# Step 1: Optimize product photo
TRANSFORM_RESULT=$(curl -s -X POST \
https://api.iterationlayer.com/image-transformation/v1/transform \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"file": {
"type": "url",
"name": "product-photo.jpg",
"url": "https://example.com/products/raw-photo.jpg"
},
"operations": [
{
"type": "resize",
"width_in_px": 1000,
"height_in_px": 1000,
"fit": "contain"
},
{ "type": "auto_contrast" },
{ "type": "sharpen", "sigma": 1.5 },
{
"type": "convert",
"format": "webp",
"quality": 90
}
]
}')
PRODUCT_IMAGE_BASE64=$(echo "$TRANSFORM_RESULT" | jq -r '.data.buffer')
# Step 2: Generate promotional banner with optimized product photo
curl -X POST \
https://api.iterationlayer.com/image-generation/v1/generate \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d "{
\"dimensions\": { \"width\": 1200, \"height\": 628 },
\"output_format\": \"png\",
\"layers\": [
{
\"index\": 0,
\"type\": \"solid-color\",
\"hex_color\": \"#1a1a2e\"
},
{
\"index\": 1,
\"type\": \"static-image\",
\"buffer\": \"$PRODUCT_IMAGE_BASE64\",
\"position\": { \"x\": 600.0, \"y\": 64.0 },
\"dimensions\": { \"width\": 500, \"height\": 500 }
},
{
\"index\": 2,
\"type\": \"text\",
\"text\": \"Flash Sale\",
\"font_name\": \"Montserrat\",
\"font_size_in_px\": 56,
\"text_color\": \"#FFFFFF\",
\"font_weight\": \"Bold\",
\"position\": { \"x\": 60.0, \"y\": 180.0 },
\"dimensions\": { \"width\": 500, \"height\": 80 }
},
{
\"index\": 3,
\"type\": \"text\",
\"text\": \"Save 25%\",
\"font_name\": \"Montserrat\",
\"font_size_in_px\": 36,
\"text_color\": \"#E94560\",
\"font_weight\": \"Bold\",
\"position\": { \"x\": 60.0, \"y\": 280.0 },
\"dimensions\": { \"width\": 500, \"height\": 60 }
}
]
}"import { IterationLayer } from "iterationlayer";
const client = new IterationLayer({ apiKey: "YOUR_API_KEY" });
// Step 1: Optimize product photo
const transformResult = await client.transform({
file: {
type: "url",
name: "product-photo.jpg",
url: "https://example.com/products/raw-photo.jpg",
},
operations: [
{
type: "resize",
width_in_px: 1000,
height_in_px: 1000,
fit: "contain",
},
{ type: "auto_contrast" },
{ type: "sharpen", sigma: 1.5 },
{ type: "convert", format: "webp", quality: 90 },
],
});
const productImageBase64 = transformResult.data.buffer;
// Step 2: Generate promotional banner with optimized product photo
const bannerResult = await client.generateImage({
dimensions: { width_in_px: 1200, height_in_px: 628 },
output_format: "png",
layers: [
{
index: 0,
type: "solid-color",
hex_color: "#1a1a2e",
},
{
index: 1,
type: "image",
buffer: productImageBase64,
position: { x: 600.0, y: 64.0 },
dimensions: { width_in_px: 500, height_in_px: 500 },
},
{
index: 2,
type: "text",
text: "Flash Sale",
font_name: "Montserrat",
font_size_in_px: 56,
text_color: "#FFFFFF",
font_weight: "Bold",
position: { x: 60.0, y: 180.0 },
dimensions: { width_in_px: 500, height_in_px: 80 },
},
{
index: 3,
type: "text",
text: "Save 25%",
font_name: "Montserrat",
font_size_in_px: 36,
text_color: "#E94560",
font_weight: "Bold",
position: { x: 60.0, y: 280.0 },
dimensions: { width_in_px: 500, height_in_px: 60 },
},
],
});
await Bun.write("banner.png", Buffer.from(bannerResult.data.buffer, "base64"));import base64
from iterationlayer import IterationLayer
client = IterationLayer(api_key="YOUR_API_KEY")
# Step 1: Optimize product photo
transform_result = client.transform(
file={
"type": "url",
"name": "product-photo.jpg",
"url": "https://example.com/products/raw-photo.jpg",
},
operations=[
{
"type": "resize",
"width_in_px": 1000,
"height_in_px": 1000,
"fit": "contain",
},
{"type": "auto_contrast"},
{"type": "sharpen", "sigma": 1.5},
{"type": "convert", "format": "webp", "quality": 90},
],
)
product_image_base64 = transform_result["data"]["buffer"]
# Step 2: Generate promotional banner with optimized product photo
banner_result = client.generate_image(
dimensions={"width_in_px": 1200, "height_in_px": 628},
output_format="png",
layers=[
{
"index": 0,
"type": "solid-color",
"hex_color": "#1a1a2e",
},
{
"index": 1,
"type": "image",
"buffer": product_image_base64,
"position": {"x": 600.0, "y": 64.0},
"dimensions": {"width_in_px": 500, "height_in_px": 500},
},
{
"index": 2,
"type": "text",
"text": "Flash Sale",
"font_name": "Montserrat",
"font_size_in_px": 56,
"text_color": "#FFFFFF",
"font_weight": "Bold",
"position": {"x": 60.0, "y": 180.0},
"dimensions": {"width_in_px": 500, "height_in_px": 80},
},
{
"index": 3,
"type": "text",
"text": "Save 25%",
"font_name": "Montserrat",
"font_size_in_px": 36,
"text_color": "#E94560",
"font_weight": "Bold",
"position": {"x": 60.0, "y": 280.0},
"dimensions": {"width_in_px": 500, "height_in_px": 60},
},
],
)
with open("banner.png", "wb") as f:
f.write(base64.b64decode(banner_result["data"]["buffer"]))package main
import (
"encoding/base64"
"os"
il "github.com/iterationlayer/sdk-go"
)
func main() {
client := il.NewClient("YOUR_API_KEY")
// Step 1: Optimize product photo
transformResult, err := client.Transform(il.TransformRequest{
File: il.NewFileFromURL(
"product-photo.jpg",
"https://example.com/products/raw-photo.jpg",
),
Operations: []il.TransformOperation{
il.NewResizeOperation(1000, 1000, "contain"),
{Type: "auto_contrast"},
il.NewSharpenOperation(1.5),
il.NewConvertOperation("webp"),
},
})
if err != nil {
panic(err)
}
// Step 2: Generate promotional banner with optimized product photo
bannerResult, err := client.GenerateImage(
il.GenerateImageRequest{
Dimensions: il.Dimensions{
WidthInPx: 1200, HeightInPx: 628,
},
OutputFormat: "png",
Layers: []il.Layer{
il.NewSolidColorBackgroundLayer(0, "#1a1a2e"),
il.NewStaticImageLayer(
1, transformResult.Data.Buffer,
il.Position{X: 600.0, Y: 64.0},
il.Dimensions{WidthInPx: 500, HeightInPx: 500},
),
il.NewTextLayer(
2, "Flash Sale", "Montserrat", 56, "#FFFFFF",
il.Position{X: 60.0, Y: 180.0},
il.Dimensions{WidthInPx: 500, HeightInPx: 80},
),
il.NewTextLayer(
3, "Save 25%", "Montserrat", 36, "#E94560",
il.Position{X: 60.0, Y: 280.0},
il.Dimensions{WidthInPx: 500, HeightInPx: 60},
),
},
},
)
if err != nil {
panic(err)
}
decoded, _ := base64.StdEncoding.DecodeString(bannerResult.Data.Buffer)
os.WriteFile("banner.png", decoded, 0644)
}