COMPOSITION
DESIGN
-
Disco Diffusion V4.1 Google Colab, Dall-E, Starryai – creating images with AI
Read more: Disco Diffusion V4.1 Google Colab, Dall-E, Starryai – creating images with AIDisco Diffusion (DD) is a Google Colab Notebook which leverages an AI Image generating technique called CLIP-Guided Diffusion to allow you to create compelling and beautiful images from just text inputs. Created by Somnai, augmented by Gandamu, and building on the work of RiversHaveWings, nshepperd, and many others.
Phone app: https://www.starryai.com/
docs.google.com/document/d/1l8s7uS2dGqjztYSjPpzlmXLjl5PM3IGkRWI3IiCuK7g
colab.research.google.com/drive/1sHfRn5Y0YKYKi1k-ifUSBFRNJ8_1sa39
Colab, or “Colaboratory”, allows you to write and execute Python in your browser, with
– Zero configuration required
– Access to GPUs free of charge
– Easy sharing
https://80.lv/articles/a-beautiful-roman-villa-made-with-disco-diffusion-5-2/
COLOR
-
Photography Basics : Spectral Sensitivity Estimation Without a Camera
Read more: Photography Basics : Spectral Sensitivity Estimation Without a Camerahttps://color-lab-eilat.github.io/Spectral-sensitivity-estimation-web/
A number of problems in computer vision and related fields would be mitigated if camera spectral sensitivities were known. As consumer cameras are not designed for high-precision visual tasks, manufacturers do not disclose spectral sensitivities. Their estimation requires a costly optical setup, which triggered researchers to come up with numerous indirect methods that aim to lower cost and complexity by using color targets. However, the use of color targets gives rise to new complications that make the estimation more difficult, and consequently, there currently exists no simple, low-cost, robust go-to method for spectral sensitivity estimation that non-specialized research labs can adopt. Furthermore, even if not limited by hardware or cost, researchers frequently work with imagery from multiple cameras that they do not have in their possession.
To provide a practical solution to this problem, we propose a framework for spectral sensitivity estimation that not only does not require any hardware (including a color target), but also does not require physical access to the camera itself. Similar to other work, we formulate an optimization problem that minimizes a two-term objective function: a camera-specific term from a system of equations, and a universal term that bounds the solution space.
Different than other work, we utilize publicly available high-quality calibration data to construct both terms. We use the colorimetric mapping matrices provided by the Adobe DNG Converter to formulate the camera-specific system of equations, and constrain the solutions using an autoencoder trained on a database of ground-truth curves. On average, we achieve reconstruction errors as low as those that can arise due to manufacturing imperfections between two copies of the same camera. We provide predicted sensitivities for more than 1,000 cameras that the Adobe DNG Converter currently supports, and discuss which tasks can become trivial when camera responses are available.
-
The 7 key elements of brand identity design + 10 corporate identity examples
Read more: The 7 key elements of brand identity design + 10 corporate identity exampleswww.lucidpress.com/blog/the-7-key-elements-of-brand-identity-design
1. Clear brand purpose and positioning
2. Thorough market research
3. Likable brand personality
4. Memorable logo
5. Attractive color palette
6. Professional typography
7. On-brand supporting graphics
LIGHTING
-
Convert between light exposure and intensity
Read more: Convert between light exposure and intensityimport math,sys def Exposure2Intensity(exposure): exp = float(exposure) result = math.pow(2,exp) print(result) Exposure2Intensity(0) def Intensity2Exposure(intensity): inarg = float(intensity) if inarg == 0: print("Exposure of zero intensity is undefined.") return if inarg < 1e-323: inarg = max(inarg, 1e-323) print("Exposure of negative intensities is undefined. Clamping to a very small value instead (1e-323)") result = math.log(inarg, 2) print(result) Intensity2Exposure(0.1)
Collections
| Explore posts
| Design And Composition
| Featured AI
Popular Searches
unreal | pipeline | virtual production | free | learn | photoshop | 360 | macro | google | nvidia | resolution | open source | hdri | real-time | photography basics | nuke
FEATURED POSTS
Social Links
DISCLAIMER – Links and images on this website may be protected by the respective owners’ copyright. All data submitted by users through this site shall be treated as freely available to share.
