An opinionated framework for optimizing operations on state dictionaries, particularly focused on Stable Diffusion model merging, leveraging Bayesian Optimization or Optuna and the sd-mecha library.

(Formerly sd-interim-bayesian-merger)

This project aims to provide a flexible and powerful tool for finding optimal parameters for sd-mecha merge methods or other state dictionary manipulations based on image generation and scoring feedback.

Note: This project is under active development and might undergo significant changes. While usability is a goal, stability is not always guaranteed on the main branch. A UI is also being worked on in spare time to provide a more user-friendly interface in the future.

• Powerful Merging Backend: Uses sd-mecha for efficient, low-memory state dictionary operations.
• Flexible Optimization:
  • Supports both Bayesian Optimization (via bayesian-optimization - untested) and Optuna for hyperparameter search.
  • Optimize parameters for built-in or custom sd-mecha merge methods.
  • Optimize hyperparameters within existing .mecha recipes (optimization_mode: recipe).
  • Optimize layer adjustments (optimization_mode: layer_adjust). (not implemented yet)
• Granular Control: Use conf/optimization_guide.yaml to define which parameters to optimize using flexible strategies:
  • all: Optimize parameters for every key in a component.
  • select: Optimize parameters for specific keys or wildcard patterns.
  • group: Optimize shared parameters for defined groups of keys/blocks.
  • single: Optimize a single shared parameter for an entire component.
  • none: Exclude a component from optimization.
• Custom Block Definitions: Define custom block groupings via configuration and utility scripts for targeted optimization (see Wiki).
• Multiple Scorers: Utilizes various image scoring models (Aesthetic, CLIP, BLIP, HPSv3, ImageReward, PickScore, CityAesthetics, etc.) to guide optimization. See [[Scoring]] wiki page.
• WebUI Integration: Designed to run alongside a running instance of A1111, Forge, SwarmUI (comfy, reforge forks planned) via their APIs for image generation.
• Asynchronous Workflow: Generates and scores images concurrently for better efficiency.

1. Prerequisites: Python 3.10+, Git, a running instance of a supported WebUI (e.g., A1111, Forge) with its API enabled.
2. Installation: Clone this repository into your WebUI's extensions folder:

   git clone -b mecha_update https://github.com/enferlain/sd-optim.git sd-optim

   (Replace URL) Then install dependencies:

   cd sd-optim
   pip install -r requirements.txt

3. Configuration: Copy .tmpl.yaml files in conf/ to .yaml and edit them (especially config.yaml and optimization_guide.yaml) to match your paths and desired settings.
4. Run: Launch your WebUI with the API enabled. Then, from the sd-optim directory, run:

   python sd_optim.py

For detailed setup, configuration options, and usage guides, please see the [Project Wiki].

• Integration with more WebUIs (ComfyUI, Reforge).
• More advanced visualization options.
• Hotkey support for interaction (scoring mode switching, early stopping).
• Dynamic adjustment of runtime parameters (batch size, payloads).
• Expanded scoring options (perceptual similarity, character consistency).
• Potential integration of other optimization libraries (e.g., Hyperactive).
• A nice webui/app is the ultimate goal.

• Based on the original concept by s1dlx.
• Relies heavily on the fantastic sd-mecha library by ljleb.
• Inspired by and utilizes concepts/code from various community projects (SuperMerger, sd-meh, etc.).
• Scoring models from multiple creators (LAION, OpenAI, Salesforce, THUDM, yuvalkirstain, etc.).