GPT Image 2.5 Flare Text-to-Image
OpenAI's GPT Image 2.5 Flare Text-to-Image generates high-quality images from natural-language prompts, with five quality tiers up to 4K. Flare is the fast, balanced GPT Image 2.5 tier for everyday generation at low latency.
उदाहरण
A stylish young European woman standing in front of a premium retro-futuristic bookstore at night, wearing a sharp black coat and silver boots, holding a red hardcover book in one hand. The storefront is filled with elegant illuminated typography and poster design. The main sign above the entrance clearly reads: “THE MIDNIGHT ARCHIVE” A large window poster clearly reads: “OPEN UNTIL 2 AM” A smaller promotional sign beside the door clearly reads: “RARE BOOKS • ART • DESIGN” The red book in her hand has the clearly legible title: “MEMORIES OF TOMORROW” Sophisticated editorial composition, c
यह prompt आज़माएँ →
A cinematic product photo of an unbranded amber glass perfume bottle with a plain frosted-glass label and no text or logo, on a marble surface, soft golden-hour lighting, shallow depth of field, elegant reflections, premium editorial photography style
यह prompt आज़माएँ →
A handsome young Mediterranean man standing on the rooftop of an old apartment building just before a storm, wearing a loose white shirt and dark trousers, struggling to pull a huge white bedsheet from a clothesline as powerful wind turns it into a sail around him, dramatic dark clouds approaching behind the city skyline, warm sunlight breaking through one gap in the clouds, poetic cinematic realism, expressive full-body action.
यह prompt आज़माएँ →
A cheerful young woman chef with rolled sleeves tossing fresh pasta in a steaming copper pan in a rustic trattoria kitchen, flames leaping from the stove, flour dust glowing in warm light, candid cinematic food photography, shallow depth of field, no signs or text anywhere
यह prompt आज़माएँ →स्पेसिफ़िकेशन
मिलते-जुलते मॉडल
कीमतें प्रति रेंडर हैं और आपके बैलेंस से तभी कटती हैं जब रेंडर सफल हो। फ़ेल हुए रेंडर का पैसा अपने आप वापस हो जाता है। उदाहरण दिखाते हैं कि मॉडल क्या कर सकता है; आपका नतीजा आपके prompt और सेटिंग्स पर निर्भर करेगा।