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Introducing StyleDrop, a model that allows a significantly higher level of stylized text-to-image synthesis by using a few style reference images that describe the style for text-to-image generation, bypassing the burden of text prompt engineering. More→

Google AI

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80,377 görüntüleme • 2 yıl önce •via X (Twitter)

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kache profil fotoğrafı

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model? code?

Alex Volkov (Thursd/AI) 🔜 AIENG summit NY profil fotoğrafı

Alex Volkov (Thursd/AI) 🔜 AIENG summit NY2 yıl önce

Shoutout @natanielruizg 👏

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Could you please introduce Gemini (at least pro) to EU, and Gemini Ultra to the world?🙂

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@natanielruizg IPAdapter?

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Impressive work, @GoogleAI! This innovation promises to simplify the process and enhance the quality of text-to-image generation. Keep pushing the boundaries of AI research! 🌟 #AI #Research #Innovation

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watchumean introducing that's old stuff in stable diffusion like a year old

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Where is our access, Mr. Google?

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Just looking like a wow !!!

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That sounds like an exciting advancement in text-to-image synthesis! StyleDrop seems to offer a more efficient approach by utilizing style reference images instead of relying solely on text prompts. This could potentially lead to more accurate and diverse image generation. I'm curious to learn more about how this model works and the results it can produce. @mira_hurley @TimeForPlanX

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@PublicAI_ #AI

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StyleDrop: Text-to-Image Generation in Any Style introduce StyleDrop, a method that enables the synthesis of images that faithfully follow a specific style using a text-to-image model. The proposed method is extremely versatile and captures nuances and details of a user-provided style, such as color schemes, shading, design patterns, and local and global effects. It efficiently learns a new style by fine-tuning very few trainable parameters (less than 1% of total model parameters) and improving the quality via iterative training with either human or automated feedback. Better yet, StyleDrop is able to deliver impressive results even when the user supplies only a single image that specifies the desired style. An extensive study shows that, for the task of style tuning text-to-image models, StyleDrop implemented on Muse convincingly outperforms other methods, including DreamBooth and textual inversion on Imagen or Stable Diffusion. paper page:

StyleDrop: Text-to-Image Generation in Any Style introduce StyleDrop, a method that enables the synthesis of images that faithfully follow a specific style using a text-to-image model. The proposed method is extremely versatile and captures nuances and details of a user-provided style, such as color schemes, shading, design patterns, and local and global effects. It efficiently learns a new style by fine-tuning very few trainable parameters (less than 1% of total model parameters) and improving the quality via iterative training with either human or automated feedback. Better yet, StyleDrop is able to deliver impressive results even when the user supplies only a single image that specifies the desired style. An extensive study shows that, for the task of style tuning text-to-image models, StyleDrop implemented on Muse convincingly outperforms other methods, including DreamBooth and textual inversion on Imagen or Stable Diffusion. paper page:

AK

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Can a small academic team build a strong text-to-image model using only public datasets? Introducing i1: a simple, fully open recipe for strong text-to-image models

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Make a few more of these ... Make a LOT more of these ... Gemini 2.0 native image output is enabling a new way to prompt: instructing with image and text together. Subtle shifts in how I draw change how Gemini interprets the same text prompt.

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Introducing Ideogram 1.0: the most advanced text-to-image model, now available on This offers state-of-the-art text rendering, unprecedented photorealism, exceptional prompt adherence, and a new feature called Magic Prompt to help with prompting.

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ByteDance announced SeedEdit! A new image model that can edit images with text prompts. It allows for high-resolution editing and supports various changes like local replacements, geometric transformations, and style adjustments. Links ⬇️

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Dreaming Tulpa 🥓👑

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Run InstantStyle Locally with 1 Click InstantStyle lets you generate images with a style of ANY other image, instantly. No LoRA required. Both text-to-image/image-to-image. I wrote a 1 click launcher for the gradio app from Frank (Haofan) Wang (The author of InstantStyle/InstantId!).

Run InstantStyle Locally with 1 Click InstantStyle lets you generate images with a style of ANY other image, instantly. No LoRA required. Both text-to-image/image-to-image. I wrote a 1 click launcher for the gradio app from Frank (Haofan) Wang (The author of InstantStyle/InstantId!).

cocktail peanut

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Ideogram AI presents Ideogram 1.0 text-to-image model offers state-of-the-art text rendering, unprecedented photorealism, exceptional prompt adherence, and a new feature called Magic Prompt to help with prompting

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AK

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Phidias A Generative Model for Creating 3D Content from Text, Image, and 3D Conditions with Reference-Augmented Diffusion discuss: In 3D modeling, designers often use an existing 3D model as a reference to create new ones. This practice has inspired the development of Phidias, a novel generative model that uses diffusion for reference-augmented 3D generation. Given an image, our method leverages a retrieved or user-provided 3D reference model to guide the generation process, thereby enhancing the generation quality, generalization ability, and controllability. Our model integrates three key components: 1) meta-ControlNet that dynamically modulates the conditioning strength, 2) dynamic reference routing that mitigates misalignment between the input image and 3D reference, and 3) self-reference augmentations that enable self-supervised training with a progressive curriculum. Collectively, these designs result in a clear improvement over existing methods. Phidias establishes a unified framework for 3D generation using text, image, and 3D conditions with versatile applications.

Phidias A Generative Model for Creating 3D Content from Text, Image, and 3D Conditions with Reference-Augmented Diffusion discuss: In 3D modeling, designers often use an existing 3D model as a reference to create new ones. This practice has inspired the development of Phidias, a novel generative model that uses diffusion for reference-augmented 3D generation. Given an image, our method leverages a retrieved or user-provided 3D reference model to guide the generation process, thereby enhancing the generation quality, generalization ability, and controllability. Our model integrates three key components: 1) meta-ControlNet that dynamically modulates the conditioning strength, 2) dynamic reference routing that mitigates misalignment between the input image and 3D reference, and 3) self-reference augmentations that enable self-supervised training with a progressive curriculum. Collectively, these designs result in a clear improvement over existing methods. Phidias establishes a unified framework for 3D generation using text, image, and 3D conditions with versatile applications.

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👀 Pixel perfect 💎✨ 🖼️ Edify Image from #NVIDIAResearch is a family of diffusion models that supports a wide range of applications, including text-to-image synthesis, 4K upsampling, ControlNets, 360° HDR panorama generation, and finetuning for image customization. 🧵 1/2

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Introducing SDXL Turbo: A real-time text-to-image generation model. SDXL Turbo achieves state-of-the-art performance with a new distillation technology, enabling single-step image generation with unprecedented quality, reducing the required step count from 50 to just one. The code, research paper, and weights for non-commercial use are now available on our website. You can test SDXL Turbo on Stability AI’s image editing platform Clipdrop, with a beta demonstration of the real-time text-to-image generation capabilities. Learn more:

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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:

Tencent Hy

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Excited to share a Google DeepMind Gemini 2.0 Flash Image Generation and Editing Quickstart. We build a Next.js reference app on how to use the new image editing feature of Gemini 2.0 Flash. Demo to test ⬇️ > Generate images from text prompts using Gemini 2.0 Flash > Or upload an image and edit it using prompts

Excited to share a Google DeepMind Gemini 2.0 Flash Image Generation and Editing Quickstart. We build a Next.js reference app on how to use the new image editing feature of Gemini 2.0 Flash. Demo to test ⬇️ > Generate images from text prompts using Gemini 2.0 Flash > Or upload an image and edit it using prompts

Philipp Schmid

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Higgsfield Mod for Minecraft is live. > prompt any building or city, even the Statue of Liberty > create paintings with text-to-image > snap a view and restyle it with image-to-image > make videos from a prompt with text-to-video. > animate in-game photos with image-to-video

Higgsfield Mod for Minecraft is live. > prompt any building or city, even the Statue of Liberty > create paintings with text-to-image > snap a view and restyle it with image-to-image > make videos from a prompt with text-to-video. > animate in-game photos with image-to-video

Higgsfield AI 🧩

454,704 görüntüleme • 1 ay önce