TL;DR: An image model reads your sketch as a composition, not as instructions. Clean, high-contrast lines condition better than a faint pencil scan, and one setting decides how much survives, running in opposite directions across tools. Below: what to draw versus write, honest limits on floor plans and dimensions, and 25+ prompts organized by eight kinds of sketch.
What does an image model actually do with a sketch?
It treats the sketch as a map of shapes, edges, and rough values, then paints inside that map according to your prompt. It does not read your sketch as a set of instructions.
That distinction matters more than any setting. Write "make the roof red" next to a roofline in your sketch and nothing happens, because the model isn't parsing your handwriting as a command; the annotation has to move into the actual prompt text. Draw an arrow pointing at a wall with "move this" scrawled beside it, and the model will most likely render the arrow as a shape in the scene. Stability describes its purpose-built sketch endpoint as reading "contour lines and edges within the image" for control, not reading intent. Whatever you want to happen, say it in words. Whatever you want kept, put it in the drawing.
This is also why a sketch behaves differently from a text-only prompt or from a reference photo. A reference photo carries lighting, material, and detail the model can copy directly. A sketch carries almost none of that; it's mostly a silhouette and a layout, so the model has to invent everything else, materials, lighting, texture, from your written description. The thinner the input, the more the prompt is doing. It's also the reverse of the job in our guide to reverse-engineering an image into a prompt: there you start with a finished picture and extract the words that made it; here you start with the drawing and have to supply the words yourself.
Which setting decides how much of your sketch survives?
The parameter, and this is the entire mechanism this style of generation runs on. Every tool below implements the same trade: how much the finished image matches your sketch's shapes and layout, against how freely the model can depart from it. What differs is which direction the number runs, and whether the tool was built for this job specifically.
Here's the direction, per tool, for the ones people actually reach for on a sketch:
| Tool | Parameter | Which direction keeps the sketch |
|---|---|---|
Stability control/sketch, control/structure | control_strength (0–1, default 0.7) | Higher |
Stable Diffusion WebUI, ComfyUI, diffusers (generic img2img) | Denoising strength / denoise / strength (0–1) | Lower |
Stability generic generate/sd3, generate/ultra | strength (0–1, required, no default) | Lower |
| Midjourney image prompts | --iw (0–3 on V8.1/V7, 0–2 on Niji 7, default 1) | Higher |
| Ideogram Remix | image_weight (1–100 integer) | Higher |
| OpenAI Images API (GPT Image) | input_fidelity (high/low, default low) | high |
| BFL FLUX1.1 Ultra | image_prompt_strength (0–1, default 0.1) | Higher (weak by default) |
| Leonardo.ai | strength (LOW/MID/HIGH, default MID) | Not stated by the vendor |
| Gemini, FLUX.2, FLUX Kontext | none published | Control moves into the prompt |
That last row matters as much as the numbers. Google's Gemini image documentation contains no strength or denoise field anywhere; its answer is a prompting technique it calls high-fidelity detail preservation: "To ensure critical details (like a face or logo) are preserved during an edit, describe them in great detail along with your edit request." On these models, what you keep is entirely a function of what you write, not a slider.
This table covers the tools people bring a sketch to most often. For the exhaustive per-tool reference, every parameter name, every documented default, and the step-count behavior that makes 0.5 look different in ComfyUI than in Stable Diffusion WebUI, see our denoising strength reference.
What should you actually draw, and what belongs in the prompt instead?
Two different jobs, and conflating them is the second most common way this fails.
The sketch's job is composition. Silhouette, proportion, where things sit relative to each other, and roughly how big they are relative to the frame. That's the information a diffusion or control model conditions on well, because it's reading edges and contrast, not meaning.
The prompt's job is everything the sketch can't show. Material, lighting direction and quality, color, mood, camera angle if it isn't obvious from the drawing, and anything the sketch only gestures at with a label or an arrow. If you wrote "brick" next to a wall in your sketch, that word has to appear in the prompt text too. The drawing doesn't carry vocabulary.
Contrast matters more than most people expect. A faint graphite sketch scanned at low contrast gives an edge-detection-style process very little signal, so ambiguous areas get filled in with the model's own guess rather than your intended line. A clean ink line, a bold marker trace, or a sketch with the contrast pushed up in any basic photo editor gives the model a cleaner map to follow. If your only version of a drawing is a faint pencil scan, darkening it or tracing the main lines before you upload it is worth the extra minute.
Scale and proportion drift no matter how you calibrate this. A diffusion model has no ruler; it has learned what plausible objects and rooms look like, and it will nudge your drawing toward that learned plausibility even at a low, sketch-preserving strength. Expect a render to be close to your proportions, not identical to them. For anything measured, treat the sketch as a starting composition, not a locked layout.
Turning a rough thumbnail composition into a finished frame
A thumbnail is the loosest input in this whole list: a few shapes standing in for a subject, a horizon line, maybe a rough light source. Keep it that loose in the drawing and put every real decision, subject, setting, mood, medium, into the prompt.
Render this thumbnail composition as a finished illustration. Keep the exact
placement, scale, and rough silhouette of every shape. The large central shape
is a lone figure standing at a cliff edge; render them in a weathered hiking
jacket, backlit by a low sun. The smaller shape at lower right is a boulder.
Sky and ground fill in freely; the composition and horizon line must not move.
[control_strength 0.55 on a structure endpoint, or denoising strength 0.45 in
SD WebUI / ComfyUI]
Take this loose thumbnail and render it as a cinematic wide shot. The three
rough blocks are, left to right: a low table, a standing person, a doorway with
light coming through it. Preserve their left-to-right order and relative sizes
exactly. Render in warm interior light, shallow depth of field, 35mm lens feel.
[control_strength 0.6, or denoising strength 0.4]
Turn this rough thumbnail into a finished graphic novel panel. Keep the panel's
internal composition, the horizon height, and the silhouette proportions.
Render the scene as a rain-lit street at night with one figure under a
streetlamp. Ink line art with flat color, no photographic texture.
[control_strength 0.5, or denoising strength 0.5]
Turning a line drawing into a rendered image
A clean line drawing is the strongest input on this page, because edges are exactly what these models condition on best. This is the demo case for design and architecture crossover: a hand-drawn elevation or a product line drawing, rendered while every line stays where you put it. If you're starting from a blank page instead of an existing sketch, our interior and architecture image prompts piece has the text-only version of the same jobs.
Render this architectural line drawing as a photoreal exterior at golden hour.
Keep every wall position, window opening, and roofline exactly as drawn.
Use the material labels written on the drawing: brick lower facade, standing
seam metal roof, painted timber trim. Landscaping and sky are yours to invent.
[control_strength 0.4 on Stability's control/structure endpoint, or --iw 2.2]
Take this hand-drawn furniture elevation and render it as a studio product
photograph. Preserve the exact silhouette, proportions, and joinery lines.
Render in oiled walnut with brushed brass hardware, three-point studio
lighting, seamless light-gray backdrop.
[control_strength 0.45, or denoising strength 0.35]
Render this line drawing of a building section as a clean architectural cutaway
illustration. Keep every wall thickness, floor level, and stair run as drawn.
Use a flat, desaturated palette with a single warm accent color for occupied
space, in the style of a competition presentation drawing.
[control_strength 0.5, or image_weight 70]
Turn this pen line drawing of a chair into a photoreal render. Keep the exact
proportions, leg angles, and seat curve. Render in stitched tan leather over
a blackened steel frame, on a polished concrete floor, soft overhead light.
[control_strength 0.4, or denoising strength 0.3]
Turning a wireframe sketch into a UI mock
A wireframe is composition in the purest sense: boxes standing in for real components. Say what each box becomes and how the whole thing should feel, because the sketch will not tell the model whether a box is a button or a photo.
Render this rough UI wireframe as a finished mobile app screen. Keep every
block's exact position, size, and reading order top to bottom. The top bar
becomes a navigation bar with a back arrow and a title. The large upper block
becomes a product photo. The row of small squares becomes star-rating icons.
The bottom block becomes a full-width primary button reading "Add to Bag."
Light theme, rounded corners, a single accent color.
[denoising strength 0.5 to 0.65; wireframes tolerate more change than a
finished screenshot would]
Turn this hand-drawn dashboard wireframe into a finished web app screenshot.
Keep the exact grid: sidebar left, stat cards across the top, a large chart
block center, a table below it. Use a dark theme, a single teal accent, and
realistic placeholder data in the chart and table.
[denoising strength 0.55]
Render this rough sketch of a landing page as a finished, polished design.
Keep the section order and each block's relative height exactly as sketched:
hero, three feature cards, testimonial, footer. Invent copy, imagery, and color
consistent with a calm, professional SaaS brand.
[denoising strength 0.6]
Can AI turn a sketched floor plan into a real drawing?
No, and this is the limit worth being explicit about before you try it. Rendering a floor-plan sketch produces a picture that looks like a plan; it does not produce geometry.
OpenAI's own GPT Image guide names this directly under its own "Limitations" heading, not as a footnote: "the model may have difficulty placing elements precisely in structured or layout-sensitive compositions." Google's Gemini guidance points at the same gap from the other side, recommending you "break your prompt into steps" for anything with many elements, because the model has no internal check that walls close or a hallway is wide enough. Nothing in this category runs a constraint solver on your walls, and a diffusion model has no ruler.
What a sketch-to-image pass on a floor plan is genuinely good for: turning a rough hand-drawn layout into a cleaner-looking concept image for a client conversation, a moodboard, or a pitch deck slide, where "looks roughly right" is the actual goal. What it cannot do is produce a dimensioned drawing you would hand to a contractor. Leave scale numbers and dimension strings out of the render entirely; the model will invent plausible-looking ones if you leave the labels in, and an invented dimension is worse than no dimension. Our floor plans and space planning piece covers the text-to-concept side of this same limitation in more depth.
Render this hand-sketched floor plan as a clean, colored concept diagram for a
client presentation. Keep every wall position, room boundary, and door opening
exactly as drawn. Do not add dimension numbers or measurements of any kind.
Color rooms by function: living areas warm neutral, wet rooms pale blue,
outdoor space green. This is a concept illustration, not a construction drawing.
[control_strength 0.75 to keep wall positions close to fixed, or denoising
strength 0.2 to 0.3]
Turn this rough bubble diagram into a clean, presentation-ready relationship
diagram. Keep the exact circles, their relative sizes, and every connecting
line. Render as a flat vector-style graphic with a simple labeled legend.
Do not add room dimensions, scale, or any numeric measurement.
[denoising strength 0.3]
Render this hand-drawn site plan sketch as a clean aerial concept illustration.
Keep the building footprint, driveway path, and planting areas exactly as
sketched. Add a soft top-down lighting look and a simple ground texture legend.
No dimension strings, no scale bar; this is for early-stage discussion only.
[control_strength 0.7]
Turning a character sketch into a rendered character
Character work rewards being explicit about what must not drift, because faces and proportions are exactly where a mid-range strength value erodes identity first.
Render this character sketch as a finished digital painting. Keep the exact
pose, proportions, and facial structure as drawn; do not reinterpret the face.
Render in painterly fantasy-illustration style: worn leather armor, a short
cloak, warm rim light from the left, muted earth-tone palette.
[control_strength 0.55, or --iw 1.8]
Turn this rough character concept sketch into a 3D-render-style illustration.
Keep the silhouette, stance, and proportions exactly as drawn. Render with
soft studio lighting, subsurface-scattered skin shading, and a plain gradient
background, in the style of a game character-select screen.
[control_strength 0.5]
Render this character sketch as a clean flat-color vector illustration for a
brand mascot. Keep the pose and proportions exactly. Simplify all interior line
work to clean, consistent stroke weight with two-tone flat shading.
[denoising strength 0.35, or image_weight 65]
Turning a product concept sketch into a render
Industrial-design sketches carry form and proportion; materials and finish belong in the prompt, because a sketch has no way to show "brushed aluminum" versus "matte plastic."
Render this product concept sketch as a photorealistic studio product shot.
Keep the exact form, proportions, and silhouette as drawn. Render in
brushed aluminum with a matte-black rubberized grip section, on a seamless
light-gray backdrop, soft three-point studio lighting, subtle contact shadow.
[control_strength 0.45, or denoising strength 0.35]
Turn this rough industrial-design sketch into a clean rendered concept image.
Keep the exact silhouette and proportions of every component. Render in
translucent frosted polycarbonate with visible internal components suggested
through the shell, on a plain white background.
[control_strength 0.4]
Render this hand-drawn packaging concept as a finished mockup. Keep the exact
silhouette and panel layout as sketched. Apply a matte kraft-paper texture,
single-color letterpress-style logo, and a soft studio light setup.
[denoising strength 0.3, or image_weight 75]
Turning a napkin diagram into a clean graphic
Napkin diagrams and quick process sketches carry structure, not style. The render's whole job is legibility, so keep the layout locked and only ask for a visual style change.
Render this napkin diagram as a clean, professional flowchart graphic. Keep
every box, arrow, and their exact positions and connections. Do not add,
remove, or reroute any connection. Use a simple flat color scheme, one accent
color per box type, consistent rounded rectangles and clean arrowheads.
[denoising strength 0.2 to 0.3; this is a legibility pass, not a reinterpretation]
Turn this rough hand-drawn process diagram into a polished presentation
graphic. Preserve every step, its position, and every arrow exactly as drawn.
Render with a light background, a single brand accent color, and clean
sans-serif labels matching the words already on the sketch.
[denoising strength 0.25]
Render this napkin sketch of a system architecture as a clean technical
diagram. Keep every box and every connecting line exactly as positioned.
Use a dark background with light strokes, in the style of a cloud-provider
architecture diagram, with subtle icons in place of plain boxes.
[denoising strength 0.3]
Turning handwriting or a whiteboard photo into a tidy version
The goal here is legibility and layout, not artistic reinterpretation, so this is the lowest-strength job on the whole page. A model with no strength slider, prompted correctly, often does this better than a strength-based one, because instruction-following beats structural conditioning when the input is text, not shapes.
Clean up this photo of a whiteboard. Straighten the perspective, remove glare
and reflections, and redraw every word, number, and line exactly as written,
in a neat handwriting-style font. Do not add, remove, or reword anything.
Keep the exact layout and grouping of the original.
[input_fidelity: high, or denoising strength 0.1 to 0.15]
Turn this handwritten note into a clean, typed-looking version. Preserve every
word, bullet, and indentation level exactly as written. Use a simple
sans-serif layout on a plain white background. Do not summarize, shorten, or
paraphrase any of the content.
[input_fidelity: high]
Clean up this scanned whiteboard photo from a planning session. Straighten the
page, remove shadow and marker glare, and sharpen every mark so it's legible.
Every arrow, box, and word must remain in its original position.
[denoising strength 0.1, or input_fidelity: high]
Calibrating a model you haven't sketched into before
Every model responds to strength differently on a sketch specifically, because a sketch has far less information in it than a photograph, so the same number preserves less than it would on a photo.
Same sketch, same prompt, four runs at your tool's lowest, quarter, half, and
three-quarter strength setting, same seed if the tool supports one. Note which
run first stops looking like your sketch's composition. That value, not one
from a tutorial, is this model's working ceiling for this kind of drawing.
Describe this sketch in enough written detail that the description alone,
with no image attached, would let someone redraw its composition from scratch.
I will pair that description with a control or strength value so I can see how
much the model is relying on the image versus the words.
Stop rewriting prompts. Start shipping.
Works with ChatGPT, Claude, Gemini, Grok, Midjourney, Ideogram, Veo3 & Kling. 4.8★ on the Chrome Web Store.
Create An AccountSources and dates
Every parameter name, range, default, and direction above was checked against the vendor's own documentation, API specification, or source code on September 2, 2026.
Stability AI: v2beta OpenAPI specification (control/sketch, control/structure, generate/sd3, generate/ultra request schemas, read directly). Midjourney: Image Prompts, read via the Help Center JSON API since docs.midjourney.com returns 403 to a direct fetch, updated August 31, 2026. Ideogram: Remix v4 API reference. OpenAI: openai-openapi schema for input_fidelity, and the image generation guide for its own stated composition-control limitation. Black Forest Labs: docs.bfl.ai full reference. Google: Gemini image generation guide. Leonardo.ai: Create Generation reference.
Source code, read directly rather than summarized: diffusers img2img pipeline, ComfyUI nodes.py for the denoise tooltip, ComfyUI's KSampler docs page for the contradicting table wording, and Stable Diffusion WebUI's ui.py for the 0.75 img2img and 0.7 hires-fix defaults.
Product facts checked against our own live pages on September 2, 2026: /faq for the free daily allowance and image prompt generation starting at Pro, and /pricing for the Pro price and plan comparison.
Where this page says something is not documented, an inferred default, or our own observed band rather than a published one, that's stated in the text next to the claim, not left implicit.