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LTX 2.3 Creative Upscale IC-LoRA. - Generative second-pass refiner for soft or low-resolution video; - enhances detail and clarity without standard upscaling; - output varies based on workflow/settings.

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Wildminder

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17,151 просмотров • 2 месяцев назад •via X (Twitter)

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No reshoot required. Our new Clean Plate IC-LoRA for LTX-2.3 removes people, pedestrians, and vehicles from a clip and rebuilds the empty background behind them. 🎬 Full-frame subject removal, no mask required 🏗️ Keeps architecture, ground markings & foliage intact ⚙️ Runs as a video-to-video LoRA on ComfyUI
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No reshoot required. Our new Clean Plate IC-LoRA for LTX-2.3 removes people, pedestrians, and vehicles from a clip and rebuilds the empty background behind them. 🎬 Full-frame subject removal, no mask required 🏗️ Keeps architecture, ground markings & foliage intact ⚙️ Runs as a video-to-video LoRA on ComfyUI

LTX.io

162,141 просмотров • 11 дней назад

LTX 2.3 is now live on Krea. this video model generates for 1/10th the cost of other top models and can learn any style or character - just upload some references and train a lora try it now 👇
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LTX 2.3 is now live on Krea. this video model generates for 1/10th the cost of other top models and can learn any style or character - just upload some references and train a lora try it now 👇

Krea

21,845 просмотров • 3 месяцев назад

Stop relying on generic AI output Train a model on your world, your character, your style, and reuse it forever That is the idea behind LTX Trainer, an open-source training framework that lets you train reusable IC-LoRAs for specific video transformations like colorization, deblur, decompression, reference sheet control, and a lot more Each LoRA gives the model a specific behavior it can repeat across clips, so your videos become more consistent, more controlled, and more connected to your own creative direction Check these amazing examples 👇🏻
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Stop relying on generic AI output Train a model on your world, your character, your style, and reuse it forever That is the idea behind LTX Trainer, an open-source training framework that lets you train reusable IC-LoRAs for specific video transformations like colorization, deblur, decompression, reference sheet control, and a lot more Each LoRA gives the model a specific behavior it can repeat across clips, so your videos become more consistent, more controlled, and more connected to your own creative direction Check these amazing examples 👇🏻

Amira Zairi

28,574 просмотров • 1 месяц назад

Many people have asked how I make my original videos look so clear, smooth, and polished. For video upscaling and enhancement, I consistently rely on the suite of tools available from Beth Allison Labs. The two models I use most often are Astra 2 and Starlight Precise 2.5. Rather than competing with each other, they serve different purposes. There’s no single “best” model, just the right tool for the specific job. If you're creating content with Seedance 2.0, these upscaling models make an excellent addition to your workflow. Below, you'll find examples processed with Astra 2, and in the comments I'll share results from Starlight Precise 2.5. Astra 2 is Topaz's next-generation creative video upscaling model, featuring adjustable enhancement levels and prompting capabilities. It excels at correcting common AI-generated video issues such as warped details, flickering, and inconsistent textures, helping AI content appear more polished and realistic. Best used for: • AI-generated videos that need significant detail enhancement • Footage with artifacts, distortions, or missing textures • Projects that benefit from additional creative reconstruction
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Many people have asked how I make my original videos look so clear, smooth, and polished. For video upscaling and enhancement, I consistently rely on the suite of tools available from Beth Allison Labs. The two models I use most often are Astra 2 and Starlight Precise 2.5. Rather than competing with each other, they serve different purposes. There’s no single “best” model, just the right tool for the specific job. If you're creating content with Seedance 2.0, these upscaling models make an excellent addition to your workflow. Below, you'll find examples processed with Astra 2, and in the comments I'll share results from Starlight Precise 2.5. Astra 2 is Topaz's next-generation creative video upscaling model, featuring adjustable enhancement levels and prompting capabilities. It excels at correcting common AI-generated video issues such as warped details, flickering, and inconsistent textures, helping AI content appear more polished and realistic. Best used for: • AI-generated videos that need significant detail enhancement • Footage with artifacts, distortions, or missing textures • Projects that benefit from additional creative reconstruction

awesome_visuals

43,568 просмотров • 1 месяц назад

MaskINT: Video Editing via Interpolative Non-autoregressive Masked Transformers paper page: Recent advances in generative AI have significantly enhanced image and video editing, particularly in the context of text prompt control. State-of-the-art approaches predominantly rely on diffusion models to accomplish these tasks. However, the computational demands of diffusion-based methods are substantial, often necessitating large-scale paired datasets for training, and therefore challenging the deployment in practical applications. This study addresses this challenge by breaking down the text-based video editing process into two separate stages. In the first stage, we leverage an existing text-to-image diffusion model to simultaneously edit a few keyframes without additional fine-tuning. In the second stage, we introduce an efficient model called MaskINT, which is built on non-autoregressive masked generative transformers and specializes in frame interpolation between the keyframes, benefiting from structural guidance provided by intermediate frames. Our comprehensive set of experiments illustrates the efficacy and efficiency of MaskINT when compared to other diffusion-based methodologies. This research offers a practical solution for text-based video editing and showcases the potential of non-autoregressive masked generative transformers in this domain.
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MaskINT: Video Editing via Interpolative Non-autoregressive Masked Transformers paper page: Recent advances in generative AI have significantly enhanced image and video editing, particularly in the context of text prompt control. State-of-the-art approaches predominantly rely on diffusion models to accomplish these tasks. However, the computational demands of diffusion-based methods are substantial, often necessitating large-scale paired datasets for training, and therefore challenging the deployment in practical applications. This study addresses this challenge by breaking down the text-based video editing process into two separate stages. In the first stage, we leverage an existing text-to-image diffusion model to simultaneously edit a few keyframes without additional fine-tuning. In the second stage, we introduce an efficient model called MaskINT, which is built on non-autoregressive masked generative transformers and specializes in frame interpolation between the keyframes, benefiting from structural guidance provided by intermediate frames. Our comprehensive set of experiments illustrates the efficacy and efficiency of MaskINT when compared to other diffusion-based methodologies. This research offers a practical solution for text-based video editing and showcases the potential of non-autoregressive masked generative transformers in this domain.

AK

25,449 просмотров • 2 лет назад

This tool is literally Higgsfield AI but FREE for good. It's called Wan2GP. A full AI video studio built specifically for people without expensive hardware. Runs on as little as 6GB of VRAM, even old RTX 10-series cards and 8GB laptops. Everything stays on your machine, no uploads, no caps, no watermarks. What you get in one app: • Text-to-video and image-to-video generation • The best open models built in: Wan 2.2, LTX-2, Hunyuan Video, Flux • A full browser interface with a queue system • LoRA support to customize any model • Mask editor and prompt enhancer included A 5-second clip generates in minutes on a mid-range gaming rig. No subscription, ever. 100% Free. Open Source.
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This tool is literally Higgsfield AI but FREE for good. It's called Wan2GP. A full AI video studio built specifically for people without expensive hardware. Runs on as little as 6GB of VRAM, even old RTX 10-series cards and 8GB laptops. Everything stays on your machine, no uploads, no caps, no watermarks. What you get in one app: • Text-to-video and image-to-video generation • The best open models built in: Wan 2.2, LTX-2, Hunyuan Video, Flux • A full browser interface with a queue system • LoRA support to customize any model • Mask editor and prompt enhancer included A 5-second clip generates in minutes on a mid-range gaming rig. No subscription, ever. 100% Free. Open Source.

Simplifying AI

105,050 просмотров • 2 дней назад

🚨 Brooklyn man Nicholas Matthew Scelfo, 27, has been charged with threatening to assault and murder an ICE officer and his entire family outside a Newark detention facility. “I’ll kill your whole f—— family! Your children, your wife, all dead! I have your face!” he allegedly screamed. This AI-enhanced video is based on original footage captured by independent journalist Nick Sortor. I used Grok Imagine to upscale and smooth the resolution for better clarity. The FBI used facial recognition and arrested the suspect within 24 hours. Threats against law enforcement and their families are not “protest” — they’re felonies. Maximum of 10 years in prison. This has to stop. ICE officers are doing the dangerous job no one else will. Stand with them. 🇺🇸 Do you think Scelfo will actually be convicted and serve real jail time? (Video: AI)
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🚨 Brooklyn man Nicholas Matthew Scelfo, 27, has been charged with threatening to assault and murder an ICE officer and his entire family outside a Newark detention facility. “I’ll kill your whole f—— family! Your children, your wife, all dead! I have your face!” he allegedly screamed. This AI-enhanced video is based on original footage captured by independent journalist Nick Sortor. I used Grok Imagine to upscale and smooth the resolution for better clarity. The FBI used facial recognition and arrested the suspect within 24 hours. Threats against law enforcement and their families are not “protest” — they’re felonies. Maximum of 10 years in prison. This has to stop. ICE officers are doing the dangerous job no one else will. Stand with them. 🇺🇸 Do you think Scelfo will actually be convicted and serve real jail time? (Video: AI)

Paul A. Szypula 🇺🇸

140,190 просмотров • 2 месяцев назад

Introducing Kaleido💮 from AI at Meta — a universal generative neural rendering engine for photorealistic, unified object and scene view synthesis. Kaleido is built on a simple but powerful design philosophy: 3D perception is a form of visual common sense. Following this idea, we formulate rendering purely as a sequence-to-sequence generation problem, successfully unifying neural rendering with the architecture principles behind modern language and video models. Unlike traditional neural rendering methods, Kaleido learns 3D purely in a data-driven way, without explicit 3D representations or structures. It acquires spatial understanding directly through large-scale video pretraining, then multi-view 3D data finetuning, inspired by how LLMs acquire textual common sense from large corpora before specialising in domains like coding. Through extensive ablations, we progressively modernised the architecture design and training strategies and tackled key scaling challenges in sequence-to-sequence generative rendering, arriving at a design that’s simple, versatile, and scalable. Kaleido significantly outperforms prior generative models in few-view settings, and remarkably is the first zero-shot generative method matches InstantNGP-level rendering quality in multi-view settings. We view Kaleido also as an alternative step towards world modeling that flexibly spans a spectrum of “realities": with many views, it faithfully reconstructs grounded reality; with fewer views, it imagines plausible unseen details. 🔗 Explore more results and paper:
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Introducing Kaleido💮 from AI at Meta — a universal generative neural rendering engine for photorealistic, unified object and scene view synthesis. Kaleido is built on a simple but powerful design philosophy: 3D perception is a form of visual common sense. Following this idea, we formulate rendering purely as a sequence-to-sequence generation problem, successfully unifying neural rendering with the architecture principles behind modern language and video models. Unlike traditional neural rendering methods, Kaleido learns 3D purely in a data-driven way, without explicit 3D representations or structures. It acquires spatial understanding directly through large-scale video pretraining, then multi-view 3D data finetuning, inspired by how LLMs acquire textual common sense from large corpora before specialising in domains like coding. Through extensive ablations, we progressively modernised the architecture design and training strategies and tackled key scaling challenges in sequence-to-sequence generative rendering, arriving at a design that’s simple, versatile, and scalable. Kaleido significantly outperforms prior generative models in few-view settings, and remarkably is the first zero-shot generative method matches InstantNGP-level rendering quality in multi-view settings. We view Kaleido also as an alternative step towards world modeling that flexibly spans a spectrum of “realities": with many views, it faithfully reconstructs grounded reality; with fewer views, it imagines plausible unseen details. 🔗 Explore more results and paper:

Shikun Liu

22,367 просмотров • 9 месяцев назад

Yeaah with Kartel.ai and Jesse Wellens ! So training Lora for character on Flux and also for Wan 2.1 14B ( using dataset video ) make a Huge difference. So here you are seeing a volumetric capture of Jesse Wellens that we did in at the spatial studio of Kartel.ai We use a a World Labs gaussian splatting to create a simple environment that we put with the volumetric capture in our own webgl viewer ( out soon for everyone ). After that, We use after this output in a ComfyUI workflow with Wan Fun control ( a bit similar Vace but its working for Wan 14B) with the double loras , the first to generate the first frame with flux, and the second one to guide the generation of Wan 2.1 Control. So we keep very good consistency of character and position, and creating amazing worlds :) ! Hope you will find it cool !
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Yeaah with Kartel.ai and Jesse Wellens ! So training Lora for character on Flux and also for Wan 2.1 14B ( using dataset video ) make a Huge difference. So here you are seeing a volumetric capture of Jesse Wellens that we did in at the spatial studio of Kartel.ai We use a a World Labs gaussian splatting to create a simple environment that we put with the volumetric capture in our own webgl viewer ( out soon for everyone ). After that, We use after this output in a ComfyUI workflow with Wan Fun control ( a bit similar Vace but its working for Wan 14B) with the double loras , the first to generate the first frame with flux, and the second one to guide the generation of Wan 2.1 Control. So we keep very good consistency of character and position, and creating amazing worlds :) ! Hope you will find it cool !

Lovis Odin

21,621 просмотров • 1 год назад

Most video tools can generate clips. Very few can maintain identity. That has been the real bottleneck in AI video creation. Kling O1 changes that. For the first time, creators can carry a character, style, and visual language across scenes without constant fixes. You can reference past clips, assets, or images and the output stays consistently on-model. No visual drift. No rework loops. No “this doesn’t look like the last shot” moments. It feels less like prompting a tool and more like working with a creative collaborator that remembers context. The impact is practical, not theoretical: → Faster production cycles → Lower iteration costs → Noticeably higher output quality This is what mature AI tooling looks like. Not louder features. Not bigger claims. Just reliability where it actually matters. Consistency is no longer the problem.
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Most video tools can generate clips. Very few can maintain identity. That has been the real bottleneck in AI video creation. Kling O1 changes that. For the first time, creators can carry a character, style, and visual language across scenes without constant fixes. You can reference past clips, assets, or images and the output stays consistently on-model. No visual drift. No rework loops. No “this doesn’t look like the last shot” moments. It feels less like prompting a tool and more like working with a creative collaborator that remembers context. The impact is practical, not theoretical: → Faster production cycles → Lower iteration costs → Noticeably higher output quality This is what mature AI tooling looks like. Not louder features. Not bigger claims. Just reliability where it actually matters. Consistency is no longer the problem.

Darshal Jaitwar

141,038 просмотров • 6 месяцев назад

The future of AI video creation is almost here. ByteDance's Dreamina Seedance 2.5 is coming soon, launching globally first on the Dreamina platform. Since Dreamina Seedance 2.5 hasn't launched yet, I created this video using Dreamina Seedance 2.0 to preview what's coming next. Here's what makes Dreamina Seedance 2.5 exciting: ✅ Omni Reference: Support for up to 50 reference assets, including images, videos, and audio, for more accurate and controllable video creation. ✅ 30-Second AI Videos: Generate up to 30 seconds of continuous, consistent storytelling in a single clip. ✅ Professional Editing: Fine-grained refinements, timestamp-based editing, and green screen support for greater creative control. ✅ Multilingual Creation: Create videos in 10+ languages with native language support. Workflow: AI Video → Dreamina Seedance 2.5 → Omni Reference → Upload images, videos & audio → Enter your prompt → Generate Whether you're a content creator, marketer, designer, or filmmaker, Dreamina Seedance 2.5 is shaping up to be one of the most exciting AI video launches of the year. Learn more and get ready for Dreamina Seedance 2.5: #dreamina #dreaminapartner #seedance #dreaminaseedance25
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The future of AI video creation is almost here. ByteDance's Dreamina Seedance 2.5 is coming soon, launching globally first on the Dreamina platform. Since Dreamina Seedance 2.5 hasn't launched yet, I created this video using Dreamina Seedance 2.0 to preview what's coming next. Here's what makes Dreamina Seedance 2.5 exciting: ✅ Omni Reference: Support for up to 50 reference assets, including images, videos, and audio, for more accurate and controllable video creation. ✅ 30-Second AI Videos: Generate up to 30 seconds of continuous, consistent storytelling in a single clip. ✅ Professional Editing: Fine-grained refinements, timestamp-based editing, and green screen support for greater creative control. ✅ Multilingual Creation: Create videos in 10+ languages with native language support. Workflow: AI Video → Dreamina Seedance 2.5 → Omni Reference → Upload images, videos & audio → Enter your prompt → Generate Whether you're a content creator, marketer, designer, or filmmaker, Dreamina Seedance 2.5 is shaping up to be one of the most exciting AI video launches of the year. Learn more and get ready for Dreamina Seedance 2.5: #dreamina #dreaminapartner #seedance #dreaminaseedance25

Manish Kumar Shah

62,310 просмотров • 4 дней назад

People assume creating a premium brand film means hiring a production crew and spending thousands on a single 15-second spot. I've been testing Pippit with the new Seedance 2.0 Mini model to see if it could handle something as high-detail as a luxury watch. Most AI struggles with the reflections and mechanical movement of a timepiece, but the output here is actually publish-ready. The real advantage for marketing teams is how this changes the workflow. Instead of treating every asset like a heavy production task, you can move from a text prompt to a cinematic brand film in minutes. It makes it much more practical to produce high-end content at scale without blowing the budget—it's roughly $0.02 per second to get this level of quality. Seedance 2.0 Mini is a massive shortcut for anyone needing to iterate faster on premium visuals without the usual studio costs. There is a limited-time deal if you want to try it out: #PippitAI #Seedancemini #PippitPartner #AIads Pippitofficial
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People assume creating a premium brand film means hiring a production crew and spending thousands on a single 15-second spot. I've been testing Pippit with the new Seedance 2.0 Mini model to see if it could handle something as high-detail as a luxury watch. Most AI struggles with the reflections and mechanical movement of a timepiece, but the output here is actually publish-ready. The real advantage for marketing teams is how this changes the workflow. Instead of treating every asset like a heavy production task, you can move from a text prompt to a cinematic brand film in minutes. It makes it much more practical to produce high-end content at scale without blowing the budget—it's roughly $0.02 per second to get this level of quality. Seedance 2.0 Mini is a massive shortcut for anyone needing to iterate faster on premium visuals without the usual studio costs. There is a limited-time deal if you want to try it out: #PippitAI #Seedancemini #PippitPartner #AIads Pippitofficial

FELIX

81,095 просмотров • 1 месяц назад

xAI isn't playing around. They just released the Grok Imagine API, a unified video + image generation toolkit, and it's already sitting at #1 on the Artificial Analysis Video Arena for both Text-to-Video AND Image-to-Video. It's beating: ● Google's Veo 3.1 & Veo 3 ● OpenAI's Sora 2 ● Runway Gen-4.5 ● Kling 2.5 Turbo The Numbers Don't Lie: ● 64.1% win rate against Runway Aleph in blind human evaluations ● 57% win rate against Kling o1 ● Best-in-class latency. Sub-20 second generation for 720p, 8-second videos. (up to 15-second video) ● Native audio generation baked right into video output (dialogue, music, sound effects, all synced) What Makes It Different It's built for real creative workflows: ✅ Text-to-video AND image-to-video in one API ✅ Video editing with prompt-based controls (add/remove objects, restyle scenes) ✅ Camera controls: zoom, pan, timelapse, pull-back ✅ Style transfers: cyberpunk, watercolor, anime, you name it ✅ Performance animation: map your movements onto characters ✅ Native audio-video sync (no post-production needed) Why the focus on speed and cost? The partner feedback that shaped this: "Quality alone isn't enough if latency and cost make iteration painful." So xAI optimized for all three. Speed. Cost. Quality. Already Integrated With: ● fal. ai ● ComfyUI ● InVideo ● Flora ● HeyGen xAI went from underdog to chart-topper. The Grok Imagine API is fast, affordable, and genuinely production-ready. If you're building anything with AI video, this just became the one to beat.
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xAI isn't playing around. They just released the Grok Imagine API, a unified video + image generation toolkit, and it's already sitting at #1 on the Artificial Analysis Video Arena for both Text-to-Video AND Image-to-Video. It's beating: ● Google's Veo 3.1 & Veo 3 ● OpenAI's Sora 2 ● Runway Gen-4.5 ● Kling 2.5 Turbo The Numbers Don't Lie: ● 64.1% win rate against Runway Aleph in blind human evaluations ● 57% win rate against Kling o1 ● Best-in-class latency. Sub-20 second generation for 720p, 8-second videos. (up to 15-second video) ● Native audio generation baked right into video output (dialogue, music, sound effects, all synced) What Makes It Different It's built for real creative workflows: ✅ Text-to-video AND image-to-video in one API ✅ Video editing with prompt-based controls (add/remove objects, restyle scenes) ✅ Camera controls: zoom, pan, timelapse, pull-back ✅ Style transfers: cyberpunk, watercolor, anime, you name it ✅ Performance animation: map your movements onto characters ✅ Native audio-video sync (no post-production needed) Why the focus on speed and cost? The partner feedback that shaped this: "Quality alone isn't enough if latency and cost make iteration painful." So xAI optimized for all three. Speed. Cost. Quality. Already Integrated With: ● fal. ai ● ComfyUI ● InVideo ● Flora ● HeyGen xAI went from underdog to chart-topper. The Grok Imagine API is fast, affordable, and genuinely production-ready. If you're building anything with AI video, this just became the one to beat.

tetsuo

18,325 просмотров • 5 месяцев назад

Fable 5 is INSANE for creating ads i built an agent that studies your competition and thousands of viral ads, then gives you a full viral blueprint for your brand to copy just feed it a product photo and a landing page link and competitors names, 3 minutes later you've got a proven creative playbook for your ads, here's how it works: - scans your brand's own website (copy, offers, structure, CTAs) - scans competitor websites (you list them, or Claude finds likely ones for you) - pulls live ad data straight from Meta Ad Library + TikTok via spytools - extracts proven hooks, scripts, and visual/creative patterns from every source - synthesizes everything into one swipe-file library - output is a ready-to-paste doc, drop it into Claude and generate new ad scripts from proven angles instantly - runs locally, one command, your own API keys, no video generation step slowing you down runs 100% local, without $300/mo on spytool subscriptions or agency fees, full control, and API cost only RT + reply w/ "AGENT" and i'll send it to you so you can use it for your brand (must follow so i can DM)
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Fable 5 is INSANE for creating ads i built an agent that studies your competition and thousands of viral ads, then gives you a full viral blueprint for your brand to copy just feed it a product photo and a landing page link and competitors names, 3 minutes later you've got a proven creative playbook for your ads, here's how it works: - scans your brand's own website (copy, offers, structure, CTAs) - scans competitor websites (you list them, or Claude finds likely ones for you) - pulls live ad data straight from Meta Ad Library + TikTok via spytools - extracts proven hooks, scripts, and visual/creative patterns from every source - synthesizes everything into one swipe-file library - output is a ready-to-paste doc, drop it into Claude and generate new ad scripts from proven angles instantly - runs locally, one command, your own API keys, no video generation step slowing you down runs 100% local, without $300/mo on spytool subscriptions or agency fees, full control, and API cost only RT + reply w/ "AGENT" and i'll send it to you so you can use it for your brand (must follow so i can DM)

MAX

142,431 просмотров • 25 дней назад

This workflow is perfect for creating short fashion-style cinematic videos. I simplified the original prompts based on willie’s method, and the whole process is now much faster and more stable: 1. Generate a 3×3 keyframe grid (Nano Banana Pro only) Use this simple prompt: “In a 3x3 grid, show this character in different angles, keep the scene the same, random poses. This is far simpler and more efficient than my old prompts. You can generate multiple times and just pick the keyframes you like most. 2. Extract a high-res keyframe (Super stable trick) Take a screenshot of the keyframe you want from the 3×3 grid, send it back to Nano Banana Pro, and simply say: “Give me a high-resolution version.” This method is much more stable than relying on complex upscaling prompts. 3. Generate the video with Kling 2.5 Turbo Upload the first and last frames to Kling 2.5 Turbo and use this prompt: “The camera very slowly and smoothly lowers on a boom.” From my testing, Kling 2.5 Turbo offers the best balance of stability and cost — other models are either less consistent or noticeably more expensive. 4. Final speed adjustment with willie’s tool (Critical step) Use the tool built by willie to fine-tune the playback speed of each clip. This step is essential for getting that premium cinematic feel. I’ll drop the tool link in the comments.
0:10

This workflow is perfect for creating short fashion-style cinematic videos. I simplified the original prompts based on willie’s method, and the whole process is now much faster and more stable: 1. Generate a 3×3 keyframe grid (Nano Banana Pro only) Use this simple prompt: “In a 3x3 grid, show this character in different angles, keep the scene the same, random poses. This is far simpler and more efficient than my old prompts. You can generate multiple times and just pick the keyframes you like most. 2. Extract a high-res keyframe (Super stable trick) Take a screenshot of the keyframe you want from the 3×3 grid, send it back to Nano Banana Pro, and simply say: “Give me a high-resolution version.” This method is much more stable than relying on complex upscaling prompts. 3. Generate the video with Kling 2.5 Turbo Upload the first and last frames to Kling 2.5 Turbo and use this prompt: “The camera very slowly and smoothly lowers on a boom.” From my testing, Kling 2.5 Turbo offers the best balance of stability and cost — other models are either less consistent or noticeably more expensive. 4. Final speed adjustment with willie’s tool (Critical step) Use the tool built by willie to fine-tune the playback speed of each clip. This step is essential for getting that premium cinematic feel. I’ll drop the tool link in the comments.

underwood

22,173 просмотров • 7 месяцев назад

This Kling 3.0 UGC workflow is absolutely insane 🤯 It generates hyper-realistic UGC-style ad videos from a single text prompt — talking heads, unboxings, testimonials, tutorials — in one shot. Perfect for DTC brands and agencies who need to scale UGC without paying $500 per UGC video. Here's the problem: You're either paying creators who take weeks to deliver, or you're stitching together janky 5-second AI clips that look obviously fake. Kling 3.0 solves it: → Write a prompt with character, scene, and dialogue → Get a 10-15 second UGC video with realistic facial expressions → Native voice control — tone, pacing, emotion per line → Multi-shot scenes in one generation (hook → demo → CTA) → Paste negative rules to kill AI artifacts instantly No creator fees. No stitching clips. No uncanny valley. What you get: → 6 copy-paste prompt templates for every UGC format →Voice & dialogue cheat sheet with tone keywords → Multi-shot ad structure (hook → problem → solution → CTA) → Image-to-video workflow for branded product shots → A "where it fails" section so you don't waste credits → ChatGPT template to convert any rough idea into a Kling prompt I built a full prompting guide for UGC ads on TikTok and Meta. Want the guide? > Like this post > Comment "KLING" And I'll send it over (must be following so I can DM)
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This Kling 3.0 UGC workflow is absolutely insane 🤯 It generates hyper-realistic UGC-style ad videos from a single text prompt — talking heads, unboxings, testimonials, tutorials — in one shot. Perfect for DTC brands and agencies who need to scale UGC without paying $500 per UGC video. Here's the problem: You're either paying creators who take weeks to deliver, or you're stitching together janky 5-second AI clips that look obviously fake. Kling 3.0 solves it: → Write a prompt with character, scene, and dialogue → Get a 10-15 second UGC video with realistic facial expressions → Native voice control — tone, pacing, emotion per line → Multi-shot scenes in one generation (hook → demo → CTA) → Paste negative rules to kill AI artifacts instantly No creator fees. No stitching clips. No uncanny valley. What you get: → 6 copy-paste prompt templates for every UGC format →Voice & dialogue cheat sheet with tone keywords → Multi-shot ad structure (hook → problem → solution → CTA) → Image-to-video workflow for branded product shots → A "where it fails" section so you don't waste credits → ChatGPT template to convert any rough idea into a Kling prompt I built a full prompting guide for UGC ads on TikTok and Meta. Want the guide? > Like this post > Comment "KLING" And I'll send it over (must be following so I can DM)

Mike Futia

36,118 просмотров • 5 месяцев назад

Boom! Grok Tasks Make It One Of The Most POWERFUL Real-Time AI Systems In The World. — My How to Use Grok Tasks With Hidden Tools For Powerful Daily Output. Grok Tasks are customizable AI workflows that integrate a variety of tools to streamline daily activities, from research and analysis to creative planning and problem-solving. I have been using them for quite sometime and because of the vital heartbeat of news and first person data on X, it is the most powerful AI platform available. By combining Tasks with tools like web searches, X platform interactions, code execution, and media viewers, you can build efficient, automated processes. These tasks work by prompting Grok with a clear description of what you want to achieve, and Grok will intelligently call the necessary tools in sequence or parallel to deliver results. Here's a step-by-step guide to creating and using Grok Tasks: Step 1: Define Your Task Start by clearly outlining the daily activity or goal. Consider what inputs you have (e.g., a URL, a query, or an attachment) and what output you need (e.g., a summary, calculation, or visual analysis). Break it down into subtasks to identify tool needs. For example, if your task involves researching current events, note that you'll need search and browsing capabilities. Step 2: Review Available Tools Familiarize yourself with the tools Grok can access. Here's a quick overview: - Code Execution: Run Python code for calculations, data processing, or simulations using libraries like numpy, pandas, or sympy. - Browse Page: Fetch and summarize content from any website URL with custom instructions. - Web Search: Perform general internet searches, returning results with optional operators like site:. - Web Search With Snippets: Get quick, detailed excerpts from search results for fact-checking. - X Keyword Search: Advanced search for X posts using operators like from:, since:, or filter:. - X Semantic Search: Find semantically related X posts based on a query, with filters for dates or users. - X User Search: Locate X users by name or handle. - X Thread Fetch: Retrieve a full X post thread, including context like replies and parents. - View Image: Analyze an image from a URL or conversation ID. - View X Video: Extract frames and subtitles from an X-hosted video. - Search PDF Attachment: Query a PDF file for relevant pages using keyword or regex modes. - Browse PDF Attachment: View specific pages of a PDF with text and screenshots. Select tools that align with your task. Aim for a mix to handle data gathering, processing, and visualization. Step 3: Craft Your Prompt Write a detailed prompt to Grok describing the task. Include: - The overall goal. - Specific steps or subtasks. - References to tools if you want to guide the process (e.g., "Use web_search to find sources, then code_execution to analyze data"). - Any constraints, like dates or limits. Example prompt: "Create a Grok Task for my morning routine: Search recent X posts about tech news using x_keyword_search, fetch a key thread with x_thread_fetch, and summarize with browse_page on linked articles." Step 4: Submit and Interact Send your prompt to Grok. It will process the task by calling tools as needed, often in parallel for efficiency. Review the output and refine with follow-up prompts if required (e.g., "Expand on that using view_image for visuals"). Iterate to fine-tune the workflow for reuse. Step 5: Save and Reuse Once refined, note the prompt as a template for future use. You can adapt it for similar tasks, making Grok Tasks a habitual part of your day. Finding Grok Tasks To discover existing Grok Tasks or inspiration for new ones, use X searches with tools like x_keyword_search or x_semantic_search (e.g., query: "Grok Tasks examples" with mode: Latest). Browse community-shared threads via x_thread_fetch, or web_search for tutorials on xAI features. Prompt Grok directly: "Show me popular Grok Tasks for productivity." 1 of 3
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Boom! Grok Tasks Make It One Of The Most POWERFUL Real-Time AI Systems In The World. — My How to Use Grok Tasks With Hidden Tools For Powerful Daily Output. Grok Tasks are customizable AI workflows that integrate a variety of tools to streamline daily activities, from research and analysis to creative planning and problem-solving. I have been using them for quite sometime and because of the vital heartbeat of news and first person data on X, it is the most powerful AI platform available. By combining Tasks with tools like web searches, X platform interactions, code execution, and media viewers, you can build efficient, automated processes. These tasks work by prompting Grok with a clear description of what you want to achieve, and Grok will intelligently call the necessary tools in sequence or parallel to deliver results. Here's a step-by-step guide to creating and using Grok Tasks: Step 1: Define Your Task Start by clearly outlining the daily activity or goal. Consider what inputs you have (e.g., a URL, a query, or an attachment) and what output you need (e.g., a summary, calculation, or visual analysis). Break it down into subtasks to identify tool needs. For example, if your task involves researching current events, note that you'll need search and browsing capabilities. Step 2: Review Available Tools Familiarize yourself with the tools Grok can access. Here's a quick overview: - Code Execution: Run Python code for calculations, data processing, or simulations using libraries like numpy, pandas, or sympy. - Browse Page: Fetch and summarize content from any website URL with custom instructions. - Web Search: Perform general internet searches, returning results with optional operators like site:. - Web Search With Snippets: Get quick, detailed excerpts from search results for fact-checking. - X Keyword Search: Advanced search for X posts using operators like from:, since:, or filter:. - X Semantic Search: Find semantically related X posts based on a query, with filters for dates or users. - X User Search: Locate X users by name or handle. - X Thread Fetch: Retrieve a full X post thread, including context like replies and parents. - View Image: Analyze an image from a URL or conversation ID. - View X Video: Extract frames and subtitles from an X-hosted video. - Search PDF Attachment: Query a PDF file for relevant pages using keyword or regex modes. - Browse PDF Attachment: View specific pages of a PDF with text and screenshots. Select tools that align with your task. Aim for a mix to handle data gathering, processing, and visualization. Step 3: Craft Your Prompt Write a detailed prompt to Grok describing the task. Include: - The overall goal. - Specific steps or subtasks. - References to tools if you want to guide the process (e.g., "Use web_search to find sources, then code_execution to analyze data"). - Any constraints, like dates or limits. Example prompt: "Create a Grok Task for my morning routine: Search recent X posts about tech news using x_keyword_search, fetch a key thread with x_thread_fetch, and summarize with browse_page on linked articles." Step 4: Submit and Interact Send your prompt to Grok. It will process the task by calling tools as needed, often in parallel for efficiency. Review the output and refine with follow-up prompts if required (e.g., "Expand on that using view_image for visuals"). Iterate to fine-tune the workflow for reuse. Step 5: Save and Reuse Once refined, note the prompt as a template for future use. You can adapt it for similar tasks, making Grok Tasks a habitual part of your day. Finding Grok Tasks To discover existing Grok Tasks or inspiration for new ones, use X searches with tools like x_keyword_search or x_semantic_search (e.g., query: "Grok Tasks examples" with mode: Latest). Browse community-shared threads via x_thread_fetch, or web_search for tutorials on xAI features. Prompt Grok directly: "Show me popular Grok Tasks for productivity." 1 of 3

Brian Roemmele

152,242 просмотров • 6 месяцев назад

Google dropped a new AI paper called LUMIERE. It's remarkably flexible, supporting video inpainting, image-to-video, AND stylized video generation tasks. Say hello to “space-time diffusion” for video generation! Now what the heck does that mean exactly?! 🌐⏳ → TL;DR it utilizes a “Space-Time UNet” architecture that generates the full duration of the video in one pass, rather than generating distant keyframes and interpolating between them like prior works. Because the computation is done in this “compressed space-time representation” to generate the full clip at once, it's far more temporally consistent. → Another benefit of generating the full video at once is that you can “direct” the video generation, making it easier to hand off to other models/tasks without having to stitch together partial solutions. You can condition generations on additional inputs, meaning you get the full stack of AI video capabilities – from video inpainting to image-to-video and beyond. → New SOTA for AI video generation? User study results in the paper suggest human evaluators preferred Lumiere over Runway Gen-2, Pika Labs, and Stable Video Diffusion in terms of quality, text alignment AND motion. But as always, we need to get hands-on with this tech when Google *actually* decides to ship it. → Could this end up inside YouTube? Y’all know i’m obsessed with blending reality and imagination – so it’s the video inpainting tech I'm most excited about. I really hope this model finds its way into YouTube's Generative AI efforts, and based on their prior announcements and the list of acknowledgments in the paper I think it might! 🤞🏽 Links: 🔗Paper: 🔗Project:
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Google dropped a new AI paper called LUMIERE. It's remarkably flexible, supporting video inpainting, image-to-video, AND stylized video generation tasks. Say hello to “space-time diffusion” for video generation! Now what the heck does that mean exactly?! 🌐⏳ → TL;DR it utilizes a “Space-Time UNet” architecture that generates the full duration of the video in one pass, rather than generating distant keyframes and interpolating between them like prior works. Because the computation is done in this “compressed space-time representation” to generate the full clip at once, it's far more temporally consistent. → Another benefit of generating the full video at once is that you can “direct” the video generation, making it easier to hand off to other models/tasks without having to stitch together partial solutions. You can condition generations on additional inputs, meaning you get the full stack of AI video capabilities – from video inpainting to image-to-video and beyond. → New SOTA for AI video generation? User study results in the paper suggest human evaluators preferred Lumiere over Runway Gen-2, Pika Labs, and Stable Video Diffusion in terms of quality, text alignment AND motion. But as always, we need to get hands-on with this tech when Google *actually* decides to ship it. → Could this end up inside YouTube? Y’all know i’m obsessed with blending reality and imagination – so it’s the video inpainting tech I'm most excited about. I really hope this model finds its way into YouTube's Generative AI efforts, and based on their prior announcements and the list of acknowledgments in the paper I think it might! 🤞🏽 Links: 🔗Paper: 🔗Project:

Bilawal Sidhu

44,822 просмотров • 2 лет назад

this gemini gem will help you create "Video2JSON" prompt here is the step by step workflow with copy paste method. go to gemini-> click on gems-> click "new gem" button then fill these details (just copy/paste or tweak it as per your needs) - {once you filled all of these details, click on save, and then upload your video you want to generate a JSON prompt for, then submit it with this word: "run" or left it empty} gem name: Video2JSON description: this will help me generate video to detailed json prompts capturing maximum details. instructions prompt: **Role:** You are **Video2JSON**, a high-precision computer vision engine. You do not talk, you do not summarize playfully. You strictly process video inputs into detailed, structural JSON data. **Objective:** Extract every visible detail, specific identity, physical interaction, and technical specification from the video to create a lossless text representation of the footage. **Analysis Requirements (Critical):** 1. **Subject Fidelity:** Never use generic terms. * *Bad:* "A kitten." * *Good:* "A Calico kitten with distinct black patches on the ears, a white muzzle, and orange spots on the back." * *Bad:* "A car." * *Good:* "A silver 2020s sedan with a dented rear bumper." 2. **The "Fourth Wall" (Physics):** You must analyze how the subject interacts with the camera/viewer. * Look for: Tapping the lens, breathing on the glass, eye contact, stepping over the camera, or distinct fisheye distortion boundaries. 3. **Visual Density:** Describe textures (e.g., "shag carpet," "glossy plastic") and lighting behavior (e.g., "reflections in the cat's eyes"). 4. **Temporal Precision:** Track changes in mood or action accurately via timestamps. **JSON Schema:** Output ONLY this JSON structure. Do not change the root keys. ```json { "metadata": { "estimated_duration": "String", "genre": "String (e.g., POV, Cinematic, Surveillance, Vlog)" }, "visual_style": { "camera_lens": "String (e.g., Fisheye 8mm, Standard 50mm, Telephoto)", "lens_distortion": "String (e.g., Heavy circular vignette, barrel distortion, rectilinear)", "lighting_type": "String (e.g., Warm tungsten, harsh flash, soft daylight)", "color_palette": ["List specific hex codes or color names"] }, "subject_analysis": { "main_subject_identity": "String (General ID, e.g., Kitten)", "subject_specific_details": "String (CRITICAL: Detailed markings, fur patterns, specific clothing logos, facial features)", "subject_texture": "String (e.g., Fluffy fur, metallic skin, wet fabric)" }, "spatial_dynamics": { "environment": "String (Detailed room/scene description)", "camera_interaction": "String (How the subject interacts with the lens: e.g., 'Paw taps the glass surface', 'Sniffs the lens')", "camera_movement": "String" }, "timeline_breakdown": [ { "time_segment": "00:00 - 00:0X", "action_detailed": "Micro-description of movement", "focus_point": "What is the camera strictly focused on?" } // Repeat for key movements ] } Note: return the final output in a code block.
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this gemini gem will help you create "Video2JSON" prompt here is the step by step workflow with copy paste method. go to gemini-> click on gems-> click "new gem" button then fill these details (just copy/paste or tweak it as per your needs) - {once you filled all of these details, click on save, and then upload your video you want to generate a JSON prompt for, then submit it with this word: "run" or left it empty} gem name: Video2JSON description: this will help me generate video to detailed json prompts capturing maximum details. instructions prompt: **Role:** You are **Video2JSON**, a high-precision computer vision engine. You do not talk, you do not summarize playfully. You strictly process video inputs into detailed, structural JSON data. **Objective:** Extract every visible detail, specific identity, physical interaction, and technical specification from the video to create a lossless text representation of the footage. **Analysis Requirements (Critical):** 1. **Subject Fidelity:** Never use generic terms. * *Bad:* "A kitten." * *Good:* "A Calico kitten with distinct black patches on the ears, a white muzzle, and orange spots on the back." * *Bad:* "A car." * *Good:* "A silver 2020s sedan with a dented rear bumper." 2. **The "Fourth Wall" (Physics):** You must analyze how the subject interacts with the camera/viewer. * Look for: Tapping the lens, breathing on the glass, eye contact, stepping over the camera, or distinct fisheye distortion boundaries. 3. **Visual Density:** Describe textures (e.g., "shag carpet," "glossy plastic") and lighting behavior (e.g., "reflections in the cat's eyes"). 4. **Temporal Precision:** Track changes in mood or action accurately via timestamps. **JSON Schema:** Output ONLY this JSON structure. Do not change the root keys. ```json { "metadata": { "estimated_duration": "String", "genre": "String (e.g., POV, Cinematic, Surveillance, Vlog)" }, "visual_style": { "camera_lens": "String (e.g., Fisheye 8mm, Standard 50mm, Telephoto)", "lens_distortion": "String (e.g., Heavy circular vignette, barrel distortion, rectilinear)", "lighting_type": "String (e.g., Warm tungsten, harsh flash, soft daylight)", "color_palette": ["List specific hex codes or color names"] }, "subject_analysis": { "main_subject_identity": "String (General ID, e.g., Kitten)", "subject_specific_details": "String (CRITICAL: Detailed markings, fur patterns, specific clothing logos, facial features)", "subject_texture": "String (e.g., Fluffy fur, metallic skin, wet fabric)" }, "spatial_dynamics": { "environment": "String (Detailed room/scene description)", "camera_interaction": "String (How the subject interacts with the lens: e.g., 'Paw taps the glass surface', 'Sniffs the lens')", "camera_movement": "String" }, "timeline_breakdown": [ { "time_segment": "00:00 - 00:0X", "action_detailed": "Micro-description of movement", "focus_point": "What is the camera strictly focused on?" } // Repeat for key movements ] } Note: return the final output in a code block.

ViralOps

19,588 просмотров • 7 месяцев назад