[SDXL-IP2P] Add gif for demonstrating training processes (#4342)

* [SDXL-IP2P] Add gif for demonstrating training processes

* [SDXL-IP2P] Add gif for demonstrating training processes

* [SDXL-IP2P] Change gif to URLs

* [SDXL-IP2P] Add URLs in case gif now show

---------
Co-authored-by: Harutatsu Akiyama <kf.zy.qin@gmail.com>

examples/instruct_pix2pix/README_sdxl.md

@@ -146,3 +146,48 @@ Particularly, `image_guidance_scale` and `guidance_scale` can have a profound im
 on the generated ("edited") image (see [here](https://twitter.com/RisingSayak/status/1628392199196151808?s=20) for an example).
 
 If you're looking for some interesting ways to use the InstructPix2Pix training methodology, we welcome you to check out this blog post: [Instruction-tuning Stable Diffusion with InstructPix2Pix](https://huggingface.co/blog/instruction-tuning-sd). 
+
+## Compare between SD and SDXL
+
+We aim to understand the differences resulting from the use of SD-1.5 and SDXL-0.9 as pretrained models. To achieve this, we trained on the [small toy dataset](https://huggingface.co/datasets/fusing/instructpix2pix-1000-samples) using both of these pretrained models. The training script is as follows:
+
+```bash
+export MODEL_NAME="runwayml/stable-diffusion-v1-5" or "stabilityai/stable-diffusion-xl-base-0.9"
+export DATASET_ID="fusing/instructpix2pix-1000-samples"
+
+CUDA_VISIBLE_DEVICES=1 python train_instruct_pix2pix.py \
+    --pretrained_model_name_or_path=$MODEL_NAME \
+    --dataset_name=$DATASET_ID \
+    --use_ema \
+    --enable_xformers_memory_efficient_attention \
+    --resolution=512 --random_flip \
+    --train_batch_size=4 --gradient_accumulation_steps=4 --gradient_checkpointing \
+    --max_train_steps=15000 \
+    --checkpointing_steps=5000 --checkpoints_total_limit=1 \
+    --learning_rate=5e-05 --lr_warmup_steps=0 \
+    --conditioning_dropout_prob=0.05 \
+    --seed=42 \
+    --val_image_url="https://datasets-server.huggingface.co/assets/fusing/instructpix2pix-1000-samples/--/fusing--instructpix2pix-1000-samples/train/23/input_image/image.jpg" \
+    --validation_prompt="make it in Japan" \
+    --report_to=wandb
+```
+
+We discovered that compared to training with SD-1.5 as the pretrained model, SDXL-0.9 results in a lower training loss value (SD-1.5 yields 0.0599, SDXL scores 0.0254). Moreover, from a visual perspective, the results obtained using SDXL demonstrated fewer artifacts and a richer detail. Notably, SDXL starts to preserve the structure of the original image earlier on.
+
+The following two GIFs provide intuitive visual results. We observed, for each step, what kind of results could be achieved using the image 
+<p align="center">
+    <img src="https://datasets-server.huggingface.co/assets/fusing/instructpix2pix-1000-samples/--/fusing--instructpix2pix-1000-samples/train/23/input_image/image.jpg" alt="input for make it Japan" width=600/>
+</p>
+with "make it in Japan” as the prompt. It can be seen that SDXL starts preserving the details of the original image earlier, resulting in higher fidelity outcomes sooner.
+
+* SD-1.5: https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sd_ip2p_training_val_img_progress.gif
+
+<p align="center">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sd_ip2p_training_val_img_progress.gif" alt="input for make it Japan" width=600/>
+</p>
+
+* SDXL: https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl_ip2p_training_val_img_progress.gif
+
+<p align="center">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl_ip2p_training_val_img_progress.gif" alt="input for make it Japan" width=600/>
+</p>