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introducing: Image Sniper Automatically buys the token when a specific user posts or interacts with a specific image. Detects based off perpetual hash, allowing the detection for similar, modified, and resized versions of the image.

uxento

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13,507 views • 1 month ago •via X (Twitter)

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introducing: Auto-Sniper Automated token buying. CA - CA is detected. Keyword - Keywords are detected. Interaction - User interacts with another user. Post Interaction - User interacts with a specific post. Activity - Any activity is detected from specific accounts.

introducing: Auto-Sniper Automated token buying. CA - CA is detected. Keyword - Keywords are detected. Interaction - User interacts with another user. Post Interaction - User interacts with a specific post. Activity - Any activity is detected from specific accounts.

uxento

24,736 views • 2 months ago

TurboEdit Instant text-based image editing discuss: We address the challenges of precise image inversion and disentangled image editing in the context of few-step diffusion models. We introduce an encoder based iterative inversion technique. The inversion network is conditioned on the input image and the reconstructed image from the previous step, allowing for correction of the next reconstruction towards the input image. We demonstrate that disentangled controls can be easily achieved in the few-step diffusion model by conditioning on an (automatically generated) detailed text prompt. To manipulate the inverted image, we freeze the noise maps and modify one attribute in the text prompt (either manually or via instruction based editing driven by an LLM), resulting in the generation of a new image similar to the input image with only one attribute changed. It can further control the editing strength and accept instructive text prompt. Our approach facilitates realistic text-guided image edits in real-time, requiring only 8 number of functional evaluations (NFEs) in inversion (one-time cost) and 4 NFEs per edit. Our method is not only fast, but also significantly outperforms state-of-the-art multi-step diffusion editing techniques.

TurboEdit Instant text-based image editing discuss: We address the challenges of precise image inversion and disentangled image editing in the context of few-step diffusion models. We introduce an encoder based iterative inversion technique. The inversion network is conditioned on the input image and the reconstructed image from the previous step, allowing for correction of the next reconstruction towards the input image. We demonstrate that disentangled controls can be easily achieved in the few-step diffusion model by conditioning on an (automatically generated) detailed text prompt. To manipulate the inverted image, we freeze the noise maps and modify one attribute in the text prompt (either manually or via instruction based editing driven by an LLM), resulting in the generation of a new image similar to the input image with only one attribute changed. It can further control the editing strength and accept instructive text prompt. Our approach facilitates realistic text-guided image edits in real-time, requiring only 8 number of functional evaluations (NFEs) in inversion (one-time cost) and 4 NFEs per edit. Our method is not only fast, but also significantly outperforms state-of-the-art multi-step diffusion editing techniques.

AK

16,062 views • 1 year ago

Using a pasta machine to send a photo through creates pixelated, simplified versions of the image. This is a great way to illustrate how Convolutional Neural Networks (CNNs) work. Each pass through the machine represents a layer in a CNN, where images are processed step by step. In CNNs, each layer identifies specific features like edges, textures, or colors. Just as the pasta machine alters the photo with each pass, CNNs refine and simplify the image for better understanding. This step-by-step process allows CNNs to recognize complex images, making them useful in areas like image classification and object detection. It ain’t perfect but it’s a simple presentation.

Using a pasta machine to send a photo through creates pixelated, simplified versions of the image. This is a great way to illustrate how Convolutional Neural Networks (CNNs) work. Each pass through the machine represents a layer in a CNN, where images are processed step by step. In CNNs, each layer identifies specific features like edges, textures, or colors. Just as the pasta machine alters the photo with each pass, CNNs refine and simplify the image for better understanding. This step-by-step process allows CNNs to recognize complex images, making them useful in areas like image classification and object detection. It ain’t perfect but it’s a simple presentation.

Brian Roemmele

97,202 views • 1 year ago

Grok just dropped a crazy AI feature you can now edit image by chatting to Grok and.. it can even change specific object in the image this is incredible for AI artist

Grok just dropped a crazy AI feature you can now edit image by chatting to Grok and.. it can even change specific object in the image this is incredible for AI artist

el.cine

159,815 views • 1 year ago

Introducing Reverse Image Search! Drag any image into the search bar and we'll find similar assets from our library. Try it at

Introducing Reverse Image Search! Drag any image into the search bar and we'll find similar assets from our library. Try it at

Poliigon

18,499 views • 25 days ago

Anamorphosis is a clever artistic technique where the image appears distorted unless viewed from a specific vantage point.

Anamorphosis is a clever artistic technique where the image appears distorted unless viewed from a specific vantage point.

Interesting As Fuck

34,749 views • 7 months ago

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

56,357 views • 3 years ago

. Grok Imagine Update very useful. Different animation versions of the same image are all available and no longer overwrite . In the example below, we have 4 versions of the same image.

. Grok Imagine Update very useful. Different animation versions of the same image are all available and no longer overwrite . In the example below, we have 4 versions of the same image.

Déborah

55,647 views • 7 months ago

Introducing ClickDiffusion! We developed a system for precise image manipulation and generation that combines natural language instructions with visual feedback provided by the user through a direct manipulation interface.

Introducing ClickDiffusion! We developed a system for precise image manipulation and generation that combines natural language instructions with visual feedback provided by the user through a direct manipulation interface.

Alec Helbling

36,293 views • 2 years ago

InstantDrag Improving Interactivity in Drag-based Image Editing discuss: Drag-based image editing has recently gained popularity for its interactivity and precision. However, despite the ability of text-to-image models to generate samples within a second, drag editing still lags behind due to the challenge of accurately reflecting user interaction while maintaining image content. Some existing approaches rely on computationally intensive per-image optimization or intricate guidance-based methods, requiring additional inputs such as masks for movable regions and text prompts, thereby compromising the interactivity of the editing process. We introduce InstantDrag, an optimization-free pipeline that enhances interactivity and speed, requiring only an image and a drag instruction as input. InstantDrag consists of two carefully designed networks: a drag-conditioned optical flow generator (FlowGen) and an optical flow-conditioned diffusion model (FlowDiffusion). InstantDrag learns motion dynamics for drag-based image editing in real-world video datasets by decomposing the task into motion generation and motion-conditioned image generation. We demonstrate InstantDrag's capability to perform fast, photo-realistic edits without masks or text prompts through experiments on facial video datasets and general scenes. These results highlight the efficiency of our approach in handling drag-based image editing, making it a promising solution for interactive, real-time applications.

InstantDrag Improving Interactivity in Drag-based Image Editing discuss: Drag-based image editing has recently gained popularity for its interactivity and precision. However, despite the ability of text-to-image models to generate samples within a second, drag editing still lags behind due to the challenge of accurately reflecting user interaction while maintaining image content. Some existing approaches rely on computationally intensive per-image optimization or intricate guidance-based methods, requiring additional inputs such as masks for movable regions and text prompts, thereby compromising the interactivity of the editing process. We introduce InstantDrag, an optimization-free pipeline that enhances interactivity and speed, requiring only an image and a drag instruction as input. InstantDrag consists of two carefully designed networks: a drag-conditioned optical flow generator (FlowGen) and an optical flow-conditioned diffusion model (FlowDiffusion). InstantDrag learns motion dynamics for drag-based image editing in real-world video datasets by decomposing the task into motion generation and motion-conditioned image generation. We demonstrate InstantDrag's capability to perform fast, photo-realistic edits without masks or text prompts through experiments on facial video datasets and general scenes. These results highlight the efficiency of our approach in handling drag-based image editing, making it a promising solution for interactive, real-time applications.

AK

71,201 views • 1 year ago

Depressing. Did a little image search for the fake ai generated image of a native American with wolf (based on a photo of someone from Siberia with a dog) to see how far it hat polluted the internet... judge for yourself:

Depressing. Did a little image search for the fake ai generated image of a native American with wolf (based on a photo of someone from Siberia with a dog) to see how far it hat polluted the internet... judge for yourself:

Fake History Hunter

17,488 views • 10 months ago

Left: President Trump posts a tacky AI image of himself for May the Fourth Right: President Obama posts a video with Mark Hamill for May the Fourth

Left: President Trump posts a tacky AI image of himself for May the Fourth Right: President Obama posts a video with Mark Hamill for May the Fourth

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2. Use the generated image as an image prompt for Runway or any video model that supports End-Frames. Use the image as the final/last frame and create a prompt that reveals the image in an ink-style animation. Example prompt:

2. Use the generated image as an image prompt for Runway or any video model that supports End-Frames. Use the image as the final/last frame and create a prompt that reveals the image in an ink-style animation. Example prompt:

Halim Alrasihi

14,596 views • 1 year ago

Introducing image references 🪄✨ Choose any photo, and Wand will swap out the selected area with your image.

Introducing image references 🪄✨ Choose any photo, and Wand will swap out the selected area with your image.

wand

19,329 views • 1 year ago

LINGO-1 doesn't just describe what it sees; it can also show us what it sees! 👀 Introducing referential segmentation - a new feature that grounds what #LINGO1 describes to specific objects in the image 🖼💬, allowing for more accurate and trustworthy responses. Watch below👇

LINGO-1 doesn't just describe what it sees; it can also show us what it sees! 👀 Introducing referential segmentation - a new feature that grounds what #LINGO1 describes to specific objects in the image 🖼💬, allowing for more accurate and trustworthy responses. Watch below👇

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Cami onboards normie to memecoins, buys Pika based on the image and it went up 746% 🤪🤪

Cami onboards normie to memecoins, buys Pika based on the image and it went up 746% 🤪🤪

eevanss LY

22,034 views • 8 months ago

Amazing! A Catholic priest sheds light on the symbolisms behind the iconic image of Our Lady of Perpetual Help.

Amazing! A Catholic priest sheds light on the symbolisms behind the iconic image of Our Lady of Perpetual Help.

Sachin Jose

155,047 views • 1 year ago

remember when ray got mad at chat for guessing a video game based off an image of grass 🙄

remember when ray got mad at chat for guessing a video game based off an image of grass 🙄

rayofsunshine ☀️

46,409 views • 1 year ago

I've further improved my shader for projecting AI-generated images. This time, I added a feature where the image changes based on the player's viewing angle, which fixes a lot of problems. I'm having fun switching the image projection on and off in this video.

I've further improved my shader for projecting AI-generated images. This time, I added a feature where the image changes based on the player's viewing angle, which fixes a lot of problems. I'm having fun switching the image projection on and off in this video.

Alex

14,467 views • 4 months ago