How to improve 3D people in your renders using AI (in Stable Diffusion) - Tutorial
TLDRIn this tutorial, the presenter from Digital Bunch introduces viewers to the use of Stable Diffusion, an open-source software project for deep learning text-to-image models, in enhancing 3D people in renders. They discuss the importance of staying updated with AI advancements and share their experiences with mixed results. The video guides users through installing Stable Diffusion, using the web interface, and cropping images for processing. It emphasizes selecting the right model for editing, crafting prompts with positive and negative examples, and adjusting settings like resolution, batch size, and noise strength for optimal results. The presenter demonstrates how Stable Diffusion can improve photorealism in renders, even fixing AI-generated people, and invites viewers to share their outcomes and suggestions for future tests.
Takeaways
- π Introduction to Stable Diffusion: The tutorial begins with an introduction to Stable Diffusion, an open-source software project for deep learning text-to-image models.
- π Recent Tests and Feedback: The digital bunch has conducted tests with Stable Diffusion and received amazing feedback from the community, leading to the creation of this tutorial.
- π Selecting the Right Model: Users should select a data model specialized in faces and people, such as 'realistic vision', for editing images involving people.
- ποΈ Editing with Brushes: The interface allows users to select elements to edit using a brush, providing precision and control over the editing process.
- π Crafting Prompts: Effective use of prompts is crucial, with positive and negative prompts guiding the AI in generating desired results while avoiding undesired ones.
- π¨ Fine-Tuning Settings: The tutorial emphasizes the importance of adjusting settings like resolution, batch size, and noise strength to achieve optimal results.
- πΌοΈ Cropping Images: Due to limitations with processing large images, users should crop the area of interest and save it as a separate file before using Stable Diffusion.
- β±οΈ Processing Time: The AI processing time is noted to be approximately 1 minute on a 4070 TI card, computed locally.
- π Comparing Results: The tutorial suggests comparing the generated images to select the best one, and pasting it back into the original visualization for the final result.
- π» AI Limitations: The tutorial acknowledges that AI tools like Stable Diffusion can sometimes produce unexpected or 'creepy' results due to their generative nature.
Q & A
What is the main topic of the tutorial?
-The tutorial is about how to improve 3D people in your renders using AI, specifically with Stable Diffusion.
Who is the presenter of the tutorial?
-The presenter is Dear from the Digital Bunch.
What kind of feedback did the Digital Bunch receive after their tests with Stable Diffusion?
-They received amazing feedback and many people asked for a tutorial on how to use Stable Diffusion.
What is Stable Diffusion?
-Stable Diffusion is an open-source software project that uses deep learning for text-to-image models.
Why is it important to keep an eye on AI developments in the creative industry?
-It's important because AI was not previously thought to impact the creative industry, but now it offers new tools and possibilities for artists.
What is the first step in using Stable Diffusion for editing images?
-The first step is to install Stable Diffusion and use its web interface, which is accessible through a desktop shortcut or a URL.
What is the limitation of Stable Diffusion when processing images?
-Stable Diffusion does not process large images; it requires users to crop the part of the image they are interested in and save it as a separate file.
How does one select the model for editing in Stable Diffusion?
-In the 'Image' tab, under the 'Inpaint' section, users can drag and drop the cropped image and select the elements they want to edit with a brush. They then choose a model that is specialized in the type of editing they want to perform.
What is a positive and negative prompt in Stable Diffusion?
-A positive prompt is a description of the desired outcome, while a negative prompt lists the results that are not wanted. Both should be kept simple and clear.
What is the optimal resolution for the Stable Diffusion model mentioned in the tutorial?
-The optimal resolution is 768 pixels, as it provides the best quality and detail for the final image.
What is the purpose of setting the batch size in Stable Diffusion?
-The batch size determines how many different images Stable Diffusion generates at once, allowing users to choose from multiple options.
What is the significance of the denoising strength setting in Stable Diffusion?
-The denoising strength (usually set between 25 to 45) determines how different the newly generated image will be from the original. A higher value results in a more significant difference.
Outlines
π¨ Introduction to Stable Diffusion in Digital Art Projects
The video begins with the host introducing themselves and their team, the Digital Bunch, and expressing gratitude for the positive feedback on their previous experiments with stable diffusion and artificial intelligence. They announce a tutorial on how to use stable diffusion, an open-source software project for text-to-image models, released in December 2022. The host discusses the importance of staying updated with evolving AI technologies and their impact on the creative industry. The tutorial starts with instructions on installing stable diffusion and using the web interface called Automatic 1111, highlighting its features and options for users.
πΌοΈ Enhancing Images with Stable Diffusion and Photoshop
This paragraph focuses on the process of enhancing images using stable diffusion, particularly when dealing with issues such as processing large images. The host explains how to crop the desired part of an image and save it as a separate file before using stable diffusion. The tutorial continues with instructions on how to use the image-to-image tab in stable diffusion, select the relevant model (e.g., Realistic Vision for faces), and type effective positive and negative prompts to guide the AI in generating the desired output. The host also discusses important settings like masked options, resolution, and denoising strength, and shares their experiences with the results, including tips for selecting the best images and potential limitations of the AI tool.
Mindmap
Keywords
π‘Stable Diffusion
π‘Artificial Intelligence (AI)
π‘Deep Learning
π‘Text-to-Image Model
π‘Photoshop
π‘Cropping
π‘Data Model
π‘Prompt
π‘Noising Strength
π‘Resolution
π‘Batch Size
Highlights
The tutorial demonstrates how to use stable diffusion in 3D renders to improve people's appearance using AI.
Stable diffusion is an open-source software project released in December 2022, focusing on deep learning text to image models.
The digital bunch has been experimenting with stable diffusion, achieving both great and mixed results.
AI was not initially expected to impact the creative industry, but stable diffusion is changing that perspective.
To use stable diffusion, one must install it and use the web interface, which can be initially confusing but offers many features.
For optimal results, crop the image to a smaller size since stable diffusion does not process large images well.
Select a model specialized in faces and people, such as Realistic Vision, for editing people in images.
When typing prompts, include both positive and negative prompts to guide the AI towards the desired outcome.
Define the element to change and use adjectives like 'photorealistic' and 'high quality' in the positive prompt.
In the negative prompt, specify unwanted results with adjectives such as 'anime', 'cartoon', and 'ugly'.
Set the resolution to 768 pixels, which is optimal for the model, and the batch size to four for a quicker selection process.
The denoising strength is crucial; a value between 25 to 45 is recommended for a realistic look without drastic changes.
Stable diffusion can generate four different images from which the best can be chosen, usually taking about 1 minute on a 4070 TI card.
The tool is adept at tweaking clothes and can sometimes produce more realistic results than 3D models.
Fixing people that were already generated by stable diffusion can lead to further improvements in the output.
While stable diffusion is a powerful tool, it can sometimes produce unexpected or 'hallucinated' results, especially with higher denoising values.
The tutorial encourages users to share their experiences and outcomes with stable diffusion for community feedback and improvement.
The digital bunch is excited about the potential of AI in the creative industry and looks forward to further research and development.