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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.

Wildminder

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17,151 просмотров • 4 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

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

165,402 просмотров • 2 месяцев 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 👇

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 • 4 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 👇🏻

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 • 3 месяцев назад

If you've been waiting for a more capable AI video workflow, Seedance 2.5 is now available on CapCut. The combination of video generation and editing in one platform makes the creative process much simpler, especially when working on longer videos or more detailed projects. What stands out: → More controllable video generation & editing → Timestamp-based storyline control → Support for up to 50 references → Up to 90-second video generation → Improved multilingual performance Experience Seedance 2.5 on CapCut: → Web: → App: #CapCut #Seedance25 #CapCutai #CapCutDidThat

If you've been waiting for a more capable AI video workflow, Seedance 2.5 is now available on CapCut. The combination of video generation and editing in one platform makes the creative process much simpler, especially when working on longer videos or more detailed projects. What stands out: → More controllable video generation & editing → Timestamp-based storyline control → Support for up to 50 references → Up to 90-second video generation → Improved multilingual performance Experience Seedance 2.5 on CapCut: → Web: → App: #CapCut #Seedance25 #CapCutai #CapCutDidThat

Ricardo Momo

48,339 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

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 • 3 месяцев 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.

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

25,460 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.

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

179,495 просмотров • 1 месяц назад

For years, AI video has forced creators to choose: More creative freedom — or more control. More references — or a manageable workflow. Longer scenes — or consistent characters, lighting, and story. That trade-off is exactly what Dreamina Seedance 2.5 is here to challenge. Dreamina, ByteDance’s proprietary AI product, has unveiled the **world-first global launch** of Seedance 2.5. Early access is live on Dreamina, while US availability is expected roughly one week later. With up to 50 multimodal references, 30-second single-generation video, and the lowest-cost access to the full Seedance family, Dreamina is the most cost-effective place to experience Seedance 2.5 at full power.

For years, AI video has forced creators to choose: More creative freedom — or more control. More references — or a manageable workflow. Longer scenes — or consistent characters, lighting, and story. That trade-off is exactly what Dreamina Seedance 2.5 is here to challenge. Dreamina, ByteDance’s proprietary AI product, has unveiled the world-first global launch of Seedance 2.5. Early access is live on Dreamina, while US availability is expected roughly one week later. With up to 50 multimodal references, 30-second single-generation video, and the lowest-cost access to the full Seedance family, Dreamina is the most cost-effective place to experience Seedance 2.5 at full power.

Adarsh Chetan

24,268 просмотров • 1 месяц 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)

🚨 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 🇺🇸

140,312 просмотров • 3 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:

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

22,471 просмотров • 11 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 !

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

23,066 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.

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 • 8 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

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

62,793 просмотров • 1 месяц 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

FELIX

81,095 просмотров • 2 месяцев 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.

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 • 7 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)

MAX

144,616 просмотров • 2 месяцев назад

Shipped. Minimax H3 EZLaunch. One install script. Full optimized stack for RTX 3090 and RTX 4090. Windows AND Linux. Ships with every model you need. T2V, I2V, and Ref2V all supported. 5 second clips in about 90 seconds. 15 second clips in under 8 minutes. Zero OOMs across 80 minutes of continuous generation. Same stack that held clean across all 10 space battle renders. What it actually does: - Downloads and configures the correct weight set automatically: pruned INT8 UNET with ConvRot, Turbo LoRA (8-step quality band), Qwen3-VL text encoder in NVFP4, MiniMax video + audio VAEs. No hunting through HuggingFace for the right quants. - Full mode coverage. Text-to-video out of the box. Image-to-video with first/last frame. Reference-to-video for style-guided generation. O - Patches SageAttention v2 sm89 to Triton INT8. - Enables kitchen CUDA with FORCE_CUDA on torch cu128 + driver 580 - Full flag set applied: lowvram, disable-smart-memory, disable-pinned-memory, disable-cuda-malloc, fp16-intermediates, CLIP on CPU - Timed smoke test runner included. Verify your stack without guessing. - Cross-platform installer. Same script, same results, Windows or Linux. The weight selection alone saves people hours. The Sage patch is the difference between launch-fail and sub-90 second renders on Ada. Nobody had either documented publicly. Now it is one command. Try it on similar cards and tweak if you want. MVP. More GPU configurations coming. Stay Tuned. Repo link in reply.

Shipped. Minimax H3 EZLaunch. One install script. Full optimized stack for RTX 3090 and RTX 4090. Windows AND Linux. Ships with every model you need. T2V, I2V, and Ref2V all supported. 5 second clips in about 90 seconds. 15 second clips in under 8 minutes. Zero OOMs across 80 minutes of continuous generation. Same stack that held clean across all 10 space battle renders. What it actually does: - Downloads and configures the correct weight set automatically: pruned INT8 UNET with ConvRot, Turbo LoRA (8-step quality band), Qwen3-VL text encoder in NVFP4, MiniMax video + audio VAEs. No hunting through HuggingFace for the right quants. - Full mode coverage. Text-to-video out of the box. Image-to-video with first/last frame. Reference-to-video for style-guided generation. O - Patches SageAttention v2 sm89 to Triton INT8. - Enables kitchen CUDA with FORCE_CUDA on torch cu128 + driver 580 - Full flag set applied: lowvram, disable-smart-memory, disable-pinned-memory, disable-cuda-malloc, fp16-intermediates, CLIP on CPU - Timed smoke test runner included. Verify your stack without guessing. - Cross-platform installer. Same script, same results, Windows or Linux. The weight selection alone saves people hours. The Sage patch is the difference between launch-fail and sub-90 second renders on Ada. Nobody had either documented publicly. Now it is one command. Try it on similar cards and tweak if you want. MVP. More GPU configurations coming. Stay Tuned. Repo link in reply.

Yume_X

13,279 просмотров • 1 месяц 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.

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

22,240 просмотров • 9 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)

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

36,317 просмотров • 7 месяцев назад