docs: cleanup of runway model (#12503)

* cleanup of runway model

* quality fixes

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")