Stable Cascade released Within 24 Hours! A New Better And Faster Diffusion Model!

Summary

The video discusses the latest AI diffusion model, Stable Cascade, released by Stability AI. The model is built on the Versatile architecture, which allows for faster training with smaller pixel images and produces high-quality images. It supports Latent Control Net IP and LCM, and has been compared favorably to other models in terms of prompt alignment and aesthetic quality. The video demonstrates the model's ability to handle complex text prompts and generate detailed images, showcasing its potential for future AI animations. However, it is currently for research purposes only and not yet available for commercial use.

Takeaways

• 🚀 Stable Cascade is a new AI diffusion model released by Stability AI, showcasing rapid advancements in AI development.
• 🌟 The model is built upon the Verchin architecture, which allows for faster training with smaller pixel images, leading to more efficient image generation.
• 📈 Stable Cascade uses a 24x24 pixel encoding, which is 42 times smaller in training data compared to traditional stable diffusions, enhancing processing speed.
• 🔍 The model supports Latent Control Net (LCN) and LCM, offering more control over image generation and potentially enabling advanced features like face swapping.
• 🔗 A demo page is available for testing the Stable Cascade model, allowing users to experiment with the new diffusion model's capabilities.
• 📝 The model separates the image generation process into three stages: latent generator, latent decoder, and refinement, improving the quality and detail of the final image.
• 🎨 Evaluations show that Stable Cascade outperforms other models in prompt alignment and aesthetic quality, offering better image recognition and handling of multiple elements in text prompts.
• 📊 The model introduces advanced options such as prior guidance scale, prior inference steps, and decoder guidance scale, providing users with more control over the image generation process.
• 📸 Users can input text prompts in a more natural language manner, which the model handles effectively, generating images that closely align with the input prompts.
• 🚫 It's important to note that Stable Cascade is not yet available for commercial purposes and is intended for research and testing at this stage.
• 🔄 The model's capabilities suggest potential future applications in AI animations, offering higher quality and more detailed images compared to current models.

Q & A

What is the name of the new AI diffusion model discussed in the transcript?

The new AI diffusion model discussed is called 'Stable Cascade'.

Which company developed the Stable Cascade AI model?

The Stable Cascade AI model was developed by Stability AI.

What is the basis for the Stable Cascade model's architecture?

The Stable Cascade model is built upon the Verchin architecture.

What is the advantage of using a smaller pixel size for the encoder training in the Stable Cascade model?

Using a smaller pixel size for the encoder training allows for faster processing and a reduction in training data size, which is 42 times smaller compared to traditional stable diffusions.

How does Stable Cascade support image generation with text input?

Stable Cascade separates the image generation process into three stages: latent generator, latent decoder, and refinement. It uses text input to generate brief ideas of the image in the latent generator stage, decodes it into pixel representations in the latent decoder stage, and refines the objects in the final stage to produce the full image.

What is the significance of the ControlNet and LCM support in Stable Cascade?

Support for ControlNet and LCM allows for more precise control over facial identity and other elements during the image generation process, including the ability to handle face swap features within the model.

How does Stable Cascade compare to previous models in terms of prompt alignment and aesthetic quality?

Stable Cascade outperforms older models in prompt alignment and has a better aesthetic quality score than most, except for Playground version 2, which has a slightly higher score.

What is the current status of Stable Cascade's compatibility with web UI systems like Automatic1111 or Comy UI?

As of the time of the transcript, Stable Cascade has not been officially released for support in Automatic1111 or Comy UI. However, updates may come in the future to support these systems.

What are the advanced options available for users in the Stable Cascade demo page?

The advanced options include negative prompts, seed numbers for image generation, width and height settings, prior guidance scale, prior inference steps, and decoder guidance scale.

How does Stable Cascade handle multiple elements in a text prompt for image generation?

Stable Cascade handles multiple elements of a text prompt effectively, generating images that incorporate all the principles of the prompt, unlike some previous models that struggled with multiple element handling.

What is the current intended purpose of the Stable Cascade AI model?

As of the time of the transcript, Stable Cascade is intended for research purposes and not yet for commercial use.

What is the significance of the demo page for Stable Cascade on Hugging Face?

The demo page on Hugging Face allows users to test the Stable Cascade model, explore its capabilities, and see the results of image generation based on various text prompts.

Outlines

00:00

🚀 Introduction to Stable Cascade: The New AI Diffusion Model

The video script introduces Stable Cascade, a recently released AI diffusion model by Stability AI. It discusses the rapid development in AI, with new models being released frequently. The presenter highlights the model's foundation on the Versatile architecture, which allows for faster training with smaller pixel images, leading to more efficient image generation. The model also supports advanced features like Laura control net IP adapter and LCM, indicating its potential for integration with various UI systems. A new demo page is mentioned for testing the model, and the presenter expresses enthusiasm about exploring the technical background and capabilities of Stable Cascade.

05:00

📈 Evaluating Stable Cascade's Performance and Features

This paragraph delves into the technical performance of Stable Cascade, comparing it with other models like Playground version 2 and SDXL Turbo. It emphasizes the model's superior prompt alignment and aesthetic quality. The presenter discusses the model's three-stage image generation process, which includes a latent generator, latent decoder, and a refinement stage. The benefits of using smaller pixels for encoding are highlighted, along with the model's ability to handle multiple elements from text prompts effectively. The paragraph also mentions additional features like control net for face identity, super resolution for image upscaling, and the potential for training the model with various objects. A live demonstration of the model using a natural language prompt is provided, showcasing its ability to generate detailed and aligned images.

10:01

🌐 Exploring Stable Cascade's Demo and GitHub Resources

The presenter shares links to the Stable Cascade demo page and its corresponding GitHub page, inviting viewers to explore and test the model. It is mentioned that the model is not yet available for commercial use but is intended for research purposes. The paragraph includes a discussion about the model's advanced options, such as negative prompts, image dimensions, and new parameters like prior guidance scale and inference steps. The presenter conducts another test using the model with different prompts, demonstrating its ability to generate images with complex details and actions. The limitations regarding the model's commercial use and the need for potential updates to UI systems for compatibility are also discussed.

15:02

🎬 Potential Applications and Future Prospects of Stable Cascade

The final paragraph speculates on the potential applications of Stable Cascade in creating AI animations with higher quality than current models. The presenter expresses excitement about the new model and encourages viewers to try it out. They also mention their intention to cover the Stable Video Diffusions update in a future video. The video concludes with a note of inspiration and a farewell, promising to see the audience in the next video.

Keywords

💡AI Diffusion Model

An AI diffusion model is a type of machine learning model that is used to generate images or videos from textual descriptions. In the context of the video, the AI diffusion model is a significant development in AI technology, with new models being released frequently. The video discusses the 'stable Cascade', a new AI diffusion model by Stability AI, which is built upon the Verchin architecture and is capable of faster training and image generation.

💡Stability AI

Stability AI is a company that is mentioned in the script as the creator of the 'stable Cascade' AI model. The company is at the forefront of AI development, particularly in the field of diffusion models for image generation. Their work is showcased in the video as a significant advancement in the technology, with the stable Cascade model demonstrating improved performance over previous models.

💡Verchin Architecture

The Verchin architecture is referenced in the script as the foundational structure upon which the stable Cascade model is built. It is a design that allows for faster training of diffusion models with smaller pixel images, leading to more efficient image generation. The architecture is significant to the video's narrative as it contributes to the improved capabilities of the stable Cascade model.

💡Image Generation Process

The image generation process is the method by which AI models create images from textual prompts. The video outlines that the stable Cascade model separates this process into three stages: latent generator, latent decoder, and refinement. Each stage plays a crucial role in converting text prompts into detailed images, with the stable Cascade model demonstrating enhanced performance in this process.

💡Text Prompt

A text prompt is a textual description used as input for AI models to generate images. The video emphasizes the evolution from traditional text prompts to more natural language prompts, which the stable Cascade model handles effectively. The text prompt is central to the theme of the video as it directly influences the output of the AI-generated images.

💡ControlNet

ControlNet is mentioned in the script as a feature supported by the stable Cascade model, allowing for control over specific aspects of the generated image, such as facial identity. This feature is significant as it provides users with more control over the image generation process and contributes to the model's ability to produce high-quality images.

💡Super Resolution

Super resolution is a technique used to enhance the quality of images by increasing their resolution. In the context of the video, the stable Cascade model incorporates super resolution to upscale images, resulting in more detailed and refined AI-generated images. This feature is highlighted as a key advantage of the model.

💡Demo Page

The demo page is an interactive platform where users can test out the stable Cascade model. The video script discusses the author's experience using the demo page to generate images with the new model. It serves as a practical example of how the model can be utilized and is a central component in demonstrating the model's capabilities.

💡GitHub Page

The GitHub page is a repository where the code for the stable Cascade model is made available. It is mentioned in the script as a resource for those interested in the technical aspects of the model or who wish to run the demo page locally. The GitHub page is significant as it provides transparency into the development of the model and allows for community contributions.

💡Prompt Alignment

Prompt alignment refers to how well an AI model can generate images that match the textual description provided by the user. The video emphasizes that the stable Cascade model excels in prompt alignment, effectively handling multiple elements within a text prompt to create images that closely resemble the user's description.

💡Aesthetic Quality

Aesthetic quality pertains to the visual appeal and artistic value of an image. The video script discusses the stable Cascade model's performance in aesthetic quality, noting that it surpasses other diffusion models in tests. This is a critical aspect when evaluating the success of an AI-generated image, as it speaks to the model's ability to produce visually pleasing results.

Highlights

Stable Cascade is a new AI diffusion model released by Stability AI, built on the Versatile architecture.

The model can train faster with smaller 24x24 pixel images compared to traditional 128x128 pixel images.

It uses 42 times less training data compared to Stable Diffusion 1.5, allowing faster processing even on lower-end GPUs.

The model has three stages - latent generator, latent decoder, and image refinement - using text prompts to generate images.

Stable Cascade outperforms Stable Diffusion 1.5 and SDXL in prompt alignment and aesthetic quality.

The model supports facial control, identity swapping, and advanced control net features.

It also has super resolution capabilities for more detailed image generation.

Stability AI has released a demo page for testing the Stable Cascade model.

The model handles multiple elements in text prompts better than previous Stable Diffusion models.

Users can input prompts in a more natural language style, unlike the older comma-separated style.

The demo page allows users to adjust various settings like negative prompts, image size, and inference steps.

The model is not yet available for commercial use, only for research purposes.

The model can generate high-quality images with detailed elements and actions based on complex text prompts.

The model's image recognition capabilities surpass those of Stable Diffusion 1.5 and SDXL.

The model could potentially be used for creating AI animations with higher quality than current models.

Stability AI may release updates in the future to make the model compatible with web UIs like Automatic1111 and Comfy UI.

Transcripts

let's talk about stable Cascade the new

AI diffusion model just

released so AI have been going really

fast in the development and everything

new AI models release every day look at

hugging face they have everything

listing in here that you see the metav

voice IO well I'm going to talk about

this in uh the large language model

Channel soon and then scroll down into

here I see well stability AI have stable

video diffusions 1.1 well I was going to

do this but then when I scroll up a

little

bit they showed stability AI stable

Cascade and I saw this is like not even

a days ago and 16 hours ago and then I

was checking out this one and I say okay

forget about stable video diffusions 1.1

updates and let's do this

one because when I saw this one they

said this model is built up on the ver

Chen architecture and I have mentioned

this diffusion model previously in my

YouTube channel which is here this is

the

sausage and where this name I search it

is actually a German language of a

sausage and well that's how I came up

with the thumbnails of this videos and

then I saw this is very interesting that

stable diffusion newer model from

stability AI they are creating a new

diffusions model using the vers chin and

then we had talked about this

architecture before it is able to train

diffusions model faster speed with

smaller pixels image and you are able to

produce sdxl standard size image like

this one we have 1024x 1024 image but

then their encoding of this architecture

is using 24x 24 PX eels instead of that

it is 42 times smaller training data

compared with traditional stable

diffusions 1.5 128 by 128 pixels and it

is even faster than sdxl because well

why not right you do something newer

model of course it's going to be

performed better than the older AI

models and one thing is really good

about this model stable Cascade created

by stability AI they are also supporting

for Laura control net IP adapter and LCM

like oh my goodness this is insane if

there's new updates for automatic 1111

or comy UI any web UI system that will

support stable diffusions I believe

later on we will have an update that can

support this stable Cascade AI models to

run image Generations on those system

and one of the very good news about that

is that they have a new demo page that

we will be testing out this model and

right now they have not officially

released any support in auto automatic

11 or com vui at this moment they just

updated today within just 24 hours so

I'm going to just say okay forget about

stable video diffusions update and let's

do this one first so this model let's go

through their overview and some

technical backgrounds about this one

first of all you see this model stable

Cascade they are separating with three

stages of image generation process which

we call it the latent generator and the

latent generator in stage C they

generate this using the text your input

text that means your text prompt

generating the brief ideas of the image

and then they pass it to the Stage B to

do the latent decoder so that it allows

the AI to put those pixels those little

little do pixels put it back into this

whole objects and then through these

objects they can be refined tuned in

stage a and you will be getting a full

image of your result so within this one

they are using better I would say better

performance than stable diffusions since

they are using a smaller size pixels for

their encoder training and then the

processing data is smaller size that is

like 42 times compared with the

traditional stable diffusion so that is

really Advantage for us to processing

faster even you have lower-end GPU

graphic card or high-end graphic cards

both of them are able to have advantage

to generate image faster and one of the

really good thing that I saw in here

they have the evaluations you got the

prom alignment and the aesthetic quality

they have compared with playground

version 2 and sdxl turbo and sdxl and

then the version version 2 so in the

prompt alignment of them are suppressing

those older model that currently on the

market and then in the aesthetic quality

the playground 2 version 2 is a little

bit higher score than the stable Cascade

but it is way better than other three

diffusions model in here as they test in

the this is like the benchmarking result

from their testing phase so let's go to

their demo page in hugging

phase right now we have this page now I

will share the links of this hugging

face demo page and also the model card

in here as well and and also they have

GitHub page that is talking about the

same thing of what we saw in the hugging

face model card the same information is

in here and you can check this out as

well and also they have more details

about the text prompt that you are going

to input that is not like those stable

diffusions 1.5 stylus text prompt and

this is more like a natural language

manner of input prompts for creating a

new image in this new model and also

they have control net here as you can

see you can control the face the face

identity and then well if you have the

face identity that means they have

already handled the face swap features

within the model I will believe that is

so and then they have the candy's

control net that is going to just like

other stable diffusion control net that

we used to do and then the super

resolutions that means they have

something like an upscaling for making

your image more details and more

refinement on all the detail small part

of your AI image and then you can easily

train your lorus with any objects for

example they have a dog of this and then

they train with this image and they can

reproduce this image with a space suit

of the dog wearing a space suit and then

the image recognitions are well I can

say it's better than stable diffusion

1.5 or sdxl because their training is

they have more image training for their

their models without this principle it

has already suppressed the image

recognition of the older stable

diffusions model okay so let's go to the

demo page right here let's try this out

now I have tried one time with a very

simple prompt in here I say the

playground an old man walking with his

grandson holding hand and sunset time

now this is not like the old days the

traditional text prompts in stable

diffusions 1.5 as you can see we are

using a more natural language sentence

almost like a sentence to create an

image like this now as I can see the

image in here is well it's pretty nice

let's go to a new tab and we can check

out the full size of it now as you can

see all these prompts in here it does

generating into the image already

there's a grandson and then the old man

is holding hands together in a

playground and then the sunset time

basically all the principle of my

prompts is already appear on this image

and it does really well to handle

multiple elements of a text prompt

rather than in stable diffusions 1.5 or

even

sdxl sometimes you cannot do multiple

element handling which means they are

not in here they have set the prompt

alignment which means they have not did

that quite good in sdxl and even the

older one the SD 1.5 but then in here

it's done really well right and then you

can see in here they have the advanced

options that you can of course you put

negative prompts you can generate seat

numbers that is very typical for all AI

models especially for image generation

models and then you can set your width

and height in here and that is the same

size of sdxl by default of this AI model

and then numbers of image and then this

one is kind of a new thing for us as a

stable diffusions user the prior guide

idance scale and then the prior

inference steps and then the decoder

guidance scale this is something and

also the lastly in here the inference

steps this is something that we don't

have in stable diffusions well the steps

we can classify this one can be the

sampling steps like how much you want to

set in 25 step or 30 step Etc but then

the other two from this decoder scales

and then the decoder steps in here it is

something that we don't have in stable

diff fusions currently so I guess if

they have to implement this model into

comfy UI or automatic 1111 they have to

create a new notes in there or a new

input data area for us to set these two

parameters in automatic 1111 as well so

I'm waiting if they have an update about

this model can be compatible with both

automatic or comy UI but at this moment

right now we are able to test the stable

Cascade in this demo page of hugging

face and the GitHub page in here allows

you to download the coding on the top of

here you can download this code and this

is also the same demo page in here but

you can run it in locally but I guess it

is not the point for us to download this

demo page locally using the GitHub page

project instead just enjoy and try in

this GitHub demo page at this moment and

let's wait for the updates if they are

supporting in other web UI like

automatic 1111 or comy UI then we can

fully enjoy this model using in those

system right so let's try another

example in here that using their default

text I will say this is a pretty cool

thing like a city of Los Angeles this

one and let's try

this okay so here we have the result

here that is kind of funny thing you're

putting something that is not realistic

but it is in realistic styles of Los

Angeles Street and you see all these

details of the street and then of the

concrete on the top here see all those

Mark they have did very detail on this

and it looks pretty good and let's try

some prompts that not by default in here

uh let's say uh you know in previous

videos I have tests about the versen

diffusions model I have test John Wick

and let's try with this one John Wick in

cyberpunk and let's try in diffusion

Cascade so let's say John Wick John Wick

closeup shot

okay actually let's not going to do the

traditional text Palm let's do something

like John Wick in Disco clapping places

he hold pistol ready to shoot the place

with cyberpunk Leon

light okay let's try this one like more

natural language prompts that is not

like those one keyword and comma one

keyword and then another comma those

table diffusions 1.5 text prompt Styles

hopefully it will generate something for

me

and there you go more clear and

hopefully there is something and let's

see the full view of

this well the eyes is not kind of clear

at this moment but we can see okay

there's like the Assassin's ring and

he's showing very detail of that and

then the wash but then John Wick is

carrying the wash in other side and

actually inside the hand wrist of the

watch should be facing inside I should

say but it does doing something

realistic kind of like everything in

here is following my prom really well

like in a disco clubbing Pace holding a

pistol ready to shoot so the action of

John Wick is ready to shooting the

pistol and then you see the Cyber Punk

Leon light is all over here so I can say

this is a pass but then the eyes of this

we might need a refinder to do that if

we need to enhance this image let's say

if let's let's try with this problem

again with more content and I would say

let's fix the eyes okay with John Wick

picture with clear face and eyes just do

that okay just add one more content in

here let's hope that it can help for our

character face with better

quality

now one thing I have to mention about

this AI model is that it is not for

commercial purpose yet okay maybe one

day you can purchase this AI models for

the license for commercial purpose but

right now we are just doing it for

research purpose okay so another one

here yeah we have a better phas more

clear and similar Styles I would say

well the pistol is kind of awkward in

this direction Direction well if you

guys have played with Firearms before

and you would know that the wrists of

this and then the angles of the pistol

pointing is kind of awkward way it

should be more pointing in the center of

the character instead of pointing

outward of the character but oh well I

would still give it a pass for this

right this style compared with the

previous one the purely trained one chai

diffusions model the older diffusion

model of this sausage model's name is

always giving me the close upshot of a

character but then in the stable Cascade

they have given me more element and

actions of the characters there is more

content in the generate image within

this sort of prompts so I would say yes

their quality have suppressed the one

child version to already a lot I mean it

should say a lot and then of course they

are suppressing the sdxl a lot as well

and I can see that if we are able to use

this model in the future and we can make

AI animations using this model instead

of SD 1.5 or

sdxl and of course we can do way better

quality than what we have in today in AI

elements so it I hope you guys enjoyed

these videos have a quick test I just

did a very fast videos quick fast videos

about this newer models I really want to

do it today to share it with you guys

and um yeah maybe the stable video

diffusions uh newer update 1.1 I would

do it next time in other videos but then

I hope you guys can get inspired of this

new models stable Cascade and then try

it out this is a very exciting news for

me and I hope you guys do so I will see

you guys in the next videos and have a

nice day

bye