My problem was: what the heck was “color”? After some more looking around and testing, I guessed that “color” might be the actual light wavelength of the source light. My light was white (6500K) and to get that wavelength, I averaged the top and bottom numbers on this converter and I came up with:

First I would check that you have correct color management settings. I’m not too familiar with recreating blender scenes in three.js, but as long as those settings are correct I would expect lighting intensity to be similar.

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Sohl-Dickstein, Jascha, Eric A. Weiss, Niru Maheswaranathan, and Surya Ganguli. “Deep Unsupervised Learning Using Nonequilibrium Thermodynamics.” arXiv, November 18, 2015. https://doi.org/10.48550/arXiv.1503.03585.

Audio synthesis is another domain where these models shine. From generating music tracks to synthesizing speech, diffusion models offer a level of granularity and control that’s hard to achieve with other techniques. Their iterative refinement process ensures that the generated audio is smooth, clear, and free from abrupt artifacts.

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lightintensity中文

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Or in other words, for a spotlight with an angle of 90 degrees, 1 watt = 217 candela. Note this is a guess, I would test a simple export with one spotlight from blender to see if it gives a decent result. If not, you’ll have to study the linked PDF from that issue to figure out the correct formula.

Entertainment: From generating background music for indie games to creating concept art for movies, these models are becoming a staple in the creative process.

As promising as diffusion models are, they’re not without their challenges. One of the primary limitations is the computational cost. The iterative nature of these models, while powerful, can be resource-intensive, especially for high-resolution tasks. This makes real-time applications, like video game graphics or live audio synthesis, a challenge.

From the intricate dance of particles in a physical system to the generation of breathtaking visuals and sounds in the digital realm, the journey of diffusion models has been nothing short of remarkable. They stand as a testament to the power of interdisciplinary research, where principles from one domain breathe life into innovations in another.

is the intensity value needed to correct for the numbers coming out of Blender (for white light specifically). It’s so close to my estimation of 10% that it makes me wonder if it was even worth all the time I spent figuring it out.

Contrasting with traditional neural networks, which often rely on deterministic processes and fixed architectures, diffusion models embrace randomness. While conventional networks might take an input and produce an output through a series of transformations, diffusion models start with a noisy version of the target data and gradually refine it. This approach is distinct from other generative models like Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs). While GANs involve a game between two networks and VAEs use probabilistic encoders and decoders, diffusion models rely on a process that’s more akin to a random walk.

I found that a value of about 10% of the original “Watts” value from Blender was pretty close. But I wanted it to be as close to the original as possible since this 3d implementation was going to be only one of the uses across multiple platforms.

A diffusion model is a generative model that leverages stochastic processes to iteratively refine an initial random sample over multiple steps, simulating the way substances spread or diffuse over time. In the context of AI, it represents a blend of physics and artificial intelligence principles, producing data outputs through a series of guided random walks in a latent space.

In essence, diffusion models offer a fresh perspective on data generation, blending principles of physics with the power of AI, and opening doors to new possibilities in the world of generative tasks.

Zhang, Chenshuang, Chaoning Zhang, Mengchun Zhang, and In So Kweon. “Text-to-Image Diffusion Models in Generative AI: A Survey.” arXiv, April 2, 2023. https://doi.org/10.48550/arXiv.2303.07909.

I will definitely go back and triple check the color spaces of the blender and threejs files, but surprisingly as noted in this github issue:

Radiantintensity

I’m working with a gltf model that was made in Blender by another designer, and threejs is loading in the intensity as the Watt values that are used in Blender which is blowing out the image. I’m trying to get the scene as close to the original designer’s intent as possible, and being able to make the accurate calculation would probably help.

Saharia, Chitwan, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, et al. “Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding.” arXiv, May 23, 2022. https://doi.org/10.48550/arXiv.2205.11487.

Ho, Jonathan, Ajay Jain, and Pieter Abbeel. “Denoising Diffusion Probabilistic Models.” In Advances in Neural Information Processing Systems, 33:6840–51. Curran Associates, Inc., 2020. https://proceedings.neurips.cc/paper/2020/hash/4c5bcfec8584af0d967f1ab10179ca4b-Abstract.html.

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Neils Rogge and Kashif Rasul. “The Annotated Diffusion Model.” Accessed September 22, 2023. https://huggingface.co/blog/annotated-diffusion.

luminous intensity中文

Looking like the calculation for spot lights requires some integration, but at a guess you might get decent results by replacing the 4pi in the above equation by the spotlight angle. So if the spotlight has an angle of 90 degrees (pi radian), the equation becomes:

And just to add, I’m coming from more of a design background than a programming background so my math abilities/knowledge probably aren’t where they would need to be in order to make some of the more advanced calculations (matrices mostly). —Thank you!

In summary, diffusion models, with their unique approach and advantages, are rapidly becoming a go-to choice for a myriad of generative tasks, pushing the boundaries of what’s possible in AI-driven content creation.

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Alammar, Jay. “The Illustrated Stable Diffusion.” Accessed September 22, 2023. https://jalammar.github.io/illustrated-stable-diffusion/.

Ah, until they fix that bug I guess you’ll have to perform the conversion yourself. If you put Φe = 1 (watts) in the point light conversion calculations you get a ratio:

Wiggers, Kyle. “A Brief History of Diffusion, the Tech at the Heart of Modern Image-Generating AI.” TechCrunch (blog), December 22, 2022. https://techcrunch.com/2022/12/22/a-brief-history-of-diffusion-the-tech-at-the-heart-of-modern-image-generating-ai/.

Luo, Calvin. “Understanding Diffusion Models: A Unified Perspective.” arXiv, August 25, 2022. https://doi.org/10.48550/arXiv.2208.11970.

Ananthaswamy, Anil. “The Physics Principle That Inspired Modern AI Art.” Quanta Magazine, January 5, 2023. https://www.quantamagazine.org/the-physics-principle-that-inspired-modern-ai-art-20230105/.

Noise plays a pivotal role in this process. It’s the initial randomness, the starting point of our walk. As the model progresses through its steps, the level of noise decreases, allowing the data to emerge from the chaos and become more refined. This controlled reduction of noise over time is what enables the model to produce coherent and high-quality outputs.

However, these challenges are also avenues for future research. As computational power continues to grow and algorithms become more efficient, the speed and resource concerns might become things of the past. On the interpretability front, there’s active research into making AI models, in general, more transparent, and diffusion models will undoubtedly benefit from these advancements.

Diffusion models, with their unique blend of physics and AI, are poised to shape the next wave of generative AI. Their transformative potential, combined with ongoing research and advancements, ensures that they’ll remain at the forefront of AI innovation for years to come.

How islight intensitymeasured

This might be a terribly dumb question, but I’ve been searching for a while and unable to find an answer. On the Light documentation page it states that:

The mathematics behind diffusion is elegantly captured by Fick’s laws. At a high level, these laws describe the rate at which substances diffuse, taking into account the concentration gradient—the difference in concentration between two points. While the equations can dive deep into complexities, the primary takeaway is that the rate of diffusion is proportional to this gradient. The steeper the gradient, the faster the diffusion.

Blender uses watts as units for power of point light sources. On the other hand, KHR_punctual_lights use lm/sr as light intensity units. I...

Diversity in Outputs: While some generative models might get stuck producing similar-looking outputs, the inherent randomness in diffusion models ensures a diverse range of generated samples, capturing the breadth of the data distribution.

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Stability in Training: One of the perennial challenges with GANs is the instability during training, often leading to mode collapse. Diffusion models, with their iterative refinement approach, tend to be more stable and less prone to such pitfalls.

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But how does a process so deeply rooted in physics find its way into the world of artificial intelligence? The answer lies in the parallels between the random movements of particles in diffusion and the behavior of data in high-dimensional spaces. Just as particles seek equilibrium in physical systems, data in AI models, especially generative ones, can be thought of as seeking an optimal distribution or representation. By leveraging the principles of diffusion, researchers and AI practitioners have found innovative ways to model data, leading to breakthroughs in generative tasks and beyond.

So, as I mentioned above and as @looeee helped me out with, I found some conversion formulas in a github post that discussed this issue. But unfortunately the formula that @looeee provided seemed to still be missing something when I tried to implement it. So I tinkered around a bit and finally found something that seemed to make it work. Here’s how it went:

Fashion: Brands have experimented with diffusion models to come up with novel design patterns for apparel, tapping into the model’s ability to generate unique and aesthetically pleasing visuals.

Healthcare: In medical imaging, diffusion models assist in enhancing low-resolution scans, making them clearer for diagnosis.

Diffusion models have carved a niche for themselves in the vast landscape of generative AI. Their unique approach to data generation has made them particularly suited for a range of tasks that require both precision and creativity.

Light intensityformula

Dhariwal, Prafulla, and Alex Nichol. “Diffusion Models Beat GANs on Image Synthesis.” arXiv, June 1, 2021. https://doi.org/10.48550/arXiv.2105.05233.

Honestly? It looked absolutely perfect when I applied that multiplier. Am I sure that 570 was the right number to use? Not totally, but it seemed to work.

Light intensityunit

Yang, Ling, Zhilong Zhang, Yang Song, Shenda Hong, Runsheng Xu, Yue Zhao, Wentao Zhang, Bin Cui, and Ming-Hsuan Yang. “Diffusion Models: A Comprehensive Survey of Methods and Applications.” arXiv, March 23, 2023. http://arxiv.org/abs/2209.00796.

Base values: KV = 683 – This is a constant related to the modern definition of the candela Watts – Unit of light measurement used in blender Lumens – This seems to be an unnecessary intermediary conversion, but it keeps the formulas simpler Candela – Unit of light measurement used by GLTF Color – According to Threejs documentation part of the formula for candela calculation Intensity – Unit of light measurement used by Threejs, second part of the threejs candela calculation

Controlled Generation: The step-by-step generation process of diffusion models allows for more control over the output. This is especially useful in tasks where specific attributes or features need to be emphasized or de-emphasized.

One of the most prominent applications of diffusion models is in image generation. Whether it’s creating lifelike portraits, artistic landscapes, or even detailed objects, diffusion models have showcased their prowess in producing high-resolution and coherent images. Beyond static images, they’ve also been employed in video generation, adding temporal coherence to the mix.

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Rombach, Robin, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. “High-Resolution Image Synthesis with Latent Diffusion Models.” arXiv, April 13, 2022. https://doi.org/10.48550/arXiv.2112.10752.

Luminousintensity

The significance of diffusion models in AI cannot be understated. They offer a fresh perspective and approach to generative tasks, standing apart from traditional neural networks and other generative models. As we delve deeper into this topic, we’ll explore the journey of diffusion from its roots in physics modeling to its transformative role in artificial intelligence.

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Looking ahead, the potential of diffusion models is vast. They could revolutionize areas like virtual reality, with lifelike graphics generated on the fly, or personalized music, where tracks are synthesized in real-time based on the listener’s mood or surroundings. The fusion of diffusion models with other AI techniques, like reinforcement learning or transfer learning, could also open up new horizons.

In the world of physics, diffusion processes describe the way particles move from regions of high concentration to areas of lower concentration, striving for equilibrium. This seemingly simple process is governed by intricate mathematical equations and principles. Fast forward to the modern age of technology, and these very principles have been adapted and transformed to serve as the foundation for some of the most advanced AI algorithms.

The constant 683 is chosen based on a wavelength of 555nm (green) since that’s where human vision is most sensitive. You should probably use this throughout the calculation, but in any case the difference will be pretty small compared to 570nm.

Hello! I just wanted to follow up with my findings on this issue in case it could help out anybody else in the future. Before I posted initially, I just tried to eyeball the correct intensity values for the lights that were imported from the Blender GLTF.

What is the formula used for multiplying color by intensity? Specifically, what format are the color values converted to in that equation (an example would be extremely helpful).

Diffusion models in the context of AI can be thought of as a series of generative models that leverage stochastic processes to produce data. Instead of directly generating an output, these models iteratively refine an initial random sample over multiple steps, much like how substances diffuse over time.

I’m also not sure what you need to do for directional lights. I guess first figure out what units Blender uses for them and then try to find a conversion formula.

Diffusion models, at their core, are a fascinating blend of physics and artificial intelligence principles. Originating from the study of how substances spread or diffuse through space and time, these models have found a unique and impactful place in the realm of AI.

Blender currently uses watts as its standard light measurement whereas gltf (and threejs) uses the candela as its measure. One of the posters in the github issue generously posted some of the conversion formulas, but the numbers I was getting weren’t lining up with the visuals. I then went back to the documentation and noticed that the luminous intensity (candela) figure I needed was not just the intensity value of the light, but the intensity multiplied by the color. So in order to calculate the values I need, I think I need the rest of the formula. Does that sound right? Let me know if I’m off somewhere.

Nichol, Alexander Quinn, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob Mcgrew, Ilya Sutskever, and Mark Chen. “GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models.” In Proceedings of the 39th International Conference on Machine Learning, 16784–804. PMLR, 2022. https://proceedings.mlr.press/v162/nichol22a.html.

Diving into the mechanics, the heart of diffusion models lies in simulating this random walk in a latent space. Imagine a space where each point represents a possible data sample. The model starts at a random point (a noisy version of the target) and takes small, guided steps, with the aim of reaching a point that represents the desired output. Each step is influenced by the gradient of the data distribution, guiding the walk towards regions of higher likelihood.

Diffusion, in the realm of physics, is a natural phenomenon that describes the passive spread of particles or substances. Imagine a drop of ink dispersing in a glass of water. Over time, the ink molecules move from an area of high concentration, where the drop was initially placed, to areas of lower concentration, eventually leading to a uniform distribution throughout the water. This movement, driven by the inherent desire for systems to reach a state of equilibrium, is the essence of diffusion.

Another area of concern is the interpretability of these models. Given their stochastic nature and the complex interplay of noise and data, understanding precisely why a model made a particular decision or produced a specific output can be elusive.