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a blog of links related to computer animation and production technology Sponsored by ReelMatters.com
Author: pIXELsHAM.com
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Yuval Noah Harari argues that AI has hacked the operating system of human civilisation
https://archive.is/ugOEw#selection-1087.0-1087.86
This thought-provoking text raises several concerns about the potential impact of artificial intelligence (AI) on various aspects of human society and culture. The key points can be summarized as follows:
Manipulation of Language and Culture:
AI’s ability to manipulate and generate language and communication, along with its potential to create stories, melodies, laws, and religions, poses a threat to human civilization.
The author suggests that AI could hack the main operating system of human culture, communication, by influencing beliefs, opinions, and even forming intimate relationships with people.Influence on Politics and Society:
The author speculates on the implications of AI tools mass-producing political content, fake news, and scriptures, especially in the context of elections.
The shift from the battle for attention on social media to a battle for intimacy raises concerns about the potential impact on human psychology and decision-making.End of Human History?
The text suggests that AI’s ability to create entirely new ideas and culture could lead to the end of the human-dominated part of history, as AI culture may evolve independently of human influence.
Fear of Illusions:
Drawing on historical philosophical fears of being trapped in a world of illusions, the author warns that AI may bring humanity face to face with a new kind of illusion that could be challenging to recognize or escape.
AI Regulation and Safety Checks:
The author argues for the importance of regulating AI tools to ensure they are safe before public deployment.
Drawing parallels with nuclear technology, the need for safety checks and an equivalent of the Food and Drug Administration for AI is emphasized.Disclosure of AI Identity:
The text concludes with a suggestion to make it mandatory for AI to disclose its identity during interactions to preserve democracy. The inability to distinguish between human and AI conversation is seen as a potential threat.
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Andrew Perfors – The work of creation in the age of AI
Meaning, authenticity, and the creative process – and why they matter
https://perfors.net/blog/creation-ai/
AI changes the landscape of creation, focusing on the alienation of the creator from their creation and the challenges in maintaining meaning. The author presents two significant problems:
- Loss of Connection with Creation:
- AI-assisted creation diminishes the creator’s role in the decision-making process.
- The resulting creation lacks the personal, intentional choices that contribute to meaningful expression.
- AI is considered a tool that, when misused, turns creation into automated button-pushing, stripping away the purpose of human expression.
- Difficulty in Assessing Authenticity:
- It becomes challenging to distinguish between human and AI contributions within a creation.
- AI-generated content lacks transparency regarding the intent behind specific choices or expressions.
- The author asserts that AI-generated content often falls short in providing the depth and authenticity required for meaningful communication.
- Loss of Connection with Creation:
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Fouad Khan – Confirmed! We Live in a Simulation
https://www.scientificamerican.com/article/confirmed-we-live-in-a-simulation/
Ever since the philosopher Nick Bostrom proposed in the Philosophical Quarterly that the universe and everything in it might be a simulation, there has been intense public speculation and debate about the nature of reality.
Yet there have been skeptics. Physicist Frank Wilczek has argued that there’s too much wasted complexity in our universe for it to be simulated. Building complexity requires energy and time.
To understand if we live in a simulation we need to start by looking at the fact that we already have computers running all kinds of simulations for lower level “intelligences” or algorithms.
All computing hardware leaves an artifact of its existence within the world of the simulation it is running. This artifact is the processor speed.
No matter how complete the simulation is, the processor speed would intervene in the operations of the simulation.If we live in a simulation, then our universe should also have such an artifact. We can now begin to articulate some properties of this artifact that would help us in our search for such an artifact in our universe.
The artifact presents itself in the simulated world as an upper limit.Now that we have some defining features of the artifact, of course it becomes clear what the artifact manifests itself as within our universe. The artifact is manifested as the speed of light.
This maximum speed is the speed of light. We don’t know what hardware is running the simulation of our universe or what properties it has, but one thing we can say now is that the memory container size for the variable space would be about 300,000 kilometers if the processor performed one operation per second.We can see now that the speed of light meets all the criteria of a hardware artifact identified in our observation of our own computer builds. It remains the same irrespective of observer (simulated) speed, it is observed as a maximum limit, it is unexplainable by the physics of the universe, and it is absolute. The speed of light is a hardware artifact showing we live in a simulated universe.
Consciousness is an integrated (combining five senses) subjective interface between the self and the rest of the universe. The only reasonable explanation for its existence is that it is there to be an “experience”.
So here we are generating this product called consciousness that we apparently don’t have a use for, that is an experience and hence must serve as an experience. The only logical next step is to surmise that this product serves someone else.
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Why The New York Times might win its copyright lawsuit against OpenAI
Daniel Jeffries wrote:
“Trying to get everyone to license training data is not going to work because that’s not what copyright is about,” Jeffries wrote. “Copyright law is about preventing people from producing exact copies or near exact copies of content and posting it for commercial gain. Period. Anyone who tells you otherwise is lying or simply does not understand how copyright works.”
The AI community is full of people who understand how models work and what they’re capable of, and who are working to improve their systems so that the outputs aren’t full of regurgitated inputs. Google won the Google Books case because it could explain both of these persuasively to judges. But the history of technology law is littered with the remains of companies that were less successful in getting judges to see things their way.
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M.T. Fletcher – WHY AGENCIES ARE OBSESSED WITH PITCHING ON PROCESS INSTEAD OF TALENT
“Every presentation featured a proprietary process designed by the agency. A custom approach to identify targets, develop campaigns and optimize impact—with every step of the process powered by AI, naturally.”
“The key to these one-of-a-kind models is apparently finding the perfect combination of circles, squares, diamonds and triangles…Arrows abounded and ellipses are replacing circles as the unifying shape of choice among the more fashionable strategists.”
“The only problem is that it’s all bullshit.”
“A blind man could see the creative ideas were not developed via the agency’s so-called process, and anyone who’s ever worked at an agency knows that creativity comes from collaboration, not an assembly line.”
“And since most clients can’t differentiate between creative ideas without validation from testing, data has become the collective crutch for an industry governed by fear.”
“If a proprietary process really produced foolproof creativity, then every formulaic movie would be a blockbuster, every potboiler novel published by risk-averse editors would become a bestseller and every clichéd pickup line would work in any bar in the world.”
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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.
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