• Picasso AI Logo
    Logo Picasso IA
  • Home
  • AI Image
    Nano Banana 2
  • AI Video
    Veo 3.1 Lite
  • AI Chat
    Gemini 3 Pro
  • Edit Images
  • Upscale Image
  • Remove Background
  • Text to Speech
  • Effects
    NEW
  • Generations
  • Billing
  • Support
  • Account
  1. Collection
  2. Text to Image
  3. Realvisxl V3 Multi Controlnet Lora

RealVisXL v3 Multi Controlnet LoRA: Realistic AI Art

RealVisXL v3 Multi Controlnet LoRA is a photorealistic image generator that gives creators, designers, and retouchers hands-on control over how every image is composed. Unlike basic text-to-image tools, it lets you attach reference data, such as a pose skeleton, a depth map, or an edge outline, to steer the output toward a specific visual structure. You can describe what you want in a prompt and also show the model how it should be arranged spatially, so the result looks deliberate, not accidental. You can stack up to three ControlNets at once, each targeting a different aspect of the image. Choose one for edge detection, another for human pose, and a third for depth. Load a LoRA file to inject a specific art style or character design into every generation. The model also supports img2img and inpainting, so you can edit existing photos or fill in specific areas without repainting the whole image from scratch. If you work in visual design, photography retouching, or character art, this model fits into your existing process without replacing it. Start with a reference image, layer on the controls you need, and get a polished result in seconds. Picasso IA makes it easy to iterate quickly without spending hours on manual corrections.

Fofr

1.86m runs

Realvisxl V3 Multi Controlnet Lora

2024-01-05

Commercial Use

RealVisXL v3 Multi Controlnet LoRA: Realistic AI Art

Table of contents

  • Overview
  • How It Works
  • Frequently Asked Questions
  • Credit Cost
  • Features
  • Use Cases
  • Examples
Get Nano Banana Pro

Overview

RealVisXL v3 Multi Controlnet LoRA is a photorealistic image generator available on Picasso IA that gives creators and designers hands-on control over how every output is composed. Unlike basic text-to-image tools, it lets you attach reference data, such as a pose skeleton, a depth map, or an edge outline, to steer the output toward a specific visual structure. Describe what you want in a prompt and also show the model how the image should be arranged spatially. The result is an image that looks intentional, not left to chance.

How It Works

  • Write a text prompt describing the subject, style, lighting, and mood of the image you want.
  • Optionally upload one to three ControlNet reference images and select a condition type for each, such as Canny edge detection, depth, pose, or lineart.
  • Set the output dimensions, number of inference steps, and guidance scale from the side panel, or leave the defaults for a quick first result.
  • If working with an existing image, upload it for img2img mode or add a black-and-white mask for inpainting to specify which areas get regenerated.
  • Hit generate and receive your output in seconds, ready to download or pass into the next step of your project.

Frequently Asked Questions

Do I need programming skills or technical knowledge to use this? No, just open RealVisXL v3 Multi Controlnet LoRA on Picasso IA, adjust the settings you want, and hit generate.

Is it free to try? Yes, you can run the model on Picasso IA without any upfront payment. A first image can be generated right away without signing up for a paid plan.

How long does it take to generate an image? Most outputs are ready in under 20 seconds at default settings with 30 inference steps. Reducing the step count or output dimensions speeds things up further.

Can I control which parts of an existing image get changed? Yes. Upload a black-and-white mask alongside your base image. Black areas stay exactly as they are, while white areas get regenerated using your text prompt.

What ControlNet types are supported? The model includes Canny edge detection, two depth options (LReS and MiDaS), two soft-edge options (PIDI and HED), lineart, anime lineart, OpenPose for body positioning, and an illusion mode. You can run any combination of three at once.

Can I load my own LoRA weights? Yes. Enter a link to compatible LoRA weights in the designated field and use the lora_scale slider to adjust how strongly that style is applied to the output.

Where can I use the generated images? The output files are standard PNG or JPEG images with no watermark applied by default. You can use them in any design tool, on a website, or in a print project.

Credit Cost

Each generation consumes 1 credit

1 credit

or 5 credits for 5 generations

Features

Everything this model can do for you

Multi-ControlNet stacking

Apply up to three ControlNets simultaneously to control pose, depth, and edges in a single generation.

LoRA weight loading

Inject a custom art style or character design by loading external LoRA weights before generating.

Inpainting support

Paint over a specific region of any image and regenerate only that area while preserving the rest.

img2img mode

Use an existing photo as a starting point and modify it with a text prompt and an adjustable prompt strength slider.

Scheduler selection

Choose from seven sampling schedulers to balance generation speed against output sharpness.

Photorealistic outputs

Produces detailed, lifelike images with accurate textures and natural lighting at up to 1024x1024 pixels.

Seed control

Reuse the same seed to reproduce an identical result or test small variations from a controlled baseline.

Guidance scale and inference step controls for precise outputs

Use Cases

Generate a full-body character illustration that matches a specific pose by uploading a reference skeleton image and applying the OpenPose ControlNet

Retouch a product photo by using inpainting to replace the background while keeping the product exactly as it is

Create a photorealistic portrait in a specific lighting style by combining a depth map ControlNet with a custom LoRA for that artist's look

Turn a rough sketch into a finished illustration by uploading it as a lineart ControlNet reference and writing a detailed style prompt

Edit a specific region of an existing image, such as swapping an outfit or replacing a backdrop, without altering the surrounding areas

Produce a consistent set of on-brand visuals by loading a style LoRA and running multiple prompt variations from the same seed

Reconstruct a blurry or low-resolution photo into a sharp, detailed image by using img2img with a high step count and a detailed prompt

Experimenting with multi-style AI art workflows

Examples

1024x1024
27.3s
Refine: no_refiner
Scheduler: K_EULER
Lora Scale: 0.8
Num Outputs: 1
Controlnet 1: soft_edge_hed
Controlnet 2: illusion
Controlnet 3: none
Guidance Scale: 7.5
Apply Watermark: No
Prompt Strength: 0.8
Sizing Strategy: width_height
Controlnet 1 End: 0.8
Controlnet 2 End: 0.65
Controlnet 3 End: 1
Controlnet 1 Start: 0
Controlnet 2 Start: 0.08
Controlnet 3 Start: 0
Num Inference Steps: 50
Controlnet 1 Conditioning Scale: 0.5
Controlnet 2 Conditioning Scale: 1
Controlnet 3 Conditioning Scale: 0.75
low quality, worst quality, ugly, soft, blurry

A detailed photo of an astronaut riding a unicorn through a field of flowers

768x768
9.4s
Refine: no_refiner
Scheduler: K_EULER
Lora Scale: 0.8
Num Outputs: 1
Controlnet 1: soft_edge_hed
Controlnet 2: none
Controlnet 3: none
Guidance Scale: 7.5
Apply Watermark: No
Prompt Strength: 0.8
Sizing Strategy: width_height
Controlnet 1 End: 1
Controlnet 2 End: 1
Controlnet 3 End: 1
Controlnet 1 Start: 0
Controlnet 2 Start: 0
Controlnet 3 Start: 0
Num Inference Steps: 30
Controlnet 1 Conditioning Scale: 0.8
Controlnet 2 Conditioning Scale: 0.8
Controlnet 3 Conditioning Scale: 0.75

A detailed photo of an astronaut riding a unicorn through a field of flowers

1024x1024
13.4s
Refine: no_refiner
Scheduler: K_EULER
Lora Scale: 0.8
Num Outputs: 1
Controlnet 1: depth_leres
Controlnet 2: none
Controlnet 3: none
Guidance Scale: 7.5
Apply Watermark: No
Prompt Strength: 0.8
Sizing Strategy: width_height
Controlnet 1 End: 0.8
Controlnet 2 End: 1
Controlnet 3 End: 1
Controlnet 1 Start: 0
Controlnet 2 Start: 0
Controlnet 3 Start: 0
Num Inference Steps: 30
Controlnet 1 Conditioning Scale: 0.7
Controlnet 2 Conditioning Scale: 0.8
Controlnet 3 Conditioning Scale: 0.75
(worst quality, low quality, illustration, 3d, 2d, painting, cartoons, sketch), open mouth

A portrait photo

768x768
9.1s
Refine: no_refiner
Scheduler: K_EULER
Lora Scale: 0.8
Num Outputs: 1
Controlnet 1: soft_edge_hed
Controlnet 2: none
Controlnet 3: none
Guidance Scale: 7.5
Apply Watermark: No
Prompt Strength: 0.8
Sizing Strategy: width_height
Controlnet 1 End: 1
Controlnet 2 End: 1
Controlnet 3 End: 1
Controlnet 1 Start: 0
Controlnet 2 Start: 0
Controlnet 3 Start: 0
Num Inference Steps: 30
Controlnet 1 Conditioning Scale: 0.8
Controlnet 2 Conditioning Scale: 0.8
Controlnet 3 Conditioning Scale: 0.75

A detailed photo of an obsidian statue of an astronaut riding a unicorn, bokeh, museum background

Switch Category

Effects

Text To Image

Text To Image

Text To Video

Large Language Models

Large Language Models

Text To Speech

Text To Speech

Super Resolution

Super Resolution

Lipsync

AI Music Generation

AI Music Generation

Video Editing

Speech To Text

Speech To Text

AI Enhance Videos

Remove Backgrounds

Remove Backgrounds