The Open Source KING is BACK. Stability's NEW AI Image Generator!

MattVidPro AI
13 Feb 202418:49

TLDRStability AI introduces Stable Cascade, an open-source AI image generation model that offers impressive results with faster inference times and cheaper training compared to previous models. The model, built on a new architecture, achieves a higher compression factor while maintaining image quality, making it competitive with other leading AI models. Despite its current non-commercial license, the potential for commercial use in the future adds to its appeal, promising to democratize AI technology further.


  • ๐Ÿš€ Stability AI has released a new AI image generation model called Stable Cascade, which is an open-source software.
  • ๐ŸŒŸ The new model is competitive with existing models like Dolly 3 and Mid Journey, offering high-quality image generation.
  • ๐Ÿ“ˆ Stable Cascade uses a smaller latent space, which results in faster inference times and cheaper training.
  • ๐ŸŽจ The model is capable of generating images with properly spelled and displayed text, improving upon previous versions.
  • ๐Ÿ”ข It achieves a compression factor of 42, significantly better than the previous stable diffusion models.
  • ๐Ÿ’ก The architecture of Stable Cascade is based on the Worin architecture, which allows for high compression without losing image quality.
  • ๐Ÿ“– Known extensions like fine-tuning, control net, and IP adapter (LCM) are possible with this method.
  • ๐Ÿ“Š The model has shown to be more efficient than its predecessors, with faster generation times and better prompt alignment.
  • ๐ŸŒ The codebase for Stable Cascade is available on GitHub, and the model can be run locally using the Pinocchio app.
  • ๐Ÿ”ง Users can experiment with various settings and prompts to fine-tune the model and achieve desired results.
  • ๐ŸŽ‰ The open-source nature of Stable Cascade is expected to democratize AI technology and encourage further innovation in the field.

Q & A

  • What is the main topic of the video transcript?

    -The main topic of the video transcript is the introduction and discussion of a new AI image generation model called Stable Cascade, developed by Stability AI.

  • How does the new Stable Cascade model differ from previous models like Stable Diffusion and Stable Diffusion XL?

    -Stable Cascade differs from previous models in its architecture and efficiency. It uses a smaller latent space, which allows for faster inference times and cheaper training, while still maintaining high-quality image generation. It also has a higher compression factor, enabling it to encode a 1024x1024 image into a 24x24 space with crisp reconstructions.

  • What is the significance of Stable AI releasing their models as open source?

    -The significance of releasing models as open source is that it allows for wider accessibility and collaboration. It promotes the democratization of AI technology, enabling more people to use, modify, and build upon the existing models, which can lead to rapid advancements and innovations in the field.

  • What are some of the features and capabilities of the Stable Cascade model mentioned in the transcript?

    -The Stable Cascade model is capable of text-image generation, cinematic photos, and anthropomorphic character rendering. It also supports extensions like fine-tuning, control net, and IP adapter (Laura). Additionally, it offers image variation, image-to-image generation, and a control net notebook for inpainting and outpainting functionalities.

  • How does the video transcript compare the performance of Stable Cascade with other models like Dolly 3 and Mid Journey?

    -The transcript compares the models based on prompt alignment, aesthetic quality, and generation time. It suggests that while Stable Cascade may not always outperform Dolly 3 or Mid Journey in terms of realism and aesthetic appeal, it offers competitive results and has the advantage of being open source and free to use.

  • What is the Worin architecture mentioned in the transcript?

    -The Worin architecture is the underlying structure of the Stable Cascade model. It is a different approach from traditional Stable Diffusion models, focusing on a smaller latent space for increased efficiency and faster generation times without sacrificing image quality.

  • How can users access and experiment with the Stable Cascade model?

    -Users can access the Stable Cascade model through various platforms. There is an unofficial Hugging Face demo available, and the model can also be run locally using a one-click launcher through the Pinocchio app. Additionally, the GitHub codebase for Stable Cascade provides training and inference scripts for more involved users.

  • What is the current licensing situation for the Stable Cascade model?

    -As of the time of the transcript, the Stable Cascade model's code is released under the MIT license, allowing for commercial use, modification, and distribution. However, the weights on Hugging Face are under a non-commercial research community license agreement. The CEO of Stability AI has indicated that once the model is refined, it will be released under a commercial use license that is free and accessible to all.

  • What are the implications of the Stable Cascade model being open source for the AI community?

    -The open-source nature of the Stable Cascade model means that it can be freely accessed, modified, and built upon by the AI community. This can lead to rapid innovation, as developers can create custom versions of the model or integrate it into their own applications. It also fosters a collaborative environment that can accelerate the advancement of AI technology.

  • What is the role of negative prompting in the Stable Cascade model?

    -Negative prompting is a technique used to refine the output of AI image generation models, including Stable Cascade. By adding negative prompts, users can guide the model to avoid generating certain elements or to correct errors, thereby improving the accuracy and relevance of the generated images.

  • How does the video transcript address concerns about the commercial viability of the Stable Cascade model?

    -The transcript acknowledges that while the Stable Cascade model is not initially released with commercial terms, the CEO of Stability AI has indicated that it will eventually be released under a commercial use license. This suggests that the model will become accessible for commercial use in the future, while currently being available for free for non-commercial purposes.



๐Ÿš€ Introduction to Stable Cascade AI Model

The paragraph introduces the Stable Cascade, a new AI image generation model by Stability AI, the company behind Stable Diffusion. The model is noted for its high-quality, realistic, and detailed images, and its ability to properly display and spell text within the generated images. It is highlighted as an open-source release, allowing for wider accessibility and potential for customization and extension, such as fine-tuning and control net integration. The Worin architecture is mentioned as the basis for the model's efficiency, with a significant compression factor that enables faster inference times and cheaper training, without compromising on image quality.


๐ŸŒ Open Source and Community Engagement

This paragraph emphasizes the open-source nature of the Stable Cascade model and its implications for the AI community. It mentions the MIT license under which the code is released and the non-commercial research community license for the model weights. The CEO of Stability AI clarifies that while the initial releases are non-commercial, a commercial use license is anticipated in the future. The paragraph also discusses the availability of various features such as image variation, image-to-image generation, and control net notebook, which are accessible for free. Additionally, it highlights the potential for the model to compete with other prominent AI models like Dolly 3 and Mid Journey.


๐ŸŽจ Comparative Analysis with Other AI Models

The focus of this paragraph is on the comparison of Stable Cascade with other AI models like Dolly 3 and Mid Journey. It discusses the quality of image generation and the model's ability to understand and execute complex prompts. The paragraph provides examples of different prompts and how Stable Cascade performs in comparison to Dolly 3 and Mid Journey, noting that while there are areas where Stable Cascade could improve, its open-source nature gives it a significant advantage in terms of adaptability and potential for improvement by the community. The paragraph also touches on the unique aspects of the model, such as its ability to generate images of famous people and handle challenging prompts.


๐Ÿ› ๏ธ Customization and Future Prospects

The final paragraph discusses the customization options and future prospects of the Stable Cascade model. It mentions the availability of the model on platforms like Hugging Face and Pinocchio, allowing users to run it locally and explore its functionalities. The paragraph also highlights the community's response to the model, showcasing examples of custom creations and the excitement around open-source AI models. While acknowledging that the model may not currently surpass Dolly 3 or Mid Journey in all aspects, the paragraph emphasizes the significance of having an open-source, freely accessible model that can drive innovation and democratize AI technology.



๐Ÿ’กAI image generation

AI image generation refers to the process where artificial intelligence algorithms are utilized to create visual content or images based on given inputs or prompts. In the context of the video, this technology is demonstrated through the introduction of Stability AI's new model, which generates realistic and detailed images, setting the stage for the discussion on the capabilities and potential of AI in the field of image creation.

๐Ÿ’กStable Cascade

Stable Cascade is the name of the new AI image generation model released by Stability AI. It is noted for its efficiency and high-quality image generation capabilities, which are attributed to its unique architecture and smaller latent space. The model's open-source nature is highlighted, emphasizing its potential to democratize AI technology and encourage further innovation.

๐Ÿ’กOpen source

Open source refers to a type of software licensing where the source code is made publicly available, allowing anyone to view, use, modify, and distribute the software freely. In the video, the emphasis on Stability AI's open-source approach underscores the company's commitment to fostering community engagement and collaborative development in AI technologies.

๐Ÿ’กLatent space

Latent space is a term in machine learning that refers to the underlying, often multidimensional, space where the data points exist before being transformed or mapped into a different space for analysis or visualization. In AI image generation, a smaller latent space can lead to faster inference times and cheaper training, as highlighted by the video's discussion on Stable Cascade's architecture.

๐Ÿ’กCompression factor

Compression factor is a measure of how much data is reduced during the compression process. In the context of AI image generation, a higher compression factor indicates a more efficient encoding of images, which can lead to faster processing times and reduced computational resources needed for training.


Fine-tuning is the process of making small adjustments to a machine learning model to improve its performance on a specific task or dataset. In AI image generation, fine-tuning can involve tweaking parameters or adding additional training data to make the model generate images that better align with desired outcomes.


Inference in the context of machine learning and AI refers to the process of using a trained model to make predictions or generate new data. For AI image generation, inference involves running input through the model to produce an output, such as an image, based on the learned patterns and relationships within the training data.

๐Ÿ’กPrompt alignment

Prompt alignment refers to the degree to which the output of an AI model, such as an image, accurately reflects the content and intent of the input prompt provided by the user. High prompt alignment indicates that the AI has successfully understood and executed the user's request.

๐Ÿ’กAesthetic quality

Aesthetic quality pertains to the visual appeal or beauty of an object, in this case, AI-generated images. It is a subjective measure that can vary from person to person, but generally reflects how pleasing or artistic the image is, often based on factors like composition, color, and detail.

๐Ÿ’กControl net notebook

A control net notebook is a tool or feature within AI image generation platforms that allows users to exercise more control over the generation process, often through inpainting and outpainting functionalities. These tools enable users to edit or extend existing images in a way that blends seamlessly with the original content.

๐Ÿ’กSuper resolution

Super resolution is a technique in image processing that aims to increase the resolution of an image while maintaining or improving its quality. In the context of AI, this can involve upscaling lower resolution images to higher resolutions through the use of machine learning algorithms.


Stability AI releases a new AI image generation model called Stable Cascade.

Stable Cascade is different from typical Stable Diffusion and Stable Diffusion XL models.

The new model generates very realistic and detailed images with properly spelled and displayed text.

Stable Cascade is open source, with its GitHub codebase available for public use.

The model is built upon a different architecture called the Worin architecture.

Stable Cascade achieves a compression factor of 42, allowing for faster inference and cheaper training.

The text conditional model is trained in a highly compressed latent space.

Stable Cascade is more efficient and cost-effective compared to previous versions of Stable Diffusion.

The model supports known extensions like fine-tuning, control net, and IP adapter LCM.

Stable Cascade has better prompt alignment than other models like SDXL Turbo and Playground V2.

The aesthetic quality of Stable Cascade is impressive, though opinions may vary.

Stable Cascade focuses on the efficiency of the model, featuring faster inference times despite a larger model size.

The model can generate images in various ways, including text-image, cinematic photo, and image variation.

Stable Cascade offers inpainting and out painting functionality, as well as super resolution.

The model allows users to train on their own images and has image reconstruction capabilities.

Stable AI's CEO, Emad, confirms that the model will eventually be released under a commercial use license.

There are unofficial Hugging Face demos available for users to try Stable Cascade.

Stable Cascade's open-source nature has the potential to significantly impact the AI art generation market.