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BREAKING: SpaceXAI just released Grok Imagine Image 2.0, its latest model for professional image generation and editing. • Improved prompt accuracy, typography and layouts • Precise edits using Magic Wand and segmentation • Background removal and smart resizing • Supports up to 5 input images • Better consistency across... show more

DogeDesigner

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42,402 görüntüleme • 5 gün önce •via X (Twitter)

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Announcing Imagine Image 2.0, our next generation image model with precision editing, crisp text rendering, improved factuality, and real world usefulness. Image 2.0 helps you make images for real work.

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Introducing Quality mode on Grok Imagine – powered by our most advanced image generation model. Quality mode gives you enhanced details, stronger text rendering, and higher levels of creative control. Now available on web and mobile. Try it at

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Updated my Grok Rate Limit Display Chrome extension. Added Grok Imagine support. Now you can easily track how many chat queries, and imagine video and image generations you have left and how long you have to wait for more. Download. or just search the chrome web store for "Grok Rate Limit Display" note: Waiting for google to approve ver 1.6.0 with imagine support

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Wan2.6: Commercial-Grade Image Output Wan2.6-Image is now available. 🖼️ Interleaved Text-and-Image Output：Generate interleaved text-and-image content with logical reasoning capabilities — enabling layered, narrative-driven visual storytelling. 🖼️ Multi-Image Conditioned Generation：Support flexible referencing, combining, and replacement of multiple images, integrating varied visual inspirations to generate novel and compelling results 🖼️ Commercial-Grade ID Preservation：High consistency in characters, styles, and elements for commercial scenarios. 🖼️ Extract creative elements — such as color, style, and composition — from reference images to enable aesthetically driven image generation. 🖼️ Precise Control of Camera Angles and Lighting：Support specifying camera perspective, spatial depth (foreground/background), and lighting details — enabling precise control over spatial composition and atmospheric mood. Try Wan2.6 today.

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We've officially released and open-sourced HunyuanImage 2.1, our latest text-to-image model. The new model delivers on our commitment to balancing performance and quality. With native 2K image generation, HunyuanImage 2.1 is an advanced open-source text-to-image model.🎨 ✨ New in 2.1: 🔹Advanced Semantics: Supports ultra-long and complex prompts of up to 1000 tokens, and precisely controls the generation of multiple subjects in a single image. 🔹Precise Chinese and English Text Rendering with seamless image–text integration: The model naturally integrates text into images, making it suitable for a wide range of applications such as product covers, illustrations, and poster design to meet the needs of various fields. 🔹Rich Styles and High Aesthetic: Capable of generating images in various styles—including photorealistic portraits, comics, and vinyl figures—it delivers outstanding visual appeal and artistic quality. 🔹High-Quality Generation: Efficiently produces ultra-high-definition (2K) images in the same time other models take to generate a 1K image. HunyuanImage 2.1 uses two text encoders: a multimodal large language model (MLLM) to improve the model's image and text alignment capabilities, and a multi-language character-aware encoder to improve text rendering capabilities. The model is a single- and double-stream diffusion transformer with 17B parameters. We've also open-sourced the weights of the the accelerated version with meanflow which reduces inference steps from 100 to just 8, and PromptEnhancer, the first industrial-grade rewriting model that enhances your prompts for more nuanced and expressive image generation. Now, creators turn complex ideas—like posters with slogans or multi-panel comics—into visuals faster than ever. We’re just getting started. Stay tuned for our native multimodal image generation model coming soon. 🌐Website: 🔗Github: 🤗Hugging Face: ✨Hugging Face Demo:

We've officially released and open-sourced HunyuanImage 2.1, our latest text-to-image model. The new model delivers on our commitment to balancing performance and quality. With native 2K image generation, HunyuanImage 2.1 is an advanced open-source text-to-image model.🎨 ✨ New in 2.1: 🔹Advanced Semantics: Supports ultra-long and complex prompts of up to 1000 tokens, and precisely controls the generation of multiple subjects in a single image. 🔹Precise Chinese and English Text Rendering with seamless image–text integration: The model naturally integrates text into images, making it suitable for a wide range of applications such as product covers, illustrations, and poster design to meet the needs of various fields. 🔹Rich Styles and High Aesthetic: Capable of generating images in various styles—including photorealistic portraits, comics, and vinyl figures—it delivers outstanding visual appeal and artistic quality. 🔹High-Quality Generation: Efficiently produces ultra-high-definition (2K) images in the same time other models take to generate a 1K image. HunyuanImage 2.1 uses two text encoders: a multimodal large language model (MLLM) to improve the model's image and text alignment capabilities, and a multi-language character-aware encoder to improve text rendering capabilities. The model is a single- and double-stream diffusion transformer with 17B parameters. We've also open-sourced the weights of the the accelerated version with meanflow which reduces inference steps from 100 to just 8, and PromptEnhancer, the first industrial-grade rewriting model that enhances your prompts for more nuanced and expressive image generation. Now, creators turn complex ideas—like posters with slogans or multi-panel comics—into visuals faster than ever. We’re just getting started. Stay tuned for our native multimodal image generation model coming soon. 🌐Website: 🔗Github: 🤗Hugging Face: ✨Hugging Face Demo:

Tencent Hy

89,257 görüntüleme • 11 ay önce

Had to try Grok Imagine after seeing Heavy Pulp’s latest work. I used Grok Imagine’s still image creation and new multi-image video reference feature for the video, no other AI model were used. I have no movie editing or movie creation skills whatsoever, just an eye for aesthetic. I added a consistent cinematic style, character and scene setting along with simple prompts. Extending scenes was flawless, the speed to render iterations was nuts, and realism is high level. 3 hours in total to produce. Must try Grok Imagine.

Had to try Grok Imagine after seeing Heavy Pulp’s latest work. I used Grok Imagine’s still image creation and new multi-image video reference feature for the video, no other AI model were used. I have no movie editing or movie creation skills whatsoever, just an eye for aesthetic. I added a consistent cinematic style, character and scene setting along with simple prompts. Extending scenes was flawless, the speed to render iterations was nuts, and realism is high level. 3 hours in total to produce. Must try Grok Imagine.

AGRIPPA █

308,219 görüntüleme • 5 ay önce

Early look at Imagine Agent Mode on Grok app for iOS! Users will be able to use Imagine Agent via a mobile optimised native UI to generate images and videos that require more complex workflows. SpaceXAI is getting quite ahead of everyone else on this front! We just need Imagine v2 now 👀 Additionally, Skills are coming soon on mobile as well.

Early look at Imagine Agent Mode on Grok app for iOS! Users will be able to use Imagine Agent via a mobile optimised native UI to generate images and videos that require more complex workflows. SpaceXAI is getting quite ahead of everyone else on this front! We just need Imagine v2 now 👀 Additionally, Skills are coming soon on mobile as well.

🚨 AI News | TestingCatalog

46,601 görüntüleme • 3 ay önce

Nano Banana Pro is here 🍌 Unlimited for ONE WEEK The model that sparked billions of creations just leveled up. Sharper quality, unbreakable character consistency and multi-image blending Available now on Freepik. Read more below

Nano Banana Pro is here 🍌 Unlimited for ONE WEEK The model that sparked billions of creations just leveled up. Sharper quality, unbreakable character consistency and multi-image blending Available now on Freepik. Read more below

Magnific

192,833 görüntüleme • 8 ay önce

In Prompt Engineering for Vision Models, taught by Abby Jacques Verre and Caleb Kaiser of Comet , you’ll learn how to prompt and fine-tune vision models for personalized image generation, image editing, object detection and segmentation. The prompts you'll use for vision models could be text, point coordinates, or bounding boxes, depending on the model. You'll also learn to tune hyperparameters to shape the output. Models you'll use include Segment-Anything Model (SAM), OWL-ViT, and Stable Diffusion. You'll also learn to fine-tune Stable Diffusion to generate personalized images (say, an image of a specific person), using a handful of images for training. As an example of a multi-step workflow, you'll use OWL-ViT to detect an object based on a text prompt, then pass the bounding box to SAM to create a segmentation mask, and input that mask into Stable Diffusion to replace the original object with a new one based on a text prompt. Controlling vision models can be tricky; this course will teach prompting and fine-tuning techniques to get precise control over their output. Get started here:

In Prompt Engineering for Vision Models, taught by Abby Jacques Verre and Caleb Kaiser of Comet , you’ll learn how to prompt and fine-tune vision models for personalized image generation, image editing, object detection and segmentation. The prompts you'll use for vision models could be text, point coordinates, or bounding boxes, depending on the model. You'll also learn to tune hyperparameters to shape the output. Models you'll use include Segment-Anything Model (SAM), OWL-ViT, and Stable Diffusion. You'll also learn to fine-tune Stable Diffusion to generate personalized images (say, an image of a specific person), using a handful of images for training. As an example of a multi-step workflow, you'll use OWL-ViT to detect an object based on a text prompt, then pass the bounding box to SAM to create a segmentation mask, and input that mask into Stable Diffusion to replace the original object with a new one based on a text prompt. Controlling vision models can be tricky; this course will teach prompting and fine-tuning techniques to get precise control over their output. Get started here:

Andrew Ng

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