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What is DALL·E 2? Art Meets AI

April 8, 2023


DALL·E 2 has taken the world by storm as a groundbreaking AI image generator that creates art from text descriptions, edits existing images, and generates new images with unprecedented accuracy and realism. In this comprehensive guide, we will explore what is DALL·E 2, how it works, its potential impact on creative jobs, and how you can use it to generate stunning images.

Introducing DALL·E 2: The AI Art Generator

Launched in April 2022, DALL·E 2 is an AI art generator created by OpenAI. It allows users to generate images from text descriptions, upload images to create variations, and even outpaint images beyond their original borders. The platform has garnered attention due to the accuracy and realism of the images it creates, but there are also concerns about potential misuse and copyright infringement.

what is dall e 2

To use DALL·E 2, users need to create an account and receive 50 free credits. The interface is browser-based, and users type a description of the image they want to generate. DALL·E 2 then attempts to create four 1024x1024 images based on the prompt, but users might have to tweak the prompt for desired results.

Tips for Generating Images with DALL·E 2

To achieve better results, users should provide detailed prompts, specifying styles of art, camera angles, lighting details, or other relevant information. DALL·E 2 might struggle with requests for images of multiple subjects, so users can create separate images and edit them together if necessary. To assist users in creating effective prompts, Guy Parsons has created a DALL·E 2 prompt book.

Competing AI Art Generators

Although DALL·E 2 is widely popular, there are competing AI art generators, such as Artbreeder-collages, Stable Diffusion, and Midjourney. However, DALL·E 2 seems to excel at generating more photorealistic images.

DALL·E 2: Publicly Accessible and Commercially Usable

After five months of limited access, DALL·E 2 is now publicly available. OpenAI has transitioned to a credit-based model, offering new users 50 free credits and 15 free credits per month thereafter. Additional credits can be purchased at $15 for 115 credits.

DALL·E 2 now allows commercial use of the images generated on its platform, including selling, reprinting, and using them on merchandise. However, copyright implications of training an AI model on existing images remain a concern.

Mitigating Bias and Toxicity in DALL·E 2

OpenAI has implemented policy changes and made advancements in mitigating bias and toxicity in the images generated by DALL·E 2. It has improved the representation of the diversity of the world's population in the images and restricted the platform from accepting image uploads containing realistic human faces or public figures' likenesses.

OpenAI prohibits the use of DALL·E 2 to create harmful images and employs both automated and human monitoring systems to prevent misuse. Images created by DALL·E 2 contain a signature row of colored squares at the bottom right corner, which can be removed per DALL·E 2's terms.

How DALL·E 2 Works: The Technology Behind the Magic

DALL·E 2 works as AI for graphic design that inputs a text prompt into a text encoder, which maps the prompt to a representation space. A model called the prior maps the text encoding to an image encoding that captures the prompt's semantic information. Finally, an image decoder stochastically generates an image representing the semantic information.

what is dall e 2

The link between textual and visual representations in DALL·E 2 is learned by another OpenAI model called CLIP (Contrastive Language-Image Pre-training). CLIP is trained on hundreds of millions of images and their associated captions. The training objective is to maximize the cosine similarity between correct encoded image/caption pairs and minimize the cosine similarity between incorrect pairs.

After training, CLIP is frozen, and DALL·E 2 moves on to learning to reverse the image encoding process. OpenAI employs a modified version of another model, GLIDE, to perform this image generation. GLIDE learns to invert the image encoding process in order to stochastically decode CLIP image embeddings using a Diffusion Model.

Diffusion Models are thermodynamics-inspired models that generate data by reversing a gradual noising process. GLIDE extends the core concept of Diffusion Models by augmenting the training process with additional textual information, resulting in text-conditional image generation.

DALL·E 2 uses a modified GLIDE model that incorporates projected CLIP text embeddings in two ways: adding the CLIP text embeddings to GLIDE's existing timestep embedding, and creating four extra tokens of context. The modified GLIDE model generates semantically consistent images conditioned on CLIP image encodings.

The model called the prior maps from the text encodings of image captions to the image encodings of their corresponding images. DALL·E 2 experiments with both Autoregressive Models and Diffusion Models for the prior, with the Diffusion Model being more computationally efficient.

In summary, DALL·E 2 demonstrates the power of Diffusion Models in deep learning and highlights the need for using natural language to train state-of-the-art models. Additionally, it reaffirms the position of Transformers as the leading models for web-scale datasets due to their parallelizability.

Integrating DALL·E 2 into Apps and Products

Developers can integrate DALL·E 2 into their apps and products using the API. For example, Microsoft is using DALL·E 2 for its new graphic design app called Designer and integrating it into Bing and Microsoft Edge with Image Creator.

DALL·E 2 Alternatives

While DALL·E 2 is a powerful tool, there are free alternatives such as Art Breeder Collage, Craiyon (formerly DALL-E Mini), and Stable Diffusion. However, DALL·E 2 stands out for its commercial usability and the quality of images it generates.

The Future of DALL·E 2 and Its Impact on Creative Jobs

Impact of DALL-E 2 on Creative Jobs

DALL·E 2 has been controversial due to potential misuse, such as generating fake news, violent images, or non-consensual porn. OpenAI has taken precautions to mitigate these risks and continues to learn from real-world use. There are also concerns about the impact of AI logo design and image generators like DALL·E 2 on creative jobs.

As AI image generation technology continues to advance, the implications for the creative industry will become clearer. However, the potential for collaboration between AI tools like DALL·E 2 and human creativity should not be underestimated, as these tools can augment and inspire new avenues for artistic expression.

In conclusion, DALL·E 2 has set a new standard for AI image generation, and its potential applications are vast. By understanding what is DALL·E 2 and learning how to use it effectively, marketers, business owners, and creatives can unlock new possibilities for their work and explore the ever-evolving world of AI-powered art.

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