Kling AI Style Transfer & Effects
Overview
Apply artistic styles through prompt engineering, use the Effects API for pre-built visual transformations, and leverage Kolors for image-based style references. Available on v1.6+ models.
Style via Prompt Engineering
The most direct approach -- include style descriptors in your prompt:
import jwt, time, os, requests
BASE = "https://api.klingai.com/v1"
def get_headers():
ak, sk = os.environ["KLING_ACCESS_KEY"], os.environ["KLING_SECRET_KEY"]
token = jwt.encode(
{"iss": ak, "exp": int(time.time()) + 1800, "nbf": int(time.time()) - 5},
sk, algorithm="HS256", headers={"alg": "HS256", "typ": "JWT"}
)
return {"Authorization": f"Bearer {token}", "Content-Type": "application/json"}
# Style: Studio Ghibli watercolor
response = requests.post(f"{BASE}/videos/text2video", headers=get_headers(), json={
"model_name": "kling-v2-6",
"prompt": "A cozy cottage in a meadow, hand-painted watercolor style, "
"soft pastel colors, Studio Ghibli aesthetic, gentle breeze",
"negative_prompt": "photorealistic, harsh lighting, dark, gritty",
"duration": "5",
"mode": "professional",
"cfg_scale": 0.7, # higher = stricter prompt adherence
})
Style Prompt Recipes
| Style | Prompt Keywords | cfg_scale |
|---|---|---|
| Cinematic | "cinematic lighting, anamorphic lens, film grain, 35mm" | 0.5-0.6 |
| Anime | "anime style, cel-shaded, vibrant colors, clean lines" | 0.6-0.7 |
| Watercolor | "watercolor painting, soft edges, pastel, hand-painted" | 0.7-0.8 |
| Oil painting | "oil painting, thick brushstrokes, impasto, canvas texture" | 0.7-0.8 |
| Neon/cyberpunk | "neon lights, cyberpunk, rain, dark city, purple and blue" | 0.5-0.6 |
| Vintage film | "vintage 8mm film, warm tones, light leaks, soft focus" | 0.6-0.7 |
| Pixel art | "pixel art style, retro 16-bit, limited palette" | 0.8-0.9 |
| Photorealistic | "photorealistic, 4K, natural lighting, DSLR quality" | 0.4-0.5 |
Effects API
The Effects API applies pre-built transformations to existing images. Available on v1.6+.
Endpoint: POST https://api.klingai.com/v1/videos/effects
# Apply an effect to an image
response = requests.post(f"{BASE}/videos/effects", headers=get_headers(), json={
"model_name": "kling-v1-6",
"image": "https://example.com/portrait.jpg",
"effect_type": "hug", # effect to apply
"duration": "5",
"mode": "standard",
})
task_id = response.json()["data"]["task_id"]
# Poll for result as usual
Available Effects
| Effect | Description |
|---|---|
hug |
Embrace/hug motion between subjects |
kiss |
Kiss animation between subjects |
heart |
Heart gesture or heart-shaped framing |
expand |
Zoom/expand outward effect |
squish |
Compression/squish animation |
Kolors Image Restyle
Use Kolors to restyle images before converting to video:
# Generate styled image with Kolors
image_response = requests.post(f"{BASE}/images/kolors", headers=get_headers(), json={
"prompt": "A cyberpunk city street, neon signs, rain-slicked roads",
"aspect_ratio": "16:9",
"imageCount": 1,
})
# Then use the generated image as input for I2V
image_url = image_response.json()["data"]["images"][0]["url"]
video_response = requests.post(f"{BASE}/videos/image2video", headers=get_headers(), json={
"model_name": "kling-v2-1",
"image": image_url,
"prompt": "Camera slowly pushes forward through the rain, neon reflections",
"duration": "5",
"mode": "professional",
})
cfg_scale Tuning
The cfg_scale parameter (0.0-1.0) controls how strictly the model follows your prompt:
| cfg_scale | Effect |
|---|---|
| 0.0-0.3 | More creative freedom, may drift from prompt |
| 0.4-0.5 | Balanced (default), natural results |
| 0.6-0.7 | Stronger prompt adherence |
| 0.8-1.0 | Very strict, may reduce quality/naturalness |
For style transfer: Use 0.6-0.8 to ensure the style keywords are respected.
Style Consistency Across Clips
# Use a consistent style template for all clips in a project
STYLE_TEMPLATE = {
"suffix": ", cinematic lighting, 35mm film grain, warm color grading, "
"anamorphic lens flare, shallow depth of field",
"negative": "cartoon, anime, painting, illustration, CGI, digital art",
"cfg_scale": 0.6,
"model": "kling-v2-6",
"mode": "professional",
}
def styled_generation(scene_prompt: str):
return requests.post(f"{BASE}/videos/text2video", headers=get_headers(), json={
"model_name": STYLE_TEMPLATE["model"],
"prompt": scene_prompt + STYLE_TEMPLATE["suffix"],
"negative_prompt": STYLE_TEMPLATE["negative"],
"cfg_scale": STYLE_TEMPLATE["cfg_scale"],
"duration": "5",
"mode": STYLE_TEMPLATE["mode"],
})