diff --git a/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_xl.md b/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_xl.md
index e4bea11c5a..5070cb33ce 100644
--- a/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_xl.md
+++ b/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_xl.md
@@ -26,8 +26,8 @@ The abstract of the paper is the following:
 
 ### Available checkpoints:
 
-- *Text-to-Image (1024x1024 resolution)*: [stabilityai/stable-diffusion-xl-base-0.9](https://huggingface.co/stabilityai/stable-diffusion-xl-base-0.9) with [`StableDiffusionXLPipeline`]
-- *Image-to-Image / Refiner (1024x1024 resolution)*: [stabilityai/stable-diffusion-xl-refiner-0.9](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-0.9) with [`StableDiffusionXLImg2ImgPipeline`]
+- *Text-to-Image (1024x1024 resolution)*: [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) with [`StableDiffusionXLPipeline`]
+- *Image-to-Image / Refiner (1024x1024 resolution)*: [stabilityai/stable-diffusion-xl-refiner-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0) with [`StableDiffusionXLImg2ImgPipeline`]
 
 ## Usage Example
 
@@ -50,7 +50,7 @@ from diffusers import StableDiffusionXLPipeline
 import torch
 
 pipe = StableDiffusionXLPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-xl-base-0.9", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
+    "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
 )
 pipe.to("cuda")
 
@@ -68,7 +68,7 @@ from diffusers import StableDiffusionXLImg2ImgPipeline
 from diffusers.utils import load_image
 
 pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-xl-refiner-0.9", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
+    "stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
 )
 pipe = pipe.to("cuda")
 url = "https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/aa_xl/000000009.png"
@@ -88,7 +88,7 @@ from diffusers import StableDiffusionXLInpaintPipeline
 from diffusers.utils import load_image
 
 pipe = StableDiffusionXLInpaintPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-xl-base-0.9", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
+    "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
 )
 pipe.to("cuda")
 
@@ -104,8 +104,8 @@ image = pipe(prompt=prompt, image=init_image, mask_image=mask_image, num_inferen
 
 ### Refining the image output
 
-In addition to the [base model checkpoint](https://huggingface.co/stabilityai/stable-diffusion-xl-base-0.9), 
-StableDiffusion-XL also includes a [refiner checkpoint](huggingface.co/stabilityai/stable-diffusion-xl-refiner-0.9)
+In addition to the [base model checkpoint](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0), 
+StableDiffusion-XL also includes a [refiner checkpoint](huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0)
 that is specialized in denoising low-noise stage images to generate images of improved high-frequency quality.
 This refiner checkpoint can be used as a "second-step" pipeline after having run the base checkpoint to improve
 image quality.
@@ -149,12 +149,12 @@ from diffusers import DiffusionPipeline
 import torch
 
 base = DiffusionPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-xl-base-0.9", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
+    "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
 )
 pipe.to("cuda")
 
 refiner = DiffusionPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-xl-refiner-0.9",
+    "stabilityai/stable-diffusion-xl-refiner-1.0",
     text_encoder_2=base.text_encoder_2,
     vae=base.vae,
     torch_dtype=torch.float16,
@@ -219,7 +219,7 @@ The ensemble-of-experts method works well on all available schedulers!
 #### 2.) Refining the image output from fully denoised base image
 
 In standard [`StableDiffusionImg2ImgPipeline`]-fashion, the fully-denoised image generated of the base model 
-can be further improved using the [refiner checkpoint](huggingface.co/stabilityai/stable-diffusion-xl-refiner-0.9).
+can be further improved using the [refiner checkpoint](huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0).
 
 For this, you simply run the refiner as a normal image-to-image pipeline after the "base" text-to-image 
 pipeline. You can leave the outputs of the base model in latent space.
@@ -229,12 +229,12 @@ from diffusers import DiffusionPipeline
 import torch
 
 pipe = DiffusionPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-xl-base-0.9", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
+    "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
 )
 pipe.to("cuda")
 
 refiner = DiffusionPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-xl-refiner-0.9",
+    "stabilityai/stable-diffusion-xl-refiner-1.0",
     text_encoder_2=pipe.text_encoder_2,
     vae=pipe.vae,
     torch_dtype=torch.float16,
@@ -267,12 +267,12 @@ from diffusers import StableDiffusionXLInpaintPipeline
 from diffusers.utils import load_image
 
 pipe = StableDiffusionXLInpaintPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-xl-base-0.9", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
+    "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
 )
 pipe.to("cuda")
 
 refiner = StableDiffusionXLInpaintPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-xl-refiner-0.9",
+    "stabilityai/stable-diffusion-xl-refiner-1.0",
     text_encoder_2=pipe.text_encoder_2,
     vae=pipe.vae,
     torch_dtype=torch.float16,
@@ -321,12 +321,12 @@ from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipelin
 import torch
 
 pipe = StableDiffusionXLPipeline.from_single_file(
-    "./sd_xl_base_0.9.safetensors", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
+    "./sd_xl_base_1.0.safetensors", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
 )
 pipe.to("cuda")
 
 refiner = StableDiffusionXLImg2ImgPipeline.from_single_file(
-    "./sd_xl_refiner_0.9.safetensors", torch_dtype=torch.float16, use_safetensors=True, variant="fp16"
+    "./sd_xl_refiner_1.0.safetensors", torch_dtype=torch.float16, use_safetensors=True, variant="fp16"
 )
 refiner.to("cuda")
 ```
@@ -399,7 +399,7 @@ from diffusers import StableDiffusionXLPipeline
 import torch
 
 pipe = StableDiffusionXLPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-xl-base-0.9", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
+    "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
 )
 pipe.to("cuda")
 
diff --git a/examples/controlnet/README_sdxl.md b/examples/controlnet/README_sdxl.md
index 4e4035066f..db8dada654 100644
--- a/examples/controlnet/README_sdxl.md
+++ b/examples/controlnet/README_sdxl.md
@@ -61,7 +61,7 @@ wget https://huggingface.co/datasets/huggingface/documentation-images/resolve/ma
 Then run `huggingface-cli login` to log into your Hugging Face account. This is needed to be able to push the trained ControlNet parameters to Hugging Face Hub.
 
 ```bash
-export MODEL_DIR="stabilityai/stable-diffusion-xl-base-0.9"
+export MODEL_DIR="stabilityai/stable-diffusion-xl-base-1.0"
 export OUTPUT_DIR="path to save model"
 
 accelerate launch train_controlnet_sdxl.py \
@@ -98,7 +98,7 @@ from diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel, UniP
 from diffusers.utils import load_image
 import torch
 
-base_model_path = "stabilityai/stable-diffusion-xl-base-0.9"
+base_model_path = "stabilityai/stable-diffusion-xl-base-1.0"
 controlnet_path = "path to controlnet"
 
 controlnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16)
diff --git a/examples/controlnet/train_controlnet_sdxl.py b/examples/controlnet/train_controlnet_sdxl.py
index 7acb1c259b..50cbfa5a78 100644
--- a/examples/controlnet/train_controlnet_sdxl.py
+++ b/examples/controlnet/train_controlnet_sdxl.py
@@ -231,7 +231,7 @@ These are controlnet weights trained on {base_model} with new type of conditioni
 
 ## License
 
-[SDXL 0.9 Research License](https://huggingface.co/stabilityai/stable-diffusion-xl-base-0.9/blob/main/LICENSE.md)
+[SDXL 1.0 License](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENSE.md)
 """
     with open(os.path.join(repo_folder, "README.md"), "w") as f:
         f.write(yaml + model_card)
diff --git a/examples/dreambooth/README_sdxl.md b/examples/dreambooth/README_sdxl.md
index 688ec7149e..7dcde78f2c 100644
--- a/examples/dreambooth/README_sdxl.md
+++ b/examples/dreambooth/README_sdxl.md
@@ -76,7 +76,7 @@ This will also allow us to push the trained LoRA parameters to the Hugging Face
 Now, we can launch training using:
 
 ```bash
-export MODEL_NAME="stabilityai/stable-diffusion-xl-base-0.9"
+export MODEL_NAME="stabilityai/stable-diffusion-xl-base-1.0"
 export INSTANCE_DIR="dog"
 export OUTPUT_DIR="lora-trained-xl"
 
@@ -127,7 +127,7 @@ image = pipe("A picture of a sks dog in a bucket", num_inference_steps=25).image
 image.save("sks_dog.png")
 ```
 
-We can further refine the outputs with the [Refiner](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-0.9):
+We can further refine the outputs with the [Refiner](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0):
 
 ```python
 from huggingface_hub.repocard import RepoCard
@@ -145,7 +145,7 @@ pipe.load_lora_weights(lora_model_id)
 
 # Load the refiner.
 refiner = StableDiffusionXLImg2ImgPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-xl-refiner-0.9", torch_dtype=torch.float16, use_safetensors=True, variant="fp16"
+    "stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16, use_safetensors=True, variant="fp16"
 )
 refiner.to("cuda")
 
diff --git a/examples/dreambooth/train_dreambooth_lora_sdxl.py b/examples/dreambooth/train_dreambooth_lora_sdxl.py
index 3ba8b5a49c..17e62c39ce 100644
--- a/examples/dreambooth/train_dreambooth_lora_sdxl.py
+++ b/examples/dreambooth/train_dreambooth_lora_sdxl.py
@@ -97,7 +97,7 @@ Special VAE used for training: {vae_path}.
 
 ## License
 
-[SDXL 0.9 Research License](https://huggingface.co/stabilityai/stable-diffusion-xl-base-0.9/blob/main/LICENSE.md)
+[SDXL 1.0 License](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENSE.md)
 """
     with open(os.path.join(repo_folder, "README.md"), "w") as f:
         f.write(yaml + model_card)
diff --git a/examples/instruct_pix2pix/README_sdxl.md b/examples/instruct_pix2pix/README_sdxl.md
index f2eeee6d87..8e3e6c8812 100644
--- a/examples/instruct_pix2pix/README_sdxl.md
+++ b/examples/instruct_pix2pix/README_sdxl.md
@@ -15,7 +15,7 @@ training procedure while being faithful to the [original implementation](https:/
 
 Refer to the original InstructPix2Pix training example for installing the dependencies.
 
-You will also need to get access of SDXL by filling the [form](https://huggingface.co/stabilityai/stable-diffusion-xl-base-0.9). 
+You will also need to get access of SDXL by filling the [form](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0). 
 
 ### Toy example
 
@@ -26,7 +26,7 @@ Configure environment variables such as the dataset identifier and the Stable Di
 checkpoint:
 
 ```bash
-export MODEL_NAME="stabilityai/stable-diffusion-xl-base-0.9"
+export MODEL_NAME="stabilityai/stable-diffusion-xl-base-1.0"
 export DATASET_ID="fusing/instructpix2pix-1000-samples"
 ```
 
@@ -51,7 +51,7 @@ with Weights and Biases. You can enable this feature with `report_to="wandb"`:
 
 ```bash
 python train_instruct_pix2pix_xl.py \
-    --pretrained_model_name_or_path=stabilityai/stable-diffusion-xl-base-0.9 \
+    --pretrained_model_name_or_path=stabilityai/stable-diffusion-xl-base-1.0 \
     --dataset_name=$DATASET_ID \
     --use_ema \
     --enable_xformers_memory_efficient_attention \
@@ -80,7 +80,7 @@ for running distributed training with `accelerate`. Here is an example command:
 
 ```bash 
 accelerate launch --mixed_precision="fp16" --multi_gpu train_instruct_pix2pix.py \
-    --pretrained_model_name_or_path=stabilityai/stable-diffusion-xl-base-0.9 \
+    --pretrained_model_name_or_path=stabilityai/stable-diffusion-xl-base-1.0 \
     --dataset_name=$DATASET_ID \
     --use_ema \
     --enable_xformers_memory_efficient_attention \
diff --git a/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py b/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py
index 342c8862b4..0db2ec81ec 100644
--- a/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py
+++ b/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py
@@ -50,7 +50,7 @@ EXAMPLE_DOC_STRING = """
         >>> from diffusers import StableDiffusionXLPipeline
 
         >>> pipe = StableDiffusionXLPipeline.from_pretrained(
-        ...     "stabilityai/stable-diffusion-xl-base-0.9", torch_dtype=torch.float16
+        ...     "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
         ... )
         >>> pipe = pipe.to("cuda")
 
diff --git a/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py b/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py
index 6125537868..4357d40562 100644
--- a/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py
+++ b/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py
@@ -52,7 +52,7 @@ EXAMPLE_DOC_STRING = """
         >>> from diffusers.utils import load_image
 
         >>> pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained(
-        ...     "stabilityai/stable-diffusion-xl-refiner-0.9", torch_dtype=torch.float16
+        ...     "stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16
         ... )
         >>> pipe = pipe.to("cuda")
         >>> url = "https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/aa_xl/000000009.png"
diff --git a/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py b/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py
index a5940e4bc0..06ca880910 100644
--- a/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py
+++ b/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py
@@ -47,7 +47,7 @@ EXAMPLE_DOC_STRING = """
         >>> from diffusers.utils import load_image
 
         >>> pipe = StableDiffusionXLInpaintPipeline.from_pretrained(
-        ...     "stabilityai/stable-diffusion-xl-base-0.9",
+        ...     "stabilityai/stable-diffusion-xl-base-1.0",
         ...     torch_dtype=torch.float16,
         ...     variant="fp16",
         ...     use_safetensors=True,