Gemini 2.0 Flash Image Generation

gemini

Partner Model
Fast Inference
REST API

Model Information

Response Time~5 sec
StatusActive
Version
2.0-flash-exp-image-generation
Updated9 days ago

Prerequisites

  • Create an API Key from the Eachlabs Console
  • Install the required dependencies for your chosen language (e.g., requests for Python)

API Integration Steps

1. Create a Prediction

Send a POST request to create a new prediction. This will return a prediction ID that you'll use to check the result. The request should include your model inputs and API key.

import requests
import time
API_KEY = "YOUR_API_KEY" # Replace with your API key
HEADERS = {
"X-API-Key": API_KEY,
"Content-Type": "application/json"
}
def create_prediction():
response = requests.post(
"https://api.eachlabs.ai/v1/prediction/",
headers=HEADERS,
json={
"model": "gemini",
"version": "2.0-flash-exp-image-generation",
"input": {
"image_url": "your_file.image/png",
"prompt": "your prompt here"
}
}
)
prediction = response.json()
if prediction["status"] != "success":
raise Exception(f"Prediction failed: {prediction}")
return prediction["predictionID"]

2. Get Prediction Result

Poll the prediction endpoint with the prediction ID until the result is ready. The API uses long-polling, so you'll need to repeatedly check until you receive a success status.

def get_prediction(prediction_id):
while True:
result = requests.get(
f"https://api.eachlabs.ai/v1/prediction/{prediction_id}",
headers=HEADERS
).json()
if result["status"] == "success":
return result
elif result["status"] == "error":
raise Exception(f"Prediction failed: {result}")
time.sleep(1) # Wait before polling again

3. Complete Example

Here's a complete example that puts it all together, including error handling and result processing. This shows how to create a prediction and wait for the result in a production environment.

try:
# Create prediction
prediction_id = create_prediction()
print(f"Prediction created: {prediction_id}")
# Get result
result = get_prediction(prediction_id)
print(f"Output URL: {result['output']}")
print(f"Processing time: {result['metrics']['predict_time']}s")
except Exception as e:
print(f"Error: {e}")

Additional Information

  • The API uses a two-step process: create prediction and poll for results
  • Response time: ~5 seconds
  • Rate limit: 60 requests/minute
  • Concurrent requests: 10 maximum
  • Use long-polling to check prediction status until completion

Overview

Gemini 2.0 Flash Image Generation is a model designed for generating high-quality images based on a text prompt and an optional reference image. It provides fast image synthesis with a focus on accuracy and coherence. Gemini 2.0 Flash Image Generation is capable of understanding detailed textual descriptions and can generate images that align with given inputs.

Technical Specifications

  • Uses advanced text-to-image generation capabilities to produce detailed images.
  • Supports multimodal input, allowing both text and image references.
  • Optimized for speed and efficiency, providing rapid response times.
  • Can generate diverse styles and compositions depending on input parameters.
  • Incorporates AI-driven enhancements to maintain visual consistency and realism.

Key Considerations

  • Gemini 2.0 Flash Image Generation may not always interpret highly abstract or ambiguous descriptions accurately.
  • Generated images might have slight inconsistencies in finer details.
  • Certain complex requests may require prompt refinement for optimal results.
  • When using image_url, ensure the reference image is relevant and clear to improve accuracy.

Tips & Tricks

  • prompt:
    • Use structured prompts that include subject, action, environment, and style for more accurate results.
    • Avoid overly generic phrases; be specific about the desired image elements.
    • Example: Instead of "a cat," use "a fluffy orange cat sitting on a wooden bench in a park during sunset."
    • If a particular artistic style is desired, explicitly mention it in the prompt.
  • image_url:
    • Ensure the reference image is clear and relevant to guide Gemini 2.0 Flash Image Generation effectively.
    • High-resolution images yield better results compared to low-quality references.
    • The image_url should complement the prompt rather than contradict it.
    • When using an image_url, try adjusting the prompt slightly to fine-tune the outcome.

Capabilities

  • Generates images based on textual descriptions with high fidelity.
  • Can adapt to various artistic styles depending on the prompt.
  • Supports conditional generation using both text and image inputs.
  • Maintains a balance between creativity and realism in outputs.
  • Handles a wide range of themes, from realistic to illustrative visuals.

What can I use for?

  • Creating concept art based on textual descriptions.
  • Generating variations of existing images using a reference.
  • Producing images for storytelling, content creation, and visual design.
  • Exploring different artistic interpretations of a single idea.
  • Enhancing creative workflows with AI-assisted image generation.

Things to be aware of

  • Experiment with different levels of detail in the prompt to see how it affects image composition.
  • Use descriptive adjectives and scene-setting words to refine results.
  • Test how modifying a single aspect of the prompt influences the generated image.
  • Provide an image_url with slight variations in the prompt to explore different creative outcomes.
  • Compare results using only a prompt versus using both prompt and image_url.

Limitations

  • May struggle with highly complex or abstract requests that lack clear direction.
  • Some generated images may have minor inconsistencies in fine details.
  • Requires careful prompt crafting to achieve specific artistic effects.
  • The effectiveness of image_url depends on the quality and relevance of the reference image.

Output Format: PNG