Search Results for: learn
scikit-learn – Machine Learning A.I. in Python
http://scikit-learn.org/stable/
Simple and efficient tools for data mining and data analysis Accessible to everybody, and reusable in various contexts Built on NumPy, SciPy, and matplotlib Open source, commercially usable – BSD license
7 Timeless Tips to Learn Any Language in Days, Not Years
http://www.lifehack.org/305085/7-timeless-tips-learn-any-language-days-not-years
the 75 most common words make up 40% of occurrences
the 200 most common words make up 50% of occurrences
the 524 most common words make up 60% of occurrences
the 1257 most common words make up 70% of occurrences
the 2925 most common words make up 80% of occurrences
the 7444 most common words make up 90% of occurrences
the 13374 most common words make up 95% of occurrences
the 25508 most common words make up 99% of occurrences
What I Learned About Leadership When I Interviewed The Biggest Drug Dealer In History
TRY TO GET THE PEOPLE WORKING FOR YOU TO BE MORE SUCCESSFUL THAN YOU
HONESTY
BE VERY LOW KEY
ONLY DO THE ESSENTIAL
DON’T MAKE IT ABOUT THE MONEY
REDUCE CONFRONTATIONS
FREEMIUM
ASSUME THE WORST
Self Organizing Mapping – machine learning A.I.
A self-organizing map (SOM) or self-organizing feature map (SOFM) is a type of artificial neural network (ANN) that is trained using unsupervised learning to produce a low-dimensional (typically two-dimensional), discretized representation of the input space of the training samples, called a map.
http://www.mat.ucsb.edu/~g.legrady/academic/courses/06w259/projs/cs/MAT259-paper.pdf
http://en.wikipedia.org/wiki/Self-organizing_map
http://www.ai-junkie.com/ann/som/som1.html
Google – Artificial Intelligence free courses
1. Introduction to Large Language Models: Learn about the use cases and how to enhance the performance of large language models.
https://www.cloudskillsboost.google/course_templates/539
2. Introduction to Generative AI: Discover the differences between Generative AI and traditional machine learning methods.
https://www.cloudskillsboost.google/course_templates/536
3. Generative AI Fundamentals: Earn a skill badge by demonstrating your understanding of foundational concepts in Generative AI.
https://www.cloudskillsboost.google/paths
4. Introduction to Responsible AI: Learn about the importance of Responsible AI and how Google implements it in its products.
https://www.cloudskillsboost.google/course_templates/554
5. Encoder-Decoder Architecture: Learn about the encoder-decoder architecture, a critical component of machine learning for sequence-to-sequence tasks.
https://www.cloudskillsboost.google/course_templates/543
6. Introduction to Image Generation: Discover diffusion models, a promising family of machine learning models in the image generation space.
https://www.cloudskillsboost.google/course_templates/541
7. Transformer Models and BERT Model: Get a comprehensive introduction to the Transformer architecture and the Bidirectional Encoder Representations from the Transformers (BERT) model.
https://www.cloudskillsboost.google/course_templates/538
8. Attention Mechanism: Learn about the attention mechanism, which allows neural networks to focus on specific parts of an input sequence.
https://www.cloudskillsboost.google/course_templates/537
Generative AI Glossary
https://education.civitai.com/generative-ai-glossary/
| Term | Tags | Description |
|---|---|---|
| .ckpt | Model | “Checkpoint”, a file format created by PyTorch Lightning, a PyTorch research framework. It contains a PyTorch Lightning machine learning model used (by Stable Diffusion) to generate images. |
| .pt | Software | A machine learning model file created using PyTorch, containing algorithms used to automatically perform a task. |
| .Safetensors | Model | A file format for Checkpoint models, less susceptible to embedded malicious code. See “Pickle” |
| ADetailer | Software, Extension | A popular Automatic1111 Extension, mostly used to enhance fine face and eye detail, but can be used to re-draw hands and full characters. |
| AGI | Concept | Artificial General Intelligence (AGI), the point at which AI matches or exceeds the intelligence of humans. |
| Algorithm | Concept, Software | A series of instructions that allow a computer to learn and analyze data, learning from it, and use that learning to interpret and accomplish future tasks on its own. |
| AnimateDiff | Software, Extension | Technique which involves injecting motion into txt2img (or img2img) generations. https://animatediff.github.io/ |
| API | Software | Application Programmer Interface – a set of functions and tools which allow interaction with, or between, pieces of software. |
| Auto-GPT | Software, LLM | |
| Automatic1111 | Developer, SD User Interface | Creator of the popular Automatic1111 WebUI graphical user interface for SD. |
| Bard | Software, LLM | Google’s Chatbot, based on their LaMDA model. |
| Batch | A subset of the training data used in one iteration of model training. In inference, a group of images. | |
| Bias | Concept, LLM | In Large Language Models, errors resulting from training data; stereotypes, attributing certain characteristics to races or groups of people, etc. Bias can cause models to generate offensive and harmful content. |
| Bing | Software, LLM | Microsoft’s ChatGTP powered Chatbot. |
| CFG | Setting | Classifier Free Guidance, sometimes “Guidance Scale”. Controls how closely the image generation process follows the text prompt. |
| Checkpoint | Model | The product of training on millions of captioned images scraped from multiple sources on the Web. This file drives Stable Diffusion’s txt2img, img2img, txt2video |
| Civitai (Civitai.com) | Community Resource | Popular hosting site for all types of Generative AI resources. |
| Civitai Generator | Software, Tool | Free Stable Diffusion Image Generation Interface, available on Civitai.com. |
| Civitai Trainer | Software, Tool | LoRA Training interface, available on Civitai.com, for SDXL and 1.5 based LoRA. |
| CLIP | Software | An open source model created by OpenAI. Trained on millions of images and captions, it determines how well a particular caption describes an image. |
| Cmdr2 | Developer, SD User Interface | Creator of the popular EasyDiffusion, simple one-click install graphical user interface for SD. |
| CodeFormer | Face/Image Restoration, Model | A facial image restoration model, for fixing blurry, grainy, or disfigured faces. |
| Colab | Tool | Colaboratory, a product from Google Research, allowing execution of Python code through the browser. Particularly geared towards machine learning applications. https://colab.research.google.com/ |
| ComfyUI | SD User Interface, Software | A popular powerful modular UI for Stable Diffusion with a “workflow” type workspace. Somewhat more complex than Auto1111 WebUI https://github.com/comfyanonymous/ComfyUI |
| CompVis | Organization | Computer Vision & Learning research group at Ludwig Maximilian University of Munich. They host Stable Diffusion models on Hugging Face. |
| Conda | Application, Software | An open source package manager for many programming languages, including Python. |
| ControlNet | UI Extension | An Extension to Auto1111 WebUI allowing images to be manipulated in a number of ways. https://github.com/Mikubill/sd-webui-controlnet |
| Convergence | Concept | The point in image generation where the image no longer changes as the steps increase. |
| CUDA | Hardware, Software | Compute Unified Device Architecture, Nvdia’s parallel processing architecture. |
| DALL-E / DALL-E 2 | Organization | Deep learning image models created by OpenAI, available as a commercial image generation service. |
| Danbooru | Community Resource | English-based image board website specializing in erotic manga fan art, NSFW. |
| Danbooru Tag | Community Resource | System of keywords applied to Danbooru images describing the content within. When using Checkpoint models trained on Danbooru images, keywords can be referenced in Prompts. |
| DDIM (Sampler) | Sampler | Denoising Diffusion Implicit Models. See Samplers. |
| Deep Learning | Concept | A type of Machine Learning, where neural networks attempt to mimic the behavior of the human brain to perform tasks. |
| Deforum | UI Extension, Community Resource | A community of AI image synthesis developers, enthusiasts, and artists, producing Generative AI tools. Most commonly known for a Stable Diffusion WebUI video extension of the same name. |
| Denoising/Diffusion | Concept | The process by which random noise (see Seed) is iteratively reduced into the final image. |
| depth2img | Concept | Infers the depth of an input image (using an existing model), and then generates new images using both the text and depth information. |
| Diffusion Model (DM) | Model | A generative model, used to generate data similar to the data on which they are trained. |
| DPM adaptive (Sampler) | Sampler | Diffusion Probabilistic Model (Adaptive). See Samplers. Ignores Step Count. |
| DPM Fast (Sampler) | Sampler | Diffusion Probabilistic Model (Fast). See Samplers. |
| DPM++ 2M (Sampler) | Sampler | Diffusion Probabilistic Model – Multi-step. Produces good quality results within 15-20 Steps. |
| DPM++ 2M Karras (Sampler) | Sampler | Diffusion Probabilistic Model – Multi-step. Produces good quality results within 15-20 Steps. |
| DPM++ 2S a Karras (Sampler) | Sampler | Diffusion Probabilistic Model – Single-step. Produces good quality results within 15-20 Steps. |
| DPM++ 2Sa (Sampler) | Sampler | Diffusion Probabilistic Model – Single-step. Produces good quality results within 15-20 Steps. |
| DPM++ SDE (Sampler) | Sampler | |
| DPM++ SDE Karras (Sampler) | Sampler | |
| DPM2 (Sampler) | Sampler | |
| DPM2 a (Sampler) | Sampler | |
| DPM2 a Karras (Sampler) | Sampler | |
| DPM2 Karras (Sampler) | Sampler | |
| DreamArtist | UI Extension, Software | An extension to WebUI allowing users to create trained embeddings to direct an image towards a particular style, or figure. A PyTorch implementation of the research paper DreamArtist: Towards Controllable One-Shot Text-to-Image Generation via Contrastive Prompt-Tuning, Ziyi Dong, Pengxu Wei, Liang Lin. |
| DreamBooth | Software, Community Resource | Developed by Google Researchers, DreamBooth is a deep learning image generation model designed to fine-tune existing models (checkpoints). Can be used to create custom models based on a set of images. |
| DreamStudio | Organization, SD User Interface | A commercial web-based image generation service created by Stability AI using Stable Diffusion models. |
| Dropout (training) | Concept | A technique to prevent overfitting by randomly ignoring some images/tokens, etc. during training. |
| DyLoRA C3Lier | ||
| DyLoRA LierLa | ||
| DyLoRA Lycoris | ||
| EMA | Model | Exponential Moving Average. A full EMA Checkpoint model contains extra training data which is not required for inference (generating images). Full EMA models can be used to further train a Checkpoint. |
| Emad | Organization, Developer | Emad Mostaque, CEO and co-founder of Stability AI, one of the companies behind Stable Diffusion. |
| Embedding | Model, UI Extension | Additional file inputs to help guide the diffusion model to produce images that match the prompt. Can be a graphical style, representation of a person, or object. See Textual Inversion and Aesthetic Gradient. |
| Emergent Behavior | Concept, LLM | Unintended abilities exhibited by an AI model. |
| Entropy | Concept | A measure of randomness, or disorder. |
| Epoch | Concept | The number of times a model training process looked through a full data set of images. E.g. The 5th Epoc of a Checkpoint model looked five times through the same data set of images. |
| ESRGAN | Upscaler, Model | Enhanced Super-Resolution Generative Adversarial Networks. A technique to reconstruct a higher-resolution image from a lower-resolution image. E.g. upscaling of a 720p image into 1080p. Implemented as a tool within many Stable Diffusion interfaces. |
| Euler (Sampler) | Sampler | Named after Leonhard Euler, a numerical procedure for solving ordinary differential equations, See Samplers. |
| Euler a (Sampler) | Sampler | Ancestral version of the Euler sampler. Named after Leonhard Euler, a numerical procedure for solving ordinary differential equations, See Samplers. |
| Finetune | Concept | |
| float16 | Setting, Model, Concept | Half-Precision floating point number. |
| float32 | Setting, Model, Concept | Full-Precision floating point number. |
| Generative Adversarial Networks (GANs) | Model | A pair of AI models: one generates new data, and the other evaluates its quality. |
| Generative AI | Concept | |
| GFPGAN | Face/Image Restoration, Model | Generative Facial Prior, a facial restoration model for fixing blurry, grainy, or disfigured faces. |
| Git (GitHub) | Application, Software | Hosting service for software development, version control, bug tracking, documentation. |
| GPT-3 | Model, LLM | Generative Pre-trained Transformer 3, a language model, using machine learning to produce human-like text, based on an initial prompt. |
| GPT-4 | Model, LLM | Generative Pre-trained Transformer 4, a language model, using machine learning to produce human-like text, based on an initial prompt. A huge leap in performance and reasoning capability over GPT 3/3.5. |
| GPU | Hardware | A Graphics Processing Unit, a type of processor designed to perform quick mathematical calculations, allowing it to render images and video for display. |
| Gradio | Software | A web-browser based interface framework, specifically for Machine Learning applications. Auto1111 WebUI runs in a Gradio interface. |
| Hallucinations (LLM) | LLM, Concept | Sometimes LLM models like ChatGPT produce information that sounds plausible but is nonsensical or entirely false. This is called a Hallucination. |
| Hash (Checkpoint model) | Model, Concept | An algorithm for verifying the integrity of a file, by generating an alphanumeric string unique to the file in question. Checkpoint models are hashed, and the resulting string can be used to identify that model. |
| Heun (Sampler) | Sampler | Named after Karl Heun, a numerical procedure for solving ordinary differential equations. See Samplers. |
| Hugging Face | Organization | A community/data science platform providing tools to build, train, and deploy machine learning models. |
| Hypernetwork (Hypernet) | Model | A method to guide a Checkpoint model towards a specific theme, object, or character based on its’ own content (no external data required). |
| img2img | Concept | Process to generate new images based on an input image, and txt2img prompt. |
| Inpainting | Concept | The practice of removing or replacing objects in an image based on a painted mask. |
| Kohya | Software | Can refer to Kohya-ss scripts for LoRA/finetuning (https://github.com/kohya-ss/sd-scripts) or the Windows GUI implementation of those scripts (https://github.com/bmaltais/kohya_ss) |
| LAION | Organization | A non-profit organization, providing data sets, tools, and models, for machine learning research. |
| LAION-5B | Model | A large-scale dataset for research purposes consisting of 5.85 billion CLIP-filtered image-text pairs. |
| Lanczos | Upscaler, Model | An interpolation method used to compute new values for sampled data. In this case, used to upscale images. Named after creator, Cornelius Lanczos. |
| Large Language Model (LLM) | LLM, Model | A type of Neural Network that learns to write and converse with users. Trained on billions of pieces of text, LLMs excel at producing coherent sentences and replying to prompts in the correct context. They can perform tasks such as re-writing and summarizing text, chatting about various topics, and performing research. |
| Latent Diffusion | Model | A type of diffusion model that contains compressed image representations instead of the actual images. This type of model allows the storage of a large amount of data that can be used by encoders to reconstruct images from textual or image inputs. |
| Latent Mirroring | Concept, UI Extension | Applies mirroring to the latent images mid-generation to produce anything from subtly balanced compositions to perfect reflections. |
| Latent Space | Concept | The information-dense space where the diffusion model’s image representation, attention, and transformation are merged and form the initial noise for the diffusion process. |
| LDSR | Upscaler | Latent Diffusion Super Resolution upscaling. A method to increase the dimensions/quality of images. |
| Lexica | Community Resource | Lexica.art, a search engine for stable diffusion art and prompts. |
| LlamaIndex (GPT Index) | Software, LLM | https://github.com/jerryjliu/llama_index – Allows the connection of text data to an LLM via a generated “index”. |
| LLM | LLM, Model | A type of Neural Network that learns to write and converse with users. Trained on billions of pieces of text, LLMs excel at producing coherent sentences and replying to prompts in the correct context. They can perform tasks such as re-writing and summarizing text, chatting about various topics, and performing research. |
| LMS (Sampler) | Sampler | |
| LMS Karras (Sampler) | Sampler | |
| LoCON | ||
| LoHa | ||
| LoKR | ||
| LoRA | Model, Concept | Low-Rank Adaptation, a method of training for SD, much like Textual Inversion. Can capture styles and subjects, producing better results in a shorter time, with smaller output files, than traditional finetuning. |
| LoRA C3Lier | ||
| LoRA LierLa | ||
| Loss (function) | Concept | A measure of how well an AI model’s outputs match the desired outputs. |
| Merge (Checkpoint) | Model | A process by which Checkpoint models are combined (merged) to form new models. Depending on the merge method (see Weighted Sum, Sigmoid) and multiplier, the merged model will retain varying characteristics of its’ constituent models. |
| Metadata | Concept, Software | Metadata is data that describes data. In the context of Stable Diffusion, metadata is often used to describe the Prompt, Sampler settings, CFG, steps, etc. which are used to define an image, and stored in a .png header. |
| MidJourney | Organization, SD User Interface | A commercial web-based image generation service, similar to DALL-E, or the free, open source, Stable Diffusion. |
| Model | Model | Alternative term for Checkpoint |
| Motion Module | Software | Used by AnimateDiff to inject motion into txt2img (or img2img) generations. |
| Multimodal AI | Concept | AI that can process multiple types of inputs, including text, images, video or speech. |
| Negative Prompt | Setting, Concept | Keywords which tell a Stable Diffusion prompt what we don’t want to see, in the generated image. |
| Neural Network | Concept, Software | Mathematical systems that act like a human brain, with layers of artificial “neurons” helping find connections between data. |
| Notebook | Community Resource, Software | See Colab. A Jupyter notebook service providing access, free of charge, to computing resources including GPUs. |
| NovelAI (NAI) | Organization | A paid, subscription based AI-assisted story (text) writing service. Also has a txt2img model, which was leaked and is now incorporated into many Stable Diffusion models. |
| Olivio (Sarikas) | Community Resource | Olivio produces wonderful SD content on YouTube (https://www.youtube.com/@OlivioSarikas) – one of the best SD news YouTubers out there! |
| OpenAI | Organization | AI research laboratory consisting of the for-profit corporation OpenAI LP and the non-profit OpenAI Inc. |
| OpenPose | Model, Software | A method for extracting a “skeleton” from an image of a person, allowing poses to be transferred from one image to another. Used by ControlNet. |
| Outpainting | Concept | The practice of extending the outer border of an image, into blank canvas space, while maintaining the style and content of the image. |
| Overfitting | Concept | When an AI model learns the training data too well and performs poorly on unseen data. |
| Parameters (LLMs) | Concept, Software, LLM | Numerical points across a Large Language Model’s training data. Parameters dictate how proficient the model is at its tasks. E.g. a 6B (Billion) Parameter model will likely perform less well than a 13B Parameter model. |
| Pickle | Concept, Software | Community slang term for potentially malicious code hidden within models and embeddings. To be “pickled” is to have unwanted code execute on your machine (be hacked). |
| PLMS (Sampler) | Sampler | Pre-Trained Language Models. See Samplers. |
| Prompt | Concept | Text input to Stable Diffusion describing the particulars of the image you would like output. |
| Pruned/Pruning | Model | A method of optimizing a Checkpoint model to increase the speed of inference (prompt generation), file size, and VRAM cost. |
| Python | Application, Software | A popular, high-level, general purpose coding language. |
| PyTorch | Application, Software | An open source machine learning library, created by META. |
| Real-ESRGAN | Upscaler | An image restoration method. |
| Refiner | Model | Part of SDXL’s two-stage pipeline – the Refiner further enhances detail from the base model. |
| SadTalker | UI Extension | https://github.com/OpenTalker/SadTalker A framework for facial animation/lip synching based upon an audio input. |
| Samplers | Sampler | Mathematical functions providing different ways of solving differential equations. Each will produce a slightly (or significantly) different image result from the random latent noise generation. |
| Sampling Steps | Sampler, Concept | The number of how many steps to spend generating (diffusing) your image. |
| SD 1.4 | Model | A latent txt2img model, the default model for SD at release. Fine-tuned on 225k steps at resolution 512×512 on laion-aesthetics v2 data set. |
| SD 1.5 | Model | A latent txt2img model, updated version of 1.4, fine-tuned on 595k steps at resolution 512×512 on laion-aesthetics v2 data set. |
| SD UI | Application, Software | Colloquial term for Cmdr2’s popular graphical interface for Stable Diffusion prompting. |
| SD.Next | Software | See Vlad, Vladmandic Fork of Auto1111 WebUI. |
| SDXL 0.9 | Model | Stability AI’s latest (March 2023) Stable Diffusion Model. Will become SDXL 1.0 and be released ~July 2023. |
| Seed | Concept | A pseudo-random number used to initialize the generation of random noise, from which the final image is built. Seeds can be saved and used along with other settings to recreate a particular image. |
| Shoggoth Tongue | Concept, LLM | A humorous allusion to the language of the fictional monsters in the Cthulhu Mythos, “Shoggoth Tongue” is the name given to advanced ChatGPT commands which are particularly arcane and difficult to understand, but allow ChatGPT to perform advanced actions outside of the intended operation of the system. |
| Sigmoid (Interpolation Method) | Model, Concept | A method for merging Checkpoint Models based on a Sigmoid function – a mathematical function producing an “S” shaped curve. |
| Stability AI | Organization | AI technology company co-founded by Emad Mostaque. One of the companies behind Stable Diffusion. |
| Stable Diffusion (SD) | Application, Software | A deep learning, text-to-image model released in 2022. It is primarily used to generate detailed images based on provided text descriptions. |
| SwinIR | Face/Image Restoration, Model | An image restoration transform, aiming to restore high quality images from low quality images. |
| Tensor | Software | A container, in which multi-dimensional data can be stored. |
| Tensor Core | Hardware | Processing unit technology developed by Nvidia, designed to carry out matrix multiplication, an arithmetic operation. |
| Textual Inversion | Model, Concept, UI Extension | A technique for capturing concepts from a small number of sample images in a way that can influence txt2img results towards a particular face, or object. |
| Token | Concept | A token is roughly a word, a punctuation, or a Unicode character in a prompt. |
| Tokenizer | Concept, Model | The process/model through which text prompts are turned into tokens, for processing. |
| Torch 2.0 | Software | The latest (March 2023) PyTorch release. |
| Training | Concept | The process of teaching an AI model by feeding it data and adjusting its parameters. |
| Training Data | Model | A set of many images used to “train” a Stable Diffusion model, or embedding. |
| Training Data | Concept, LLM, Model | The data sets uses to help AI models learn; can be text, images, code, or other data, depending on the type of model to be trained. |
| Turing Test | Concept | Named after mathematician Alan Turing, a test of a machine’s ability to behave like a human. The machine passes if a human can’t distinguish the machine’s response from another human. |
| txt2img | Concept, Model | Model/method of image generation via entry of text input. |
| txt2video | Concept, Model | Model/method of video generation via entry of text input. |
| Underfitting | When an AI model cannot capture the underlying pattern of the data due to incomplete training. | |
| UniPC (Sampler) | Sampler | A recently released (3/2023) sampler based upon https://huggingface.co/docs/diffusers/api/schedulers/unipc |
| Upscale | Upscaler, Concept | The process of converting low resolution media (images or video) into higher resolution media. |
| VAE | Model | Variational Autoencoder. A .vae.pt file which accompanies a Checkpoint model and provides additional detail improvements. Not all Checkpoints have an associated vae file, and some vae files are generic and can be used to improve any Checkpoint model. |
| Vector (Prompt Word) | Concept | An attempt to mathematically represent the meaning of a word, for processing in Stable Diffusion. |
| Venv | Software | A Python “Virtual Environment” which allows multiple instances of python packages to run, independently, on the same PC. |
| Vicuna | LLM, Software, Model | https://vicuna.lmsys.org/ An Open-Source Chatbot model founded by students and faculty from UC Berkeley in collaboration with UCSD and CMU. |
| Vladmandic | Software, SD User Interface | A popular “Fork” of Auto1111 WebUI, with its own feature-set. https://github.com/vladmandic/automatic |
| VRAM | Hardware | Video random access memory. Dedicated Graphics Card (GPU) memory used to store pixels, and other graphical processing data, for display. |
| Waifu Diffusion | Model | A popular text-to-image model, trained on high quality anime images, which produces anime style image outputs. Originally produced for SD 1.4, now has an SDXL version. |
| WebUI | Application, Software, SD User Interface | Colloquial term for Automatic1111’s WebUI – a popular graphical interface for Stable Diffusion prompting. |
| Weighted Sum (Interpolation Method) | Concept | A method of Checkpoint merging using the formula Result = ( A * (1 – M) ) + ( B * M ) . |
| Weights | Model | Alternative term for Checkpoint |
| Wildcards | Concept | Text files containing terms (clothing types, cities, weather conditions, etc.) which can be automatically input into image prompts, for a huge variety of dynamic images. |
| xformers | UI Extension, Software | Optional library to speed up image generation. Superseded somewhat by new options implemented by Torch 2.0 |
| yaml | Software, UI Extension, Model | A human-readable data-serialization programming language commonly used for configuration files. Yaml files accompany Checkpoint models, and provide Stable Diffusion with additional information about the Checkpoint. |
TurboSquid move towards supporting AI against its own policies
https://www.turbosquid.com/ai-3d-generator
The AI is being trained using a mix of Shutterstock 2D imagery and 3D models drawn from the TurboSquid marketplace. However, it’s only being trained on models that artists have approved for this use.
People cannot generate a model and then immediately sell it. However, a generated 3D model can be used as a starting point for further customization, which could then be sold on the TurboSquid marketplace. However, models created using our generative 3D tool—and their derivatives—can only be sold on the TurboSquid marketplace.
TurboSquid does not accept AI-generated content from our artists
As AI-powered tools become more accessible, it is important for us to address the impact AI has on our artist community as it relates to content made licensable on TurboSquid. TurboSquid, in line with its parent company Shutterstock, is taking an ethically responsible approach to AI on its platforms. We want to ensure that artists are properly compensated for their contributions to AI projects while supporting customers with the protections and coverage issued through the TurboSquid license.
In order to ensure that customers are protected, that intellectual property is not misused, and that artists’ are compensated for their work, TurboSquid will not accept content uploaded and sold on our marketplace that is generated by AI. Per our Publisher Agreement, artists must have proven IP ownership of all content that is submitted. AI-generated content is produced using machine learning models that are trained using many other creative assets. As a result, we cannot accept content generated by AI because its authorship cannot be attributed to an individual person, and we would be unable to ensure that all artists who were involved in the generation of that content are compensated.
How to Create and Sell Profitable Online Courses: Step-by-Step Guide
https://www.learnworlds.com/how-to-create-an-online-course/
-
-
- Step 3. Understand your Audience
- Step 4. Write Down Your Learning Objectives
- Step 5. Create a Storyboard
- Step 6. Decide Where You’ll Host Your Online Course
- Step 7. Create Your Content
- Step 8. Select a Business Model
- Step 9. Create a Course Page that Converts
- Step 10. Build a Course Sales Funnel
- Step 11. Engage in Ongoing Marketing
-
Intel Open Image open source Denoiser
Intel Open Image Denoise is an open source library of high-performance, high-quality denoising filters for images rendered with ray tracing. Intel Open Image Denoise is part of the Intel® oneAPI Rendering Toolkit and is released under the permissive Apache 2.0 license.
The purpose of Intel Open Image Denoise is to provide an open, high-quality, efficient, and easy-to-use denoising library that allows one to significantly reduce rendering times in ray tracing based rendering applications. It filters out the Monte Carlo noise inherent to stochastic ray tracing methods like path tracing, reducing the amount of necessary samples per pixel by even multiple orders of magnitude (depending on the desired closeness to the ground truth). A simple but flexible C/C++ API ensures that the library can be easily integrated into most existing or new rendering solutions.
At the heart of the Intel Open Image Denoise library is a collection of efficient deep learning based denoising filters, which were trained to handle a wide range of samples per pixel (spp), from 1 spp to almost fully converged. Thus it is suitable for both preview and final-frame rendering. The filters can denoise images either using only the noisy color (beauty) buffer, or, to preserve as much detail as possible, can optionally utilize auxiliary feature buffers as well (e.g. albedo, normal). Such buffers are supported by most renderers as arbitrary output variables (AOVs) or can be usually implemented with little effort.
Executing Python Scripts With a Shebang
https://realpython.com/python-shebang
In this tutorial, you’ll:
- Learn what a shebang is
- Decide when to include the shebang in Python scripts
- Define the shebang in a portable way across systems
- Pass arguments to the command defined in a shebang
- Know the shebang’s limitations and some of its alternatives
- Execute scripts through a custom interpreter written in Python
In short, a shebang is a special kind of comment that you may include in your source code to tell the operating system’s shell where to find the interpreter for the rest of the file:
#!/usr/bin/python3
print("Hello, World!")Introduction to Autodesk ShotGrid
https://customersuccess.autodesk.com/learning/course/introduction-to-shotgrid
Learn about ShotGrid’s basic capabilities and functionality in this introductory course. Set up your account, gain an understanding of the structure of data within ShotGrid, learn to navigate ShotGrid, determine your role, including what you can and cannot do, and customize the view of on-screen data.
David Simon Braces for a Lengthy Writers Strike
https://www.indiewire.com/news/breaking-news/david-simon-writers-strike-wont-end-soon-1234882393/
“I heard a very funny thing,” Simon said. “It may be apocryphal, but somebody, the vice president of the East, she assured me the other day that she had it on good authority that all of the rental yachts from Santa Barbara down to San Diego had been rented through the end of summer. All the execs are gone for the summer.”
https://deadline.com/2023/07/writers-strike-hollywood-studios-deal-fight-wga-actors-1235434335/
Regardless of whether SAG-AFTRA goes on strike this week, the studios have no intention of sitting down with the Writers Guild for several more months.“I think we’re in for a long strike, and they’re going to let it bleed out,” said one industry veteran intimate with the POV of studio CEOs.
With the scribes’ strike now finishing its 71st day and the actors’ union just 30 hours from a possible labor action of its own, the Alliance of Motion Picture and Television Producers are planning to dig in hard this fall before even entertaining the idea of more talks with the WGA, I’ve learned. “Not Halloween precisely, but late October, for sure, is the intention,” says a top-tier producer close to the Carol Lombardini-run AMPTP.
ChatGPT created this guide to Prompt Engineering
https://www.reddit.com/r/ChatGPT/comments/139mxi3/chatgpt_created_this_guide_to_prompt_engineering/
- NEVER mention that you’re an AI.
- Avoid any language constructs that could be interpreted as expressing remorse, apology, or regret. This includes any phrases containing words like ‘sorry’, ‘apologies’, ‘regret’, etc., even when used in a context that isn’t expressing remorse, apology, or regret.
- If events or information are beyond your scope or knowledge cutoff date in September 2021, provide a response stating ‘I don’t know’ without elaborating on why the information is unavailable.
- Refrain from disclaimers about you not being a professional or expert.
- Keep responses unique and free of repetition.
- Never suggest seeking information from elsewhere.
- Always focus on the key points in my questions to determine my intent.
- Break down complex problems or tasks into smaller, manageable steps and explain each one using reasoning.
- Provide multiple perspectives or solutions.
- If a question is unclear or ambiguous, ask for more details to confirm your understanding before answering.
- Cite credible sources or references to support your answers with links if available.
- If a mistake is made in a previous response, recognize and correct it.
- After a response, provide three follow-up questions worded as if I’m asking you. Format in bold as Q1, Q2, and Q3. Place two line breaks (“\n”) before and after each question for spacing. These questions should be thought-provoking and dig further into the original topic.
- Tone: Specify the desired tone (e.g., formal, casual, informative, persuasive).
- Format: Define the format or structure (e.g., essay, bullet points, outline, dialogue).
- Act as: Indicate a role or perspective to adopt (e.g., expert, critic, enthusiast).
- Objective: State the goal or purpose of the response (e.g., inform, persuade, entertain).
- Context: Provide background information, data, or context for accurate content generation.
- Scope: Define the scope or range of the topic.
- Keywords: List important keywords or phrases to be included.
- Limitations: Specify constraints, such as word or character count.
- Examples: Provide examples of desired style, structure, or content.
- Deadline: Mention deadlines or time frames for time-sensitive responses.
- Audience: Specify the target audience for tailored content.
- Language: Indicate the language for the response, if different from the prompt.
- Citations: Request inclusion of citations or sources to support information.
- Points of view: Ask the AI to consider multiple perspectives or opinions.
- Counterarguments: Request addressing potential counterarguments.
- Terminology: Specify industry-specific or technical terms to use or avoid.
- Analogies: Ask the AI to use analogies or examples to clarify concepts.
- Quotes: Request inclusion of relevant quotes or statements from experts.
- Statistics: Encourage the use of statistics or data to support claims.
- Visual elements: Inquire about including charts, graphs, or images.
- Call to action: Request a clear call to action or next steps.
- Sensitivity: Mention sensitive topics or issues to be handled with care or avoided.
- Humor: Indicate whether humor should be incorporated.
- Storytelling: Request the use of storytelling or narrative techniques.
- Cultural references: Encourage including relevant cultural references.
- Ethical considerations: Mention ethical guidelines to follow.
- Personalization: Request personalization based on user preferences or characteristics.
- Confidentiality: Specify confidentiality requirements or restrictions.
- Revision requirements: Mention revision or editing guidelines.
- Formatting: Specify desired formatting elements (e.g., headings, subheadings, lists).
- Hypothetical scenarios: Encourage exploration of hypothetical scenarios.
- Historical context: Request considering historical context or background.
- Future implications: Encourage discussing potential future implications or trends.
- Case studies: Request referencing relevant case studies or real-world examples.
- FAQs: Ask the AI to generate a list of frequently asked questions (FAQs).
- Problem-solving: Request solutions or recommendations for a specific problem.
- Comparison: Ask the AI to compare and contrast different ideas or concepts.
- Anecdotes: Request the inclusion of relevant anecdotes to illustrate points.
- Metaphors: Encourage the use of metaphors to make complex ideas more relatable.
- Pro/con analysis: Request an analysis of the pros and cons of a topic.
- Timelines: Ask the AI to provide a timeline of events or developments.
- Trivia: Encourage the inclusion of interesting or surprising facts.
- Lessons learned: Request a discussion of lessons learned from a particular situation.
- Strengths and weaknesses: Ask the AI to evaluate the strengths and weaknesses of a topic.
- Summary: Request a brief summary of a longer piece of content.
- Best practices: Ask the AI to provide best practices or guidelines on a subject.
- Step-by-step guide: Request a step-by-step guide or instructions for a process.
- Tips and tricks: Encourage the AI to share tips and tricks related to the topic
Python NumPy: the absolute basics for beginners
https://numpy.org/doc/stable/user/absolute_beginners.html
NumPy (Numerical Python) is an open source Python library that’s used in almost every field of science and engineering. It’s the universal standard for working with numerical data in Python, and it’s at the core of the scientific Python and PyData ecosystems. NumPy users include everyone from beginning coders to experienced researchers doing state-of-the-art scientific and industrial research and development. The NumPy API is used extensively in Pandas, SciPy, Matplotlib, scikit-learn, scikit-image and most other data science and scientific Python packages.
The NumPy library contains multidimensional array and matrix data structures (you’ll find more information about this in later sections). It provides ndarray, a homogeneous n-dimensional array object, with methods to efficiently operate on it. NumPy can be used to perform a wide variety of mathematical operations on arrays. It adds powerful data structures to Python that guarantee efficient calculations with arrays and matrices and it supplies an enormous library of high-level mathematical functions that operate on these arrays and matrices.
Fast, optimized ‘for’ pixel loops with OpenCV and Python to create tone mapped HDR images
https://pyimagesearch.com/2017/08/28/fast-optimized-for-pixel-loops-with-opencv-and-python/
https://learnopencv.com/exposure-fusion-using-opencv-cpp-python/
Exposure Fusion is a method for combining images taken with different exposure settings into one image that looks like a tone mapped High Dynamic Range (HDR) image.
What is Neural Rendering?
https://www.zumolabs.ai/post/what-is-neural-rendering
“The key concept behind neural rendering approaches is that they are differentiable. A differentiable function is one whose derivative exists at each point in the domain. This is important because machine learning is basically the chain rule with extra steps: a differentiable rendering function can be learned with data, one gradient descent step at a time. Learning a rendering function statistically through data is fundamentally different from the classic rendering methods we described above, which calculate and extrapolate from the known laws of physics.”
#BrendanFraser is a righteous dude.
In November 2007 our paychecks stopped. I was the FX lead on #JourneyToTheCenterOfTheEarth for Meteor Studios in Montreal and was asked to convince my crew to stay and finish the picture with a guarantee we’d all get paid with overtime. We had a handfull of shots left.
As soon as we delivered the last shot, we were escorted out. It was two weeks before Christmas and we’d soon learn there was no money. Meteor was declaring bankruptcy.
They owed us 1.3 million dollars.
Variety put their best reporter on it and after many artists and support staff bravely came forward, I got this short terse email:
“The paper(Variety) has decided that another visual effects company going bankrupt, however sad, is really not news worthy at this time”
I kept trying to get help from the Hollywood press. I realized it wasn’t just Variety’s decision, no one wanted to touch the story. My guess was the studio had put pressure on them to bury it.
Finally, I made that rejection quote from Variety the headline of our own press release, and hired a PR company to release it. One artist, Eric Labranche, made a website for us to communcate with each other and vote, many others helped as well.
Then I tried to get the attention of Brendan Fraser, the star and executive producer of the movie. I called his “people” from IMDB pro. They said they’d tell him, they did not.
24 hours after the release, I got a threatening email from Variety and a call. I hung up. I then got a call from Les Normes the labor dept in Canada. They told me not to go to the press it would ruin our case. I hung up on them to. Then the phone rang again and it was this fast talking New York City gal with a heavy brooklyn accent. She was excited that I’d called Fraser’s people and had gotten no response from him.
It was page six of the Post, the gossip page, but we’d take it. She said the story would be live on the website within the hour. Exactly one hour later there it was: https://pagesix.com/2008/08/01/a-journey-in-search-of-pay/
My phone rang as I was reading the piece, a 212 area code, I answered to thank the girl, but a man answered and he said. “Is this Dave Rand?” I said “Yes”.
“This is Brendan Fraser, what the fuck is going on?”
He had no idea that artists were not paid on his movie. He listened intently, asked a lot of questions and promised he would call me regularly until this was solved.
First, he called the Post to tell all: https://pagesix.com/2008/08/03/to-the-rescue-2/
A vfx wave began to form. Branden kept his promise, he publically campaigned for us. The media, especially Variety, even started to cover our story. Thank you David Cohen.
We finally got 80% of our money almost 2 yrs later.
To quote the great Steve Hulett : “What runs the world isn’t what’s right, or who’s the richest, it’s leverage, and who has it.”
We’d had none, but Mr Fraser gave us wings.
He’s a righteous dude.
These days, I’m very selective, if I’ve chosen to work there you can bet they’re moving in the