SDXL Multi Controlnet LoRA is a text-to-image model that gives you direct control over the structure, style, and composition of generated images, all in one place on Picasso IA. Most AI image models accept a text prompt and return a result, but when you need a specific pose, a particular spatial layout, or a distinct visual style applied consistently, a prompt alone often falls short. This model lets you feed in reference images through up to three ControlNet inputs and layer them simultaneously, so the output conforms to the shapes, depths, and contours you actually specify. The model supports LoRA weight loading, which means you can bring in a trained style or character and blend it into your generation at a tunable scale. It also handles img2img workflows, letting you feed an existing photo as a base and adjust how much the prompt reshapes it. Inpainting adds a third layer: mask a specific area, describe what you want there, and the model fills it in while leaving the rest of the image intact. This is a practical tool for illustrators who need to match a reference pose, product designers testing color or texture variations, and art directors who want repeatable visual styles across a campaign. Open it on Picasso IA, upload your reference images, set your ControlNet conditions, and run your first generation in minutes.
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.
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.
Everything this model can do for you
Stack up to three independent conditioning inputs, such as edge, depth, and pose, in a single generation pass.
Apply any compatible LoRA weights and dial in the blend with a dedicated scale slider.
Feed an existing photo as the base image and use prompt strength to control how far the output drifts from the original.
Mask any area of an image and fill it with new content described in the prompt, leaving surrounding pixels intact.
Choose from seven schedulers, including K_EULER and DPMSolverMultistep, to match your preferred generation behavior.
Set start, end, and conditioning scale for each ControlNet to control exactly when and how strongly each input takes effect.
Set custom output dimensions or match the size automatically to an input or ControlNet image.
Fine control over controlnet start/end and conditioning scale
A TOK photo, extreme macro photo of a golden astronaut riding a unicorn statue, in a museum, bokeh, 50mm
A TOK photo, extreme macro photo of a golden astronaut riding a unicorn statue, in a museum, 18mm
A TOK photo, extreme macro photo of a golden astronaut riding a unicorn statue, in a museum, 18mm
