Apply suggestions from code review

Author: yiyixuxu

docs/source/en/api/models/autoencoder_kl_wan.md

@@ -18,7 +18,7 @@ The model can be loaded with the following code snippet.
 ```python
 from diffusers import AutoencoderKLWan
 
-vae = AutoencoderKLWan.from_pretrained("Wan-AI/Wan2.1-T2V-1.3B", subfolder="vae", torch_dtype=torch.float32)
+vae = AutoencoderKLWan.from_pretrained("Wan-AI/Wan2.1-T2V-1.3B-Diffusers", subfolder="vae", torch_dtype=torch.float32)
 ```
 
 ## AutoencoderKLWan

docs/source/en/api/models/wan_transformer_3d.md

@@ -18,7 +18,7 @@ The model can be loaded with the following code snippet.
 ```python
 from diffusers import WanTransformer3DModel
 
-transformer = WanTransformer3DModel.from_pretrained("Wan-AI/Wan2.1-T2V-1.3B", subfolder="transformer", torch_dtype=torch.bfloat16)
+transformer = WanTransformer3DModel.from_pretrained("Wan-AI/Wan2.1-T2V-1.3B-Diffusers", subfolder="transformer", torch_dtype=torch.bfloat16)
 ```
 
 ## WanTransformer3DModel

src/diffusers/pipelines/wan/pipeline_wan.py

@@ -47,8 +47,8 @@
         >>> from diffusers import AutoencoderKLWan, WanPipeline
         >>> from diffusers.utils import export_to_video
 
-        >>> # Available models: Wan-AI/Wan2.1-T2V-14B, Wan-AI/Wan2.1-T2V-1.3B
-        >>> model_id = "Wan-AI/Wan2.1-T2V-14B"
+        >>> # Available models: Wan-AI/Wan2.1-T2V-14B-Diffusers, Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+        >>> model_id = "Wan-AI/Wan2.1-T2V-14B-Diffusers"
         >>> vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
         >>> pipe = WanPipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)
         >>> pipe.to("cuda")
@@ -178,7 +178,7 @@ def encode_prompt(
         self,
         prompt: Union[str, List[str]],
         negative_prompt: Optional[Union[str, List[str]]] = None,
-        do_classifier_free_guidance: bool = False,
+        do_classifier_free_guidance: bool = True,
         num_videos_per_prompt: int = 1,
         prompt_embeds: Optional[torch.Tensor] = None,
         negative_prompt_embeds: Optional[torch.Tensor] = None,

src/diffusers/pipelines/wan/pipeline_wan_i2v.py

@@ -49,8 +49,8 @@
         >>> from diffusers import AutoencoderKLWan, WanImageToVideoPipeline
         >>> from diffusers.utils import export_to_video, load_image
 
-        >>> # Available models: Wan-AI/Wan2.1-I2V-14B-480P, Wan-AI/Wan2.1-I2V-1.3B-720P
-        >>> model_id = "Wan-AI/Wan2.1-I2V-14B-480P"
+        >>> # Available models: Wan-AI/Wan2.1-I2V-14B-480P-Diffusers, Wan-AI/Wan2.1-I2V-1.3B-720P-Diffusers
+        >>> model_id = "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"
         >>> vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
         >>> pipe = WanImageToVideoPipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)
         >>> pipe.to("cuda")
@@ -211,7 +211,7 @@ def encode_prompt(
         self,
         prompt: Union[str, List[str]],
         negative_prompt: Optional[Union[str, List[str]]] = None,
-        do_classifier_free_guidance: bool = False,
+        do_classifier_free_guidance: bool = True,
         num_videos_per_prompt: int = 1,
         prompt_embeds: Optional[torch.Tensor] = None,
         negative_prompt_embeds: Optional[torch.Tensor] = None,