Image Generation APIs

Generate stunning images from text prompts using state-of-the-art diffusion models. Choose from FLUX.1-dev for best quality, SDXL for versatility, or Stable Diffusion for classic reliability.

Endpoint

POST https://api.hyperbolic.xyz/v1/image/generation

Basic Example

Python
cURL

import requests
import base64

url = "https://api.hyperbolic.xyz/v1/image/generation"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer YOUR_API_KEY"
}
data = {
    "model_name": "FLUX.1-dev",
    "prompt": "A futuristic city skyline at sunset, cyberpunk style, neon lights",
    "height": 1024,
    "width": 1024,
    "steps": 30,
    "cfg_scale": 5
}

response = requests.post(url, headers=headers, json=data)
result = response.json()

# Decode and save the image
image_data = base64.b64decode(result["images"][0]["image"])
with open("generated_image.png", "wb") as f:
    f.write(image_data)

curl -X POST "https://api.hyperbolic.xyz/v1/image/generation" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model_name": "FLUX.1-dev",
    "prompt": "A futuristic city skyline at sunset, cyberpunk style, neon lights",
    "height": 1024,
    "width": 1024,
    "steps": 30,
    "cfg_scale": 5
  }' | jq -r ".images[0].image" | base64 -d > generated_image.png

Request Parameters

Required Parameters

Parameter	Type	Description
`model_name`	string	Model ID (e.g., `FLUX.1-dev`, `SDXL1.0-base`)
`prompt`	string	Text description of the image to generate

Optional Parameters

Parameter	Type	Default	Description
`height`	integer	1024	Image height in pixels
`width`	integer	1024	Image width in pixels
`steps`	integer	30	Number of inference steps (more = higher quality, slower)
`cfg_scale`	float	5	Prompt relevance (higher = closer to prompt, typically 5-15)
`negative_prompt`	string	-	What to avoid in the generated image
`seed`	integer	-	Random seed for reproducible results
`sampler`	string	-	Sampling algorithm to use
`backend`	string	auto	Computation backend: `auto`, `tvm`, or `torch`

SDXL-Specific Parameters

Parameter	Type	Description
`prompt_2`	string	Secondary prompt for SDXL models
`negative_prompt_2`	string	Secondary negative prompt for SDXL
`enable_refiner`	boolean	Enable SDXL refiner for enhanced details

Response Format

The API returns a JSON object containing base64-encoded image data:

{
  "images": [
    {
      "image": "iVBORw0KGgoAAAANSUhEUgAA..."
    }
  ]
}

Decoding the Response

import base64

def save_image(response_json, filename="output.png"):
    """Decode and save the generated image."""
    image_data = base64.b64decode(response_json["images"][0]["image"])
    with open(filename, "wb") as f:
        f.write(image_data)

Available Models

Model	Model ID	Best For
FLUX.1-dev	`FLUX.1-dev`	Best quality, outstanding prompt following
SDXL 1.0	`SDXL1.0-base`	High-quality, versatile, supports LoRA
SDXL Turbo	`SDXL-turbo`	Fast generation
Stable Diffusion 2	`SD2`	Good balance of speed and quality
Stable Diffusion 1.5	`SD1.5`	Classic, reliable, supports LoRA
Segmind SD 1B	`SSD`	Domain-specific applications

Model Recommendations

Choosing the right model:

Best quality: FLUX.1-dev for outstanding prompt following and visual quality
Fast generation: SDXL Turbo for quick iterations
Image-to-image: SDXL 1.0 or SD1.5 (FLUX does not support image-to-image)
LoRA support: SDXL 1.0 or SD1.5 for style customization

Supported Resolutions

All image models support the following resolutions:

Resolution	Aspect Ratio
1024 x 1024	1:1 (Square)
1152 x 896	~4:3
1216 x 832	~3:2
1344 x 768	~16:9
1536 x 640	~2.4:1 (Ultrawide)
1664 x 2432	~2:3 (Portrait)
2048 x 2048	1:1 (Large Square)
2432 x 1664	~3:2 (Landscape)
640 x 1536	~1:2.4 (Tall)
768 x 1344	~9:16 (Portrait)
832 x 1216	~2:3

Pricing

Base rate: $0.01 per image at 1024x1024 resolution with 25 steps. Pricing formula:

Price = $0.01 × (width/1024) × (height/1024) × (steps/25)

Pricing Examples

Resolution	Steps	Price
1024 x 1024	25	$0.01
1024 x 1024	50	$0.02
2048 x 2048	25	$0.04
2048 x 2048	50	$0.08
512 x 512	25	$0.0025

Image-to-Image Generation

Transform existing images using a reference image. Supported by Stable Diffusion models only.

Python
cURL

import requests
import base64
from PIL import Image
from io import BytesIO

def encode_image(image_path):
    """Encode an image file to base64."""
    with Image.open(image_path) as img:
        buffered = BytesIO()
        img.save(buffered, format="PNG")
        return base64.b64encode(buffered.getvalue()).decode("utf-8")

# Encode your reference image
reference_image = encode_image("input_image.png")

url = "https://api.hyperbolic.xyz/v1/image/generation"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer YOUR_API_KEY"
}
data = {
    "model_name": "SDXL1.0-base",
    "prompt": "A watercolor painting of the same scene",
    "image": reference_image,
    "strength": 0.7,
    "height": 1024,
    "width": 1024,
    "steps": 30
}

response = requests.post(url, headers=headers, json=data)
result = response.json()

# Save the result
image_data = base64.b64decode(result["images"][0]["image"])
with open("transformed_image.png", "wb") as f:
    f.write(image_data)

# First encode your image: base64 -i input.png -o input_base64.txt

curl -X POST "https://api.hyperbolic.xyz/v1/image/generation" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model_name": "SDXL1.0-base",
    "prompt": "A watercolor painting of the same scene",
    "image": "YOUR_BASE64_ENCODED_IMAGE",
    "strength": 0.7,
    "height": 1024,
    "width": 1024,
    "steps": 30
  }' | jq -r ".images[0].image" | base64 -d > transformed_image.png

Image-to-Image Parameters

Parameter	Type	Description
`image`	string	Base64-encoded reference image
`strength`	float	Transformation strength (0-1). Higher values = more change from original

Strength guide:

0.3-0.5: Subtle changes, preserves most of the original
0.5-0.7: Moderate transformation
0.7-1.0: Major changes, original is mostly a guide

Image-to-image is supported by Stable Diffusion models only. FLUX.1-dev does not support this feature.

LoRA Adapters

LoRA (Low-Rank Adaptation) adapters let you apply custom styles to your generated images. Available for SDXL and SD1.5 models.

Available LoRAs

SDXL LoRAs:

LoRA Name	Style
`Add_Detail`	Enhanced details
`More_Art`	Artistic style
`Pixel_Art`	Pixel art style
`Logo`	Logo design
`Sci-fi`	Science fiction
`Crayons`	Crayon drawing
`Paint_Splash`	Paint splash effect
`Outdoor_Product_Photography`	Product photography

SD1.5 LoRAs:

LoRA Name	Style
`Add_Detail`	Enhanced details
`Superhero`	Comic superhero style
`Lineart`	Line art
`Anime_Lineart`	Anime-style line art
`Cartoon_Background`	Cartoon backgrounds
`Pencil_Sketch`	Pencil sketch

Using LoRAs

Python
cURL

import requests
import base64

url = "https://api.hyperbolic.xyz/v1/image/generation"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer YOUR_API_KEY"
}
data = {
    "model_name": "SDXL1.0-base",
    "prompt": "a cute cat",
    "height": 1024,
    "width": 1024,
    "lora": {"Pixel_Art": 1.0}
}

response = requests.post(url, headers=headers, json=data)
result = response.json()

image_data = base64.b64decode(result["images"][0]["image"])
with open("pixel_cat.png", "wb") as f:
    f.write(image_data)

curl -X POST "https://api.hyperbolic.xyz/v1/image/generation" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model_name": "SDXL1.0-base",
    "prompt": "a cute cat",
    "height": 1024,
    "width": 1024,
    "lora": {"Pixel_Art": 1.0}
  }' | jq -r ".images[0].image" | base64 -d > pixel_cat.png

Combining Multiple LoRAs

Mix multiple LoRAs by adjusting their weights (0.0-1.0):

data = {
    "model_name": "SDXL1.0-base",
    "prompt": "a cute cat logo",
    "height": 1024,
    "width": 1024,
    "lora": {
        "Pixel_Art": 0.5,
        "Logo": 0.5,
        "Paint_Splash": 0.9
    }
}

Experiment with different weight combinations to achieve unique styles. Lower weights apply subtle influence, while higher weights create stronger effects.

Backend Options

Backend	Description
`auto`	Automatically selects the best backend (recommended)
`tvm`	Optimized for speed using TVM
`torch`	PyTorch backend, more flexible

Tips for Better Results

Prompt Writing

Be specific: “A golden retriever puppy playing in autumn leaves, soft sunlight” works better than “a dog”
Include style: Add artistic style keywords like “photorealistic”, “oil painting”, “anime style”
Describe lighting: Mention lighting conditions like “soft natural light”, “dramatic shadows”, “neon glow”

Using Negative Prompts

Exclude unwanted elements:

data = {
    "model_name": "FLUX.1-dev",
    "prompt": "professional portrait photo of a woman",
    "negative_prompt": "blurry, low quality, distorted, deformed",
    "height": 1024,
    "width": 1024
}

Reproducible Results

Use the seed parameter to generate the same image:

data = {
    "model_name": "FLUX.1-dev",
    "prompt": "a magical forest",
    "seed": 42,
    "height": 1024,
    "width": 1024
}

CFG Scale Guide

1-5: More creative, may deviate from prompt
5-10: Balanced (recommended)
10-15: Closely follows prompt
15+: Very strict, may reduce quality

Next Steps

Text APIs

Generate text with large language models

Vision Language Models

Analyze images with multimodal AI

Audio APIs

Text-to-speech generation

Overview

On-Demand GPU

Serverless Inference

Reserved Clusters

General Platform

Image Generation APIs

Image Generation APIs

Endpoint

Basic Example

Request Parameters

Required Parameters

Optional Parameters

SDXL-Specific Parameters

Response Format

Decoding the Response

Available Models

Model Recommendations

Supported Resolutions

Pricing

Pricing Examples

Image-to-Image Generation

Image-to-Image Parameters

LoRA Adapters

Available LoRAs

Using LoRAs

Combining Multiple LoRAs

Backend Options

Tips for Better Results

Prompt Writing

Using Negative Prompts

Reproducible Results

CFG Scale Guide

Next Steps

Text APIs

Vision Language Models

Audio APIs

Overview

On-Demand GPU

Serverless Inference

Reserved Clusters

General Platform

​Image Generation APIs

​Endpoint

​Basic Example

​Request Parameters

​Required Parameters

​Optional Parameters

​SDXL-Specific Parameters

​Response Format

​Decoding the Response

​Available Models

​Model Recommendations

​Supported Resolutions

​Pricing

​Pricing Examples

​Image-to-Image Generation

​Image-to-Image Parameters

​LoRA Adapters

​Available LoRAs

​Using LoRAs

​Combining Multiple LoRAs

​Backend Options

​Tips for Better Results

​Prompt Writing

​Using Negative Prompts

​Reproducible Results

​CFG Scale Guide

​Next Steps

Text APIs

Vision Language Models

Audio APIs

Image Generation APIs

Endpoint

Basic Example

Request Parameters

Required Parameters

Optional Parameters

SDXL-Specific Parameters

Response Format

Decoding the Response

Available Models

Model Recommendations

Supported Resolutions

Pricing

Pricing Examples

Image-to-Image Generation

Image-to-Image Parameters

LoRA Adapters

Available LoRAs

Using LoRAs

Combining Multiple LoRAs

Backend Options

Tips for Better Results

Prompt Writing

Using Negative Prompts

Reproducible Results

CFG Scale Guide

Next Steps