Revise Pix2Pix documentation for clarity and scope

docs/explainers/image-generation/pix2pix-models.md

@@ -1,34 +1,69 @@
 ---
-title: Pix2Pix Models in XWhy Image Generation
-description: Planned examples showing how Pix2Pix-style image-to-image models may be explained within the broader XWhy image-generation documentation.
+title: Image Generation and Image Editing
+description: An overview of model-agnostic explanation approaches for image-generation and image-editing models in XWhy.
 ---
 
-# Pix2Pix model examples
+# Image Generation and Image Editing
 
-!!! danger "Experimental interface — under construction"
-    XWhy currently exports `Pix2PixExplainer`, but its `explain()` method raises `NotImplementedError`. The examples described here are a documentation roadmap, not an executable workflow.
+Generative image models either create new images or transform existing ones. Within XWhy, these tasks are organised into two related categories:
+
+- **Image generation**, where a model creates an image from a conditioning input such as text, noise, or another representation.
+- **Image editing**, where a source image is modified according to an instruction, mask, or target condition.
+
+Unlike image classification, these models do not return a single class score. Their explanations must therefore consider the relationship between the input condition, the source image, and the generated output.
+
+## Model-agnostic explanation workflow
 
-Pix2Pix-style models are one example of conditional image-to-image generation. They belong under **Image Generation → Image Editing** because the model transforms a source image into a target image rather than returning a classification score.
+A local explanation workflow for image-generation and image-editing models may involve:
 
-## Planned first example
+1. generating a reference output using the original input;
+2. perturbing the source image, conditioning input, or editing instruction;
+3. generating outputs for the perturbed inputs;
+4. comparing each perturbed output with the reference output;
+5. measuring differences using pixel-space, perceptual, or semantic distances;
+6. fitting a local interpretable surrogate model;
+7. producing feature, region, or word-level attributions.
 
-A future worked example should document:
+This approach can be applied without requiring access to the internal architecture or gradients of the generative model.
 
-1. the source image and target transformation task;
-2. the Pix2Pix model, weights, and preprocessing;
+## Image-editing example: Pix2Pix
+
+Pix2Pix-style models are an example of conditional image-to-image generation. They transform a source image into a target image rather than returning a classification score.
+
+!!! danger "Experimental interface — under construction"
+    XWhy currently exports `Pix2PixExplainer`, but its `explain()` method raises `NotImplementedError`. The interface is therefore experimental and should not yet be treated as an executable explanation workflow.
+
+A future Pix2Pix example should document:
+
+1. the source image and target transformation;
+2. the model, weights, and preprocessing steps;
 3. the source-image regions selected for perturbation;
 4. the output-distance or perceptual-similarity measure;
 5. the local surrogate configuration;
-6. the generated attribution map;
+6. the resulting attribution map;
 7. fidelity, stability, and runtime evidence;
-8. limitations of interpreting generative outputs.
+8. limitations when interpreting generative outputs.
+
+## Planned examples
+
+Future documentation may include:
+
+- text-to-image generation;
+- instruction-based image editing;
+- mask-based image editing;
+- Pix2Pix-style image translation;
+- perturbation of textual conditioning inputs;
+- comparison of pixel, perceptual, and semantic distances;
+- detection of unintended changes outside the requested edit region.
+
+## Evaluation considerations
 
-## Planned comparison cases
+Explanations for generative models should be assessed across multiple dimensions, including:
 
-- removing or masking source-image regions;
-- perturbing the conditioning input;
-- comparing pixel-space and perceptual distances;
-- checking attribution stability across generation seeds;
-- detecting unintended changes outside the target edit region.
+- **fidelity** to the behaviour of the generative model;
+- **stability** under small or semantically irrelevant input changes;
+- **consistency** across repeated generations;
+- **runtime and computational cost**;
+- **localisation** of intended and unintended visual changes.
 
 [View the current `Pix2PixExplainer` API interface](../../reference/xwhy/explainers/pix2pix.md)