From 6e629f6fb207cdedc59b214426aff39ef1167583 Mon Sep 17 00:00:00 2001 From: richboyneedcash <273099414+richboyneedcash@users.noreply.github.com> Date: Wed, 5 Aug 2026 21:09:33 +0800 Subject: [PATCH] [docs] Fix broken Wuerstchen example links in zh training docs The Wuerstchen training example moved from examples/wuerstchen/ to examples/research_projects/wuerstchen/, but the Chinese training docs still link to the old path on main, so those links 404. Update the 5 blob/main and tree/main links across overview.md, lora.md and wuerstchen.md to the current research_projects/ path. Commit-pinned (SHA) links are intentionally left unchanged since they reference the historical file location. Co-authored-by: TRAE CLI --- docs/source/zh/training/lora.md | 2 +- docs/source/zh/training/overview.md | 2 +- docs/source/zh/training/wuerstchen.md | 6 +++--- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/docs/source/zh/training/lora.md b/docs/source/zh/training/lora.md index f80ae79f7503..c504a243c31f 100644 --- a/docs/source/zh/training/lora.md +++ b/docs/source/zh/training/lora.md @@ -18,7 +18,7 @@ specific language governing permissions and limitations under the License. [LoRA（大语言模型的低秩适配）](https://hf.co/papers/2106.09685) 是一种轻量级训练技术，能显著减少可训练参数量。其原理是通过向模型注入少量新权重参数，仅训练这些新增参数。这使得LoRA训练速度更快、内存效率更高，并生成更小的模型权重文件（通常仅数百MB），便于存储和分享。LoRA还可与DreamBooth等其他训练技术结合以加速训练过程。 > [!TIP] -> LoRA具有高度通用性，目前已支持以下应用场景：[DreamBooth](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth_lora.py)、[Kandinsky 2.2](https://github.com/huggingface/diffusers/blob/main/examples/kandinsky2_2/text_to_image/train_text_to_image_lora_decoder.py)、[Stable Diffusion XL](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_lora_sdxl.py)、[文生图](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_lora.py)以及[Wuerstchen](https://github.com/huggingface/diffusers/blob/main/examples/wuerstchen/text_to_image/train_text_to_image_lora_prior.py)。 +> LoRA具有高度通用性，目前已支持以下应用场景：[DreamBooth](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth_lora.py)、[Kandinsky 2.2](https://github.com/huggingface/diffusers/blob/main/examples/kandinsky2_2/text_to_image/train_text_to_image_lora_decoder.py)、[Stable Diffusion XL](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_lora_sdxl.py)、[文生图](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_lora.py)以及[Wuerstchen](https://github.com/huggingface/diffusers/blob/main/examples/research_projects/wuerstchen/text_to_image/train_text_to_image_lora_prior.py)。 本指南将通过解析[train_text_to_image_lora.py](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_lora.py)脚本，帮助您深入理解其工作原理，并掌握如何针对具体需求进行定制化修改。 diff --git a/docs/source/zh/training/overview.md b/docs/source/zh/training/overview.md index c0fa829ea06a..72b1481ba656 100644 --- a/docs/source/zh/training/overview.md +++ b/docs/source/zh/training/overview.md @@ -31,7 +31,7 @@ http://www.apache.org/licenses/LICENSE-2.0 | [Custom Diffusion](https://github.com/huggingface/diffusers/tree/main/examples/custom_diffusion) | | | | [T2I-Adapters](https://github.com/huggingface/diffusers/tree/main/examples/t2i_adapter) | 👍 | | | [Kandinsky 2.2](https://github.com/huggingface/diffusers/tree/main/examples/kandinsky2_2/text_to_image) | | 👍 | -| [Wuerstchen](https://github.com/huggingface/diffusers/tree/main/examples/wuerstchen/text_to_image) | | 👍 | +| [Wuerstchen](https://github.com/huggingface/diffusers/tree/main/examples/research_projects/wuerstchen/text_to_image) | | 👍 | 这些示例处于**积极维护**状态，如果遇到问题请随时提交issue。如果您认为应该添加其他训练示例，欢迎创建[功能请求](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feature_request.md&title=)与我们讨论，我们将评估其是否符合独立完整、易于调整、新手友好和单一用途的标准。 diff --git a/docs/source/zh/training/wuerstchen.md b/docs/source/zh/training/wuerstchen.md index 393a6c50157e..3a66f20de88d 100644 --- a/docs/source/zh/training/wuerstchen.md +++ b/docs/source/zh/training/wuerstchen.md @@ -16,7 +16,7 @@ specific language governing permissions and limitations under the License. 为了将先验模型放入 GPU 内存并加速训练，尝试分别启用 `gradient_accumulation_steps`、`gradient_checkpointing` 和 `mixed_precision`。 -本指南探讨 [train_text_to_image_prior.py](https://github.com/huggingface/diffusers/blob/main/examples/wuerstchen/text_to_image/train_text_to_image_prior.py) 脚本，帮助您更熟悉它，以及如何根据您的用例进行适配。 +本指南探讨 [train_text_to_image_prior.py](https://github.com/huggingface/diffusers/blob/main/examples/research_projects/wuerstchen/text_to_image/train_text_to_image_prior.py) 脚本，帮助您更熟悉它，以及如何根据您的用例进行适配。 在运行脚本之前，请确保从源代码安装库： @@ -59,7 +59,7 @@ write_basic_config() 最后，如果您想在自己的数据集上训练模型，请查看 [创建训练数据集](create_dataset) 指南，了解如何创建与训练脚本兼容的数据集。 > [!TIP] -> 以下部分重点介绍了训练脚本中对于理解如何修改它很重要的部分，但并未涵盖 [脚本](https://github.com/huggingface/diffusers/blob/main/examples/wuerstchen/text_to_image/train_text_to_image_prior.py) 的详细信息。如果您有兴趣了解更多，请随时阅读脚本，并告诉我们您是否有任何问题或疑虑。 +> 以下部分重点介绍了训练脚本中对于理解如何修改它很重要的部分，但并未涵盖 [脚本](https://github.com/huggingface/diffusers/blob/main/examples/research_projects/wuerstchen/text_to_image/train_text_to_image_prior.py) 的详细信息。如果您有兴趣了解更多，请随时阅读脚本，并告诉我们您是否有任何问题或疑虑。 ## 脚本参数 @@ -78,7 +78,7 @@ accelerate launch train_text_to_image_prior.py \ 训练脚本也与 [文本到图像](text2image#training-script) 训练指南类似，但已修改以支持 Wuerstchen。本指南重点介绍 Wuerstchen 训练脚本中独特的代码。 -[`main()`](https://github.com/huggingface/diffusers/blob/6e68c71503682c8693cb5b06a4da4911dfd655ee/examples/wuerstchen/text_to_image/train_text_to_image_prior.py#L441) 函数首先初始化图像编码器 - 一个 [EfficientNet](https://github.com/huggingface/diffusers/blob/main/examples/wuerstchen/text_to_image/modeling_efficient_net_encoder.py) - 以及通常的调度器和分词器。 +[`main()`](https://github.com/huggingface/diffusers/blob/6e68c71503682c8693cb5b06a4da4911dfd655ee/examples/wuerstchen/text_to_image/train_text_to_image_prior.py#L441) 函数首先初始化图像编码器 - 一个 [EfficientNet](https://github.com/huggingface/diffusers/blob/main/examples/research_projects/wuerstchen/text_to_image/modeling_efficient_net_encoder.py) - 以及通常的调度器和分词器。 ```py with ContextManagers(deepspeed_zero_init_disabled_context_manager()):