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

bghira · bghira · commit 78de3b763fe6 · 2024-05-07T07:16:03.000-06:00
diff --git 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 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.
 
 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 \
@@ -242,7 +242,7 @@ accelerate launch --mixed_precision="fp16"  train_text_to_image_prior.py \
 <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 \
diff --git 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 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:
 
 - saved model checkpoints
 - `pytorch_lora_weights.safetensors` (the trained LoRA weights)
@@ -187,7 +187,7 @@ A full training run takes ~5 hours on a 2080 Ti GPU with 11GB of VRAM.
 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 DATASET_NAME="lambdalabs/naruto-blip-captions"
 
 accelerate launch --mixed_precision="fp16"  train_text_to_image_lora.py \
   --pretrained_model_name_or_path=$MODEL_NAME \
diff --git 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 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.
 
 <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 \
diff --git 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 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.
 
 <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 \
@@ -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 \
diff --git 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" \
diff --git 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.
+[Pokémon 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 DATASET_NAME="lambdalabs/naruto-blip-captions"
 ```
 
 학습을 시작하기 전에 알아야 할 몇 가지 플래그가 있습니다.
diff --git 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 [Pokémon 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 \
@@ -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 \
diff --git 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 \
diff --git a/examples/consistency_distillation/train_lcm_distill_lora_sdxl.py b/examples/consistency_distillation/train_lcm_distill_lora_sdxl.py
@@ -71,7 +71,7 @@
 logger = get_logger(__name__)
 
 DATASET_NAME_MAPPING = {
-    "lambdalabs/pokemon-blip-captions": ("image", "text"),
+    "lambdalabs/naruto-blip-captions": ("image", "text"),
 }
 
 
diff --git 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" \
diff --git 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"),
 }
 
 
diff --git 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 @@
 logger = get_logger(__name__, log_level="INFO")
 
 DATASET_NAME_MAPPING = {
-    "lambdalabs/pokemon-blip-captions": ("image", "text"),
+    "lambdalabs/naruto-blip-captions": ("image", "text"),
 }
 
 
diff --git a/examples/research_projects/lora/README.md b/examples/research_projects/lora/README.md
@@ -19,15 +19,15 @@ 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.___**
 
 **___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="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 
diff --git 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"),
 }
 
 
diff --git 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 \
diff --git 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 @@
 logger = get_logger(__name__, log_level="INFO")
 
 DATASET_NAME_MAPPING = {
-    "lambdalabs/pokemon-blip-captions": ("image", "text"),
+    "lambdalabs/naruto-blip-captions": ("image", "text"),
 }
 
 
diff --git 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 @@
 logger = get_logger(__name__, log_level="INFO")
 
 DATASET_NAME_MAPPING = {
-    "lambdalabs/pokemon-blip-captions": ("image", "text"),
+    "lambdalabs/naruto-blip-captions": ("image", "text"),
 }
 
 
diff --git 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"),
 }
 
 
diff --git 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"),
 }
 
 
diff --git 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 @@
 
 
 DATASET_NAME_MAPPING = {
-    "lambdalabs/pokemon-blip-captions": ("image", "text"),
+    "lambdalabs/naruto-blip-captions": ("image", "text"),
 }
 
 
diff --git a/examples/text_to_image/README.md b/examples/text_to_image/README.md
diff --git a/examples/text_to_image/README_sdxl.md b/examples/text_to_image/README_sdxl.md
diff --git a/examples/text_to_image/train_text_to_image.py b/examples/text_to_image/train_text_to_image.py
diff --git a/examples/text_to_image/train_text_to_image_flax.py b/examples/text_to_image/train_text_to_image_flax.py
diff --git a/examples/text_to_image/train_text_to_image_lora.py b/examples/text_to_image/train_text_to_image_lora.py
diff --git a/examples/text_to_image/train_text_to_image_lora_sdxl.py b/examples/text_to_image/train_text_to_image_lora_sdxl.py
diff --git a/examples/text_to_image/train_text_to_image_sdxl.py b/examples/text_to_image/train_text_to_image_sdxl.py
diff --git a/examples/wuerstchen/text_to_image/README.md b/examples/wuerstchen/text_to_image/README.md
diff --git 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
diff --git a/examples/wuerstchen/text_to_image/train_text_to_image_prior.py b/examples/wuerstchen/text_to_image/train_text_to_image_prior.py

Original file line number	Diff line number	Diff line change
`@@ -71,7 +71,7 @@`
`71`	`71`	`logger = get_logger(__name__)`
`72`	`72`
`73`	`73`	`DATASET_NAME_MAPPING = {`
`74`		`- "lambdalabs/pokemon-blip-captions": ("image", "text"),`
	`74`	`+ "lambdalabs/naruto-blip-captions": ("image", "text"),`
`75`	`75`	`}`
`76`	`76`
`77`	`77`