Unlearn API for Diffusers

**Is your feature request related to a problem? Please describe.**
Currently, it appears there in no unlearn API that is part of diffusers library specifically in the context of T2I models. Researchers always need to fork existing repos released by various works , plumb them for execution. This is not straight forward. (Ex:- Erasing Concepts from Diffusion Models, Ablating Concepts in Text-to-Image Diffusion Models, Fine-Grained Erasure in Text-to-Image Diffusion-based Foundation Models and others)

**Describe the solution you'd like.**
An Unlearn API, that can take inputs as diffusion model, method, concept to unlearn and produce and unlearned model will be very helpful.

**Describe alternatives you've considered.**
Researchers always need to fork existing repos released by various works , plumb them for execution. This is not straight forward. (Ex:- Erasing Concepts from Diffusion Models, Ablating Concepts in Text-to-Image Diffusion Models, Fine-Grained Erasure in Text-to-Image Diffusion-based Foundation Models and others)

**Additional context.**
Similar API may also be applicable for transformers library. Let me know if another issue needs to be submitted there.

The issue names no files, tests, or entry points; start by reviewing Diffusers' text-to-image pipeline structure and the cited concept-unlearning methods. Define the supported models, methods, inputs, outputs, and validation criteria before implementation; done should mean an agreed API scope with tests or benchmarks demonstrating unlearning.

Written by the indexing model from the issue text.