Images

Our image endpoints are compatible with the OpenAI API, supporting the same v1/images/generations and v1/images/edits formats, plus additional analysis capabilities.

Image generation

Here is an example of image generation: Parameters:

model: The model ID of the model you want to use
prompt: The text description of the image to generate
n: The number of images to generate (default: 1, range: 1-10)
quality: The quality of the generated image (default: “standard”, options: “standard”, “hd”)
response_format: The format of the returned image (default: “url”, options: “url”, “b64_json”)
size: The size of the generated image (default: “1024x1024”, options: “256x256”, “512x512”, “1024x1024”, “1792x1024”, “1024x1792”)
style: The style of the generated image (default: “vivid”, options: “vivid”, “natural”)

from synapsai import SynapsAI
import base64

model = SynapsAI("your-api-key")

response = model.images.generate(
    model="model-id",
    prompt="A beautiful sunset over a mountain lake",
    n=1,
    quality="hd",
    response_format="b64_json",
    size="1024x1024",
    style="vivid"
)

with open("image.jpg", "wb") as f:
    f.write(base64.b64decode(response.data[0].b64_json))

curl -X POST https://api.synapsai.cloud/v1/images/generations \
  -H "Authorization: Bearer $SYNAPSAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
        "model": "model-id",
        "prompt": "A beautiful sunset over a mountain lake",
        "n": 1,
        "quality": "hd",
        "response_format": "url",
        "size": "1024x1024",
        "style": "vivid"
    }'

Image editing

Here is an example of image editing: Parameters:

model: The model ID of the model you want to use
image: The image to edit (base64 encoded, URL, or file)
prompt: The text description of the changes to make
mask: The mask image indicating areas to edit (optional)
n: The number of edited images to generate (default: 1, range: 1-10)
size: The size of the edited image (default: “1024x1024”)
response_format: The format of the returned image (default: “url”, options: “url”, “b64_json”)

from synapsai import SynapsAI
import base64

model = SynapsAI("your-api-key")

response = model.images.edit(
    image="path/to/image.jpg",
    model="model-id",
    prompt="Add a rainbow in the sky",
    mask="path/to/mask.png",
    n=1,
    size="1024x1024",
    response_format="b64_json"
)

with open("edited_image.jpg", "wb") as f:
    f.write(base64.b64decode(response.data[0].b64_json))

curl -X POST https://api.synapsai.cloud/v1/images/edits \
  -H "Authorization: Bearer $SYNAPSAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
        "model": "model-id",
        "image": "path/to/image.jpg",
        "prompt": "Add a rainbow in the sky",
        "mask": "path/to/mask.png",
        "n": 1,
        "size": "1024x1024",
        "response_format": "url"
    }'

Image to text

Here is an example of image analysis to extract text descriptions: Parameters:

model: The model ID of the model you want to use
inputs: The image to analyze (base64 encoded, URL, or file)
max_new_tokens: Maximum number of tokens to generate (default: 300)
generate_kwargs: Additional generation parameters (optional)

from synapsai import SynapsAI

model = SynapsAI("your-api-key")

response = model.images.to_text(
    model="model-id",
    inputs="https://example.com/image.jpg",
    max_new_tokens=300,
    generate_kwargs={"temperature": 0.7}
)

for result in response.data:
    print("Analysis:", result)

curl -X POST https://api.synapsai.cloud/v1/images/to-text \
  -H "Authorization: Bearer $SYNAPSAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
        "model": "model-id",
        "inputs": "https://example.com/image.jpg",
        "max_new_tokens": 300,
        "generate_kwargs": {"temperature": 0.7}
    }'

Image feature extraction

Here is an example of extracting features from images: Parameters:

model: The model ID of the model you want to use
inputs: The image to extract features from (base64 encoded, URL, or file)

from synapsai import SynapsAI

model = SynapsAI("your-api-key")

response = model.images.feature_extraction(
    model="model-id",
    inputs="https://example.com/image.jpg"
)

for features in response.data:
    print("Features:", features)

curl -X POST https://api.synapsai.cloud/v1/images/feature-extraction \
  -H "Authorization: Bearer $SYNAPSAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
        "model": "model-id",
        "inputs": "https://example.com/image.jpg"
    }'

Image segmentation

Here is an example of image segmentation: Parameters:

model: The model ID of the model you want to use
inputs: The image to analyze (base64 encoded, URL, or file)
subtask: The segmentation subtask (default: “panoptic”)
threshold: The confidence threshold (default: 0.9)
mask_threshold: The mask threshold (default: 0.5)
overlap_mask_area_threshold: The overlap mask area threshold (default: 0.5)

from synapsai import SynapsAI

model = SynapsAI("your-api-key")

response = model.images.segmentation(
    model="model-id",
    inputs="https://example.com/image.jpg",
    subtask="panoptic",
    threshold=0.9,
    mask_threshold=0.5,
    overlap_mask_area_threshold=0.5
)

for result in response.data:
    print("Segmentation:", result)

curl -X POST https://api.synapsai.cloud/v1/images/segmentation \
  -H "Authorization: Bearer $SYNAPSAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
        "model": "model-id",
        "inputs": "https://example.com/image.jpg",
        "subtask": "panoptic",
        "threshold": 0.9,
        "mask_threshold": 0.5,
        "overlap_mask_area_threshold": 0.5
    }'

Depth estimation

Here is an example of depth estimation: Parameters:

model: The model ID of the model you want to use
inputs: The image to analyze (base64 encoded, URL, or file)
parameters: Additional parameters for depth estimation (optional)

from synapsai import SynapsAI

model = SynapsAI("your-api-key")

response = model.images.depth_estimation(
    model="model-id",
    inputs="https://example.com/image.jpg",
    parameters={"output_format": "numpy"}
)

for result in response.data:
    print("Depth map:", result)

curl -X POST https://api.synapsai.cloud/v1/images/depth-estimation \
  -H "Authorization: Bearer $SYNAPSAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
        "model": "model-id",
        "inputs": "https://example.com/image.jpg",
        "parameters": {"output_format": "numpy"}
    }'

Object detection

Here is an example of object detection: Parameters:

model: The model ID of the model you want to use
inputs: The image to analyze (base64 encoded, URL, or file)
threshold: The confidence threshold for detections (default: 0.5)

from synapsai import SynapsAI

model = SynapsAI("your-api-key")

response = model.images.object_detection(
    model="model-id",
    inputs="https://example.com/image.jpg",
    threshold=0.5
)

for detection in response.data:
    print("Detected objects:", detection)

curl -X POST https://api.synapsai.cloud/v1/images/object-detection \
  -H "Authorization: Bearer $SYNAPSAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
        "model": "model-id",
        "inputs": "https://example.com/image.jpg",
        "threshold": 0.5
    }'

Mask Generation

Here is an example of mask generation: Parameters:

image: The image to generate masks for (base64 encoded, URL, or file)
mask_threshold: The threshold for mask generation (default: 0.0)
pred_iou_thresh: The threshold for prediction IOU (default: 0.88)
stability_score_thresh: The threshold for stability score (default: 0.95)
stability_score_offset: The offset for stability score (default: 1)
crops_nms_thresh: The threshold for crops NMS (default: 0.7)
crops_n_layers: The number of crops layers (default: 0)
crop_overlap_ratio: The overlap ratio for crops (default: 0.3413)
crop_n_points_downscale_factor: The number of points to downscale for crops (default: 1)

from synapsai import SynapsAI

model = SynapsAI("your-api-key")

response = model.images.mask_generation(
    model="model-id",
    image="https://example.com/image.jpg",
    mask_threshold=0.5
)

for mask in response.data:
    print("Mask:", mask)

curl -X POST https://api.synapsai.cloud/v1/images/mask-generation \
  -H "Authorization: Bearer $SYNAPSAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
        "model": "model-id",
        "image": "https://example.com/image.jpg",
        "mask_threshold": 0.5
    }'

You can also try image generation and editing using the Playground. For deeper insights into the image endpoints, see our API reference.

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Image generation

Image editing

Image to text

Image feature extraction

Image segmentation

Depth estimation

Object detection

Mask Generation

​Image generation

​Image editing

​Image to text

​Image feature extraction

​Image segmentation

​Depth estimation

​Object detection

​Mask Generation

Image generation

Image editing

Image to text

Image feature extraction

Image segmentation

Depth estimation

Object detection

Mask Generation