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P-Image-Ideogram, built with Ideogram, now has a Very High mode. Very High uses test-time scaling to push final image quality further. Built for hero assets, difficult prompts, and quality-critical generations when people and products have to pop. • ~5.55s at 1K · $0.033/image (1K) · $0.066/image (2K) • Five...

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Pruna AI

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10,077 görüntüleme • 13 gün önce •via X (Twitter)

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P-Image-Try-On delivers the fastest and cheapest virtual try-on on the market. • Affordable: $0.015 for the first garment, $0.008 for each additional one • Multi-garment: fit up to 11 garment references onto a person photo • Quality mode for <2s per garment • Turbo mode for even higher speeds <4s in total • Prompt guidance and pose guidance for precise control Ideal for fashion, e-commerce, and catalog teams shipping VTO without reshoots. Available day 0 on Pruna API and with our launch partners: Cloudflare Scenario Replicate Wiro AI — Ship AI Faster Tellers each::labs WaveSpeedAI Runware LookFashion 👉 Playground: 📚 API: 🧩 P-Video, P-Image, P-Image-Edit & more:
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P-Image-Try-On delivers the fastest and cheapest virtual try-on on the market. • Affordable: $0.015 for the first garment, $0.008 for each additional one • Multi-garment: fit up to 11 garment references onto a person photo • Quality mode for <2s per garment • Turbo mode for even higher speeds <4s in total • Prompt guidance and pose guidance for precise control Ideal for fashion, e-commerce, and catalog teams shipping VTO without reshoots. Available day 0 on Pruna API and with our launch partners: Cloudflare Scenario Replicate Wiro AI — Ship AI Faster Tellers each::labs WaveSpeedAI Runware LookFashion 👉 Playground: 📚 API: 🧩 P-Video, P-Image, P-Image-Edit & more:

Pruna AI

25,591 görüntüleme • 2 ay önce

P-Image-Upscale is the fastest and cheapest image upscaler in the world: supporting outputs up to 128 MP under 1 seconds. A couple of weeks ago we released p-image-upscale, and it now works better than ever. It’s the fastest image upscaler in the world, supporting outputs up to 128 MP while keeping pricing simple and predictable: - $0.005/image for 1–4 MP - $0.01/image for 4–8 MP - $0.02/image for 8–16 MP - $0.04/image for 16–32 MP - $0.06/image for 32–64 MP - $0.12/image for 64–128 MP That means you can go from low-res input to production-ready output with extreme speed, while preserving detail and keeping costs easy to understand. Available on: - Pruna AI | - each::labs | - inference shell | - Replicate | - Runware | - Segmind | - WaveSpeedAI | - Wiro AI — Ship AI Faster | If you’re building workflows where image quality, price, latency, and scale all matter, p-image-upscale is built for you.
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P-Image-Upscale is the fastest and cheapest image upscaler in the world: supporting outputs up to 128 MP under 1 seconds. A couple of weeks ago we released p-image-upscale, and it now works better than ever. It’s the fastest image upscaler in the world, supporting outputs up to 128 MP while keeping pricing simple and predictable: - $0.005/image for 1–4 MP - $0.01/image for 4–8 MP - $0.02/image for 8–16 MP - $0.04/image for 16–32 MP - $0.06/image for 32–64 MP - $0.12/image for 64–128 MP That means you can go from low-res input to production-ready output with extreme speed, while preserving detail and keeping costs easy to understand. Available on: - Pruna AI | - each::labs | - inference shell | - Replicate | - Runware | - Segmind | - WaveSpeedAI | - Wiro AI — Ship AI Faster | If you’re building workflows where image quality, price, latency, and scale all matter, p-image-upscale is built for you.

Pruna AI

23,959 görüntüleme • 3 ay önce

Happy to announce P-Video-Edit: instruction-based video-to-video editing with up to 4 references for just $0.025/s. • 𝗜𝗻𝗽𝘂𝘁: One source video (up to 15s) + one edit prompt • 𝗥𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲𝘀: Up to 4 optional images for supported style and object swaps • 𝗣𝗿𝗶𝗰𝗶𝗻𝗴: Draft from $0.025/s · standard from $0.045/s • 𝗗𝗿𝗮𝗳𝘁 𝗺𝗼𝗱𝗲: Faster preview before you commit to the full render Don't reshoot it. Just edit. Available via our inference partners Scenario, Replicate, Wiro AI — Ship AI Faster, WaveSpeedAI, Prodia, Runware, Together AI , Runpod, Cloudflare, each::labs, Tellers, Segmind Validated by our benchmark partners Design Arena, Datapoint AI, rapidata 👉 Free Playground: 📚 Model page: 🧩 API:
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Happy to announce P-Video-Edit: instruction-based video-to-video editing with up to 4 references for just $0.025/s. • 𝗜𝗻𝗽𝘂𝘁: One source video (up to 15s) + one edit prompt • 𝗥𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲𝘀: Up to 4 optional images for supported style and object swaps • 𝗣𝗿𝗶𝗰𝗶𝗻𝗴: Draft from $0.025/s · standard from $0.045/s • 𝗗𝗿𝗮𝗳𝘁 𝗺𝗼𝗱𝗲: Faster preview before you commit to the full render Don't reshoot it. Just edit. Available via our inference partners Scenario, Replicate, Wiro AI — Ship AI Faster, WaveSpeedAI, Prodia, Runware, Together AI , Runpod, Cloudflare, each::labs, Tellers, Segmind Validated by our benchmark partners Design Arena, Datapoint AI, rapidata 👉 Free Playground: 📚 Model page: 🧩 API:

Pruna AI

12,831 görüntüleme • 6 gün önce

P-Video-Animate is now the fastest model for top-quality image-to-video motion transfer. - $0.03/s at 720p and $0.06/s at 1080p — a fraction of typical motion-transfer pricing (~$0.07–$0.35/s at 720p in our benchmarks) - 5.24 s generation time per second of output video at 720p — roughly 7× faster than typical motion-transfer alternatives in the same benchmarks - Upload one still plus a driver video to preserve motion, timing, camera movement, and scene structure, with optional source audio and clips up to ~2 minutes at 720p Ideal for UGC ads, gaming cinematics, film casting, and product demos when you already have a winning take and want a new hero still to follow it. Try it in the playground: We’re launching with 70% discounted usage for the coming two days with our launch partners. each::labs, Replicate, Runware, Scenario, WaveSpeedAI, Wiro, Tellers, Soon also on Together AI We’d love your feedback as you try it. 📚 Model docs & API examples: 📚 API quickstart: ⭐️ OSS: 🌐
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P-Video-Animate is now the fastest model for top-quality image-to-video motion transfer. - $0.03/s at 720p and $0.06/s at 1080p — a fraction of typical motion-transfer pricing (~$0.07–$0.35/s at 720p in our benchmarks) - 5.24 s generation time per second of output video at 720p — roughly 7× faster than typical motion-transfer alternatives in the same benchmarks - Upload one still plus a driver video to preserve motion, timing, camera movement, and scene structure, with optional source audio and clips up to ~2 minutes at 720p Ideal for UGC ads, gaming cinematics, film casting, and product demos when you already have a winning take and want a new hero still to follow it. Try it in the playground: We’re launching with 70% discounted usage for the coming two days with our launch partners. each::labs, Replicate, Runware, Scenario, WaveSpeedAI, Wiro, Tellers, Soon also on Together AI We’d love your feedback as you try it. 📚 Model docs & API examples: 📚 API quickstart: ⭐️ OSS: 🌐

Pruna AI

22,727 görüntüleme • 3 ay önce

P-Video-Replace is now the fastest model for top-quality character replacement in existing video. - $0.03/s at 720p and $0.06/s at 1080p — a small fraction of typical character-replacement / video-edit pricing (~$0.07–$0.35/s at 720p in our benchmarks) - 3.58 s generation time per second of output video at 720p — roughly ~10× faster than typical alternatives in the same benchmarks (~36–43 s per second of output) - Upload a source video, 1–4 reference character images, and an instruction prompt to swap the character in the source video while keeping the rest unchanged. Ideal for UGC ad variations, meme remixes, film recasting, gaming cinematics, and educational videos when you already have a winning take and need a new face, creator, or persona in the same shot. Try it in the playground: We're launching with 70% discounted usage which ends on 2026-06-08 00:00 CEST! Try it at any of our launch partners: Pruna AI, each::labs, inference shell, Replicate, Runware, Scenario, Tellers, Together AI, WaveSpeedAI, Wiro AI — Ship AI Faster Also available on the Pruna API: We'd love your feedback as you try it. 📚 Model docs & API examples: 📚 API quickstart: ⭐️ OSS: 🌐
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P-Video-Replace is now the fastest model for top-quality character replacement in existing video. - $0.03/s at 720p and $0.06/s at 1080p — a small fraction of typical character-replacement / video-edit pricing (~$0.07–$0.35/s at 720p in our benchmarks) - 3.58 s generation time per second of output video at 720p — roughly ~10× faster than typical alternatives in the same benchmarks (~36–43 s per second of output) - Upload a source video, 1–4 reference character images, and an instruction prompt to swap the character in the source video while keeping the rest unchanged. Ideal for UGC ad variations, meme remixes, film recasting, gaming cinematics, and educational videos when you already have a winning take and need a new face, creator, or persona in the same shot. Try it in the playground: We're launching with 70% discounted usage which ends on 2026-06-08 00:00 CEST! Try it at any of our launch partners: Pruna AI, each::labs, inference shell, Replicate, Runware, Scenario, Tellers, Together AI, WaveSpeedAI, Wiro AI — Ship AI Faster Also available on the Pruna API: We'd love your feedback as you try it. 📚 Model docs & API examples: 📚 API quickstart: ⭐️ OSS: 🌐

Pruna AI

20,098 görüntüleme • 3 ay önce

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:
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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

89,257 görüntüleme • 1 yıl önce

Today, we’re thrilled to unveil the Arcee.ai Foundation Models, a new family of GenAI models designed from the ground up for enterprise reality. The first release—AFM-4.5B—is a 4.5-billion-parameter frontier model that delivers excellent accuracy, strict compliance, and very high cost efficiency. In short: enterprise-grade intelligence that can run anywhere—on a smartphone, at the edge, or in the cloud. For a quick taste, you can test AFM-4.5B in our playground and on For a deeper dive into the model’s training pipeline and benchmarks, details are available in our technical blog post. ➡️ Arcee AI playground: ➡️ Together AI playground: ➡️ Launch blog post: ➡️ Technical blog post: PS: As we’ll now focus on our AFM foundation models, and because we love open-source, we're opening up access to our previously closed-source language models. Details in the tech blog post!
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Today, we’re thrilled to unveil the Arcee.ai Foundation Models, a new family of GenAI models designed from the ground up for enterprise reality. The first release—AFM-4.5B—is a 4.5-billion-parameter frontier model that delivers excellent accuracy, strict compliance, and very high cost efficiency. In short: enterprise-grade intelligence that can run anywhere—on a smartphone, at the edge, or in the cloud. For a quick taste, you can test AFM-4.5B in our playground and on For a deeper dive into the model’s training pipeline and benchmarks, details are available in our technical blog post. ➡️ Arcee AI playground: ➡️ Together AI playground: ➡️ Launch blog post: ➡️ Technical blog post: PS: As we’ll now focus on our AFM foundation models, and because we love open-source, we're opening up access to our previously closed-source language models. Details in the tech blog post!

Arcee.ai

21,934 görüntüleme • 1 yıl önce

I built a Morning Brew-style daily newsletter that writes itself with AI (now at 10,000 daily readers) (and I’m nuts for open-sourcing it) The AI system behind @therecapai clones how a human writer would function, but each step is packaged into an agentic workstream. The full automation handles: - Daily scrapping hundreds of reddit threads, hackernews, twitter posts, Google news API to build a massive data lake of 'daily AI news' - Dozens of custom prompts to pull the top daily stories from this data lake, write short breakdowns, & format the newsletter - Builds custom images based on each story with ChatGPT image generator This was 5 MONTHS of iterating and fine-tuning prompts to get the output and content to an extremely high quality state (no AI slop) and call me crazy, but I'm giving it away for free. Just Retweet, follow me, and comment on this thread "NEWSLETTER" and I'll DM you the full n8n template that you can copy/paste and fine-tune to your use-case as well as a full hour long video breakdown explaining the build. Also you can see the actual contents of the newsletter for yourself with the link below—proof of the quality that is possible with AI. RIP media companies 💀 AI is here.
10:31

I built a Morning Brew-style daily newsletter that writes itself with AI (now at 10,000 daily readers) (and I’m nuts for open-sourcing it) The AI system behind @therecapai clones how a human writer would function, but each step is packaged into an agentic workstream. The full automation handles: - Daily scrapping hundreds of reddit threads, hackernews, twitter posts, Google news API to build a massive data lake of 'daily AI news' - Dozens of custom prompts to pull the top daily stories from this data lake, write short breakdowns, & format the newsletter - Builds custom images based on each story with ChatGPT image generator This was 5 MONTHS of iterating and fine-tuning prompts to get the output and content to an extremely high quality state (no AI slop) and call me crazy, but I'm giving it away for free. Just Retweet, follow me, and comment on this thread "NEWSLETTER" and I'll DM you the full n8n template that you can copy/paste and fine-tune to your use-case as well as a full hour long video breakdown explaining the build. Also you can see the actual contents of the newsletter for yourself with the link below—proof of the quality that is possible with AI. RIP media companies 💀 AI is here.

David Roberts

219,549 görüntüleme • 11 ay önce

🚀Exciting News! Tencent Hunyuan 3D AI Creation Engine v2.5 is here, revolutionizing 3D generation with ultra-high-definition modeling! 1⃣Massive Leap in Precision: Upgraded geometric model with 10B parameters (up from 1B) and 10x more mesh faces, delivering 1024-resolution geometric details—smoother surfaces, sharper edges, and richer visuals! 2⃣High-quality Textures & PBR: Supports high-quality textures and fine-grained bump mapping for lifelike surfaces. Industry-first multi-view PBR generation for realistic lighting and reflections. 3⃣Animation Boost: Optimized skeletal rigging with automatic bone binding and skin weight assignment, streamlining 3D animation workflows. 4⃣Flexible Workflows: New pipeline templates for text-to-3D and image-to-3D mesh reduction, plus multi-view 3D generation, empowering game development, animation, and more with customizable 3D assets. 5⃣Accessible Creation: Generate high-quality 3D models from text or images, slashing complexity. Free daily quota doubled to 20 for the AI Engine, with the API now live on Tencent Cloud! ☀️From games to AR/VR, Hunyuan 3D is redefining 3D AIGC, making creation faster, smarter, and more inclusive. Try it now! Tencent Hunyuan 3D AI Creation Engine: Tencent Hunyuan 3D Open Source:
1:30

🚀Exciting News! Tencent Hunyuan 3D AI Creation Engine v2.5 is here, revolutionizing 3D generation with ultra-high-definition modeling! 1⃣Massive Leap in Precision: Upgraded geometric model with 10B parameters (up from 1B) and 10x more mesh faces, delivering 1024-resolution geometric details—smoother surfaces, sharper edges, and richer visuals! 2⃣High-quality Textures & PBR: Supports high-quality textures and fine-grained bump mapping for lifelike surfaces. Industry-first multi-view PBR generation for realistic lighting and reflections. 3⃣Animation Boost: Optimized skeletal rigging with automatic bone binding and skin weight assignment, streamlining 3D animation workflows. 4⃣Flexible Workflows: New pipeline templates for text-to-3D and image-to-3D mesh reduction, plus multi-view 3D generation, empowering game development, animation, and more with customizable 3D assets. 5⃣Accessible Creation: Generate high-quality 3D models from text or images, slashing complexity. Free daily quota doubled to 20 for the AI Engine, with the API now live on Tencent Cloud! ☀️From games to AR/VR, Hunyuan 3D is redefining 3D AIGC, making creation faster, smarter, and more inclusive. Try it now! Tencent Hunyuan 3D AI Creation Engine: Tencent Hunyuan 3D Open Source:

Tencent Hy

58,225 görüntüleme • 1 yıl önce

Tested this concepting workflow. ▶️ Converting a level design image to a 3D asset gallery image ▶️ re-generating segmented subjects into separate high quality images. ▶️ using these images to generate the 3D meshes. First time using 'for loops' in ComfyUI, works great and the prompts are updated for every segment. I used the Nano Banana 2 API node in ComfyUI to generate the final high quality images. Those SAM3 nodes are crazy btw, just set some points and it creates a mask! For the 3D model generation I used the new Tripo Smart Mesh tool, which is fast! The output is not perfect yet. I think in a lot of cases not production ready right now but that depends on the input image and the goal as well. For pre-production / concepting phase it's perfect and for far distance meshes it’s fine. I’m sure these tools keep improving, curious what we can work with in 3-6 months. Minimal cleanup and scaling in Blender and tested the meshes in UEFN / Unreal Engine. After creating the ComfyUI workflow it took less than an hour to do everything.
1:28

Tested this concepting workflow. ▶️ Converting a level design image to a 3D asset gallery image ▶️ re-generating segmented subjects into separate high quality images. ▶️ using these images to generate the 3D meshes. First time using 'for loops' in ComfyUI, works great and the prompts are updated for every segment. I used the Nano Banana 2 API node in ComfyUI to generate the final high quality images. Those SAM3 nodes are crazy btw, just set some points and it creates a mask! For the 3D model generation I used the new Tripo Smart Mesh tool, which is fast! The output is not perfect yet. I think in a lot of cases not production ready right now but that depends on the input image and the goal as well. For pre-production / concepting phase it's perfect and for far distance meshes it’s fine. I’m sure these tools keep improving, curious what we can work with in 3-6 months. Minimal cleanup and scaling in Blender and tested the meshes in UEFN / Unreal Engine. After creating the ComfyUI workflow it took less than an hour to do everything.

Jerome | InsaneUnreal

17,461 görüntüleme • 5 ay önce

STEAL THE BRAINROT is expanding. We’re hiring a high-level UEFN Level Designer to lead our in-game events. We’re looking for someone very experienced and creative, who knows how to bring their talent and ideas into high-quality in-game events. There is an existing structure and visual style, and the role requires the ability to adapt and build new events based on what has already been done. Requirements: - Several years of experience in level / environment design - Strong portfolio - All assets must be custom made - Strong experience with Blender - Deep understanding of materials - Advanced UEFN experience (materials, VFX, optimization) - Full-time availability - Available to start immediately We’re looking for talented people with real experience, not beginners. Compensation is high and based on experience. A signing bonus is available for the right candidate. Below are examples of past events, this is the quality bar: 100% custom assets, modeled in Blender, with custom materials & VFX in UEFN, fully optimized. DM me with your portfolio.
0:41

STEAL THE BRAINROT is expanding. We’re hiring a high-level UEFN Level Designer to lead our in-game events. We’re looking for someone very experienced and creative, who knows how to bring their talent and ideas into high-quality in-game events. There is an existing structure and visual style, and the role requires the ability to adapt and build new events based on what has already been done. Requirements: - Several years of experience in level / environment design - Strong portfolio - All assets must be custom made - Strong experience with Blender - Deep understanding of materials - Advanced UEFN experience (materials, VFX, optimization) - Full-time availability - Available to start immediately We’re looking for talented people with real experience, not beginners. Compensation is high and based on experience. A signing bonus is available for the right candidate. Below are examples of past events, this is the quality bar: 100% custom assets, modeled in Blender, with custom materials & VFX in UEFN, fully optimized. DM me with your portfolio.

FeRinS

138,287 görüntüleme • 9 ay önce

TokenFlow: Consistent Diffusion Features for Consistent Video Editing paper page: The generative AI revolution has recently expanded to videos. Nevertheless, current state-of-the-art video models are still lagging behind image models in terms of visual quality and user control over the generated content. In this work, we present a framework that harnesses the power of a text-to-image diffusion model for the task of text-driven video editing. Specifically, given a source video and a target text-prompt, our method generates a high-quality video that adheres to the target text, while preserving the spatial layout and motion of the input video. Our method is based on a key observation that consistency in the edited video can be obtained by enforcing consistency in the diffusion feature space. We achieve this by explicitly propagating diffusion features based on inter-frame correspondences, readily available in the model. Thus, our framework does not require any training or fine-tuning, and can work in conjunction with any off-the-shelf text-to-image editing method. We demonstrate state-of-the-art editing results on a variety of real-world videos.
0:36

TokenFlow: Consistent Diffusion Features for Consistent Video Editing paper page: The generative AI revolution has recently expanded to videos. Nevertheless, current state-of-the-art video models are still lagging behind image models in terms of visual quality and user control over the generated content. In this work, we present a framework that harnesses the power of a text-to-image diffusion model for the task of text-driven video editing. Specifically, given a source video and a target text-prompt, our method generates a high-quality video that adheres to the target text, while preserving the spatial layout and motion of the input video. Our method is based on a key observation that consistency in the edited video can be obtained by enforcing consistency in the diffusion feature space. We achieve this by explicitly propagating diffusion features based on inter-frame correspondences, readily available in the model. Thus, our framework does not require any training or fine-tuning, and can work in conjunction with any off-the-shelf text-to-image editing method. We demonstrate state-of-the-art editing results on a variety of real-world videos.

AK

143,469 görüntüleme • 3 yıl önce

Today we are announcing Keenable AI, an AI-native index of the best human knowledge we have, starting with the open web. AI seems to know just about everything until you ask it about something you know deeply. The answer isn’t wrong, but it's just very average. We started Keenable to fix this problem: every model and every agent should be able to query, reason over, and continuously learn from the living web. Backed by a $26M Seed from Accel and Conviction. Built by the team that took on Google at Yandex Search, with researchers and engineers from Amazon AGI, X, and Perplexity. The Web Search API and Web Query Language for AI are live now. Together they make the web queryable at AI scale. Both are free until end of September. Seven days of benchmarks, stories, and launches ahead. Let your agents search.
1:08

Today we are announcing Keenable AI, an AI-native index of the best human knowledge we have, starting with the open web. AI seems to know just about everything until you ask it about something you know deeply. The answer isn’t wrong, but it's just very average. We started Keenable to fix this problem: every model and every agent should be able to query, reason over, and continuously learn from the living web. Backed by a $26M Seed from Accel and Conviction. Built by the team that took on Google at Yandex Search, with researchers and engineers from Amazon AGI, X, and Perplexity. The Web Search API and Web Query Language for AI are live now. Together they make the web queryable at AI scale. Both are free until end of September. Seven days of benchmarks, stories, and launches ahead. Let your agents search.

Andrey Styskin

545,494 görüntüleme • 15 gün önce

.cova on why AI made creativity more valuable: "The rise of AI has given way to a massive flooding of the internet. As a creator, I've noticed a lot of audiences have realized this, and the work that really cuts through the noise is work that is really resonant. You can tell it comes from a place the creator is deeply passionate about, and it feels very authentic. It's made for connection, but it's also very true to the person making it." "With AI producing this flood of content, people have felt like the media they're consuming feels more rootless. And therefore the high-taste, high-creativity stuff that's actually crafted by the person, people are starting to value way more." "I use so much AI, and a lot of it's invisible, a lot of it's very visible. I've found a way to automate away the logistical side of things, so I can focus on the creativity. I built a bunch of custom tools for video-making, and I built them all with AI. Even though I prefer to edit myself and I script everything myself, I still use AI to bounce ideas off of." "The way I initially blew up was I used AI to transform my dorm room into different crazy sets, and people thought I was a professional. I was by no means a professional. It was my tiny dorm room, and I added a piano there, a vinyl record player, fairy lights, a moving robot in the background, cool lighting. It was all AI. I really accredit AI to a lot of my success."
2:26

.cova on why AI made creativity more valuable: "The rise of AI has given way to a massive flooding of the internet. As a creator, I've noticed a lot of audiences have realized this, and the work that really cuts through the noise is work that is really resonant. You can tell it comes from a place the creator is deeply passionate about, and it feels very authentic. It's made for connection, but it's also very true to the person making it." "With AI producing this flood of content, people have felt like the media they're consuming feels more rootless. And therefore the high-taste, high-creativity stuff that's actually crafted by the person, people are starting to value way more." "I use so much AI, and a lot of it's invisible, a lot of it's very visible. I've found a way to automate away the logistical side of things, so I can focus on the creativity. I built a bunch of custom tools for video-making, and I built them all with AI. Even though I prefer to edit myself and I script everything myself, I still use AI to bounce ideas off of." "The way I initially blew up was I used AI to transform my dorm room into different crazy sets, and people thought I was a professional. I was by no means a professional. It was my tiny dorm room, and I added a piano there, a vinyl record player, fairy lights, a moving robot in the background, cool lighting. It was all AI. I really accredit AI to a lot of my success."

MTS

10,699 görüntüleme • 1 ay önce

Want to create an avatar from a single image? FlexAvatar is a transformer model that creates full 360°, high-quality, and expressive 3D head avatar from just a single portrait image in minutes. Real-time Demo: FlexAvatar's lightweight architecture allows both animation and rendering in real-time, enabling interactive user experiences. To create a new 3D head avatar, only one image is required, e.g., from a webcam. The final avatar is ready after 2 minutes. Architecture: Under the hood, FlexAvatar adopts a transformer-based encoder-decoder design. The encoder maps the input image onto a latent avatar space, while the decoder produces 3D Gaussian attribute maps by incorporating the animation signal via cross-attention. The model learns all facial animations directly from the data without relying on pre-built 3D face models. This equips the avatars with realistic facial expressions. The internal avatar latent space can be conveniently used to integrate additional observations of a person via fitting. This enables use-cases where more than one image of a person is available, e.g., from a phone scan of the person. We train jointly on 2D monocular videos and multi-view data. However, in monocular videos, the animation signal leaks the target viewpoint, causing the model to produce incomplete 3D heads. We call this phenomenon entanglement of driving signal and target viewpoint. To prevent entanglement, we introduce bias sinks. These are learnable tokens that indicate whether a training sample stems from a monocular or a multi-view dataset. During training, the model learns to produce incomplete 3D heads only when the monocular token is present. During inference, FlexAvatar then always uses the multi-view token for which the model has learned to produce complete 3D heads. This simple design allows to combine the generalizability from monocular data with the quality of multi-view data. FlexAvatar summary: - Input: Single-image, phone scan, or monocular video - Output: Full 360° head avatar - Expressive animations - Real-time rendering and animation - Generalization to any portrait - Create a new avatar in 2 minutes - Use bias sinks to combine 2D and 3D data 🏠 🌍 🎥 Great work by Tobias Kirschstein and Simon Giebenhain!
0:30

Want to create an avatar from a single image? FlexAvatar is a transformer model that creates full 360°, high-quality, and expressive 3D head avatar from just a single portrait image in minutes. Real-time Demo: FlexAvatar's lightweight architecture allows both animation and rendering in real-time, enabling interactive user experiences. To create a new 3D head avatar, only one image is required, e.g., from a webcam. The final avatar is ready after 2 minutes. Architecture: Under the hood, FlexAvatar adopts a transformer-based encoder-decoder design. The encoder maps the input image onto a latent avatar space, while the decoder produces 3D Gaussian attribute maps by incorporating the animation signal via cross-attention. The model learns all facial animations directly from the data without relying on pre-built 3D face models. This equips the avatars with realistic facial expressions. The internal avatar latent space can be conveniently used to integrate additional observations of a person via fitting. This enables use-cases where more than one image of a person is available, e.g., from a phone scan of the person. We train jointly on 2D monocular videos and multi-view data. However, in monocular videos, the animation signal leaks the target viewpoint, causing the model to produce incomplete 3D heads. We call this phenomenon entanglement of driving signal and target viewpoint. To prevent entanglement, we introduce bias sinks. These are learnable tokens that indicate whether a training sample stems from a monocular or a multi-view dataset. During training, the model learns to produce incomplete 3D heads only when the monocular token is present. During inference, FlexAvatar then always uses the multi-view token for which the model has learned to produce complete 3D heads. This simple design allows to combine the generalizability from monocular data with the quality of multi-view data. FlexAvatar summary: - Input: Single-image, phone scan, or monocular video - Output: Full 360° head avatar - Expressive animations - Real-time rendering and animation - Generalization to any portrait - Create a new avatar in 2 minutes - Use bias sinks to combine 2D and 3D data 🏠 🌍 🎥 Great work by Tobias Kirschstein and Simon Giebenhain!

Matthias Niessner

96,319 görüntüleme • 8 ay önce

🔥HOLY SMOKES! $TAO holders! 🚀 SUBNET 19 (VISION) ON BITTENSOR IS ABSOLUTELY CRUSHING IT! In my 5+ years covering crypto and AI, this is one of the most impressive implementations I've seen. The combination of scale, performance, and decentralization is absolutely next level! 🚀 @namoray_dev @Corcel_X 💨 INSANE Speed Performance: - Llama 3.1 8B: 196.18 tokens/s with +107.23% advantage - Llama 3.1 70B: 124.96 tokens/s with +154.96% advantage - Llama 3.2 3B: 166.69 tokens/s with +21.66% advantage 🔥 Top Tier Model Integration: - Meta-Llama-3-70B & 8B Instruct - FLUX.1-schnell for Text-to-Image - ProteusV0.4-Lightning (Text & Image) - Multiple model variations for redundancy 🔥 What Makes This INSANE: - Complete decentralization - No single point of failure - Multiple model choices for redundancy - Real-time performance tracking - Transparent incentive structure The incentive distribution curve shows a healthy network with: - Strong rewards for top performers - Fair distribution across all participants - Clear path for growth and improvement - Sustainable economic model What's truly MIND-BLOWING is how they've managed to: 1. Scale to millions of operations 2. Maintain high quality across multiple tasks 3. Create a fair, competitive marketplace 4. Build in redundancy and reliability 5. Achieve true decentralization This isn't just another subnet - this is the future of decentralized AI inference happening RIGHT NOW! 🔥 1. MASSIVE Scale & Adoption: - We're seeing 7M+ tokens being processed - 14K+ processing steps being executed - Multiple AI models running simultaneously - Incredible miner participation across the network 2. Revolutionary Task Distribution: - Llama 3.1 70B leading with 20% weighting - Avatar Generation at 15% - Perfectly balanced task distribution for optimal network performance - Multiple specialized tasks including Text-to-Image and Image-to-Image processing 3. Elite Performance Metrics: - Top miners hitting 0.00775 incentive rates - Consistent performance across the network - Impressive scaling from top to bottom performers - Strong incentive curve maintaining network quality 📈 Network Performance: - Consistent upward trend in tokens/s - Quality scores maintaining high levels (>0.9) - Steady improvement in miner performance - Rock-solid network reliability ⚡ Platform Highlights: - Permissionless, serverless architecture - Global network of Always-On GPUs - Instant API access - Full decentralization - Multi-model support with seamless switching What makes this TRULY SPECIAL is the consistent upward trajectory in both speed and quality, while maintaining a decentralized architecture. The performance advantages over industry standards (+154.96% for 70B!) are absolutely mind-blowing! 🚀 This isn't just another AI subnet - it's a glimpse into the future of decentralized AI inference! The combination of speed, reliability, and model variety makes this one of the most impressive implementations in the space! 🔥 📽 Watch Now on YouTube and TikTok: Source 🔗
1:39

🔥HOLY SMOKES! $TAO holders! 🚀 SUBNET 19 (VISION) ON BITTENSOR IS ABSOLUTELY CRUSHING IT! In my 5+ years covering crypto and AI, this is one of the most impressive implementations I've seen. The combination of scale, performance, and decentralization is absolutely next level! 🚀 @namoray_dev @Corcel_X 💨 INSANE Speed Performance: - Llama 3.1 8B: 196.18 tokens/s with +107.23% advantage - Llama 3.1 70B: 124.96 tokens/s with +154.96% advantage - Llama 3.2 3B: 166.69 tokens/s with +21.66% advantage 🔥 Top Tier Model Integration: - Meta-Llama-3-70B & 8B Instruct - FLUX.1-schnell for Text-to-Image - ProteusV0.4-Lightning (Text & Image) - Multiple model variations for redundancy 🔥 What Makes This INSANE: - Complete decentralization - No single point of failure - Multiple model choices for redundancy - Real-time performance tracking - Transparent incentive structure The incentive distribution curve shows a healthy network with: - Strong rewards for top performers - Fair distribution across all participants - Clear path for growth and improvement - Sustainable economic model What's truly MIND-BLOWING is how they've managed to: 1. Scale to millions of operations 2. Maintain high quality across multiple tasks 3. Create a fair, competitive marketplace 4. Build in redundancy and reliability 5. Achieve true decentralization This isn't just another subnet - this is the future of decentralized AI inference happening RIGHT NOW! 🔥 1. MASSIVE Scale & Adoption: - We're seeing 7M+ tokens being processed - 14K+ processing steps being executed - Multiple AI models running simultaneously - Incredible miner participation across the network 2. Revolutionary Task Distribution: - Llama 3.1 70B leading with 20% weighting - Avatar Generation at 15% - Perfectly balanced task distribution for optimal network performance - Multiple specialized tasks including Text-to-Image and Image-to-Image processing 3. Elite Performance Metrics: - Top miners hitting 0.00775 incentive rates - Consistent performance across the network - Impressive scaling from top to bottom performers - Strong incentive curve maintaining network quality 📈 Network Performance: - Consistent upward trend in tokens/s - Quality scores maintaining high levels (>0.9) - Steady improvement in miner performance - Rock-solid network reliability ⚡ Platform Highlights: - Permissionless, serverless architecture - Global network of Always-On GPUs - Instant API access - Full decentralization - Multi-model support with seamless switching What makes this TRULY SPECIAL is the consistent upward trajectory in both speed and quality, while maintaining a decentralized architecture. The performance advantages over industry standards (+154.96% for 70B!) are absolutely mind-blowing! 🚀 This isn't just another AI subnet - it's a glimpse into the future of decentralized AI inference! The combination of speed, reliability, and model variety makes this one of the most impressive implementations in the space! 🔥 📽 Watch Now on YouTube and TikTok: Source 🔗

Andy ττ

11,616 görüntüleme • 1 yıl önce

Sora 2 API + n8n for UGC videos ... absolutely insane 🤯 this n8n workflow generates realistic influencer-style videos from just a product image. No watermark. Fully automated. (and I'm giving it away for free) Here's what this system does: → Analyzes your product image with AI vision → Creates the perfect influencer persona to promote it → Generates multiple UGC video scripts automatically → Uses OpenAI's Sora 2 to create realistic videos → Outputs videos ready for Instagram, TikTok, and Facebook The results? What used to take weeks and thousands of dollars now happens in minutes for under $5 per video. This isn't just about cutting costs – it's about scaling your creative production and driving your ROAS 50%+ higher due to on-demand, high-converting video content. Brands and agencies today spend $10,000+ per month hiring creators, shipping products, waiting weeks, and hoping for a few decent videos back. This changes the whole game for $1 per video. Want the full n8n template, all of the prompts, and a full step-by-step setup video? 1. Like & RT this post 2. Follow me (so I can dm you) 3. Comment "UGC" below I'll send you the entire system for free.
3:45

Sora 2 API + n8n for UGC videos ... absolutely insane 🤯 this n8n workflow generates realistic influencer-style videos from just a product image. No watermark. Fully automated. (and I'm giving it away for free) Here's what this system does: → Analyzes your product image with AI vision → Creates the perfect influencer persona to promote it → Generates multiple UGC video scripts automatically → Uses OpenAI's Sora 2 to create realistic videos → Outputs videos ready for Instagram, TikTok, and Facebook The results? What used to take weeks and thousands of dollars now happens in minutes for under $5 per video. This isn't just about cutting costs – it's about scaling your creative production and driving your ROAS 50%+ higher due to on-demand, high-converting video content. Brands and agencies today spend $10,000+ per month hiring creators, shipping products, waiting weeks, and hoping for a few decent videos back. This changes the whole game for $1 per video. Want the full n8n template, all of the prompts, and a full step-by-step setup video? 1. Like & RT this post 2. Follow me (so I can dm you) 3. Comment "UGC" below I'll send you the entire system for free.

David Roberts

138,072 görüntüleme • 11 ay önce

Seedance 2.0 Emotional Coordinates Meet Valence-Arousal. I’ve been experimenting with valence-arousal prompting in Seedance 2.0 on mitte.ai, and surprisingly, a lot of it actually works. Most likely this should transfer to many other video/image models too. Before we start, I should mention that a lot of attempts can hit safety/violence filters depending on how extreme the emotional state becomes. My test prompts were also intentionally very simple and open-ended, which probably increased that. Valence measures how positive or negative an emotional state is. Arousal measures how calm or activated it is. So instead of writing: “sad, anxious, emotionally overwhelmed” you can try things like: valence: low arousal: high I tested mostly numeric values at first, but honestly I’ve been getting much better and more stable results with transition-based prompting like this: “The emotional state gradually shifts from high valence and low arousal to low valence and high arousal.” That feels much more model-friendly right now. I think this could become really useful for subtle acting, emotional transitions, cinematic dialogue scenes, uncanny performances and mood-driven storytelling. Leaving example Seedance 2.0 prompts, the Valence-Arousal infographic and a GPT Image 2 prompt below.
1:03

Seedance 2.0 Emotional Coordinates Meet Valence-Arousal. I’ve been experimenting with valence-arousal prompting in Seedance 2.0 on mitte.ai, and surprisingly, a lot of it actually works. Most likely this should transfer to many other video/image models too. Before we start, I should mention that a lot of attempts can hit safety/violence filters depending on how extreme the emotional state becomes. My test prompts were also intentionally very simple and open-ended, which probably increased that. Valence measures how positive or negative an emotional state is. Arousal measures how calm or activated it is. So instead of writing: “sad, anxious, emotionally overwhelmed” you can try things like: valence: low arousal: high I tested mostly numeric values at first, but honestly I’ve been getting much better and more stable results with transition-based prompting like this: “The emotional state gradually shifts from high valence and low arousal to low valence and high arousal.” That feels much more model-friendly right now. I think this could become really useful for subtle acting, emotional transitions, cinematic dialogue scenes, uncanny performances and mood-driven storytelling. Leaving example Seedance 2.0 prompts, the Valence-Arousal infographic and a GPT Image 2 prompt below.

Kōda

54,465 görüntüleme • 4 ay önce

DisCo: Disentangled Control for Referring Human Dance Generation in Real World paper page: Generative AI has made significant strides in computer vision, particularly in image/video synthesis conditioned on text descriptions. Despite the advancements, it remains challenging especially in the generation of human-centric content such as dance synthesis. Existing dance synthesis methods struggle with the gap between synthesized content and real-world dance scenarios. In this paper, we define a new problem setting: Referring Human Dance Generation, which focuses on real-world dance scenarios with three important properties: (i) Faithfulness: the synthesis should retain the appearance of both human subject foreground and background from the reference image, and precisely follow the target pose; (ii) Generalizability: the model should generalize to unseen human subjects, backgrounds, and poses; (iii) Compositionality: it should allow for composition of seen/unseen subjects, backgrounds, and poses from different sources. To address these challenges, we introduce a novel approach, DISCO, which includes a novel model architecture with disentangled control to improve the faithfulness and compositionality of dance synthesis, and an effective human attribute pre-training for better generalizability to unseen humans. Extensive qualitative and quantitative results demonstrate that DISCO can generate high-quality human dance images and videos with diverse appearances and flexible motions.
0:29

DisCo: Disentangled Control for Referring Human Dance Generation in Real World paper page: Generative AI has made significant strides in computer vision, particularly in image/video synthesis conditioned on text descriptions. Despite the advancements, it remains challenging especially in the generation of human-centric content such as dance synthesis. Existing dance synthesis methods struggle with the gap between synthesized content and real-world dance scenarios. In this paper, we define a new problem setting: Referring Human Dance Generation, which focuses on real-world dance scenarios with three important properties: (i) Faithfulness: the synthesis should retain the appearance of both human subject foreground and background from the reference image, and precisely follow the target pose; (ii) Generalizability: the model should generalize to unseen human subjects, backgrounds, and poses; (iii) Compositionality: it should allow for composition of seen/unseen subjects, backgrounds, and poses from different sources. To address these challenges, we introduce a novel approach, DISCO, which includes a novel model architecture with disentangled control to improve the faithfulness and compositionality of dance synthesis, and an effective human attribute pre-training for better generalizability to unseen humans. Extensive qualitative and quantitative results demonstrate that DISCO can generate high-quality human dance images and videos with diverse appearances and flexible motions.

AK

161,479 görüntüleme • 3 yıl önce