• By Prompt News
• Published October 13, 2024

The best AI is still free! Forge & Flux for FREE (Easy Guide) + Animation

Introduction

Welcome to my tutorial on how to use Forge UI and the model Flux to create and animate images for free. This step-by-step guide will help you harness the power of these free AI tools and bring your creative ideas to life with ease. Please see video above for step by step process.

Introduction

Forge and Flux are powerful, free AI-driven tools that allow you to create stunning images and animations effortlessly. Whether you’re an artist, content creator, or just curious about AI, this guide will walk you through everything you need to get started.

System Requirements

• Windows Tutorial: The video is aimed at Windows users, with a minimum VRAM requirement of 6 GB for Forge.
•  
• Storage Requirements: Forge requires at least 1.7 GB, while Flux needs between 12-17 GB, depending on the version you use (NF4 or FP8).

Installing Forge UI

Forge is popular because it offers a clean, user-friendly interface that makes AI image generation accessible to both beginners and advanced users. It’s free, open-source, and provides compatibility with various AI models, including Flux and Stable Diffusion, allowing for versatile creative projects. The tool is optimized for fast performance, particularly for users with strong GPUs, and supports LoRA models for further customization. Forge’s ability to deliver unlimited image generation without subscriptions, coupled with its easy-to-use design, has made it a go-to option for those looking for a powerful yet efficient AI tool.

 
Download Forge Here:

Forge Download: https://github.com/lllyasviel/stable-diffusion-webui-forge

 

 

Installing Flux

Flux is a popular AI model known for its ability to generate high-quality images, from hyperrealistic art to anime and digital paintings, while also excelling at comprehending and integrating text within images. It is designed to work efficiently on lower-end GPUs, making it accessible to a broader range of users, even those with limited hardware. With its compatibility in tools like Forge, it allows for flexible creativity and fast performance, offering a streamlined experience for AI-based image generation without sacrificing quality. These features, combined with its ability to run on systems with moderate VRAM, make Flux a go-to model for AI enthusiasts

 Download Flux Here:

Flux Download: https://github.com/lllyasviel/stable-diffusion-webui-forge/discussions/981

NF4 Version:

• Recommended for VRAM between 6 GB and 12 GB.
• This is the developer-recommended version because it is very fast and efficient.
• Ideal for users with moderate hardware specifications. It provides a good balance between speed and image quality while taking up less space.
•  

FP8 Version:

• Recommended for VRAM of 12 GB and higher.
• This version is more demanding on your system and offers higher precision and quality. It is suitable for users with more powerful GPUs.
• If you have ample VRAM (12 GB or more), the FP8 version can generate more detailed images, though it will require more system resources and take up more storage.
•  

Choosing Between NF4 and FP8:

• If you have 6-12 GB of VRAM, it’s suggested to go with the NF4 version, as it’s optimized for speed and performance with lower hardware requirements.
• For users with 12 GB of VRAM or more, you can opt for the FP8 version for higher-quality image generation.

 

1.  Prompt I used in the Youtube video:

   Cinematic composition, Digital art, The central figure is a young woman with long, flowing, silver-white hair cascading down from her head, blending seamlessly with the surrounding white to form a surreal, ethereal background. Her expression is calm and serene, with closed eyes, suggesting a state of relaxation or meditation. She is surrounded by a white Japanese dragon. The dragon’s smooth body twists its body around hers the out of the dragon’s body turning to dust. She is facing forwards towards the viewer with her eyes open, and has placed one hand elegantly place on the dragon. She has extremely sharp, long red nails. There is a glowing ring light in the background. The overall style is a blend of fantasy and majestic science fiction, with a high level of detail and smooth, polished textures.

 

Steps: 40, Sampler: Euler,
Schedule type: Simple, 
CFG scale: 1, 
Distilled CFG Scale: 3.5, 
Seed: 739566982, 
Size: 896×1152,

 

1.  

 

1.  Conclusion

   Congratulations! You’ve learned how to use Forge and Flux to create stunning AI-driven images and animations for free. With these tools, the possibilities for creativity are endless. Don’t hesitate to experiment with new features and explore more advanced techniques.

2.  

 

1. Resource links:

2. https://blackforestlabs.ai/
3.  
4.  

• •  
  •  

  Thank you for joining me, and image creation!

Promptmuse X

•  #fluxai #forge #forgeui #fluxanimation #flux
  

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<p>The post The best AI is still free! Forge & Flux for FREE (Easy Guide) + Animation first appeared on Prompt Muse.</p>

• By Prompt Muse
• Published September 3, 2024

How To Install Flux On ComfyUI

Introduction

Welcome to this written Tutorial on How to install Flux on ComfyUI. The occpomeying video can be found here:

What is Flux?

Flux, released in mid-2023, was specifically developed for ComfyUI to enhance its image generation capabilities. However, it is now avalible on webui’s such as Automtic1111, Forge and ComfyUI.

The Flux AI model excels in prompt adherence, producing high-quality images with accurate anatomy and demonstrating strong capabilities in generating text.

 

Flux and ComfyUI were developed by Black Forest Labs, and the tool is freely avavlibe to use locally, howver terms and condtions have to be agreed to.

It’s important to use it responsibly and within the bounds of ethical guidelines.

1. Resource links:

2. https://blackforestlabs.ai/
3. https://github.com/comfyanonymous/ComfyUI_examples/tree/master/flux

 

 

Hardware Requirements:

Feature/Version	FP8	Schnell	Dev
Overview	Optimized for low VRAM, best for efficient development	Fastest version for lower-end GPUs, optimized for speed	Great for quality and efficiency
Visual Quality	Good, performs slower than the Schnell version	Compromises on visual quality for speed, less detail	Better details, more prompt adherence. High Quality
Image Detail	Good	Good	High
VRAM Requirement	8-12GB+ Have seen folks with 8GB using but at a push	8-12GB+	12GB+
Model Size	17.2GB	17.2GB	23.8GB

1.  

1. For all version make sure you have updated Comfyui, simply go to the ComfyUI Manager, click on Manager > Update All, and allow the update process to complete.
2.  

1. FP8

Faster, optimized version for users with more limited GPU resources

Download Flux FP8

Save the flux1-dev-fp8.safetensors file into ComfyUI\models\checkpoints folder onto your PC.

Load up ComfyUI and Update via the ComfyUI Manager. Update ALL

Download This simple Flux worksflow below, drag and drop tje JSON file into your ComfyUI, Alterntively Load in via your manager. Update All

Download FP8 WorkFlow

2. Schnell

Faster, optimized version for users with more limited GPU resources

Download Schnell Model here and put into ComfyUI > models > unet.

Download VAE here ComfyUI > models > vae.

Download Clip model clip_l.safetensors and t5xxl_fp8_e4m3fn.safetensors

Load up ComfyUI and Update via the ComfyUI Manager. Update ALL

Download schnell WorkFlow

3. Dev

 

Development and personal use, capable of producing high-quality images but requires more powerful hardware than the [Schnell] version

Download the .safetensor Model here and put into ComfyUI > models > unet.

Download the following two CLIP models, and put them in ComfyUI > models > clip.

• clip_l.safetensors
• t5xxl_fp16.safetensors

(https://huggingface.co/comfyanonymous/flux_text_encoders/tree/main)

•  
• Download the a.e VAE  file. Put it in ComfyUI > models > vae.
• 
  

Download Dev WorkFlow

 

1. Download FLUX Model

   Go to the Flux dev model Hugging Face page. Sign up (It’s free) /Login and accept the Terms and conditions. 

   Click on the Files and versions tab and download the flux-dev.safetensors file (See Image below)

2.  

Download Dev Flux WorkFlow

 

1. Resource links:

2. https://blackforestlabs.ai/
3. https://github.com/comfyanonymous/ComfyUI_examples/tree/master/flux
4.  

• •  
  •  

  Thank you for joining me, and image creation!

Promptmuse X

•  #fluxai #fluxcomfyui #comfyui 

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• By Prompt Muse
• Published August 1, 2024

How I Made A K-Pop Singer: Full Process with phone mocap!

Introduction

Welcome to this Written Tutorial on Creating 3D Characters with Character Creator, Blender and Unreal engine. Please see the full video here

In this tutorial, I’ll guide you through the entire process of creating 3D characters using Character Creator and the free plugin CC Blender Pipeline. From character creation to importing into Blender and finally into Unreal engine. We’ll cover every step in detail and try out some new plugins! Please note that Reallusion currently has a competition to win cash prizes check it out here!

What You’ll Learn

• How to quickly put together a 3D character using character creator and the blender pipeline. How to integrate plugin into your workflow and bring everything back into character creator and add mocap.
•  

---

Prerequisites

1. Character Creator: Used to create and export your character (currently 40% off with a 30-day trial).
2.  
3. Unreal Engine: Ensure you have it installed.
4.   
5. CC Rig Plugin: Available on the Epic Store Marketplace.
6.  
7. MetaHuman Plugin: Install from the Marketplace.
8.  
9. Unreal Auto Setup: Download from the Reallusion website.
10.  
11. Live Link Face App: Free on the App Store via your phones app store
12.  

13. Ensuring All Plugins Are Active In Unreal Engine:

    To ensure a smooth workflow, you need to make sure all necessary plugins are active in Unreal Engine. Follow these steps:

    1. Activate Required Plugins:
       • In the Plugins window, use the search bar to find each of the required plugins:
         • Quixel Bridge
         • CC Rig Plugin
         • MetaHuman Plugin
         • Unreal Auto Setup
         • Live Link
         •  
         • Apple ARKit (for ARFaceKit functionality)
       • Make sure each of these plugins is enabled by checking the box next to their names.
       • Click Restart Now if prompted to restart Unreal Engine after enabling plugins.
14.  

Step 1: 

1. Creating Your 3D Character

   We start with Character Creator to design and customize our 3D character. This powerful tool allows you to sculpt detailed and lifelike characters with ease. You can easily with sliders adjust facial features, body proportions, and clothing to match your desired look.

Importing the Character into Blender

1. Once our character is ready, we’ll utilize the CC Blender Pipeline plugin to export and import the character into Blender. This is a free plugin.

2.  

3.  This plugin simplifies the process, ensuring that all character details are preserved. In Blender, we’ll begin sculpting and using additional plugins to paint details and create stylized hair quickly.

1.  

Step 2: 

1. Creating a Stylized 3D K-pop Outfit

   If you would like access to a premade market place of clothes, the Real Illusion Content store has many assets that can be downloaded directly into your CC program. This makes it easy to get quality clothing onto your character. Click here to enter the Content Store.

2.  
3.  

4. But if you would prefer to make your own clothes this comes with a additional learning curve and  the expense of a third part product. I do get asked regularly about creating clothes from scratch, so I thought I’d try out a product for you called Style 3D Artlier. which we will use to design a bespoke K-pop style outfit for our character. 

 

1.  

Step 3: 

1. Adding Mocap with Move.ai One

   Once our character is complete, we’ll add motion capture (mocap) using Move.ai One. 

2.  

3. This tool claims to produce mocap using just one camera.

4.  Although it may not be as precise as mocap suits or the multi cam option (Moveai Pro), I’m curious to see its capabilities. 

5.  

6. Setting up Move.ai was straightforward—I simply downloaded the app on my iPhone and recorded using the free trial, capturing 10-second intervals and uploading the finished .fbx files to my PC. My first impression were good, I knew it would be a wobbly animation that would require mocap clean up! But it was good enough for my little example.

 Step 4:

Cleaning Up Mocap Data

Of course, some clean-up is necessary, especially when you are recording mocap from one iPhone! I recommend using iClone for this which will help you clean up the mocap fast, but if you’re on a budget, Blender is a great alternative, however it may require a bit more of a learning curve and might not be as intuitive as Iclone.

I imported my 3D character into iClone and used a free retargeting file to streamline the process. It was easy and enjoyable!

I repeated the clean-up steps for each mocap import from my phone until I had a good selection of dance moves, which I saved in my iClone library for future use.

1.  

Step 5: 

Exporting To Unreal Engine

At this point, you can export your character to Blender using the CC Pipeline. However, I opted to take my character into Unreal Engine, so I can use the Iphone to record my facial animations. (You can do this in Iclone with Accuface).

I simple exported my character without any animations as an fbx file and then exported each of my animation as an fbx.

In Unreal Engine I imported my skeletal mesh and animation sequences into the Unreal content draw , added them to a new sequence level, and converted my character to a CC Control rig to enable facial animation.

Step 6: Recording Animation

1. To record in Unreal engine you need to set up a Level sequence, think of this as your time line to add animation to. Below are the steps to setup your level sequencer and record your facial animation via your Iphone to the sequencer. I converted my Imported Skeleton mesh to a CC control rig. This is a fantastic free plug in which you can grab here. This plug will now allow me adjust my animation further in unreal engine and copy and paste animation onto the facial control from my Metahuman.
2.  

• Recording Singing with MetaHuman and LiveLink

  To keep things organized and efficient, I opened a separate project for this step. I imported a MetaHuman in my scene, used the LiveLink and Unreal Face app on my phone to record singing, and exported the animation as an .FBX file. Finally, I imported this into my original project and applied it to my character’s facial control rig.

DOWNLOAD LINKS

• • Character Creator
  • Reallusion Auto Setup
  • UE CC Control Rig
  •  

  Thank you for joining me, and happy animating!

Promptmuse X

•  #CharacterCreator #iClone #RiggedCharacter #UnrealEngine #UE #Controlrig

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• By Prompt Muse
• Published June 28, 2024

Mocap with Custom Characters

Introduction

Welcome to this written tutorial on how to animate custom characters in Unreal Engine using the Live Link Face app. This guide will show you how to easily transfer facial animations from your iPhone to your custom characters, including setting up body animations. Full video here

What You’ll Learn

• How to set up and use the Live Link Face app with Unreal Engine
•  
• How to import and animate
•  characters from Character Creator
•  
• How to add body animations to your characters
•  

---

Prerequisites

1. Character Creator: Used to create and export your character (currently 40% off with a 30-day trial).
2.  
3. Unreal Engine: Ensure you have it installed.
4.  
5. Quixel Bridge: Download and install from the Epic Store Marketplace.
6.  
7. CC Rig Plugin: Available on the Epic Store Marketplace.
8.  
9. MetaHuman Plugin: Install from the Marketplace.
10.  
11. Unreal Auto Setup: Download from the Reallusion website.
12.  
13. Live Link Face App: Free on the App Store via your phones app store
14.  

15. Ensuring All Plugins Are Active In Unreal Engine:

    To ensure a smooth workflow, you need to make sure all necessary plugins are active in Unreal Engine. Follow these steps:

    1. Activate Required Plugins:
       • In the Plugins window, use the search bar to find each of the required plugins:
         • Quixel Bridge
         • CC Rig Plugin
         • MetaHuman Plugin
         • Unreal Auto Setup
         • Live Link
         •  
         • Apple ARKit (for ARFaceKit functionality)
       • Make sure each of these plugins is enabled by checking the box next to their names.
       • Click Restart Now if prompted to restart Unreal Engine after enabling plugins.
16.  

Step 1: Create an Unreal Project 

(Installing the Auto Setup as a bridge from Character Creator to Unreal Engine)

1. Launch Unreal Engine and create a new blank project.
2. Name the project (e.g., “AutoTutorial”) and create it.
3. Close the project to install the necessary files.

1.  

Step 2: Install Unreal Auto Setup

1. Download the Auto Setup from the Reallusion website and unzip it.
2. Run the setup executable file.
3. Copy the contents and plugin folders from the downloaded files to your Unreal Engine project folder (e.g., Documents > Unreal Engine > AutoTutorial).
4. Replace the files when prompted.

 

1.  

Step 3: Import Your Custom Character

1. Open Character Creator and select your character. (Has to be a CC3 character)
2. Export as FBX with Unreal Engine as the target preset. File>Export>FBX>Clothed Character
3. Import the character into your Unreal Engine project, ensuring CC Control Rig is installed.

 Step 4: Setup MetaHuman

We are now going to use a metahuman as a Dummy to record our facial animations onto.

1. Import a MetaHuman character via Quixel Bridge and add it to your Unreal project.
2. Set up Live Link Face App on your iPhone and ensure it is connected to your computer.
3.  

Step 5: Connect MetaHuman to Live Link

1. In Unreal Engine, select your MetaHuman character.
2. Set up the Live Link connection in the details panel and ensure facial tracking is active.
3.  

Step 6: Recording Animation

1. To record in Unreal engine you need to set up a Level sequence, think of this as your time line to add animation to. Below are the steps to setup your level sequencer and record your facial animation via your Iphone to the sequencer:

•  

Create a New Level Sequence:
In the Content Browser, right-click and go to Animation > Level Sequence.
Name your sequence and double-click to open it in the Sequencer.

 

Add Your Metahuman to the Sequence:
In the Sequencer window, click the + Track button.
Select Actor to Sequencer, then choose your Metahuman character from the list.

 

Start Recording:
During the countdown, ensure your ARFaceKit device is capturing your facial movements. Perform the desired expressions and movements.
Click the Record button in the Sequencer toolbar (Red Button Left of the screen) A countdown will begin.

Stop Recording:
Once you’ve finished the performance, click the Stop button in the Sequencer toolbar.
The recorded animation will appear as keyframes in the Sequencer timeline.

 

Review and Edit the Animation:
Scrub through the timeline to review the recorded animation.
You can adjust keyframes, refine movements, and blend animations as needed for a polished result.

 

Save Your Work:
Always save your Level Sequence and project to avoid losing any progress.

Step 7: Baking The Key Frames

1. After stopping the recording, select the recorded track in the Sequencer.
2. Right-click on the track and choose Bake To Control Rig > Face_ControlBoard_CtrlRig. This process will convert the live link data into keyframes, which we now can copy and paste on to our custom Character.

Step 8: Baking The Key Frames

Select the baked keyframes in the Sequencer for your Metahuman character.
Right-click and choose Copy.
Add your Character Creator (CC) character to the Sequencer by clicking the + Track button and selecting your CC character.
Navigate to the appropriate track on your CC character where you want to paste the keyframes.
Right-click on the track and choose Paste to apply the baked keyframes to your CC character.

 

•  

Conclusion

That’s it for this tutorial on using the Live Link Face app to animate custom characters in Unreal Engine. If you have any questions or want to share your creations, feel free to tag me on social media @PromptMuse

DOWNLOAD LINKS

• • Character Creator
  • Quixel Bridge
  • Unreal Engine Marketplace
  • Reallusion Auto Setup
  •  

  Thank you for joining me, and happy animating!

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• By Prompt Muse
• Published May 22, 2024

CC Control Rig Is Here

Rigging custom characters in Unreal Engine can be a tedious process, but the new CC Rigs plugin changes everything. This innovative tool converts CC3+ characters into Metahuman rigs for both body and face, making it easier than ever to create custom characters—whether human, creature, or cartoon. Say goodbye to the limitations of custom character creation in Unreal Engine. This is a true game-changer.

Free Download UE CC Rig

Key Features of the UE CC Rigs Plugin:

•  

• Wide Compatibility: Although CC Rig was primarily created to import Character Creator 3D models, this tool allows for Imports from popular platforms like Daz3D, Maya, and Blender, automatically converting the skeletal body for use in Unreal Engine via the Accurig free plugin, this then can be convert to CC RIG (Reallusion) 

•  

• Enhanced Animation Editing: Allows detailed animation editing within Unreal Engine, including importing motion files, creating custom animations, and organizing motions using the Perform List Editor, you can even transfer Metahuman control rig animations over….yeah!  (Reallusion) (Reallusion).

•  

• Custom MetaHumans: Facilitates the creation of custom MetaHumans by transferring CC head shapes and textures to Unreal Engine, producing highly realistic digital actors compatible with Unreal Engine and iClone (Reallusion).

•  

• Asset Optimization: Using Character Creator workflow this provides tools for importing, editing, and optimizing character assets, including mesh conversion, InstaLOD optimization, and prop management (Reallusion Magazine).

•  

Getting Started:

1. Character Creation:

   • Use Character Creator, available with a 30-day trial. Start with a base mesh template to simplify the initial steps. Adjust the facial profile and body using the intuitive slider system.
   •  

1. Additional Sculpting:

   • If you need further customization, use the Blender pipeline or the GoZ plugin for ZBrush to sculpt your character and bring it back into Character Creator.
   •  

2. Importing External Characters:

   • Import characters created externally and convert their rigs to CC3+ compatible rigs using AccuRIG.

3. Extensive Customization:

   • Add hair, skin textures, clothes, and accessories to your character. Utilize Reallusion’s extensive catalog, or create your own in Blender or ZBrush. It’s also compatible with Marvelous Designer.

4. Animation Process:

   • Once your character is ready, export it from Character Creator into Unreal Engine to convert into CC Rigs. Personally, I prefer using iClone for animation. I transfer my character with the one-click solution into iClone, set up my animations, and then convert into CC Rigs.
   •  

•  

• Step-by-Step Guide to Using CC Rigs in Unreal Engine

   

  1. Install the Plugin:

     • 1. Sign in to your Epic Games account, and download the plugin for free from the Epic Games market place here

• 1.  2. Enable the Plugin:
     
     • Create or use an existing Unreal project. Load a project and navigate toSettings > Plugins (Top right of the screen), search for Character Creator UE Control Rig, and enable it. Restart (Restart Button Will be on the bottom of the window)
     •  

     • 3. Transfer Your Character:

       • Use Iclone Unreal Live Link or Auto Setup (Free) to transfer your character to Unreal Engine.
       • If you using the auto seteup route install from here for free, and follow the instructions to setup in UE. Once installed you can go File> Export FBX > Clothed character Target Unreal engine, check delete hidden mesh.
       • Then import into unreal ready to convert to UE CC rigs.
  2. •  

•  

• 1. • •  

       • 4.Convert to CC Rig:

         • In Unreal Engine find your character’s skeletal mesh in the content panel, right-click, and choose Create CC Rig from the pop up menu.
         •  

         • 5. Animation Setup:

           • Add your character’s blueprint to the sequencer, load animations, and bake keyframes for easy editing. (Much easier to see this in the video)
           •  

•  

• 1. • •  
       • • •  

  Final Touches and Animation Refinement

  • Refine your animations by adding keyframes directly in the sequencer. This system will significantly improve your workflow and reduce your timelines.
  •  

  Final thoughts:

  This plugin is a game-changer for Character Creator and iClone users. If you’re not already a user, try the 30-day trial. I’m passionate about Reallusion’s products and wouldn’t recommend them if I didn’t believe they are great.

  Links and Resources:

  • Download CC Rigs Plugin
  • Kevin and Nia Models
  • Character Creator 30-day Trial
  • My Patreon for In-Depth Videos
  •  
• Let me know if this exciting news helps you, whether you’re a studio, an animator, or an enthusiast. And as always, it’s been a pleasure! Thanks for reading and watching my video! Leave a comment on the video to let me know someone has read this haha!

PromptMuse x

 #CharacterCreator #iClone #RiggedCharacter #UnrealEngine #UE #Controlrig

•  

PrevPreviousCustom Blender Hair To Metahuman Guide

NextMocap with Custom CharactersNext

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Tired of the default hair options for Metahumans in Unreal Engine? This comprehensive guide will walk you through creating and customizing Metahuman hair using Blender. This process involves no plugins and uses entirely free software.

Please Join my Patreon to get access to Blender Resources and additional guides! PromptMuse | 3D & AI Learning Community | Patreon

Prerequisites

• Unreal Engine installed on your PC : Download Unreal Engine
• Metahuman plugin installed: MetaHuman Plugin in Code Plugins – UE Marketplace (unrealengine.com)
• Quixel Bridge installed: Quixel Bridge – Manage 3D content and export with one click
• Blender 4.0: 4.0 — blender.org

Basic familiarity with Blender and Unreal Engine. This tutorial uses 100% free software.

If you don’t have these installed, refer to my getting started with Unreal Engine and Metahumans here (Coming soon).

1.1 Create a Metahuman

• Use the Metahuman Creator, a browser-based app, to create your custom character: MetaHuman Creator (unrealengine.com)
• Save your Metahuman for later import into Unreal Engine.

1.2 Setting Up your Unreal Project

• Open Unreal Engine via the Epic Launcher, navigate to the Marketplace on the top tab, and search “Metahuman” download the free ‘Metahuman Lighting’ rig.
• Create a new project and launch it.
• In Unreal Engine, navigate to Window and Quxiel Bridge.
• In Quxiel Bridge download your Metahuman by selecting “My Metahuman” from the lefthand side navigation window. Once downloaded Select your metahumans card and from the right handside panel select “Add” This will add your metahuman to your content draw in your UnrealProject

Step 2: Exporting Mesh to Blender

• In Unreal Engine, select your Metahuman and remove the existing hair if needed.
• Now, export the head mesh. Find the mesh in the content browser, right-click, and select “Export.” Choose FBX format for Blender compatibility. Save the file in a convenient location

Step 3: Creating Hair in Blender

3.1 Importing and Prepping the Mesh

• Open Blender, Im using Blender 4.0. In a new scene, delete the default objects.
• Go to File > Import > FBX, and select the exported head mesh.
• In the viewport, separate the scalp from the face. This isolation will help in focusing the hair creation on the scalp area.

3.2 Designing the Hair

• With the scalp selected, enter Sculpt Mode.
• Use the Add Hair tool to begin placing hair guides on the scalp. These guides will shape the overall hairstyle.
• Adjust hair length, density, and curvature. For long hairstyles, increase the length and density. For short styles, reduce these parameters.
• Apply modifiers for specific textures and effects:
  • Clump Modifier: To create grouped strands of hair.
  • Curl Modifier: For curly or wavy hair.
  • Frizz Modifier: Adds a frizzy, unstructured look to the hair.
• Focus on the hairline and parting. Add more hair guides here to ensure a natural, dense look.

3.3 Exporting Hair Back to Unreal Engine

• Once you’re satisfied with the hair design, export it as an Alembic (.abc) file. This format preserves the hair details for Unreal Engine.

Step 4: Finalizing in Unreal Engine

4.1 Importing Hair into Unreal

• Back in Unreal Engine, import the Alembic file. Navigate to the content browser, right-click, and select ‘Import to /Game’. Locate your Alembic file and import it.

4.2 Adjusting Hair Settings

• Select the imported hair in the content browser. In the details panel, fine-tune the settings:
  • Interpolation: Adjust for smooth hair transitions.
  • Root and Tip Scale: Control the thickness of the hair at the root and the tip.
  • Color Settings: Customize the hair color and texture to match your character’s style.
• Enable physics simulation for realistic hair movement.

4.3 Binding Hair to Metahuman

• To attach the hair to your character, use the ‘Create Binding’ option in Unreal Engine. This step ensures that the hair moves naturally with your character’s animations.

Conclusion

You’ve successfully created and customized hair for your Metahuman character in Unreal Engine. Experiment with different styles and modifiers to enhance your digital characters further. Don’t forget to save your project to preserve your work.

Additional Resources

• Visit my  Patreon for exclusive Blender files and additional resources:
• PromptMuse | 3D & AI Learning Community | Patreon
• Share your unique hairstyles on social media with @promptMuse.

<p>The post Custom Blender Hair To Metahuman Guide first appeared on Prompt Muse.</p>

Introduction of ComfyUI

ComfyUI is a node-based user interface specifically designed for generating AI images and animations using Stable Diffusion. Gaining popularity in 2023 as an alternative user interface to Automatic1111, ComfyUI stands out for its flexibility and user-friendly design.

The node-based layout of ComfyUI streamlines the creation of custom AI-generated images and animations. However, for someone new to the system, this node-based approach might initially seem daunting. This is exactly why I have written this guide to slowly introduce you to it. Additionally, its compatibility and free-to-use nature alongside Stable Diffusion further enhance its appeal to those interested in AI-driven creative processes. This tool appears to be particularly favoured by users who prefer a more intuitive and customizable interface for their AI image and animation generation needs.

Table Of Contents

• System Requirements and Installation
• ComfyUI Default Layout
• Understanding the Nodes in ComfyUI
• Resources for Learning and Inspiration
• Loading Checkpoints
• The CLIP Text Encode (Prompt) Nodes
• KSampler Node in Depth
• Denoising and Scheduler Nodes
• VAE Decode Node
• Saving Your AI Image
• Preview of the Next Guide

System Requirements and Installation:

•  NVIDIA Graphics card
• 12GB of VRAM

For Mac users please see here. I have made a installation video here : Ultimate Guide to Seamless AI Animations (Even on Low-End PCs!) – YouTube

ComfyUI’s user interface is built around nodes, each serving a distinct function. Upon opening ComfyUI, you’re greeted with a blank workspace. To activate the default interface, which includes a pre-connected set of basic nodes, click the ‘Load Default’ button as shown in the accompanying image. This action will display a network of fundamental nodes, arranged from left to right. For installation of ComfyUI please refer to this video.

What do all the nodes do?

ComfyUI’s versatility is showcased by its wide range of node sequences, each tailored to create specific types of AI images or animations. This flexibility allows users to experiment and find the best node combinations for their creative projects. The ComfyUI community actively contributes to this diversity by constantly creating and sharing new workflows.

The Stable Diffusion Reddit forum is an excellent resource for both beginners and those looking for creative inspiration. In this forum, users engage in sharing ideas, tips, and various workflows related to Stable Diffusion. While the forum isn’t exclusively focused on ComfyUI, it remains a valuable source of information and community support for users interested in all aspects of Stable Diffusion, including those using ComfyUI. This collaborative environment can be especially helpful for learning, troubleshooting, and discovering new techniques in image and animation generation with AI.

Load Checkpoint

The first node on your default layout is the load checkpoint node. A checkpoint file is  essentially pre-trained model focusing on a style or genre. The type of images a model can create is determined by the dataset it was trained on. This dataset could focus on general imagery or be specialized in a particular genre or style. Please be aware that many checkpoint files cannot be used for commercial use. Also it’s worth noting if the checkpoint file was made for Stable diffusion 1.5(an earlier version of stable diffusion or SDXL – the latest version. CivitAI is a great source to download checkpoint files Civitai: The Home of Open-Source Generative AI .

The CLIP Text encode(Prompt)

There are two of these nodes, you will notice that one will connect to the KSampler Postive and one the negative. They are simply your positive prompt and negative prompt.

Positive Prompt: The positive prompt guides the AI towards what you want it to draw

Negative Prompt: The negative prompt specifies what you want the AI to exclude from the image

KSampler

The KSampler lets you have control over the image process
This node has different options for both input and output to control how it processes the image.
Seed: The seed value is the initial random input used in the latent space to start the image generation process. Essentially, it determines the content of the image, with each generated image having a unique seed value.
Using the same seed with produce similar results.
Steps: Generally, a higher number of steps allows more time for refining the image generation, leading to better quality. However, using too many steps can adversely affect the image.
CFG: The CFG value emphasizes adherence to the prompt in the image generation, but excessively high scale settings can detrimentally affect image quality.

• Sampler Name: Various samplers impact how fast, how well, and how varied the image creation process is. Different samplers offer distinct results in these areas.

Example of different samplers that cna be used in ComfyUI and Automatic 1111:

Euler a, Euler, LMS, Heun, DPM2, DPM2 a, DPM++ 2S a, DPM++ 2M, DPM Fast, DPM Adaptive, LMS Karras, DPM2 Karras, DPM2 a Karras, DPM++ 2S a Karras, DPM++ 2M Karras, DDIM, PLMS etc

• Denoise: The Denoise strength setting, ranging from 0 to 1, determines the similarity or difference between the output image and the input image. A setting of 0 adds no noise, meaning the output will be exactly the same as the input.
• Scheduler: The Scheduler controls the frequency and intervals at which the sampling process is repeated. Different Schedulers can impact the quality and variety of the generated images in various ways.

VAE Decode Node

There is no need to adjust anything with the VAE decode, this node is there to automatically convert images from latent space back into pixel space, utilizing the specified Variational Autoencoder (VAE). This process essentially transforms the abstract, encoded representations of images in latent space into visible, regular images in pixel space.

Save Image

This is the last node on the default workflow and where you AI image will appear. In the next guide I will show you how to add video outputs to your node base layout.

Conclusion

As demonstrated, generating an image with ComfyUI is more straightforward than it might initially appear, and this guide aims to boost your confidence in using the tool. While some workflows may seem complex at first glance, they become much simpler once broken down and explained. In upcoming guides, I’ll delve into additional node workflows, specifically focusing on video-to-video transformations. These will enable us to alter the style of a video using some of ComfyUI’s advanced nodes. Stay tuned for more insights and tutorials. Until next time…

<p>The post An Idiots guide to ComfyUI first appeared on Prompt Muse.</p>

I was one of the early adopters of DALL·E 3.0, and I couldn’t wait to put this new AI image generator to the test. The initial results, I must say, blew me away. What set DALL·E 3.0 apart was its ability to understand not just single words or simple prompts but entire phrases and sentences. It felt like a significant leap forward in AI creativity.

The images it generated were a sight to behold – beautiful, bright, and full of life. It seemed like I could finally do away with the rigid, prompt-based language that had constrained previous AI models. DALL·E 3.0 was a breath of fresh air in the world of creative content generation.

Excitement filled my days as I used DALL·E 3.0 to create a wide array of images for a project I was passionately working on.  It quickly became an indispensable tool in my creative arsenal. However, all good things must come to an end, and in this case, it happened rather abruptly.

One fateful day, I logged onto the platform to create more AI-generated images, only to be met with a disheartening message: “Promptblocked.” I tried again, hoping it was just a minor glitch, but to my dismay, the same message appeared: “Promptblocked.”

It turned out that DALL·E 3.0 had been nerfed into existence. The issue lay in the specific prompt I had used – a celebrity’s name, Lana Del Rey, to be precise. Previously, DALL·E 3.0 had excelled in creating fantastic reference images for my project, but suddenly, it refused to cooperate. The reliable wellspring of creative inspiration had run dry.

As I reflected on my journey with DALL·E 3.0, I couldn’t help but acknowledge the rollercoaster of emotions it had taken me on. It had been an incredible tool, enabling boundless creativity with its brilliant images. But, it came with its limitations, and one had to tread carefully to avoid promptblocks.

In the end, my experience with DALL·E 3.0 was indeed remarkable, a brief and brilliant journey through the realm of AI-generated artistry. Though its limitations became evident, the potential of this technology remains undeniably promising. DALL·E 3.0 may have had its ups and downs, but it undeniably showcased the incredible power of AI in the world of creative content generation.

If you wish to try it out for yourself, a link is here: https://www.bing.com/create

It is still a fantastic tool, but not as good as it once was.

PromptMuse

<p>The post DALL·E 3.0: Is the Hype Justified? first appeared on Prompt Muse.</p>

In the rapidly advancing world of artificial intelligence (AI), content creators are continually seeking innovative ways to ensure their work bypasses AI detectors. Enter Pass AI Detection, a ground-breaking tool that not only refines your content for human readers but also ensures it’s tailored to bypass AI detectors increasingly utilised by search engines and other platforms.

What is Pass AI Detection?

Pass AI Detection is a sophisticated AI detection tool that analyses your text and modifies it to bypass both AI and human readers. It’s a game-changer for content creators, enabling them to create engaging content that meets the stringent requirements of AI detectors.

AI content detectors are utilised in a range of applications, from search engines to academic integrity tools like Turnitin. These AI detectors scrutinise the text to determine its relevance, quality, and originality. As a result, content creators need to ensure their content is tailored to bypass these AI content detection systems to improve visibility and reach.

Pass AI Detection leverages cutting-edge AI techniques to evaluate and modify your content. The process begins with the AI content detector, which scans your text and identifies areas for improvement. The AI detector looks at your distribution of keywords and topics and provides a suggested distribution that would optimise your content for AI content detection.

A Balanced Approach to Content Creation

Pass AI Detection centres on balance. It understands the negatives of keyword stuffing and the necessity for reader-friendly text. The tool doesn’t increase keyword frequency, but promotes balanced keyword distribution, crafting content that is both AI and reader-friendly.

As AI detection methodologies progress, Pass AI Detection adapts. The tool is continuously refining its algorithms to ensure your content remains optimised for the most recent AI detection techniques. This commitment to staying ahead of the curve positions Pass AI Detection as a leader in the AI content detection market.

Content creation is a critical component of successful marketing strategies. Pass AI Detection helps generate content that not only attracts your audience but also performs well with AI detectors, achieving an ideal balance between human readability and AI detectability. Experience cost-effective, innovative content creation with Pass AI Detection’s unique BYOK model. Join the revolution today.

<p>The post Meet Pass AI Detection: Your Free Tool to Bypass AI Content Detectors first appeared on Prompt Muse.</p>

As OpenAI’s ChatGPT gained popularity as a powerful language model, concerns arose regarding the lack of privacy settings for users. Initially, the AI product did not provide options for users to safeguard their data, and OpenAI utilized data from conversations to train its language model further. With privacy watchdogs and regulators demanding better data protection, OpenAI introduced a new privacy setting. This guide will walk you through the process of opting out of ChatG

PT data sharing and explain the implications of doing so.

Step-by-Step Guide to Opting Out of ChatGPT Data Sharing

To stop ChatGPT from using your private data, follow these steps:

1. Log into your ChatGPT account using a browser.
2. Click on your account icon located in the bottom left corner.

3. Select “Settings.”

4. Find the “Data Controls” section and click on the “Show menu” next to it.

5. Toggle off “Chat History & Training.”

Note that OpenAI may have tried to obscure this privacy-enhancing feature. Within this menu, you can also export your ChatGPT data or delete your account if desired.

What Happens to Your ChatGPT Chat History After Turning Off Data Collection?

When you disable OpenAI’s ability to collect and use your data for training ChatGPT, your chats will only be stored for 30 days. After that period, your conversations will be permanently deleted. OpenAI states that while they will retain your data for 30 days, it will only be reviewed to monitor for abuse and not used for training ChatGPT.

In the future, OpenAI plans to launch a ChatGPT Business account that will allow users to keep their chat history even when disabling data collection.

What Happens to Your Existing ChatGPT Data?

Unfortunately, you cannot delete the data you previously shared with ChatGPT before the new privacy settings were implemented. However, you can opt to start sharing data again by re-enabling “Chat History & Training” within the settings.

OpenAI claims to use the shared data to improve their models, making them more helpful for users. They state that they do not use the data for advertising, selling services, or creating user profiles.

Future Improvements in ChatGPT Privacy Management

While the current privacy options for ChatGPT are limited, it is essential to keep an eye on potential improvements in the future. OpenAI may develop better ways to manage user privacy, ensuring a more secure and user-friendly experience.

In conclusion, the introduction of privacy settings in ChatGPT is a step forward in data protection. By following this guide, you can easily opt out of data sharing and safeguard your private data. However, there is still room for improvement in privacy management, and users should remain vigilant regarding their data security.

<p>The post Preserve Your Privacy: Take Action and Change ChatGPT Settings to Stop OpenAI from Training on Your Data first appeared on Prompt Muse.</p>

On Thursday, President Joe Biden held a meeting at the White House with CEOs of leading AI companies, including Google, Microsoft, OpenAI, and Anthropic. The meeting emphasized the importance of ensuring the safety of AI products before deployment and addressing the risks posed by AI. However, some AI experts criticized the exclusion of ethics researchers who have warned of AI’s dangers for years.
Over the past few months, generative AI models such as ChatGPT have quickly gained popularity, driving companies to develop similar products rapidly. However, concerns have been growing about potential privacy issues, employment bias, and the potential for using them to create misinformation campaigns.

AI Ethics Researchers Respond

Critics of the companies’ ethical track records were not impressed by the meeting. They questioned the choice of inviting people to the meeting who, they argue, represent companies that have created the issues with AI that the White House seeks to address.
On Twitter, AI researcher Dr. Timnit Gebru wrote, “It seems like we spend half our time talking to various legislators and agencies and STILL we have this… A room full of the dudes who gave us the issues & fired us for talking about the risks, being called on by the damn president to ‘protect people’s rights.'” In 2020, Google fired Gebru following a dispute over a research paper she co-authored that highlighted potential risks and biases in large-scale language models.

University of Oxford AI ethics researcher Elizabeth Renieris tweeted, “Unfortunately, and with all due respect POTUS, these are not the people who can tell us what is “most needed to protect society” when it comes to #AI.”

AI Safety and AI Ethics

The criticism highlights the divide between “AI safety” (a movement concerned primarily with hypothetical existential risk from AI) and “AI ethics” (a group of researchers concerned largely about misapplications and impacts of current AI systems, including bias and misinformation).
Author Dr. Brandeis Marshall suggested organizing a “counter-meeting” that would include a diverse group of AI ethicists, practitioners, and researchers to discuss the real-world implications of AI and propose more inclusive and responsible solutions.

Looking Forward

Although the White House meeting brought attention to the potential risks and challenges posed by AI, it remains to be seen whether the discussion will lead to concrete actions that address these issues. It is crucial for government, industry, and academia to collaborate and ensure that AI development is safe, responsible, and equitable.
To foster a more comprehensive dialogue on AI ethics and safety, including voices from marginalized communities and interdisciplinary perspectives, could be a critical step in building more resilient and fair AI systems. Engaging stakeholders from various backgrounds in the decision-making process will help address concerns around bias, privacy, and the potential misuse of AI technologies.

As AI continues to advance rapidly, it will become increasingly important for policymakers and industry leaders to work together and create robust regulatory frameworks that protect the public interest while enabling innovation. Only through a collaborative approach can we ensure that AI technologies are harnessed to benefit society as a whole, while mitigating potential risks and negative consequences.

<p>The post A New Era of AI Governance: The White House Leads the Way in Promoting AI Ethics and Inclusivity first appeared on Prompt Muse.</p>

In this article, we’ll explore how Tore Knabe created his groundbreaking VR games, and what makes them such a unique and exciting experience.

The Making of Tore Knabe’s VR Game

Tore Knabe’s VR games are the result of several different technologies working together. The game was built using the Whisper which is Open AI’s new Speech to Text (STT) api. In addition to Whisper, Knabe also used Eleven Labs, a company specialising in creating human-sounding artificial voices, and the GPT-3 API, which powers the AI characters in the game.

The game takes place in various settings, including a peaceful lakeside scene with a priest and an old bar tavern with a talkative bartender. In both scenarios, the player is able to have conversations with the AI characters using natural language. The AI characters are able to respond to the player’s questions and statements in real-time, thanks to the power of GPT-3.

What’s particularly impressive about Tore Knabe’s game is the level of detail that has gone into creating the AI characters. For example, in the bar tavern scenario, the AI bartender is able to keep track of the various patrons in the bar, and can discuss each one with the player as a separate topic. This adds a level of realism to the game that is rarely seen in other AI-powered experiences.

The Future of VR and AI

Tore Knabe’s VR game is just the beginning of what could be a whole new era of gaming. As VR technology continues to improve, and AI becomes even more advanced, we can expect to see more games that combine these two technologies in innovative ways.

Imagine a virtual reality world where every character you encounter is powered by AI, and is able to converse with you in a natural and engaging way. Or a game where the AI characters can learn from your interactions, and adapt their behaviour based on your choices. The possibilities are truly endless.

Tore Knabe’s VR game is an exciting glimpse into the future of gaming, where AI and VR technology are combined in new and innovative ways. By using GPT-3 to power the AI characters in the game, Knabe has created a truly immersive experience that feels like a real conversation.

As we continue to explore the possibilities of AI and VR, it’s important to consider the potential benefits and drawbacks of this technology. While there are certainly concerns about its impact on society, there’s no denying that the combination of AI and VR has the potential to revolutionize the way we play games, and even how we interact with the world around us.

<p>The post Virtual Reality meets Artificial Intelligence: The Fascinating World of Tore Knabe’s VR Game first appeared on Prompt Muse.</p>

Musk’s X.AI: The New Contender in the Generative AI Arena

Reports are swirling that Elon Musk has created a new company called X.AI to compete with OpenAI and join the generative AI race. X.AI is incorporated in Nevada and lists the Twitter-owner as the company’s director, with Musk’s personal wealth manager, Jared Birchall, listed as its secretary.

Rumours have been circulating for some time about Musk entering the race for the best generative AI system, particularly following the massive popularity of OpenAI’s ChatGPT chatbot. A recent Business Insider report revealed that Musk purchased 10,000 graphic processing units for use at one of Twitter’s two remaining data centres. A source told the outlet that this investment shows Musk is “committed” to the effort, as there’s little reason for Twitter to spend so much on the tech if it didn’t plan to use it for AI.

Musk’s AI Dream Team: SpaceX and Tesla Investors, Unite!

According to a Financial Times report, Musk has been assembling a team of AI researchers and engineers and is in talks with SpaceX and Tesla investors to pour money into his latest venture. “A bunch of people are investing in it… it’s real, and they are excited about it,” the outlet quoted a person saying.

This news comes just a week after it was revealed that Twitter Inc no longer exists because it has been merged into X Corp, another company owned by Elon Musk. The move is likely part of a broader plan to create an “everything app” in the style of China’s WeChat.

Musk’s AI Ambitions: Contradictions and Controversies

Interestingly, Musk’s AI plans come after he signed an open letter calling for a six-month pause on AI development due to safety concerns. Musk has also been an open critic of OpenAI, a company he invested in and co-founded in 2015, and eventually left after disagreements with colleagues.

Additionally, Musk recently revealed April 20 as the final date for removing the blue tick from legacy verified accounts in a bid to promote uptake of Twitter Blue, a plan that hasn’t been going too well.

As the AI race heats up, the tech world is eagerly watching Musk’s moves with X.AI. Will his latest venture disrupt the AI industry or simply add another layer of intrigue to Musk’s growing list of companies? Only time will tell, but one thing’s for sure: with Musk at the helm, it’s bound to be an exciting ride.

Stay tuned to Prompt Muse for the latest updates on this unfolding AI showdown, as well as your daily dose of essential tech news.

<p>The post Elon Musk Sparks AI Showdown with X.AI, Taking on OpenAI and Shaking Up Twitter first appeared on Prompt Muse.</p>

AI-generated animations are becoming increasingly popular in the creative world, and it’s no wonder why. These powerful tools allow artists to quickly and easily create stunning visuals that would otherwise take hours or even days to produce. In this comprehensive guide, we’ll walk you through the process of creating your own AI-generated animation using state-of-the-art techniques, tools, and workflows. With the help of this tutorial, you’ll be well on your way to producing professional-quality animations in no time.

Preparing Your Data for Training

Before diving into the animation process, it’s essential to prepare your data for training. Here’s a step-by-step guide to help you get started:

Gather a diverse dataset: To train your AI model effectively, you’ll need a large and varied dataset. This can include a mix of images, videos, and other visual content.
Preprocess your data: To ensure your AI model can effectively learn from your dataset, you’ll need to preprocess your data. This can involve resizing images, removing any unwanted artifacts, and normalizing the data.
Split your dataset: Divide your dataset into a training set and a validation set. This will allow you to train your model on one set of data while validating its performance on another.

How I Unleashed the Power of AI to Create Next-Level Animations
Once your data is ready, you can begin the process of training your AI model.

Training Your AI Model with Google Colab

Google Colab is an excellent platform for training your AI model, as it provides a powerful and user-friendly interface. Follow these steps to train your model using Google Colab:

Upload your dataset: Import your dataset into Google Colab, either by uploading it directly or connecting to your Google Drive.
Configure your training settings: Adjust the learning rate, optimizer, and other settings to optimize your model’s performance.
Train your model: Run the training cell to begin the training process. This may take anywhere from 30 to 40 minutes, depending on the size of your dataset and the complexity of your model.
Throughout the training process, be sure to monitor your model’s performance by checking its progress in the Google Colab interface.

Once your model has completed its training, you can export it for use in your animation project.

Creating and Animating Your 3D Character

Now that your AI model is trained, it’s time to create and animate your 3D character. Here’s a step-by-step guide to help you bring your character to life:

Generate an image of your character: Use a tool like Automatic 111, Run Pod, or CalicuristroC Lab UI to generate an image of your character using your trained AI model.
Create a 3D model of your character: Import your generated image into a 3D modeling program like Headshot or iClone, and use the built-in tools to create a 3D model of your character.
Add hair and other details: Use the libraries and tools provided by your 3D modeling program to add hair, clothing, and other details to your character.
Animate your character: Use a plugin like Motion Live to record your facial movements and apply them to your 3D model in real time. This will create a realistic, lifelike animation of your character.

Once your character is fully animated, you can export it for use in your final project.

Rendering and Finalizing Your AI-Generated Animation

After you have completed setting up your animation, it’s time to render and finalize your AI-generated creation. With the right settings and techniques, you’ll be able to produce smooth and realistic animations.

Batch Rendering Frames

To create a frame-by-frame animation, you need to batch render your frames. To do this, head over to the “Batch” tab in Automatic 111, select the input folder where your 3D files are stored, and the output folder where you want the rendered frames to be saved. Then, click on “Generate.” Once the rendering is complete, you will have a seamless animation that can be easily combined and edited in post-production software.

Post-Production and Final Touches

After rendering your animation frames, import them into a post-production software like Adobe After Effects or Da Vinci Resolve. Apply the appropriate effects, such as dirt removal and deflicker, to ensure a smooth and polished final result.

Remember that you can experiment with different checkpoint models in Automatic 111 to quickly change the render style of your animation. This flexibility allows you to create a variety of looks and styles with just a few clicks.

Sharing Your AI-Generated Animation and Continuing to Learn

Once your AI-generated animation is complete, be sure to share your work on social media and tag Prompt Muse so they can see and appreciate your creative projects. Connect with others in the community and continue learning from their techniques and ideas.

Join the Prompt Muse Community

Get involved with the AI creative community by following Prompt Muse on social media. You can find them on Instagram and Twitter as @PromptMuse. Additionally, join their Discord server to engage with other AI enthusiasts and share your projects.

Stay Up-to-Date with the Latest AI Tech News

To keep up with the latest developments in AI technology, subscribe to the Prompt Muse newsletter. You’ll receive regular updates on cutting-edge techniques and tools that can help you push the boundaries of your AI-generated animations and other creative projects.

Creating AI-generated animations is an exciting and ever-evolving field. By following the detailed steps and techniques shared by Prompt Muse, you can create stunning and realistic animations using AI technology. Experiment with different settings, checkpoints, and tools to discover new possibilities and unique styles for your projects.

Remember to share your creations with the AI community and tag Prompt Muse on social media, as they are always eager to see and celebrate the innovative work being done in this space. Stay connected, keep learning, and continue pushing the boundaries of AI-generated animation.

TRANSCRIPT:

And welcome back to another prompt muse tutorial. Today, I’m going to be showing you how to create a fully animated AI character. The beauty of this technique is that you don’t have to act out the scenes before. In fact, you don’t need to look like your actor or your actress. This method could potentially be used in the movie industry. If not just the concept artwork, it could be used for an entire movie itself. For this tutorial, I’m going to be training a realistic data set for my puppet. I’m going to be using myself so you can judge the likeness at the end for yourself. I gather up loads of images of myself. I then train it with Koya Laura. Then once I’ve done that, I use Character creator to create a puppet. You do not need to know any 3D. There is no learning curve to this software. You’re basically using sliders and dragging and dropping assets onto your 3D character. It is super simple to use. Obviously, you can go down the blender routeand the meta human route if you want to, but I just warn you, there is a learning curve, so it’s up to you what you want to do.

But my workflow is through character creator. This means I can then switch out to any character using the data set. So once I’ve got those rendered files of my puppet, I can then switch out those data sets from one character to another, and it’s a really easy and efficient way of working. So let’s dive in and get on with the tutorial. Step one, preparing your training images. I’ve prepared various images of myself. Put some images that are more like you, rather than photographs with a filter on, because trust me, it works ten times better. If you do that, you will get the likeness a lot better. It’s important that you choose your head, your body, some full bodies in there, some close up, some far away. You need to ensure that your hairstyles are quite different in each image, that you’re taking from different profiles, that your background’s changed, that your outfit’s changed. If you put too many headshots into your training data, you end up with things like this. Because the AI doesn’t know what the body is, so it gets confused. You need to put some body shots in there. I’m using a website called Burmy, which is absolutely free to use and it crops your images down to the size you want.

I’m going to be using 512 by 512 because that’s pretty much my resolution. You can go up to 768 by 768, but remember that’s going to use more VRAM and take longer to train. Once I’ve done that, I want to rename all my files. So click on Rename and just put X in the box and Save as Zip. And that will save all your files in numerical order. So 1. Png, 2. Png, because that’s how you want it. Head over to Google Drive and simply save your zip file there. Step two, training your data set. I’m using the Linquf Quia Laura Dream booth. We are going to make sure that we’re logged into our Google account here, and then we’re going to press Connect here. So once you’ve done both of those, we can start the training. Simply come down to step one. All we need to do is check the Mount Drive button and run that cell. And this is going to mount your Google Drive. I’m going to click Connect to Google Drive. It’s going to give you some warnings about connecting to an unGoogle offered notebook. Now that’s running. Once that’s completed, you’ll get a green check next to it.

So this downloads all the dependencies into your file structure over here. Now, remember, this is remote. This isn’t on your PC. So once you come out of here, you’ll lose everything. But this is where we connect the Google Drive so we can pull files across from our Google Drive. And then once we’re finished, we can take the train model and export that to our Google Drive. So we’re just going to come down here, the 1.2 Open Special Fire Explorer. Ignore that, that’s fine. We don’t need to do that. The default setting here is for anything version 3, which is more of an animation style model. And if you click here, there’s a few more preloaded links in there. If I click on Stable Diffusion 1.5, that’s good for me because I’m doing a realistic character. I just want to chip in and say the Stable Diffusion 1.5 base model is a good all rounder training file to use. Obviously, you can use whatever you want, but if you’re starting off, this is a good one to learn with. You can see in this bit below, you can actually load in your custom model, which means you can go to the hugging face link.

For example, a good model to use is Realistic Vision 2. So you get the Hugging Face link for your model and place it in there and run that cell. But we’re not doing that for this tutorial. So I’m just going to leave the space underneath, which is Stable Diffusion 2Model Blanks. We’re not going to be using that. And then just ignore 2.2. We’re not going to be running our own custom model. So 2.3 download available VAE. Sometimes you might notice when you switch models in automatic one on one or whatever program you’re using, that the images are really desaturated and lost their colours. That’s usually down to the VAE not the not being detected or being corrupted. So we are going to load in the Stable Diffusion 1.5 VAE, which is already there. So it’s a Stable Diffusion VAE, and that’s going to, again, just download it all into our file structure. And then we’re just going to hit on Run on there. And then we come down to 3.1 locating train data directory. So this is going to create some file path to where our train data file is. And that’s where all our input data set images, so my face images, will be going into that folder.

And don’t worry, you don’t have to do anything. It does it automatically for you. So I’m going to hit run on that cell once again, and it says your trained data directory. So if we go and have a look, so expand the Laura down here. By the way, if you don’t have this open, it’s this folder here to go into here. Now, go to Laura and you’ve got your regularisation data and your trained data. Do not drag and drop your images into here. Wait, we’re going to be doing that automatically. On to 3.2 unzip data set. So this zip file underscore URL. So this is why we zipped our file up and put it onto our Google Drive because we’re now going to grab it. So if you go to Drive, this is essentially your Google Drive, my Drive. And then I’m going to come down and find my zip file, which is 100 prompt muse images. Zip. I’m going to click on the three dots there and copy path. And then I’m just going to paste in the top zip file underscore URL. I’m going to leave the unzip, underscore, two blank. I’m just going to close this hierarchy here so we can see what we’re doing.

And you can see there it’s extracted all my files from that zip file into that new folder. So we’re going to come down to 3.3, which is the image scraper. Again, I’m not going to use this. This is based on more or less animation databases. So what it does is scrape regularisation images, which I don’t want to do because I’m not using animation. So I’m going to ignore this. But if you are using animation, you can do it here, ignore 3.3. Data clearing. This is to do with the cell above it. You’re scraping all these images, you might not be aware what they actually are. There will probably be some in there, but hopefully… No, don’t do that. And the convert transparency images. Well, it says what it does. So if the image has a transparent background, that’s very hard for machine learning. So you want to convert that and also random colours as well. So you check that if you’re doing the animation and scraping the images, which we’re not going to do. Okay, so let’s go down to 4.2, which is data annotation. We’re going to be using a blip captioning, which is tagging images with a description.

This is used for realistic imagery. The one below it, which is the way through diffusion, is used more for animation. So we’re just going to be using this blip captioning over here. here. So I’m not going to change any of these settings. I’m going to leave them as default and run that cell. What that will be doing is reading the input images that I put into the Google collab. It’s then going to be describing what it sees in the images. Everything it needs out of the description is what it’s going to train upon. So it’s going to describe my microphone and the fact that I’m wearing a necklace or potentially a brown top. This means it’s not going to train upon those things, which makes it so much easier to create a way more flexible model when I’m generating the images later on. So I’m not stuck in this room with this necklace and a brown top on. So to show you what I mean, I’m just going to show you the files it created. So if you come to your files, Laura, and then expand on train data, you can see it’s generated these caption files here.

So if we just pick any image here, 13, and you can see I’ve got a microphone and a necklace, so it should pick up on those attributes. So I’m going to click the caption that goes along with that image. And yeah, it said a woman sitting in a chair holding a microphone. So it’s actually that. Now I can actually add on to this and add necklace if I didn’t want it to train on me wearing a necklace, but I like my necklace and yeah, it’d be good to have that in the model as well. So you can edit these further if you want to. But for this tutorial, I’m not going to do that. I’m just going to leave it as is. I’m just going to close those images there and close that window. I’m going to ignore the way for your diffusion tagger. As I said, that’s for anime, and I’m going to ignore the custom caption tag. This creates you a text file caption, which again, I’m going to ignore that. Now we’re onto training model. So in 5.1 model config. So if you’ve used StableDiffusion version 2 to train your model, you need to check these two boxes here.

I haven’t. I’ve used Stable Diffusion 1.5, so I’m going to leave those unchecked. Under the project name, give your project name as something that you will remember. So I’m going to just call my imprompt tutorial. And then underneath it, you’ve got pre trained model name all. So I need to change this to my Stable Diffusion trained model. We downloaded all these dependencies in the first cells. This would have made you a pre trained underscore folder. So if you just expand that, and then within there sits your saved Hensers model. So if you go with the three dots, click on it, copy path, and simply just paste that in there. So we have the VAE, so the VAE file, which controls the color in your images. So we also need to grab that and that would have installed during the first sell as well. So that will be in the VAE folder. So repeat that process, copy that path and just simply paste it in there. Now, this is where it’s going to be saving your finalized models. And I say models because it’s going to create multiple versions, but we’ll get to that later. Once you’ve closed this Google notebook, this will all go.

All these files will disappear. Make sure you check output to drive and that will save it to your Google Drive and just run that cell. So you can see here, the output path is content drive, my drive, Laura output. So there’ll be a folder on your Google Drive called Laura, and it will be an output file. We’re getting to the most important settings here. So we want to keep the train repeats to 10. Got the instance token. I’m just going to keep mine at mksks. Now you will see random names sometimes like sks. This is because it’s not a name that Stable Diffusion associates with something, so it’s not going to call it up. So by associating M KSKS with my model, it knows it’s calling up my model, my image. I’m going to keep that as is. If you’re not sure, just keep it as M KSKS style. So we are not training a style. We are training a woman. Or you can put person. I actually prefer to put person. You can put woman. It’s up to your own discretion if you want to do that. Resolution, we’re doing 512 by 512 because we have input images that are 512 by 512.

If you’re doing 7 6 8, put 7 6 8 here, just change it up the slider. Just leaving all these settings here as default, and I’m just going to run that cell. So we come down to 5.3, Lauren Optimisation Config, but you really need to experiment with the settings yourself to see if you can get a better result because obviously you are training different images than I am. But however, I will give you my settings because I have done a lot of tests. Come down to the Convolution DIM, which I’m going to be setting quite low at eight, and the Convolution Alpha, I’m going to be setting at one. Then we come down to network dim, which I’m going to set at 16, and then I’m going to change the network alpha to eight. These settings actually have huge influence on your model. I used to do the settings at one, two, eight by one, two, eight, but I’ve played around and I quite like these settings for my realistic models. What settings might work for me might not work for you guys because of different training sets, different resolutions and all that. But I digress.

Okay, I’m going to leave the optimiser config as adding W 8 bit. So the first learning rate, I’m going to change to five, E 4. So the text encoder learning rate, I’m going to change to 1E4. And then the learning rate scheduler, I’m going to change to CoSine with restarts. The warm up steps, I’m going to do 0.05. And to be honest, I’m quite happy with that. So this is probably going to be a learning rate of about 950 steps. But we’ll see once we hit run. So we’re going to run that cell, and then we’re going to go to 5.4 training config. I’m going to leave low RAM on. I’m going to enable sample prompt. I’m going to leave the sampler as DTM. Noise offset, I’m going to leave at zero. Sometimes I go 0.01. I’m going to be creating 10 Epochs here, which will save a file at every learning stage, which means I can test the files out in my web UI at the end to see if it’s either undercooked or overcooked or just about right. I like to do about 10 because it gives me a nice, diverse range to pull from.

The trained batch sizes. Now, you can go quite low. You can go to one. I’m probably going to go to two and see how it goes from there. So the batch sizes is how many files it’s training together. If I’m training six, it’s going to be a lot quicker than it will be for two. If I went to one, I’d probably completely run out of RAM. So if you do have a RAM issue, try sticking to six or higher. But if you don’t have any RAM issues whatsoever, you can train on anything as low as one here. The mixed and saved precision, I’m both leaving those at F P 16. My Epoch, I’m going to save every Epox. So that’s 10 Epox I should have at the end. I’m saving the model as a saved tensers model, and I’m leaving this all as default here, so that’s pretty simple. I’m going to run that cell. Now we come to our final cell. You’ll be glad to hear, all you need to do, just run that cell and leave everything as default and let the training begin. This might take probably about 30 to 40 minutes. If I wanted it to be done quicker, I would actually increase the batch size.

Hopefully, all this makes sense. I wanted to describe what I’m doing as I do it, so you have at least a understanding of what’s going on, which hopefully, again, will allow you to make changes to suit your training data. Once the training is complete, you do not have to do any of the remaining cells in the notebook. Your files will now be saved automatically into your Google Drive. So head over to your Google Drive, you will have a Lo ra file in there, an output file, and in there lives your Lo ra files. And remember, I said it would save a file at every training step, and we said 10 in this demonstration, so it’s given us 10 files here. As you’re probably aware, I use automatic 111 on Run Pod, and the link for instructions are all here. So if you don’t want to use automatic 111 locally on your computer and you don’t want to have to set up a Run Pod like I’ve got, the developer of this Koya Lo ra notebook has just come out with a brand spanking new automatic 111 notebook with control net 1 and the brand new control net 2, as well as the ability to use your newly trained Lora files.

You can use the CalicuristroC lab UI, and it’s basically automatic 111 to generate your images. So I just thought I’d throw that in there as an additional option. Now, grab these files, download them and load them into your stable diffusion model LoRA file. Just whack them all in there. Step three, creating our puppet. Now, underneath the Generate button, you’ll see this sun icon here. Give that a click, and then this will open up this panel along here. Select Laura, and you should see your Laura files now in there. Now, if you don’t, you can always click the refresh button. You can test out all these files here by clicking in on each file. That will then load its tag up into the prompt like this. So you just test them all out, just use one at a time. You can also control the weights of these Laura files as well by adjusting this figure. So I’m just going to load in my LoRA file with a prompt as well as a negative prompt and just run it through and see what it looks like. So I’m quite happy with the overall state of my LoRA file.

It does look like me. So I’m just going to create an image of me bored. I’m going to be taking this bored image of myself and dragging and dropping that into the Headshot plugin, which then generates me a 3D model of my face. I can go in and sculpt this further if I want to, but I’m just using this as a puppet or a guide for the AI. It doesn’t have to look great. It just has to look similar to me. Once I’m happy with the face and the body shape, I then add some hair. Again, it’s super easy. I’m just dragging and dropping from a library. Now, once I’ve finished with my character, I’m now going to export it to iC loan. These programmes work together in a pipeline. So iC loan is more of an animation programme. So this is where I’m going to be adding the facial animation as well as the body idle. I use a plug in called Motion Live. I just activate Motion Live and I’ve downloaded the Motion Live app on my phone here. All links are below in the description. It’s super easy to use. All you’re doing is recording your facial movement and that’s being applied to your 3D model in real time.

I’ve just dragged and dropped a lighting situation in here and got some physics on the hair and I’m pretty much ready to render. Come over to the render panel, which is here and I’m going to I have selected PNG sequence. I actually went 7 6 8 by 7 6 8 in the end. Try and be visible by 16 if you can. And we come down here and I’m just doing 300 to 500 frames and I’m going to export those as PNG. So we’re going to jump back into automatic 111and I’m going to go through the settings I’m using. I’m using image to image and I’ve loaded in my Laura here. Remember the buttons over here. And then I’ve just added a simple prompt because I don’t want the prompt to fight the original image. That’s quite important. So you want to keep the prompt as brief as possible. Do not put too many details that are not in the image that you want to create. Then we have the negative prompt, which is as important as this prompt up here. So you can copy my one. I’ll put it down in the description below. So this is our rendered image added in here.

So the sampling method I have set to DPM then the SDE. You can use whatever you want. I’ve had good results with Euler A, I’ve had good results with KM Kuhares. Sampling steps, I’m keeping relatively low for this. Width and height, I’m going for 7.68×768, the same as my input. De noising strength, I’m keeping low. I’m keeping the de noising strength at 11. Actually, you’ll notice my CFG scale is down six as well. Again, we want to pull from this image as much as we can and apply the Laura over the top without the prompt compromising things too much. I’ve already set my seed because I’ve tested this out already. When you do this, just render a minus one seed until you get something you like and then lock it in with the Reuse button. So on Control net, I’ve enabled head and I have the model enabled as well. I haven’t changed anything from the default settings here. I’ve got a secondary model in as well, which I’ve enabled canny and enabled the Cany model. Again, I haven’t changed any of the default settings. So let’s render and see what it creates us. That looks like me on a really good day.

And it’s it’s following the mouth movements as well, which we need for lip syncing. I have seen so much AI generation where the mouth is just a blurry mess. Using my workflow, you can now get precise mouth lip syncing. This is why I made the model on me so you can see that it is working, okay? So we’re now going to batch render these frames, which will give us a frame by frame animation. And to do that, head over to batch and put your input, so where your 3D files are sitting on your drive, and then your output where you want them to render to. And then hit Generate. And congratulations, we have completed our animation. All that is rendering. I want to show you something really cool. And this is why I like this workflow. I literally can come over and switch my checkpoint file and run that again. And look at that. It’s created a semi animation style version with a click of a button. So you can literally change the render type by using your diffusion checkpoint. I think this is really cool. And this is a great starting point from where things are going to go from here.

Now, we’ve got Control net 2, which has just come out, which I’m not using in this video. So that’s going to take things to a whole new another level. So I’ve simply just thrown those frames into After Effects from Automatic 111. If you have Da Vinci Resolve, use the dirt removal and the deflicker times two in there because that will give you really nice smooth results to your animation. I’m going to hit space bar so you can have a look at the animation. I’ve slowed it down so you can see the frames are blending so well into each other. It’s probably not very good animation I did there, but you can see the technique. I switched the checkpoint model over. I didn’t change any of the settings. I’d probably go back in and change some settings to make the animation version smoother. But that shows you how quickly you can flick, essentially, the AI renderer to change. And then that took about two minutes to render those animation frames. I would love to see what you guys make with this workflow. So please tag me in your social media so I can have a look because I love looking through all the creative projects everybody’s doing with AI at the moment.

I put all my AI creative projects and workflows all on my social media as well. On Instagram, I’m @PromptMuse. On Twitter, I’m @PromptMuse. And on Discord, there is a link below. I’m really excited to see what you guys create with this. This tutorial will be written up step by step on the Prompt Muse website as well. And we have a fantastic weekly newsletter that surrounds the latest and greatest in AI tech. So please join that on the Prompt News website. Thank you for watching this video. I really appreciate it if you subscribe to this channel and give it a thumbs up. I do have a buy me a Coffee link down in the description, and that is just to buy me a coffee. I don’t put any content behind any paywalls. I like to make sure that all my content is free and accessible to everybody. And having that allows me to continue to do that. So I thank you very much. This video has been Days in the making because I have had to test out so much stuff and variations to get this to work. So I know you guys appreciate that. Please share this video on your social media and @ me as well when you do it because I like to see that.

That’s amazing and I can say thank you to you as well. So yeah, I think that will do it for today. Bye bye.

<p>The post How I Unleashed the Power of AI to Create Next-Level Animations first appeared on Prompt Muse.</p>

Ancient Times: Automata and Early Mechanical Devices

During ancient times, people had a fascination with creating machines that could perform tasks on their own. These early mechanical devices were often inspired by nature and the movements of animals. One of the most famous examples of these early machines is the Antikythera mechanism. Discovered in 1901 in a sunken ship off the coast of the Greek island of Antikythera, this device is thought to have been built around 200 BCE. It consisted of a complex system of gears and was used to predict the positions of the sun, moon, and planets, as well as lunar and solar eclipses.

The Antikythera mechanism was an incredible feat of engineering for its time and is often considered to be one of the first examples of a complex mechanical device. It was also a testament to the advanced knowledge of astronomy that existed in ancient Greece.

Other examples of ancient automata include the chessboard robot. This device was reportedly built in the 9th century and used a hidden human operator to move the pieces on the chessboard. The operator would sit inside the machine and use levers and pulleys to move the pieces, making it appear as though the machine was moving them on its own.

In addition to these early mechanical devices, there were also other types of automata that were created during ancient times. These included statues that could move and speak, as well as water clocks and other timekeeping devices.

Overall, the development of automata and early mechanical devices during ancient times was an important milestone in the history of technology. It paved the way for future innovations and helped to lay the foundation for the modern world we live in today.

Late 1700s – Early 1800s: The Industrial Revolution and Early Automata

The Industrial Revolution was a period of significant change that transformed the way goods were produced, and it had a profound impact on society. During this time, there were many advances in mechanical technology, which led to the development of early automata.

One of the most famous examples of early automata from this time is the Mechanical Turk. The Turk was a chess-playing automaton that was built in 1770 by Wolfgang von Kempelen, an engineer from Austria. The Turk was a life-size figure of a man sitting at a table, and it appeared to be capable of playing chess on its own, defeating many notable opponents throughout Europe and America.

However, the reality was that the Turk was not capable of playing chess on its own. Instead, it was operated by a human chess player who was hidden inside the machine. The player sat on a small platform inside the Turk and used a series of levers and pulleys to control the movements of the chess pieces on the board.

Despite the fact that the Mechanical Turk was not truly automated, it was an impressive feat of engineering for its time and became famous for its ability to defeat skilled chess players. It toured throughout Europe and America for over 80 years, attracting crowds of people who were amazed by its apparent ability to play chess on its own.

In addition to the Mechanical Turk, there were many other examples of early automata that were developed during the Industrial Revolution. These included machines that could perform simple tasks like weaving and spinning, as well as more complex devices like the Jacquard loom, which used punch cards to control the weaving of intricate patterns.

Overall, the Industrial Revolution was a critical period in the development of mechanical technology and automation. It laid the foundation for the modern era of manufacturing and set the stage for future advancements in automation and robotics.

1950s: The Birth of Artificial Intelligence

In 1956, John McCarthy, an American computer and cognitive scientist, coined the term “artificial intelligence” or “AI.” This marked the beginning of a new era in computing, where machines were no longer limited to performing basic arithmetic operations but were instead being developed to simulate human-like reasoning and decision-making.

At the time, computers were still in their infancy and were mainly used for scientific and military purposes. They were large, expensive, and required specialized knowledge to operate. However, McCarthy saw the potential for these machines to be used for more than just number-crunching.

In his proposal for the Dartmouth Conference, which was held in the summer of 1956, McCarthy outlined his vision for a machine that could reason and learn from past experiences. He envisioned a system that could simulate human intelligence by using a combination of logic, rules, and probability to make decisions.

This idea was revolutionary at the time, and it sparked a new wave of research and development in the field of AI. Over the next few decades, researchers made significant strides in developing algorithms and techniques that could simulate human-like intelligence.

One of the early breakthroughs in AI was the development of expert systems in the 1970s. These were programs that could replicate the decision-making abilities of human experts in specific domains such as medicine, finance, and engineering. Expert systems were widely used in industry, but they were limited in their ability to generalize to new situations.

In the 1980s and 1990s, there was a renewed focus on developing machine learning algorithms that could enable machines to learn from data and improve their performance over time. This led to the development of neural networks, which were inspired by the structure of the human brain.

Today, AI is a rapidly evolving field that is being used in a wide range of applications, from speech recognition and natural language processing to image and video analysis and autonomous vehicles. While the goal of creating machines that can match or surpass human intelligence is still far off, advances in AI are driving significant changes in industry, healthcare, and other fields, and the potential for future breakthroughs is immense.

1960s – 1970s: Rule-Based Expert Systems

In the 1960s and 1970s, rule-based expert systems were a significant area of research in the field of artificial intelligence. These systems were designed to solve complex problems by breaking them down into a set of rules that the computer could follow. The idea behind rule-based expert systems was to capture the knowledge and expertise of human experts and encode it into a set of rules that a computer could use to solve similar problems.

One of the earliest examples of a rule-based expert system was MYCIN, developed by Edward Shortliffe in 1976. MYCIN was a medical expert system designed to diagnose bacterial infections based on a set of symptoms and medical history. It was designed to replicate the decision-making process of a human expert, using a set of rules and heuristics to reach a diagnosis.

Another example of a rule-based expert system was DENDRAL, developed by Joshua Lederberg and his colleagues at Stanford University. DENDRAL was designed to help chemists identify the molecular structure of organic compounds based on their mass spectrometry data. It used a set of rules to generate hypotheses about the molecular structure and then used feedback from the user to refine and improve the accuracy of its predictions.

Rule-based expert systems were widely used in industry and government during the 1970s and 1980s. They were particularly useful in areas where there was a large amount of specialized knowledge that needed to be applied in a consistent and reliable manner. However, rule-based expert systems had some limitations, particularly when it came to dealing with uncertainty and ambiguity.

Despite their limitations, rule-based expert systems paved the way for further advances in the field of artificial intelligence. They demonstrated that it was possible to encode human expertise into a computer system and use it to solve complex problems. Today, the ideas and techniques behind rule-based expert systems continue to influence the development of more advanced AI systems, including machine learning algorithms and deep neural networks.

1969: The First AI Winter

In 1969, the US government cut funding for artificial intelligence (AI) research, marking the beginning of what is now known as the first AI winter. The term “AI winter” refers to a period of reduced funding and interest in AI research that occurred several times throughout the history of AI.

The first AI winter was caused by a combination of factors, including the lack of significant progress in AI research, the high cost of hardware and software needed for AI research, and the inability of AI researchers to demonstrate practical applications for their work. As a result, the US government, along with other organizations and institutions, began to reduce funding for AI research.

The first AI winter lasted from the late 1960s to the early 1970s and had a significant impact on the development of AI research. Many AI researchers were forced to abandon their work or move on to other areas of research, and funding for AI research remained low for several years.

The AI winter also had a profound impact on the perception of AI among the general public. Many people began to view AI as a pipe dream or a science fiction concept, rather than a realistic field of research with practical applications.

However, the first AI winter eventually came to an end, as new breakthroughs and innovations in AI research led to renewed interest and funding. In the 1980s, the development of expert systems and the rise of machine learning led to a resurgence of interest in AI research, which helped to drive significant progress in the field.

Today, AI is once again a rapidly growing field with significant investment and interest from governments, corporations, and individuals around the world. While the first AI winter was a challenging time for AI researchers and the field as a whole, it ultimately served as a reminder of the importance of perseverance and continued innovation in the pursuit of scientific advancement.

1980s – 1990s: Neural Networks and Machine Learning

In the 1980s and 1990s, researchers began exploring the use of neural networks and machine learning techniques in the field of artificial intelligence. These technologies represented a significant departure from the earlier rule-based expert systems and offered new possibilities for creating intelligent machines that could learn and adapt over time.

Neural networks are computer systems that are modeled after the structure and function of the human brain. They consist of interconnected nodes or “neurons” that can learn and adapt based on new information. Neural networks can be used for a wide range of tasks, from image and speech recognition to natural language processing and decision-making.

Machine learning involves creating algorithms that can learn from data and make predictions or decisions based on that data. These algorithms can be used to classify data, detect patterns, and make predictions. One of the key benefits of machine learning is its ability to improve over time as it receives more data, making it an ideal technique for tasks like image and speech recognition.

The development of neural networks and machine learning techniques in the 1980s and 1990s led to significant advances in AI research. Researchers were able to develop sophisticated algorithms that could learn and adapt to new data, opening up new possibilities for creating intelligent machines.

One of the most significant applications of neural networks and machine learning in the 1990s was in the field of computer vision. Researchers developed algorithms that could analyze and recognize images, opening up new possibilities for applications like facial recognition, object recognition, and autonomous vehicles.

Today, neural networks and machine learning continue to be a major focus of AI research. The development of deep neural networks and other advanced machine learning techniques has led to significant breakthroughs in areas like natural language processing, speech recognition, and computer vision. As these technologies continue to evolve, we can expect to see even more significant transformations in the field of artificial intelligence.

1997: Deep Blue Defeats Kasparov

The chess match between Deep Blue and Garry Kasparov in 1997 was a major turning point in the field of AI. Deep Blue was a specially designed computer system created by IBM, designed specifically to play chess at a professional level. The match was held in New York City and attracted a lot of media attention.

The match was played over six games, with Kasparov winning the first game, but then losing the second and third games. The fourth game ended in a draw, and Kasparov won the fifth game, leaving the match tied at 2.5 games each. In the final game, Deep Blue emerged victorious, defeating Kasparov and winning the match by a score of 3.5 to 2.5.

The victory of Deep Blue over Kasparov was a significant achievement in the field of AI, as it demonstrated that machines could be developed to compete at a high level in complex games like chess. It also showed that machines were capable of analyzing and evaluating vast amounts of data in a short amount of time, far beyond what a human could do.

After the match, there was some controversy over whether or not Deep Blue’s victory was a true test of AI. Some argued that Deep Blue’s victory was due more to its brute computational power than to any real intelligence. Others argued that the machine’s ability to adapt and learn from past games made it a true example of AI.

Regardless of the debate, the match between Deep Blue and Kasparov was a pivotal moment in the history of AI. It showed that machines were capable of performing complex tasks that were once thought to be the sole domain of human intelligence. This breakthrough paved the way for further advances in the field of AI, including the development of machine learning algorithms and deep neural networks, which have led to even more significant breakthroughs in recent years.

2000s: Big Data and Deep Learning

In the 2000s, the advent of the internet and the explosion of data led to a renewed interest in artificial intelligence. Big data analytics became an essential part of AI research, with the ability to analyze vast amounts of data to find patterns and insights. Deep learning, a subset of machine learning, also emerged during this time and became an area of intense research and development.

Big data analytics involves the use of advanced algorithms and tools to analyze and make sense of large and complex data sets. The explosion of data in the 2000s, including social media, digital devices, and other sources, meant that big data analytics became increasingly important for businesses and organizations looking to gain insights and improve decision-making.

Deep learning, a subset of machine learning, involves the use of artificial neural networks with multiple layers. These networks are designed to learn from data and make predictions based on that data. Deep learning algorithms can be used for a wide range of applications, including image and speech recognition, natural language processing, and decision-making.

One of the most significant breakthroughs in deep learning came in 2012 when a deep neural network called AlexNet won the ImageNet Large Scale Visual Recognition Challenge, a competition for computer vision systems. AlexNet’s success demonstrated the potential of deep learning to revolutionize computer vision and image recognition, opening up new possibilities for applications like self-driving cars and facial recognition.

Overall, the 2000s saw significant progress in the development of AI, driven by the explosion of data and the emergence of big data analytics and deep learning. These technologies have had a significant impact on many industries, including healthcare, finance, and manufacturing, and have paved the way for further advances in AI research and development.

2010s – Present: AI Goes Mainstream

The 2010s saw a significant surge in the mainstream adoption of AI applications in various industries. This period marked the beginning of the fourth industrial revolution or Industry 4.0, which involved the convergence of technology, data, and physical systems.

One of the key drivers of this AI revolution was the growth of big data and cloud computing. The rise of the internet and digital technologies led to the collection of vast amounts of data, which could be used to train machine learning algorithms and develop sophisticated AI models. With cloud computing, businesses could access these resources on demand, without the need for significant upfront investment in hardware and software.

This period saw the emergence of virtual assistants like Siri and Alexa, which became ubiquitous in many households around the world. These assistants used natural language processing and machine learning algorithms to understand user queries and provide personalized responses.

The use of AI also expanded into various industries, including healthcare, finance, and manufacturing. In healthcare, AI is being used for early disease detection, personalized treatment recommendations, and drug discovery. In finance, AI is used for fraud detection, trading algorithms, and risk management. In manufacturing, AI is used for predictive maintenance, quality control, and supply chain optimization.

The development of self-driving cars also gained significant attention in this period, with major tech companies like Google, Tesla, and Uber investing heavily in autonomous vehicle technology. Self-driving cars use a combination of machine learning algorithms, computer vision, and sensor technologies to navigate and make decisions on the road.

Overall, the 2010s saw a massive expansion of AI applications in everyday life and across various industries. With continued advances in AI technology, we can expect to see even more significant transformations in the way we live and work in the coming years.

2011: Watson Wins Jeopardy!

In 2011, IBM’s Watson computer made history by winning a Jeopardy! match against two former champions, Ken Jennings and Brad Rutter. The match was broadcast on national television and attracted a lot of attention from the media and the public.

Watson was a highly advanced computer system designed by IBM to understand and respond to natural language clues. It was named after Thomas J. Watson, the founder of IBM. The system was built using a combination of advanced algorithms, machine learning, and natural language processing techniques.

The Jeopardy! match was a significant breakthrough in the field of natural language processing. Jeopardy! is a game show that involves answering questions in the form of answers, and the questions can be quite complex and require a deep understanding of language and culture. Watson’s ability to understand and respond to these questions in real-time was a major achievement for the field of natural language processing.

Watson’s success in the Jeopardy! match was due to its ability to analyze vast amounts of data and make connections between seemingly unrelated pieces of information. It used a combination of statistical analysis and natural language processing to understand the questions and generate responses.

The victory of Watson over human champions was a significant moment in the history of AI. It demonstrated that machines were capable of understanding and responding to natural language, a task that was once thought to be the exclusive domain of human intelligence. It also showed that machine learning algorithms and natural language processing techniques were becoming increasingly sophisticated and capable of performing complex tasks.

2016: AlphaGo Defeats Lee Sedol

In 2016, Google’s AlphaGo computer made history by defeating world champion Lee Sedol in a five-game match of the ancient Chinese game of Go. Go is considered one of the most complex games in the world, with more possible moves than there are atoms in the universe. AlphaGo’s victory was a significant achievement for the field of artificial intelligence and demonstrated the potential of deep learning and AI to solve complex problems.

AlphaGo was developed by DeepMind, a British AI research company acquired by Google in 2015. The system used a combination of deep neural networks and reinforcement learning to learn the game of Go and improve its gameplay over time. Reinforcement learning involves training a computer system by rewarding it for positive behavior and punishing it for negative behavior, allowing the system to learn from its mistakes and improve its performance.

The match between AlphaGo and Lee Sedol attracted a lot of attention from the media and the public, as it pitted human intelligence against artificial intelligence in a highly competitive and complex game. The victory of AlphaGo over Lee Sedol was a significant milestone in the development of AI, demonstrating the potential of AI to perform complex tasks that were once thought to be the exclusive domain of human intelligence.

AlphaGo’s success in the game of Go had significant implications for the future of AI research and development. It showed that deep learning and reinforcement learning techniques could be used to solve complex problems and learn new tasks, paving the way for further advances in AI technology. The victory of AlphaGo also sparked renewed interest and investment in AI research, leading to significant progress in areas like natural language processing, computer vision, and robotics.

Overall, the victory of AlphaGo over Lee Sedol was a significant moment in the history of artificial intelligence. It demonstrated the potential of deep learning and AI to solve complex problems and perform tasks that were once thought to be the exclusive domain of human intelligence. As AI technology continues to evolve, we can expect to see even more significant transformations in the way we live and work in the coming years.

2021: GPT-3 and Advanced Language Models

In 2021, OpenAI released GPT-3, a state-of-the-art natural language processing model that has been hailed as a breakthrough in AI research. GPT-3 stands for “Generative Pre-trained Transformer 3,” and it is the third iteration of a series of language models developed by OpenAI.

GPT-3 is a massive deep learning model that was trained on a vast amount of data from the internet, including books, articles, and websites. It has over 175 billion parameters, making it one of the largest and most complex language models ever created.

One of the most significant advances in GPT-3 is its ability to generate human-like text. It can write essays, stories, and even computer code with remarkable fluency and accuracy. GPT-3’s language generation capabilities have been used in a wide range of applications, from chatbots and virtual assistants to content creation and language translation.

GPT-3’s language generation capabilities are made possible by its deep learning architecture, which allows it to learn from large amounts of data and generate responses based on that learning. It also has the ability to understand context and generate responses that are appropriate to the situation.

GPT-3’s release has sparked a lot of excitement in the AI community, as it represents a significant step towards creating more advanced AI systems that can understand and interact with humans more effectively. It has the potential to revolutionize the way we interact with machines, making them more human-like and easier to use.

Final Thoughs

As we’ve seen, the history of AI is a long and fascinating one, filled with many breakthroughs and setbacks. From ancient automata to advanced deep learning models, AI has come a long way over the centuries. But where is it headed next? What new breakthroughs and innovations lie ahead?

As AI continues to evolve and develop, it raises many questions and challenges. Will machines eventually surpass human intelligence, and if so, what will that mean for our society? How can we ensure that AI is used ethically and responsibly? And what role will humans play in a world dominated by intelligent machines?

In the words of Stephen Hawking, “The rise of powerful AI will be either the best or the worst thing ever to happen to humanity. We do not yet know which.” But by continuing to push the boundaries of AI research and development, and by engaging in thoughtful and ethical discussions about its implications, we can work towards creating a future where AI is a force for good, and where humans and machines can coexist in harmony.

<p>The post A Brief History of Artificial Intelligence: From Its Humble Beginnings to Its Future Possibilities first appeared on Prompt Muse.</p>

With Expedia’s latest plugin, iOS users can now chat with an intelligent chatbot to plan their trips. The feature is part of the company’s ongoing efforts to provide a more streamlined and informative travel experience.

“Expedia is committed to delivering a core operating system for the travel industry that’s always evolving,” said Peter Kern, Expedia Group’s vice chairman and CEO. “By incorporating ChatGPT into our app, alongside other AI-driven tools like hotel comparison and price tracking, we’re offering travellers an even more intuitive way to craft their dream vacation.“

The ChatGPT-powered travel assistant provides recommendations on various travel aspects, including destinations, lodging, transportation, and activities. As users converse with the bot, the app will automatically save discussed hotels to the user’s “trip” feature, simplifying the booking process.

Expedia has also reassured users that their data will remain private, stating that customer information from ChatGPT searches will not be shared with OpenAI. However, the company may leverage this data internally to improve and personalize the user experience, as Kern explained in a Wall Street Journal interview.

Currently, in beta testing, the feature is exclusively available to iOS users and supports only the English language. Android users will have to wait for access to this groundbreaking travel planning tool.

Travel planning just got easier in the @Expedia app, thanks to the iOS beta launch of a new experience powered by #ChatGPT. See how Expedia members can start an open-ended conversation to get inspired for their next trip: https://t.co/qpMiaYxi9d pic.twitter.com/ddDzUgCigc

— Expedia Group (@ExpediaGroup) April 4, 2023

How does the new Expedia feature work?

Users can engage in conversation with the app, receiving tailored suggestions for destinations, accommodations, and activities. The chatbot saves hotel details from these discussions, allowing users to easily select date ranges, check availability, and add flights, rental cars, and other activities to their itinerary. After the chatbot generates various options, users can review their saved items for the final decision.

Expedia has even released a video demonstration of the feature on Twitter, showcasing the next step in travel planning convenience.

<p>The post Expedia Debuts ChatGPT-Driven Travel Assistant: Get an Early Look on iOS first appeared on Prompt Muse.</p>

What is GPT and How Has it Evolved?

To understand the excitement surrounding GPT-5, let’s first take a step back and examine the Generative Pre-trained Transformer (GPT) series. These models are designed to understand and generate human-like text. OpenAI, the organization behind GPT, has been continuously improving its language models since the release of the first GPT in 2018.

Each new version of GPT has showcased significant advancements in performance, scalability, and usability. GPT-3, for example, was known for its impressive capabilities in generating coherent and contextually relevant text across a variety of tasks. With GPT-4, users have experienced even greater accuracy and fluency in generated text.

What to Expect from GPT-5

As we await the release of GPT-5, it is important to consider what this next-generation AI might be capable of. Given the leaps in performance and scalability seen with previous iterations, we can reasonably expect GPT-5 to continue this trend.

Some potential advancements that GPT-5 could bring include:

Enhanced language understanding: GPT-5 could possess an even deeper understanding of context, nuance, and emotion in language, allowing it to generate more accurate and human-like responses.

Improved task-solving capabilities: By integrating advanced reasoning and problem-solving skills, GPT-5 might be able to tackle a wider range of tasks and challenges.

More advanced AI-human interaction: With better conversational skills, GPT-5 could become an indispensable tool for various industries, providing real-time assistance in customer service, healthcare, and education.

Decreased biases: OpenAI has been working to reduce biases in its AI models. With GPT-5, we might see significant progress in addressing and minimizing biases in generated text.

Increased adaptability: GPT-5 could potentially learn new skills and adapt to new situations more efficiently, making it a more versatile tool for users.

The Road to Artificial General Intelligence

A major talking point surrounding GPT-5 is the possibility of it achieving AGI. AGI refers to an AI system with the ability to understand, learn, and perform any intellectual task that a human being can do. While AGI remains a long-standing goal for AI researchers, it has yet to be achieved.

Tech entrepreneur Siqi Chen’s claim that GPT-5 could reach AGI by the end of the year has sparked debate within the AI community. While some experts believe it is possible, others remain skeptical. The truth likely lies somewhere in between.

Regardless of whether GPT-5 achieves AGI, it is undeniable that its development will bring us one step closer to this ambitious goal. As AI systems like GPT become more powerful, versatile, and adaptable, they will continue to push the boundaries of what is possible in the field of artificial intelligence.

Implications and Ethical Considerations

As we look forward to the release of GPT-5, it is crucial to consider the ethical implications of such a powerful AI system. The increasing capabilities of AI models like GPT-5 raise concerns about potential misuse, privacy, and security.

OpenAI has been proactive in addressing these concerns by implementing safety measures and guidelines for the responsible use of its technology. As AI continues to advance, the importance of developing comprehensive ethical frameworks to govern its use cannot be overstated.

What’s Next for GPT-5 and OpenAI?

While the exact release date of GPT-5 remains uncertain, the buzz surrounding its potential capabilities and impact is undeniable. As OpenAI continues to refine its language models, we can expect future iterations to build upon the successes of their predecessors, driving the AI field forward.

The potential applications of GPT-5 in various industries, its role in advancing the pursuit of AGI, and the ethical considerations that come with increasingly powerful AI systems will all be important topics of discussion in the coming months.

Ultimately, the release of GPT-5 has the potential to revolutionize the way we interact with AI and reshape our understanding of what these systems can achieve. As we eagerly await its arrival, it is essential to remain mindful of both the opportunities and challenges that come with the development of increasingly advanced AI systems.

The anticipation surrounding the release of GPT-5 highlights the rapid progress and growing impact of AI technology. With each new iteration of the GPT series, we witness remarkable advancements in AI capabilities, bringing us closer to the elusive goal of AGI. As we look forward to the release of GPT-5, it is crucial to consider both its potential benefits and the ethical implications of its use. The future of AI is undeniably exciting, and GPT-5 is set to be a pivotal milestone in its ongoing evolution.

<p>The post GPT-5 Could Release by Year-End: Unprecedented Power and the Path to Artificial General Intelligence first appeared on Prompt Muse.</p>

A Breach in Privacy?

On March 31, 2023, Italy’s Data Protection Authority announced its decision to temporarily block access to ChatGPT, an artificial intelligence software created by OpenAI, following a data breach that raised concerns over the protection of user data [ABC News]. The authority is currently investigating whether the data breach constitutes a violation of the stringent data protection rules set forth by the European Union.

This recent data breach has raised questions about the safety and security of user data, particularly in light of the fact that ChatGPT is widely used across a variety of industries and applications. The investigation will focus on whether the collection of personal information during the breach adhered to EU data protection regulations [Financial Times].

ChatGPT: A Powerful Tool with Potential Risks

ChatGPT is an AI language model developed by OpenAI, based on the GPT-4 architecture. Since its release, it has been widely adopted across industries and used for a multitude of purposes, ranging from customer support and content generation to language translation and virtual assistance. Its advanced capabilities and versatility have made it an invaluable tool for many businesses and users worldwide.

However, with great power comes great responsibility. The recent data breach has highlighted the potential risks associated with the use of advanced AI technology like ChatGPT. As the software processes vast amounts of data, including personal information, it becomes imperative for developers and regulators alike to ensure the privacy and security of user data.

European Union Data Protection Rules

The European Union has long been a pioneer in data protection regulations, and the General Data Protection Regulation (GDPR) is the cornerstone of its efforts to safeguard user data. The GDPR, which came into effect in May 2018, aims to harmonize data protection laws across the EU and empower individuals with greater control over their personal information.

Under GDPR, organizations are required to adhere to strict data protection principles, including transparency, data minimization, and accountability. Failure to comply with these rules can result in significant financial penalties, with fines up to €20 million or 4% of an organization’s global annual turnover, whichever is higher.

The ongoing investigation by Italy’s Data Protection Authority will determine whether the ChatGPT data breach violates the provisions of GDPR and if OpenAI is liable for any penalties.

Implications for AI and Data Privacy

The temporary blocking of ChatGPT in Italy raises important questions about the balance between technological innovation and data privacy. As AI continues to advance and integrate itself into various aspects of our lives, ensuring the privacy and security of user data becomes increasingly crucial.

This incident serves as a reminder that even the most advanced AI technologies are not immune to data breaches and security vulnerabilities. It is essential for developers, regulators, and users alike to remain vigilant and prioritize data privacy in order to maintain trust in these technologies.

<p>The post AI Privacy Scandal: Italy Slams the Brakes on ChatGPT Amidst Shocking Data Breach Concerns! first appeared on Prompt Muse.</p>

The Impacts of GPTs on the Workforce

The study suggests that GPTs will impact around 80% of workers, with at least 10% of their work tasks being impacted by the invention of ChatGPT. In addition, 19% of workers may have half or more of their work impacted by it. The study does not distinguish between labour-augmenting or labour-displacing effects, but it notes that higher-income jobs have greater exposure to AI language tools. The study defines exposure by reducing the human time to complete a task by at least 50%.

According to the study, GPTs are likely to help workers become more efficient rather than replacing them. The study suggests that GPTs will be most useful for tasks that require complex problem-solving skills and knowledge in specialized fields. Jobs that require analytical skills, such as mathematicians, interpreters, accountants, legal secretaries, writers, and authors, are less likely to be replaced by GPTs. In contrast, blue-collar workers are less likely to be impacted by AI language tools.

Challenges for Policymakers

The study suggests that as the capabilities of GPTs continue to evolve, the impact of AI on the economy will likely persist and increase, posing challenges for policymakers in predicting and regulating their trajectory. Policymakers will need to address social, economic, regulatory, and other factors that influence actual labor productivity outcomes.

The study also notes that while GPTs can make human labour more efficient, policymakers must consider the ethical implications of using these technologies. AI language tools can be used to automate tasks that are repetitive, dangerous, or harmful to workers. However, policymakers must ensure that workers are not replaced by machines and that workers are trained to work with AI.

The Future of AI in the Workforce

The study concludes that GPTs will have a significant impact on the workforce in the future, making tasks more efficient and enabling workers to focus on more complex tasks. As AI technology continues to evolve, policymakers will need to address the ethical implications of using AI in the workforce.

The study emphasizes that GPTs are still evolving, and the full extent of their impact on the workforce is yet to be seen. However, it is clear that GPTs will have a significant impact on the workforce, and policymakers will need to address the challenges that arise from the integration of AI into the workforce.

Overall, the study provides insights into the implications of GPTs on the workforce, highlighting the need for policymakers to address the ethical and regulatory challenges posed by these technologies. While GPTs are likely to improve the efficiency of the labour force, policymakers must ensure that workers are not replaced by machines and that workers are trained to work with AI. As AI technology continues to evolve, policymakers will need to remain vigilant and proactive in regulating the use of AI in the workforce.

<p>The post How AI’s GPTs will impact the workforce: A new study explores the implications first appeared on Prompt Muse.</p>

In a blog post, Microsoft revealed that GPT-4 is now available in preview mode in Azure OpenAI Service. Businesses and partners already using the service can now apply for access to GPT-4, which is OpenAI’s most advanced model yet.

For businesses that want access to GPT-4, the prices start at $0.03 per 1,000 tokens for an 8k context for prompt completion and $0.06 per 1,000 tokens for a 32k context. Billing for all GPT-4 usage will begin on April 1.

Epic Healthcare, Coursera, and Coca-Cola are just a few of the companies that plan to utilize Azure OpenAI Service with GPT-4 in innovative ways to bring real impact to their businesses. For instance, Coursera will be leveraging Azure OpenAI Service to create a new AI-powered learning experience on its platform, allowing students to receive personalized support throughout their learning journey.

The addition of GPT-4 in Azure OpenAI Service represents a significant development for businesses that aim to integrate advanced AI models into their operations. By using Azure OpenAI Service, they can now harness the power of the most advanced models to create AI-powered applications and improve their business processes.

<p>The post Microsoft unveils GPT-4 in Azure OpenAI Service for businesses to create their own AI-powered apps first appeared on Prompt Muse.</p>