Meta AI's Make-A-Scene Generates Artwork with Text and Sketches

Make-A-Scene is not “just another Dalle”. The goal of this new model isn’t to allow users to generate random images following text prompt as dalle does — which is really cool — but restricts the user control on the generations.

Instead, Meta wanted to push creative expression forward, merging this text-to-image trend with previous sketch-to-image models, leading to “Make-A-Scene”: a fantastic blend between text and sketch-conditioned image generation. Learn more in the video...

►Read the full article: https://www.louisbouchard.ai/make-a-scene/
►Meta's blog post: https://ai.facebook.com/blog/greater-creative-control-for-ai-image-generation
►Paper: Gafni, O., Polyak, A., Ashual, O., Sheynin, S., Parikh, D. and
Taigman, Y., 2022. Make-a-scene: Scene-based text-to-image generation
with human priors.
►My Newsletter (A new AI application explained weekly to your emails!): https://www.louisbouchard.ai/newsletter/

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this is make a scene it's not just

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another deli the goal of this new model

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isn't to allow users to generate random

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images following text prompt as dali

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does which is really cool but restricts

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the user control on the generations

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instead meta wanted to push creative

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expression forward merging this text to

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image trend with previous sketch to

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image models leading to make a scene a

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fantastic blend between text and sketch

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conditioned image generation this simply

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means that using this new approach you

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can quickly sketch out a cat and write

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what kind of image you would like and

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the image generation process will follow

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both the sketch and the guidance of your

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text it gets us even closer to being

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able to generate the perfect

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illustration we want in a few seconds

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you can see this multimodal generative

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ai method as a daily model with a bit

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more control over the generations since

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it can also take in a quick sketch as

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input this is why we call it multimodal

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since it can take multiple modalities as

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inputs like text and an image a sketch

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in this case compared to delhi which

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only takes text to generate an image

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multi-modal models are something super

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promising especially if we match the

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quality of the results we see online

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since we have more control over the

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results getting closer to a very

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interesting end goal of generating the

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perfect image we have in mind without

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any design skills of course this is

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still in the research state and is an

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exploratory ai research concept it

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doesn't mean what we see isn't

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achievable it just means it will take a

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bit more time to get to the public the

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progress is extremely fast in the field

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and i wouldn't be surprised to see it

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live very shortly or a similar model

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from other people to play with i believe

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such sketch and text-based models are

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even more interesting especially for the

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industry which is why i wanted to cover

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it on my channel even though the results

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are a bit behind those of daily 2 we see

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online and it's not only interesting for

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the industry but for artists too some

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use the sketch feature to generate even

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more unexpected results than what delhi

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could do we can ask it to generate

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something and draw a form that doesn't

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represent the specific thing like

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drawing a jellyfish in a flower shape

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which may not be impossible to have with

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dali but much more complicated without

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sketch guidance as the model will only

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reproduce what it learns from which

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comes from real world images and

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illustrations so the main question is

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how can they guide the generations with

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both text input like delhi and a sketch

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simultaneously and have the model follow

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both guidelines well it's very very

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similar to how delhi works so i won't

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enter too much into the details of a

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generative model as i covered at least

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five different approaches in the past

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two months which you should definitely

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watch if you haven't yet as these models

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like dali 2 or imogen are quite

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fantastic

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typically these models will take

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millions of training examples to learn

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how to generate images from text with

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data in the form of images and their

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captions scraped from the internet here

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during training instead of only relying

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on the caption generating the first

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version of the image and comparing it to

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the actual image and repeating this

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process numerous times with all our

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images we will also feed it a sketch

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what's cool is that the sketches are

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quite easy to produce for training

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simply take a pre-trained network you

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can download online and perform instance

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segmentation for those who wants the

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details they use a free pre-trained vgg

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model on imagenet so a quite small

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network compared to those today super

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accurate and fast producing results like

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this called a segmentation map they

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simply process all their images once and

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get these maps for training the model

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then use this map as well as the caption

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to orient the model to generate the

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initial image at inference time or when

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one of us will use it our sketch will

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replace those maps as i said they used a

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model called vgg to create fake sketches

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for training they use a transformer

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architecture for the image generation

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process which is different from dolly to

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and i invite you to watch the video i

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made introducing transformers for vision

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applications if you'd like more details

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on how it can process and generate

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images this sketch guided transformer is

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the main difference with magazine along

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with not using an image text ranker like

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clip to measure text and image pairs

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which you can also learn about in my

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daily video

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instead all the encoded text and

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segmentation maps are sent to the

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transformer model the model then

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generates the relevant image tokens

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encoded and decoded by the corresponding

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networks mainly to produce the image the

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encoder is used during training to

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calculate the difference between the

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produced and initial image but only the

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decoder is needed to take this

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transformer output and transform it into

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an image

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and voila this is how meta's new model

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is able to take a sketch and text inputs

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and generate a high definition image out

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of it allowing more control over the

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results with great quality

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and as they say it's just the beginning

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of this new kind of ai model the

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approaches will just keep improving both

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in terms of quality and availability for

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the public which is super exciting many

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artists are already using the model for

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their own work as described in meta's

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blog post and i'm excited about when we

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will be able to use it too their

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approach doesn't require any coding

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knowledge only a good sketching hand and

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some prompt engineering which means

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trial and error with the text inputs

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tweaking the formulations and words used

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to produce different and better results

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of course this was just an overview of

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the new make a scene approach and i

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invite you to read the full paper linked

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below for a complete overview of how it

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works i hope you've enjoyed this video

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and i will see you next week with

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another amazing paper

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