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>
2026-01-27 17:22:53 +03:00 · 2024-05-07 10:36:39 -06:00
parent 23e091564f
commit 8edaf3b79c
31 changed files with 94 additions and 94 deletions
--- a/docs/source/en/training/kandinsky.md
+++ b/docs/source/en/training/kandinsky.md
@@ -205,7 +205,7 @@ model_pred = unet(noisy_latents, timesteps, None, added_cond_kwargs=added_cond_k

 Once you’ve made all your changes or you’re okay with the default configuration, you’re ready to launch the training script! 🚀

-You'll train on the [Pokémon BLIP captions](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions) dataset to generate your own Pokémon, but you can also create and train on your own dataset by following the [Create a dataset for training](create_dataset) guide. Set the environment variable `DATASET_NAME` to the name of the dataset on the Hub or if you're training on your own files, set the environment variable `TRAIN_DIR` to a path to your dataset.
+You'll train on the [Naruto BLIP captions](https://huggingface.co/datasets/lambdalabs/naruto-blip-captions) dataset to generate your own Naruto characters, but you can also create and train on your own dataset by following the [Create a dataset for training](create_dataset) guide. Set the environment variable `DATASET_NAME` to the name of the dataset on the Hub or if you're training on your own files, set the environment variable `TRAIN_DIR` to a path to your dataset.

 If you’re training on more than one GPU, add the `--multi_gpu` parameter to the `accelerate launch` command.

@@ -219,7 +219,7 @@ To monitor training progress with Weights & Biases, add the `--report_to=wandb`
 <hfoption id="prior model">

 ```bash
-export DATASET_NAME="lambdalabs/pokemon-blip-captions"
+export DATASET_NAME="lambdalabs/naruto-blip-captions"

 accelerate launch --mixed_precision="fp16"  train_text_to_image_prior.py \
  --dataset_name=$DATASET_NAME \
@@ -232,17 +232,17 @@ accelerate launch --mixed_precision="fp16"  train_text_to_image_prior.py \
  --checkpoints_total_limit=3 \
  --lr_scheduler="constant" \
  --lr_warmup_steps=0 \
-  --validation_prompts="A robot pokemon, 4k photo" \
+  --validation_prompts="A robot naruto, 4k photo" \
  --report_to="wandb" \
  --push_to_hub \
-  --output_dir="kandi2-prior-pokemon-model"
+  --output_dir="kandi2-prior-naruto-model"
 ```

 </hfoption>
 <hfoption id="decoder model">

 ```bash
-export DATASET_NAME="lambdalabs/pokemon-blip-captions"
+export DATASET_NAME="lambdalabs/naruto-blip-captions"

 accelerate launch --mixed_precision="fp16"  train_text_to_image_decoder.py \
  --dataset_name=$DATASET_NAME \
@@ -256,10 +256,10 @@ accelerate launch --mixed_precision="fp16"  train_text_to_image_decoder.py \
  --checkpoints_total_limit=3 \
  --lr_scheduler="constant" \
  --lr_warmup_steps=0 \
-  --validation_prompts="A robot pokemon, 4k photo" \
+  --validation_prompts="A robot naruto, 4k photo" \
  --report_to="wandb" \
  --push_to_hub \
-  --output_dir="kandi2-decoder-pokemon-model"
+  --output_dir="kandi2-decoder-naruto-model"
 ```

 </hfoption>
@@ -279,7 +279,7 @@ prior_components = {"prior_" + k: v for k,v in prior_pipeline.components.items()
 pipeline = AutoPipelineForText2Image.from_pretrained("kandinsky-community/kandinsky-2-2-decoder", **prior_components, torch_dtype=torch.float16)

 pipe.enable_model_cpu_offload()
-prompt="A robot pokemon, 4k photo"
+prompt="A robot naruto, 4k photo"
 image = pipeline(prompt=prompt, negative_prompt=negative_prompt).images[0]
 ```

@@ -299,7 +299,7 @@ import torch
 pipeline = AutoPipelineForText2Image.from_pretrained("path/to/saved/model", torch_dtype=torch.float16)
 pipeline.enable_model_cpu_offload()

-prompt="A robot pokemon, 4k photo"
+prompt="A robot naruto, 4k photo"
 image = pipeline(prompt=prompt).images[0]
 ```

@@ -313,7 +313,7 @@ unet = UNet2DConditionModel.from_pretrained("path/to/saved/model" + "/checkpoint
 pipeline = AutoPipelineForText2Image.from_pretrained("kandinsky-community/kandinsky-2-2-decoder", unet=unet, torch_dtype=torch.float16)
 pipeline.enable_model_cpu_offload()

-image = pipeline(prompt="A robot pokemon, 4k photo").images[0]
+image = pipeline(prompt="A robot naruto, 4k photo").images[0]
 ```

 </hfoption>
--- a/docs/source/en/training/lora.md
+++ b/docs/source/en/training/lora.md
@@ -170,7 +170,7 @@ Aside from setting up the LoRA layers, the training script is more or less the s

 Once you've made all your changes or you're okay with the default configuration, you're ready to launch the training script! 🚀

-Let's train on the [Pokémon BLIP captions](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions) dataset to generate our own Pokémon. Set the environment variables `MODEL_NAME` and `DATASET_NAME` to the model and dataset respectively. You should also specify where to save the model in `OUTPUT_DIR`, and the name of the model to save to on the Hub with `HUB_MODEL_ID`. The script creates and saves the following files to your repository:
+Let's train on the [Naruto BLIP captions](https://huggingface.co/datasets/lambdalabs/naruto-blip-captions) dataset to generate your own Naruto characters. Set the environment variables `MODEL_NAME` and `DATASET_NAME` to the model and dataset respectively. You should also specify where to save the model in `OUTPUT_DIR`, and the name of the model to save to on the Hub with `HUB_MODEL_ID`. The script creates and saves the following files to your repository:

 - saved model checkpoints
 - `pytorch_lora_weights.safetensors` (the trained LoRA weights)
@@ -185,9 +185,9 @@ A full training run takes ~5 hours on a 2080 Ti GPU with 11GB of VRAM.

 ```bash
 export MODEL_NAME="runwayml/stable-diffusion-v1-5"
-export OUTPUT_DIR="/sddata/finetune/lora/pokemon"
-export HUB_MODEL_ID="pokemon-lora"
-export DATASET_NAME="lambdalabs/pokemon-blip-captions"
+export OUTPUT_DIR="/sddata/finetune/lora/naruto"
+export HUB_MODEL_ID="naruto-lora"
+export DATASET_NAME="lambdalabs/naruto-blip-captions"

 accelerate launch --mixed_precision="fp16"  train_text_to_image_lora.py \
  --pretrained_model_name_or_path=$MODEL_NAME \
@@ -208,7 +208,7 @@ accelerate launch --mixed_precision="fp16"  train_text_to_image_lora.py \
  --hub_model_id=${HUB_MODEL_ID} \
  --report_to=wandb \
  --checkpointing_steps=500 \
-  --validation_prompt="A pokemon with blue eyes." \
+  --validation_prompt="A naruto with blue eyes." \
  --seed=1337
 ```

@@ -220,7 +220,7 @@ import torch

 pipeline = AutoPipelineForText2Image.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16).to("cuda")
 pipeline.load_lora_weights("path/to/lora/model", weight_name="pytorch_lora_weights.safetensors")
-image = pipeline("A pokemon with blue eyes").images[0]
+image = pipeline("A naruto with blue eyes").images[0]
 ```

 ## Next steps
--- a/docs/source/en/training/sdxl.md
+++ b/docs/source/en/training/sdxl.md
@@ -176,7 +176,7 @@ If you want to learn more about how the training loop works, check out the [Unde

 Once you’ve made all your changes or you’re okay with the default configuration, you’re ready to launch the training script! 🚀

-Let’s train on the [Pokémon BLIP captions](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions) dataset to generate your own Pokémon. Set the environment variables `MODEL_NAME` and `DATASET_NAME` to the model and the dataset (either from the Hub or a local path). You should also specify a VAE other than the SDXL VAE (either from the Hub or a local path) with `VAE_NAME` to avoid numerical instabilities.
+Let’s train on the [Naruto BLIP captions](https://huggingface.co/datasets/lambdalabs/naruto-blip-captions) dataset to generate your own Naruto characters. Set the environment variables `MODEL_NAME` and `DATASET_NAME` to the model and the dataset (either from the Hub or a local path). You should also specify a VAE other than the SDXL VAE (either from the Hub or a local path) with `VAE_NAME` to avoid numerical instabilities.

 <Tip>

@@ -187,7 +187,7 @@ To monitor training progress with Weights & Biases, add the `--report_to=wandb`
 ```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 \
@@ -211,7 +211,7 @@ accelerate launch train_text_to_image_sdxl.py \
  --validation_prompt="a cute Sundar Pichai creature" \
  --validation_epochs 5 \
  --checkpointing_steps=5000 \
-  --output_dir="sdxl-pokemon-model" \
+  --output_dir="sdxl-naruto-model" \
  --push_to_hub
 ```

@@ -226,9 +226,9 @@ import torch

 pipeline = DiffusionPipeline.from_pretrained("path/to/your/model", torch_dtype=torch.float16).to("cuda")

-prompt = "A pokemon with green eyes and red legs."
+prompt = "A naruto with green eyes and red legs."
 image = pipeline(prompt, num_inference_steps=30, guidance_scale=7.5).images[0]
-image.save("pokemon.png")
+image.save("naruto.png")
 ```

 </hfoption>
@@ -244,11 +244,11 @@ import torch_xla.core.xla_model as xm
 device = xm.xla_device()
 pipeline = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0").to(device)

-prompt = "A pokemon with green eyes and red legs."
+prompt = "A naruto with green eyes and red legs."
 start = time()
 image = pipeline(prompt, num_inference_steps=inference_steps).images[0]
 print(f'Compilation time is {time()-start} sec')
-image.save("pokemon.png")
+image.save("naruto.png")

 start = time()
 image = pipeline(prompt, num_inference_steps=inference_steps).images[0]
--- a/docs/source/en/training/text2image.md
+++ b/docs/source/en/training/text2image.md
@@ -158,7 +158,7 @@ Once you've made all your changes or you're okay with the default configuration,
 <hfoptions id="training-inference">
 <hfoption id="PyTorch">

-Let's train on the [Pokémon BLIP captions](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions) dataset to generate your own Pokémon. Set the environment variables `MODEL_NAME` and `dataset_name` to the model and the dataset (either from the Hub or a local path). If you're training on more than one GPU, add the `--multi_gpu` parameter to the `accelerate launch` command.
+Let's train on the [Naruto BLIP captions](https://huggingface.co/datasets/lambdalabs/naruto-blip-captions) dataset to generate your own Naruto characters. Set the environment variables `MODEL_NAME` and `dataset_name` to the model and the dataset (either from the Hub or a local path). If you're training on more than one GPU, add the `--multi_gpu` parameter to the `accelerate launch` command.

 <Tip>

@@ -168,7 +168,7 @@ To train on a local dataset, set the `TRAIN_DIR` and `OUTPUT_DIR` environment va

 ```bash
 export MODEL_NAME="runwayml/stable-diffusion-v1-5"
-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 \
@@ -183,7 +183,7 @@ accelerate launch --mixed_precision="fp16"  train_text_to_image.py \
  --max_grad_norm=1 \
  --enable_xformers_memory_efficient_attention
  --lr_scheduler="constant" --lr_warmup_steps=0 \
-  --output_dir="sd-pokemon-model" \
+  --output_dir="sd-naruto-model" \
  --push_to_hub
 ```

@@ -202,7 +202,7 @@ To train on a local dataset, set the `TRAIN_DIR` and `OUTPUT_DIR` environment va

 ```bash
 export MODEL_NAME="runwayml/stable-diffusion-v1-5"
-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 \
@@ -212,7 +212,7 @@ python train_text_to_image_flax.py \
  --max_train_steps=15000 \
  --learning_rate=1e-05 \
  --max_grad_norm=1 \
-  --output_dir="sd-pokemon-model" \
+  --output_dir="sd-naruto-model" \
  --push_to_hub
 ```

@@ -231,7 +231,7 @@ import torch
 pipeline = StableDiffusionPipeline.from_pretrained("path/to/saved_model", torch_dtype=torch.float16, use_safetensors=True).to("cuda")

 image = pipeline(prompt="yoda").images[0]
-image.save("yoda-pokemon.png")
+image.save("yoda-naruto.png")
 ```

 </hfoption>
@@ -246,7 +246,7 @@ from diffusers import FlaxStableDiffusionPipeline

 pipeline, params = FlaxStableDiffusionPipeline.from_pretrained("path/to/saved_model", dtype=jax.numpy.bfloat16)

-prompt = "yoda pokemon"
+prompt = "yoda naruto"
 prng_seed = jax.random.PRNGKey(0)
 num_inference_steps = 50

@@ -261,7 +261,7 @@ prompt_ids = shard(prompt_ids)

 images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images
 images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
-image.save("yoda-pokemon.png")
+image.save("yoda-naruto.png")
 ```

 </hfoption>
--- a/docs/source/en/training/wuerstchen.md
+++ b/docs/source/en/training/wuerstchen.md
@@ -131,7 +131,7 @@ If you want to learn more about how the training loop works, check out the [Unde

 Once you’ve made all your changes or you’re okay with the default configuration, you’re ready to launch the training script! 🚀

-Set the `DATASET_NAME` environment variable to the dataset name from the Hub. This guide uses the [Pokémon BLIP captions](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions) dataset, but you can create and train on your own datasets as well (see the [Create a dataset for training](create_dataset) guide).
+Set the `DATASET_NAME` environment variable to the dataset name from the Hub. This guide uses the [Naruto BLIP captions](https://huggingface.co/datasets/lambdalabs/naruto-blip-captions) dataset, but you can create and train on your own datasets as well (see the [Create a dataset for training](create_dataset) guide).

 <Tip>

@@ -140,7 +140,7 @@ To monitor training progress with Weights & Biases, add the `--report_to=wandb`
 </Tip>

 ```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" \
@@ -156,10 +156,10 @@ accelerate launch  train_text_to_image_prior.py \
  --checkpoints_total_limit=3 \
  --lr_scheduler="constant" \
  --lr_warmup_steps=0 \
-  --validation_prompts="A robot pokemon, 4k photo" \
+  --validation_prompts="A robot naruto, 4k photo" \
  --report_to="wandb" \
  --push_to_hub \
-  --output_dir="wuerstchen-prior-pokemon-model"
+  --output_dir="wuerstchen-prior-naruto-model"
 ```

 Once training is complete, you can use your newly trained model for inference!
@@ -171,7 +171,7 @@ from diffusers.pipelines.wuerstchen import DEFAULT_STAGE_C_TIMESTEPS

 pipeline = AutoPipelineForText2Image.from_pretrained("path/to/saved/model", torch_dtype=torch.float16).to("cuda")

-caption = "A cute bird pokemon holding a shield"
+caption = "A cute bird naruto holding a shield"
 images = pipeline(
    caption,
    width=1024,
--- a/docs/source/ko/training/lora.md
+++ b/docs/source/ko/training/lora.md
@@ -49,15 +49,15 @@ huggingface-cli login

 ### 학습[[dreambooth-training]]

-[Pokémon BLIP 캡션](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions) 데이터셋으로 [`stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5)를 파인튜닝해 나만의 포켓몬을 생성해 보겠습니다.
+[Naruto BLIP 캡션](https://huggingface.co/datasets/lambdalabs/naruto-blip-captions) 데이터셋으로 [`stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5)를 파인튜닝해 나만의 포켓몬을 생성해 보겠습니다.

 시작하려면 `MODEL_NAME` 및 `DATASET_NAME` 환경 변수가 설정되어 있는지 확인하십시오. `OUTPUT_DIR` 및 `HUB_MODEL_ID` 변수는 선택 사항이며 허브에서 모델을 저장할 위치를 지정합니다.

 ```bash
 export MODEL_NAME="runwayml/stable-diffusion-v1-5"
-export OUTPUT_DIR="/sddata/finetune/lora/pokemon"
-export HUB_MODEL_ID="pokemon-lora"
-export DATASET_NAME="lambdalabs/pokemon-blip-captions"
+export OUTPUT_DIR="/sddata/finetune/lora/naruto"
+export HUB_MODEL_ID="naruto-lora"
+export DATASET_NAME="lambdalabs/naruto-blip-captions"
 ```

 학습을 시작하기 전에 알아야 할 몇 가지 플래그가 있습니다.
--- a/docs/source/ko/training/text2image.md
+++ b/docs/source/ko/training/text2image.md
@@ -73,12 +73,12 @@ xFormers는 Flax에 사용할 수 없습니다.

 <frameworkcontent>
 <pt>
-다음과 같이 [Pokémon BLIP 캡션](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions) 데이터셋에서 파인튜닝 실행을 위해 [PyTorch 학습 스크립트](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py)를 실행합니다:
+다음과 같이 [Naruto BLIP 캡션](https://huggingface.co/datasets/lambdalabs/naruto-blip-captions) 데이터셋에서 파인튜닝 실행을 위해 [PyTorch 학습 스크립트](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py)를 실행합니다:


 ```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 train_text_to_image.py \
  --pretrained_model_name_or_path=$MODEL_NAME \
@@ -93,7 +93,7 @@ accelerate launch train_text_to_image.py \
  --learning_rate=1e-05 \
  --max_grad_norm=1 \
  --lr_scheduler="constant" --lr_warmup_steps=0 \
-  --output_dir="sd-pokemon-model" 
+  --output_dir="sd-naruto-model" 
 ```

 자체 데이터셋으로 파인튜닝하려면 🤗 [Datasets](https://huggingface.co/docs/datasets/index)에서 요구하는 형식에 따라 데이터셋을 준비하세요. [데이터셋을 허브에 업로드](https://huggingface.co/docs/datasets/image_dataset#upload-dataset-to-the-hub)하거나 [파일들이 있는 로컬 폴더를 준비](https ://huggingface.co/docs/datasets/image_dataset#imagefolder)할 수 있습니다.
@@ -136,7 +136,7 @@ pip install -U -r requirements_flax.txt

 ```bash
 export MODEL_NAME="runwayml/stable-diffusion-v1-5"
-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 \
@@ -146,7 +146,7 @@ python train_text_to_image_flax.py \
  --max_train_steps=15000 \
  --learning_rate=1e-05 \
  --max_grad_norm=1 \
-  --output_dir="sd-pokemon-model" 
+  --output_dir="sd-naruto-model" 
 ```

 자체 데이터셋으로 파인튜닝하려면 🤗 [Datasets](https://huggingface.co/docs/datasets/index)에서 요구하는 형식에 따라 데이터셋을 준비하세요. [데이터셋을 허브에 업로드](https://huggingface.co/docs/datasets/image_dataset#upload-dataset-to-the-hub)하거나 [파일들이 있는 로컬 폴더를 준비](https ://huggingface.co/docs/datasets/image_dataset#imagefolder)할 수 있습니다.
@@ -166,7 +166,7 @@ python train_text_to_image_flax.py \
  --max_train_steps=15000 \
  --learning_rate=1e-05 \
  --max_grad_norm=1 \
-  --output_dir="sd-pokemon-model"
+  --output_dir="sd-naruto-model"
 ```
 </jax>
 </frameworkcontent>
@@ -189,7 +189,7 @@ pipe = StableDiffusionPipeline.from_pretrained(model_path, torch_dtype=torch.flo
 pipe.to("cuda")

 image = pipe(prompt="yoda").images[0]
-image.save("yoda-pokemon.png")
+image.save("yoda-naruto.png")
 ```
 </pt>
 <jax>
@@ -203,7 +203,7 @@ from diffusers import FlaxStableDiffusionPipeline
 model_path = "path_to_saved_model"
 pipe, params = FlaxStableDiffusionPipeline.from_pretrained(model_path, dtype=jax.numpy.bfloat16)

-prompt = "yoda pokemon"
+prompt = "yoda naruto"
 prng_seed = jax.random.PRNGKey(0)
 num_inference_steps = 50

@@ -218,7 +218,7 @@ prompt_ids = shard(prompt_ids)

 images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images
 images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
-image.save("yoda-pokemon.png")
+image.save("yoda-naruto.png")
 ```
 </jax>
 </frameworkcontent>
--- a/docs/source/ko/training/unconditional_training.md
+++ b/docs/source/ko/training/unconditional_training.md
@@ -103,13 +103,13 @@ accelerate launch train_unconditional.py \
 <div class="flex justify-center">
    <img src="https://user-images.githubusercontent.com/26864830/180248660-a0b143d0-b89a-42c5-8656-2ebf6ece7e52.png"/>
 </div>
-[Pokemon](https://huggingface.co/datasets/huggan/pokemon) 데이터셋을 사용할 경우:
+[Naruto](https://huggingface.co/datasets/lambdalabs/naruto-blip-captions) 데이터셋을 사용할 경우:

 ```bash
 accelerate launch train_unconditional.py \
-  --dataset_name="huggan/pokemon" \
+  --dataset_name="lambdalabs/naruto-blip-captions" \
  --resolution=64 \
-  --output_dir="ddpm-ema-pokemon-64" \
+  --output_dir="ddpm-ema-naruto-64" \
  --train_batch_size=16 \
  --num_epochs=100 \
  --gradient_accumulation_steps=1 \
@@ -129,9 +129,9 @@ accelerate launch train_unconditional.py \

 ```bash
 accelerate launch --mixed_precision="fp16" --multi_gpu train_unconditional.py \
-  --dataset_name="huggan/pokemon" \
+  --dataset_name="lambdalabs/naruto-blip-captions" \
  --resolution=64 --center_crop --random_flip \
-  --output_dir="ddpm-ema-pokemon-64" \
+  --output_dir="ddpm-ema-naruto-64" \
  --train_batch_size=16 \
  --num_epochs=100 \
  --gradient_accumulation_steps=1 \
--- a/examples/consistency_distillation/README_sdxl.md
+++ b/examples/consistency_distillation/README_sdxl.md
@@ -115,11 +115,11 @@ accelerate launch train_lcm_distill_lora_sdxl_wds.py \

 We provide another version for LCM LoRA SDXL that follows best practices of `peft` and leverages the `datasets` library for quick experimentation. The script doesn't load two UNets unlike `train_lcm_distill_lora_sdxl_wds.py` which reduces the memory requirements quite a bit. 

-Below is an example training command that trains an LCM LoRA on the [Pokemons dataset](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions):
+Below is an example training command that trains an LCM LoRA on the [Pokemons dataset](https://huggingface.co/datasets/lambdalabs/naruto-blip-captions):

 ```bash
 export MODEL_NAME="stabilityai/stable-diffusion-xl-base-1.0"
-export DATASET_NAME="lambdalabs/pokemon-blip-captions"
+export DATASET_NAME="lambdalabs/naruto-blip-captions"
 export VAE_PATH="madebyollin/sdxl-vae-fp16-fix"

 accelerate launch train_lcm_distill_lora_sdxl.py \
--- a/examples/consistency_distillation/train_lcm_distill_lora_sdxl.py
+++ b/examples/consistency_distillation/train_lcm_distill_lora_sdxl.py
@@ -71,7 +71,7 @@ check_min_version("0.28.0.dev0")
 logger = get_logger(__name__)

 DATASET_NAME_MAPPING = {
-    "lambdalabs/pokemon-blip-captions": ("image", "text"),
+    "lambdalabs/naruto-blip-captions": ("image", "text"),
 }


--- a/examples/kandinsky2_2/text_to_image/README.md
+++ b/examples/kandinsky2_2/text_to_image/README.md
@@ -57,7 +57,7 @@ To disable wandb logging, remove the `--report_to=="wandb"` and `--validation_pr

 <!-- accelerate_snippet_start -->
 ```bash
-export DATASET_NAME="lambdalabs/pokemon-blip-captions"
+export DATASET_NAME="lambdalabs/naruto-blip-captions"

 accelerate launch --mixed_precision="fp16"  train_text_to_image_decoder.py \
  --dataset_name=$DATASET_NAME \
@@ -139,7 +139,7 @@ You can fine-tune the Kandinsky prior model with `train_text_to_image_prior.py`

 <!-- accelerate_snippet_start -->
 ```bash
-export DATASET_NAME="lambdalabs/pokemon-blip-captions"
+export DATASET_NAME="lambdalabs/naruto-blip-captions"

 accelerate launch --mixed_precision="fp16"  train_text_to_image_prior.py \
  --dataset_name=$DATASET_NAME \
@@ -183,7 +183,7 @@ If you want to use a fine-tuned decoder checkpoint along with your fine-tuned pr
 for running distributed training with `accelerate`. Here is an example command:

 ```bash
-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_decoder.py \
  --dataset_name=$DATASET_NAME \
@@ -227,13 +227,13 @@ on consumer GPUs like Tesla T4, Tesla V100.

 ### Training

-First, you need to set up your development environment as explained in the [installation](#installing-the-dependencies). Make sure to set the `MODEL_NAME` and `DATASET_NAME` environment variables. Here, we will use [Kandinsky 2.2](https://huggingface.co/kandinsky-community/kandinsky-2-2-decoder) and the [Pokemons dataset](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions).  
+First, you need to set up your development environment as explained in the [installation](#installing-the-dependencies). Make sure to set the `MODEL_NAME` and `DATASET_NAME` environment variables. Here, we will use [Kandinsky 2.2](https://huggingface.co/kandinsky-community/kandinsky-2-2-decoder) and the [Pokemons dataset](https://huggingface.co/datasets/lambdalabs/naruto-blip-captions).  


 #### Train decoder 

 ```bash
-export DATASET_NAME="lambdalabs/pokemon-blip-captions"
+export DATASET_NAME="lambdalabs/naruto-blip-captions"

 accelerate launch --mixed_precision="fp16" train_text_to_image_decoder_lora.py \
  --dataset_name=$DATASET_NAME --caption_column="text" \
@@ -252,7 +252,7 @@ accelerate launch --mixed_precision="fp16" train_text_to_image_decoder_lora.py \
 #### Train prior

 ```bash
-export DATASET_NAME="lambdalabs/pokemon-blip-captions"
+export DATASET_NAME="lambdalabs/naruto-blip-captions"

 accelerate launch --mixed_precision="fp16" train_text_to_image_prior_lora.py \
  --dataset_name=$DATASET_NAME --caption_column="text" \
--- a/examples/kandinsky2_2/text_to_image/train_text_to_image_lora_prior.py
+++ b/examples/kandinsky2_2/text_to_image/train_text_to_image_lora_prior.py
@@ -332,7 +332,7 @@ def parse_args():


 DATASET_NAME_MAPPING = {
-    "lambdalabs/pokemon-blip-captions": ("image", "text"),
+    "lambdalabs/naruto-blip-captions": ("image", "text"),
 }


--- a/examples/kandinsky2_2/text_to_image/train_text_to_image_prior.py
+++ b/examples/kandinsky2_2/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"),
 }


--- a/examples/research_projects/lora/README.md
+++ b/examples/research_projects/lora/README.md
@@ -19,7 +19,7 @@ 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.___**

@@ -27,7 +27,7 @@ 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 
--- a/examples/research_projects/lora/train_text_to_image_lora.py
+++ b/examples/research_projects/lora/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"),
 }


--- a/examples/research_projects/onnxruntime/text_to_image/README.md
+++ b/examples/research_projects/onnxruntime/text_to_image/README.md
@@ -55,7 +55,7 @@ The command to train a DDPM UNetCondition model on the Pokemon dataset with onnx

 ```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=$MODEL_NAME \
  --dataset_name=$dataset_name \
--- a/examples/research_projects/onnxruntime/text_to_image/train_text_to_image.py
+++ b/examples/research_projects/onnxruntime/text_to_image/train_text_to_image.py
@@ -59,7 +59,7 @@ check_min_version("0.17.0.dev0")
 logger = get_logger(__name__, log_level="INFO")

 DATASET_NAME_MAPPING = {
-    "lambdalabs/pokemon-blip-captions": ("image", "text"),
+    "lambdalabs/naruto-blip-captions": ("image", "text"),
 }


--- a/examples/research_projects/scheduled_huber_loss_training/text_to_image/train_text_to_image.py
+++ b/examples/research_projects/scheduled_huber_loss_training/text_to_image/train_text_to_image.py
@@ -61,7 +61,7 @@ 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"),
 }


--- a/examples/research_projects/scheduled_huber_loss_training/text_to_image/train_text_to_image_lora.py
+++ b/examples/research_projects/scheduled_huber_loss_training/text_to_image/train_text_to_image_lora.py
@@ -406,7 +406,7 @@ def parse_args():


 DATASET_NAME_MAPPING = {
-    "lambdalabs/pokemon-blip-captions": ("image", "text"),
+    "lambdalabs/naruto-blip-captions": ("image", "text"),
 }


--- a/examples/research_projects/scheduled_huber_loss_training/text_to_image/train_text_to_image_lora_sdxl.py
+++ b/examples/research_projects/scheduled_huber_loss_training/text_to_image/train_text_to_image_lora_sdxl.py
@@ -468,7 +468,7 @@ def parse_args(input_args=None):


 DATASET_NAME_MAPPING = {
-    "lambdalabs/pokemon-blip-captions": ("image", "text"),
+    "lambdalabs/naruto-blip-captions": ("image", "text"),
 }


--- a/examples/research_projects/scheduled_huber_loss_training/text_to_image/train_text_to_image_sdxl.py
+++ b/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"),
 }


--- a/examples/text_to_image/README.md
+++ b/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 \
--- a/examples/text_to_image/README_sdxl.md
+++ b/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 \
--- a/examples/text_to_image/train_text_to_image.py
+++ b/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"),
 }


--- a/examples/text_to_image/train_text_to_image_flax.py
+++ b/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"),
 }


--- a/examples/text_to_image/train_text_to_image_lora.py
+++ b/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"),
 }


--- a/examples/text_to_image/train_text_to_image_lora_sdxl.py
+++ b/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"),
 }


--- a/examples/text_to_image/train_text_to_image_sdxl.py
+++ b/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"),
 }


--- a/examples/wuerstchen/text_to_image/README.md
+++ b/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" \
--- a/examples/wuerstchen/text_to_image/train_text_to_image_lora_prior.py
+++ b/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"),
 }


--- a/examples/wuerstchen/text_to_image/train_text_to_image_prior.py
+++ b/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"),
 }