Author: Nguyễn Thu Thủy

Struggling to make your content stand out? With Ideogram and ChatGPT, you can create eye-catching visual hooks in just seconds that will captivate your audience.

Ideogram AI is a top-tier generative AI tool, designed to help you create stunning images and artwork using simple prompts.

What makes it exceptional is its ability to excel at:

– Incorporating text into designs

– Adding intricate details

– Delivering crystal-clear visuals

– Offering customizable shapes

Whether you’re a marketer, designer, or content creator, this powerful duo ensures you can craft engaging visuals quickly and effortlessly. With the recent launch of Ideogram Canvas, creating impressive visuals has never been easier or faster.

Ideogram Canvas has solved a challenge I used to face, and it can do the same for you.

[1] Before, I had to download images from Ideogram, then edit, extend, and upscale them using Canva.

[2] Now, with Ideogram Canvas, you can handle all of those tasks directly on the platform. This update streamlines your workflow and saves time by eliminating the need to switch between apps.

It’s a flexible creative board that lets you organize, generate, and edit images in one place.

You can upload your own images or create new ones, then easily edit or combine them with powerful tools like Magic Fill and Extend.

Try out Ideogram Canvas here: https://ideogram.ai/canvas

In this article, you’ll discover how to use ChatGPT and the new Ideogram Canvas to craft stunning visual hooks for your content in just four simple steps.

[Step 1] – Create prompt
Open ChatGPT and use the ‘Ideogram 2.0 Prompt Creator’ GPT.

Navigate to ChatGPT, go to ‘Explore GPTs,’ and search for ‘Ideogram prompts.’ You’ll find a list of available GPTs designed to help you generate prompts for Ideogram.


Now, Select the “Ideogram 2.0 Prompt Creator” GPT.

[Step 2] — Use this prompt template with the GPT

Once you’ve selected the GPT, use the following prompt:

“Please help me generate 5 Ideogram 2.0 prompts to create a unique, visually appealing, and attention-grabbing visual hook cover image for a [Usecase: social media ad, podcast thumbnail, brochure, blog post, etc.] on the topic of [TOPIC: e.g., How to Boost Engagement with Creative Visuals].”

This will provide you with five creative prompts to try in Ideogram. You can then choose the one that stands out the most and aligns with your vision.

Here’s an example:

[Step 3] — Copy the prompts into Ideogram and generate designs  

Now, go tohttps://ideogram.ai/canvas and paste the prompts generated by ChatGPT.Configure the Ideogram settings according to your preferences. Here’s a quick overview of what each setting means:

– Magic Prompt: ON — This feature automatically expands and enhances your input prompt with more descriptive details, helping to create richer, more visually appealing outputs. It makes your prompt “smarter” by adding vivid and expressive elements.

– Prompt — Describe what you want the AI to generate.

– Model — Choose the AI model for image generation. Use the latest 2.0 version for the best results.

– Style — Set the visual style, such as General, Realistic, Design, 3D, or Anime.

– Aspect Ratio — Define the height-to-width ratio. For example, select 1:1 for a 1024px x 1024px image.

– Seed — Control randomness for consistency; using the same seed will produce similar outputs.

– Negative Prompt — Specify any elements or features you don’t want included in the image.

Once everything is set, generate your designs! Here are the outcomes from the prompts I used on Ideogram to create a LinkedIn carousel cover for a post.

[Step 4] — Edit your designs in the new Ideogram Canvas  

After your design is generated, simply select it, click the ‘…’ button, and choose ‘Edit in Canvas’ to make any adjustments or enhancements to the image.”


This is how it look


Here are two amazing features of Ideogram Canvas:

Magic Fill 

This powerful inpainting tool allows you to edit specific areas of an image by replacing objects, adding text, fixing imperfections, or changing backgrounds. With Ideogram Canvas, you can zoom in for high-resolution, detailed edits.

How to use Magic Fill:

1) Select the area you want to edit.

2) Adjust the generation window as needed.

3) Enter a text prompt to guide the tool in making the changes.

Extend 

Extend is an outpainting tool that expands images beyond their original borders while preserving a consistent style. It lets you adjust the composition and aspect ratio, making your image adaptable to any screen size without losing its original essence.

How to use Extend:

1) Adjust the generation window.

2) Enter a text prompt to guide the Extend tool in expanding the image.

Combine Magic Fill and Extend for impressive results.I upgraded to the Pro version of Ideogram Canvas, which is only $8 per month. Along with the subscription, I received 100 complimentary credits to explore all the features, and it’s been a fantastic tool.

You can try out Ideogram Canvas here: https://ideogram.ai/canvas

Reference from @anishsingh20

Llama 3.2 is a new large language model (LLM) from Meta, designed to be smaller and more lightweight compared to Llama 3.1. It includes a range of models in various sizes, such as small and medium-sized vision models (11B and 90B) and lightweight text models (1B and 3B). The 1B and 3B models are specifically designed for use on edge devices and mobile platforms.

Llama 3.1, which was launched last July, is an open-source model with an extremely large parameter count of 405B, making it difficult to deploy on a large scale for widespread use. This challenge led to the development of Llama 3.2.

Llama 3.2’s 1B and 3B models are lightweight AI models specifically designed for mobile devices, which is why they only support text-based tasks. Larger models, on the other hand, are meant to handle more complex processing on cloud servers. Due to the smaller parameter count, the 1B and 3B models can operate directly on-device, capable of handling up to 128K tokens (approximately 96,240 words) for tasks like text summarization, sentence rewriting, and more. Because the processing occurs on-device, it also ensures enhanced data security, as user data remains on their own devices.

Meta’s latest Llama 3.2 models are taking a leap forward in AI technology, especially for mobile and on-device applications. The 1B and 3B models, specifically, are designed to run smoothly on hardware like smartphones or even on SoCs (System on Chips) from Qualcomm, MediaTek, and other ARM-based processors. This opens up new possibilities for bringing advanced AI capabilities directly to your pocket, without needing a powerful server.

Meta revealed that the Llama 3.2 1B and 3B models are actually optimized versions of the larger Llama 3.1 models (8B and 70B). These smaller models are created using a process called “knowledge distillation,” where larger models “teach” the smaller ones. The output of the large models is used as a target during the training of the smaller models. This process adjusts the smaller models’ weights in such a way that they maintain much of the performance of the original larger model. In simple terms, this approach helps the smaller models achieve a higher level of efficiency compared to training them from scratch.

For more complex tasks, Meta has also introduced the larger Llama 3.2 vision models, sized at 11B and 90B. These models not only handle text but also have impressive image-processing capabilities. For example, the mid-sized 11B and 90B models can be applied to tasks like understanding charts and graphs. Businesses can use these models to get deeper insights from sales data, analyzing financial reports, or even automating complex visual tasks that go beyond just text analysis.

With Llama 3.2, Meta is pushing the boundaries of AI, from mobile-optimized, secure, on-device processing to more advanced cloud-based visual intelligence.

In its earlier versions, Llama was primarily focused on processing language (text) data. However, with Llama 3.2, Meta has expanded its capabilities to handle images as well. This transformation required significant architectural changes and the addition of new components to the model. Here’s how Meta made it possible:

1. Introducing an Image Encoder: To enable Llama to process images, Meta added an image encoder to the model. This encoder translates visual data into a form that the language model can understand, effectively bridging the gap between images and text processing.

2. Adding an Adapter: To seamlessly integrate the image encoder with the existing language model, Meta introduced an adapter. This adapter connects the image encoder to the language model using cross-attention layers, which allow the model to combine information from both images and text. Cross-attention helps the model focus on relevant parts of the image while processing related textual information.

3. Training the Adapter: The adapter was trained on paired datasets consisting of images and corresponding text, allowing it to learn how to accurately link visual information to its textual context. This step is crucial for tasks like image captioning, where the model needs to interpret an image and generate a relevant description.

4. Additional Training for Better Visual Understanding: Meta took the model’s training further by feeding it various datasets, including both noisy and high-quality data. This additional training phase ensures that the model becomes proficient at understanding and reasoning about visual content, even in less-than-ideal conditions.

5. Post-Training Optimization: After the training phase, Llama 3.2 underwent optimization using several advanced techniques. One of these involved leveraging synthetic data and a reward model to fine-tune the model’s performance. These strategies help improve the overall quality of the model, allowing it to generate better outputs, especially when dealing with visual information.

With these changes, Meta has evolved Llama from a purely text-based model into a powerful multimodal AI capable of processing both text and images, broadening its potential applications across industries.

When it comes to Llama 3.2’s smaller models, both the 1B and 3B versions show promising results. The Llama 3.2 3B model, in particular, demonstrates impressive performance across a range of tasks, especially on more complex benchmarks such as MMLU, IFEval, GSM8K, and Hellaswag, where it competes favorably against Google’s Gemma 2B IT model.

Even the smaller Llama 3.2 1B model holds its own, showing respectable scores despite its size, which makes it a great option for devices with limited resources. This performance highlights the efficiency of the model, especially for mobile or edge applications where resources are constrained.

Overall, the Llama 3.2 3B model stands out as a small but highly capable language model, with the potential to perform well across a variety of language processing tasks. It’s a testament to how even compact models can achieve excellent results when optimized effectively.

Virtual Try-On is an advanced technology in the field of e-commerce and user experience, particularly in the fashion and beauty industries. This technology allows users to virtually try on products like eyeglasses, hats, jewelry, or makeup directly on their faces using a mobile device or computer.

Key Features of Virtual Try-On:

• Accurate Facial Recognition: The technology uses artificial intelligence and facial recognition algorithms to identify the user’s facial features, adjusting the product to fit perfectly.
• Interactive Viewing: Users can rotate, tilt their heads, or change their viewing angle to see the product from different perspectives, simulating a real-life try-on experience.
• Augmented Reality (AR) Technology: Often combined with AR technology, it overlays the product onto the live image from the camera, creating the impression that the product is truly present on the user’s face.
• Diverse Applications: This technology is not limited to eyewear but also applies to other products like hats, earrings, lipstick, and other makeup items.
• Enhanced Shopping Experience: By allowing users to try products before purchasing, Virtual Try-On helps minimize the risk of buying the wrong item and improves the online shopping experience.

With the rapid development of AI, there has been a fusion of Virtual Try-On technologies. Developers have created interactive spaces that allow users to virtually try on different fashion items using artificial intelligence. This application lets you see how clothes or accessories might look on you by overlaying them onto your image. It’s a tool designed to enhance the online shopping experience, making it easier for you to visualize the product before buying.

How It Works:

1. Upload an Image: Users can upload their photo or use a webcam for the system to scan and recognize their body shape or face.
2. Apply the Product: The system applies virtual products onto the user’s image, allowing them to see how the items would look when worn or used.
3. Customize and Choose: Users can adjust the size, color, and style of the product to see which option best suits their style.

Virtual Try-On aims to reduce uncertainty and boost consumer confidence when shopping online, while also improving the overall shopping experience. Currently, platforms like Github and Hugger Face offer many open-source resources that allow developers to use and advance this technology to create applications serving e-commerce and user experience.

A Simple Practical Example:

Let’s create a product, like a t-shirt with the word “Scuti,” and use this image with a model to generate a promotional image for the product.

Step 1: Upload the desired model’s image.

Step 2: Upload the desired product.

To create this promotional image, we need both the model’s image and the product image. We can use OpenAI’s DALLE 3 model to generate suitable images, then use Hugger Face’s Virtual Try-On to proceed.

Here is the result: the model is now wearing the new product you uploaded. Depending on your creativity, you can design your own products for personal or brand use.

Virtual Try-On là một công nghệ tiên tiến trong lĩnh vực thương mại điện tử và trải nghiệm người dùng, đặc biệt là trong ngành thời trang và làm đẹp. Công nghệ này cho phép người dùng thử nghiệm các sản phẩm như kính mắt, mũ, trang sức, hoặc trang điểm trực tiếp trên khuôn mặt của mình thông qua một thiết bị di động hoặc máy tính.

Các đặc điểm chính của Virtual Try-On:

1. Nhận diện khuôn mặt chính xác: Công nghệ sử dụng trí tuệ nhân tạo và các thuật toán nhận diện khuôn mặt để xác định các đặc điểm khuôn mặt của người dùng, từ đó điều chỉnh sản phẩm để phù hợp hoàn hảo.
2. Tương tác trực quan: Người dùng có thể xoay, nghiêng đầu hoặc thay đổi góc nhìn để xem sản phẩm từ nhiều góc độ khác nhau, giống như đang thử trực tiếp.
3. Công nghệ AR (Augmented Reality): Hugger Face thường kết hợp với công nghệ thực tế tăng cường, giúp chèn sản phẩm vào hình ảnh trực tiếp từ camera, tạo cảm giác sản phẩm thật sự hiện diện trên khuôn mặt.
4. Ứng dụng đa dạng: Công nghệ này không chỉ áp dụng cho kính mắt mà còn cho các sản phẩm khác như mũ, khuyên tai, son môi, và các sản phẩm trang điểm khác.
5. Tăng cường trải nghiệm mua sắm: Bằng cách cho phép người dùng thử trước sản phẩm, Virtual Try-On giúp giảm thiểu rủi ro mua hàng sai lầm và cải thiện trải nghiệm mua sắm trực tuyến.

Hiện nay với sự phát triển mạnh mẽ của AI, đã có những sự kết hợp giữa virtual Try-on. Các nhà phát triển đã tạo ra các không gian tương tác cho phép người dùng thử ảo các mặt hàng thời trang khác nhau bằng trí tuệ nhân tạo. Ứng dụng này cho phép bạn xem quần áo hoặc phụ kiện có thể trông như thế nào trên người bạn bằng cách phủ chúng lên hình ảnh của bạn. Đây là một công cụ được thiết kế để nâng cao trải nghiệm mua sắm trực tuyến, giúp bạn dễ dàng hình dung sản phẩm trước khi mua.

Cách hoạt động:

1. Tải lên hình ảnh: Người dùng có thể tải lên hình ảnh của họ hoặc sử dụng webcam để hệ thống có thể quét và nhận diện hình dáng cơ thể hoặc khuôn mặt của họ.
2. Áp dụng sản phẩm: Hệ thống sẽ áp dụng các sản phẩm ảo lên hình ảnh của người dùng, giúp họ thấy được cách sản phẩm trông như thế nào khi được mặc hoặc sử dụng.
3. Tùy chỉnh và lựa chọn: Người dùng có thể điều chỉnh kích thước, màu sắc, và kiểu dáng của sản phẩm để xem lựa chọn nào phù hợp nhất với phong cách của họ.

Virtual Try-On đều nhằm mục đích làm giảm bớt sự không chắc chắn và tăng cường sự tự tin của người tiêu dùng khi mua sắm trực tuyến, đồng thời cải thiện trải nghiệm mua sắm tổng thể. Hiện tại trên Github, Hugger face hay các nền tảng khác cung cấp nhiều Opensource cho phép những nhà phát triển sử dụng và phát triển công nghệ này tạo ra các ứng dụng nhằm phục vụ lĩnh vực thương mại điện tử và trải nghiệm người dùng.

Dưới đây là một ví dụ thực tế đơn giản: Chúng ta sẽ tạo sản phẩm một chiếc áo phông, có chữ Scuti và sử dụng hình ảnh này với người mẫu để tạo ra hình ảnh quảng cáo cho sản phẩm chỉ với 2 bước cực đơn giản

・Bước 1: Upload hình ảnh người mẫu mong muốn sử dụng sản phẩm.

・Bước 2: Upload sản phẩm mong muốn

Để tạo được hình ảnh quảng cáo này chúng ta cần hình ảnh của người mẫu và hình ảnh sản phẩm. Chúng ta có thể sử dụng model DALLE 3 của OpenAI để tạo các hình ảnh phù hợp với mong muốn. Sau đó sử dụng Virtual try-on Hugger face để có thể thực hiện.

Và dưới đây là kết quả, người mẫu sẽ được thay sản phẩm mới mà bạn đã thực hiện upload. Dựa vào sự sáng tạo của bạn, bạn hoàn toàn có thể tự thiết kế những sản phẩm cho cá nhân và thương hiệu.