curl-X POST 'https://pollo.ai/api/platform/v1/generation/kling-ai/kling-v3-image/image'\-H'Content-Type: application/json'\-H'x-api-key: YOUR_API_KEY'\-d'{ "input": { "prompt": "A cinematic shot of a golden retriever running through a field of sunflowers at sunset, warm rim light, shallow depth of field", "aspectRatio": "1:1", "resolution": "1K" }}'
{"input":{"prompt":"A cinematic shot of a golden retriever running through a field of sunflowers at sunset, warm rim light, shallow depth of field","aspectRatio":"1:1","resolution":"1K"},"webhookUrl":"https://example.com/webhooks/pollo"}
แผนผัง
ช่องป้อนข้อมูลสำหรับปลายทางและโหมดที่เลือก
สนาม
ประเภท
ที่จำเป็น
คำอธิบาย
prompt
string
ใช่
Text prompt describing the content to generate.
aspectRatio
string
เลขที่
Output aspect ratio, as width:height (e.g. 16:9).
resolution
string
เลขที่
Output resolution of the generated media (e.g. 720p, 1080p, 2K, 4K). Values are case-insensitive.
Feed Kling 3.0 several reference images and it keeps one character, outfit, and art style steady across every frame, making it suited to image sets that must belong together.
With Kling Image 3.0 on Pollo API, developers add text-to-image, single-reference editing, and multi-image composition to their apps, rendering up to 2K across aspect ratios from ultrawide 21:9 to vertical 9:16.
Key Features of Kling Image 3.0 API
Multi-Image Composition
Combine several reference images in one request, so a character, a product, and a setting can be merged into a single coherent scene instead of separate renders.
Character and Style Consistency
Recurring subjects hold their face, wardrobe, and look across generations, which lets you build a related sequence rather than a batch of unconnected one-off images.
Wide Aspect Range
Output spans ultrawide 21:9 panoramas, standard landscape and portrait ratios, square, and vertical 9:16, so one model covers banners, posters, and mobile-first frames.
2K Detail
Renders reach 2K resolution, holding fine texture in faces, fabric, and typography, which keeps images usable for print layouts and large-format placements without upscaling.
Reference-Guided Editing
Pair a prompt with an existing image to restyle, extend, or re-stage it, giving precise control over an established composition instead of starting each idea from scratch.
Use Cases of Kling Image 3.0 API
Generate a run of panels that share one protagonist and setting, useful for pre-visualization tools and shot-planning apps.
Storyboard Frames:
Illustrated Book and Comic Panels: Keep a recurring character on-model across pages, so illustration platforms can produce consistent sequential art.
Game Character Sheets: Render a hero from multiple angles and outfits while the face and proportions stay fixed for design pipelines.
Product-in-Scene Composites: Blend a product image with a model or environment reference into one staged shot for ecommerce listing builders.
Concept and Environment Art: Turn dense scene descriptions into detailed locations and props for worldbuilding and level-design workflows.
Brand Image Sets: Produce a matching series of marketing visuals that share palette, subject, and layout for campaign automation tools.
Reference Restyling: Take an uploaded photo and re-render it in a new style or lighting through image-to-image editing.
How to Use Kling Image 3.0 API
Get an API Key: Create a Pollo API account and generate your API key from the developer dashboard.
Choose the Model: Send requests to the Kling 3.0 image endpoint at /generation/kling-ai/kling-v3-image/image.
Add Your Inputs: Provide a prompt for text-to-image, add imageUrl for single-reference editing, or pass an images array for multi-image composition, then set aspectRatio and resolution (1k or 2k).
Generate and Retrieve: Submit the task, poll the returned taskId and status, and download the finished image once it succeeds.
Prompting Best Practices for Kling Image 3.0 API
Describe the subject, the frame, and the reference relationship clearly. When combining images, say what each reference contributes, so the model knows which element to keep and which to change.
A Simple Prompt Formula
Subject and its defining traits + what each reference contributes + composition and framing + style and lighting + aspect ratio intent
What You Should Notice
Anchor The Character: Name the identity traits you want preserved, such as hair, wardrobe, or age, so consistency carries across a series.
Assign Each Reference: When using multiple images, state which one is the subject, the product, or the backdrop to avoid blended mistakes.
Frame Deliberately: Choose an aspect ratio that fits the end use, since 21:9 and 9:16 change composition more than a simple crop.
Write Dense Prompts: The model reads long, detailed descriptions, so spend words on layout, materials, and mood rather than one vague line.
Reserve 2K For Detail: Pick 2K when texture or typography matters, and 1K for faster drafts while you iterate on a look.
Example Prompts
Sequential Picture Book Panel
"A curious red-haired girl in a yellow raincoat kneels beside a glowing tide pool at dusk, examining a starfish. Soft watercolor illustration, warm rim light, gentle depth of field, 4:3 framing to match the previous storybook page."
Multi-Reference Product Composite
"Place the sneaker from the first image onto the model in the second image, laced and worn while she sits on concrete steps. Natural overcast light, editorial catalog style, sharp product focus, 3:4 portrait for a listing."
Game Character Turnaround
"A stoic desert ranger with a scarred jaw, dust-worn leather coat, and a brass compass, shown in a three-quarter front pose. Consistent face and outfit, painterly concept-art style, neutral studio backdrop, 2:3 character sheet framing."
Ultrawide Environment Concept
"An abandoned greenhouse reclaimed by vines, shattered glass panels scattering afternoon light across mossy stone paths and rusted planters. Detailed environment art, layered depth, cool green palette, 21:9 panorama for an establishing shot."
Kling Image 3.0 vs Seedream 4.0 vs GPT Image 1
Capability
Kling Image 3.0
Seedream 4.0
GPT Image 1
Multi-image composition
✅ Merge several references in one request
✅ Strong reference blending
✅ Supports multiple inputs
Character consistency across frames
✅ Holds subject across a series
✅ Good subject retention
✅ Reliable, less series-focused
Max resolution
Up to 2K
Up to 4K
High, model-managed
Aspect ratio range
21:9 to 9:16, eight ratios
Wide range
Fixed set of sizes
Prompt length
Long, detailed prompts
Long prompts
Long prompts
Recommended For
Consistent sequential image sets
High-resolution reference edits
Text rendering and conversational edits
Why Choose Kling Image 3.0 API?
Kling Image 3.0 fits products that need related images, not isolated ones, keeping a character and style consistent across storyboards, book panels, character sheets, and campaign sets.
Through Pollo API, you reach Kling Image 3.0 with one API key alongside 300+ leading image and video models, with clean docs, task status polling, and generation that costs less than Fal AI.
Integrate the Kling Image 3.0 endpoint to ship consistent multi-reference image features, then compare it against Seedream, GPT Image, and Midjourney without rewriting your integration.
Kling Image 3.0 API FAQs
What is Kling Image 3.0?
Kling Image 3.0 is Kling AI's image generation model supporting text-to-image, image-to-image, and multi-image composition, with a focus on character consistency across a related set of images.
Does Kling Image 3.0 API support multiple reference images?
Yes. You can pass several image URLs in one request, letting the model merge a subject, a product, or a backdrop into a single coherent scene.
What resolutions and aspect ratios are available?
It renders at 1K or 2K across eight aspect ratios, from ultrawide 21:9 through square and portrait to vertical 9:16, chosen per request.
How does it keep characters consistent?
By working from reference images and detailed prompts, it preserves identity traits like face, wardrobe, and style, which helps build sequential panels rather than unrelated images.
Can it edit an existing image?
Yes. Provide an image URL with a prompt to restyle, re-stage, or extend it, giving controlled changes to an established composition through image-to-image.
Why run Kling Image 3.0 through Pollo API?
Pollo API gives one integration for Kling Image 3.0 and 300+ other models, with documentation, task tracking, and lower-cost generation than comparable providers.
