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Acto3D: an open-source user-friendly volume rendering software for high-resolution 3D fluorescence imaging in biology Read this Techniques and Resources Article by Naoki Takeshita, Kenta Yashiro Kenta Yashiro and colleagues:

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13,564 Aufrufe • vor 2 Jahren •via X (Twitter)

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Kenta Yashirovor 2 Jahren

Thrilled to launch our 3D imaging software! Easy, cost-effective, precise. No more expensive hardware/software!

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PHOTON COUNTING CT is NOT a better CT It is a NEW imaging modality Photon Counting CT (PCCT) represents a transformative leap in medical imaging, not only as a molecular imaging modality but also as a technology offering ultra-high resolution and functional imaging capabilities. It is fundamentally more than just an enhanced version of traditional CT—PCCT introduces new ways of seeing and understanding the human body, providing critical insights at the molecular, structural, and functional levels. This positions PCCT as a unique imaging modality that requires a fresh approach to technical implementation, operational workflows, and financial planning. Despite the larger upfront investment, PCCT’s ability to drastically reduce downstream healthcare costs makes it a highly valuable investment in the long run. 1. Technical Innovations • Molecular Imaging and Energy Discrimination: Unlike traditional CT, which simply measures the total absorbed energy, PCCT counts individual X-ray photons and differentiates their energy levels. This allows for precise molecular imaging, revealing the composition of tissues and materials at a biochemical level. By distinguishing between different tissue types and contrast agents, PCCT opens up new diagnostic possibilities, such as identifying molecular biomarkers in tumors or distinguishing between stable and unstable plaque in coronary arteries. This capability shifts the focus of imaging from purely anatomical to both anatomical and molecular, offering more comprehensive diagnostic information. • Ultra-High Spatial Resolution: PCCT features significantly smaller detector elements compared to conventional CT scanners, allowing for ultra-high resolution imaging. This means clinicians can visualize fine structures such as microcalcifications in arteries, small lesions in soft tissues, or the intricate architecture of bones. This level of detail was previously unattainable with traditional CT. When combined with molecular imaging, this ultra-high resolution allows for the precise localization and characterization of disease at very early stages, which is essential for early diagnosis and intervention. • Functional Imaging Capabilities: PCCT also excels as a functional imaging modality. By capturing energy-resolved information, PCCT can provide insights into tissue functionality and dynamic physiological processes. For instance, it can detect changes in blood flow, tissue perfusion, and oxygenation without the need for additional contrast agents or scans. This functionality allows for real-time assessment of physiological processes, making it particularly valuable in cardiology, oncology, and neurology for evaluating organ function and monitoring disease progression. • Reduced Noise and Artifact Reduction: Photon-counting technology dramatically reduces electronic noise and imaging artifacts, such as beam hardening, resulting in clearer and more accurate images. The ability to deliver ultra-high resolution images with minimal artifacts improves diagnostic accuracy, reducing the need for repeat scans and ensuring that even subtle abnormalities are detected. 2. Operational Considerations • New Workflow for Molecular, High-Resolution, and Functional Imaging: The integration of molecular, ultra-high resolution, and functional imaging into routine clinical workflows introduces complexity that requires adaptation. Radiologists and technicians need specialized training to interpret and analyze multi-energy datasets that include molecular and functional information. PCCT produces a vast amount of detailed data, requiring clinicians to adopt new imaging protocols and refine their diagnostic approaches to fully leverage its capabilities. • Post-Processing and Data Management: PCCT generates richer, more complex datasets, which necessitates advanced post-processing tools and data management systems. Existing PACS and imaging software may not be equipped to handle such large volumes of data or to process functional and molecular information effectively. This means healthcare institutions must invest in robust IT infrastructure, including upgraded software and storage solutions, as well as provide additional training for staff on new imaging analysis techniques. • Revised Clinical Protocols: The molecular, functional, and ultra-high resolution imaging capabilities of PCCT will likely prompt changes in clinical protocols. For instance, the need for contrast agents may be reduced, simplifying patient preparation and decreasing the risk of adverse reactions. Additionally, the ability to monitor physiological functions in real-time through functional imaging could lead to more dynamic diagnostic procedures, such as assessing the effectiveness of interventions or treatments in real-time. 3. Financial Impact • Higher Initial Investment: PCCT systems are more expensive than traditional CT scanners due to their advanced technology, which includes photon-counting detectors and the computational power required for high-resolution, molecular, and functional imaging. While this upfront cost is significant, it is crucial to view it in the broader context of the downstream benefits and cost reductions that PCCT offers. • Downstream Cost Reductions: Although the initial capital investment is higher, PCCT’s ability to combine molecular, functional, and ultra-high resolution imaging leads to substantial reductions in downstream healthcare costs. Its superior diagnostic accuracy minimizes the need for follow-up tests, repeat scans, or invasive diagnostic procedures, such as diagnostic coronary angiographies. For example, in cardiology, PCCT can precisely differentiate between types of coronary plaque, reducing the need for invasive procedures to assess risk. • Lower Overall Healthcare Expenditures: By enabling earlier, more accurate diagnoses, PCCT can reduce the overall cost of patient care. Early detection of disease, particularly through its molecular and functional imaging capabilities, allows for more targeted treatments, potentially preventing the need for more aggressive and expensive interventions down the line. For instance, early-stage tumor detection via molecular imaging could lead to less invasive treatments, reducing hospital stays and improving patient outcomes, ultimately driving down healthcare costs. • Increased ROI Through Enhanced Patient Outcomes: Over time, the combination of molecular, functional, and ultra-high resolution imaging enhances diagnostic precision, which translates into better patient outcomes. Improved diagnostic accuracy reduces the incidence of unnecessary procedures, minimizes treatment delays, and results in more personalized and effective care. This leads to increased patient satisfaction, better healthcare outcomes, and greater patient throughput—all factors that improve the institution’s return on investment (ROI). • Competitive Advantage and New Revenue Streams: By adopting PCCT, healthcare institutions position themselves at the forefront of advanced imaging technologies. The ability to offer molecular, functional, and ultra-high resolution imaging creates a competitive advantage, attracting more complex and high-value cases. This can boost the institution’s reputation for excellence in diagnostics, leading to increased referrals, new patient populations, and expanded revenue opportunities. Summary Photon Counting CT (PCCT) is not just an evolution of existing CT technology—it is a molecular, ultra-high resolution, and functional imaging modality that fundamentally transforms the diagnostic landscape. Its ability to capture detailed molecular data, visualize minute anatomical structures with ultra-high resolution, and provide real-time functional imaging opens new possibilities for earlier and more precise diagnoses. While the financial investment in PCCT is larger, the reduction in downstream healthcare costs through improved diagnostic accuracy, fewer unnecessary interventions, and earlier disease detection far outweighs the initial expense. For institutions committed to advancing patient care and improving long-term financial outcomes, PCCT is an essential investment in the future of medical imaging. The video attached shows a patient accessing the Hospital for ACS. PCCT can provide ALL the imaging information of the concurrent imaging modalities (CXR, CAG, Echo, CMR) that you see around it... that's a lot! #PhotonCountingCT #MolecularImaging #UltraHighResolution #FunctionalImaging #FutureOfImaging #AdvancedMedicalImaging #EarlyDiseaseDetection #InnovativeCT #CuttingEdgeHealthcare #PrecisionDiagnostics #HealthcareInnovation #MedicalTechnology #CostEffectiveImaging #NextGenCT #PatientCareRevolution
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PHOTON COUNTING CT is NOT a better CT It is a NEW imaging modality Photon Counting CT (PCCT) represents a transformative leap in medical imaging, not only as a molecular imaging modality but also as a technology offering ultra-high resolution and functional imaging capabilities. It is fundamentally more than just an enhanced version of traditional CT—PCCT introduces new ways of seeing and understanding the human body, providing critical insights at the molecular, structural, and functional levels. This positions PCCT as a unique imaging modality that requires a fresh approach to technical implementation, operational workflows, and financial planning. Despite the larger upfront investment, PCCT’s ability to drastically reduce downstream healthcare costs makes it a highly valuable investment in the long run. 1. Technical Innovations • Molecular Imaging and Energy Discrimination: Unlike traditional CT, which simply measures the total absorbed energy, PCCT counts individual X-ray photons and differentiates their energy levels. This allows for precise molecular imaging, revealing the composition of tissues and materials at a biochemical level. By distinguishing between different tissue types and contrast agents, PCCT opens up new diagnostic possibilities, such as identifying molecular biomarkers in tumors or distinguishing between stable and unstable plaque in coronary arteries. This capability shifts the focus of imaging from purely anatomical to both anatomical and molecular, offering more comprehensive diagnostic information. • Ultra-High Spatial Resolution: PCCT features significantly smaller detector elements compared to conventional CT scanners, allowing for ultra-high resolution imaging. This means clinicians can visualize fine structures such as microcalcifications in arteries, small lesions in soft tissues, or the intricate architecture of bones. This level of detail was previously unattainable with traditional CT. When combined with molecular imaging, this ultra-high resolution allows for the precise localization and characterization of disease at very early stages, which is essential for early diagnosis and intervention. • Functional Imaging Capabilities: PCCT also excels as a functional imaging modality. By capturing energy-resolved information, PCCT can provide insights into tissue functionality and dynamic physiological processes. For instance, it can detect changes in blood flow, tissue perfusion, and oxygenation without the need for additional contrast agents or scans. This functionality allows for real-time assessment of physiological processes, making it particularly valuable in cardiology, oncology, and neurology for evaluating organ function and monitoring disease progression. • Reduced Noise and Artifact Reduction: Photon-counting technology dramatically reduces electronic noise and imaging artifacts, such as beam hardening, resulting in clearer and more accurate images. The ability to deliver ultra-high resolution images with minimal artifacts improves diagnostic accuracy, reducing the need for repeat scans and ensuring that even subtle abnormalities are detected. 2. Operational Considerations • New Workflow for Molecular, High-Resolution, and Functional Imaging: The integration of molecular, ultra-high resolution, and functional imaging into routine clinical workflows introduces complexity that requires adaptation. Radiologists and technicians need specialized training to interpret and analyze multi-energy datasets that include molecular and functional information. PCCT produces a vast amount of detailed data, requiring clinicians to adopt new imaging protocols and refine their diagnostic approaches to fully leverage its capabilities. • Post-Processing and Data Management: PCCT generates richer, more complex datasets, which necessitates advanced post-processing tools and data management systems. Existing PACS and imaging software may not be equipped to handle such large volumes of data or to process functional and molecular information effectively. This means healthcare institutions must invest in robust IT infrastructure, including upgraded software and storage solutions, as well as provide additional training for staff on new imaging analysis techniques. • Revised Clinical Protocols: The molecular, functional, and ultra-high resolution imaging capabilities of PCCT will likely prompt changes in clinical protocols. For instance, the need for contrast agents may be reduced, simplifying patient preparation and decreasing the risk of adverse reactions. Additionally, the ability to monitor physiological functions in real-time through functional imaging could lead to more dynamic diagnostic procedures, such as assessing the effectiveness of interventions or treatments in real-time. 3. Financial Impact • Higher Initial Investment: PCCT systems are more expensive than traditional CT scanners due to their advanced technology, which includes photon-counting detectors and the computational power required for high-resolution, molecular, and functional imaging. While this upfront cost is significant, it is crucial to view it in the broader context of the downstream benefits and cost reductions that PCCT offers. • Downstream Cost Reductions: Although the initial capital investment is higher, PCCT’s ability to combine molecular, functional, and ultra-high resolution imaging leads to substantial reductions in downstream healthcare costs. Its superior diagnostic accuracy minimizes the need for follow-up tests, repeat scans, or invasive diagnostic procedures, such as diagnostic coronary angiographies. For example, in cardiology, PCCT can precisely differentiate between types of coronary plaque, reducing the need for invasive procedures to assess risk. • Lower Overall Healthcare Expenditures: By enabling earlier, more accurate diagnoses, PCCT can reduce the overall cost of patient care. Early detection of disease, particularly through its molecular and functional imaging capabilities, allows for more targeted treatments, potentially preventing the need for more aggressive and expensive interventions down the line. For instance, early-stage tumor detection via molecular imaging could lead to less invasive treatments, reducing hospital stays and improving patient outcomes, ultimately driving down healthcare costs. • Increased ROI Through Enhanced Patient Outcomes: Over time, the combination of molecular, functional, and ultra-high resolution imaging enhances diagnostic precision, which translates into better patient outcomes. Improved diagnostic accuracy reduces the incidence of unnecessary procedures, minimizes treatment delays, and results in more personalized and effective care. This leads to increased patient satisfaction, better healthcare outcomes, and greater patient throughput—all factors that improve the institution’s return on investment (ROI). • Competitive Advantage and New Revenue Streams: By adopting PCCT, healthcare institutions position themselves at the forefront of advanced imaging technologies. The ability to offer molecular, functional, and ultra-high resolution imaging creates a competitive advantage, attracting more complex and high-value cases. This can boost the institution’s reputation for excellence in diagnostics, leading to increased referrals, new patient populations, and expanded revenue opportunities. 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Introducing LayerDrone — the world's largest decentralized drone network. Built by pilots, for pilots. Our mission: Create an open, standardized, and continuously updated visual record of our planet in ultra-high resolution.
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This may seem "too good to be true", but it isn't AI, fake, or BS. The creator, "function.3d", is known for specializing in open-source recycling projects. The machine you see in the video is likely the PETALOT, a high-quality, open-source filament recycler designed by Function 3D. You can even find the technical CAD files and assembly guides on GitHub if you want to build one yourself.
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This may seem "too good to be true", but it isn't AI, fake, or BS. The creator, "function.3d", is known for specializing in open-source recycling projects. The machine you see in the video is likely the PETALOT, a high-quality, open-source filament recycler designed by Function 3D. You can even find the technical CAD files and assembly guides on GitHub if you want to build one yourself.

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GPS-Gaussian+: Generalizable Pixel-wise 3D Gaussian Splatting for Real-Time Human-Scene Rendering from Sparse Views TL;DR: Are we witnessing the first steps towards 3DGS live streaming? Contributions: • We introduce a generalizable 3D Gaussian Splatting methodology that employs pixel-wise Gaussian parameter maps defined on 2D source image planes to formulate 3D Gaussians in a feed-forward manner. • We propose a fully differentiable framework composed of an iterative depth estimation module and a Gaussian parameter regression module. The intermediate depth prediction bridges the two components and allows them to benefit from joint training. • We introduce a regularization term and an epipolar attention mechanism to preserve geometry consistency between the two source views when using only rendering loss. Our method generalizes well to unseen characters even in complicated scenes. • We develop a real-time FVV system that achieves high-resolution rendering of characters in the scene without any geometry supervision.
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GPS-Gaussian+: Generalizable Pixel-wise 3D Gaussian Splatting for Real-Time Human-Scene Rendering from Sparse Views TL;DR: Are we witnessing the first steps towards 3DGS live streaming? Contributions: • We introduce a generalizable 3D Gaussian Splatting methodology that employs pixel-wise Gaussian parameter maps defined on 2D source image planes to formulate 3D Gaussians in a feed-forward manner. • We propose a fully differentiable framework composed of an iterative depth estimation module and a Gaussian parameter regression module. The intermediate depth prediction bridges the two components and allows them to benefit from joint training. • We introduce a regularization term and an epipolar attention mechanism to preserve geometry consistency between the two source views when using only rendering loss. Our method generalizes well to unseen characters even in complicated scenes. • We develop a real-time FVV system that achieves high-resolution rendering of characters in the scene without any geometry supervision.

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This Photoshop workflow is fully automated. All images generated and the scene harmonized. Write a prompt and MCP lets Claude control PS directly. You're an art director and AI is your production team. Open source MCP for PS by Mike Chambers. We're experimenting. Read more 👇
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This Photoshop workflow is fully automated. All images generated and the scene harmonized. Write a prompt and MCP lets Claude control PS directly. You're an art director and AI is your production team. Open source MCP for PS by Mike Chambers. We're experimenting. Read more 👇

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Today, we're announcing an expansion of Vantor’s industry-leading imaging constellation—eliminating a long-standing tradeoff between accurate, high-resolution imagery and high-frequency monitoring. For the first time, both come together in a single commercial system. 20 cm-class imaging. Global revisits as often as every 15 minutes. This expansion introduces two new satellite classes: 🛰️ Vantor Vantage™, next-generation 20 cm-class imaging satellites delivering the highest commercial resolution on orbit, coming online as early as 2029 🛰️ Vantor Pulse™, a fleet of 40 cm-class satellites designed for persistent, high-frequency monitoring, coming online as early as 2027 These new satellites build on the strength of the Vantor constellation—including our WorldView Legion satellites, which already deliver the most accurate, high-resolution imagery on orbit. By increasing capacity and refresh rates, the expansion reinforces the spatial foundation behind our Tensorglobe spatial intelligence platform, powering a real-time, AI-ready view of the world in 2D and 3D. These capabilities automate the full intelligence cycle within customer environments, enabling a new level of sovereign capability that helps governments and businesses detect change, track activity, and act faster. 👉 Read the full announcement here: Total clarity from space to ground.

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⚡🎉 We are thrilled to introduce VORTEX, an AI-powered computational framework for predicting 3D Spatial Transcriptomics (ST) using 3D tissue images and minimal 2D ST! 🧬 By combining cutting-edge 3D non-destructive tissue imaging with AI, VORTEX imputes the 3D molecular landscape of large tissue samples in a cost-effective and scalable manner. 🧠💡Our approach: By pretraining on diverse 3D morphology–2D transcriptomic pairs from heterogeneous tissue samples, and then fine-tuning on minimal 2D ST data from a volume of interest, VORTEX leverages both generic tissue-specific and sample-specific morphomolecular correlates to predict 3D ST. Congratulations to our superstar co-leads Cristina Almagro Pérez and Andrew H. Song, this was an exciting collaboration with Jonathan Liu Sizun Jiang Ali Bashashati. Preprint: Demo: Read the excellent blog from our superstar grad student Cristina Almagro Pérez: Also see our previous work on 3D Computational Pathology from Andrew H. Song published in Cell last year: Stay tuned for more to come.

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The $600 Open-Source Home Robot🤖 Meet AlohaMini, the new dual-arm mobile robot designed to make real-world manipulation and embodied AI research accessible. This bot is fully 3D-printable and aimed at home builders and research labs. Key Features: 💰 Cost: The Bill of Materials (BOM) totals around $600 (USD) for self-printed parts, making it highly accessible. 🦾 Hardware: Dual-arm mobile base with a motorized vertical lift (0–60 cm travel). 💻 Software: Completely open-source hardware and software, compatible out-of-the-box with the LeRobot framework. The platform supports a fully 3D-printed Mini and a stiffer Pro model (hybrid materials). It assembles in approximately 60 minutes. 🔗 GitHub Open-Source Code & Files:

RoboHub🤖

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Weiroll: A Unique and Powerful Primitive for DeFi by Colin Platt from Makina This session will focus on how Weiroll ( and later modified in an open source initiative, creates a powerful primitive for DeFi interactions, including in building and maintaining vaults. Full Video Below 👇🧵
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Weiroll: A Unique and Powerful Primitive for DeFi by Colin Platt from Makina This session will focus on how Weiroll ( and later modified in an open source initiative, creates a powerful primitive for DeFi interactions, including in building and maintaining vaults. Full Video Below 👇🧵

ETHDenver 🏔🦬🦄

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Luma’s mission is to make 3D creation accessible to all. We believe that neural rendering will be a significant piece of this puzzle. Sparked by NeRF and follow-up papers – including works from the now Luma research team members – neural rendering has become an incredibly productive field of research! To further accelerate the rate of advancement and help the research community grow and build, today we are excited to sponsor nerfstudio along with the Berkeley AI Research Lab (Berkeley AI Research), led by Professor Angjoo Kanazawa (Angjoo Kanazawa). Nerfstudio is a collaboration-friendly project that has greatly benefited NeRF research by providing a well-built codebase to experiment with and try new ideas in, and we are proud to be a part of this effort. 3D creation is about to change, deeply. It’s time for new ideas, weird ideas, cool ideas. Open source and product-driven research are both crucial to realize this huge vision. Join us at Luma or contribute to Nerfstudio to help us build this future!
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Luma’s mission is to make 3D creation accessible to all. We believe that neural rendering will be a significant piece of this puzzle. Sparked by NeRF and follow-up papers – including works from the now Luma research team members – neural rendering has become an incredibly productive field of research! To further accelerate the rate of advancement and help the research community grow and build, today we are excited to sponsor nerfstudio along with the Berkeley AI Research Lab (Berkeley AI Research), led by Professor Angjoo Kanazawa (Angjoo Kanazawa). Nerfstudio is a collaboration-friendly project that has greatly benefited NeRF research by providing a well-built codebase to experiment with and try new ideas in, and we are proud to be a part of this effort. 3D creation is about to change, deeply. It’s time for new ideas, weird ideas, cool ideas. Open source and product-driven research are both crucial to realize this huge vision. Join us at Luma or contribute to Nerfstudio to help us build this future!

Luma AI

55,591 Aufrufe • vor 2 Jahren

Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors paper page: present Magic123, a two-stage coarse-to-fine approach for high-quality, textured 3D meshes generation from a single unposed image in the wild using both2D and 3D priors. In the first stage, we optimize a neural radiance field to produce a coarse geometry. In the second stage, we adopt a memory-efficient differentiable mesh representation to yield a high-resolution mesh with a visually appealing texture. In both stages, the 3D content is learned through reference view supervision and novel views guided by a combination of 2D and 3D diffusion priors. We introduce a single trade-off parameter between the 2D and 3D priors to control exploration (more imaginative) and exploitation (more precise) of the generated geometry. Additionally, we employ textual inversion and monocular depth regularization to encourage consistent appearances across views and to prevent degenerate solutions, respectively. Magic123 demonstrates a significant improvement over previous image-to-3D techniques, as validated through extensive experiments on synthetic benchmarks and diverse real-world images.
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Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors paper page: present Magic123, a two-stage coarse-to-fine approach for high-quality, textured 3D meshes generation from a single unposed image in the wild using both2D and 3D priors. In the first stage, we optimize a neural radiance field to produce a coarse geometry. In the second stage, we adopt a memory-efficient differentiable mesh representation to yield a high-resolution mesh with a visually appealing texture. In both stages, the 3D content is learned through reference view supervision and novel views guided by a combination of 2D and 3D diffusion priors. We introduce a single trade-off parameter between the 2D and 3D priors to control exploration (more imaginative) and exploitation (more precise) of the generated geometry. Additionally, we employ textual inversion and monocular depth regularization to encourage consistent appearances across views and to prevent degenerate solutions, respectively. Magic123 demonstrates a significant improvement over previous image-to-3D techniques, as validated through extensive experiments on synthetic benchmarks and diverse real-world images.

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Open-source robot arm meets hand tracking [📍GitHub below] It is designed with an industrial mindset but built as a 3D-printed desktop system. PAROL6 paired with a LEAP Motion controller is a nice example of how accessible robot teleoperation has become. • Hand motion is streamed to the robot at 100 Hz via UDP • A pneumatic gripper is controlled by simple fist open and close gestures • The entire robot stack is open source, from mechanics to control software Combine that with low-latency hand tracking and you get a very practical platform for learning manipulation, teleoperation, and human-robot interfaces. This kind of setup is great for experimentation, teleop, data collection, and teaching robots by demonstration All without proprietary hardware or locked software. Credit to SourceRobotics 📍Code: —— Weekly robotics and AI insights. Subscribe free:
0:35

Open-source robot arm meets hand tracking [📍GitHub below] It is designed with an industrial mindset but built as a 3D-printed desktop system. PAROL6 paired with a LEAP Motion controller is a nice example of how accessible robot teleoperation has become. • Hand motion is streamed to the robot at 100 Hz via UDP • A pneumatic gripper is controlled by simple fist open and close gestures • The entire robot stack is open source, from mechanics to control software Combine that with low-latency hand tracking and you get a very practical platform for learning manipulation, teleoperation, and human-robot interfaces. This kind of setup is great for experimentation, teleop, data collection, and teaching robots by demonstration All without proprietary hardware or locked software. Credit to SourceRobotics 📍Code: —— Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

32,025 Aufrufe • vor 5 Monaten

zeroscope_v2 XL, A watermark-free Modelscope-based video model capable of generating high quality video at 1024 x 576 Model on Hugging Face : This model was trained with offset noise using 9,923 clips and 29,769 tagged frames at 24 frames, 1024x576 resolution. zeroscope_v2_XL is specifically designed for upscaling content made with zeroscope_v2_576w using vid2vid in the 1111 text2video extension by kabachuha. Leveraging this model as an upscaler allows for superior overall compositions at higher resolutions, permitting faster exploration in 576x320 (or 448x256) before transitioning to a high-resolution render. zeroscope_v2_XL uses 15.3gb of vram when rendering 30 frames at 1024x576
0:35

zeroscope_v2 XL, A watermark-free Modelscope-based video model capable of generating high quality video at 1024 x 576 Model on Hugging Face : This model was trained with offset noise using 9,923 clips and 29,769 tagged frames at 24 frames, 1024x576 resolution. zeroscope_v2_XL is specifically designed for upscaling content made with zeroscope_v2_576w using vid2vid in the 1111 text2video extension by kabachuha. Leveraging this model as an upscaler allows for superior overall compositions at higher resolutions, permitting faster exploration in 576x320 (or 448x256) before transitioning to a high-resolution render. zeroscope_v2_XL uses 15.3gb of vram when rendering 30 frames at 1024x576

AK

714,316 Aufrufe • vor 3 Jahren