Google's highest quality text-to-image model, capable of generating images with detail, rich lighting and beauty.
import requests
import time
# Step 1: Start image generation
generate_url = "https://api.atlascloud.ai/api/v1/model/generateImage"
headers = {
"Content-Type": "application/json",
"Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
"model": "google/imagen3",
"prompt": "A beautiful landscape with mountains and lake",
"width": 512,
"height": 512,
"steps": 20,
"guidance_scale": 7.5
}
generate_response = requests.post(generate_url, headers=headers, json=data)
generate_result = generate_response.json()
prediction_id = generate_result["data"]["id"]
# Step 2: Poll for result
poll_url = f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}"
def check_status():
while True:
response = requests.get(poll_url, headers={"Authorization": "Bearer $ATLASCLOUD_API_KEY"})
result = response.json()
if result["data"]["status"] == "completed":
print("Generated image:", result["data"]["outputs"][0])
return result["data"]["outputs"][0]
elif result["data"]["status"] == "failed":
raise Exception(result["data"]["error"] or "Generation failed")
else:
# Still processing, wait 2 seconds
time.sleep(2)
image_url = check_status()Imagen 3
Imagen 3 is DeepMind’s latest text-to-image generative model, focusing on high-quality image generation with improved detail, lighting, and reduced artifacts.
Core Capabilities
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Enhanced prompt understanding for complex image generation tasks
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Improved text rendering for applications like presentations and typography
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Support for diverse artistic styles from photorealism to animation
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Better handling of lighting, textures, and fine details
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Natural language prompt processing without requiring complex prompt engineering
Technical Improvements
Image Quality
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Enhanced color balance and vibrancy
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Improved texture rendering
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Better detail preservation in complex scenes
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Reduced artifact generation
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More accurate style reproduction across different artistic genres
Prompt Processing
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Support for longer, more detailed prompts
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Better understanding of camera angles and composition requirements
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Improved handling of specific style requests
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Enhanced text rendering capabilities
Benchmarks
Performance metrics based on human evaluation using GenAI-Bench:
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Highest score for visual quality among compared models
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High accuracy in prompt response adherence
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Strong performance in overall preference benchmarks
Detailed benchmark methodology and results are available in Appendix D of the technical report.
Security Features
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Built-in content filtering system
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Dataset filtering to minimize harmful content
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SynthID watermarking integration for image identification
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Extensive red teaming and evaluations for: Fairness, Bias, Content safety
Technical Documentation
For detailed technical specifications and methodology, refer to the full technical report.
