Scalero

scalero

Storybook V2 Comic Book Builder: Part 2, The Document Generator

An Elegant Three Button HTML and JavaScript Audio Animation Player

Using CSS to Create a GIF Animation

The Storybook Landing Page: A CSS/JavaScript Animated Splash Screen Landing Page

As the founder of ScaleVoice, I'm leveraging my background in AI and automotive e-commerce to revolutionize sales operations. Our company provides autonomous AI voice sales agents that act as "superhero sidekicks" for automotive sales teams, handling outbound calls, SMS, and chat conversations with human-like proficiency. With a track record of facilitating more than $7 million in monthly car purchases for our clients and processing over one million calls per month, ScaleVoice is at the forefront of AI-powered sales solutions, delivering tangible results and redefining customer engagement in the automotive industry. 1. What is your company in 2–5 words? Ondrej Kuchta: Autonomous AI voice sales agent 2. Why is now the time for your company to exist? OK: VC firm Andreessen Horowitz recently stated, "We believe AI will fundamentally reimagine core systems and sales workflows, leaving no incumbent safe." This paradigm shift presents opportunities for companies like ScaleVoice that specialize in complex, long-term customer journeys. ScaleVoice combines cutting-edge AI expertise with deep automotive sales knowledge. Unlike traditional chatbots that follow strict scripts, our AI agents make decisions based on the conversation context. We focus on delivering tangible results rather than just selling software. Our approach manages end-to-end workflows, from initial customer contact through appointment scheduling and follow-ups. This comprehensive solution sets us apart in the market, where many competitors offer more limited functionality. 3. What do you love about your team, and why are you the ones to solve this problem? OK: I believe we are at the forefront of combining cutting-edge AI expertise with deep industry knowledge. We're not just tech experts - we're domain specialists who understand the unique challenges facing businesses in the automotive space. Our team's blend of skills and experience is what makes us uniquely qualified. We've got seasoned AI researchers and engineers working alongside former automotive executives and sales professionals. This cross-pollination allows us to develop solutions that are technologically sophisticated and grounded in real-world practicality. Equally important, we're passionate problem-solvers driven by results. This outcome-focused mindset permeates everything we do. 4. If you weren’t building your startup, what would you be doing? OK: I'd likely explore other AI applications in business or work on another transformative tech startup. My background spans roles from kernel development to founding my own startups in fitness, retail and home security.Prior to ScaleVoice, I served as the CTO at Driverama, a Germany-based e-commerce platform using data to facilitate used car transactions in Western Europe. Under my leadership, we experienced remarkable growth, expanding from four employees to 250 and increasing revenue from zero to $40 million in less than two years. My success in helping auto dealerships acquire vehicles at Driverama revealed even more opportunities to leverage cutting-edge AI technologies that previously weren't sufficiently advanced or didn't exist. These experiences, combined with the emergence of powerful new AI tools, made the timing perfect for launching ScaleVoice. So while I'm always open to new opportunities, I'm currently laser-focused on ScaleVoice's mission to redefine the future of sales through AI-powered operations. The potential is limitless, and I can't imagine doing anything else. 5. At the moment, how do you measure success? What are your metrics? OK: We have a unique success-based pricing model, which means we only make money when our clients make a sale. Our key metrics include sales volume for our customers, agent success rate, lead response time, and overall call volume which currently exceeds one million per month. At the end of the day, we're not selling software - we're delivering work. 6. In a few sentences, what do you offer to whom? OK: ScaleVoice acts as a "superhero sidekick" for sales teams in automotive and related sectors. Our autonomous AI agents handle outbound calls, SMS, and chat conversations as well as humans. They contact potential car sellers within seconds of their expressed interest, which helps convert more leads into qualified appointments. Most customers don't even realize they're interacting with an AI agent, which means more efficient, consistent conversations and happier customers overall. 7. What’s most exciting about your traction to date? The most exciting aspect of our traction is the consistent, measurable performance of our AI agents in real-world applications. Our work with Aures, the largest Central European dealer of pre-owned cars, has been a particularly significant milestone for us. We're currently facilitating more than $7 million in monthly car purchases for Aures. This impressive figure demonstrates the scalability and effectiveness of our product, even in uncertain market conditions. These kinds of results truly showcase the impact of our technology. Our AI agents aren't just talking the talk - they're delivering tangible business results for our clients. It's exciting to see our solution making such a substantial difference in the automotive sales landscape. 8. Where do you think your growth will be next year? Next year, we expect significant growth as we expand into the US market this fall, targeting states with high concentrations of car dealerships like California, New York, and Texas. We're confident that our proven track record in Europe will translate well to the American market, where businesses are equally hungry for innovative solutions to streamline their customer operations.We also plan to expand into related verticals like car insurance, financing and even the energy sector. These are ambitious moves, but we’ll never shy away from going after challenging verticals as the demand for our technology continues to grow. 9. Tell us about your first paying customer and revenue expectations over the next year. While I can't disclose specific client details, our success with Aures is indicative of our revenue potential. 10. What’s your biggest threat? One of our biggest threats is the rapid pace of AI development and potential competition. We’re staying ahead by continuously enhancing our AI-driven solution to provide even greater value to dealerships and sales teams. We're also developing deeper integrations with management systems and CRMs, enabling more tailored interactions and ability to handle even more complex workflows. For example, our system will soon be able to recognize when a customer's vehicle needs a tire change and facilitate that service on the spot. Beyond single calls, we're developing our AI's ability to manage and convert customers across a series of interactions, creating a cohesive and long-term engagement strategy. Ultimately, our deep industry understanding and laser-focus on tangible outcomes are our biggest advantages. While competitors may try to catch up, we're committed to staying steps ahead through continuous innovation and an unwavering commitment to results. This startup founder interview template is based on HackerNoon Founder & CEO David Smooke’s ten questions for startup founders. Would you like to take a stab at answering some of these questions? The link for the template is HERE. As the founder of ScaleVoice, I'm leveraging my background in AI and automotive e-commerce to revolutionize sales operations. Our company provides autonomous AI voice sales agents that act as "superhero sidekicks" for automotive sales teams, handling outbound calls, SMS, and chat conversations with human-like proficiency. With a track record of facilitating more than $7 million in monthly car purchases for our clients and processing over one million calls per month, ScaleVoice is at the forefront of AI-powered sales solutions, delivering tangible results and redefining customer engagement in the automotive industry. 1. What is your company in 2–5 words? Ondrej Kuchta: Autonomous AI voice sales agent 2. Why is now the time for your company to exist? OK: VC firm Andreessen Horowitz recently stated , "We believe AI will fundamentally reimagine core systems and sales workflows, leaving no incumbent safe." This paradigm shift presents opportunities for companies like ScaleVoice that specialize in complex, long-term customer journeys. Andreessen Horowitz recently stated Andreessen Horowitz recently stated ScaleVoice ScaleVoice ScaleVoice combines cutting-edge AI expertise with deep automotive sales knowledge. Unlike traditional chatbots that follow strict scripts, our AI agents make decisions based on the conversation context. We focus on delivering tangible results rather than just selling software. Our approach manages end-to-end workflows, from initial customer contact through appointment scheduling and follow-ups. This comprehensive solution sets us apart in the market, where many competitors offer more limited functionality. 3. What do you love about your team, and why are you the ones to solve this problem? OK: I believe we are at the forefront of combining cutting-edge AI expertise with deep industry knowledge. We're not just tech experts - we're domain specialists who understand the unique challenges facing businesses in the automotive space. Our team's blend of skills and experience is what makes us uniquely qualified. We've got seasoned AI researchers and engineers working alongside former automotive executives and sales professionals. This cross-pollination allows us to develop solutions that are technologically sophisticated and grounded in real-world practicality. Equally important, we're passionate problem-solvers driven by results. This outcome-focused mindset permeates everything we do. 4. If you weren’t building your startup, what would you be doing? OK: I'd likely explore other AI applications in business or work on another transformative tech startup. My background spans roles from kernel development to founding my own startups in fitness, retail and home security. Prior to ScaleVoice, I served as the CTO at Driverama, a Germany-based e-commerce platform using data to facilitate used car transactions in Western Europe. Under my leadership, we experienced remarkable growth, expanding from four employees to 250 and increasing revenue from zero to $40 million in less than two years. My success in helping auto dealerships acquire vehicles at Driverama revealed even more opportunities to leverage cutting-edge AI technologies that previously weren't sufficiently advanced or didn't exist. These experiences, combined with the emergence of powerful new AI tools, made the timing perfect for launching ScaleVoice. So while I'm always open to new opportunities, I'm currently laser-focused on ScaleVoice's mission to redefine the future of sales through AI-powered operations. The potential is limitless, and I can't imagine doing anything else. 5. At the moment, how do you measure success? What are your metrics? OK: We have a unique success-based pricing model, which means we only make money when our clients make a sale. Our key metrics include sales volume for our customers, agent success rate, lead response time, and overall call volume which currently exceeds one million per month. At the end of the day, we're not selling software - we're delivering work. 6. In a few sentences, what do you offer to whom? OK: ScaleVoice acts as a "superhero sidekick" for sales teams in automotive and related sectors. Our autonomous AI agents handle outbound calls, SMS, and chat conversations as well as humans. They contact potential car sellers within seconds of their expressed interest, which helps convert more leads into qualified appointments. Most customers don't even realize they're interacting with an AI agent, which means more efficient, consistent conversations and happier customers overall. 7. What’s most exciting about your traction to date? The most exciting aspect of our traction is the consistent, measurable performance of our AI agents in real-world applications. Our work with Aures , the largest Central European dealer of pre-owned cars, has been a particularly significant milestone for us. Our work with Aures Our work with Aures We're currently facilitating more than $7 million in monthly car purchases for Aures. This impressive figure demonstrates the scalability and effectiveness of our product, even in uncertain market conditions. These kinds of results truly showcase the impact of our technology. Our AI agents aren't just talking the talk - they're delivering tangible business results for our clients. It's exciting to see our solution making such a substantial difference in the automotive sales landscape. 8. Where do you think your growth will be next year? Next year, we expect significant growth as we expand into the US market this fall, targeting states with high concentrations of car dealerships like California, New York, and Texas. We're confident that our proven track record in Europe will translate well to the American market, where businesses are equally hungry for innovative solutions to streamline their customer operations.We also plan to expand into related verticals like car insurance, financing and even the energy sector. These are ambitious moves, but we’ll never shy away from going after challenging verticals as the demand for our technology continues to grow. 9. Tell us about your first paying customer and revenue expectations over the next year. While I can't disclose specific client details, our success with Aures is indicative of our revenue potential. 10. What’s your biggest threat? One of our biggest threats is the rapid pace of AI development and potential competition. We’re staying ahead by continuously enhancing our AI-driven solution to provide even greater value to dealerships and sales teams. We're also developing deeper integrations with management systems and CRMs, enabling more tailored interactions and ability to handle even more complex workflows. For example, our system will soon be able to recognize when a customer's vehicle needs a tire change and facilitate that service on the spot. Beyond single calls, we're developing our AI's ability to manage and convert customers across a series of interactions, creating a cohesive and long-term engagement strategy. Ultimately, our deep industry understanding and laser-focus on tangible outcomes are our biggest advantages. While competitors may try to catch up, we're committed to staying steps ahead through continuous innovation and an unwavering commitment to results. This startup founder interview template is based on HackerNoon Founder & CEO David Smooke’s ten questions for startup founders. This startup founder interview template is based on HackerNoon Founder & CEO David Smooke ’s ten questions for startup founders . HackerNoon Founder & CEO David Smooke ten questions for startup founders Would you like to take a stab at answering some of these questions? The link for the template is HERE. Would you like to take a stab at answering some of these questions? The link for the template is HERE. The link for the template is HERE.

10 Questions for Every Startup Founder with ScaleVoice Co-founder & CEO, Ondrej Kuchta

A New Approach to Funding SaaS Companies By Lew Moorman, GP/co-founder Scaleworks During the deployment of our first $60M venture equity fund, we came across so many good software companies that were not a great fit for traditional venture capital. Some were not growing quite fast enough to be viewed as “venture scale”, others wanted to take a more controlled invest as you grow approach. As we studied these situations case-by-case, one thing became very clear: injection of capital could absolutely accelerate their momentum. However, right now there are not many options for companies at that stage — some continue to bootstrap and miss their opportunity, and others raise VC even though they may not really be suitable for that path. some do Unfortunately, traditional banks still require consistent cash flow or tangible assets to collateralize any loan, something that growing SaaS companies typically don’t have. They do, however, have consistent recurring revenue streams, with high gross margins. We believe that is a bankable asset, banks don’t. So we thought…hey, we can provide yet another path for founders and teams with great products. We call it our “venture finance” fund, and we’re ready to deploy $10M immediately. Karl Pichler, GP, Venture Finance The fund will make loans to growing SaaS companies in value of up to 6 months of their recurring revenue. To manage the fund, we’ve brought in Rackspace’s former CFO of 6 years, Karl Pichler as GP, Venture Finance. His experience with financing and advising companies on lowering its cost of capital is second to none. He has also seen the same pushback from banks that we have. Now we get to work on solving these problems together! We’re looking forward to hearing from companies who want to chat about their place in the SaaS world and how we can help scale them from great products to great businesses. Whether we take a majority equity position or help with debt financing, we hope to continue to build a community of SaaS companies building smart and sustainable businesses. Lew and team Scaleworks

Introducing the Scaleworks Venture Finance Fund

Authors:
(1) Yuwei Guo, The Chinese University of Hong Kong;
(2) Ceyuan Yang, Shanghai Artificial Intelligence Laboratory with Corresponding Author;
(3) Anyi Rao, Stanford University;
(4) Zhengyang Liang, Shanghai Artificial Intelligence Laboratory;
(5) Yaohui Wang, Shanghai Artificial Intelligence Laboratory;
(6) Yu Qiao, Shanghai Artificial Intelligence Laboratory;
(7) Maneesh Agrawala, Stanford University;
(8) Dahua Lin, Shanghai Artificial Intelligence Laboratory;
(9) Bo Dai, The Chinese University of Hong Kong and The Chinese University of Hong Kong. Table of Links Abstract and 1 Introduction 2 Work Related 3 Preliminary AnimateDiff 4.1 Alleviate Negative Effects from Training Data with Domain Adapter 4.2 Learn Motion Priors with Motion Module 4.3 Adapt to New Motion Patterns with MotionLora 4.4 AnimateDiff in Practice 5 Experiments and 5.1 Qualitative Results 5.2 Qualitative Comparison 5.3 Ablative Study 5.4 Controllable Generation 6 Conclusion 7 Ethics Statement 8 Reproducibility Statement, Acknowledgement and References 5.3 ABLATIVE STUDY Domain adapter. To investigate the impact of the domain adapter in AnimateDiff, we conducted a study by adjusting the scaler in the adapter layers during inference, ranging from 1 (full impact) to 0 (complete removal). As illustrated in Figure 6, as the scaler of the adapter decreases, there is an improvement in overall visual quality, accompanied by a reduction in the visual content distribution learned from the video dataset (the watermark in the case of WebVid (Bain et al., 2021)). These results indicate the successful role of the domain adapter in enhancing the visual quality of AnimateDiff by alleviating the motion module from learning the visual distribution gap. Motion module design. We compare our motion module design of the temporal Transformer with its full convolution counterpart, which is motivated by the fact that both designs are widely employed in recent works on video generation. We replace the temporal attention with 1D temporal convolution and ensured that the two model parameters were closely aligned. As depicted in supplementary materials, the convolutional motion module aligns all frames to be identical but does not incorporate any motion compared to the Transformer architecture. Efficiency of MotionLoRA. The efficiency of MotionLoRA in AnimateDiff was examined in terms of parameter efficiency and data efficiency. Parameter efficiency is crucial for efficient model training and sharing among users, while data efficiency is essential for real-world applications where collecting an adequate number of reference videos for specific motion patterns may be challenging. To investigate these aspects, we trained multiple MotionLoRA models with varying parameter scales and reference video quantities. In Fig. 7, the first two samples demonstrate that MotionLoRA is capable of learning new camera motions (e.g., zoom-in) with a small parameter scale while maintaining comparable motion quality. Furthermore, even with a modest number of reference videos (e.g., N = 50), the model successfully learns the desired motion patterns. However, when the number of reference videos is excessively limited (e.g., N = 5), significant degradation in quality is observed, suggesting that MotionLoRA encounters difficulties in learning shared motion patterns and instead relies on capturing texture information from the reference videos. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Yuwei Guo, The Chinese University of Hong Kong; (2) Ceyuan Yang, Shanghai Artificial Intelligence Laboratory with Corresponding Author; (3) Anyi Rao, Stanford University; (4) Zhengyang Liang, Shanghai Artificial Intelligence Laboratory; (5) Yaohui Wang, Shanghai Artificial Intelligence Laboratory; (6) Yu Qiao, Shanghai Artificial Intelligence Laboratory; (7) Maneesh Agrawala, Stanford University; (8) Dahua Lin, Shanghai Artificial Intelligence Laboratory; (9) Bo Dai, The Chinese University of Hong Kong and The Chinese University of Hong Kong. Authors: Authors: (1) Yuwei Guo, The Chinese University of Hong Kong; (2) Ceyuan Yang, Shanghai Artificial Intelligence Laboratory with Corresponding Author; (3) Anyi Rao, Stanford University; (4) Zhengyang Liang, Shanghai Artificial Intelligence Laboratory; (5) Yaohui Wang, Shanghai Artificial Intelligence Laboratory; (6) Yu Qiao, Shanghai Artificial Intelligence Laboratory; (7) Maneesh Agrawala, Stanford University; (8) Dahua Lin, Shanghai Artificial Intelligence Laboratory; (9) Bo Dai, The Chinese University of Hong Kong and The Chinese University of Hong Kong. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Work Related 2 Work Related 3 Preliminary 3 Preliminary AnimateDiff AnimateDiff 4.1 Alleviate Negative Effects from Training Data with Domain Adapter 4.1 Alleviate Negative Effects from Training Data with Domain Adapter 4.2 Learn Motion Priors with Motion Module 4.2 Learn Motion Priors with Motion Module 4.3 Adapt to New Motion Patterns with MotionLora 4.3 Adapt to New Motion Patterns with MotionLora 4.4 AnimateDiff in Practice 4.4 AnimateDiff in Practice 5 Experiments and 5.1 Qualitative Results 5 Experiments and 5.1 Qualitative Results 5.2 Qualitative Comparison 5.2 Qualitative Comparison 5.3 Ablative Study 5.3 Ablative Study 5.4 Controllable Generation 5.4 Controllable Generation 6 Conclusion 6 Conclusion 7 Ethics Statement 7 Ethics Statement 8 Reproducibility Statement, Acknowledgement and References 8 Reproducibility Statement, Acknowledgement and References 5.3 ABLATIVE STUDY Domain adapter. To investigate the impact of the domain adapter in AnimateDiff, we conducted a study by adjusting the scaler in the adapter layers during inference, ranging from 1 (full impact) to 0 (complete removal). As illustrated in Figure 6, as the scaler of the adapter decreases, there is an improvement in overall visual quality, accompanied by a reduction in the visual content distribution learned from the video dataset (the watermark in the case of WebVid (Bain et al., 2021)). These results indicate the successful role of the domain adapter in enhancing the visual quality of AnimateDiff by alleviating the motion module from learning the visual distribution gap. Domain adapter. Motion module design. We compare our motion module design of the temporal Transformer with its full convolution counterpart, which is motivated by the fact that both designs are widely employed in recent works on video generation. We replace the temporal attention with 1D temporal convolution and ensured that the two model parameters were closely aligned. As depicted in supplementary materials, the convolutional motion module aligns all frames to be identical but does not incorporate any motion compared to the Transformer architecture. Motion module design. Efficiency of MotionLoRA. The efficiency of MotionLoRA in AnimateDiff was examined in terms of parameter efficiency and data efficiency. Parameter efficiency is crucial for efficient model training and sharing among users, while data efficiency is essential for real-world applications where collecting an adequate number of reference videos for specific motion patterns may be challenging. To investigate these aspects, we trained multiple MotionLoRA models with varying parameter scales and reference video quantities. In Fig. 7, the first two samples demonstrate that MotionLoRA is capable of learning new camera motions (e.g., zoom-in) with a small parameter scale while maintaining comparable motion quality. Furthermore, even with a modest number of reference videos (e.g., N = 50), the model successfully learns the desired motion patterns. However, when the number of reference videos is excessively limited (e.g., N = 5), significant degradation in quality is observed, suggesting that MotionLoRA encounters difficulties in learning shared motion patterns and instead relies on capturing texture information from the reference videos. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Ablative Study on Domain Adapter, Motion Module Design, and MotionLoRA Efficiency

Authors:
(1) Yuwei Guo, The Chinese University of Hong Kong;
(2) Ceyuan Yang, Shanghai Artificial Intelligence Laboratory with Corresponding Author;
(3) Anyi Rao, Stanford University;
(4) Zhengyang Liang, Shanghai Artificial Intelligence Laboratory;
(5) Yaohui Wang, Shanghai Artificial Intelligence Laboratory;
(6) Yu Qiao, Shanghai Artificial Intelligence Laboratory;
(7) Maneesh Agrawala, Stanford University;
(8) Dahua Lin, Shanghai Artificial Intelligence Laboratory;
(9) Bo Dai, The Chinese University of Hong Kong and The Chinese University of Hong Kong. Table of Links Abstract and 1 Introduction 2 Work Related 3 Preliminary AnimateDiff 4.1 Alleviate Negative Effects from Training Data with Domain Adapter 4.2 Learn Motion Priors with Motion Module 4.3 Adapt to New Motion Patterns with MotionLora 4.4 AnimateDiff in Practice 5 Experiments and 5.1 Qualitative Results 5.2 Qualitative Comparison 5.3 Ablative Study 5.4 Controllable Generation 6 Conclusion 7 Ethics Statement 8 Reproducibility Statement, Acknowledgement and References 4.4 ANIMATEDIFF IN PRACTICE We elaborate on the training and inference here and put the detailed configurations in supplementary materials. It’s worth noting that when training the domain adapter, the motion module, and the MotionLoRA, parameters outside the trainable part remain frozen. Inference. At inference time (Fig. 2), the personalized T2I model will first be inflated in the same way discussed in Section 4.2, then injected with the motion module for general animation generation, and the optional MotionLoRA for generating animation with personalized motion. As for the domain adapter, instead of simply dropping it during the inference time, in practice, we can also inject it into the personalized T2I model and adjust its contribution by changing the scaler α in Eq. (4). An ablation study on the value of α is conducted in experiments. Finally, the animation frames can be obtained by performing the reverse diffusion process and decoding the latent codes. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Yuwei Guo, The Chinese University of Hong Kong; (2) Ceyuan Yang, Shanghai Artificial Intelligence Laboratory with Corresponding Author; (3) Anyi Rao, Stanford University; (4) Zhengyang Liang, Shanghai Artificial Intelligence Laboratory; (5) Yaohui Wang, Shanghai Artificial Intelligence Laboratory; (6) Yu Qiao, Shanghai Artificial Intelligence Laboratory; (7) Maneesh Agrawala, Stanford University; (8) Dahua Lin, Shanghai Artificial Intelligence Laboratory; (9) Bo Dai, The Chinese University of Hong Kong and The Chinese University of Hong Kong. Authors: Authors: (1) Yuwei Guo, The Chinese University of Hong Kong; (2) Ceyuan Yang, Shanghai Artificial Intelligence Laboratory with Corresponding Author; (3) Anyi Rao, Stanford University; (4) Zhengyang Liang, Shanghai Artificial Intelligence Laboratory; (5) Yaohui Wang, Shanghai Artificial Intelligence Laboratory; (6) Yu Qiao, Shanghai Artificial Intelligence Laboratory; (7) Maneesh Agrawala, Stanford University; (8) Dahua Lin, Shanghai Artificial Intelligence Laboratory; (9) Bo Dai, The Chinese University of Hong Kong and The Chinese University of Hong Kong. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Work Related 2 Work Related 3 Preliminary 3 Preliminary AnimateDiff AnimateDiff 4.1 Alleviate Negative Effects from Training Data with Domain Adapter 4.1 Alleviate Negative Effects from Training Data with Domain Adapter 4.2 Learn Motion Priors with Motion Module 4.2 Learn Motion Priors with Motion Module 4.3 Adapt to New Motion Patterns with MotionLora 4.3 Adapt to New Motion Patterns with MotionLora 4.4 AnimateDiff in Practice 4.4 AnimateDiff in Practice 5 Experiments and 5.1 Qualitative Results 5 Experiments and 5.1 Qualitative Results 5.2 Qualitative Comparison 5.2 Qualitative Comparison 5.3 Ablative Study 5.3 Ablative Study 5.4 Controllable Generation 5.4 Controllable Generation 6 Conclusion 6 Conclusion 7 Ethics Statement 7 Ethics Statement 8 Reproducibility Statement, Acknowledgement and References 8 Reproducibility Statement, Acknowledgement and References 4.4 ANIMATEDIFF IN PRACTICE We elaborate on the training and inference here and put the detailed configurations in supplementary materials. It’s worth noting that when training the domain adapter, the motion module, and the MotionLoRA, parameters outside the trainable part remain frozen. Inference. At inference time (Fig. 2), the personalized T2I model will first be inflated in the same way discussed in Section 4.2, then injected with the motion module for general animation generation, and the optional MotionLoRA for generating animation with personalized motion. As for the domain adapter, instead of simply dropping it during the inference time, in practice, we can also inject it into the personalized T2I model and adjust its contribution by changing the scaler α in Eq. (4). An ablation study on the value of α is conducted in experiments. Finally, the animation frames can be obtained by performing the reverse diffusion process and decoding the latent codes. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

AnimateDiff in the Wild

Flighty App Can Now Predict Flight Delays: Here’s How You Can Also Do It Using Machine Learning

ফ্লাইটি অ্যাপ এখন ফ্লাইট বিলম্বের পূর্বাভাস দিতে পারে: এখানে আপনি কীভাবে মেশিন লার্নিং ব্যবহার করে এটি করতে পারেন

Die Flighty-App kann jetzt Flugverspätungen vorhersagen: So können Sie dies auch mithilfe von maschinellem Lernen tun

La aplicación Flighty ahora puede predecir retrasos en los vuelos: así es como puedes hacerlo tú también mediante aprendizaje automático

L’application Flighty peut désormais prédire les retards de vol : voici comment vous pouvez également le faire grâce au machine learning

फ्लाइटी ऐप अब उड़ान में देरी का अनुमान लगा सकता है: यहां बताया गया है कि आप मशीन लर्निंग का उपयोग करके ऐसा कैसे कर सकते हैं

Flighty アプリがフライトの遅延を予測できるようになりました。機械学習を使用してこれを行う方法は次のとおりです。

Flighty 앱이 이제 항공편 지연을 예측할 수 있습니다. 머신 러닝을 사용하여 예측하는 방법도 있습니다.

O aplicativo Flighty agora pode prever atrasos de voos: veja como você também pode fazer isso usando aprendizado de máquina

Приложение Flighty теперь может предсказывать задержки рейсов: вот как это можно сделать с помощью машинного обучения

Flighty Uygulaması Artık Uçuş Gecikmelerini Tahmin Edebiliyor: Makine Öğrenimini Kullanarak Bunu Nasıl Yapabileceğinizi Öğrenin

Ứng dụng Flighty hiện có thể dự đoán sự chậm trễ của chuyến bay: Đây là cách bạn cũng có thể thực hiện bằng cách sử dụng máy học

Flighty 应用程序现在可以预测航班延误：您也可以使用机器学习来实现这一点

Score Big With Football Analytics: Design Your Country's Perfect National Team with Machine Learning

Stories, News & Mentions about SCALERO

Scalero’s Stories on HackerNoon