docs: cleanup of runway model (#12503)

* cleanup of runway model

* quality fixes

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`.
     """

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`.

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"

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}" \

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"

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`.

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"
 #

scripts/convert_consistency_decoder.py

@@ -24,7 +24,8 @@ args = args.parse_args()
 
 
 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("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 +1093,7 @@ 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 +1120,7 @@ print((sample_consistency_orig - sample_consistency_new_3).abs().sum())
 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")
 

src/diffusers/models/auto_model.py

@@ -128,13 +128,13 @@ class AutoModel(ConfigMixin):
         ```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.
         ```

src/diffusers/models/modeling_flax_utils.py

@@ -113,14 +113,14 @@ class FlaxModelMixin(PushToHubMixin):
         >>> 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 @@ class FlaxModelMixin(PushToHubMixin):
         >>> 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 @@ class FlaxModelMixin(PushToHubMixin):
         >>> 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"))
@@ -216,8 +216,8 @@ class FlaxModelMixin(PushToHubMixin):
             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
-                      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`):
@@ -271,7 +271,7 @@ class FlaxModelMixin(PushToHubMixin):
         >>> 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 @@ class FlaxModelMixin(PushToHubMixin):
         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.
         ```

src/diffusers/models/modeling_utils.py

@@ -923,13 +923,13 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
         ```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 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
         ```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

src/diffusers/modular_pipelines/stable_diffusion_xl/denoise.py

@@ -115,7 +115,7 @@ class StableDiffusionXLInpaintLoopBeforeDenoiser(ModularPipelineBlocks):
     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]

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")

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/runwayml/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`.
     """

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/runwayml/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`.
     """
@@ -266,8 +266,8 @@ class AltDiffusionPipeline(
                 "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"

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/runwayml/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`.
     """
@@ -294,8 +294,8 @@ class AltDiffusionImg2ImgPipeline(
                 "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"

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/runwayml/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`.
     """
@@ -226,8 +226,8 @@ class CycleDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, Sta
                 "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"

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/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`.
     """

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/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 +197,8 @@ class StableDiffusionInpaintPipelineLegacy(
                 "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"

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/runwayml/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`]):