[docs] Clarify training args (#3146)

* clarify training arg

* apply feedback

docs/source/en/training/controlnet.mdx

@@ -74,6 +74,7 @@ wget https://huggingface.co/datasets/huggingface/documentation-images/resolve/ma
 wget https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_2.png
 ```
 
+Specify the `MODEL_NAME` environment variable (either a Hub model repository id or a path to the directory containing the model weights) and pass it to the [`~diffusers.DiffusionPipeline.from_pretrained.pretrained_model_name_or_path`] argument.
 
 ```bash
 export MODEL_DIR="runwayml/stable-diffusion-v1-5"

docs/source/en/training/dreambooth.mdx

@@ -50,6 +50,20 @@ from accelerate.utils import write_basic_config
 write_basic_config()
 ```
 
+Finally, download a [few images of a dog](https://huggingface.co/datasets/diffusers/dog-example) to DreamBooth with:
+
+```py
+from huggingface_hub import snapshot_download
+
+local_dir = "./dog"
+snapshot_download(
+    "diffusers/dog-example",
+    local_dir=local_dir,
+    repo_type="dataset",
+    ignore_patterns=".gitattributes",
+)
+```
+
 ## Finetuning
 
 <Tip warning={true}>
@@ -60,22 +74,13 @@ DreamBooth finetuning is very sensitive to hyperparameters and easy to overfit.
 
 <frameworkcontent>
 <pt>
-Let's try DreamBooth with a
-[few images of a dog](https://huggingface.co/datasets/diffusers/dog-example);
-download and save them to a directory and then set the `INSTANCE_DIR` environment variable to that path:
+Set the `INSTANCE_DIR` environment variable to the path of the directory containing the dog images.
 
-```python 
-local_dir = "./path_to_training_images"
-snapshot_download(
-    "diffusers/dog-example",
-    local_dir=local_dir, repo_type="dataset",
-    ignore_patterns=".gitattributes",
-)
-```
+Specify the `MODEL_NAME` environment variable (either a Hub model repository id or a path to the directory containing the model weights) and pass it to the [`~diffusers.DiffusionPipeline.from_pretrained.pretrained_model_name_or_path`] argument.
 
 ```bash
 export MODEL_NAME="CompVis/stable-diffusion-v1-4"
-export INSTANCE_DIR="path_to_training_images"
+export INSTANCE_DIR="./dog"
 export OUTPUT_DIR="path_to_saved_model"
 ```
 
@@ -105,11 +110,13 @@ Before running the script, make sure you have the requirements installed:
 pip install -U -r requirements.txt
 ```
 
+Specify the `MODEL_NAME` environment variable (either a Hub model repository id or a path to the directory containing the model weights) and pass it to the [`~diffusers.DiffusionPipeline.from_pretrained.pretrained_model_name_or_path`] argument.
+
 Now you can launch the training script with the following command:
 
 ```bash
 export MODEL_NAME="duongna/stable-diffusion-v1-4-flax"
-export INSTANCE_DIR="path-to-instance-images"
+export INSTANCE_DIR="./dog"
 export OUTPUT_DIR="path-to-save-model"
 
 python train_dreambooth_flax.py \
@@ -135,7 +142,7 @@ The authors recommend generating `num_epochs * num_samples` images for prior pre
 <pt>
 ```bash
 export MODEL_NAME="CompVis/stable-diffusion-v1-4"
-export INSTANCE_DIR="path_to_training_images"
+export INSTANCE_DIR="./dog"
 export CLASS_DIR="path_to_class_images"
 export OUTPUT_DIR="path_to_saved_model"
 
@@ -160,7 +167,7 @@ accelerate launch train_dreambooth.py \
 <jax>
 ```bash
 export MODEL_NAME="duongna/stable-diffusion-v1-4-flax"
-export INSTANCE_DIR="path-to-instance-images"
+export INSTANCE_DIR="./dog"
 export CLASS_DIR="path-to-class-images"
 export OUTPUT_DIR="path-to-save-model"
 
@@ -197,7 +204,7 @@ Pass the `--train_text_encoder` argument to the training script to enable finetu
 <pt>
 ```bash
 export MODEL_NAME="CompVis/stable-diffusion-v1-4"
-export INSTANCE_DIR="path_to_training_images"
+export INSTANCE_DIR="./dog"
 export CLASS_DIR="path_to_class_images"
 export OUTPUT_DIR="path_to_saved_model"
 
@@ -224,7 +231,7 @@ accelerate launch train_dreambooth.py \
 <jax>
 ```bash
 export MODEL_NAME="duongna/stable-diffusion-v1-4-flax"
-export INSTANCE_DIR="path-to-instance-images"
+export INSTANCE_DIR="./dog"
 export CLASS_DIR="path-to-class-images"
 export OUTPUT_DIR="path-to-save-model"
 
@@ -360,7 +367,7 @@ Then pass the `--use_8bit_adam` option to the training script:
 
 ```bash
 export MODEL_NAME="CompVis/stable-diffusion-v1-4"
-export INSTANCE_DIR="path_to_training_images"
+export INSTANCE_DIR="./dog"
 export CLASS_DIR="path_to_class_images"
 export OUTPUT_DIR="path_to_saved_model"
 
@@ -389,7 +396,7 @@ To run DreamBooth on a 12GB GPU, you'll need to enable gradient checkpointing, t
 
 ```bash
 export MODEL_NAME="CompVis/stable-diffusion-v1-4"
-export INSTANCE_DIR="path-to-instance-images"
+export INSTANCE_DIR="./dog"
 export CLASS_DIR="path-to-class-images"
 export OUTPUT_DIR="path-to-save-model"
 
@@ -436,7 +443,7 @@ Launch training with the following command:
 
 ```bash
 export MODEL_NAME="CompVis/stable-diffusion-v1-4"
-export INSTANCE_DIR="path_to_training_images"
+export INSTANCE_DIR="./dog"
 export CLASS_DIR="path_to_class_images"
 export OUTPUT_DIR="path_to_saved_model"
 

docs/source/en/training/instructpix2pix.mdx

@@ -74,8 +74,7 @@ write_basic_config()
 As mentioned before, we'll use a [small toy dataset](https://huggingface.co/datasets/fusing/instructpix2pix-1000-samples) for training. The dataset 
 is a smaller version of the [original dataset](https://huggingface.co/datasets/timbrooks/instructpix2pix-clip-filtered) used in the InstructPix2Pix paper.
 
-Configure environment variables such as the dataset identifier and the Stable Diffusion
-checkpoint:
+Specify the `MODEL_NAME` environment variable (either a Hub model repository id or a path to the directory containing the model weights) and pass it to the [`~diffusers.DiffusionPipeline.from_pretrained.pretrained_model_name_or_path`] argument. You'll also need to specify the dataset name in `DATASET_ID`:
 
 ```bash
 export MODEL_NAME="runwayml/stable-diffusion-v1-5"

docs/source/en/training/lora.mdx

@@ -52,7 +52,9 @@ Finetuning a model like Stable Diffusion, which has billions of parameters, can
 
 Let's finetune [`stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) on the [Pokémon BLIP captions](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions) dataset to generate your own Pokémon.
 
-To start, make sure you have the `MODEL_NAME` and `DATASET_NAME` environment variables set. The `OUTPUT_DIR` and `HUB_MODEL_ID` variables are optional and specify where to save the model to on the Hub:
+Specify the `MODEL_NAME` environment variable (either a Hub model repository id or a path to the directory containing the model weights) and pass it to the [`~diffusers.DiffusionPipeline.from_pretrained.pretrained_model_name_or_path`] argument. You'll also need to set the `DATASET_NAME` environment variable to the name of the dataset you want to train on.
+
+The `OUTPUT_DIR` and `HUB_MODEL_ID` variables are optional and specify where to save the model to on the Hub:
 
 ```bash
 export MODEL_NAME="runwayml/stable-diffusion-v1-5"
@@ -140,7 +142,9 @@ Load the LoRA weights from your finetuned model *on top of the base model weight
 
 Let's finetune [`stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) with DreamBooth and LoRA with some 🐶 [dog images](https://drive.google.com/drive/folders/1BO_dyz-p65qhBRRMRA4TbZ8qW4rB99JZ). Download and save these images to a directory.
 
-To start, make sure you have the `MODEL_NAME` and `INSTANCE_DIR` (path to directory containing images) environment variables set. The `OUTPUT_DIR` variables is optional and specifies where to save the model to on the Hub:
+To start, specify the `MODEL_NAME` environment variable (either a Hub model repository id or a path to the directory containing the model weights) and pass it to the [`~diffusers.DiffusionPipeline.from_pretrained.pretrained_model_name_or_path`] argument. You'll also need to set `INSTANCE_DIR` to the path of the directory containing the images. 
+
+The `OUTPUT_DIR` variables is optional and specifies where to save the model to on the Hub:
 
 ```bash
 export MODEL_NAME="runwayml/stable-diffusion-v1-5"

docs/source/en/training/text2image.mdx

@@ -72,7 +72,9 @@ To load a checkpoint to resume training, pass the argument `--resume_from_checkp
 
 <frameworkcontent>
 <pt>
-Launch the [PyTorch training script](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py) for a fine-tuning run on the [Pokémon BLIP captions](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions) dataset like this:
+Launch the [PyTorch training script](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py) for a fine-tuning run on the [Pokémon BLIP captions](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions) dataset like this.
+
+Specify the `MODEL_NAME` environment variable (either a Hub model repository id or a path to the directory containing the model weights) and pass it to the [`~diffusers.DiffusionPipeline.from_pretrained.pretrained_model_name_or_path`] argument.
 
 <literalinclude>
 {"path": "../../../../examples/text_to_image/README.md",
@@ -141,6 +143,8 @@ Before running the script, make sure you have the requirements installed:
 pip install -U -r requirements_flax.txt
 ```
 
+Specify the `MODEL_NAME` environment variable (either a Hub model repository id or a path to the directory containing the model weights) and pass it to the [`~diffusers.DiffusionPipeline.from_pretrained.pretrained_model_name_or_path`] argument.
+
 Now you can launch the [Flax training script](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_flax.py) like this:
 
 ```bash

docs/source/en/training/text_inversion.mdx

-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+ <!--Copyright 2023 The HuggingFace Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at
@@ -81,9 +81,20 @@ To resume training from a saved checkpoint, pass the following argument to the t
 
 ## Finetuning
 
-For your training dataset, download these [images of a cat statue](https://drive.google.com/drive/folders/1fmJMs25nxS_rSNqS5hTcRdLem_YQXbq5) and store them in a directory. 
+For your training dataset, download these [images of a cat toy](https://huggingface.co/datasets/diffusers/cat_toy_example) and store them in a directory:
 
-Set the `MODEL_NAME` environment variable to the model repository id, and the `DATA_DIR` environment variable to the path of the directory containing the images. Now you can launch the [training script](https://github.com/huggingface/diffusers/blob/main/examples/textual_inversion/textual_inversion.py):
+```py
+from huggingface_hub import snapshot_download
+
+local_dir = "./cat"
+snapshot_download(
+    "diffusers/cat_toy_example", local_dir=local_dir, repo_type="dataset", ignore_patterns=".gitattributes"
+)
+```
+
+Specify the `MODEL_NAME` environment variable (either a Hub model repository id or a path to the directory containing the model weights) and pass it to the [`~diffusers.DiffusionPipeline.from_pretrained.pretrained_model_name_or_path`] argument, and the `DATA_DIR` environment variable to the path of the directory containing the images. 
+
+Now you can launch the [training script](https://github.com/huggingface/diffusers/blob/main/examples/textual_inversion/textual_inversion.py):
 
 <Tip>
 
@@ -95,7 +106,7 @@ Set the `MODEL_NAME` environment variable to the model repository id, and the `D
 <pt>
 ```bash
 export MODEL_NAME="runwayml/stable-diffusion-v1-5"
-export DATA_DIR="path-to-dir-containing-images"
+export DATA_DIR="./cat"
 
 accelerate launch textual_inversion.py \
   --pretrained_model_name_or_path=$MODEL_NAME \
@@ -121,11 +132,13 @@ Before you begin, make sure you install the Flax specific dependencies:
 pip install -U -r requirements_flax.txt
 ```
 
+Specify the `MODEL_NAME` environment variable (either a Hub model repository id or a path to the directory containing the model weights) and pass it to the [`~diffusers.DiffusionPipeline.from_pretrained.pretrained_model_name_or_path`] argument.
+
 Then you can launch the [training script](https://github.com/huggingface/diffusers/blob/main/examples/textual_inversion/textual_inversion_flax.py):
 
 ```bash
 export MODEL_NAME="duongna/stable-diffusion-v1-4-flax"
-export DATA_DIR="path-to-dir-containing-images"
+export DATA_DIR="./cat"
 
 python textual_inversion_flax.py \
   --pretrained_model_name_or_path=$MODEL_NAME \