meta/sam-3/image-to-image

What makes SAM 3 stand out

SAM 3 is a high-fidelity image-to-image system that unifies detection, segmentation, and editing in a structure-aware workflow. SAM 3 preserves geometry, materials, and composition while applying targeted, text-driven changes, producing realistic results without full-frame resynthesis. With strong region understanding and mask precision, SAM 3 enables reliable localized edits, global restyling, and asset preparation across creative, AR, and research pipelines. Thanks to its adaptability to varied scenes and lighting, SAM 3 remains robust on cluttered layouts and dependable in production where consistency and editability are crucial. Key capabilities of SAM 3:

• Structure-preserving edits that maintain pose, layout, depth cues, and material response.
• Segmentation-driven control with high-quality masks for region-constrained operations.
• Text-conditioned changes to add, remove, or modify elements without drifting the base scene.
• Background cleanup or replacement while keeping the subject intact and edges stable.
• Consistent lighting and realism with perspective-aware harmonization of shadows and reflections.
• Edge-safe transformations that avoid halos, bleed, and unintended warping on fine details.
• Resolution- and aspect-robust behavior suitable for batch workflows and pipelines with SAM 3.

Prompting guide for SAM 3

To use SAM 3, start by providing image_url as the required base image. Optionally include text_prompt to describe the edit in clear, concrete terms. State what to change and what to preserve so SAM 3 can limit its operations to the intended regions. Use spatial language to help SAM 3 localize changes, and specify lighting, material, or style targets only when needed. Keep prompts for SAM 3 concise and iterative to refine outcomes without destabilizing structure.

Example prompts for SAM 3:

• "Preserve the person and pose; remove the power lines from the background only."
• "Only modify the background; replace the sky with overcast clouds; keep building edges sharp."
• "Add a wooden bench to the right of the subject; match perspective and lighting; preserve shadows."
• "Replace the storefront sign with 'BLOSSOM' in white sans-serif; perspective-matched; do not change facade."
• "Subtle cinematic grade across the scene; do not alter skin tone or composition."
• "Remove reflections on the left window; keep interior and signage unchanged."

Pro tips for SAM 3:

• Specify scope and constraints first: what to keep, what to edit, and where.
• Use precise spatial terms like left, right, foreground, background, upper-right quadrant.
• Prefer a few strong descriptors over many competing adjectives.
• Iterate with short updates; adjust nouns and constraints rather than rewriting the whole prompt.
• Supply a high-resolution, well-cropped image_url; remove irrelevant regions before processing with SAM 3.