7879 - adjust documentation to use naruto dataset, since pokemon is now gated (#7880)

* 7879 - adjust documentation to use naruto dataset, since pokemon is now gated

* replace references to pokemon in docs

* more references to pokemon replaced

* Japanese translation update

---------
Co-authored-by: bghira <bghira@users.github.com>

examples/research_projects/scheduled_huber_loss_training/text_to_image/train_text_to_image_sdxl.py

@@ -60,7 +60,7 @@ logger = get_logger(__name__)
 
 
 DATASET_NAME_MAPPING = {
-    "lambdalabs/pokemon-blip-captions": ("image", "text"),
+    "lambdalabs/naruto-blip-captions": ("image", "text"),
 }
 
 

examples/text_to_image/README.md

@@ -57,7 +57,7 @@ With `gradient_checkpointing` and `mixed_precision` it should be possible to fin
 <!-- accelerate_snippet_start -->
 ```bash
 export MODEL_NAME="CompVis/stable-diffusion-v1-4"
-export DATASET_NAME="lambdalabs/pokemon-blip-captions"
+export DATASET_NAME="lambdalabs/naruto-blip-captions"
 
 accelerate launch --mixed_precision="fp16"  train_text_to_image.py \
   --pretrained_model_name_or_path=$MODEL_NAME \
@@ -136,7 +136,7 @@ for running distributed training with `accelerate`. Here is an example command:
 
 ```bash
 export MODEL_NAME="CompVis/stable-diffusion-v1-4"
-export DATASET_NAME="lambdalabs/pokemon-blip-captions"
+export DATASET_NAME="lambdalabs/naruto-blip-captions"
 
 accelerate launch --mixed_precision="fp16" --multi_gpu  train_text_to_image.py \
   --pretrained_model_name_or_path=$MODEL_NAME \
@@ -192,7 +192,7 @@ on consumer GPUs like Tesla T4, Tesla V100.
 
 ### Training
 
-First, you need to set up your development environment as is explained in the [installation section](#installing-the-dependencies). Make sure to set the `MODEL_NAME` and `DATASET_NAME` environment variables. Here, we will use [Stable Diffusion v1-4](https://hf.co/CompVis/stable-diffusion-v1-4) and the [Pokemons dataset](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions).
+First, you need to set up your development environment as is explained in the [installation section](#installing-the-dependencies). Make sure to set the `MODEL_NAME` and `DATASET_NAME` environment variables. Here, we will use [Stable Diffusion v1-4](https://hf.co/CompVis/stable-diffusion-v1-4) and the [Pokemons dataset](https://huggingface.co/datasets/lambdalabs/naruto-blip-captions).
 
 **___Note: Change the `resolution` to 768 if you are using the [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) 768x768 model.___**
 
@@ -200,7 +200,7 @@ First, you need to set up your development environment as is explained in the [i
 
 ```bash
 export MODEL_NAME="CompVis/stable-diffusion-v1-4"
-export DATASET_NAME="lambdalabs/pokemon-blip-captions"
+export DATASET_NAME="lambdalabs/naruto-blip-captions"
 ```
 
 For this example we want to directly store the trained LoRA embeddings on the Hub, so
@@ -282,7 +282,7 @@ pip install -U -r requirements_flax.txt
 
 ```bash
 export MODEL_NAME="duongna/stable-diffusion-v1-4-flax"
-export DATASET_NAME="lambdalabs/pokemon-blip-captions"
+export DATASET_NAME="lambdalabs/naruto-blip-captions"
 
 python train_text_to_image_flax.py \
   --pretrained_model_name_or_path=$MODEL_NAME \

examples/text_to_image/README_sdxl.md

@@ -52,7 +52,7 @@ Note also that we use PEFT library as backend for LoRA training, make sure to ha
 ```bash
 export MODEL_NAME="stabilityai/stable-diffusion-xl-base-1.0"
 export VAE_NAME="madebyollin/sdxl-vae-fp16-fix"
-export DATASET_NAME="lambdalabs/pokemon-blip-captions"
+export DATASET_NAME="lambdalabs/naruto-blip-captions"
 
 accelerate launch train_text_to_image_sdxl.py \
   --pretrained_model_name_or_path=$MODEL_NAME \
@@ -76,7 +76,7 @@ accelerate launch train_text_to_image_sdxl.py \
 
 **Notes**:
 
-*  The `train_text_to_image_sdxl.py` script pre-computes text embeddings and the VAE encodings and keeps them in memory. While for smaller datasets like [`lambdalabs/pokemon-blip-captions`](https://hf.co/datasets/lambdalabs/pokemon-blip-captions), it might not be a problem, it can definitely lead to memory problems when the script is used on a larger dataset. For those purposes, you would want to serialize these pre-computed representations to disk separately and load them during the fine-tuning process. Refer to [this PR](https://github.com/huggingface/diffusers/pull/4505) for a more in-depth discussion.
+*  The `train_text_to_image_sdxl.py` script pre-computes text embeddings and the VAE encodings and keeps them in memory. While for smaller datasets like [`lambdalabs/naruto-blip-captions`](https://hf.co/datasets/lambdalabs/naruto-blip-captions), it might not be a problem, it can definitely lead to memory problems when the script is used on a larger dataset. For those purposes, you would want to serialize these pre-computed representations to disk separately and load them during the fine-tuning process. Refer to [this PR](https://github.com/huggingface/diffusers/pull/4505) for a more in-depth discussion.
 * The training script is compute-intensive and may not run on a consumer GPU like Tesla T4.
 * The training command shown above performs intermediate quality validation in between the training epochs and logs the results to Weights and Biases. `--report_to`, `--validation_prompt`, and `--validation_epochs` are the relevant CLI arguments here.
 * SDXL's VAE is known to suffer from numerical instability issues. This is why we also expose a CLI argument namely `--pretrained_vae_model_name_or_path` that lets you specify the location of a better VAE (such as [this one](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix)).
@@ -142,14 +142,14 @@ on consumer GPUs like Tesla T4, Tesla V100.
 
 ### Training
 
-First, you need to set up your development environment as is explained in the [installation section](#installing-the-dependencies). Make sure to set the `MODEL_NAME` and `DATASET_NAME` environment variables and, optionally, the `VAE_NAME` variable. Here, we will use [Stable Diffusion XL 1.0-base](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) and the [Pokemons dataset](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions).
+First, you need to set up your development environment as is explained in the [installation section](#installing-the-dependencies). Make sure to set the `MODEL_NAME` and `DATASET_NAME` environment variables and, optionally, the `VAE_NAME` variable. Here, we will use [Stable Diffusion XL 1.0-base](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) and the [Pokemons dataset](https://huggingface.co/datasets/lambdalabs/naruto-blip-captions).
 
 **___Note: It is quite useful to monitor the training progress by regularly generating sample images during training. [Weights and Biases](https://docs.wandb.ai/quickstart) is a nice solution to easily see generating images during training. All you need to do is to run `pip install wandb` before training to automatically log images.___**
 
 ```bash
 export MODEL_NAME="stabilityai/stable-diffusion-xl-base-1.0"
 export VAE_NAME="madebyollin/sdxl-vae-fp16-fix"
-export DATASET_NAME="lambdalabs/pokemon-blip-captions"
+export DATASET_NAME="lambdalabs/naruto-blip-captions"
 ```
 
 For this example we want to directly store the trained LoRA embeddings on the Hub, so
@@ -219,7 +219,7 @@ You need to save the mentioned configuration as an `accelerate_config.yaml` file
 ```shell
 export MODEL_NAME="stabilityai/stable-diffusion-xl-base-1.0"
 export VAE_NAME="madebyollin/sdxl-vae-fp16-fix"
-export DATASET_NAME="lambdalabs/pokemon-blip-captions"
+export DATASET_NAME="lambdalabs/naruto-blip-captions"
 export ACCELERATE_CONFIG_FILE="your accelerate_config.yaml"
 
 accelerate launch  --config_file $ACCELERATE_CONFIG_FILE train_text_to_image_lora_sdxl.py \

examples/text_to_image/train_text_to_image.py

@@ -62,7 +62,7 @@ check_min_version("0.28.0.dev0")
 logger = get_logger(__name__, log_level="INFO")
 
 DATASET_NAME_MAPPING = {
-    "lambdalabs/pokemon-blip-captions": ("image", "text"),
+    "lambdalabs/naruto-blip-captions": ("image", "text"),
 }
 
 

examples/text_to_image/train_text_to_image_flax.py

@@ -250,7 +250,7 @@ def parse_args():
 
 
 dataset_name_mapping = {
-    "lambdalabs/pokemon-blip-captions": ("image", "text"),
+    "lambdalabs/naruto-blip-captions": ("image", "text"),
 }
 
 

examples/text_to_image/train_text_to_image_lora.py

@@ -387,7 +387,7 @@ def parse_args():
 
 
 DATASET_NAME_MAPPING = {
-    "lambdalabs/pokemon-blip-captions": ("image", "text"),
+    "lambdalabs/naruto-blip-captions": ("image", "text"),
 }
 
 

examples/text_to_image/train_text_to_image_lora_sdxl.py

@@ -454,7 +454,7 @@ def parse_args(input_args=None):
 
 
 DATASET_NAME_MAPPING = {
-    "lambdalabs/pokemon-blip-captions": ("image", "text"),
+    "lambdalabs/naruto-blip-captions": ("image", "text"),
 }
 
 

examples/text_to_image/train_text_to_image_sdxl.py

@@ -61,7 +61,7 @@ logger = get_logger(__name__)
 
 
 DATASET_NAME_MAPPING = {
-    "lambdalabs/pokemon-blip-captions": ("image", "text"),
+    "lambdalabs/naruto-blip-captions": ("image", "text"),
 }
 
 

examples/wuerstchen/text_to_image/README.md

@@ -37,7 +37,7 @@ You can fine-tune the Würstchen prior model with the `train_text_to_image_prior
 
 <!-- accelerate_snippet_start -->
 ```bash
-export DATASET_NAME="lambdalabs/pokemon-blip-captions"
+export DATASET_NAME="lambdalabs/naruto-blip-captions"
 
 accelerate launch  train_text_to_image_prior.py \
   --mixed_precision="fp16" \
@@ -72,10 +72,10 @@ In a nutshell, LoRA allows adapting pretrained models by adding pairs of rank-de
 
 ### Prior Training
 
-First, you need to set up your development environment as explained in the [installation](#Running-locally-with-PyTorch) section. Make sure to set the `DATASET_NAME` environment variable. Here, we will use the [Pokemon captions dataset](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions).
+First, you need to set up your development environment as explained in the [installation](#Running-locally-with-PyTorch) section. Make sure to set the `DATASET_NAME` environment variable. Here, we will use the [Pokemon captions dataset](https://huggingface.co/datasets/lambdalabs/naruto-blip-captions).
 
 ```bash
-export DATASET_NAME="lambdalabs/pokemon-blip-captions"
+export DATASET_NAME="lambdalabs/naruto-blip-captions"
 
 accelerate launch train_text_to_image_lora_prior.py \
   --mixed_precision="fp16" \

examples/wuerstchen/text_to_image/train_text_to_image_lora_prior.py

@@ -55,7 +55,7 @@ check_min_version("0.28.0.dev0")
 logger = get_logger(__name__, log_level="INFO")
 
 DATASET_NAME_MAPPING = {
-    "lambdalabs/pokemon-blip-captions": ("image", "text"),
+    "lambdalabs/naruto-blip-captions": ("image", "text"),
 }
 
 

examples/wuerstchen/text_to_image/train_text_to_image_prior.py

@@ -56,7 +56,7 @@ check_min_version("0.28.0.dev0")
 logger = get_logger(__name__, log_level="INFO")
 
 DATASET_NAME_MAPPING = {
-    "lambdalabs/pokemon-blip-captions": ("image", "text"),
+    "lambdalabs/naruto-blip-captions": ("image", "text"),
 }