Build your own Stable Doodle: Sketch to Image

Abhishek Thakur
28 Jul 202309:26

TLDRIn this video tutorial, the creator demonstrates how to build an app that transforms sketches into high-quality images using a unified diffusion model. The process involves utilizing a dataset with training pairs, a language prompt, task instruction, and visual condition to generate images. The creator simplifies the process by replacing the image uploader with a sketch pad and refining the output with stable diffusion for enhanced results. The video includes a walkthrough of the code, a modified demo, and a live demonstration of drawing a starfish and generating refined images from it.

Takeaways

  • 🎨 The video demonstrates how to create an app that generates images from sketches.
  • 🐬 The creator shows an example of drawing a dolphin and generating an image that matches the sketch.
  • 📚 The app is based on a paper titled 'A Unified Diffusion Model for Controllable Visual Generation in the Wild'.
  • 🔍 The data set used in the paper contains different tasks with training pairs of text prompts, task instructions, and visual conditions.
  • 📈 The code for the app is open-source and available on a platform called Hugging Face Spaces.
  • 🖌️ The creator suggests replacing the image uploader with a sketch pad for the app.
  • 🤖 The app uses a stable diffusion refiner to enhance the quality of the generated images.
  • 👀 The video provides a walkthrough of the code and its modifications.
  • 🛠️ The creator modified the original app.py to include a sketch function and removed unnecessary functions for the demo.
  • 📹 The final app allows users to draw a sketch and generate a refined image through a web interface.
  • 🎥 The video concludes with a demonstration of drawing a starfish and generating two versions of the image, one original and one refined.

Q & A

  • What is the main topic of the video?

    -The main topic of the video is about creating an app that can generate images based on user-drawn sketches, similar to the way the presenter drew a dolphin.

  • What is the paper 'paper uni control' about?

    -The paper 'paper uni control' is about a unified diffusion model for controllable visual generation in the wild, which contains a dataset with different tasks and training pairs for generating visual content based on text prompts and task instructions.

  • How can viewers access the paper and the code used in the video?

    -The paper is available on arXiv and the code is open-source, which means viewers can access it by looking at the provided links or repositories mentioned in the video.

  • What is the role of the Hugging Face Spaces demo in the video?

    -The Hugging Face Spaces demo is used as a reference for the presenter's app creation process, showcasing different image conditions and how users can upload images and write prompts to generate new images.

  • How does the presenter modify the existing code to create the sketch-based app?

    -The presenter clones the repository, modifies the app.py file, and focuses on the sketch part of the code. They also integrate a stable diffusion refiner to enhance the output image quality.

  • What is the significance of the stable diffusion image-to-image pipeline?

    -The stable diffusion image-to-image pipeline is used to refine the output image generated by the sketch-based app, improving its quality and resolution.

  • How does the presenter handle the inversion of the sketch image?

    -The presenter inverts the sketch image by subtracting each pixel value from 255, as the sketch pad provides images with a black background and white drawn pixels, which needs to be reversed for the processing.

  • What is the purpose of the 'result image list' in the code?

    -The 'result image list' stores the generated images based on the prompt and sketch. The first image from this list is used for further processing and demonstration in the app.

  • How does the presenter modify the demo section of the app?

    -The presenter replaces the image uploader with a sketchpad, simplifies the interface to focus on sketch input, and modifies the demo to display results from both the original code and the refined output.

  • What are the additional options available in the app demo?

    -The app demo includes advanced options for users to tweak, although the presenter suggests that the default settings work well for most sketches and encourages users to explore these options for better results.

  • How long does it take for the app to load and start running?

    -The app takes a few seconds to load once everything is set up. However, the initial setup, which involves downloading all the models, can take a longer time, especially for first-time users.

Outlines

00:00

🎨 Introduction to App Development for Sketch-Based Image Generation

The video begins with the creator welcoming viewers to their YouTube channel and introducing the topic of the day: creating an app that generates images based on user sketches. The creator shares their surprise at the quality of the generated images, using a dolphin sketch as an example. They explain that the app will utilize a unified diffusion model for controllable visual generation and mention that the paper on this model is available on arXiv. The creator also notes that the dataset, code, and a demo on Hugging Face Spaces are accessible for further exploration. The plan is to modify the existing code to replace the image uploader with a sketch pad and integrate a stable diffusion refiner for enhanced output quality.

05:02

📝 Coding and Customization of the Sketch-Based Image Generation App

In this paragraph, the creator dives into the technical aspects of the app development. They discuss the structure of the dataset used for training, which includes text prompts, task instructions, and visual conditions. The creator mentions that they will be using the code directly and will not be coding much in this session. They explain their approach to modifying the existing app by cloning the repository and altering the app.py file. The focus is on the sketch part of the app, where an input image and prompt are used to generate an image, which is then refined using the stable diffusion image-to-image pipeline. The creator provides a brief overview of the process, including the inversion of the sketch's pixel values to prepare it for the diffusion refiner. They also mention the removal of unnecessary functions for this demo and encourage viewers to explore the original code for a complete understanding.

🚀 Running the Demo and Viewing the Results

The creator proceeds to the examples section, where they demonstrate the app's functionality. They explain that the app generates a number of images based on the user's sketch and prompt, with the results stored in a list. The first image from the list is taken and further refined using the SDExcel refiner. The creator shares their modifications to the demo section, converting the image uploader to a sketchpad with a specific resolution and RGB mode. They discuss the options available for users to customize their sketches and the results. The creator then launches the demo, showing the process of drawing a starfish and running the app to produce both the original and refined images. They compare the two, noting the improved quality and resolution of the refined image. The video concludes with the creator thanking the viewers for watching and encouraging them to like, subscribe, and share the video.

Mindmap

Keywords

💡YouTube channel

A YouTube channel is a public webpage where individuals or organizations can upload and share videos on various topics. In the context of the video, it is the platform where the creator is hosting their tutorial on building an app for sketch-based image generation.

💡App creation

App creation refers to the process of designing and developing a software application for users to perform specific tasks or activities. In this video, the main focus is on teaching viewers how to create an app that can generate images from user-drawn sketches.

💡Sketch recognition

Sketch recognition is a technology that interprets and understands hand-drawn sketches, converting them into digital information that can be used by software. In the video, sketch recognition is essential for the app to identify the user's input sketch and generate an appropriate image.

💡Unified Diffusion Model

A Unified Diffusion Model is a machine learning model that uses a diffusion process to generate images or perform other visual tasks. It is trained on datasets with various tasks and can produce outputs based on prompts and visual conditions. In the video, this model is used to generate images from text prompts and sketches.

💡Hugging Face Spaces

Hugging Face Spaces is a platform that hosts machine learning models and provides demos for users to interact with these models. It allows developers and users to explore various AI applications without having to set up their own infrastructure. In the video, the creator mentions using a demo on Hugging Face Spaces as a starting point for their app.

💡Stable Diffusion

Stable Diffusion is a type of generative model that uses diffusion processes to create high-quality images from textual descriptions or other inputs. It refines and enhances the initial output to produce more realistic and detailed images. In the video, Stable Diffusion is used to improve the quality of the images generated from sketches.

💡Code modification

Code modification involves making changes to existing software code to achieve new functionalities or improve existing ones. In the video, the creator modifies the original code of a demo to adapt it for their specific app that generates images from sketches.

💡Visual generation

Visual generation refers to the process of creating visual content, such as images or videos, using computational models. It is a key aspect of AI and machine learning, where models are trained to generate new visual data based on input data. In the video, visual generation is the main goal, with the app creating images from textual prompts and sketches.

💡Image refinement

Image refinement is the process of improving the quality or details of an image, often using AI or machine learning techniques. It can involve adjusting contrast, sharpness, or other visual elements to create a more polished result. In the video, image refinement is achieved through the use of Stable Diffusion.

💡Demo launch

A demo launch refers to the process of running a demonstration version of a software application to showcase its features and functionality. It allows users to interact with the app and see it in action without the need to install or download it. In the video, the creator launches a demo of their app for viewers to see how it works.

Highlights

The video demonstrates how to create an app that generates images from sketches.

The app uses a unified diffusion model for controllable visual generation.

The paper and code for the model are publicly available on the internet.

The model is trained on a dataset containing different tasks and training pairs.

The video shows the process of drawing a dolphin and generating a corresponding image.

The app can generate different kinds of dolphins based on the sketch and prompt.

A demo of the model is available on Hugging Face Spaces.

The video creator proposes replacing the image uploader with a sketch pad for the app.

The output from the sketch pad is refined using Stable Diffusion to improve quality.

The video provides a walkthrough of the code used for the app.

The app's code is modified to work with a sketch input instead of an image upload.

The video explains how the sketch is inverted to create the correct input for the model.

The final output is a combination of the original sketch-based image and the refined image.

The video includes a demo section where viewers can draw and see the generated images.

The app is launched by running a Python script, and the process is detailed in the video.

The video concludes with the creator drawing a starfish and showcasing the generated images.

The video encourages viewers to like, subscribe, and share if they enjoyed the content.