Update model card (#2)

- Upload folder using huggingface_hub (ef7fcf78c36475845ed0c567434ac9c5f962f3bb)

README.md

@@ -13,18 +13,19 @@ These motion modules are applied after the ResNet and Attention blocks in the St
         <td><center>
         masterpiece, bestquality, sunset.
         <br>
-        <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/animatediff-realistic-doc.gif"
+        <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/animatediff-v3-euler-a.gif"
             alt="masterpiece, bestquality, sunset"
             style="width: 300px;" />
         </center></td>
     </tr>
 </table>
 
+
 The following example demonstrates how you can utilize the motion modules with an existing Stable Diffusion text to image model.
 
 ```python
 import torch
-from diffusers import MotionAdapter, AnimateDiffPipeline, DDIMScheduler
+from diffusers import MotionAdapter, AnimateDiffPipeline, EulerAncestralDiscreteScheduler
 from diffusers.utils import export_to_gif
 
 # Load the motion adapter
@@ -32,13 +33,10 @@ adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-
 # load SD 1.5 based finetuned model
 model_id = "SG161222/Realistic_Vision_V5.1_noVAE"
 pipe = AnimateDiffPipeline.from_pretrained(model_id, motion_adapter=adapter)
-scheduler = DDIMScheduler.from_pretrained(
+scheduler = EulerAncestralDiscreteScheduler.from_pretrained(
     model_id,
     subfolder="scheduler",
-    clip_sample=False,
     beta_schedule="linear",
-    timestep_spacing="linspace",
-    steps_offset=1
 )
 pipe.scheduler = scheduler
 
@@ -62,9 +60,3 @@ output = pipe(
 frames = output.frames[0]
 export_to_gif(frames, "animation.gif")
 ```
-
-<Tip>
-
-AnimateDiff tends to work better with finetuned Stable Diffusion models. If you plan on using a scheduler that can clip samples, make sure to disable it by setting `clip_sample=False` in the scheduler as this can also have an adverse effect on generated samples.
-
-</Tip>