docs: add image generation documentation and image-to-image editing tutorial

docs/code_reference/models.md

@@ -1,11 +1,12 @@
 # Models
 
-The `models` module defines configuration objects for model-based generation. [ModelProvider](#data_designer.config.models.ModelProvider), specifies connection and authentication details for custom providers. [ModelConfig](#data_designer.config.models.ModelConfig) encapsulates model details including the model alias, identifier, and inference parameters. [Inference Parameters](../concepts/models/inference-parameters.md) controls model behavior through settings like `temperature`, `top_p`, and `max_tokens`, with support for both fixed values and distribution-based sampling. The module includes [ImageContext](#data_designer.config.models.ImageContext) for providing image inputs to multimodal models.
+The `models` module defines configuration objects for model-based generation. [ModelProvider](#data_designer.config.models.ModelProvider) specifies connection and authentication details for custom providers. [ModelConfig](#data_designer.config.models.ModelConfig) encapsulates model details including the model alias, identifier, and inference parameters. [Inference Parameters](../concepts/models/inference-parameters.md) controls model behavior through settings like `temperature`, `top_p`, and `max_tokens`, with support for both fixed values and distribution-based sampling. The module includes [ImageContext](#data_designer.config.models.ImageContext) for providing image inputs to multimodal models, and [ImageInferenceParams](#data_designer.config.models.ImageInferenceParams) for configuring image generation models.
 
 For more information on how they are used, see below:
 
 - **[Model Providers](../concepts/models/model-providers.md)**
 - **[Model Configs](../concepts/models/model-configs.md)**
-- **[Image Context](/notebooks/4-providing-images-as-context/)**
+- **[Image Context](../notebooks/4-providing-images-as-context.ipynb)**
+- **[Generating Images](../notebooks/5-generating-images.ipynb)**
 
 ::: data_designer.config.models

docs/colab_notebooks/1-the-basics.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "96178d08",
+   "id": "a3262d09",
    "metadata": {},
    "source": [
     "# 🎨 Data Designer Tutorial: The Basics\n",
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1d02a1d6",
+   "id": "88e11b2c",
    "metadata": {},
    "source": [
     "### 📦 Import Data Designer\n",
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2292d817",
+   "id": "fdab52ed",
    "metadata": {},
    "source": [
     "### ⚡ Colab Setup\n",
@@ -37,7 +37,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8af621fc",
+   "id": "ed0c5f01",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -48,7 +48,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "70e6a11c",
+   "id": "1b1bd7f5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -66,7 +66,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "41031828",
+   "id": "d5c521d8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -76,7 +76,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0b480b10",
+   "id": "f4fef50a",
    "metadata": {},
    "source": [
     "### ⚙️ Initialize the Data Designer interface\n",
@@ -89,7 +89,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d434a8e2",
+   "id": "5ac50e65",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -98,7 +98,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f88f6792",
+   "id": "a7b10975",
    "metadata": {},
    "source": [
     "### 🎛️ Define model configurations\n",
@@ -115,7 +115,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4261574c",
+   "id": "d7b5b9ac",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -145,7 +145,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bbbc3d58",
+   "id": "34e0b268",
    "metadata": {},
    "source": [
     "### 🏗️ Initialize the Data Designer Config Builder\n",
@@ -160,7 +160,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "92c0cf35",
+   "id": "7a269413",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -169,7 +169,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "44246c7d",
+   "id": "17bd706d",
    "metadata": {},
    "source": [
     "## 🎲 Getting started with sampler columns\n",
@@ -186,7 +186,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "07d20f3f",
+   "id": "2c6b6b39",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -195,7 +195,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9d3c87b0",
+   "id": "47b7ee01",
    "metadata": {},
    "source": [
     "Let's start designing our product review dataset by adding product category and subcategory columns.\n"
@@ -204,7 +204,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c646b021",
+   "id": "c71acecd",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -285,7 +285,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ff18b032",
+   "id": "c756d439",
    "metadata": {},
    "source": [
     "Next, let's add samplers to generate data related to the customer and their review.\n"
@@ -294,7 +294,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "78846d99",
+   "id": "f3544473",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -331,7 +331,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "97059bfc",
+   "id": "1f64a4c3",
    "metadata": {},
    "source": [
     "## 🦜 LLM-generated columns\n",
@@ -346,7 +346,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "98c66eff",
+   "id": "2a115f81",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -382,7 +382,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ff2d52b9",
+   "id": "10b41627",
    "metadata": {},
    "source": [
     "### 🔁 Iteration is key – preview the dataset!\n",
@@ -399,7 +399,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6e622478",
+   "id": "5dd66ce6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -409,7 +409,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1addc7d8",
+   "id": "4e42ad0f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -420,7 +420,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7af4b9c3",
+   "id": "63c40f3b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -430,7 +430,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "91d0ee89",
+   "id": "27fb4965",
    "metadata": {},
    "source": [
     "### 📊 Analyze the generated data\n",
@@ -443,7 +443,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e1e3aed0",
+   "id": "e1fa96ac",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -453,7 +453,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6eaa402e",
+   "id": "f837b52f",
    "metadata": {},
    "source": [
     "### 🆙 Scale up!\n",
@@ -466,7 +466,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f6b148d4",
+   "id": "9593ba9d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -476,7 +476,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f4e62e5b",
+   "id": "6a6e359f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -489,7 +489,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7d426ab0",
+   "id": "d14a08e9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -501,7 +501,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "449d003c",
+   "id": "54a90cee",
    "metadata": {},
    "source": [
     "## ⏭️ Next Steps\n",