Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

Claude charges for image inputs based on pixel size, not the amount of text inside the image So you can literally turn long prompts into an image and save a lot of tokens Here is a repo which does this

karankendre's profile picture

Karan

15,781 subscribers

2,684,424 Aufrufe • vor 14 Tagen •via X (Twitter)

BildungWissenschaft & Technologie
LIVE

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

This new AI image generator is impressive. You can now create an image based on a simple sketch and text prompt.
0:36

This new AI image generator is impressive. You can now create an image based on a simple sketch and text prompt.

Rowan Cheung

543,208 Aufrufe • vor 3 Jahren

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.
0:46

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

71,232 Aufrufe • vor 1 Jahr

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.
0:22

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 Aufrufe • vor 1 Jahr

Introducing Image to App. Turn any image into a native mobile app. Just paste an image. Claude Code Opus 4.6 will design, code, deploy and translate a mobile app inspired by original design. We’ve used this internally a lot for consumer apps on iOS/Android.
0:30

Introducing Image to App. Turn any image into a native mobile app. Just paste an image. Claude Code Opus 4.6 will design, code, deploy and translate a mobile app inspired by original design. We’ve used this internally a lot for consumer apps on iOS/Android.

David Ch

173,647 Aufrufe • vor 3 Monaten

PLEASE Stop saving JSON prompts for Gemini ! You don’t need it. JSON format is only for interactions like below, not for your creative workflow. What you actually need (watch the video): ✅ Save the image ✅ Upload it into an AI Media Gallery ✅ Reuse it anytime (image → image) AI can already generate what you want from an image. The missing piece is where all those assets live.
1:10

PLEASE Stop saving JSON prompts for Gemini ! You don’t need it. JSON format is only for interactions like below, not for your creative workflow. What you actually need (watch the video): ✅ Save the image ✅ Upload it into an AI Media Gallery ✅ Reuse it anytime (image → image) AI can already generate what you want from an image. The missing piece is where all those assets live.

Enes Kırca

77,074 Aufrufe • vor 7 Monaten

not long now before we can simply drag and drop an image into an editor to create a 3D scene! the scenes below are generated from the image on the left 👀
0:25

not long now before we can simply drag and drop an image into an editor to create a 3D scene! the scenes below are generated from the image on the left 👀

Dreaming Tulpa 🥓👑

156,028 Aufrufe • vor 1 Jahr

wow.. this AI is crazy you can now use ChatGPT image 2.0 on infinite canvas and its much more advanced you can edit image with simple text and turn it into commercial video directly
0:39

wow.. this AI is crazy you can now use ChatGPT image 2.0 on infinite canvas and its much more advanced you can edit image with simple text and turn it into commercial video directly

Abhishek

31,469 Aufrufe • vor 2 Monaten

this workflow save you tons of time for creating longer films the image include everything in the scene, so you dont need to @ things multiple time in the prompts to keep consistent on with shot list image, you can generate multiple shot using 1 prompt
0:25

this workflow save you tons of time for creating longer films the image include everything in the scene, so you dont need to @ things multiple time in the prompts to keep consistent on with shot list image, you can generate multiple shot using 1 prompt

el.cine

15,213 Aufrufe • vor 2 Monaten

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.
0:54

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 Aufrufe • vor 5 Monaten

A new real-time world model is here 👀 I tested out Dynamics Lab's Mirage 2, which was just publicly released. You can upload an image and step inside it - with game controls that guide movement + the ability to change the scene with text prompts.
0:25

A new real-time world model is here 👀 I tested out Dynamics Lab's Mirage 2, which was just publicly released. You can upload an image and step inside it - with game controls that guide movement + the ability to change the scene with text prompts.

Justine Moore

24,297 Aufrufe • vor 11 Monaten

We’re excited to announce the release and open-source of HunyuanImage 3.0 — the largest and most powerful open-source text-to-image model to date, with over 80 billion total parameters, of which 13 billion are activated per token during inference.The effect is completely comparable to the industry’s flagship closed-source model.🚀🚀🚀 HunyuanImage 3.0 originates from our internally developed native multimodal large language model, with fine-tuning and post-training focused on text-to-image generation. This unique foundation gives the model a powerful set of capabilities: ✅Reason with world knowledge ✅Understand complex, thousand-word prompts ✅Generate precise text within images Different from traditional DiT architecture image generation models, HunyuanImage 3.0’s MoE architecture uses a Transfusion-based approach to deeply couple Diffusion and LLM training for a single, powerful system. Built on Hunyuan-A13B, HunyuanImage 3.0 was trained on a massive dataset: 5 billion image-text pairs, video frames, interleaved image-text data, and 6 trillion tokens of text corpora. This hybrid training across multimodal generation, understanding, and LLM capabilities allows the model to seamlessly integrate multiple tasks. Whether you're an illustrator, designer, or creator, this is built to slash your workflow from hours to minutes. HunyuanImage 3.0 can generate intricate text, detailed comics, expressive emojis, and lively, engaging illustrations for educational content. The current release focuses solely on text-to-image generation and future updates will include image-to-image, image editing, multi-turn interaction, and more. 👉🏻Try it now: 🔗GitHub: 🤗Hugging Face:
2:09

We’re excited to announce the release and open-source of HunyuanImage 3.0 — the largest and most powerful open-source text-to-image model to date, with over 80 billion total parameters, of which 13 billion are activated per token during inference.The effect is completely comparable to the industry’s flagship closed-source model.🚀🚀🚀 HunyuanImage 3.0 originates from our internally developed native multimodal large language model, with fine-tuning and post-training focused on text-to-image generation. This unique foundation gives the model a powerful set of capabilities: ✅Reason with world knowledge ✅Understand complex, thousand-word prompts ✅Generate precise text within images Different from traditional DiT architecture image generation models, HunyuanImage 3.0’s MoE architecture uses a Transfusion-based approach to deeply couple Diffusion and LLM training for a single, powerful system. Built on Hunyuan-A13B, HunyuanImage 3.0 was trained on a massive dataset: 5 billion image-text pairs, video frames, interleaved image-text data, and 6 trillion tokens of text corpora. This hybrid training across multimodal generation, understanding, and LLM capabilities allows the model to seamlessly integrate multiple tasks. Whether you're an illustrator, designer, or creator, this is built to slash your workflow from hours to minutes. HunyuanImage 3.0 can generate intricate text, detailed comics, expressive emojis, and lively, engaging illustrations for educational content. The current release focuses solely on text-to-image generation and future updates will include image-to-image, image editing, multi-turn interaction, and more. 👉🏻Try it now: 🔗GitHub: 🤗Hugging Face:

Tencent Hy

412,658 Aufrufe • vor 9 Monaten

"This is how GPT-4 sees and hears itself" I used GPT-4 to describe itself. Then I used its description to generate an image, a video based on this image and a soundtrack. Tools I used: GPT-4, Midjourney, Kainber AI, Mubert, RunwayML This is the description I used that GPT-4 had of itself as a prompt to text-to-image, image-to-video, and text-to-music. I put the video and sound together in RunwayML.
0:59

"This is how GPT-4 sees and hears itself" I used GPT-4 to describe itself. Then I used its description to generate an image, a video based on this image and a soundtrack. Tools I used: GPT-4, Midjourney, Kainber AI, Mubert, RunwayML This is the description I used that GPT-4 had of itself as a prompt to text-to-image, image-to-video, and text-to-music. I put the video and sound together in RunwayML.

Kris Kashtanova

1,233,452 Aufrufe • vor 3 Jahren

👀 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
2:48

👀 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

NVIDIA AI Developer

14,747 Aufrufe • vor 1 Jahr

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:
1:39

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 Aufrufe • vor 10 Monaten

MatrixGPT - The state-of-the-art Text to Image AI app 💎An all-in-one #AI solution offering a suite of innovative apps including Text-to-image, Image-to-image, Text-to-speech & Talking face. Let's create art and earn with #MatrixGPT! JOIN US NOW!👉 $MAI
0:44

MatrixGPT - The state-of-the-art Text to Image AI app 💎An all-in-one #AI solution offering a suite of innovative apps including Text-to-image, Image-to-image, Text-to-speech & Talking face. Let's create art and earn with #MatrixGPT! JOIN US NOW!👉 $MAI

MatrixGPT

13,519 Aufrufe • vor 3 Jahren

Image Gen is here. If you can make a page, you can make a graphic for it too. Just hit /image and Notion handles the rest. Find some of our favorite use cases below ↓
1:05

Image Gen is here. If you can make a page, you can make a graphic for it too. Just hit /image and Notion handles the rest. Find some of our favorite use cases below ↓

Notion

114,371 Aufrufe • vor 2 Monaten

Step into the future of AI image generation with Qwen-Image! From superior text rendering to consistent image editing across multiple languages, it sets a new benchmark! 💡What will you create with Qwen-Image?
0:28

Step into the future of AI image generation with Qwen-Image! From superior text rendering to consistent image editing across multiple languages, it sets a new benchmark! 💡What will you create with Qwen-Image?

Alibaba Group

203,475 Aufrufe • vor 11 Monaten

wow.. this AI is crazy you can now use Google Gemini 2.0 image editor on FLORA and its much more advanced you can edit image with simple text and turn it into commercial video directly it's free to use for a limited time now, link in comment 10 examples:
0:22

wow.. this AI is crazy you can now use Google Gemini 2.0 image editor on FLORA and its much more advanced you can edit image with simple text and turn it into commercial video directly it's free to use for a limited time now, link in comment 10 examples:

el.cine

1,371,115 Aufrufe • vor 1 Jahr

Image-Blaster is a Claude skill that can create an entire 3D environment from a single image. The special sauce here is that it also extracts key environment elements and converts them into their own separate 3D models. Full YT video:
0:53

Image-Blaster is a Claude skill that can create an entire 3D environment from a single image. The special sauce here is that it also extracts key environment elements and converts them into their own separate 3D models. Full YT video:

Matt Workman

25,206 Aufrufe • vor 2 Monaten