[Docs] Add supported models & introduction pages

site/docs/getting-started/introduction.mdx

@@ -4,3 +4,58 @@ sidebar_label: Introduction
 ---
 
 # Introduction to OpenVINO GenAI
+
+## What is OpenVINO GenAI?
+
+OpenVINO™ GenAI is a library of the most popular Generative AI model pipelines, optimized execution methods, and samples that run on top of highly performant [OpenVINO Runtime](https://docs.openvino.ai/).
+It provides simplified APIs for running generative models, hiding the complexity of the generation process and enabling developers to easily integrate state-of-the-art generative models into their applications with minimal code.
+
+As a lightweight solution designed for efficient inference, OpenVINO GenAI includes all the core functionality needed for generative model execution (e.g. tokenization via `openvino-tokenizers`) with no external dependencies required.
+This library is friendly to PC and laptop execution, and optimized for resource consumption.
+
+## Key Features and Benefits
+
+- **📦 Pre-built Generative AI Pipelines:** Ready-to-use pipelines for text generation (LLMs), image generation (Diffuser-based), speech processing (Whisper), and visual language models (VLMs). See all [supported use cases](/docs/category/use-cases).
+- **👣 Minimal Footprint:** Smaller binary size and reduced memory footprint compared to other frameworks.
+- **🚀 Performance Optimization:** Hardware-specific optimizations for CPU, GPU, and NPU devices.
+- **👨‍💻 Programming Language Support:** Comprehensive APIs in both Python and C++.
+- **🗜️ Model Compression:** Support for 8-bit and 4-bit weight compression, including embedding layers.
+- **🎓 Advanced Inference Capabilities:** In-place KV-cache, dynamic quantization, speculative sampling, and more.
+- **🎨 Wide Model Compatibility:** Support for popular models including Llama, Mistral, Phi, Qwen, Stable Diffusion, Flux, Whisper, and others. Refer to the [Supported Models](/docs/supported-models) for more details.
+
+## Workflow Overview
+
+Using OpenVINO GenAI typically involves three main steps:
+
+1. **Model Preparation:**
+    - Convert model from other frameworks to the OpenVINO IR format (e.g. using `optimum-intel`), optionally applying weights compression.
+    - Download pre-converted model in OpenVINO IR format (e.g. from [OpenVINO Toolkit](https://huggingface.co/OpenVINO) organization on Hugging Face).
+    :::info
+
+    You can use models from [Hugging Face](https://huggingface.co/) and [ModelScope](https://modelscope.cn/home)
+
+    :::
+2. **Pipeline Setup:** Initialize the appropriate pipeline for your task (`LLMPipeline`, `Text2ImagePipeline`, `WhisperPipeline`, `VLMPipeline`, etc.) with the converted model.
+3. **Inference:** Run the model with your inputs using the pipeline's simple API.
+
+![OpenVINO GenAI Workflow](/img/openvino-genai-workflow.svg)
+
+## Comparison with Alternatives
+
+Unlike base OpenVINO, which requires manual implementation of generation loops, tokenization, scheduling etc., OpenVINO GenAI provides these components in a ready-to-use package.
+
+Compared to Hugging Face Optimum Intel, OpenVINO GenAI offers a smaller footprint, fewer dependencies, and better performance optimization options, particularly for C++ applications.
+
+| Feature | OpenVINO GenAI | Base OpenVINO | Hugging Face Optimum Intel |
+|-|-|-|-|
+| Easy-to-use APIs | ✅ | ❌ | ✅ |
+| Low footprint | ✅ | ✅ | ❌ |
+| C++ support | ✅ | ✅ | ❌ |
+| Pre-built pipelines | ✅ | ❌ | ✅ |
+| Model variety | Medium | High | High |
+
+## System Requirements
+
+OpenVINO GenAI is built on top of OpenVINO Runtime and shares the same system requirements.
+
+Refer to the [OpenVINO System Requirements](https://docs.openvino.ai/2025/about-openvino/release-notes-openvino/system-requirements.html) for more details.

site/docs/supported-models/_category_.json

@@ -0,0 +1,8 @@
+{
+  "label": "Supported Models",
+  "position": 4,
+  "link": {
+    "type": "doc",
+    "id": "supported-models/index"
+  }
+}

site/docs/supported-models/_components/base-models-table/index.tsx

@@ -0,0 +1,38 @@
+import Link from '@docusaurus/Link';
+import { Children } from 'react';
+
+type BaseModelsTableProps = {
+  headers: string[];
+  rows: React.JSX.Element[];
+};
+
+export function BaseModelsTable({ headers, rows }: BaseModelsTableProps): React.JSX.Element {
+  return (
+    <table>
+      <thead>
+        <tr>
+          {headers.map((v) => (
+            <th key={v}>{v}</th>
+          ))}
+        </tr>
+      </thead>
+      <tbody style={{ verticalAlign: 'baseline' }}>{Children.map(rows, (row) => row)}</tbody>
+    </table>
+  );
+}
+
+export const LinksCell = ({ links }: { links: string[] }) => (
+  <td>
+    <ul>
+      {links.map((link) => (
+        <li key={link}>
+          <Link href={link}>{new URL(link).pathname.slice(1)}</Link>
+        </li>
+      ))}
+    </ul>
+  </td>
+);
+
+export const StatusCell = ({ value }: { value: boolean }) => (
+  <td style={{ textAlign: 'center' }}>{value ? '✅' : '❌'}</td>
+);

site/docs/supported-models/_components/image-generation-models-table/index.tsx

@@ -0,0 +1,29 @@
+import React from 'react';
+import { BaseModelsTable, LinksCell, StatusCell } from '../base-models-table';
+import { IMAGE_GENERATION_MODELS } from './models';
+
+export default function ImageGenerationModelsTable(): React.JSX.Element {
+  const headers = [
+    'Architecture',
+    'Text to Image',
+    'Image to Image',
+    'LoRA Support',
+    'Example HuggingFace Models',
+  ];
+
+  const rows = IMAGE_GENERATION_MODELS.map(
+    ({ architecture, textToImage, imageToImage, loraSupport, links }) => (
+      <tr key={architecture}>
+        <td>
+          <code style={{ whiteSpace: 'pre' }}>{architecture}</code>
+        </td>
+        <StatusCell value={textToImage} />
+        <StatusCell value={imageToImage} />
+        <StatusCell value={loraSupport} />
+        <LinksCell links={links} />
+      </tr>
+    )
+  );
+
+  return <BaseModelsTable headers={headers} rows={rows} />;
+}

site/docs/supported-models/_components/image-generation-models-table/models.ts

@@ -0,0 +1,85 @@
+type ImageGenerationModelType = {
+  architecture: string;
+  textToImage: boolean;
+  imageToImage: boolean;
+  loraSupport: boolean;
+  links: string[];
+};
+
+export const IMAGE_GENERATION_MODELS: ImageGenerationModelType[] = [
+  {
+    architecture: 'Latent Consistency Model',
+    textToImage: true,
+    imageToImage: true,
+    loraSupport: true,
+    links: ['https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7'],
+  },
+  {
+    architecture: 'Stable Diffusion',
+    textToImage: true,
+    imageToImage: true,
+    loraSupport: true,
+    links: [
+      'https://huggingface.co/CompVis/stable-diffusion-v1-1',
+      'https://huggingface.co/CompVis/stable-diffusion-v1-2',
+      'https://huggingface.co/CompVis/stable-diffusion-v1-3',
+      'https://huggingface.co/CompVis/stable-diffusion-v1-4',
+      'https://huggingface.co/junnyu/stable-diffusion-v1-4-paddle',
+      'https://huggingface.co/jcplus/stable-diffusion-v1-5',
+      'https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5',
+      'https://huggingface.co/botp/stable-diffusion-v1-5',
+      'https://huggingface.co/dreamlike-art/dreamlike-anime-1.0',
+      'https://huggingface.co/stabilityai/stable-diffusion-2',
+      'https://huggingface.co/stabilityai/stable-diffusion-2-base',
+      'https://huggingface.co/stabilityai/stable-diffusion-2-1',
+      'https://huggingface.co/bguisard/stable-diffusion-nano-2-1',
+      'https://huggingface.co/justinpinkney/pokemon-stable-diffusion',
+      'https://huggingface.co/stablediffusionapi/architecture-tuned-model',
+      'https://huggingface.co/IDEA-CCNL/Taiyi-Stable-Diffusion-1B-Chinese-EN-v0.1',
+      'https://huggingface.co/ZeroCool94/stable-diffusion-v1-5',
+      'https://huggingface.co/pcuenq/stable-diffusion-v1-4',
+      'https://huggingface.co/rinna/japanese-stable-diffusion',
+      'https://huggingface.co/benjamin-paine/stable-diffusion-v1-5',
+      'https://huggingface.co/philschmid/stable-diffusion-v1-4-endpoints',
+      'https://huggingface.co/naclbit/trinart_stable_diffusion_v2',
+      'https://huggingface.co/Fictiverse/Stable_Diffusion_PaperCut_Model',
+    ],
+  },
+  {
+    architecture: 'Stable Diffusion XL',
+    textToImage: true,
+    imageToImage: true,
+    loraSupport: true,
+    links: [
+      'https://huggingface.co/stabilityai/stable-diffusion-xl-base-0.9',
+      'https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0',
+      'https://huggingface.co/stabilityai/sdxl-turbo',
+    ],
+  },
+  {
+    architecture: 'Stable Diffusion 3',
+    textToImage: true,
+    imageToImage: false,
+    loraSupport: false,
+    links: [
+      'https://huggingface.co/stabilityai/stable-diffusion-3-medium-diffusers',
+      'https://huggingface.co/stabilityai/stable-diffusion-3.5-medium',
+      'https://huggingface.co/stabilityai/stable-diffusion-3.5-large',
+      'https://huggingface.co/stabilityai/stable-diffusion-3.5-large-turbo',
+    ],
+  },
+  {
+    architecture: 'Flux',
+    textToImage: true,
+    imageToImage: false,
+    loraSupport: false,
+    links: [
+      'https://huggingface.co/black-forest-labs/FLUX.1-schnell',
+      'https://huggingface.co/Freepik/flux.1-lite-8B-alpha',
+      'https://huggingface.co/black-forest-labs/FLUX.1-dev',
+      'https://huggingface.co/shuttleai/shuttle-3-diffusion',
+      'https://huggingface.co/shuttleai/shuttle-3.1-aesthetic',
+      'https://huggingface.co/Shakker-Labs/AWPortrait-FL',
+    ],
+  },
+];

site/docs/supported-models/_components/inpainting-models-table/index.tsx

@@ -0,0 +1,19 @@
+import React from 'react';
+import { BaseModelsTable, LinksCell, StatusCell } from '../base-models-table';
+import { INPAINTING_MODELS } from './models';
+
+export default function InpaintingModelsTable(): React.JSX.Element {
+  const headers = ['Architecture', 'LoRA Support', 'Example HuggingFace Models'];
+
+  const rows = INPAINTING_MODELS.map(({ architecture, loraSupport, links }) => (
+    <tr key={architecture}>
+      <td>
+        <code style={{ whiteSpace: 'pre' }}>{architecture}</code>
+      </td>
+      <StatusCell value={loraSupport} />
+      <LinksCell links={links} />
+    </tr>
+  ));
+
+  return <BaseModelsTable headers={headers} rows={rows} />;
+}

site/docs/supported-models/_components/inpainting-models-table/models.ts

@@ -0,0 +1,23 @@
+type InpaintingModelType = {
+  architecture: string;
+  loraSupport: boolean;
+  links: string[];
+};
+
+export const INPAINTING_MODELS: InpaintingModelType[] = [
+  {
+    architecture: 'Stable Diffusion',
+    loraSupport: true,
+    links: [
+      'https://huggingface.co/stabilityai/stable-diffusion-2-inpainting',
+      'https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting',
+      'https://huggingface.co/botp/stable-diffusion-v1-5-inpainting',
+      'https://huggingface.co/parlance/dreamlike-diffusion-1.0-inpainting',
+    ],
+  },
+  {
+    architecture: 'Stable Diffusion XL',
+    loraSupport: true,
+    links: ['https://huggingface.co/diffusers/stable-diffusion-xl-1.0-inpainting-0.1'],
+  },
+];

site/docs/supported-models/_components/llm-models-table/index.tsx

@@ -0,0 +1,27 @@
+import React from 'react';
+import { BaseModelsTable, LinksCell } from '../base-models-table';
+import { LLM_MODELS } from './models';
+
+export default function LLMModelsTable(): React.JSX.Element {
+  const headers = ['Architecture', 'Models', 'Example HuggingFace Models'];
+
+  const rows = LLM_MODELS.map(({ architecture, models }) => (
+    <>
+      <tr key={architecture}>
+        <td rowSpan={models.length}>
+          <code>{architecture}</code>
+        </td>
+        <td>{models[0].name}</td>
+        <LinksCell links={models[0].links} />
+      </tr>
+      {models.slice(1).map(({ name, links }) => (
+        <tr key={name}>
+          <td>{name}</td>
+          <LinksCell links={links} />
+        </tr>
+      ))}
+    </>
+  ));
+
+  return <BaseModelsTable headers={headers} rows={rows} />;
+}