[Docs] Add Falcon H1, Hunyuan-Large, Qwen3-Omni support and update Diffusion usage (#17888)

Co-authored-by: Rishitshivam <164783543+Rishitshivam@users.noreply.github.com>
Co-authored-by: Ratish P <114130421+Ratish1@users.noreply.github.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
Co-authored-by: Adarsh Shirawalmath <114558126+adarshxs@users.noreply.github.com>
Co-authored-by: zhaochenyang20 <zhaochen20@outlook.com>

docs/basic_usage/diffusion.md

@@ -0,0 +1,19 @@
+# Diffusion
+
+SGLang supports two categories of diffusion models for different use cases. This page covers image and video generation; for diffusion LLMs, see [Diffusion LLMs](diffusion_llms.md).
+
+## Image & Video Generation Models
+
+For generating images and videos from text prompts, SGLang supports [many](../supported_models/image_generation/diffusion_models.md#image-generation-models) models like:
+
+- **FLUX, Qwen-Image** - High-quality image generation
+- **Wan 2.2, HunyuanVideo** - Video generation
+
+```bash
+# Example: Launch FLUX for image generation
+python3 -m sglang.launch_server \
+  --model-path black-forest-labs/FLUX.2-klein-4B \
+  --host 0.0.0.0 --port 30000
+```
+
+**Full model list:** [Diffusion Models](../supported_models/image_generation/diffusion_models.md)

docs/basic_usage/diffusion_llms.md

@@ -0,0 +1,14 @@
+# Diffusion Language Models (dLLMs)
+
+These are text-generation models that use diffusion (denoising) instead of autoregressive decoding:
+
+- **LLaDA** - Large Language Diffusion with mAsking
+
+```bash
+# Example: Launch LLaDA for text generation
+python3 -m sglang.launch_server \
+  --model-path GSAI-ML/LLaDA-8B-Instruct \
+  --host 0.0.0.0 --port 30000
+```
+
+**Full model list:** [Diffusion Language Models](../supported_models/text_generation/diffusion_language_models.md)

docs/basic_usage/openai_api_embeddings.ipynb

@@ -9,7 +9,7 @@
     "SGLang provides OpenAI-compatible APIs to enable a smooth transition from OpenAI services to self-hosted local models.\n",
     "A complete reference for the API is available in the [OpenAI API Reference](https://platform.openai.com/docs/guides/embeddings).\n",
     "\n",
-    "This tutorial covers the embedding APIs for embedding models. For a list of the supported models see the [corresponding overview page](../supported_models/embedding_models.md)\n"
+    "This tutorial covers the embedding APIs for embedding models. For a list of the supported models see the [corresponding overview page](../supported_models/retrieval_ranking/embedding_models.md)\n"
    ]
   },
   {
@@ -173,7 +173,7 @@
    "metadata": {},
    "source": [
     "## Multi-Modal Embedding Model\n",
-    "Please refer to [Multi-Modal Embedding Model](../supported_models/embedding_models.md)"
+    "Please refer to [Multi-Modal Embedding Model](../supported_models/retrieval_ranking/embedding_models.md)"
    ]
   }
  ],

docs/basic_usage/openai_api_vision.ipynb

@@ -10,7 +10,7 @@
     "A complete reference for the API is available in the [OpenAI API Reference](https://platform.openai.com/docs/guides/vision).\n",
     "This tutorial covers the vision APIs for vision language models.\n",
     "\n",
-    "SGLang supports various vision language models such as Llama 3.2, LLaVA-OneVision, Qwen2.5-VL, Gemma3 and [more](../supported_models/multimodal_language_models.md).\n",
+    "SGLang supports various vision language models such as Llama 3.2, LLaVA-OneVision, Qwen2.5-VL, Gemma3 and [more](../supported_models/text_generation/multimodal_language_models.md).\n",
     "\n",
     "As an alternative to the OpenAI API, you can also use the [SGLang offline engine](https://github.com/sgl-project/sglang/blob/main/examples/runtime/engine/offline_batch_inference_vlm.py)."
    ]