在使用的任何深度学习框架中。
在你正在使用的任意深度学习框架中安装 🤗 Diffusers 。
🤗 Diffusers已在Python 3.7+、PyTorch 1.7.0+和Flax上进行了测试。按照下面的安装说明，针对你正在使用的深度学习框架进行安装：
......@@ -21,11 +21,11 @@ specific language governing permissions and limitations under the License.
## 使用pip安装
你需要在[虚拟环境](https://docs.python.org/3/library/venv.html)中安装🤗 Diffusers 。
你需要在[虚拟环境](https://docs.python.org/3/library/venv.html)中安装 🤗 Diffusers 。
如果你对 Python 虚拟环境不熟悉，可以看看这个[教程](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/).
使用虚拟环境你可以轻松管理不同的项目，避免了依赖项之间的兼容性问题。
在虚拟环境中，你可以轻松管理不同的项目，避免依赖项之间的兼容性问题。
首先，在你的项目目录下创建一个虚拟环境：
......@@ -39,7 +39,7 @@ python -m venv .env
source .env/bin/activate
```
现在你就可以安装 🤗 Diffusers了！使用下边这个命令：
现在，你就可以安装 🤗 Diffusers了！使用下边这个命令：
**PyTorch**
......@@ -55,7 +55,7 @@ pip install diffusers["flax"]
## 从源代码安装
在从源代码安装 `diffusers` 之前，你先确定你已经安装了 `torch` 和 `accelerate`。
在从源代码安装 `diffusers` 之前，确保你已经安装了 `torch` 和 `accelerate`。
`torch`的安装教程可以看 `torch` [文档](https://pytorch.org/get-started/locally/#start-locally).
......@@ -65,17 +65,17 @@ pip install diffusers["flax"]
pip install accelerate
```
从源码安装 🤗 Diffusers 使用以下命令:
从源码安装 🤗 Diffusers 需要使用以下命令:
```bash
pip install git+https://github.com/huggingface/diffusers
```
这个命令安装的是最新的 `main`版本，而不是最近的`stable`版。
`main`是一直和最新进展保持一致的。比如，上次正式版发布了，有bug，新的正式版还没推出，但是`main`中可以看到这个bug被修复了。
但是这也意味着 `main`版本并不总是稳定的。
`main`是一直和最新进展保持一致的。比如，上次发布的正式版中有bug，在`main`中可以看到这个bug被修复了，但是新的正式版此时尚未推出。
但是这也意味着 `main`版本不保证是稳定的。
我们努力保持`main`版本正常运行,大多数问题都能在几个小时或一天之内解决
我们努力保持`main`版本正常运行，大多数问题都能在几个小时或一天之内解决
如果你遇到了问题，可以提 [Issue](https://github.com/huggingface/transformers/issues)，这样我们就能更快修复问题了。
......@@ -105,8 +105,8 @@ pip install -e ".[torch]"
pip install -e ".[flax]"
```
这些命令将连接你克隆的版本库和你的 Python 库路径。
现在，除了正常的库路径外，Python 还会在你克隆的文件夹内寻找。
这些命令将连接到你克隆的版本库和你的 Python 库路径。
现在，不只是在通常的库路径，Python 还会在你克隆的文件夹内寻找包。
例如，如果你的 Python 包通常安装在 `~/anaconda3/envs/main/lib/python3.7/Site-packages/`，Python 也会搜索你克隆到的文件夹。`~/diffusers/`。
<Tip warning={true}>
......@@ -116,32 +116,31 @@ pip install -e ".[flax]"
</Tip>
现在你可以用下面的命令轻松地将你克隆的🤗Diffusers仓库更新到最新版本。
现在你可以用下面的命令轻松地将你克隆的 🤗 Diffusers 库更新到最新版本。
```bash
cd ~/diffusers/
git pull
```
你的Python环境将在下次运行时找到`main`版本的🤗 Diffusers。
你的Python环境将在下次运行时找到`main`版本的 🤗 Diffusers。
## 注意遥测日志
## 注意 Telemetry 日志
我们的库会在使用`from_pretrained()`请求期间收集信息。这些数据包括Diffusers和PyTorch/Flax的版本，请求的模型或管道，以及预训练检查点的路径（如果它被托管在Hub上）。
我们的库会在使用`from_pretrained()`请求期间收集 telemetry 信息。这些数据包括Diffusers和PyTorch/Flax的版本，请求的模型或管道类，以及预训练检查点的路径（如果它被托管在Hub上的话）。
这些使用数据有助于我们调试问题并确定新功能的开发优先级。
Telemetry 数据仅在从 HuggingFace Hub 中加载模型和管道时发送，而不会在本地使用期间收集。
这些使用数据有助于我们调试问题并优先考虑新功能。
当从HuggingFace Hub加载模型和管道时才会发送遥测数据，并且在本地使用时不会收集数据。
我们知道，并不是每个人都想分享这些的信息，我们尊重您的隐私，
因此您可以通过在终端中设置 `DISABLE_TELEMETRY` 环境变量从而禁用 Telemetry 数据收集：
我们知道并不是每个人都想分享这些的信息，我们尊重您的隐私，
因此您可以通过在终端中设置“DISABLE_TELEMETRY”环境变量来禁用遥测数据的收集：
在Linux/MacOS中:
Linux/MacOS :
```bash
export DISABLE_TELEMETRY=YES
```
在Windows中:
Windows :
```bash
set DISABLE_TELEMETRY=YES
```
\ No newline at end of file
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