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Kling 2.1
Kling 2.1
Fast response and reliable output

Kling 2.1: Use one image to generate images with more depth and spatial sense

Upload one image as a base, and with just one simple description, such as adjusting composition, changing lighting, optimizing details, or enhancing image layers, Kling 2.1 will perform optimization based on understanding the image structure, making the generated results more natural and stable in spatial relationships, lighting performance, and overall composition。 It is more suitable for scenarios that require “texture and visual expression” of images。 For example, in e-commerce visuals, making product i

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Kling Image｜
Multi-reference consistency locking

Upload up to 10 reference images to lock subject outline and key elements, then use one sentence to decide how to change: background, props, style. Iterate for reusable series visuals.

Next-gen multimodal creation platform

Start with reference images
Upload reference images
Write one sentence
Generate result
Step 1: Upload reference images - Upload 3–10 reference images Lock subject outline and key elements
Step 2: Write one sentence - Enter your instruction "Change to starry sky background, holding star wand" Describe the desired change in natural language
Step 3: Generate result - Generate result

Success in one minute

3 fixed actions to master the core workflow

Series consistency

  1. 1Pick references Upload 3–10 images, more angles/details the better
  2. 2Write constraints What must stay: identity, shape, color, LOGO, material
  3. 3Write changes Change one thing at a time: background, style, props, camera

Precise edit

  1. 1Upload original Upload the image you want to modify
  2. 2One-sentence instruction What to change + to what + rest unchanged
  3. 3Fine-tune output Second-round refinement only in the changed area

Why is this more stable?

Kling Image favors "reference-driven semantic accuracy and context preservation"—not random redraws. It better understands "what to change and what to keep" for stable, controllable results.

What makes it special?

See the difference at a glance

Typical text-to-image

Each generation "re-guesses" what the character looks like

  • Hard to keep character consistency
  • Difficult to filter multiple generations

Kling Image

Uses multi-reference to "lock" key features

  • Better for continuous series output
  • Precise control—change exactly what you want

Important note Kling Image prioritizes "semantic accuracy and consistency" over fastest output. Its strength is precise control, not fast random generation.

Core capabilities

4 core capabilities for your creation needs

Up to 10 reference images to lock features

Upload 10 references to lock outline, core elements, tone—easier to keep stable consistency

Reference-driven composition editing

Better at "who/what" into "where" context composition, emphasizing scene logic

Precise static image editing

Add/remove/change objects, swap style, local changes—leave the rest untouched

Multi-view consistency

Front/side/back or different angles—identity and outfit details preserved

Template library

No need to "understand the model"—just pick what you want to do

Product scene swap

Swap white-back product shots to café, outdoor, office, etc. 1 Upload product image 2 Choose target scene 3 Generate multi-angle display

Use template

Same-character series posters

Keep character consistent, generate cyberpunk, retro, cartoon styles 1 Upload character multi-angle images 2 Define character constraints 3 Generate different style posters

Use template

Multi-view character assets

Front, side, back views for games/animation 1 Upload front view 2 Add side/back references 3 Generate complete asset pack

Use template

Local outfit/material swap

Change only clothes or material, rest unchanged 1 Upload original 2 Specify edit region 3 Describe desired effect

Use template

Subject + scene composition

Place character in specific scene, natural and realistic 1 Upload subject image 2 Upload scene reference 3 Smart fusion generation

Use template

Multi-style deployment

Same subject, different styles for A/B testing 1 Define subject 2 Choose multiple styles 3 Batch generate for comparison

Use template

Case study

Reference stack → Constraints → Changes → Result

Reference stack

Up to 10

Constraints

  • Shape unchanged
  • Color unchanged
  • LOGO position unchanged

Changes

  • Scene: Forest camp
  • Style: Natural lighting

Output

Create once, reuse many times

On Dovoo AI you don't just generate one image—you build an asset library

Reference combinations

Save role/product asset packs, upload once and reuse

Prompt templates

Save constraints + changes skeleton for quick reuse

Output history

Replay every iteration version—never miss an idea

Prompt writing

1

Lock sentence

Keep 【subject】's 【shape/ratio/color/key identifiers】 unchanged

2

Change sentence

Change 【background/props/style/camera】 to 【target】

3

Protection sentence

Other areas unchanged; lighting/perspective match original

One-click fill examples

"Keep the character's black short hair, blue eyes and white T-shirt unchanged, change background to café scene"

"Keep product shape and LOGO unchanged, change material to metallic"

"Keep character facial features unchanged, change outfit to red dress"

FAQ

Up to 10. More references help the model understand subject features and improve consistency.

The same asset pack can be switched to other models for comparison—all on Dovoo AI.

Start creating