The first time I used ControlNet, the result made me laugh - it was so far from what I'd intended. Then I learned how pose skeletons actually work, and everything changed overnight. Without ControlNet, you write a prompt and hope the AI composes the scene the way you envision it. With ControlNet, you specify exact poses, edges, depth maps, compositions, and spatial layouts, and the AI generates within those precise constraints. It bridges the gap between random generation and professional art direction, giving you the kind of control that photographers have over their subjects and that directors have over their scenes. If you are serious about AI art - whether for commercial work, personal projects, or professional portfolios - ControlNet is an essential skill. This guide covers everything from basic concepts to advanced multi-ControlNet workflows.
What is ControlNet?
ControlNet is a neural network architecture that conditions Stable Diffusion's image generation on an additional input image - a "control image" that guides the composition, structure, or pose of the output. It works as an extension in both A1111 and ComfyUI. The concept is elegant: you provide a reference - a pose skeleton, edge map, depth map, segmentation map, or even a rough hand-drawn sketch - and the AI generates a new image that follows those structural constraints while applying the style, content, and quality described in your text prompt. Think of it as giving the AI an architectural blueprint: the blueprint determines the structure, while your prompt determines the aesthetic. The power of ControlNet is that it separates composition from content. You can take the pose from a fashion photograph, the style from a fantasy prompt, and the lighting from a cinematic description, combining them into a single, intentional image that matches your exact creative vision. ControlNet models are free, open-source, and available for both SD 1.5 and SDXL architectures. Multiple ControlNet models can be used simultaneously on a single generation, allowing you to control pose AND depth AND edge detail all at once.The Most Useful ControlNet Models
There are over a dozen ControlNet model types, but these are the ones you will use 90% of the time:OpenPose detects and controls body poses. Upload any reference photo of a person and ControlNet extracts the pose skeleton - a stick figure showing joint positions. The AI then generates a completely new person in that exact pose, with whatever style and clothing your prompt describes. OpenPose is the most popular ControlNet model because pose control is the most common need. It also has variants: OpenPose Face adds facial landmark detection (expression control), and OpenPose Hand improves hand positioning.
Canny Edge detects edges in a reference image and uses them as compositional guidelines. It creates a line drawing of the reference, and the AI fills in the details according to your prompt. Canny is incredibly versatile - use it to maintain the composition of an existing photo while completely changing the style, to turn a sketch into a finished illustration, or to ensure architectural lines remain straight and precise.
Depth creates a depth map from a reference image, showing the spatial relationship between near and far elements. The AI uses this depth information to maintain the same spatial layout - foreground elements stay in front, background elements stay behind, and the overall three-dimensional structure is preserved. Perfect for landscapes, architectural interiors, and any scene where spatial accuracy matters.
Lineart is specifically designed for line drawings and sketches. It extracts clean line art from a reference and uses it to guide generation. This is the best ControlNet for artists who want to sketch a rough composition and have the AI render it into a polished illustration.
Scribble is the most forgiving model - it works from rough, messy sketches and scribbly drawings. Even a 30-second rough sketch provides enough structural guidance for a coherent image. This is the most accessible ControlNet for non-artists who want compositional control without drawing skill.
Tile upscales and adds detail to images. It divides the image into tiles and regenerates each with added detail, guided by the original composition. This is the go-to ControlNet for AI image upscaling that adds genuine new detail rather than just interpolating pixels.
Practical Workflow: Step by Step
Here is the exact workflow for using ControlNet effectively, from reference to finished image:Step 1: Choose or create your reference image. This can be a photograph (for pose or composition reference), a sketch you drew yourself, a screenshot from a movie (for composition inspiration), or even a 3D posed figure from a free tool like Magic Poser or Daz3D. The reference does not need to look good - it just needs to convey the structural information you want.
Step 2: Load the reference into ControlNet and choose the right preprocessor. In A1111, expand the ControlNet section below the prompt fields, upload your image, and select the preprocessor and model. OpenPose for body poses, Canny for edges and composition, Depth for spatial layout, Scribble for rough sketches. Click the preview button to see the extracted control map before generating.
Step 3: Write your prompt as usual. Your text prompt handles everything that ControlNet does not - art style, lighting, colors, mood, clothing, character details. ControlNet handles the WHERE and the STRUCTURE; your prompt handles the WHAT and the HOW.
Step 4: Adjust the ControlNet weight and guidance. Weight (0.0-2.0, default 1.0) controls how strongly the control image influences the generation. At 1.0, it follows the reference closely. Lower weights (0.4-0.7) allow more creative freedom. Higher weights (1.2-1.5) enforce stricter adherence. Start at 1.0 and adjust based on results. Control guidance start/end lets you specify at which step of the generation process ControlNet activates and deactivates - this is useful for advanced techniques where you want ControlNet to set the initial composition but let the AI refine freely in later steps.
Step 5: Generate and iterate. Generate 4 images, evaluate which best matches your vision, and adjust the weight or prompt for the next batch. The iterative loop with ControlNet is much faster than without it because the composition is already locked in - you are only refining style and details.
Real-World Use Cases
ControlNet unlocks professional applications that text-only prompting cannot achieve:Character art and comics: Use OpenPose to generate the same character in different poses for a comic page or character sheet. Pose reference images for each panel, maintain character consistency with --cref or LoRA, and produce a complete comic page with professional-level pose variety.
Product photography and placement: Use Depth ControlNet to place products in specific locations within a scene. Take a depth map of your desired product placement, and the AI generates the environment around it with perfect spatial accuracy.
Architectural visualization: Use Canny edges from architectural sketches or CAD renders to generate photorealistic architectural renders. The edges ensure structural accuracy while the prompt controls materials, lighting, and atmosphere.
Fashion and clothing design: Use OpenPose to generate models in specific runway poses wearing AI-generated outfits. Design clothing in your prompt, control the model's pose with OpenPose, and produce fashion lookbook images without a photographer or model.
Animation and motion: Use OpenPose with sequential pose references to create frame-by-frame animations. Combined with AnimateDiff, this enables AI-assisted animation with consistent character poses.
Interior design: Use Depth maps from room photographs to redesign interiors. The depth map preserves the room's spatial layout while your prompt completely transforms the style, furniture, materials, and lighting.
Advanced: Multi-ControlNet and Stacking
The real power emerges when you stack multiple ControlNet models simultaneously. In A1111, you can enable multiple ControlNet units (up to 3 or more). In ComfyUI, simply add multiple ControlNet Apply nodes to your workflow. Example: use OpenPose for character pose + Depth for background spatial layout + Canny for architectural detail - all in one generation. Each ControlNet controls a different aspect of the image. Set different weights for each: a strong OpenPose weight (1.0) for precise pose matching with a lighter Depth weight (0.5) for general spatial guidance. This multi-ControlNet approach is how professional AI artists achieve the level of compositional control that makes their work look intentionally directed rather than randomly generated.Getting Started Today
Install the ControlNet extension in A1111 (search "ControlNet" in the Extensions tab) or add ControlNet nodes in ComfyUI (install via ComfyUI Manager). Download ControlNet models from Hugging Face - they are free and typically 700MB-1.4GB each. Start with OpenPose - it is the most intuitive and immediately impactful. Take a selfie in the pose you want, load it as your reference, add a PromptSpace prompt for the style, and generate. The combination of precise composition control from ControlNet and high-quality prompts from PromptSpace produces professional, intentional results that random text-only prompting simply cannot match. Browse promptspace.in for prompts optimized for ControlNet workflows and start creating directed AI art today.Frequently Asked Questions
Is this free to use? Yes — PromptSpace provides free AI prompts for any AI tool. Try them instantly with our free AI image generator — no account needed.Which AI model works best with these prompts? These prompts work across Midjourney, DALL-E 3, FLUX, and Stable Diffusion. Test in two tools to compare — results vary by model.
Can I use AI-generated images commercially? Yes on paid tiers of Midjourney, ChatGPT Plus, and Adobe Firefly. Always verify the specific platform's terms before commercial use.
How do I improve results if the first output isn't right? Add specific lighting, camera angle, and visual style references. Iterate 3–4 times with small changes to find the best output.
