Fix moved Wuerstchen example path in training docs

docs/source/en/training/lora.md

@@ -18,7 +18,7 @@ specific language governing permissions and limitations under the License.
 [LoRA (Low-Rank Adaptation of Large Language Models)](https://hf.co/papers/2106.09685) is a popular and lightweight training technique that significantly reduces the number of trainable parameters. It works by inserting a smaller number of new weights into the model and only these are trained. This makes training with LoRA much faster, memory-efficient, and produces smaller model weights (a few hundred MBs), which are easier to store and share. LoRA can also be combined with other training techniques like DreamBooth to speedup training.
 
 > [!TIP]
-> LoRA is very versatile and supported for [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), [text-to-image](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_lora.py), and [Wuerstchen](https://github.com/huggingface/diffusers/blob/main/examples/wuerstchen/text_to_image/train_text_to_image_lora_prior.py).
+> LoRA is very versatile and supported for [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), [text-to-image](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_lora.py), and [Wuerstchen](https://github.com/huggingface/diffusers/blob/main/examples/research_projects/wuerstchen/text_to_image/train_text_to_image_lora_prior.py).
 
 This guide will explore the [train_text_to_image_lora.py](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_lora.py) script to help you become more familiar with it, and how you can adapt it for your own use-case.
 

docs/source/en/training/overview.md

@@ -34,7 +34,7 @@ Our current collection of training scripts include:
 | [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) |  | 👍 |
 
 These examples are **actively** maintained, so please feel free to open an issue if they aren't working as expected. If you feel like another training example should be included, you're more than welcome to start a [Feature Request](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feature_request.md&title=) to discuss your feature idea with us and whether it meets our criteria of being self-contained, easy-to-tweak, beginner-friendly, and single-purpose.
 

docs/source/en/training/wuerstchen.md

@@ -16,7 +16,7 @@ The [Wuerstchen](https://hf.co/papers/2306.00637) model drastically reduces comp
 
 To fit the prior model into GPU memory and to speedup training, try enabling `gradient_accumulation_steps`, `gradient_checkpointing`, and `mixed_precision` respectively.
 
-This guide explores the [train_text_to_image_prior.py](https://github.com/huggingface/diffusers/blob/main/examples/wuerstchen/text_to_image/train_text_to_image_prior.py) script to help you become more familiar with it, and how you can adapt it for your own use-case.
+This guide explores the [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) script to help you become more familiar with it, and how you can adapt it for your own use-case.
 
 Before running the script, make sure you install the library from source:
 
@@ -29,7 +29,7 @@ pip install .
 Then navigate to the example folder containing the training script and install the required dependencies for the script you're using:
 
 ```bash
-cd examples/wuerstchen/text_to_image
+cd examples/research_projects/wuerstchen/text_to_image
 pip install -r requirements.txt
 ```
 
@@ -59,11 +59,11 @@ write_basic_config()
 Lastly, if you want to train a model on your own dataset, take a look at the [Create a dataset for training](create_dataset) guide to learn how to create a dataset that works with the training script.
 
 > [!TIP]
-> The following sections highlight parts of the training scripts that are important for understanding how to modify it, but it doesn't cover every aspect of the [script](https://github.com/huggingface/diffusers/blob/main/examples/wuerstchen/text_to_image/train_text_to_image_prior.py) in detail. If you're interested in learning more, feel free to read through the scripts and let us know if you have any questions or concerns.
+> The following sections highlight parts of the training scripts that are important for understanding how to modify it, but it doesn't cover every aspect of the [script](https://github.com/huggingface/diffusers/blob/main/examples/research_projects/wuerstchen/text_to_image/train_text_to_image_prior.py) in detail. If you're interested in learning more, feel free to read through the scripts and let us know if you have any questions or concerns.
 
 ## Script parameters
 
-The training scripts provides many parameters to help you customize your training run. All of the parameters and their descriptions are found in the [`parse_args()`](https://github.com/huggingface/diffusers/blob/6e68c71503682c8693cb5b06a4da4911dfd655ee/examples/wuerstchen/text_to_image/train_text_to_image_prior.py#L192) function. It provides default values for each parameter, such as the training batch size and learning rate, but you can also set your own values in the training command if you'd like.
+The training scripts provides many parameters to help you customize your training run. All of the parameters and their descriptions are found in the [`parse_args()`](https://github.com/huggingface/diffusers/blob/6e68c71503682c8693cb5b06a4da4911dfd655ee/examples/research_projects/wuerstchen/text_to_image/train_text_to_image_prior.py#L192) function. It provides default values for each parameter, such as the training batch size and learning rate, but you can also set your own values in the training command if you'd like.
 
 For example, to speedup training with mixed precision using the fp16 format, add the `--mixed_precision` parameter to the training command:
 
@@ -78,7 +78,7 @@ Most of the parameters are identical to the parameters in the [Text-to-image](te
 
 The training script is also similar to the [Text-to-image](text2image#training-script) training guide, but it's been modified to support Wuerstchen. This guide focuses on the code that is unique to the Wuerstchen training script.
 
-The [`main()`](https://github.com/huggingface/diffusers/blob/6e68c71503682c8693cb5b06a4da4911dfd655ee/examples/wuerstchen/text_to_image/train_text_to_image_prior.py#L441) function starts by initializing the image encoder - an [EfficientNet](https://github.com/huggingface/diffusers/blob/main/examples/wuerstchen/text_to_image/modeling_efficient_net_encoder.py) - in addition to the usual scheduler and tokenizer.
+The [`main()`](https://github.com/huggingface/diffusers/blob/6e68c71503682c8693cb5b06a4da4911dfd655ee/examples/research_projects/wuerstchen/text_to_image/train_text_to_image_prior.py#L441) function starts by initializing the image encoder - an [EfficientNet](https://github.com/huggingface/diffusers/blob/main/examples/research_projects/wuerstchen/text_to_image/modeling_efficient_net_encoder.py) - in addition to the usual scheduler and tokenizer.
 
 ```py
 with ContextManagers(deepspeed_zero_init_disabled_context_manager()):
@@ -103,7 +103,7 @@ optimizer = optimizer_cls(
 )
 ```
 
-Next, you'll apply some [transforms](https://github.com/huggingface/diffusers/blob/65ef7a0c5c594b4f84092e328fbdd73183613b30/examples/wuerstchen/text_to_image/train_text_to_image_prior.py#L656) to the images and [tokenize](https://github.com/huggingface/diffusers/blob/65ef7a0c5c594b4f84092e328fbdd73183613b30/examples/wuerstchen/text_to_image/train_text_to_image_prior.py#L637) the captions:
+Next, you'll apply some [transforms](https://github.com/huggingface/diffusers/blob/65ef7a0c5c594b4f84092e328fbdd73183613b30/examples/research_projects/wuerstchen/text_to_image/train_text_to_image_prior.py#L656) to the images and [tokenize](https://github.com/huggingface/diffusers/blob/65ef7a0c5c594b4f84092e328fbdd73183613b30/examples/research_projects/wuerstchen/text_to_image/train_text_to_image_prior.py#L637) the captions:
 
 ```py
 def preprocess_train(examples):
@@ -113,7 +113,7 @@ def preprocess_train(examples):
     return examples
 ```
 
-Finally, the [training loop](https://github.com/huggingface/diffusers/blob/65ef7a0c5c594b4f84092e328fbdd73183613b30/examples/wuerstchen/text_to_image/train_text_to_image_prior.py#L656) handles compressing the images to latent space with the `EfficientNetEncoder`, adding noise to the latents, and predicting the noise residual with the [`WuerstchenPrior`] model.
+Finally, the [training loop](https://github.com/huggingface/diffusers/blob/65ef7a0c5c594b4f84092e328fbdd73183613b30/examples/research_projects/wuerstchen/text_to_image/train_text_to_image_prior.py#L656) handles compressing the images to latent space with the `EfficientNetEncoder`, adding noise to the latents, and predicting the noise residual with the [`WuerstchenPrior`] model.
 
 ```py
 pred_noise = prior(noisy_latents, timesteps, prompt_embeds)