[doc wip] literalinclude (#2718)

Author: Mishig

docs/source/en/training/text2image.mdx

@@ -74,25 +74,13 @@ To load a checkpoint to resume training, pass the argument `--resume_from_checkp
 <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:
 
-```bash
-export MODEL_NAME="CompVis/stable-diffusion-v1-4"
-export dataset_name="lambdalabs/pokemon-blip-captions"
-
-accelerate launch train_text_to_image.py \
-  --pretrained_model_name_or_path=$MODEL_NAME \
-  --dataset_name=$dataset_name \
-  --use_ema \
-  --resolution=512 --center_crop --random_flip \
-  --train_batch_size=1 \
-  --gradient_accumulation_steps=4 \
-  --gradient_checkpointing \
-  --mixed_precision="fp16" \
-  --max_train_steps=15000 \
-  --learning_rate=1e-05 \
-  --max_grad_norm=1 \
-  --lr_scheduler="constant" --lr_warmup_steps=0 \
-  --output_dir="sd-pokemon-model" 
-```
+<literalinclude>
+{"path": "../../../../examples/text_to_image/README.md",
+"language": "bash",
+"start-after": "accelerate_snippet_start",
+"end-before": "accelerate_snippet_end",
+"dedent": 0}
+</literalinclude>
 
 To finetune on your own dataset, prepare the dataset according to the format required by 🤗 [Datasets](https://huggingface.co/docs/datasets/index). You can [upload your dataset to the Hub](https://huggingface.co/docs/datasets/image_dataset#upload-dataset-to-the-hub), or you can [prepare a local folder with your files](https://huggingface.co/docs/datasets/image_dataset#imagefolder).
 

examples/text_to_image/README.md

@@ -52,7 +52,7 @@ If you have already cloned the repo, then you won't need to go through these ste
 With `gradient_checkpointing` and `mixed_precision` it should be possible to fine tune the model on a single 24GB GPU. For higher `batch_size` and faster training it's better to use GPUs with >30GB memory.
 
 **___Note: Change the `resolution` to 768 if you are using the [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) 768x768 model.___**
-
+<!-- accelerate_snippet_start -->
 ```bash
 export MODEL_NAME="CompVis/stable-diffusion-v1-4"
 export dataset_name="lambdalabs/pokemon-blip-captions"
@@ -71,6 +71,7 @@ accelerate launch --mixed_precision="fp16"  train_text_to_image.py \
   --lr_scheduler="constant" --lr_warmup_steps=0 \
   --output_dir="sd-pokemon-model" 
 ```
+<!-- accelerate_snippet_end -->
 
 
 To run on your own training files prepare the dataset according to the format required by `datasets`, you can find the instructions for how to do that in this [document](https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder-with-metadata).