From 9e18f02764baba5fac36a3a953e62966057563c8 Mon Sep 17 00:00:00 2001
From: EazyAl <muhammadaliimran310@gmail.com>
Date: Fri, 17 Oct 2025 17:44:02 +0200
Subject: [PATCH 1/2] cleanup of runway model

---
 README.md                                     |  2 +-
 .../en/api/models/asymmetricautoencoderkl.md  |  2 +-
 .../pipelines/stable_diffusion/depth2img.md   |  2 +-
 .../api/pipelines/stable_diffusion/inpaint.md |  4 +--
 .../stable_diffusion/latent_upscale.md        |  2 +-
 .../pipelines/stable_diffusion/overview.md    |  4 +--
 .../stable_diffusion/stable_diffusion_2.md    |  2 +-
 .../pipelines/stable_diffusion/text2img.md    |  2 +-
 .../api/pipelines/stable_diffusion/upscale.md |  2 +-
 docs/source/en/training/adapt_a_model.md      |  4 +--
 .../en/using-diffusers/inference_with_lcm.md  |  2 +-
 docs/source/en/using-diffusers/inpaint.md     | 30 +++++++++----------
 docs/source/ko/optimization/torch2.0.md       |  2 +-
 docs/source/ko/training/adapt_a_model.md      |  4 +--
 docs/source/ko/using-diffusers/inpaint.md     | 13 ++------
 docs/source/zh/training/adapt_a_model.md      |  4 +--
 examples/community/README.md                  |  2 +-
 .../community/adaptive_mask_inpainting.py     | 12 ++++----
 .../community/composable_stable_diffusion.py  |  6 ++--
 examples/community/fresco_v2v.py              |  2 +-
 examples/community/hd_painter.py              |  2 +-
 examples/community/img2img_inpainting.py      |  2 +-
 examples/community/instaflow_one_step.py      |  6 ++--
 examples/community/ip_adapter_face_id.py      |  6 ++--
 examples/community/kohya_hires_fix.py         |  2 +-
 .../latent_consistency_interpolate.py         |  2 +-
 examples/community/llm_grounded_diffusion.py  |  6 ++--
 examples/community/lpw_stable_diffusion.py    |  4 +--
 examples/community/lpw_stable_diffusion_xl.py |  2 +-
 examples/community/matryoshka.py              |  4 +--
 .../multilingual_stable_diffusion.py          |  2 +-
 .../pipeline_controlnet_xl_kolors_inpaint.py  |  2 +-
 examples/community/pipeline_fabric.py         |  6 ++--
 .../community/pipeline_kolors_inpainting.py   |  2 +-
 examples/community/pipeline_prompt2prompt.py  |  6 ++--
 .../community/pipeline_sdxl_style_aligned.py  |  2 +-
 .../pipeline_stable_diffusion_boxdiff.py      |  8 ++---
 .../pipeline_stable_diffusion_pag.py          |  8 ++---
 ...pipeline_stable_diffusion_upscale_ldm3d.py |  2 +-
 ...ne_stable_diffusion_xl_attentive_eraser.py |  2 +-
 ..._stable_diffusion_xl_controlnet_adapter.py |  2 +-
 ...diffusion_xl_controlnet_adapter_inpaint.py |  4 +--
 examples/community/pipeline_zero1to3.py       |  8 ++---
 examples/community/rerender_a_video.py        |  2 +-
 examples/community/run_onnx_controlnet.py     |  2 +-
 examples/community/run_tensorrt_controlnet.py |  2 +-
 examples/community/sd_text2img_k_diffusion.py |  2 +-
 examples/community/sde_drag.py                |  2 +-
 .../community/stable_diffusion_comparison.py  |  2 +-
 .../stable_diffusion_controlnet_img2img.py    |  2 +-
 .../stable_diffusion_controlnet_inpaint.py    |  2 +-
 ...le_diffusion_controlnet_inpaint_img2img.py |  2 +-
 .../stable_diffusion_controlnet_reference.py  |  2 +-
 examples/community/stable_diffusion_ipex.py   |  8 ++---
 examples/community/stable_diffusion_mega.py   |  2 +-
 .../community/stable_diffusion_reference.py   |  8 ++---
 .../community/stable_diffusion_repaint.py     |  6 ++--
 .../stable_diffusion_tensorrt_img2img.py      |  6 ++--
 .../stable_diffusion_tensorrt_inpaint.py      |  6 ++--
 .../stable_diffusion_tensorrt_txt2img.py      |  6 ++--
 examples/community/text_inpainting.py         |  2 +-
 examples/research_projects/anytext/anytext.py |  2 +-
 .../dreambooth_inpaint/README.md              |  8 ++---
 .../research_projects/ip_adapter/README.md    |  4 +--
 .../README.md                                 |  2 +-
 .../pipeline_prompt_diffusion.py              |  2 +-
 .../research_projects/vae/vae_roundtrip.py    |  2 +-
 scripts/convert_consistency_decoder.py        |  6 ++--
 src/diffusers/models/auto_model.py            |  4 +--
 src/diffusers/models/modeling_flax_utils.py   | 16 +++++-----
 src/diffusers/models/modeling_utils.py        |  6 ++--
 .../stable_diffusion_xl/denoise.py            |  2 +-
 src/diffusers/pipelines/README.md             |  2 +-
 .../controlnet_xs/pipeline_controlnet_xs.py   |  2 +-
 .../alt_diffusion/pipeline_alt_diffusion.py   |  6 ++--
 .../pipeline_alt_diffusion_img2img.py         |  6 ++--
 .../pipeline_cycle_diffusion.py               |  6 ++--
 ...ne_onnx_stable_diffusion_inpaint_legacy.py |  2 +-
 ...ipeline_stable_diffusion_inpaint_legacy.py |  6 ++--
 ...pipeline_stable_diffusion_model_editing.py |  2 +-
 .../pipeline_stable_diffusion_paradigms.py    |  6 ++--
 .../pipeline_stable_diffusion_pix2pix_zero.py |  2 +-
 .../pipeline_versatile_diffusion.py           |  2 +-
 .../pipeline_latent_consistency_img2img.py    |  2 +-
 .../pipeline_latent_consistency_text2img.py   |  2 +-
 .../pipeline_leditspp_stable_diffusion.py     |  6 ++--
 .../pag/pipeline_pag_controlnet_sd.py         |  4 +--
 .../pag/pipeline_pag_controlnet_sd_inpaint.py | 10 +++----
 .../pipelines/pag/pipeline_pag_sd.py          |  8 ++---
 .../pipelines/pag/pipeline_pag_sd_img2img.py  |  8 ++---
 .../pipelines/pag/pipeline_pag_sd_inpaint.py  | 10 +++----
 .../pag/pipeline_pag_sd_xl_inpaint.py         |  2 +-
 .../pipeline_paint_by_example.py              |  2 +-
 .../pipeline_semantic_stable_diffusion.py     |  4 +--
 .../pipeline_stable_diffusion_3_inpaint.py    |  2 +-
 .../pipeline_stable_diffusion_xl_inpaint.py   |  2 +-
 src/diffusers/utils/hub_utils.py              |  2 +-
 tests/pipelines/pag/test_pag_sd_inpaint.py    |  2 +-
 98 files changed, 205 insertions(+), 214 deletions(-)

diff --git a/README.md b/README.md
index 68202ba095ee..07334416ed1b 100644
--- a/README.md
+++ b/README.md
@@ -171,7 +171,7 @@ Also, say 👋 in our public Discord channel <a href="https://discord.gg/G7tWnz9
   <tr style="border-top: 2px solid black">
     <td>Text-guided Image Inpainting</td>
     <td><a href="https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/inpaint">Stable Diffusion Inpainting</a></td>
-      <td><a href="https://huggingface.co/runwayml/stable-diffusion-inpainting"> runwayml/stable-diffusion-inpainting </a></td>
+      <td><a href="https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting"> stable-diffusion-v1-5/stable-diffusion-inpainting </a></td>
   </tr>
   <tr style="border-top: 2px solid black">
     <td>Image Variation</td>
diff --git a/docs/source/en/api/models/asymmetricautoencoderkl.md b/docs/source/en/api/models/asymmetricautoencoderkl.md
index fbadf9bd4009..0c7fbd38fb54 100644
--- a/docs/source/en/api/models/asymmetricautoencoderkl.md
+++ b/docs/source/en/api/models/asymmetricautoencoderkl.md
@@ -39,7 +39,7 @@ mask_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images
 original_image = load_image(img_url).resize((512, 512))
 mask_image = load_image(mask_url).resize((512, 512))
 
-pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting")
+pipe = StableDiffusionInpaintPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-inpainting")
 pipe.vae = AsymmetricAutoencoderKL.from_pretrained("cross-attention/asymmetric-autoencoder-kl-x-1-5")
 pipe.to("cuda")
 
diff --git a/docs/source/en/api/pipelines/stable_diffusion/depth2img.md b/docs/source/en/api/pipelines/stable_diffusion/depth2img.md
index aa43cf7db903..9ac9b7f90619 100644
--- a/docs/source/en/api/pipelines/stable_diffusion/depth2img.md
+++ b/docs/source/en/api/pipelines/stable_diffusion/depth2img.md
@@ -21,7 +21,7 @@ The Stable Diffusion model can also infer depth based on an image using [MiDaS](
 > [!TIP]
 > Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!
 >
-> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!
+> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis) and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!
 
 ## StableDiffusionDepth2ImgPipeline
 
diff --git a/docs/source/en/api/pipelines/stable_diffusion/inpaint.md b/docs/source/en/api/pipelines/stable_diffusion/inpaint.md
index 84cc31e15897..d60f472d0a7f 100644
--- a/docs/source/en/api/pipelines/stable_diffusion/inpaint.md
+++ b/docs/source/en/api/pipelines/stable_diffusion/inpaint.md
@@ -21,14 +21,14 @@ The Stable Diffusion model can also be applied to inpainting which lets you edit
 ## Tips
 
 It is recommended to use this pipeline with checkpoints that have been specifically fine-tuned for inpainting, such
-as [runwayml/stable-diffusion-inpainting](https://huggingface.co/runwayml/stable-diffusion-inpainting). Default
+as [stable-diffusion-v1-5/stable-diffusion-inpainting](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting). Default
 text-to-image Stable Diffusion checkpoints, such as
 [stable-diffusion-v1-5/stable-diffusion-v1-5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) are also compatible but they might be less performant.
 
 > [!TIP]
 > Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!
 >
-> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!
+> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis) and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!
 
 ## StableDiffusionInpaintPipeline
 
diff --git a/docs/source/en/api/pipelines/stable_diffusion/latent_upscale.md b/docs/source/en/api/pipelines/stable_diffusion/latent_upscale.md
index 4f0521740cab..19eae9a9ce44 100644
--- a/docs/source/en/api/pipelines/stable_diffusion/latent_upscale.md
+++ b/docs/source/en/api/pipelines/stable_diffusion/latent_upscale.md
@@ -17,7 +17,7 @@ The Stable Diffusion latent upscaler model was created by [Katherine Crowson](ht
 > [!TIP]
 > Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!
 >
-> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!
+> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis) and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!
 
 ## StableDiffusionLatentUpscalePipeline
 
diff --git a/docs/source/en/api/pipelines/stable_diffusion/overview.md b/docs/source/en/api/pipelines/stable_diffusion/overview.md
index 7e6e16c347db..2d2de39c91a8 100644
--- a/docs/source/en/api/pipelines/stable_diffusion/overview.md
+++ b/docs/source/en/api/pipelines/stable_diffusion/overview.md
@@ -22,7 +22,7 @@ Stable Diffusion is trained on 512x512 images from a subset of the LAION-5B data
 
 For more details about how Stable Diffusion works and how it differs from the base latent diffusion model, take a look at the Stability AI [announcement](https://stability.ai/blog/stable-diffusion-announcement) and our own [blog post](https://huggingface.co/blog/stable_diffusion#how-does-stable-diffusion-work) for more technical details.
 
-You can find the original codebase for Stable Diffusion v1.0 at [CompVis/stable-diffusion](https://github.com/CompVis/stable-diffusion) and Stable Diffusion v2.0 at [Stability-AI/stablediffusion](https://github.com/Stability-AI/stablediffusion) as well as their original scripts for various tasks. Additional official checkpoints for the different Stable Diffusion versions and tasks can be found on the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations. Explore these organizations to find the best checkpoint for your use-case!
+You can find the original codebase for Stable Diffusion v1.0 at [CompVis/stable-diffusion](https://github.com/CompVis/stable-diffusion) and Stable Diffusion v2.0 at [Stability-AI/stablediffusion](https://github.com/Stability-AI/stablediffusion) as well as their original scripts for various tasks. Additional official checkpoints for the different Stable Diffusion versions and tasks can be found on the [CompVis](https://huggingface.co/CompVis) and [Stability AI](https://huggingface.co/stabilityai) Hub organizations. Explore these organizations to find the best checkpoint for your use-case!
 
 The table below summarizes the available Stable Diffusion pipelines, their supported tasks, and an interactive demo:
 
@@ -64,7 +64,7 @@ The table below summarizes the available Stable Diffusion pipelines, their suppo
             <a href="./inpaint">StableDiffusionInpaint</a>
             </td>
             <td class="px-4 py-2 text-gray-700">inpainting</td>
-            <td class="px-4 py-2"><a href="https://huggingface.co/spaces/runwayml/stable-diffusion-inpainting"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue"/></a>
+            <td class="px-4 py-2"><a href="https://huggingface.co/spaces/stable-diffusion-v1-5/stable-diffusion-inpainting"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue"/></a>
             </td>
         </tr>
         <tr>
diff --git a/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_2.md b/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_2.md
index 67729cd195ca..31164fb9ae8f 100644
--- a/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_2.md
+++ b/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_2.md
@@ -36,7 +36,7 @@ Here are some examples for how to use Stable Diffusion 2 for each task:
 > [!TIP]
 > Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!
 >
-> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!
+> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis) and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!
 
 ## Text-to-image
 
diff --git a/docs/source/en/api/pipelines/stable_diffusion/text2img.md b/docs/source/en/api/pipelines/stable_diffusion/text2img.md
index 59a0f00d22e6..2033638a1a36 100644
--- a/docs/source/en/api/pipelines/stable_diffusion/text2img.md
+++ b/docs/source/en/api/pipelines/stable_diffusion/text2img.md
@@ -25,7 +25,7 @@ The abstract from the paper is:
 > [!TIP]
 > Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!
 >
-> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!
+> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis) and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!
 
 ## StableDiffusionPipeline
 
diff --git a/docs/source/en/api/pipelines/stable_diffusion/upscale.md b/docs/source/en/api/pipelines/stable_diffusion/upscale.md
index 14393370bec7..e7d571e73cdf 100644
--- a/docs/source/en/api/pipelines/stable_diffusion/upscale.md
+++ b/docs/source/en/api/pipelines/stable_diffusion/upscale.md
@@ -21,7 +21,7 @@ The Stable Diffusion upscaler diffusion model was created by the researchers and
 > [!TIP]
 > Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!
 >
-> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!
+> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis) and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!
 
 ## StableDiffusionUpscalePipeline
 
diff --git a/docs/source/en/training/adapt_a_model.md b/docs/source/en/training/adapt_a_model.md
index f528c8bfb656..9b7efd2abfd8 100644
--- a/docs/source/en/training/adapt_a_model.md
+++ b/docs/source/en/training/adapt_a_model.md
@@ -16,12 +16,12 @@ pipeline.unet.config["in_channels"]
 4
 ```
 
-Inpainting requires 9 channels in the input sample. You can check this value in a pretrained inpainting model like [`runwayml/stable-diffusion-inpainting`](https://huggingface.co/runwayml/stable-diffusion-inpainting):
+Inpainting requires 9 channels in the input sample. You can check this value in a pretrained inpainting model like [`stable-diffusion-v1-5/stable-diffusion-inpainting`](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting):
 
 ```py
 from diffusers import StableDiffusionPipeline
 
-pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-inpainting", use_safetensors=True)
+pipeline = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-inpainting", use_safetensors=True)
 pipeline.unet.config["in_channels"]
 9
 ```
diff --git a/docs/source/en/using-diffusers/inference_with_lcm.md b/docs/source/en/using-diffusers/inference_with_lcm.md
index cde4168d38e0..258ed2979ae0 100644
--- a/docs/source/en/using-diffusers/inference_with_lcm.md
+++ b/docs/source/en/using-diffusers/inference_with_lcm.md
@@ -215,7 +215,7 @@ from diffusers import AutoPipelineForInpainting, LCMScheduler
 from diffusers.utils import load_image, make_image_grid
 
 pipe = AutoPipelineForInpainting.from_pretrained(
-    "runwayml/stable-diffusion-inpainting",
+    "stable-diffusion-v1-5/stable-diffusion-inpainting",
     torch_dtype=torch.float16,
     variant="fp16",
 ).to("cuda")
diff --git a/docs/source/en/using-diffusers/inpaint.md b/docs/source/en/using-diffusers/inpaint.md
index 28da3a68a59f..232dbf2c6b92 100644
--- a/docs/source/en/using-diffusers/inpaint.md
+++ b/docs/source/en/using-diffusers/inpaint.md
@@ -112,7 +112,7 @@ blurred_mask
 
 ## Popular models
 
-[Stable Diffusion Inpainting](https://huggingface.co/runwayml/stable-diffusion-inpainting), [Stable Diffusion XL (SDXL) Inpainting](https://huggingface.co/diffusers/stable-diffusion-xl-1.0-inpainting-0.1), and [Kandinsky 2.2 Inpainting](https://huggingface.co/kandinsky-community/kandinsky-2-2-decoder-inpaint) are among the most popular models for inpainting. SDXL typically produces higher resolution images than Stable Diffusion v1.5, and Kandinsky 2.2 is also capable of generating high-quality images.
+[Stable Diffusion Inpainting](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting), [Stable Diffusion XL (SDXL) Inpainting](https://huggingface.co/diffusers/stable-diffusion-xl-1.0-inpainting-0.1), and [Kandinsky 2.2 Inpainting](https://huggingface.co/kandinsky-community/kandinsky-2-2-decoder-inpaint) are among the most popular models for inpainting. SDXL typically produces higher resolution images than Stable Diffusion v1.5, and Kandinsky 2.2 is also capable of generating high-quality images.
 
 ### Stable Diffusion Inpainting
 
@@ -124,7 +124,7 @@ from diffusers import AutoPipelineForInpainting
 from diffusers.utils import load_image, make_image_grid
 
 pipeline = AutoPipelineForInpainting.from_pretrained(
-    "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
+    "stable-diffusion-v1-5/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
 )
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
@@ -244,7 +244,7 @@ make_image_grid([init_image, image], rows=1, cols=2)
 ```
 
 </hfoption>
-<hfoption id="runwayml/stable-diffusion-inpainting">
+<hfoption id="stable-diffusion-v1-5/stable-diffusion-inpainting">
 
 ```py
 import torch
@@ -252,7 +252,7 @@ from diffusers import AutoPipelineForInpainting
 from diffusers.utils import load_image, make_image_grid
 
 pipeline = AutoPipelineForInpainting.from_pretrained(
-    "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
+    "stable-diffusion-v1-5/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
 )
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
@@ -278,7 +278,7 @@ make_image_grid([init_image, image], rows=1, cols=2)
   </div>
   <div>
     <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint-specific.png"/>
-    <figcaption class="mt-2 text-center text-sm text-gray-500">runwayml/stable-diffusion-inpainting</figcaption>
+    <figcaption class="mt-2 text-center text-sm text-gray-500">stable-diffusion-v1-5/stable-diffusion-inpainting</figcaption>
   </div>
 </div>
 
@@ -308,7 +308,7 @@ make_image_grid([init_image, image], rows=1, cols=2)
 ```
 
 </hfoption>
-<hfoption id="runwayml/stable-diffusion-inpaint">
+<hfoption id="stable-diffusion-v1-5/stable-diffusion-inpaint">
 
 ```py
 import torch
@@ -316,7 +316,7 @@ from diffusers import AutoPipelineForInpainting
 from diffusers.utils import load_image, make_image_grid
 
 pipeline = AutoPipelineForInpainting.from_pretrained(
-    "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
+    "stable-diffusion-v1-5/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
 )
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
@@ -340,7 +340,7 @@ make_image_grid([init_image, image], rows=1, cols=2)
   </div>
   <div>
     <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/specific-inpaint-basic.png"/>
-    <figcaption class="mt-2 text-center text-sm text-gray-500">runwayml/stable-diffusion-inpainting</figcaption>
+    <figcaption class="mt-2 text-center text-sm text-gray-500">stable-diffusion-v1-5/stable-diffusion-inpainting</figcaption>
   </div>
 </div>
 
@@ -358,7 +358,7 @@ from diffusers.utils import load_image, make_image_grid
 
 device = "cuda"
 pipeline = AutoPipelineForInpainting.from_pretrained(
-    "runwayml/stable-diffusion-inpainting",
+    "stable-diffusion-v1-5/stable-diffusion-inpainting",
     torch_dtype=torch.float16,
     variant="fp16"
 )
@@ -396,7 +396,7 @@ from diffusers import AutoPipelineForInpainting
 from diffusers.utils import load_image, make_image_grid
 
 pipeline = AutoPipelineForInpainting.from_pretrained(
-    "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
+    "stable-diffusion-v1-5/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
 )
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
@@ -441,7 +441,7 @@ from diffusers import AutoPipelineForInpainting
 from diffusers.utils import load_image, make_image_grid
 
 pipeline = AutoPipelineForInpainting.from_pretrained(
-    "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
+    "stable-diffusion-v1-5/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
 )
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
@@ -481,7 +481,7 @@ from diffusers import AutoPipelineForInpainting
 from diffusers.utils import load_image, make_image_grid
 
 pipeline = AutoPipelineForInpainting.from_pretrained(
-    "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
+    "stable-diffusion-v1-5/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
 )
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
@@ -606,7 +606,7 @@ from diffusers import AutoPipelineForInpainting, AutoPipelineForImage2Image
 from diffusers.utils import load_image, make_image_grid
 
 pipeline = AutoPipelineForInpainting.from_pretrained(
-    "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
+    "stable-diffusion-v1-5/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
 )
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
@@ -683,7 +683,7 @@ from diffusers import AutoPipelineForInpainting
 from diffusers.utils import make_image_grid
 
 pipeline = AutoPipelineForInpainting.from_pretrained(
-    "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16,
+    "stable-diffusion-v1-5/stable-diffusion-inpainting", torch_dtype=torch.float16,
 )
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
@@ -714,7 +714,7 @@ controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_inpai
 
 # pass ControlNet to the pipeline
 pipeline = StableDiffusionControlNetInpaintPipeline.from_pretrained(
-    "runwayml/stable-diffusion-inpainting", controlnet=controlnet, torch_dtype=torch.float16, variant="fp16"
+    "stable-diffusion-v1-5/stable-diffusion-inpainting", controlnet=controlnet, torch_dtype=torch.float16, variant="fp16"
 )
 pipeline.enable_model_cpu_offload()
 # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
diff --git a/docs/source/ko/optimization/torch2.0.md b/docs/source/ko/optimization/torch2.0.md
index c78c4a87b622..354f7243cf50 100644
--- a/docs/source/ko/optimization/torch2.0.md
+++ b/docs/source/ko/optimization/torch2.0.md
@@ -173,7 +173,7 @@ mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data
 init_image = download_image(img_url).resize((512, 512))
 mask_image = download_image(mask_url).resize((512, 512))
 
-path = "runwayml/stable-diffusion-inpainting"
+path = "stable-diffusion-v1-5/stable-diffusion-inpainting"
 
 run_compile = True  # Set True / False
 
diff --git a/docs/source/ko/training/adapt_a_model.md b/docs/source/ko/training/adapt_a_model.md
index 3795558f5fc7..fe6fde05b784 100644
--- a/docs/source/ko/training/adapt_a_model.md
+++ b/docs/source/ko/training/adapt_a_model.md
@@ -28,12 +28,12 @@ pipeline.unet.config["in_channels"]
 4
 ```
 
-인페인팅은 입력 샘플에 9개의 채널이 필요합니다. [`runwayml/stable-diffusion-inpainting`](https://huggingface.co/runwayml/stable-diffusion-inpainting)와 같은 사전학습된 인페인팅 모델에서 이 값을 확인할 수 있습니다:
+인페인팅은 입력 샘플에 9개의 채널이 필요합니다. [`stable-diffusion-v1-5/stable-diffusion-inpainting`](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting)와 같은 사전학습된 인페인팅 모델에서 이 값을 확인할 수 있습니다:
 
 ```py
 from diffusers import StableDiffusionPipeline
 
-pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-inpainting")
+pipeline = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-inpainting")
 pipeline.unet.config["in_channels"]
 9
 ```
diff --git a/docs/source/ko/using-diffusers/inpaint.md b/docs/source/ko/using-diffusers/inpaint.md
index cefb89218621..6c0c08bf7312 100644
--- a/docs/source/ko/using-diffusers/inpaint.md
+++ b/docs/source/ko/using-diffusers/inpaint.md
@@ -14,7 +14,7 @@ specific language governing permissions and limitations under the License.
 
 [[open-in-colab]]
 
-[`StableDiffusionInpaintPipeline`]은 마스크와 텍스트 프롬프트를 제공하여 이미지의 특정 부분을 편집할 수 있도록 합니다. 이 기능은 인페인팅 작업을 위해 특별히 훈련된 [`runwayml/stable-diffusion-inpainting`](https://huggingface.co/runwayml/stable-diffusion-inpainting)과 같은 Stable Diffusion 버전을 사용합니다.
+[`StableDiffusionInpaintPipeline`]은 마스크와 텍스트 프롬프트를 제공하여 이미지의 특정 부분을 편집할 수 있도록 합니다. 이 기능은 인페인팅 작업을 위해 특별히 훈련된 [`stable-diffusion-v1-5/stable-diffusion-inpainting`](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting)과 같은 Stable Diffusion 버전을 사용합니다.
 
 먼저 [`StableDiffusionInpaintPipeline`] 인스턴스를 불러옵니다:
 
@@ -27,7 +27,7 @@ from io import BytesIO
 from diffusers import StableDiffusionInpaintPipeline
 
 pipeline = StableDiffusionInpaintPipeline.from_pretrained(
-    "runwayml/stable-diffusion-inpainting",
+    "stable-diffusion-v1-5/stable-diffusion-inpainting",
     torch_dtype=torch.float16,
 )
 pipeline = pipeline.to("cuda")
@@ -61,12 +61,3 @@ image = pipe(prompt=prompt, image=init_image, mask_image=mask_image).images[0]
 
 > [!WARNING]
 > 이전의 실험적인 인페인팅 구현에서는 품질이 낮은 다른 프로세스를 사용했습니다. 이전 버전과의 호환성을 보장하기 위해 새 모델이 포함되지 않은 사전학습된 파이프라인을 불러오면 이전 인페인팅 방법이 계속 적용됩니다.
-
-아래 Space에서 이미지 인페인팅을 직접 해보세요!
-
-<iframe
-  src="https://runwayml-stable-diffusion-inpainting.hf.space"
-  frameborder="0"
-  width="850"
-  height="500"
-></iframe>
diff --git a/docs/source/zh/training/adapt_a_model.md b/docs/source/zh/training/adapt_a_model.md
index b5f915569739..7dbf46ec1290 100644
--- a/docs/source/zh/training/adapt_a_model.md
+++ b/docs/source/zh/training/adapt_a_model.md
@@ -16,12 +16,12 @@ pipeline.unet.config["in_channels"]
 4
 ```
 
-而图像修复任务需要输入样本具有9个通道。您可以在 [`runwayml/stable-diffusion-inpainting`](https://huggingface.co/runwayml/stable-diffusion-inpainting) 这样的预训练修复模型中验证此参数：
+而图像修复任务需要输入样本具有9个通道。您可以在 [`stable-diffusion-v1-5/stable-diffusion-inpainting`](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting) 这样的预训练修复模型中验证此参数：
 
 ```python
 from diffusers import StableDiffusionPipeline
 
-pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-inpainting", use_safetensors=True)
+pipeline = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-inpainting", use_safetensors=True)
 pipeline.unet.config["in_channels"]
 9
 ```
diff --git a/examples/community/README.md b/examples/community/README.md
index e314463077f0..4a4b0f5fd9f5 100644
--- a/examples/community/README.md
+++ b/examples/community/README.md
@@ -1328,7 +1328,7 @@ model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined"
 
 # Load Stable Diffusion Inpainting Pipeline with custom pipeline
 pipe = DiffusionPipeline.from_pretrained(
-    "runwayml/stable-diffusion-inpainting",
+    "stable-diffusion-v1-5/stable-diffusion-inpainting",
     custom_pipeline="text_inpainting",
     segmentation_model=model,
     segmentation_processor=processor
diff --git a/examples/community/adaptive_mask_inpainting.py b/examples/community/adaptive_mask_inpainting.py
index aac460cb4609..da67debe72ec 100644
--- a/examples/community/adaptive_mask_inpainting.py
+++ b/examples/community/adaptive_mask_inpainting.py
@@ -126,7 +126,7 @@
         ...     "lllyasviel/control_v11p_sd15_inpaint", torch_dtype=torch.float16
         ... )
         >>> pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
-        ...     "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
+        ...     "stable-diffusion-v1-5/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
         ... )
 
         >>> pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
@@ -347,7 +347,7 @@ class AdaptiveMaskInpaintPipeline(
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
+            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
             about a model's potential harms.
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
@@ -429,8 +429,8 @@ def __init__(
                 "The configuration file of the unet has set the default `sample_size` to smaller than"
                 " 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
                 " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
+                " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                 " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                 " in the config might lead to incorrect results in future versions. If you have downloaded this"
                 " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
@@ -970,7 +970,7 @@ def __call__(
         >>> default_mask_image = download_image(mask_url).resize((512, 512))
 
         >>> pipe = AdaptiveMaskInpaintPipeline.from_pretrained(
-        ...     "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16
+        ...     "stable-diffusion-v1-5/stable-diffusion-inpainting", torch_dtype=torch.float16
         ... )
         >>> pipe = pipe.to("cuda")
 
@@ -1095,7 +1095,7 @@ def __call__(
 
         # 8. Check that sizes of mask, masked image and latents match
         if num_channels_unet == 9:
-            # default case for runwayml/stable-diffusion-inpainting
+            # default case for stable-diffusion-v1-5/stable-diffusion-inpainting
             num_channels_mask = mask.shape[1]
             num_channels_masked_image = masked_image_latents.shape[1]
             if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:
diff --git a/examples/community/composable_stable_diffusion.py b/examples/community/composable_stable_diffusion.py
index a7c540ceb984..a7c610ad4355 100644
--- a/examples/community/composable_stable_diffusion.py
+++ b/examples/community/composable_stable_diffusion.py
@@ -62,7 +62,7 @@ class ComposableStableDiffusionPipeline(DiffusionPipeline, StableDiffusionMixin)
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
+            Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details.
         feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
@@ -145,8 +145,8 @@ def __init__(
                 "The configuration file of the unet has set the default `sample_size` to smaller than"
                 " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
                 " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
+                " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                 " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                 " in the config might lead to incorrect results in future versions. If you have downloaded this"
                 " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
diff --git a/examples/community/fresco_v2v.py b/examples/community/fresco_v2v.py
index 47ba71299de8..b79834db5eed 100644
--- a/examples/community/fresco_v2v.py
+++ b/examples/community/fresco_v2v.py
@@ -1276,7 +1276,7 @@ class FrescoV2VPipeline(StableDiffusionControlNetImg2ImgPipeline):
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
+            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
             about a model's potential harms.
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
diff --git a/examples/community/hd_painter.py b/examples/community/hd_painter.py
index 20bb43a76f32..70e5656855ff 100644
--- a/examples/community/hd_painter.py
+++ b/examples/community/hd_painter.py
@@ -678,7 +678,7 @@ def __call__(
 
         # 8. Check that sizes of mask, masked image and latents match
         if num_channels_unet == 9:
-            # default case for runwayml/stable-diffusion-inpainting
+            # default case for stable-diffusion-v1-5/stable-diffusion-inpainting
             num_channels_mask = mask.shape[1]
             num_channels_masked_image = masked_image_latents.shape[1]
             if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:
diff --git a/examples/community/img2img_inpainting.py b/examples/community/img2img_inpainting.py
index 499230b1e2cd..595df107cacb 100644
--- a/examples/community/img2img_inpainting.py
+++ b/examples/community/img2img_inpainting.py
@@ -78,7 +78,7 @@ class ImageToImageInpaintingPipeline(DiffusionPipeline):
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
+            Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details.
         feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
diff --git a/examples/community/instaflow_one_step.py b/examples/community/instaflow_one_step.py
index 06be1d10b65e..0f16707eadf5 100644
--- a/examples/community/instaflow_one_step.py
+++ b/examples/community/instaflow_one_step.py
@@ -86,7 +86,7 @@ class InstaFlowPipeline(
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
+            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
             about a model's potential harms.
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
@@ -165,8 +165,8 @@ def __init__(
                 "The configuration file of the unet has set the default `sample_size` to smaller than"
                 " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
                 " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
+                " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                 " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                 " in the config might lead to incorrect results in future versions. If you have downloaded this"
                 " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
diff --git a/examples/community/ip_adapter_face_id.py b/examples/community/ip_adapter_face_id.py
index 5b420882e9d8..d16aaf5a54c6 100644
--- a/examples/community/ip_adapter_face_id.py
+++ b/examples/community/ip_adapter_face_id.py
@@ -166,7 +166,7 @@ class IPAdapterFaceIDStableDiffusionPipeline(
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
+            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
             about a model's potential harms.
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
@@ -247,8 +247,8 @@ def __init__(
                 "The configuration file of the unet has set the default `sample_size` to smaller than"
                 " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
                 " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
+                " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                 " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                 " in the config might lead to incorrect results in future versions. If you have downloaded this"
                 " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
diff --git a/examples/community/kohya_hires_fix.py b/examples/community/kohya_hires_fix.py
index 63f6b8973c91..c968ecf2af0e 100644
--- a/examples/community/kohya_hires_fix.py
+++ b/examples/community/kohya_hires_fix.py
@@ -414,7 +414,7 @@ class StableDiffusionHighResFixPipeline(StableDiffusionPipeline):
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
+            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
             about a model's potential harms.
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
diff --git a/examples/community/latent_consistency_interpolate.py b/examples/community/latent_consistency_interpolate.py
index 9fc423368231..e8349ba317e9 100644
--- a/examples/community/latent_consistency_interpolate.py
+++ b/examples/community/latent_consistency_interpolate.py
@@ -222,7 +222,7 @@ class LatentConsistencyModelWalkPipeline(
             supports [`LCMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
+            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
             about a model's potential harms.
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
diff --git a/examples/community/llm_grounded_diffusion.py b/examples/community/llm_grounded_diffusion.py
index 8f047615023b..5bf6674a43e3 100644
--- a/examples/community/llm_grounded_diffusion.py
+++ b/examples/community/llm_grounded_diffusion.py
@@ -302,7 +302,7 @@ class LLMGroundedDiffusionPipeline(
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
+            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
             about a model's potential harms.
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
@@ -392,8 +392,8 @@ def __init__(
                 "The configuration file of the unet has set the default `sample_size` to smaller than"
                 " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
                 " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
+                " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                 " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                 " in the config might lead to incorrect results in future versions. If you have downloaded this"
                 " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
diff --git a/examples/community/lpw_stable_diffusion.py b/examples/community/lpw_stable_diffusion.py
index cb017c0bbe29..58e932bbcf74 100644
--- a/examples/community/lpw_stable_diffusion.py
+++ b/examples/community/lpw_stable_diffusion.py
@@ -552,8 +552,8 @@ def __init__(
                 "The configuration file of the unet has set the default `sample_size` to smaller than"
                 " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
                 " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
+                " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                 " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                 " in the config might lead to incorrect results in future versions. If you have downloaded this"
                 " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
diff --git a/examples/community/lpw_stable_diffusion_xl.py b/examples/community/lpw_stable_diffusion_xl.py
index 272c5d5652c5..95d3405df50d 100644
--- a/examples/community/lpw_stable_diffusion_xl.py
+++ b/examples/community/lpw_stable_diffusion_xl.py
@@ -1765,7 +1765,7 @@ def denoising_value_valid(dnv):
 
             # Check that sizes of mask, masked image and latents match
             if num_channels_unet == 9:
-                # default case for runwayml/stable-diffusion-inpainting
+                # default case for stable-diffusion-v1-5/stable-diffusion-inpainting
                 num_channels_mask = mask.shape[1]
                 num_channels_masked_image = masked_image_latents.shape[1]
                 if num_channels_latents + num_channels_mask + num_channels_masked_image != num_channels_unet:
diff --git a/examples/community/matryoshka.py b/examples/community/matryoshka.py
index 3871552672a6..97ad8b9e86c6 100644
--- a/examples/community/matryoshka.py
+++ b/examples/community/matryoshka.py
@@ -3729,8 +3729,8 @@ def __init__(
                 "The configuration file of the unet has set the default `sample_size` to smaller than"
                 " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
                 " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
+                " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                 " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                 " in the config might lead to incorrect results in future versions. If you have downloaded this"
                 " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
diff --git a/examples/community/multilingual_stable_diffusion.py b/examples/community/multilingual_stable_diffusion.py
index afef4e9e9719..436803f201fe 100644
--- a/examples/community/multilingual_stable_diffusion.py
+++ b/examples/community/multilingual_stable_diffusion.py
@@ -78,7 +78,7 @@ class MultilingualStableDiffusion(DiffusionPipeline, StableDiffusionMixin):
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
+            Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details.
         feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
diff --git a/examples/community/pipeline_controlnet_xl_kolors_inpaint.py b/examples/community/pipeline_controlnet_xl_kolors_inpaint.py
index 4b6123cc1f8b..3abd98482980 100644
--- a/examples/community/pipeline_controlnet_xl_kolors_inpaint.py
+++ b/examples/community/pipeline_controlnet_xl_kolors_inpaint.py
@@ -1607,7 +1607,7 @@ def denoising_value_valid(dnv):
 
         # 9. Check that sizes of mask, masked image and latents match
         if num_channels_unet == 9:
-            # default case for runwayml/stable-diffusion-inpainting
+            # default case for stable-diffusion-v1-5/stable-diffusion-inpainting
             num_channels_mask = mask.shape[1]
             num_channels_masked_image = masked_image_latents.shape[1]
             if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:
diff --git a/examples/community/pipeline_fabric.py b/examples/community/pipeline_fabric.py
index dcc7730cbe33..d29e98df5e50 100644
--- a/examples/community/pipeline_fabric.py
+++ b/examples/community/pipeline_fabric.py
@@ -135,7 +135,7 @@ class FabricPipeline(DiffusionPipeline):
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
+            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
             about a model's potential harms.
     """
 
@@ -163,8 +163,8 @@ def __init__(
                 "The configuration file of the unet has set the default `sample_size` to smaller than"
                 " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
                 " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
+                " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                 " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                 " in the config might lead to incorrect results in future versions. If you have downloaded this"
                 " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
diff --git a/examples/community/pipeline_kolors_inpainting.py b/examples/community/pipeline_kolors_inpainting.py
index 3cab8ecac002..26517819ebd0 100644
--- a/examples/community/pipeline_kolors_inpainting.py
+++ b/examples/community/pipeline_kolors_inpainting.py
@@ -1487,7 +1487,7 @@ def denoising_value_valid(dnv):
 
         # 8. Check that sizes of mask, masked image and latents match
         if num_channels_unet == 9:
-            # default case for runwayml/stable-diffusion-inpainting
+            # default case for stable-diffusion-v1-5/stable-diffusion-inpainting
             num_channels_mask = mask.shape[1]
             num_channels_masked_image = masked_image_latents.shape[1]
             if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:
diff --git a/examples/community/pipeline_prompt2prompt.py b/examples/community/pipeline_prompt2prompt.py
index 8d94dc9248c1..eb19667970b0 100644
--- a/examples/community/pipeline_prompt2prompt.py
+++ b/examples/community/pipeline_prompt2prompt.py
@@ -106,7 +106,7 @@ class Prompt2PromptPipeline(
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
+            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
             about a model's potential harms.
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
@@ -187,8 +187,8 @@ def __init__(
                 "The configuration file of the unet has set the default `sample_size` to smaller than"
                 " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
                 " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
+                " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                 " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                 " in the config might lead to incorrect results in future versions. If you have downloaded this"
                 " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
diff --git a/examples/community/pipeline_sdxl_style_aligned.py b/examples/community/pipeline_sdxl_style_aligned.py
index 10438af365f9..51547599f531 100644
--- a/examples/community/pipeline_sdxl_style_aligned.py
+++ b/examples/community/pipeline_sdxl_style_aligned.py
@@ -1730,7 +1730,7 @@ def denoising_value_valid(dnv):
 
             # Check that sizes of mask, masked image and latents match
             if num_channels_unet == 9:
-                # default case for runwayml/stable-diffusion-inpainting
+                # default case for stable-diffusion-v1-5/stable-diffusion-inpainting
                 num_channels_mask = mask.shape[1]
                 num_channels_masked_image = masked_image_latents.shape[1]
                 if num_channels_latents + num_channels_mask + num_channels_masked_image != num_channels_unet:
diff --git a/examples/community/pipeline_stable_diffusion_boxdiff.py b/examples/community/pipeline_stable_diffusion_boxdiff.py
index 07e29b9c05b7..c05a6143132c 100644
--- a/examples/community/pipeline_stable_diffusion_boxdiff.py
+++ b/examples/community/pipeline_stable_diffusion_boxdiff.py
@@ -59,7 +59,7 @@
         >>> import torch
         >>> from diffusers import StableDiffusionPipeline
 
-        >>> pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
+        >>> pipe = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16)
         >>> pipe = pipe.to("cuda")
 
         >>> prompt = "a photo of an astronaut riding a horse on mars"
@@ -392,7 +392,7 @@ class StableDiffusionBoxDiffPipeline(
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
+            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
             about a model's potential harms.
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
@@ -473,8 +473,8 @@ def __init__(
                 "The configuration file of the unet has set the default `sample_size` to smaller than"
                 " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
                 " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
+                " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                 " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                 " in the config might lead to incorrect results in future versions. If you have downloaded this"
                 " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
diff --git a/examples/community/pipeline_stable_diffusion_pag.py b/examples/community/pipeline_stable_diffusion_pag.py
index 6b62b610afa2..3f98dca0b986 100644
--- a/examples/community/pipeline_stable_diffusion_pag.py
+++ b/examples/community/pipeline_stable_diffusion_pag.py
@@ -42,7 +42,7 @@
         ```py
         >>> import torch
         >>> from diffusers import StableDiffusionPipeline
-        >>> pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
+        >>> pipe = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16)
         >>> pipe = pipe.to("cuda")
         >>> prompt = "a photo of an astronaut riding a horse on mars"
         >>> image = pipe(prompt).images[0]
@@ -359,7 +359,7 @@ class StableDiffusionPAGPipeline(
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
+            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
             about a model's potential harms.
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
@@ -440,8 +440,8 @@ def __init__(
                 "The configuration file of the unet has set the default `sample_size` to smaller than"
                 " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
                 " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
+                " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                 " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                 " in the config might lead to incorrect results in future versions. If you have downloaded this"
                 " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
diff --git a/examples/community/pipeline_stable_diffusion_upscale_ldm3d.py b/examples/community/pipeline_stable_diffusion_upscale_ldm3d.py
index 9777633535ac..e358f66b4acc 100644
--- a/examples/community/pipeline_stable_diffusion_upscale_ldm3d.py
+++ b/examples/community/pipeline_stable_diffusion_upscale_ldm3d.py
@@ -100,7 +100,7 @@ class StableDiffusionUpscaleLDM3DPipeline(
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
+            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
             about a model's potential harms.
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
diff --git a/examples/community/pipeline_stable_diffusion_xl_attentive_eraser.py b/examples/community/pipeline_stable_diffusion_xl_attentive_eraser.py
index a881814c2a91..65df4c03eb2c 100644
--- a/examples/community/pipeline_stable_diffusion_xl_attentive_eraser.py
+++ b/examples/community/pipeline_stable_diffusion_xl_attentive_eraser.py
@@ -2042,7 +2042,7 @@ def denoising_value_valid(dnv):
 
         # 8. Check that sizes of mask, masked image and latents match
         if num_channels_unet == 9:
-            # default case for runwayml/stable-diffusion-inpainting
+            # default case for stable-diffusion-v1-5/stable-diffusion-inpainting
             num_channels_mask = mask.shape[1]
             num_channels_masked_image = masked_image_latents.shape[1]
             if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:
diff --git a/examples/community/pipeline_stable_diffusion_xl_controlnet_adapter.py b/examples/community/pipeline_stable_diffusion_xl_controlnet_adapter.py
index 564a19e923d2..6dade126f282 100644
--- a/examples/community/pipeline_stable_diffusion_xl_controlnet_adapter.py
+++ b/examples/community/pipeline_stable_diffusion_xl_controlnet_adapter.py
@@ -188,7 +188,7 @@ class StableDiffusionXLControlNetAdapterPipeline(
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
+            Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details.
         feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
diff --git a/examples/community/pipeline_stable_diffusion_xl_controlnet_adapter_inpaint.py b/examples/community/pipeline_stable_diffusion_xl_controlnet_adapter_inpaint.py
index c73433b20f88..9ec6a90b072d 100644
--- a/examples/community/pipeline_stable_diffusion_xl_controlnet_adapter_inpaint.py
+++ b/examples/community/pipeline_stable_diffusion_xl_controlnet_adapter_inpaint.py
@@ -330,7 +330,7 @@ class StableDiffusionXLControlNetAdapterInpaintPipeline(
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
+            Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details.
         feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
         requires_aesthetics_score (`bool`, *optional*, defaults to `"False"`):
@@ -1569,7 +1569,7 @@ def denoising_value_valid(dnv):
 
         # 8. Check that sizes of mask, masked image and latents match
         if num_channels_unet == 9:
-            # default case for runwayml/stable-diffusion-inpainting
+            # default case for stable-diffusion-v1-5/stable-diffusion-inpainting
             num_channels_mask = mask.shape[1]
             num_channels_masked_image = masked_image_latents.shape[1]
             if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:
diff --git a/examples/community/pipeline_zero1to3.py b/examples/community/pipeline_zero1to3.py
index 9e29566978e8..1be59fd8323e 100644
--- a/examples/community/pipeline_zero1to3.py
+++ b/examples/community/pipeline_zero1to3.py
@@ -46,7 +46,7 @@
         >>> import torch
         >>> from diffusers import StableDiffusionPipeline
 
-        >>> pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
+        >>> pipe = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16)
         >>> pipe = pipe.to("cuda")
 
         >>> prompt = "a photo of an astronaut riding a horse on mars"
@@ -86,7 +86,7 @@ class Zero1to3StableDiffusionPipeline(DiffusionPipeline, StableDiffusionMixin):
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
+            Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details.
         feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
         cc_projection ([`CCProjection`]):
@@ -164,8 +164,8 @@ def __init__(
                 "The configuration file of the unet has set the default `sample_size` to smaller than"
                 " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
                 " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
+                " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                 " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                 " in the config might lead to incorrect results in future versions. If you have downloaded this"
                 " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
diff --git a/examples/community/rerender_a_video.py b/examples/community/rerender_a_video.py
index 78a15a03b099..840f9e206d4d 100644
--- a/examples/community/rerender_a_video.py
+++ b/examples/community/rerender_a_video.py
@@ -288,7 +288,7 @@ class RerenderAVideoPipeline(StableDiffusionControlNetImg2ImgPipeline):
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
+            Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details.
         feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
diff --git a/examples/community/run_onnx_controlnet.py b/examples/community/run_onnx_controlnet.py
index f0ab2a2b9643..2b56e8a1e51b 100644
--- a/examples/community/run_onnx_controlnet.py
+++ b/examples/community/run_onnx_controlnet.py
@@ -54,7 +54,7 @@
         >>> # load control net and stable diffusion v1-5
         >>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
         >>> pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
-        ...     "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
+        ...     "stable-diffusion-v1-5/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
         ... )
 
         >>> # speed up diffusion process with faster scheduler and memory optimization
diff --git a/examples/community/run_tensorrt_controlnet.py b/examples/community/run_tensorrt_controlnet.py
index e4f1abc83b0b..b62eb4f58e8f 100644
--- a/examples/community/run_tensorrt_controlnet.py
+++ b/examples/community/run_tensorrt_controlnet.py
@@ -158,7 +158,7 @@ def __call__(self, **kwargs):
         >>> # load control net and stable diffusion v1-5
         >>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
         >>> pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
-        ...     "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
+        ...     "stable-diffusion-v1-5/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
         ... )
 
         >>> # speed up diffusion process with faster scheduler and memory optimization
diff --git a/examples/community/sd_text2img_k_diffusion.py b/examples/community/sd_text2img_k_diffusion.py
index 4d5cea497f8c..e351420f786a 100755
--- a/examples/community/sd_text2img_k_diffusion.py
+++ b/examples/community/sd_text2img_k_diffusion.py
@@ -64,7 +64,7 @@ class StableDiffusionPipeline(DiffusionPipeline, StableDiffusionMixin):
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
+            Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details.
         feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
diff --git a/examples/community/sde_drag.py b/examples/community/sde_drag.py
index f408ee64dbb9..63899ce738bb 100644
--- a/examples/community/sde_drag.py
+++ b/examples/community/sde_drag.py
@@ -114,7 +114,7 @@ def __call__(
         >>> from diffusers import DDIMScheduler, DiffusionPipeline
 
         >>> # Load the pipeline
-        >>> model_path = "runwayml/stable-diffusion-v1-5"
+        >>> model_path = "stable-diffusion-v1-5/stable-diffusion-v1-5"
         >>> scheduler = DDIMScheduler.from_pretrained(model_path, subfolder="scheduler")
         >>> pipe = DiffusionPipeline.from_pretrained(model_path, scheduler=scheduler, custom_pipeline="sde_drag")
         >>> pipe.to('cuda')
diff --git a/examples/community/stable_diffusion_comparison.py b/examples/community/stable_diffusion_comparison.py
index 22f3b3e0c385..ce6e77c87ff0 100644
--- a/examples/community/stable_diffusion_comparison.py
+++ b/examples/community/stable_diffusion_comparison.py
@@ -46,7 +46,7 @@ class StableDiffusionComparisonPipeline(DiffusionPipeline, StableDiffusionMixin)
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionMegaSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
+            Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details.
         feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
diff --git a/examples/community/stable_diffusion_controlnet_img2img.py b/examples/community/stable_diffusion_controlnet_img2img.py
index 6d8038cfd4ae..aa116112be1c 100644
--- a/examples/community/stable_diffusion_controlnet_img2img.py
+++ b/examples/community/stable_diffusion_controlnet_img2img.py
@@ -36,7 +36,7 @@
         >>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
 
         >>> pipe_controlnet = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
-                "runwayml/stable-diffusion-v1-5",
+                "stable-diffusion-v1-5/stable-diffusion-v1-5",
                 controlnet=controlnet,
                 safety_checker=None,
                 torch_dtype=torch.float16
diff --git a/examples/community/stable_diffusion_controlnet_inpaint.py b/examples/community/stable_diffusion_controlnet_inpaint.py
index fe7b808b6beb..6d710e0d73c7 100644
--- a/examples/community/stable_diffusion_controlnet_inpaint.py
+++ b/examples/community/stable_diffusion_controlnet_inpaint.py
@@ -81,7 +81,7 @@
         >>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-seg", torch_dtype=torch.float16)
 
         >>> pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
-                "runwayml/stable-diffusion-inpainting", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16
+                "stable-diffusion-v1-5/stable-diffusion-inpainting", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16
             )
 
         >>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
diff --git a/examples/community/stable_diffusion_controlnet_inpaint_img2img.py b/examples/community/stable_diffusion_controlnet_inpaint_img2img.py
index 2b5dc77fe5aa..fcb5ed059bb4 100644
--- a/examples/community/stable_diffusion_controlnet_inpaint_img2img.py
+++ b/examples/community/stable_diffusion_controlnet_inpaint_img2img.py
@@ -80,7 +80,7 @@
         >>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-seg", torch_dtype=torch.float16)
 
         >>> pipe = StableDiffusionControlNetInpaintImg2ImgPipeline.from_pretrained(
-                "runwayml/stable-diffusion-inpainting", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16
+                "stable-diffusion-v1-5/stable-diffusion-inpainting", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16
             )
 
         >>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
diff --git a/examples/community/stable_diffusion_controlnet_reference.py b/examples/community/stable_diffusion_controlnet_reference.py
index e5dd249e0424..74c81b63626d 100644
--- a/examples/community/stable_diffusion_controlnet_reference.py
+++ b/examples/community/stable_diffusion_controlnet_reference.py
@@ -37,7 +37,7 @@
 
         >>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
         >>> pipe = StableDiffusionControlNetReferencePipeline.from_pretrained(
-                "runwayml/stable-diffusion-v1-5",
+                "stable-diffusion-v1-5/stable-diffusion-v1-5",
                 controlnet=controlnet,
                 safety_checker=None,
                 torch_dtype=torch.float16
diff --git a/examples/community/stable_diffusion_ipex.py b/examples/community/stable_diffusion_ipex.py
index 7d1cd4f5d09e..4f545aa09ded 100644
--- a/examples/community/stable_diffusion_ipex.py
+++ b/examples/community/stable_diffusion_ipex.py
@@ -43,7 +43,7 @@
         >>> import torch
         >>> from diffusers import StableDiffusionPipeline
 
-        >>> pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", custom_pipeline="stable_diffusion_ipex")
+        >>> pipe = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", custom_pipeline="stable_diffusion_ipex")
 
         >>> # For Float32
         >>> pipe.prepare_for_ipex(prompt, dtype=torch.float32, height=512, width=512) #value of image height/width should be consistent with the pipeline inference
@@ -85,7 +85,7 @@ class StableDiffusionIPEXPipeline(
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
+            Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details.
         feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
@@ -161,8 +161,8 @@ def __init__(
                 "The configuration file of the unet has set the default `sample_size` to smaller than"
                 " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
                 " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
+                " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                 " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                 " in the config might lead to incorrect results in future versions. If you have downloaded this"
                 " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
diff --git a/examples/community/stable_diffusion_mega.py b/examples/community/stable_diffusion_mega.py
index 77e5011d2a70..c67ebc80b006 100644
--- a/examples/community/stable_diffusion_mega.py
+++ b/examples/community/stable_diffusion_mega.py
@@ -47,7 +47,7 @@ class StableDiffusionMegaPipeline(DiffusionPipeline, StableDiffusionMixin):
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionMegaSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
+            Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details.
         feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
diff --git a/examples/community/stable_diffusion_reference.py b/examples/community/stable_diffusion_reference.py
index 6f7dce982339..d0372bbeba65 100644
--- a/examples/community/stable_diffusion_reference.py
+++ b/examples/community/stable_diffusion_reference.py
@@ -46,7 +46,7 @@
         >>> input_image = load_image("https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png")
 
         >>> pipe = StableDiffusionReferencePipeline.from_pretrained(
-                "runwayml/stable-diffusion-v1-5",
+                "stable-diffusion-v1-5/stable-diffusion-v1-5",
                 safety_checker=None,
                 torch_dtype=torch.float16
                 ).to('cuda:0')
@@ -112,7 +112,7 @@ class StableDiffusionReferencePipeline(
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
+            Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details.
         feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
@@ -194,8 +194,8 @@ def __init__(
                 "The configuration file of the unet has set the default `sample_size` to smaller than"
                 " 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
                 " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
+                " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                 " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                 " in the config might lead to incorrect results in future versions. If you have downloaded this"
                 " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
diff --git a/examples/community/stable_diffusion_repaint.py b/examples/community/stable_diffusion_repaint.py
index 94b9f8b01b51..b974e3c7ae74 100644
--- a/examples/community/stable_diffusion_repaint.py
+++ b/examples/community/stable_diffusion_repaint.py
@@ -167,7 +167,7 @@ class StableDiffusionRepaintPipeline(
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
+            Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details.
         feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
@@ -249,8 +249,8 @@ def __init__(
                 "The configuration file of the unet has set the default `sample_size` to smaller than"
                 " 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
                 " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
+                " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                 " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                 " in the config might lead to incorrect results in future versions. If you have downloaded this"
                 " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
diff --git a/examples/community/stable_diffusion_tensorrt_img2img.py b/examples/community/stable_diffusion_tensorrt_img2img.py
index dc11703b6aea..5b7733fe5784 100755
--- a/examples/community/stable_diffusion_tensorrt_img2img.py
+++ b/examples/community/stable_diffusion_tensorrt_img2img.py
@@ -678,7 +678,7 @@ class TensorRTStableDiffusionImg2ImgPipeline(DiffusionPipeline):
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
+            Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details.
         feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
@@ -766,8 +766,8 @@ def __init__(
                 "The configuration file of the unet has set the default `sample_size` to smaller than"
                 " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
                 " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
+                " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                 " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                 " in the config might lead to incorrect results in future versions. If you have downloaded this"
                 " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
diff --git a/examples/community/stable_diffusion_tensorrt_inpaint.py b/examples/community/stable_diffusion_tensorrt_inpaint.py
index fff7309e9cf6..fc81e4c28917 100755
--- a/examples/community/stable_diffusion_tensorrt_inpaint.py
+++ b/examples/community/stable_diffusion_tensorrt_inpaint.py
@@ -682,7 +682,7 @@ class TensorRTStableDiffusionInpaintPipeline(DiffusionPipeline):
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
+            Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details.
         feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
@@ -770,8 +770,8 @@ def __init__(
                 "The configuration file of the unet has set the default `sample_size` to smaller than"
                 " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
                 " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
+                " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                 " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                 " in the config might lead to incorrect results in future versions. If you have downloaded this"
                 " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
diff --git a/examples/community/stable_diffusion_tensorrt_txt2img.py b/examples/community/stable_diffusion_tensorrt_txt2img.py
index 15a6e69c413c..e1d09edf9342 100755
--- a/examples/community/stable_diffusion_tensorrt_txt2img.py
+++ b/examples/community/stable_diffusion_tensorrt_txt2img.py
@@ -594,7 +594,7 @@ class TensorRTStableDiffusionPipeline(DiffusionPipeline):
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
+            Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details.
         feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
@@ -682,8 +682,8 @@ def __init__(
                 "The configuration file of the unet has set the default `sample_size` to smaller than"
                 " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
                 " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
+                " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                 " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                 " in the config might lead to incorrect results in future versions. If you have downloaded this"
                 " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
diff --git a/examples/community/text_inpainting.py b/examples/community/text_inpainting.py
index f262cf2cac6d..bdf9eca498e7 100644
--- a/examples/community/text_inpainting.py
+++ b/examples/community/text_inpainting.py
@@ -52,7 +52,7 @@ class TextInpainting(DiffusionPipeline, StableDiffusionMixin):
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
+            Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details.
         feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
diff --git a/examples/research_projects/anytext/anytext.py b/examples/research_projects/anytext/anytext.py
index 38f0adb891fc..7ae6ae57c22a 100644
--- a/examples/research_projects/anytext/anytext.py
+++ b/examples/research_projects/anytext/anytext.py
@@ -1223,7 +1223,7 @@ class AnyTextPipeline(
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
+            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
             about a model's potential harms.
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
diff --git a/examples/research_projects/dreambooth_inpaint/README.md b/examples/research_projects/dreambooth_inpaint/README.md
index 46703fa982e5..b6ee1d72f6e9 100644
--- a/examples/research_projects/dreambooth_inpaint/README.md
+++ b/examples/research_projects/dreambooth_inpaint/README.md
@@ -5,7 +5,7 @@ This script was added by @thedarkzeno .
 Please note that this script is not actively maintained, you can open an issue and tag @thedarkzeno or @patil-suraj though.
 
 ```bash
-export MODEL_NAME="runwayml/stable-diffusion-inpainting"
+export MODEL_NAME="stable-diffusion-v1-5/stable-diffusion-inpainting"
 export INSTANCE_DIR="path-to-instance-images"
 export OUTPUT_DIR="path-to-save-model"
 
@@ -29,7 +29,7 @@ Prior-preservation is used to avoid overfitting and language-drift. Refer to the
 According to the paper, it's recommended to generate `num_epochs * num_samples` images for prior-preservation. 200-300 works well for most cases.
 
 ```bash
-export MODEL_NAME="runwayml/stable-diffusion-inpainting"
+export MODEL_NAME="stable-diffusion-v1-5/stable-diffusion-inpainting"
 export INSTANCE_DIR="path-to-instance-images"
 export CLASS_DIR="path-to-class-images"
 export OUTPUT_DIR="path-to-save-model"
@@ -60,7 +60,7 @@ With the help of gradient checkpointing and the 8-bit optimizer from bitsandbyte
 To install `bitandbytes` please refer to this [readme](https://github.com/TimDettmers/bitsandbytes#requirements--installation).
 
 ```bash
-export MODEL_NAME="runwayml/stable-diffusion-inpainting"
+export MODEL_NAME="stable-diffusion-v1-5/stable-diffusion-inpainting"
 export INSTANCE_DIR="path-to-instance-images"
 export CLASS_DIR="path-to-class-images"
 export OUTPUT_DIR="path-to-save-model"
@@ -92,7 +92,7 @@ Pass the `--train_text_encoder` argument to the script to enable training `text_
 ___Note: Training text encoder requires more memory, with this option the training won't fit on 16GB GPU. It needs at least 24GB VRAM.___
 
 ```bash
-export MODEL_NAME="runwayml/stable-diffusion-inpainting"
+export MODEL_NAME="stable-diffusion-v1-5/stable-diffusion-inpainting"
 export INSTANCE_DIR="path-to-instance-images"
 export CLASS_DIR="path-to-class-images"
 export OUTPUT_DIR="path-to-save-model"
diff --git a/examples/research_projects/ip_adapter/README.md b/examples/research_projects/ip_adapter/README.md
index 3df9644ddf8d..0bead5ae859d 100644
--- a/examples/research_projects/ip_adapter/README.md
+++ b/examples/research_projects/ip_adapter/README.md
@@ -55,7 +55,7 @@ The Accelerate launch command is used to train a model using multiple GPUs and m
 ```
 accelerate launch --mixed_precision "fp16" \
 tutorial_train_ip-adapter.py \
---pretrained_model_name_or_path="runwayml/stable-diffusion-v1-5/" \
+--pretrained_model_name_or_path="stable-diffusion-v1-5/stable-diffusion-v1-5/" \
 --image_encoder_path="{image_encoder_path}" \
 --data_json_file="{data.json}" \
 --data_root_path="{image_path}" \
@@ -73,7 +73,7 @@ tutorial_train_ip-adapter.py \
 ```
 accelerate launch --num_processes 8 --multi_gpu --mixed_precision "fp16" \
   tutorial_train_ip-adapter.py \
-  --pretrained_model_name_or_path="runwayml/stable-diffusion-v1-5/" \
+  --pretrained_model_name_or_path="stable-diffusion-v1-5/stable-diffusion-v1-5/" \
   --image_encoder_path="{image_encoder_path}" \
   --data_json_file="{data.json}" \
   --data_root_path="{image_path}" \
diff --git a/examples/research_projects/multi_subject_dreambooth_inpainting/README.md b/examples/research_projects/multi_subject_dreambooth_inpainting/README.md
index 8ddef1b83c3b..3412de662f58 100644
--- a/examples/research_projects/multi_subject_dreambooth_inpainting/README.md
+++ b/examples/research_projects/multi_subject_dreambooth_inpainting/README.md
@@ -27,7 +27,7 @@ You can build multiple datasets for every subject and upload them to the 🤗 hu
 Before launching the training script, make sure to select the inpainting the target model, the output directory and the 🤗 datasets.
 
 ```bash
-export MODEL_NAME="runwayml/stable-diffusion-inpainting"
+export MODEL_NAME="stable-diffusion-v1-5/stable-diffusion-inpainting"
 export OUTPUT_DIR="path-to-save-model"
 
 export DATASET_1="gzguevara/mr_potato_head_masked"
diff --git a/examples/research_projects/promptdiffusion/pipeline_prompt_diffusion.py b/examples/research_projects/promptdiffusion/pipeline_prompt_diffusion.py
index 1bd9c0161ff6..233df1276563 100644
--- a/examples/research_projects/promptdiffusion/pipeline_prompt_diffusion.py
+++ b/examples/research_projects/promptdiffusion/pipeline_prompt_diffusion.py
@@ -177,7 +177,7 @@ class PromptDiffusionPipeline(
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
+            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
             about a model's potential harms.
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
diff --git a/examples/research_projects/vae/vae_roundtrip.py b/examples/research_projects/vae/vae_roundtrip.py
index cdc3a54fdf74..922cb42615e9 100644
--- a/examples/research_projects/vae/vae_roundtrip.py
+++ b/examples/research_projects/vae/vae_roundtrip.py
@@ -238,7 +238,7 @@ def parse_args() -> argparse.Namespace:
 
 # EXAMPLE USAGE:
 #
-# python vae_roundtrip.py --use_cuda --pretrained_model_name_or_path "runwayml/stable-diffusion-v1-5" --subfolder "vae" --input_image "foo.png"
+# python vae_roundtrip.py --use_cuda --pretrained_model_name_or_path "stable-diffusion-v1-5/stable-diffusion-v1-5" --subfolder "vae" --input_image "foo.png"
 #
 # python vae_roundtrip.py --use_cuda --pretrained_model_name_or_path "madebyollin/taesd" --use_tiny_nn --input_image "foo.png"
 #
diff --git a/scripts/convert_consistency_decoder.py b/scripts/convert_consistency_decoder.py
index 629c784c095a..d6a19690c77f 100644
--- a/scripts/convert_consistency_decoder.py
+++ b/scripts/convert_consistency_decoder.py
@@ -507,7 +507,7 @@ def rename_state_dict(sd, embedding):
 
 
 # encode with stable diffusion vae
-pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
+pipe = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16)
 pipe.vae.cuda()
 
 # construct original decoder with jitted model
@@ -1090,7 +1090,7 @@ def new_constructor(self, **kwargs):
 Encoder.__init__ = new_constructor
 
 
-vae = AutoencoderKL.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="vae")
+vae = AutoencoderKL.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="vae")
 consistency_vae = ConsistencyDecoderVAE(
     encoder_args=vae.encoder.constructor_arguments,
     decoder_args=unet.config,
@@ -1117,7 +1117,7 @@ def new_constructor(self, **kwargs):
 print("running with diffusers pipeline")
 
 pipe = DiffusionPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5", vae=consistency_vae, torch_dtype=torch.float16
+    "stable-diffusion-v1-5/stable-diffusion-v1-5", vae=consistency_vae, torch_dtype=torch.float16
 )
 pipe.to("cuda")
 
diff --git a/src/diffusers/models/auto_model.py b/src/diffusers/models/auto_model.py
index a95b0ae64a8e..947b610ea61a 100644
--- a/src/diffusers/models/auto_model.py
+++ b/src/diffusers/models/auto_model.py
@@ -128,13 +128,13 @@ def from_pretrained(cls, pretrained_model_or_path: Optional[Union[str, os.PathLi
         ```py
         from diffusers import AutoModel
 
-        unet = AutoModel.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="unet")
+        unet = AutoModel.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="unet")
         ```
 
         If you get the error message below, you need to finetune the weights for your downstream task:
 
         ```bash
-        Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:
+        Some weights of UNet2DConditionModel were not initialized from the model checkpoint at stable-diffusion-v1-5/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:
         - conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated
         You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
         ```
diff --git a/src/diffusers/models/modeling_flax_utils.py b/src/diffusers/models/modeling_flax_utils.py
index fd195783212e..2bcf7f6476ae 100644
--- a/src/diffusers/models/modeling_flax_utils.py
+++ b/src/diffusers/models/modeling_flax_utils.py
@@ -113,14 +113,14 @@ def to_bf16(self, params: Union[Dict, FrozenDict], mask: Any = None):
         >>> from diffusers import FlaxUNet2DConditionModel
 
         >>> # load model
-        >>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5")
+        >>> model, params = FlaxUNet2DConditionModel.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5")
         >>> # By default, the model parameters will be in fp32 precision, to cast these to bfloat16 precision
         >>> params = model.to_bf16(params)
         >>> # If you don't want to cast certain parameters (for example layer norm bias and scale)
         >>> # then pass the mask as follows
         >>> from flax import traverse_util
 
-        >>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5")
+        >>> model, params = FlaxUNet2DConditionModel.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5")
         >>> flat_params = traverse_util.flatten_dict(params)
         >>> mask = {
         ...     path: (path[-2] != ("LayerNorm", "bias") and path[-2:] != ("LayerNorm", "scale"))
@@ -149,7 +149,7 @@ def to_fp32(self, params: Union[Dict, FrozenDict], mask: Any = None):
         >>> from diffusers import FlaxUNet2DConditionModel
 
         >>> # Download model and configuration from huggingface.co
-        >>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5")
+        >>> model, params = FlaxUNet2DConditionModel.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5")
         >>> # By default, the model params will be in fp32, to illustrate the use of this method,
         >>> # we'll first cast to fp16 and back to fp32
         >>> params = model.to_f16(params)
@@ -179,14 +179,14 @@ def to_fp16(self, params: Union[Dict, FrozenDict], mask: Any = None):
         >>> from diffusers import FlaxUNet2DConditionModel
 
         >>> # load model
-        >>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5")
+        >>> model, params = FlaxUNet2DConditionModel.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5")
         >>> # By default, the model params will be in fp32, to cast these to float16
         >>> params = model.to_fp16(params)
         >>> # If you want don't want to cast certain parameters (for example layer norm bias and scale)
         >>> # then pass the mask as follows
         >>> from flax import traverse_util
 
-        >>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5")
+        >>> model, params = FlaxUNet2DConditionModel.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5")
         >>> flat_params = traverse_util.flatten_dict(params)
         >>> mask = {
         ...     path: (path[-2] != ("LayerNorm", "bias") and path[-2:] != ("LayerNorm", "scale"))
@@ -216,7 +216,7 @@ def from_pretrained(
             pretrained_model_name_or_path (`str` or `os.PathLike`):
                 Can be either:
 
-                    - A string, the *model id* (for example `runwayml/stable-diffusion-v1-5`) of a pretrained model
+                    - A string, the *model id* (for example `stable-diffusion-v1-5/stable-diffusion-v1-5`) of a pretrained model
                       hosted on the Hub.
                     - A path to a *directory* (for example `./my_model_directory`) containing the model weights saved
                       using [`~FlaxModelMixin.save_pretrained`].
@@ -271,7 +271,7 @@ def from_pretrained(
         >>> from diffusers import FlaxUNet2DConditionModel
 
         >>> # Download model and configuration from huggingface.co and cache.
-        >>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5")
+        >>> model, params = FlaxUNet2DConditionModel.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5")
         >>> # Model was saved using *save_pretrained('./test/saved_model/')* (for example purposes, not runnable).
         >>> model, params = FlaxUNet2DConditionModel.from_pretrained("./test/saved_model/")
         ```
@@ -279,7 +279,7 @@ def from_pretrained(
         If you get the error message below, you need to finetune the weights for your downstream task:
 
         ```bash
-        Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:
+        Some weights of UNet2DConditionModel were not initialized from the model checkpoint at stable-diffusion-v1-5/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:
         - conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated
         You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
         ```
diff --git a/src/diffusers/models/modeling_utils.py b/src/diffusers/models/modeling_utils.py
index 1af7ba9ac511..ce26785f63ea 100644
--- a/src/diffusers/models/modeling_utils.py
+++ b/src/diffusers/models/modeling_utils.py
@@ -923,13 +923,13 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
         ```py
         from diffusers import UNet2DConditionModel
 
-        unet = UNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="unet")
+        unet = UNet2DConditionModel.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="unet")
         ```
 
         If you get the error message below, you need to finetune the weights for your downstream task:
 
         ```bash
-        Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:
+        Some weights of UNet2DConditionModel were not initialized from the model checkpoint at stable-diffusion-v1-5/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:
         - conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated
         You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
         ```
@@ -1800,7 +1800,7 @@ def num_parameters(self, only_trainable: bool = False, exclude_embeddings: bool
         ```py
         from diffusers import UNet2DConditionModel
 
-        model_id = "runwayml/stable-diffusion-v1-5"
+        model_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
         unet = UNet2DConditionModel.from_pretrained(model_id, subfolder="unet")
         unet.num_parameters(only_trainable=True)
         859520964
diff --git a/src/diffusers/modular_pipelines/stable_diffusion_xl/denoise.py b/src/diffusers/modular_pipelines/stable_diffusion_xl/denoise.py
index 8a8025747332..83e6007e3fc1 100644
--- a/src/diffusers/modular_pipelines/stable_diffusion_xl/denoise.py
+++ b/src/diffusers/modular_pipelines/stable_diffusion_xl/denoise.py
@@ -115,7 +115,7 @@ def inputs(self) -> List[str]:
     def check_inputs(components, block_state):
         num_channels_unet = components.num_channels_unet
         if num_channels_unet == 9:
-            # default case for runwayml/stable-diffusion-inpainting
+            # default case for stable-diffusion-v1-5/stable-diffusion-inpainting
             if block_state.mask is None or block_state.masked_image_latents is None:
                 raise ValueError("mask and masked_image_latents must be provided for inpainting-specific Unet")
             num_channels_latents = block_state.latents.shape[1]
diff --git a/src/diffusers/pipelines/README.md b/src/diffusers/pipelines/README.md
index 363caffe20ba..6f9ab7b291ad 100644
--- a/src/diffusers/pipelines/README.md
+++ b/src/diffusers/pipelines/README.md
@@ -159,7 +159,7 @@ init_image = download_image(img_url).resize((512, 512))
 mask_image = download_image(mask_url).resize((512, 512))
 
 pipe = StableDiffusionInpaintPipeline.from_pretrained(
-    "runwayml/stable-diffusion-inpainting",
+    "stable-diffusion-v1-5/stable-diffusion-inpainting",
     torch_dtype=torch.float16,
 )
 pipe = pipe.to("cuda")
diff --git a/src/diffusers/pipelines/controlnet_xs/pipeline_controlnet_xs.py b/src/diffusers/pipelines/controlnet_xs/pipeline_controlnet_xs.py
index 1545027a280c..2ca1ac2d45f1 100644
--- a/src/diffusers/pipelines/controlnet_xs/pipeline_controlnet_xs.py
+++ b/src/diffusers/pipelines/controlnet_xs/pipeline_controlnet_xs.py
@@ -133,7 +133,7 @@ class StableDiffusionControlNetXSPipeline(
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
+            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
             about a model's potential harms.
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
diff --git a/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py b/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py
index 003e7482741f..f2b187dedc34 100644
--- a/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py
+++ b/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py
@@ -185,7 +185,7 @@ class AltDiffusionPipeline(
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
+            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
             about a model's potential harms.
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
@@ -266,8 +266,8 @@ def __init__(
                 "The configuration file of the unet has set the default `sample_size` to smaller than"
                 " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
                 " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
+                " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                 " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                 " in the config might lead to incorrect results in future versions. If you have downloaded this"
                 " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
diff --git a/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py b/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py
index 64bd06d02ea7..d7fb8f65393c 100644
--- a/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py
+++ b/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py
@@ -213,7 +213,7 @@ class AltDiffusionImg2ImgPipeline(
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
+            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
             about a model's potential harms.
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
@@ -294,8 +294,8 @@ def __init__(
                 "The configuration file of the unet has set the default `sample_size` to smaller than"
                 " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
                 " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
+                " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                 " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                 " in the config might lead to incorrect results in future versions. If you have downloaded this"
                 " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
diff --git a/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_cycle_diffusion.py b/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_cycle_diffusion.py
index 59c79e134e07..d6d79d179c44 100644
--- a/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_cycle_diffusion.py
+++ b/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_cycle_diffusion.py
@@ -162,7 +162,7 @@ class CycleDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, Sta
             instance of [`DDIMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
+            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
             about a model's potential harms.
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
@@ -226,8 +226,8 @@ def __init__(
                 "The configuration file of the unet has set the default `sample_size` to smaller than"
                 " 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
                 " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
+                " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                 " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                 " in the config might lead to incorrect results in future versions. If you have downloaded this"
                 " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
diff --git a/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_onnx_stable_diffusion_inpaint_legacy.py b/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_onnx_stable_diffusion_inpaint_legacy.py
index 2d9eaa493f1a..7d5eee971c90 100644
--- a/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_onnx_stable_diffusion_inpaint_legacy.py
+++ b/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_onnx_stable_diffusion_inpaint_legacy.py
@@ -62,7 +62,7 @@ class OnnxStableDiffusionInpaintPipelineLegacy(DiffusionPipeline):
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
+            Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details.
         feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
diff --git a/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_inpaint_legacy.py b/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_inpaint_legacy.py
index 205ace65eeb6..4412783b1442 100644
--- a/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_inpaint_legacy.py
+++ b/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_inpaint_legacy.py
@@ -111,7 +111,7 @@ class StableDiffusionInpaintPipelineLegacy(
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
+            Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details.
         feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
@@ -196,8 +196,8 @@ def __init__(
                 "The configuration file of the unet has set the default `sample_size` to smaller than"
                 " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
                 " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
+                " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                 " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                 " in the config might lead to incorrect results in future versions. If you have downloaded this"
                 " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
diff --git a/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_model_editing.py b/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_model_editing.py
index d81f0d262552..ff1471afd995 100644
--- a/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_model_editing.py
+++ b/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_model_editing.py
@@ -64,7 +64,7 @@ class StableDiffusionModelEditingPipeline(
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
+            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
             about a model's potential harms.
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
diff --git a/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_paradigms.py b/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_paradigms.py
index 2331157ba5e6..4466aefbb89a 100644
--- a/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_paradigms.py
+++ b/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_paradigms.py
@@ -46,10 +46,10 @@
         >>> from diffusers import DDPMParallelScheduler
         >>> from diffusers import StableDiffusionParadigmsPipeline
 
-        >>> scheduler = DDPMParallelScheduler.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="scheduler")
+        >>> scheduler = DDPMParallelScheduler.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="scheduler")
 
         >>> pipe = StableDiffusionParadigmsPipeline.from_pretrained(
-        ...     "runwayml/stable-diffusion-v1-5", scheduler=scheduler, torch_dtype=torch.float16
+        ...     "stable-diffusion-v1-5/stable-diffusion-v1-5", scheduler=scheduler, torch_dtype=torch.float16
         ... )
         >>> pipe = pipe.to("cuda")
 
@@ -95,7 +95,7 @@ class StableDiffusionParadigmsPipeline(
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
+            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
             about a model's potential harms.
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
diff --git a/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py b/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py
index d000d87e6a7b..0c37650435d8 100644
--- a/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py
+++ b/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py
@@ -303,7 +303,7 @@ class StableDiffusionPix2PixZeroPipeline(DiffusionPipeline, StableDiffusionMixin
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], [`EulerAncestralDiscreteScheduler`], or [`DDPMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
+            Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details.
         feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
         requires_safety_checker (bool):
diff --git a/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion.py b/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion.py
index 61582853b055..4670e7a6f19c 100644
--- a/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion.py
+++ b/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion.py
@@ -38,7 +38,7 @@ class VersatileDiffusionPipeline(DiffusionPipeline):
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
+            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
             about a model's potential harms.
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
diff --git a/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py b/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py
index 1d7733982ef5..b8e6d295ca5f 100644
--- a/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py
+++ b/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py
@@ -186,7 +186,7 @@ class LatentConsistencyModelImg2ImgPipeline(
             supports [`LCMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
+            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
             about a model's potential harms.
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
diff --git a/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py b/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py
index 3e96b44663e1..2104510a590c 100644
--- a/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py
+++ b/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py
@@ -165,7 +165,7 @@ class LatentConsistencyModelPipeline(
             supports [`LCMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
+            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
             about a model's potential harms.
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
diff --git a/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py b/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py
index 5b61aaf9b6fa..fbf4dc23d043 100644
--- a/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py
+++ b/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py
@@ -49,7 +49,7 @@
         >>> from diffusers.utils import load_image
 
         >>> pipe = LEditsPPPipelineStableDiffusion.from_pretrained(
-        ...     "runwayml/stable-diffusion-v1-5", variant="fp16", torch_dtype=torch.float16
+        ...     "stable-diffusion-v1-5/stable-diffusion-v1-5", variant="fp16", torch_dtype=torch.float16
         ... )
         >>> pipe.enable_vae_tiling()
         >>> pipe = pipe.to("cuda")
@@ -381,8 +381,8 @@ def __init__(
                 "The configuration file of the unet has set the default `sample_size` to smaller than"
                 " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
                 " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
+                " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                 " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                 " in the config might lead to incorrect results in future versions. If you have downloaded this"
                 " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
diff --git a/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py b/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py
index de66871922e6..565d852eab66 100644
--- a/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py
+++ b/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py
@@ -80,7 +80,7 @@
         >>> # load control net and stable diffusion v1-5
         >>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
         >>> pipe = AutoPipelineForText2Image.from_pretrained(
-        ...     "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16, enable_pag=True
+        ...     "stable-diffusion-v1-5/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16, enable_pag=True
         ... )
 
         >>> # speed up diffusion process with faster scheduler and memory optimization
@@ -202,7 +202,7 @@ class StableDiffusionControlNetPAGPipeline(
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
+            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
             about a model's potential harms.
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
diff --git a/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.py b/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.py
index 3daaac328caa..00995b66f7f6 100644
--- a/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.py
+++ b/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.py
@@ -93,7 +93,7 @@
         ...     "lllyasviel/control_v11p_sd15_inpaint", torch_dtype=torch.float16
         ... )
         >>> pipe = AutoPipelineForInpainting.from_pretrained(
-        ...     "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16, enable_pag=True
+        ...     "stable-diffusion-v1-5/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16, enable_pag=True
         ... )
 
         >>> pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
@@ -151,9 +151,9 @@ class StableDiffusionControlNetPAGInpaintPipeline(
         - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
 
     > [!TIP] > This pipeline can be used with checkpoints that have been specifically fine-tuned for inpainting >
-    ([runwayml/stable-diffusion-inpainting](https://huggingface.co/runwayml/stable-diffusion-inpainting)) as well as >
+    ([stable-diffusion-v1-5/stable-diffusion-inpainting](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting)) as well as >
     default text-to-image Stable Diffusion checkpoints >
-    ([runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5)). Default text-to-image >
+    ([stable-diffusion-v1-5/stable-diffusion-v1-5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5)). Default text-to-image >
     Stable Diffusion checkpoints might be preferable for ControlNets that have been fine-tuned on those, such as >
     [lllyasviel/control_v11p_sd15_inpaint](https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint).
 
@@ -175,7 +175,7 @@ class StableDiffusionControlNetPAGInpaintPipeline(
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
+            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
             about a model's potential harms.
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
@@ -1328,7 +1328,7 @@ def __call__(
 
         # 7.1 Check that sizes of mask, masked image and latents match
         if num_channels_unet == 9:
-            # default case for runwayml/stable-diffusion-inpainting
+            # default case for stable-diffusion-v1-5/stable-diffusion-inpainting
             num_channels_mask = mask.shape[1]
             num_channels_masked_image = masked_image_latents.shape[1]
             if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:
diff --git a/src/diffusers/pipelines/pag/pipeline_pag_sd.py b/src/diffusers/pipelines/pag/pipeline_pag_sd.py
index 349d006aada3..e21f440840bd 100644
--- a/src/diffusers/pipelines/pag/pipeline_pag_sd.py
+++ b/src/diffusers/pipelines/pag/pipeline_pag_sd.py
@@ -57,7 +57,7 @@
         >>> from diffusers import AutoPipelineForText2Image
 
         >>> pipe = AutoPipelineForText2Image.from_pretrained(
-        ...     "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, enable_pag=True
+        ...     "stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, enable_pag=True
         ... )
         >>> pipe = pipe.to("cuda")
 
@@ -190,7 +190,7 @@ class StableDiffusionPAGPipeline(
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
+            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
             about a model's potential harms.
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
@@ -272,8 +272,8 @@ def __init__(
                 "The configuration file of the unet has set the default `sample_size` to smaller than"
                 " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
                 " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
+                " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                 " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                 " in the config might lead to incorrect results in future versions. If you have downloaded this"
                 " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
diff --git a/src/diffusers/pipelines/pag/pipeline_pag_sd_img2img.py b/src/diffusers/pipelines/pag/pipeline_pag_sd_img2img.py
index e9a846b5e2c4..e28000047c03 100644
--- a/src/diffusers/pipelines/pag/pipeline_pag_sd_img2img.py
+++ b/src/diffusers/pipelines/pag/pipeline_pag_sd_img2img.py
@@ -61,7 +61,7 @@
         >>> from diffusers.utils import load_image
 
         >>> pipe = AutoPipelineForImage2Image.from_pretrained(
-        ...     "runwayml/stable-diffusion-v1-5",
+        ...     "stable-diffusion-v1-5/stable-diffusion-v1-5",
         ...     torch_dtype=torch.float16,
         ...     enable_pag=True,
         ... )
@@ -185,7 +185,7 @@ class StableDiffusionPAGImg2ImgPipeline(
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
+            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
             about a model's potential harms.
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
@@ -267,8 +267,8 @@ def __init__(
                 "The configuration file of the unet has set the default `sample_size` to smaller than"
                 " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
                 " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
+                " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                 " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                 " in the config might lead to incorrect results in future versions. If you have downloaded this"
                 " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
diff --git a/src/diffusers/pipelines/pag/pipeline_pag_sd_inpaint.py b/src/diffusers/pipelines/pag/pipeline_pag_sd_inpaint.py
index ee9d20f3682b..c8a07809c1a7 100644
--- a/src/diffusers/pipelines/pag/pipeline_pag_sd_inpaint.py
+++ b/src/diffusers/pipelines/pag/pipeline_pag_sd_inpaint.py
@@ -58,7 +58,7 @@
         >>> from diffusers import AutoPipelineForInpainting
 
         >>> pipe = AutoPipelineForInpainting.from_pretrained(
-        ...     "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, enable_pag=True
+        ...     "stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, enable_pag=True
         ... )
         >>> pipe = pipe.to("cuda")
         >>> img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
@@ -217,7 +217,7 @@ class StableDiffusionPAGInpaintPipeline(
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
+            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
             about a model's potential harms.
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
@@ -299,8 +299,8 @@ def __init__(
                 "The configuration file of the unet has set the default `sample_size` to smaller than"
                 " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
                 " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
+                " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                 " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                 " in the config might lead to incorrect results in future versions. If you have downloaded this"
                 " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
@@ -1183,7 +1183,7 @@ def __call__(
 
         # 8. Check that sizes of mask, masked image and latents match
         if num_channels_unet == 9:
-            # default case for runwayml/stable-diffusion-inpainting
+            # default case for stable-diffusion-v1-5/stable-diffusion-inpainting
             num_channels_mask = mask.shape[1]
             num_channels_masked_image = masked_image_latents.shape[1]
             if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:
diff --git a/src/diffusers/pipelines/pag/pipeline_pag_sd_xl_inpaint.py b/src/diffusers/pipelines/pag/pipeline_pag_sd_xl_inpaint.py
index 2e12a4a97fbe..2a8f7a448d5e 100644
--- a/src/diffusers/pipelines/pag/pipeline_pag_sd_xl_inpaint.py
+++ b/src/diffusers/pipelines/pag/pipeline_pag_sd_xl_inpaint.py
@@ -1501,7 +1501,7 @@ def denoising_value_valid(dnv):
 
         # 8. Check that sizes of mask, masked image and latents match
         if num_channels_unet == 9:
-            # default case for runwayml/stable-diffusion-inpainting
+            # default case for stable-diffusion-v1-5/stable-diffusion-inpainting
             num_channels_mask = mask.shape[1]
             num_channels_masked_image = masked_image_latents.shape[1]
             if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:
diff --git a/src/diffusers/pipelines/paint_by_example/pipeline_paint_by_example.py b/src/diffusers/pipelines/paint_by_example/pipeline_paint_by_example.py
index 61435b80ca5a..0e4d582014a8 100644
--- a/src/diffusers/pipelines/paint_by_example/pipeline_paint_by_example.py
+++ b/src/diffusers/pipelines/paint_by_example/pipeline_paint_by_example.py
@@ -179,7 +179,7 @@ class PaintByExamplePipeline(DeprecatedPipelineMixin, DiffusionPipeline, StableD
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
+            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
             about a model's potential harms.
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
diff --git a/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py b/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py
index a5f67bffe6f0..1c17c4b92428 100644
--- a/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py
+++ b/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py
@@ -48,7 +48,7 @@ class SemanticStableDiffusionPipeline(DeprecatedPipelineMixin, DiffusionPipeline
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`Q16SafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
+            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
             about a model's potential harms.
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
@@ -332,7 +332,7 @@ def __call__(
         >>> from diffusers import SemanticStableDiffusionPipeline
 
         >>> pipe = SemanticStableDiffusionPipeline.from_pretrained(
-        ...     "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16
+        ...     "stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16
         ... )
         >>> pipe = pipe.to("cuda")
 
diff --git a/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_inpaint.py b/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_inpaint.py
index bed596e57c34..5b2cca0378cd 100644
--- a/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_inpaint.py
+++ b/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_inpaint.py
@@ -1247,7 +1247,7 @@ def __call__(
 
         # match the inpainting pipeline and will be updated with input + mask inpainting model later
         if num_channels_transformer == 33:
-            # default case for runwayml/stable-diffusion-inpainting
+            # default case for stable-diffusion-v1-5/stable-diffusion-inpainting
             num_channels_mask = mask.shape[1]
             num_channels_masked_image = masked_image_latents.shape[1]
             if (
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 18f8536a7510..88cc7515b051 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
@@ -1501,7 +1501,7 @@ def denoising_value_valid(dnv):
 
         # 8. Check that sizes of mask, masked image and latents match
         if num_channels_unet == 9:
-            # default case for runwayml/stable-diffusion-inpainting
+            # default case for stable-diffusion-v1-5/stable-diffusion-inpainting
             num_channels_mask = mask.shape[1]
             num_channels_masked_image = masked_image_latents.shape[1]
             if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:
diff --git a/src/diffusers/utils/hub_utils.py b/src/diffusers/utils/hub_utils.py
index b6e99452aa88..f30663660062 100644
--- a/src/diffusers/utils/hub_utils.py
+++ b/src/diffusers/utils/hub_utils.py
@@ -113,7 +113,7 @@ def load_or_create_model_card(
 
     Args:
         repo_id_or_path (`str`):
-            The repo id (e.g., "runwayml/stable-diffusion-v1-5") or local path where to look for the model card.
+            The repo id (e.g., "stable-diffusion-v1-5/stable-diffusion-v1-5") or local path where to look for the model card.
         token (`str`, *optional*):
             Authentication token. Will default to the stored token. See https://huggingface.co/settings/token for more
             details.
diff --git a/tests/pipelines/pag/test_pag_sd_inpaint.py b/tests/pipelines/pag/test_pag_sd_inpaint.py
index 709df6837055..754158bbf138 100644
--- a/tests/pipelines/pag/test_pag_sd_inpaint.py
+++ b/tests/pipelines/pag/test_pag_sd_inpaint.py
@@ -255,7 +255,7 @@ def test_encode_prompt_works_in_isolation(self):
 @require_torch_accelerator
 class StableDiffusionPAGPipelineIntegrationTests(unittest.TestCase):
     pipeline_class = StableDiffusionPAGInpaintPipeline
-    repo_id = "runwayml/stable-diffusion-v1-5"
+    repo_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
 
     def setUp(self):
         super().setUp()

From 74ad847e47d2cf74acfbc63e54f8f5c0bf6a4d13 Mon Sep 17 00:00:00 2001
From: EazyAl <muhammadaliimran310@gmail.com>
Date: Fri, 17 Oct 2025 19:32:47 +0200
Subject: [PATCH 2/2] quality fixes

---
 scripts/convert_consistency_decoder.py         |  7 +++++--
 src/diffusers/models/modeling_flax_utils.py    |  4 ++--
 .../controlnet_xs/pipeline_controlnet_xs.py    |  4 ++--
 .../alt_diffusion/pipeline_alt_diffusion.py    |  4 ++--
 .../pipeline_alt_diffusion_img2img.py          |  4 ++--
 .../pipeline_cycle_diffusion.py                |  4 ++--
 ...ine_onnx_stable_diffusion_inpaint_legacy.py |  3 ++-
 ...pipeline_stable_diffusion_inpaint_legacy.py |  3 ++-
 .../pipeline_stable_diffusion_model_editing.py |  4 ++--
 .../pipeline_stable_diffusion_paradigms.py     |  8 +++++---
 .../pipeline_stable_diffusion_pix2pix_zero.py  |  3 ++-
 .../pipeline_versatile_diffusion.py            |  4 ++--
 .../pipeline_latent_consistency_img2img.py     |  4 ++--
 .../pipeline_latent_consistency_text2img.py    |  4 ++--
 .../pag/pipeline_pag_controlnet_sd.py          |  9 ++++++---
 .../pag/pipeline_pag_controlnet_sd_inpaint.py  | 18 +++++++++++-------
 src/diffusers/pipelines/pag/pipeline_pag_sd.py |  4 ++--
 .../pipelines/pag/pipeline_pag_sd_img2img.py   |  4 ++--
 .../pipelines/pag/pipeline_pag_sd_inpaint.py   |  4 ++--
 .../pipeline_paint_by_example.py               |  4 ++--
 .../pipeline_semantic_stable_diffusion.py      |  4 ++--
 src/diffusers/utils/hub_utils.py               |  3 ++-
 22 files changed, 63 insertions(+), 47 deletions(-)

diff --git a/scripts/convert_consistency_decoder.py b/scripts/convert_consistency_decoder.py
index d6a19690c77f..9e289457752b 100644
--- a/scripts/convert_consistency_decoder.py
+++ b/scripts/convert_consistency_decoder.py
@@ -24,7 +24,8 @@
 
 
 def _extract_into_tensor(arr, timesteps, broadcast_shape):
-    # from: https://github.com/openai/guided-diffusion/blob/22e0df8183507e13a7813f8d38d51b072ca1e67c/guided_diffusion/gaussian_diffusion.py#L895    """
+    # from: https://github.com/openai/guided-diffusion/blob/22e0df8183507e13a7813f8d38d51b072ca1e67c/guided_diffusion/gaussian_diffusion.py#L895
+    #  """
     res = arr[timesteps].float()
     dims_to_append = len(broadcast_shape) - len(res.shape)
     return res[(...,) + (None,) * dims_to_append]
@@ -507,7 +508,9 @@ def rename_state_dict(sd, embedding):
 
 
 # encode with stable diffusion vae
-pipe = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16)
+pipe = StableDiffusionPipeline.from_pretrained(
+    "stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16
+)
 pipe.vae.cuda()
 
 # construct original decoder with jitted model
diff --git a/src/diffusers/models/modeling_flax_utils.py b/src/diffusers/models/modeling_flax_utils.py
index 2bcf7f6476ae..3f060993190f 100644
--- a/src/diffusers/models/modeling_flax_utils.py
+++ b/src/diffusers/models/modeling_flax_utils.py
@@ -216,8 +216,8 @@ def from_pretrained(
             pretrained_model_name_or_path (`str` or `os.PathLike`):
                 Can be either:
 
-                    - A string, the *model id* (for example `stable-diffusion-v1-5/stable-diffusion-v1-5`) of a pretrained model
-                      hosted on the Hub.
+                    - A string, the *model id* (for example `stable-diffusion-v1-5/stable-diffusion-v1-5`) of a
+                      pretrained model hosted on the Hub.
                     - A path to a *directory* (for example `./my_model_directory`) containing the model weights saved
                       using [`~FlaxModelMixin.save_pretrained`].
             dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`):
diff --git a/src/diffusers/pipelines/controlnet_xs/pipeline_controlnet_xs.py b/src/diffusers/pipelines/controlnet_xs/pipeline_controlnet_xs.py
index 2ca1ac2d45f1..3682ddc91156 100644
--- a/src/diffusers/pipelines/controlnet_xs/pipeline_controlnet_xs.py
+++ b/src/diffusers/pipelines/controlnet_xs/pipeline_controlnet_xs.py
@@ -133,8 +133,8 @@ class StableDiffusionControlNetXSPipeline(
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
-            about a model's potential harms.
+            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for
+            more details about a model's potential harms.
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
     """
diff --git a/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py b/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py
index f2b187dedc34..6f484aa3e298 100644
--- a/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py
+++ b/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py
@@ -185,8 +185,8 @@ class AltDiffusionPipeline(
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
-            about a model's potential harms.
+            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for
+            more details about a model's potential harms.
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
     """
diff --git a/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py b/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py
index d7fb8f65393c..d6bf90120755 100644
--- a/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py
+++ b/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py
@@ -213,8 +213,8 @@ class AltDiffusionImg2ImgPipeline(
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
-            about a model's potential harms.
+            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for
+            more details about a model's potential harms.
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
     """
diff --git a/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_cycle_diffusion.py b/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_cycle_diffusion.py
index d6d79d179c44..08f8c7e26fae 100644
--- a/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_cycle_diffusion.py
+++ b/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_cycle_diffusion.py
@@ -162,8 +162,8 @@ class CycleDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, Sta
             instance of [`DDIMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
-            about a model's potential harms.
+            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for
+            more details about a model's potential harms.
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
     """
diff --git a/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_onnx_stable_diffusion_inpaint_legacy.py b/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_onnx_stable_diffusion_inpaint_legacy.py
index 7d5eee971c90..fcd8bf317adf 100644
--- a/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_onnx_stable_diffusion_inpaint_legacy.py
+++ b/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_onnx_stable_diffusion_inpaint_legacy.py
@@ -62,7 +62,8 @@ class OnnxStableDiffusionInpaintPipelineLegacy(DiffusionPipeline):
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details.
+            Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for
+            details.
         feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
diff --git a/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_inpaint_legacy.py b/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_inpaint_legacy.py
index 4412783b1442..ba0dd66c2938 100644
--- a/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_inpaint_legacy.py
+++ b/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_inpaint_legacy.py
@@ -111,7 +111,8 @@ class StableDiffusionInpaintPipelineLegacy(
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details.
+            Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for
+            details.
         feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
diff --git a/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_model_editing.py b/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_model_editing.py
index ff1471afd995..b7a0be57c12b 100644
--- a/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_model_editing.py
+++ b/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_model_editing.py
@@ -64,8 +64,8 @@ class StableDiffusionModelEditingPipeline(
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
-            about a model's potential harms.
+            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for
+            more details about a model's potential harms.
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
         with_to_k ([`bool`]):
diff --git a/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_paradigms.py b/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_paradigms.py
index 4466aefbb89a..c236e73bf448 100644
--- a/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_paradigms.py
+++ b/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_paradigms.py
@@ -46,7 +46,9 @@
         >>> from diffusers import DDPMParallelScheduler
         >>> from diffusers import StableDiffusionParadigmsPipeline
 
-        >>> scheduler = DDPMParallelScheduler.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="scheduler")
+        >>> scheduler = DDPMParallelScheduler.from_pretrained(
+        ...     "stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="scheduler"
+        ... )
 
         >>> pipe = StableDiffusionParadigmsPipeline.from_pretrained(
         ...     "stable-diffusion-v1-5/stable-diffusion-v1-5", scheduler=scheduler, torch_dtype=torch.float16
@@ -95,8 +97,8 @@ class StableDiffusionParadigmsPipeline(
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
-            about a model's potential harms.
+            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for
+            more details about a model's potential harms.
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
     """
diff --git a/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py b/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py
index 0c37650435d8..2a461ae20cc9 100644
--- a/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py
+++ b/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py
@@ -303,7 +303,8 @@ class StableDiffusionPix2PixZeroPipeline(DiffusionPipeline, StableDiffusionMixin
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], [`EulerAncestralDiscreteScheduler`], or [`DDPMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details.
+            Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for
+            details.
         feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
         requires_safety_checker (bool):
diff --git a/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion.py b/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion.py
index 4670e7a6f19c..9ff8e9857791 100644
--- a/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion.py
+++ b/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion.py
@@ -38,8 +38,8 @@ class VersatileDiffusionPipeline(DiffusionPipeline):
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
-            about a model's potential harms.
+            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for
+            more details about a model's potential harms.
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
     """
diff --git a/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py b/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py
index b8e6d295ca5f..59f733a498ed 100644
--- a/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py
+++ b/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py
@@ -186,8 +186,8 @@ class LatentConsistencyModelImg2ImgPipeline(
             supports [`LCMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
-            about a model's potential harms.
+            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for
+            more details about a model's potential harms.
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
         requires_safety_checker (`bool`, *optional*, defaults to `True`):
diff --git a/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py b/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py
index 2104510a590c..e463884618f5 100644
--- a/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py
+++ b/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py
@@ -165,8 +165,8 @@ class LatentConsistencyModelPipeline(
             supports [`LCMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
-            about a model's potential harms.
+            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for
+            more details about a model's potential harms.
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
         requires_safety_checker (`bool`, *optional*, defaults to `True`):
diff --git a/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py b/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py
index 565d852eab66..1abef014301a 100644
--- a/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py
+++ b/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py
@@ -80,7 +80,10 @@
         >>> # load control net and stable diffusion v1-5
         >>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
         >>> pipe = AutoPipelineForText2Image.from_pretrained(
-        ...     "stable-diffusion-v1-5/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16, enable_pag=True
+        ...     "stable-diffusion-v1-5/stable-diffusion-v1-5",
+        ...     controlnet=controlnet,
+        ...     torch_dtype=torch.float16,
+        ...     enable_pag=True,
         ... )
 
         >>> # speed up diffusion process with faster scheduler and memory optimization
@@ -202,8 +205,8 @@ class StableDiffusionControlNetPAGPipeline(
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
-            about a model's potential harms.
+            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for
+            more details about a model's potential harms.
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
     """
diff --git a/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.py b/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.py
index 00995b66f7f6..2781af789018 100644
--- a/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.py
+++ b/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.py
@@ -93,7 +93,10 @@
         ...     "lllyasviel/control_v11p_sd15_inpaint", torch_dtype=torch.float16
         ... )
         >>> pipe = AutoPipelineForInpainting.from_pretrained(
-        ...     "stable-diffusion-v1-5/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16, enable_pag=True
+        ...     "stable-diffusion-v1-5/stable-diffusion-v1-5",
+        ...     controlnet=controlnet,
+        ...     torch_dtype=torch.float16,
+        ...     enable_pag=True,
         ... )
 
         >>> pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
@@ -151,10 +154,11 @@ class StableDiffusionControlNetPAGInpaintPipeline(
         - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
 
     > [!TIP] > This pipeline can be used with checkpoints that have been specifically fine-tuned for inpainting >
-    ([stable-diffusion-v1-5/stable-diffusion-inpainting](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting)) as well as >
-    default text-to-image Stable Diffusion checkpoints >
-    ([stable-diffusion-v1-5/stable-diffusion-v1-5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5)). Default text-to-image >
-    Stable Diffusion checkpoints might be preferable for ControlNets that have been fine-tuned on those, such as >
+    ([stable-diffusion-v1-5/stable-diffusion-inpainting](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting))
+    as well as > default text-to-image Stable Diffusion checkpoints >
+    ([stable-diffusion-v1-5/stable-diffusion-v1-5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5)).
+    Default text-to-image > Stable Diffusion checkpoints might be preferable for ControlNets that have been fine-tuned
+    on those, such as >
     [lllyasviel/control_v11p_sd15_inpaint](https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint).
 
     Args:
@@ -175,8 +179,8 @@ class StableDiffusionControlNetPAGInpaintPipeline(
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
-            about a model's potential harms.
+            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for
+            more details about a model's potential harms.
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
     """
diff --git a/src/diffusers/pipelines/pag/pipeline_pag_sd.py b/src/diffusers/pipelines/pag/pipeline_pag_sd.py
index e21f440840bd..ea64f8be2c50 100644
--- a/src/diffusers/pipelines/pag/pipeline_pag_sd.py
+++ b/src/diffusers/pipelines/pag/pipeline_pag_sd.py
@@ -190,8 +190,8 @@ class StableDiffusionPAGPipeline(
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
-            about a model's potential harms.
+            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for
+            more details about a model's potential harms.
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
     """
diff --git a/src/diffusers/pipelines/pag/pipeline_pag_sd_img2img.py b/src/diffusers/pipelines/pag/pipeline_pag_sd_img2img.py
index e28000047c03..8351112ce409 100644
--- a/src/diffusers/pipelines/pag/pipeline_pag_sd_img2img.py
+++ b/src/diffusers/pipelines/pag/pipeline_pag_sd_img2img.py
@@ -185,8 +185,8 @@ class StableDiffusionPAGImg2ImgPipeline(
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
-            about a model's potential harms.
+            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for
+            more details about a model's potential harms.
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
     """
diff --git a/src/diffusers/pipelines/pag/pipeline_pag_sd_inpaint.py b/src/diffusers/pipelines/pag/pipeline_pag_sd_inpaint.py
index c8a07809c1a7..6b1b294e10f5 100644
--- a/src/diffusers/pipelines/pag/pipeline_pag_sd_inpaint.py
+++ b/src/diffusers/pipelines/pag/pipeline_pag_sd_inpaint.py
@@ -217,8 +217,8 @@ class StableDiffusionPAGInpaintPipeline(
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
-            about a model's potential harms.
+            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for
+            more details about a model's potential harms.
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
     """
diff --git a/src/diffusers/pipelines/paint_by_example/pipeline_paint_by_example.py b/src/diffusers/pipelines/paint_by_example/pipeline_paint_by_example.py
index 0e4d582014a8..c09992befbcb 100644
--- a/src/diffusers/pipelines/paint_by_example/pipeline_paint_by_example.py
+++ b/src/diffusers/pipelines/paint_by_example/pipeline_paint_by_example.py
@@ -179,8 +179,8 @@ class PaintByExamplePipeline(DeprecatedPipelineMixin, DiffusionPipeline, StableD
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`StableDiffusionSafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
-            about a model's potential harms.
+            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for
+            more details about a model's potential harms.
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
 
diff --git a/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py b/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py
index 1c17c4b92428..49b09e205cc5 100644
--- a/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py
+++ b/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py
@@ -48,8 +48,8 @@ class SemanticStableDiffusionPipeline(DeprecatedPipelineMixin, DiffusionPipeline
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
         safety_checker ([`Q16SafetyChecker`]):
             Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
-            about a model's potential harms.
+            Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for
+            more details about a model's potential harms.
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
     """
diff --git a/src/diffusers/utils/hub_utils.py b/src/diffusers/utils/hub_utils.py
index f30663660062..d0b05c7d9541 100644
--- a/src/diffusers/utils/hub_utils.py
+++ b/src/diffusers/utils/hub_utils.py
@@ -113,7 +113,8 @@ def load_or_create_model_card(
 
     Args:
         repo_id_or_path (`str`):
-            The repo id (e.g., "stable-diffusion-v1-5/stable-diffusion-v1-5") or local path where to look for the model card.
+            The repo id (e.g., "stable-diffusion-v1-5/stable-diffusion-v1-5") or local path where to look for the model
+            card.
         token (`str`, *optional*):
             Authentication token. Will default to the stored token. See https://huggingface.co/settings/token for more
             details.