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Tsundere Malfoid slop will continue until morale improves. One thing I'm using is a 3D latent upscaling with minimax H3... and one of the great things is that it's faster to get high-resolution videos. I can generate quickly at low resolution and then upscale it to finish. The main... show more

Emerald Apple

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37,310 просмотров • 5 дней назад •via X (Twitter)

Искусство Игры Наука и технологии

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Low Resolution to 4K in seconds 😱 I have been using this tool a lot in recent days to improve low-resolution images If you want to try it out, comment “Upscale” and I'll send you the link ⬇️

Low Resolution to 4K in seconds 😱 I have been using this tool a lot in recent days to improve low-resolution images If you want to try it out, comment “Upscale” and I'll send you the link ⬇️

Matthew

55,286 просмотров • 10 месяцев назад

TAA VS FSR 4 VS DLSS 4.5 at a super-low resolution so you can see how each one reconstructs the image better. In this scenario, I've used Assassins Creed: Shadows on two different machines using the same settings and set the following settings for upscaling. In-game resolution at 720p and changed the resolution scaling to 20% (144p) while keeping the sharpness at 30% Which one looks better?

TAA VS FSR 4 VS DLSS 4.5 at a super-low resolution so you can see how each one reconstructs the image better. In this scenario, I've used Assassins Creed: Shadows on two different machines using the same settings and set the following settings for upscaling. In-game resolution at 720p and changed the resolution scaling to 20% (144p) while keeping the sharpness at 30% Which one looks better?

NikTek

55,738 просмотров • 9 месяцев назад

Seedream 4.0 is available in OpenGradient Chat Image Studio. It's the one to reach for when you want detail. Razor-sharp, photoreal, high resolution. And like everything here, you generate it privately, with nothing logged or traced back to you. 1/5 🧵

Seedream 4.0 is available in OpenGradient Chat Image Studio. It's the one to reach for when you want detail. Razor-sharp, photoreal, high resolution. And like everything here, you generate it privately, with nothing logged or traced back to you. 1/5 🧵

OpenGradient (∇, ∇)

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NVIDIA just released a very impressive text-to-video paper. Video Latent Diffusion Models (Video LDMs) use a diffusion model in a compressed latent space to generate high-resolution videos. Here's a brief overview of how it works: 1. Pre-train image LDM on a dataset of images. 2. Turn the image LDM into a Video LDM by adding temporal layers to model video frames. 3. Fine-tune the Video LDM on encoded video sequences to create a video generator. 4. Temporally align diffusion model upsamplers to generate high-resolution videos. 5. Validate Video LDM on real driving videos of 512x1024 resolution, achieving state-of-the-art performance. 6. Apply the approach in creative content creation with text-to-video modeling. Paper: Project:

NVIDIA just released a very impressive text-to-video paper. Video Latent Diffusion Models (Video LDMs) use a diffusion model in a compressed latent space to generate high-resolution videos. Here's a brief overview of how it works: 1. Pre-train image LDM on a dataset of images. 2. Turn the image LDM into a Video LDM by adding temporal layers to model video frames. 3. Fine-tune the Video LDM on encoded video sequences to create a video generator. 4. Temporally align diffusion model upsamplers to generate high-resolution videos. 5. Validate Video LDM on real driving videos of 512x1024 resolution, achieving state-of-the-art performance. 6. Apply the approach in creative content creation with text-to-video modeling. Paper: Project:

Lior Alexander

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A steady and settled mind feels happy. If that settledness is not there, then one keeps jumping from one thing to another without any resolution. There will be no closure, and one is left with a feeling of dissatisfaction and a loss of inner peace. Settledness is the key, and Heartfulness meditation done correctly will automatically make you feel settled and calm. Learn to meditate with Heartfulness at the #GlobalSpiritualityMahotsav

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Daaji - Kamlesh Patel

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Markandey Sharma

96,964 просмотров • 1 месяц назад

FIFA president reacting to the news of UEFA boycotting FIFA World Cup; “It’s a difficult situation I must say, but the vision is clear, we are committed to making football better and better everyday day, we will continue without them until we come to a resolution. Football is a great sport and I don’t see why the business should be a problem, the goal is to generate more money for the expansion of the great tournament”

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ŠNØŴ

94,258 просмотров • 2 месяцев назад

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Dreaming Tulpa 🥓👑

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CCS adapter doesn’t interface with the Cybertruck due to the plastic wheel arches. We ripped those suckers off and plugged it in Wasn’t able to charge at a high voltage charger but it’s possible a low voltage one could work, but then what’s the point? Still more testing to be done, I have to try a few more units to see if this was a comms oddity with the Delta high power

CCS adapter doesn’t interface with the Cybertruck due to the plastic wheel arches. We ripped those suckers off and plugged it in Wasn’t able to charge at a high voltage charger but it’s possible a low voltage one could work, but then what’s the point? Still more testing to be done, I have to try a few more units to see if this was a comms oddity with the Delta high power

Kyle Conner

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Colmap 4.0 was very recently released, so it inspired me to do some work to better understand it and its new capabilities with Rerun. I want to really understand how Colmap, and in particular, pycolmap, works outside of just calling it via the CLI. So my goal is to use the low-level pycolmap API to log every part of the pipeline. The explicit goal is to have an alternative to the SQLite database that I can utilize. Instead of SQLite, I want to try logging everything directly to rerun and use RRD. This means I can have deep inspectability and still save the features/matches/2D view geometry, but be able to view it directly in rerun. I think this is one of the superpowers that rerun provides; data and visualizations are deeply integrated. As I'm often working with sequential data (videos), I'm going to specifically focus on four things: 1. Monocular Video Simple: Calls high-level APIs such as pycolmap.extract_features, pycolmap.match_sequential, pycolmap.incremental_mapping. These are basically identical to the CLI options and provide a good baseline. 2. Monocular Video Streamed: Take the above high-level APIs and break them down to their iterator version, logging each component in a streamed manner. This way, I can stream the intermediate features to rerun while the extraction/matching/mapping is happening. 3. Rig with unknown calibration: <- WHAT THE VIDEO SHOWS This is probably the most interesting version and the first one I've been working on. It allows one to set a rig between known sensors, such as in VR/AR devices, leading to much better reconstructions with multiple cameras. This is the case where we don't know the calibration a priori, so we have to run a reconstruction twice: once as a normal Colmap reconstruction with no rig constraints, use this to generate the constraints, and then do it again with the newly found rig. 4. Rig with known calibration: This is the RoboCap example, where we have a pre-calibrated set of sensors, so we don't need to run the two reconstructions and also gain better matching between cameras, both spatially and temporally. Again, this leads to a much better reconstruction! Along with all this, GLOMAP has become a first-class global mapper, making it super easy to use directly within pycolmap! I'm excited to do more with this and compare it to things like pycuvslam, vipe, and other alternatives.

Colmap 4.0 was very recently released, so it inspired me to do some work to better understand it and its new capabilities with Rerun. I want to really understand how Colmap, and in particular, pycolmap, works outside of just calling it via the CLI. So my goal is to use the low-level pycolmap API to log every part of the pipeline. The explicit goal is to have an alternative to the SQLite database that I can utilize. Instead of SQLite, I want to try logging everything directly to rerun and use RRD. This means I can have deep inspectability and still save the features/matches/2D view geometry, but be able to view it directly in rerun. I think this is one of the superpowers that rerun provides; data and visualizations are deeply integrated. As I'm often working with sequential data (videos), I'm going to specifically focus on four things: 1. Monocular Video Simple: Calls high-level APIs such as pycolmap.extract_features, pycolmap.match_sequential, pycolmap.incremental_mapping. These are basically identical to the CLI options and provide a good baseline. 2. Monocular Video Streamed: Take the above high-level APIs and break them down to their iterator version, logging each component in a streamed manner. This way, I can stream the intermediate features to rerun while the extraction/matching/mapping is happening. 3. Rig with unknown calibration: <- WHAT THE VIDEO SHOWS This is probably the most interesting version and the first one I've been working on. It allows one to set a rig between known sensors, such as in VR/AR devices, leading to much better reconstructions with multiple cameras. This is the case where we don't know the calibration a priori, so we have to run a reconstruction twice: once as a normal Colmap reconstruction with no rig constraints, use this to generate the constraints, and then do it again with the newly found rig. 4. Rig with known calibration: This is the RoboCap example, where we have a pre-calibrated set of sensors, so we don't need to run the two reconstructions and also gain better matching between cameras, both spatially and temporally. Again, this leads to a much better reconstruction! Along with all this, GLOMAP has become a first-class global mapper, making it super easy to use directly within pycolmap! I'm excited to do more with this and compare it to things like pycuvslam, vipe, and other alternatives.

Pablo Vela

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I was informed that the team will be seeking a trade immediately and will be working with me and my family to find the right place to continue competing for championships. I don’t agree with the decision and always believed it was going to begin and end in LA. Still, if there’s one thing that I have learned over the years: there are so many things that are out of your control, but it is how you respond to these things that you will look back on and remember. I have taken so much pride in playing alongside my teammates for the LA community, so thank you for embracing my family and making this such a special place for us. 2024 began with one of the best training camps of my career. Preparations start now for 2025. Highly motivated, as healthy as ever, and looking forward to playing elite football for years to come. Love you guys.. But coming for it all.

I was informed that the team will be seeking a trade immediately and will be working with me and my family to find the right place to continue competing for championships. I don’t agree with the decision and always believed it was going to begin and end in LA. Still, if there’s one thing that I have learned over the years: there are so many things that are out of your control, but it is how you respond to these things that you will look back on and remember. I have taken so much pride in playing alongside my teammates for the LA community, so thank you for embracing my family and making this such a special place for us. 2024 began with one of the best training camps of my career. Preparations start now for 2025. Highly motivated, as healthy as ever, and looking forward to playing elite football for years to come. Love you guys.. But coming for it all.

Cooper Kupp

19,597,983 просмотров • 1 год назад

🚨 one person can now do the work of an entire creative team. i just tested it on a real one. a friend needed an ad for his brand, so I opened the new Runway Agent 2.0 to try it out. here's how it went: → it generated the music and the key image first, so I could approve the direction → once I gave the ok, it built the full video around it → and when something was off, i changed just that one piece, without redoing the rest one prompt, and I had the ad we needed, work that used to take weeks. this is what it made 👇 if you want to try it → · 30% off 3 months with code RUNWAYAGENT — made with Runway · #MadeWithRunway · #ad

brenz.

28,770 просмотров • 3 месяцев назад

Clarity Upscaler now works with Flux! 🥳 Clarity started 1 year ago as an upscaler to convert images with Stable Diffusion 1.5 into high-resolution, photorealistic images It took awhile to make it work with Flux but now it does. With Flux it's even better at upscaling faces while preserving identity and resemblance (a common problem with other upscalers) LoRAs are also supported, so upscaling with a specific style or face works perfectly Now available on Clarity AI and through my own API Link in the reply!

Clarity Upscaler now works with Flux! 🥳 Clarity started 1 year ago as an upscaler to convert images with Stable Diffusion 1.5 into high-resolution, photorealistic images It took awhile to make it work with Flux but now it does. With Flux it's even better at upscaling faces while preserving identity and resemblance (a common problem with other upscalers) LoRAs are also supported, so upscaling with a specific style or face works perfectly Now available on Clarity AI and through my own API Link in the reply!

philz1337x

71,216 просмотров • 1 год назад

Many generative models work in an autoencoder's latent space; They train faster and generate better when this space is well organized. One way to organize it is distilling a pretrained model into it. But how can it work when the two spaces have different structures?

Many generative models work in an autoencoder's latent space; They train faster and generate better when this space is well organized. One way to organize it is distilling a pretrained model into it. But how can it work when the two spaces have different structures?

kyutai

11,748 просмотров • 10 дней назад

Always wanted to try combining my paper with NCA, and turns out this approach is a perfect starting point It decouples grid with rules from rendering by using SIREN as a decoder, so I just replaced it with SAD Same rules, different way to render them in higher resolution

Always wanted to try combining my paper with NCA, and turns out this approach is a perfect starting point It decouples grid with rules from rendering by using SIREN as a decoder, so I just replaced it with SAD Same rules, different way to render them in higher resolution

Lucky Iyinbor

17,357 просмотров • 2 месяцев назад

A line in AI video was crossed, in my experiments with just the web interface, H3 Max can now create reasonably high quality AI video in less time than it takes you to watch it. This is realtime from the moment I pushed the "generate" button (and also includes prompt enhancement)

A line in AI video was crossed, in my experiments with just the web interface, H3 Max can now create reasonably high quality AI video in less time than it takes you to watch it. This is realtime from the moment I pushed the "generate" button (and also includes prompt enhancement)

Ethan Mollick

89,911 просмотров • 1 месяц назад