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+# CalbeNodes
+
+A collection of custom nodes created for personal use and convenience.
+
+## Table of Contents
+- [Installation](#installation)
+- [Usage](#usage)
+- [Nodes](#nodes)
+  - [Character Manager](#character-manager)
+  - [Film Grain](#film-grain)
+  - [Flip Flopper](#flip-flopper)
+- [Contributing](#contributing)
+- [License](#license)
+
+## Installation
+
+git clone this repository into your custom nodes folder
+
+I tried to make it so all requirements come with comfy, so hopefully no installs needed.
+
+## Usage
+
+The nodes will appear under a calbenodes heading and can be searched
+
+## Nodes
+
+### Character Manager
+
+The Character Manager node is a versatile tool for managing and applying character-specific attributes in your image generation pipeline. It allows you to create, select, and apply character settings, including LoRA models, face images, and textual descriptions.
+
+#### Features:
+- Create and manage multiple characters
+- Apply character-specific LoRA models
+- Select preferred face images for characters
+- Generate random face selections
+- Create face image grids
+- Apply character-specific activation text and descriptions
+
+#### Inputs:
+- `model`: The base model to apply character settings to
+- `clip`: The CLIP model for text processing
+- `character`: Select from existing characters, create a new one, or choose randomly
+- `lora_strength`: Strength of the LoRA application (-10.0 to 10.0)
+- `seed`: Random seed for consistent results
+- `new_name`: Name for creating a new character
+- `lora_path`: Path to the character's LoRA file
+- `face_images_dir`: Directory containing character face images
+- `preferred_face_image`: Path to the preferred face image
+- `activation_text`: Text to activate the character in prompts
+- `description`: Character description
+- `negative_prompt`: Negative prompt for the character
+
+#### Outputs:
+- `model`: Updated model with applied LoRA
+- `clip`: Updated CLIP model
+- `lora_activation`: Character activation text
+- `description`: Character description
+- `negative_prompt`: Character-specific negative prompt
+- `preferred_face`: Preferred face image (as tensor)
+- `random_face`: Randomly selected face image (as tensor)
+- `face_grid`: Grid of all character face images (as tensor)
+- `character_name`: Name of the selected or created character
+- `seed`: The seed used for this execution
+
+#### Usage:
+1. Select an existing character or choose "New Character" to create one.
+2. If creating a new character, provide necessary information like name, LoRA path, and face images directory.
+3. Adjust the LoRA strength as needed.
+4. The node will apply the character settings and return the updated model along with character-specific information and images.
+
+### Film Grain
+
+The Film Grain node adds a realistic film grain effect to images, simulating the appearance of traditional photographic film.
+
+#### Features:
+- Adds customizable film grain to images
+- Supports batch processing of multiple images
+- Adjustable grain intensity
+
+#### Inputs:
+- `image`: The input image or batch of images (IMAGE type)
+- `intensity`: The strength of the film grain effect (FLOAT, range 0.01 to 1.0, default 0.07)
+
+#### Outputs:
+- `IMAGE`: The processed image(s) with added film grain
+
+#### Usage:
+1. Connect an image or batch of images to the "image" input.
+2. Adjust the "intensity" parameter to control the strength of the film grain effect.
+3. The node will output the processed image(s) with the film grain applied.
+
+### Flip Flopper
+
+The Flip Flopper node (Same Architecture) is an advanced sampling node that alternates between two models during the sampling process, allowing for unique and creative image generation.
+
+#### Features:
+- Alternates between two models during sampling
+- Supports different VAEs for each model
+- Customizable sampling parameters for each model
+- Option to invert the order of model application
+
+#### Inputs:
+- `model1` and `model2`: The two models to alternate between
+- `vae1` and `vae2`: VAEs corresponding to each model
+- `add_noise`: Enable or disable noise addition
+- `noise_seed`: Seed for noise generation
+- `steps`: Total number of sampling steps
+- `cfg1` and `cfg2`: CFG scales for each model
+- `sampler_name1` and `sampler_name2`: Sampler types for each model
+- `scheduler1` and `scheduler2`: Scheduler types for each model
+- `positive1`, `negative1`, `positive2`, `negative2`: Conditioning for each model
+- `latent_image`: Input latent image
+- `denoise`: Denoising strength
+- `chunks`: Number of steps per chunk
+- `invert`: Option to invert the order of model application
+
+#### Outputs:
+- `LATENT`: The resulting latent image after sampling
+- `FINAL_VAE`: The VAE used in the final iteration
+
+#### Usage:
+1. Connect two models, their corresponding VAEs, and other required inputs.
+2. Set the sampling parameters for each model (CFG, sampler, scheduler, etc.).
+3. Adjust the number of steps and chunks as needed.
+4. The node will alternate between the two models during sampling, producing a unique result.
+
+## Contributing
+
+This project is primarily for personal use, but if you have any suggestions or improvements, feel free to open an issue or submit a pull request.
+
+## License
+
+MIT