docs: cleanup of runway model (#12503)

* cleanup of runway model

* quality fixes

src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_paradigms.py

@@ -46,10 +46,12 @@ EXAMPLE_DOC_STRING = """
         >>> 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")
 
@@ -95,8 +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/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/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/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):

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/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/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/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`.
         requires_safety_checker (`bool`, *optional*, defaults to `True`):

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/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`.
         requires_safety_checker (`bool`, *optional*, defaults to `True`):

src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py

@@ -49,7 +49,7 @@ EXAMPLE_DOC_STRING = """
         >>> 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 @@ class LEditsPPPipelineStableDiffusion(
                 "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/pag/pipeline_pag_controlnet_sd.py

@@ -80,7 +80,10 @@ EXAMPLE_DOC_STRING = """
         >>> # 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
@@ -202,8 +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/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/pag/pipeline_pag_controlnet_sd_inpaint.py

@@ -93,7 +93,10 @@ EXAMPLE_DOC_STRING = """
         ...     "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)
@@ -151,10 +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 >
-    ([runwayml/stable-diffusion-inpainting](https://huggingface.co/runwayml/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 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/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`.
     """
@@ -1328,7 +1332,7 @@ class StableDiffusionControlNetPAGInpaintPipeline(
 
         # 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:

src/diffusers/pipelines/pag/pipeline_pag_sd.py

@@ -57,7 +57,7 @@ EXAMPLE_DOC_STRING = """
         >>> 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")
 
@@ -190,8 +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/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`.
     """
@@ -272,8 +272,8 @@ class StableDiffusionPAGPipeline(
                 "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/pag/pipeline_pag_sd_img2img.py

@@ -61,7 +61,7 @@ EXAMPLE_DOC_STRING = """
         >>> 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,
         ... )
@@ -185,8 +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/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`.
     """
@@ -267,8 +267,8 @@ class StableDiffusionPAGImg2ImgPipeline(
                 "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/pag/pipeline_pag_sd_inpaint.py

@@ -58,7 +58,7 @@ EXAMPLE_DOC_STRING = """
         >>> 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"
@@ -217,8 +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/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`.
     """
@@ -299,8 +299,8 @@ class StableDiffusionPAGInpaintPipeline(
                 "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 @@ class StableDiffusionPAGInpaintPipeline(
 
         # 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:

src/diffusers/pipelines/pag/pipeline_pag_sd_xl_inpaint.py

@@ -1501,7 +1501,7 @@ class StableDiffusionXLPAGInpaintPipeline(
 
         # 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:

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/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/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/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`.
     """
@@ -332,7 +332,7 @@ class SemanticStableDiffusionPipeline(DeprecatedPipelineMixin, DiffusionPipeline
         >>> 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")
 

src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_inpaint.py

@@ -1247,7 +1247,7 @@ class StableDiffusion3InpaintPipeline(DiffusionPipeline, SD3LoraLoaderMixin, Fro
 
         # 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 (

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py

@@ -1501,7 +1501,7 @@ class StableDiffusionXLInpaintPipeline(
 
         # 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:

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., "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.

tests/pipelines/pag/test_pag_sd_inpaint.py

@@ -255,7 +255,7 @@ class StableDiffusionPAGInpaintPipelineFastTests(
 @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()