Overview
SDXL Multi Controlnet LoRA is a text-to-image model built for creators who need direct, repeatable control over the structure and style of generated images, available on Picasso IA. A single text prompt works well for quick ideation, but it rarely delivers the precise pose, spatial layout, or visual consistency that a professional project demands. This model accepts up to three ControlNet reference images simultaneously, layering conditions like edge detection, depth maps, and body pose to steer the output toward a specific visual target. Pair that with LoRA weight support and inpainting, and you have a single tool that handles complex, multi-step image projects without switching between separate apps.
How It Works
- Write your text prompt and, optionally, a negative prompt to filter out unwanted elements from the output.
- Select up to three ControlNet types, such as edge detection, depth, or OpenPose, and upload a reference image for each one you want active.
- Upload a base image and switch to img2img or inpainting mode if you want to edit an existing photo rather than generate from scratch.
- Paste a LoRA weights URL into the weights field and set the LoRA scale to blend in a specific trained style.
- Click generate and download the result from the output panel; change the seed, conditioning strength, or prompt and re-run to refine.
Frequently Asked Questions
Do I need programming skills or technical knowledge to use this?
No, just open SDXL Multi Controlnet LoRA on Picasso IA, adjust the settings you want, and hit generate.
Is it free to try?
Yes, you can run generations without any upfront cost. The number of free runs available depends on your account plan.
How long does it take to get results?
A standard 768x768 generation at 30 inference steps typically finishes in 20 to 40 seconds. Enabling the refiner or increasing the step count adds time proportionally.
What output formats are supported?
The model returns image files you can download directly from the results panel. You can generate up to four images per run by adjusting the number of outputs setting.
Can I customize the output quality or style?
Yes. You can adjust inference steps, the classifier-free guidance scale, scheduler type, LoRA scale, and each ControlNet's conditioning strength. Each parameter changes the result in a measurable way.
How many times can I run the model?
There is no hard cap built into the model itself. How many generations you can run depends on your current account plan.
What happens if I am not happy with the result?
Change the seed, lower or raise the ControlNet conditioning scale, or adjust the prompt strength slider. Small parameter changes often produce noticeably different outputs without rebuilding the prompt from scratch.
