[doc] standardize diffusion cookbook model pages (#34247)

Co-authored-by: Claude Opus 5 <noreply@anthropic.com>

docs/cookbook/diffusion/Wan/Wan2.1.mdx

@@ -7,24 +7,13 @@ metatags:
 import { DiffusionModelTags } from '/src/snippets/diffusion/model-tags.jsx';
 import { Wan21Deployment } from '/src/snippets/diffusion/wan21-deployment.jsx';
 
-<DiffusionModelTags tags={["video", "text-to-video", "image-to-video", "LoRA", "text rendering"]} />
+<DiffusionModelTags tags={["video", "text-to-video", "image-to-video", "1.3B / 14B", "bilingual text"]} />
 
 ## 1. Model Introduction
 
-[Wan2.1 series](https://github.com/Wan-Video/Wan2.1) is an open and advanced suite of large-scale video generative models from Wan-AI.
+[Wan2.1](https://github.com/Wan-Video/Wan2.1) is a broad open video family covering text-to-video and image-to-video across 1.3B and 14B checkpoints. Its practical strengths are motion-rich generation, temporal consistency, and readable Chinese/English text, with 480p and 720p variants for different quality and memory targets.
 
-Key characteristics:
-
-- **State-of-the-art video quality**: Consistently outperforms many open-source and commercial video models on internal and public benchmarks, especially for motion richness and temporal consistency.
-- **Consumer GPU friendly**: The T2V-1.3B variant can generate 5-second 480P videos on consumer GPUs with modest VRAM requirements.
-- **Multi-capability suite**: Supports Text-to-Video (T2V), Image-to-Video (I2V), video editing, text-to-image, and video-to-audio generation.
-- **Robust text rendering**: First-generation Wan model capable of generating both Chinese and English text in videos with strong readability.
-- **Powerful Wan-VAE**: A 3D causal VAE that encodes/decodes long 1080P videos while preserving temporal information, enabling efficient high-resolution video generation.
-
-For more details, refer to the official Wan2.1 resources:
-
-- **GitHub**: [Wan-Video/Wan2.1](https://github.com/Wan-Video/Wan2.1)
-- **Hugging Face collection**: [Wan-AI Wan2.1](https://huggingface.co/Wan-AI/Wan2.1-T2V-14B)
+Choose the 1.3B T2V model for consumer-GPU experiments and the 14B models when quality matters more than footprint. Wan2.1 is a dense DiT family; for timestep-specialized MoE capacity or the unified 5B TI2V path, use Wan2.2 instead.
 
 ## 2. SGLang-diffusion Installation