Image-guided creation
Use stable diffusion img2img to explore variations
Stable Diffusion img2img is the subject of this guide: it begins with a source image and a text prompt, but cannot guarantee exact preservation. The partner link opens Supavisual’s paid photo editor, not Stable Diffusion or a model-selection screen.
Partner link only. Sign in if asked, open the paid photo editor at /edit, and upload your image and enter any instructions there. Img2img does not send an image or prompt; there is no free generation here.
Find the right starting point
Image guidance is useful when you already have a composition worth developing. If you need a narrower edit or a first-time walkthrough, these guides address those decisions directly.
- what is img2img tutorial Start here for the basic terms behind source images, prompts, and generated variations.
- how to use img2img for beginners Follow a beginner-friendly workflow for preparing an input and reviewing the result.
- img2img vs inpaint Compare whole-image variation with edits aimed at a selected region.
Three mechanisms behind a variation
In a Stable Diffusion workflow, the input image, prompt, and denoising setting work together. None acts as a precise lock on the final pixels.
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1
Encode the source
The source image is converted into a representation the model can work from. A clear input with recognizable shapes gives the process more useful structure than an ambiguous or heavily compressed image.
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2
Add and remove noise
A denoising setting controls how far the generation can move from its starting image. Lower values tend to retain more of the original; higher values permit broader changes but can disrupt a pose or layout.
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3
Guide the reconstruction
The text prompt steers what the model draws as it resolves the noise. Describe the desired change and the details worth keeping, then judge the output against the source rather than the prompt alone.
Choose a whole-image pass or a targeted edit
A practical first step is deciding how much of the image may change. This comparison describes the methods, not a promise that every editor offers both.
| Whole-image img2img | Inpainting | |
|---|---|---|
| Edit area | The full source image influences the new result. | A selected region is the main area to replace. |
| Best starting goal | Explore a new style or broader variation. | Change one object or repair a local detail. |
| Input preparation | Supply a source image and describe the intended result. | Supply an image, mark an edit region, and describe the replacement. |
| Preserving surroundings | Unrequested areas can change during reconstruction. | Unselected areas are intended to remain more stable. |
| Typical review | Check composition, subject identity, and overall style. | Check mask boundaries, lighting, and how the edit blends. |
| Better choice when | You welcome several interpretations of the source. | You need a specific correction without redesigning the scene. |
Limits to check before you keep a result
Stable diffusion can produce convincing variations without reliably obeying every constraint. Inspect each output at full size before treating it as a finished edit.
Small details may drift
Letters, logos, jewelry, and facial details can change even when the broader composition looks familiar.
What to do instead
Compare the result with the source at full resolution and restore critical details separately.
A prompt is not a mask
Telling a whole-image pass to change only a jacket does not prevent nearby hair, hands, or background objects from changing.
What to do instead
For a strictly local replacement, use an editor with a masking or inpainting workflow.
More change can cost continuity
Stronger transformations may improve a new style while losing the original pose, proportions, or scene geometry.
What to do instead
Try a less aggressive setting if your chosen tool exposes one, and compare several results.
Outputs still need permission checks
Generation does not establish rights to a source photo, a recognizable person's likeness, or a protected design.
What to do instead
Use material you are allowed to edit and review the intended use before publishing.
Take an image idea further
Bring a source image and a specific creative direction to your chosen workflow. Look for the changes you wanted, check what unexpectedly moved, and keep the source available for comparison.
Explore your next image variation
- Describe the change you want
- Name the details you want to preserve
- Inspect the result before sharing
Stable Diffusion img2img questions
It uses the image as a starting point for generation rather than treating it as an unchangeable background. The amount of visible change depends on the workflow settings, prompt, model, and source.
Text-only generation starts without your image's composition. An image-guided workflow gives the model existing visual structure to work from, which is useful when you want a variation of a particular scene.
If your tool exposes denoising strength, a lower setting generally favors more of the original structure. Write a focused prompt, change one goal at a time, and compare the outputs rather than assuming a setting will preserve every detail.
A whole-image pass can affect areas you did not intend to edit. If the task is to replace one specific region while retaining its surroundings, look for a masking or inpainting workflow instead.