The Current Absurd State of Generating AI Images

5 Sept 202315:52

TLDRThe video discusses the rapid advancements in AI-generated images and their increasing popularity, as exemplified by the rise of an AI influencer with 2.7 million Instagram followers. It explores the evolution of AI image generation from incomprehensible images to ultra-realistic faces and full scenes. The script delves into model mixes, the development of Laura models, and their variations like Locon and Loja, which offer better identity preservation and style combination. It also touches on the use of tools like After Detailer and Control Net Tile for refining images. The video highlights the release of SDXL, a new model that generates highly detailed images but requires significant computational resources. Despite the challenges, the potential for fine-tuning and generating impressive results with SDXL is promising. The discussion concludes with the mention of Focus, a new GUI for SDXL that prioritizes optimal image generation with minimal user control, and a brief sponsorship message for, an educational platform for STEM fields.


  • πŸ“ˆ The popularity of AI-generated images has surged, with an example being an Instagram model with 2.7 million followers created using rendered images.
  • πŸš€ AI technology for image generation has improved significantly in recent months, raising questions about the potential for creating more influential AI personalities.
  • 🎨 AI models can now generate highly realistic images from text, including details like lighting, shadows, and camera lens effects.
  • πŸ€– Model mixes, which combine the best parts of several models, have led to stronger AI models capable of creating more aesthetically pleasing images.
  • 🌟 'Locon' and 'Loha' are two new variations of the AI model 'Lora' that show promise in improving identity preservation and style combination in image generation.
  • πŸ” The differences between Lora, Locon, Loha, and other methods like Locker and Dilora are subtle but significant for those in the field of AI image generation.
  • πŸ“± Tools like After Detailer and ControlNet are used to improve specific features of AI-generated images, such as faces and hands, after the main image has been generated.
  • 🧩 SDXL, a new model released by Stability AI, is a significant upgrade from previous models, offering higher resolution and better fine-tuning capabilities.
  • 🚧 Despite its potential, SDXL faces challenges in being adopted widely due to its high computational requirements and the need for optimized GUI support.
  • 🌐 The development of extensions for SDXL is slower compared to SD 1.5, but early test results are promising for the future of fine-tuning and image generation.
  • πŸŽ“ Platforms like are excellent resources for beginners to learn the foundational skills in STEM fields, including those necessary for understanding AI and machine learning.

Q & A

  • What is the current state of AI-generated images?

    -AI-generated images have become increasingly sophisticated, with advancements in technology allowing for the creation of highly realistic and aesthetically pleasing images. The latest models can generate images with detailed faces, lighting, shadows, and even camera lens effects from just text prompts.

  • How has the popularity of AI influencers grown?

    -AI influencers have become so popular that there is an Instagram model with 2.7 million followers, which is a rendered image superimposed onto a real person. This demonstrates the potential of AI technology to create influential figures that can attract a large following.

  • What is the role of model mixes in AI image generation?

    -Model mixes involve combining the best aspects of several AI models to create a new model that is stronger and more capable of generating aesthetically pleasing images. These mixes can learn specific characteristics about a style, face type, or clothing, and are trained with just a handful of images.

  • What are some of the variations of Laura that are gaining popularity in AI fine-tuned spaces?

    -Two notable variations of Laura gaining popularity are Locon, which trains both the Transformer block and the res block for better identity preservation, and Loja, which combines two Laura models using the Hadamard product for better expressiveness and style combination.

  • How does the AI model SDXL differ from its predecessors?

    -SDXL is a new base model trained on a higher resolution and incorporates a refiner for built-in image detail enhancement. It is capable of generating highly realistic images but requires significant computational resources, making it less accessible for most users.

  • What is the significance of the pseudo photorealism effect in AI-generated images?

    -The pseudo photorealism effect, often achieved through the bokeh effect, creates a sense of depth of field that can make AI-generated images appear more realistic. However, it can also camouflage artifacts or bad details, potentially leading to biased model evaluations based on user ratings.

  • How does the process of text-to-image generation evolve with new techniques?

    -Text-to-image generation has evolved to include not just the conversion of text to images but also the use of various extensions and tools to fix or improve the image quality. Techniques like After Detailer and ControlNet help refine specific features or upscale images with relevant details.

  • What is the role of fine-tuning in the development of AI-generated images?

    -Fine-tuning allows AI models to learn specific faces or subjects with high accuracy, which can significantly improve the quality and realism of the generated images. It is a crucial step in the development of advanced AI-generated images.

  • What are some of the challenges faced by the AI community in adopting new models like SDXL?

    -Challenges include finding the right fine-tuning parameters for large and computationally expensive models, as well as compatibility issues with existing open-source GUIs and extensions. These hurdles can slow down the adoption of new models in the community.

  • How does the Focus GUI differ from other GUIs for running SDXL?

    -Focus GUI is designed to run SDXL optimally with minimal user control, focusing on generating good images with short prompts. It incorporates state-of-the-art image generation techniques and automates choices like Samplers or CFG values for newcomers.

  • What is the potential impact of user ratings on the development of AI models?

    -User ratings can provide valuable feedback for model improvement. However, if the ratings are based on aesthetic preferences that are enhanced by techniques like the bokeh effect, they might lead to a misdirection in the AI model's development, focusing more on style than on generating realistic details.

  • How can someone interested in AI and machine learning get started with building their skills?

    -Platforms like offer interactive learning experiences in STEM fields, including AI and machine learning. They provide a clear roadmap for different knowledge levels, making complex subjects like calculus and linear algebra more digestible and accessible.



πŸš€ AI Influencers and Image Generation Evolution

The first paragraph discusses the rise of AI influencers, particularly noting an Instagram model with 2.7 million followers generated by AI. It reflects on the advancements in AI technology and its potential to create highly realistic and influential digital personas. The script also delves into the journey of text-based AI image generation, the improvements in image quality over the years, and the emergence of model mixes that leverage the best attributes of various models to produce high-aesthetic images. It touches on the evolution of AI models like Laura, which can learn specific characteristics from a handful of training images, and the various methods and their impact on the field of AI-generated content.


πŸ€” The Dominance of Laura and the Emergence of SDXL

The second paragraph explores why certain AI models like Laura have remained dominant despite the existence of potentially superior alternatives. It discusses the challenges of adopting new models, the advantages of sticking with established models with more resources and support, and the various uses of Laura models beyond art style replication. The paragraph also introduces Textual Inversion and its limitations compared to Laura's ability to understand and edit generating noise. It further explains additional tools and techniques used to refine AI-generated images, such as After Detailer and Control Net Tile, and how they contribute to higher resolution and detail. The impact of SDXL, a new model that surpasses previous versions, is also highlighted, along with its current limitations due to hardware requirements.


🎨 The Impact of AI Image Generation on Model Evaluation

The third paragraph contemplates the potential downsides of AI-generated images that use techniques like blurring to create a sense of depth, which might mislead user ratings and model evaluation. It suggests that these artifacts could bias the AI towards styles that are aesthetically pleasing rather than technically accurate. The paragraph also provides insights into the recommended resolutions for generating images with SDXL and its proficiency in fine-tuning. Early test results of fine-tuning with SDXL are promising, and there is a discussion about the progress of other models like Waifu Diffusion XL. The challenges of integrating SDXL with existing open-source GUIs are mentioned, and the introduction of a new GUI called Focus is highlighted for its optimization and user-friendliness for newcomers.


πŸ“š Learning STEM Fields with

The fourth and final paragraph shifts the focus to education and learning, particularly in the fields of machine learning and AI. It promotes as an ideal platform for beginners to build a strong foundation in math, coding, and machine learning skills. The script emphasizes the effectiveness of interactive learning and how provides a clear roadmap for various subjects across different knowledge levels. It also mentions a special offer for new users, including a free 30-day experience and a discount on an annual membership, and thanks the sponsor for their support.



πŸ’‘Artificial Influencer

An 'Artificial Influencer' refers to a virtual character or persona that is created using artificial intelligence and is often used for social media influence, marketing, or other forms of digital engagement. In the context of the video, it highlights the growing popularity of AI-generated personalities, such as an Instagram model with 2.7 million followers, which are not real people but rendered images superimposed onto real photos.

πŸ’‘AI Generated Images

AI Generated Images are visual outputs created by artificial intelligence algorithms that can produce images from textual descriptions or through other forms of data input. The video discusses the evolution and advancement in AI image generation, noting how these images have become increasingly realistic and detailed, to the point where they can mimic real-life scenarios and human faces with high fidelity.

πŸ’‘Model Mixes

Model Mixes refer to the combination of different AI models to create a new model that leverages the strengths of its components. The video explains that these mixes can generate high-quality images by merging the best features of various models, allowing for the creation of more aesthetically pleasing and diverse outputs.

πŸ’‘Locon (Laura for Convolutional Layer)

Locon is a variation of the AI model 'Laura' that is trained on both the Transformer block and the Res block for improved detail preservation and identity maintenance in generated images. The video suggests that Locon may offer better image quality by retaining more details from the original reference image compared to the standard Laura model.

πŸ’‘Loha (Hadamard Product of Two Lauras)

Loha is a method that combines two instances of the Laura model using the Hadamard product, which theoretically enhances the model's expressiveness. The video posits that Loha is particularly adept at blending styles with characters, making it a powerful tool for generating images with complex stylistic elements.


πŸ’‘Textual Inversion

Textual Inversion is a technique used in AI image generation where the trigger word or concept is translated into a numerical form that the model can interpret and use to generate an image. The video contrasts this with Laura's ability to understand the connection between concepts and trigger words, suggesting that Textual Inversion may be less flexible and effective outside the model it was trained on.

πŸ’‘After Detailer

After Detailer is an automatic painting tool used to enhance specific features of an AI-generated image, such as the face, hands, or body. The video describes it as a tool that can fix details post-image generation, improving the overall quality of the final output.

πŸ’‘Control Net

Control Net is an image upscaling model that uses text prompts to upscale images in different tiles, allowing it to generate relevant details for a larger image context. The video mentions it as a tool that can significantly improve the resolution and detail of AI-generated images.

πŸ’‘Dynamic Thresholding

Dynamic Thresholding is a technique that allows users to achieve a higher CFG scale, which influences the level of detail and adherence to the input prompt in AI-generated images. The video discusses how it can help create images that are more aligned with the input prompt without causing the AI to generate unwanted or unrealistic elements.

πŸ’‘Latent Coupling

Latent Coupling is a method mentioned in the video that involves separating an image into different regions and prompting them accordingly. This allows for more precise control over the generation process, ensuring that different parts of the image are generated without interference from other elements.


SDXL is a new base model for AI image generation that has been trained on a higher resolution and includes a refiner for adding image details. The video highlights SDXL as a significant advancement in AI image generation, capable of producing highly realistic images, but also notes its current limitations in terms of computational requirements and the challenges of fine-tuning.


Artificial influencer popularity has grown significantly, with an Instagram model amassing 2.7 million followers.

AI-generated images have improved drastically in recent months, raising questions about the potential for AI influencer creation.

The evolution of AI image generation includes the rise of incomprehensible images, big buba ladies illustrations, and ultra-realistic faces.

AI can now generate images with realistic lighting, shadows, and camera lens effects from text prompts alone.

Model mixes, which combine the best models to create stronger and more aesthetically pleasing images, have been discussed.

Laura, an AI model, can learn specific characteristics about a style, face type, or clothing with just a handful of training images.

The AI fine-tuned space has seen the rise of methods like licorice, named after an anime, which is gaining popularity.

Two notable Laura variations, locon and loha, have the potential to become extremely popular for their improved identity preservation and expressiveness.

Loha and locon are theoretically better than Laura but have not yet dominated the scene due to various factors.

Text-to-image generation has evolved to include text plus multiple extensions for improved image quality.

Tools like After Detailer and ControlNet are used to enhance specific features and upscale images with text prompts.

Dynamic thresholding allows for higher CFG scale, generating images more aligned with the input prompt.

Latent coupling and the break keyword are techniques for managing prompts to avoid interference in large canvas images.

Sdxl, a new model by Stability AI, offers a significant upgrade with a base resolution of 1024x1024 and a built-in refiner.

Sdxl's pseudo photorealism uses the bokeh effect to blur backgrounds, potentially influencing model evaluation.

Sdxl is excellent for fine-tuning and learning faces or subjects accurately, offering hope for future image generation.

Waifu Diffusion XL has shown promising results in early tests, surpassing previous models in its fine-tuning process.

The development of extensions for Sdxl is slower due to the challenge of finding the right fine-tuning parameters for large models.

Focus, a new GUI for Sdxl, prioritizes optimal operation and generating good images with minimal user control. is recommended for beginners interested in learning about AI, ML, or STEM fields, offering interactive and visual learning experiences.