Build your own Stable Doodle: Sketch to Image
TLDRIn this YouTube tutorial, the creator demonstrates how to build an app that transforms a user's sketch into a detailed image. Utilizing a unified diffusion model and stable diffusion refiner, the app generates high-quality images from simple sketches. The video provides a step-by-step guide on modifying the existing code, cloning the repository, and setting up the app with a sketchpad interface. The result is an app that accurately reflects the user's artistic input, offering a unique blend of creativity and technology.
Takeaways
- 🎨 The video demonstrates how to create an app that turns sketches into images.
- 🐬 The creator is surprised by the quality of the generated images, such as a dolphin sketch.
- 📄 The app uses a paper titled 'UniDiffuse' for controllable visual generation.
- 🔗 The paper and dataset are available on arXiv, and the code is open-source.
- 💻 The video focuses on modifying the app to use a sketch pad instead of an image uploader.
- 🖥️ The process involves using a stable diffusion model called 'Stable Diffusion Refiner'.
- 🖌️ The sketch pad allows users to draw on a black background with white pixels.
- 🔄 The sketch is inverted to match the model's expected input format.
- 📸 The app generates images from the sketch and refines them using the stable diffusion model.
- 🌐 The demo is accessible through a web browser, and the code will be available for viewers.
- 🌟 The video concludes with a live demo where the creator draws a starfish and generates images from it.
Q & A
What is the main topic of the video?
-The video is about creating an app that can generate images from sketches using a unified diffusion model.
What is the purpose of the paper mentioned in the video?
-The paper discusses a unified diffusion model for controllable visual generation, which is used as the basis for the app demonstrated in the video.
What does the dataset used in the app contain?
-The dataset contains K different tasks, each denoted by training pairs that include a language prompt, task instruction, and a visual condition.
How does the app handle the user's sketch?
-The app takes the user's sketch, inverts it to match the required format, and then uses it as a visual condition to generate an image.
What is the role of the Stable Diffusion refiner in the app?
-The Stable Diffusion refiner is used to enhance the quality and resolution of the generated images by refining the output from the diffusion model.
How does the video creator modify the original app code?
-The video creator clones the repository, modifies the app.py file, and integrates the Stable Diffusion refiner to improve the output image quality.
What is the significance of inverting the image in the app's process?
-Inverting the image is necessary because the sketch pad has a black background with white sketches, and the model requires the opposite for processing.
What is the purpose of the 'process_sketch' function in the app?
-The 'process_sketch' function takes the input image, converts it to an array, and prepares it for the diffusion model by inverting the colors.
How does the video creator handle the generation of multiple images based on the sketch and prompt?
-The creator generates multiple images using the 'examples' section of the code, which stores the results in a list and allows the user to view different samples.
What changes were made to the demo section of the app?
-The demo section was modified to include a sketchpad instead of an image uploader, and the results are displayed in a gallery with options to view both the original and refined images.
What is the final step the viewer needs to take to use the app?
-The final step is to run the app using 'python app.py' in the terminal, which will launch the demo and allow the user to draw a sketch and generate images.
Outlines
🎨 Creating a Sketch-Based App
The speaker introduces a YouTube tutorial on developing an application that generates images based on user sketches. They demonstrate the app's functionality by drawing a dolphin and showing how the app produces a matching image. The project is based on a paper titled 'A Unified Diffusion Model for Controllable Visual Generation in the Wild,' which is publicly available for review. The speaker mentions that the dataset and code are open-source and will be utilized in the tutorial. They plan to modify the app's code to replace the image uploader with a sketch pad and integrate stable diffusion to enhance the output image quality.
💻 Coding and Refining the Sketch-Based App
The tutorial continues with the speaker detailing the coding process for the app. They explain the structure of the original code and their modifications, focusing on the sketch functionality. The speaker clarifies that they are not going into the details of the entire codebase but are instead concentrating on the parts relevant to sketch input. They discuss the process of inverting the sketch image to prepare it for the stable diffusion refiner, which is used to enhance the generated image's quality. The speaker also mentions removing unnecessary functions to streamline the demo. They guide viewers on how to run the demo, which now includes a sketchpad for drawing instead of an image uploader, and how to view the results in a result gallery. The speaker concludes by running the demo, demonstrating the app's ability to generate images from a sketch of a starfish, and compares the original and refined outputs.
Mindmap
Keywords
Stable Doodle
Sketch
Stable Diffusion
Hugging Face
SDXL Refiner
Diffusers
App.py
Sketchpad
Image-to-Image Pipeline
Python
Highlights
Introduction to creating an app that generates images from sketches.
Demonstration of the app's ability to create a dolphin image that matches a sketch.
Explanation of the unified diffusion model for controllable visual generation.
Mention of the paper and dataset used for the app's development.
Overview of the data set containing K different tasks with training pairs.
Description of the process involving language prompts, task instructions, and visual conditions.
The code and dataset are open-source and available for exploration.
Hugging Face Spaces provides a demo for different image conditions.
Idea to replace the image uploader with a sketch pad for the app.
Plan to use the sketch pad output with Stable Diffusion to enhance the image.
Instructions on cloning the repository and modifying the app.py.
Details on using the output from the sketch pad through Stable Diffusion's refiner.
Explanation of the Stable Diffusion image-to-image pipeline from Hugging Face Diffusers.
Process of inverting the sketch pad image to match the model's input requirements.
Removal of unnecessary functions to streamline the demo.
Description of the examples and how they are stored in the result image list.
Modifications to the demo section to include a sketchpad instead of image upload.
Final steps to launch the demo and view the results in the browser.
Demonstration of drawing a starfish and generating images from the sketch.
Comparison of the original image output and the refined image from Stable Diffusion.
Conclusion and call to action for viewers to like, subscribe, and share the video.