A beginner's guide to the Realvisxl-V3-Multi-Controlnet-Lora model by Sdxl-Based on Replicate

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This is a simplified guide to an AI model called Realvisxl-V3-Multi-Controlnet-Lora maintained by Sdxl-Based. If you like these kinds of analysis, you should join AImodels.fyi or follow us on Twitter.

Model overview

realvisxl-v3-multi-controlnet-lora is a text-to-image generation model built on the SDXL architecture from sdxl-based. It combines RealVis XL V3.0 with advanced control mechanisms, allowing you to guide image generation through multiple simultaneous controlnets. This model stands apart from similar offerings like sdxl-multi-controlnet-lora and sdxl-lcm-multi-controlnet-lora by focusing on photorealistic outputs while maintaining the flexibility of multi-controlnet composition.

Model inputs and outputs

The model accepts text prompts alongside optional image inputs and accepts extensive parameters for controlling the generation process. You can stack up to three different controlnets simultaneously, each with independent configuration. The model outputs one to four generated images depending on your settings.

Inputs

Prompt - Text description of the image you want to generate
Negative Prompt - Text describing what you don't want in the image
Image - An existing image for img2img or inpainting workflows
Mask - A mask image specifying areas to inpaint (black areas preserved, white areas regenerated)
Width and Height - Output dimensions up to your preferred size
Guidance Scale - Controls how strictly the model follows your prompt (1-50)
Controlnet 1, 2, and 3 - Up to three simultaneous controlnets including edge detection (canny), depth mapping (midas, leres), soft edges (pidi, hed), pose detection (openpose), line art modes, and QR code illusions
Controlnet Images - Separate images for each controlnet to guide composition
Controlnet Conditioning Scales - Strength of each controlnet's influence (0-4)
Controlnet Start and End - When each controlnet's influence begins and ends during generation (0-1)
LoRA Weights - Custom trained model weights to apply
LoRA Scale - Strength of custom model application (0-1)
Scheduler - Sampling method (DDIM, DPMSolverMultistep, Euler variants, and others)
Num Inference Steps - Generation quality through sampling iterations (1-500)
Refine - Optional SDXL refiner for enhanced detail
Seed - Random seed for reproducible results
Sizing Strategy - How to resize based on width/height, input image, or control images
Apply Watermark - Toggle watermarking to identify generated content
Disable Safety Checker - API-only option to bypass content filtering