Krita AI Trick to Removing Objects With AI diffusion Plugin
TLDRIn this informative video, the creator explores the use of Krita's AI diffusion plugin to remove unwanted objects from images. They introduce a technique involving the use of a static image uploaded to their cloud, which viewers can download to aid in the process. The video demonstrates how to manipulate the AI to achieve desired results, comparing the effectiveness of different models and settings. The creator also discusses the benefits of using Krita over Adobe, highlighting its open-source nature and the potential for future improvements with community support.
Takeaways
- 🎨 **Using Krita and AI Diffusion:** The video discusses how to use Krita, a free and open-source painting program, with an AI diffusion plugin to manipulate images, specifically for removing objects from the background.
- 📚 **Static Diffusion Technique:** The presenter introduces a method involving a static diffusion pattern to trick the AI into generating a unique image based on the static pattern, which can be used to replace unwanted elements in a photo.
- 🔍 **Model Selection:** It's important to choose the right AI model suited for the task at hand, such as photo restoration or nature scenes, as different models are trained on different datasets.
- 🖥️ **Hardware Limitations:** The video mentions the limitations of older hardware like the GTX 1070 and how it affects the resolution and speed of rendering AI images.
- 📉 **Resolution Adjustment:** To accommodate slower systems, the presenter suggests lowering the resolution during the rendering process and then upscaling the image afterward to maintain detail.
- 🖌️ **Paint Bucket Tool:** The Paint Bucket tool is used with a static diffusion pattern to flood a new layer with static colors, which the AI then uses to generate a new image section.
- 🔄 **AI Diffusion Process:** The AI diffusion process starts with a static image and builds a unique image each time, sometimes getting caught in loops, which is likened to the old magic eye images.
- ✅ **Blending Results:** The method described can result in exceptionally well-blended images, effectively removing the unwanted object while maintaining the integrity of the surrounding scene.
- 🔧 **Tool Options and Adjustments:** The video emphasizes the need to adjust tool options and padding settings for optimal results when using the AI diffusion plugin.
- 🌐 **Open Source Advantage:** The presenter advocates for the use of open-source tools like Krita and the AI diffusion plugin due to their cost-effectiveness and the potential for community-driven improvements.
- ❓ **Reflections and Water Effects:** There's a curiosity expressed about the effectiveness of the method when applied to reflective water, comparing it to Adobe's capabilities, and noting that it could be a hit-and-miss depending on the model used.
Q & A
What is the main topic of the video?
-The main topic of the video is about using Krita with an AI diffusion plugin to remove objects from an image's background.
What is the AI diffusion process like?
-The AI diffusion process involves creating a static image and using that static image to build a unique image each time. Sometimes the process can get caught in a loop.
How does the speaker suggest manipulating the AI in Krita?
-The speaker suggests uploading a static diffusion file to the cloud and using it within Krita to manipulate the AI into removing objects from the image.
What is the advantage of using a lower resolution for the AI diffusion process?
-Using a lower resolution allows the renders to run faster, especially on older or slower systems. The image can always be upscaled afterward without losing much detail, thanks to AI upscaling.
Why does the speaker prefer Krita's AI diffusion over Adobe?
-The speaker prefers Krita's AI diffusion because it allows for the selection of specific models, offers a live preview, and has a user interface similar to Adobe or Corel. Additionally, it is free and open source.
What is the significance of the static pattern file shared by the speaker?
-The static pattern file is used to flood a new layer with static colors in Krita, which helps the AI diffusion process to interpret the static as a new render and attempt to build an image out of it.
How can the AI diffusion process be adjusted for better blending?
-The AI diffusion process can be adjusted by modifying the padding and blending settings in the tool options to achieve better integration with the surrounding image.
What is the speaker's opinion on Adobe's AI model?
-The speaker believes that Adobe's AI model has a better language model associated with the diffused images, which can lead to more accurate interpretations of user commands like 'remove'.
How does the speaker suggest choosing the right model for the AI diffusion process?
-The speaker suggests looking for models that are suited to the style of the artwork being worked on, such as models focused on photo restoration for detailed work or models trained on nature for outdoor scenes.
What is the potential future development the speaker hopes to see in Krita's AI diffusion?
-The speaker hopes for the development of more tools within the AI diffusion plugin, such as an automatic flood feature and scripted steps for rendering, which could be implemented as AI removal tools.
Why is it important to provide feedback to the creators of open-source tools?
-Providing feedback, especially positive feedback, to the creators of open-source tools is important because it motivates them to continue developing and improving the tools, leading to better functionality and more features for users.
Outlines
🎥 Introduction to AI Manipulation in Stream Tabulous
The speaker begins by welcoming viewers to Stream Tabulous and expresses an intention to explore AI manipulation techniques, specifically within the Cryer platform. The focus is on learning how to influence AI diffusion to perform certain tasks, such as adding or removing objects within an image. The speaker references a previous video where they discussed adding elements like a cat to an image and plans to delve into background object removal in this session. They mention the lack of a dedicated AI removal tool but are hopeful that they can find a workaround. The introduction also includes a brief mention of the speaker's background and their approach to exploring and utilizing AI in image manipulation.
📁 Navigating Cryer Data File and Uploading Static Diffusion Pattern
In this segment, the speaker guides viewers on how to navigate the Cryer data file and introduces a new method involving the use of a static diffusion pattern. They explain that they will upload a file to the cloud which viewers can download to enhance their AI usage experience. The speaker provides an analogy of the static diffusion pattern to a magic eye picture, where the AI builds a unique image from a static base image. They also discuss the limitations of different AI models and their resolutions, comparing the capabilities of Adobe's AI model with Cryer's. The speaker emphasizes the benefits of Cryer, such as model selection, live preview, and the graphic user interface, and expresses gratitude for the open-source community providing this tool for free.
🖼️ Adjusting Resolution and Removing Objects with AI
The speaker proceeds to demonstrate how to adjust the image resolution to accommodate their older system and discusses the intention to remove a vehicle from the image. They explain that despite their efforts, the AI seems to struggle with the object removal, as it continues to recognize and include the vehicle in the render. The speaker then introduces a new pattern fill, 'static diffusion', which they believe could aid in the manipulation of the AI to achieve the desired result. They guide viewers on how to access and apply this new tool within the Cryer platform, emphasizing the importance of the static image in influencing the AI's output. The speaker also shares their thoughts on how the AI might interpret and generate images based on the static pattern.
🌳 Selecting Appropriate Models and Adjusting Settings for Nature Scenes
Here, the speaker discusses the importance of selecting the right AI models based on the type of artwork being created. They share their experience with different models, highlighting that certain models are better suited for photo restoration and detailed textures, while others might be more appropriate for natural scenes. The speaker demonstrates how to adjust settings and blend the AI-generated elements with the existing image for a more realistic outcome. They also touch on the potential of future AI models, particularly those trained to understand language better, which could improve the accuracy of object removal and addition in AI-generated images.
💡 Final Thoughts on AI Manipulation and Support for Open Source Projects
In the concluding paragraph, the speaker reflects on their curiosity about the AI's ability to render reflective water and compares it to Adobe's performance. They express a desire to see Cryer and AI diffusion technology become more widely known and used. The speaker promotes the open-source nature of the project, encourages viewers to support the creators of useful plugins, and emphasizes the potential for future development and innovation within the community. They end the video by encouraging viewers to like, share, and subscribe to their content to help support their YouTube channel and increase its visibility.
Mindmap
Keywords
💡Krita AI
💡AI diffusion
💡Background removal
💡Cloud storage
💡Static image
💡Patterns
💡Resolution
💡AI model
💡Open source
💡Adobe
Highlights
Introduction to using Krita AI with the diffusion plugin for object removal in images.
Exploring the limitations of Krita's AI background removal tools and seeking alternative methods.
Utilizing a static image uploaded to the cloud for AI manipulation purposes.
Understanding how AI diffusion works by creating a unique image from a static base.
The comparison between Krita and Adobe in terms of AI capabilities and rendering speed.
Demonstration of the process to install and use the static diffusion pattern in Krita.
Adjusting the resolution of the image to accommodate older hardware and improve rendering speed.
The use of the paint bucket tool with the static diffusion pattern for object removal.
Explanation of the blending and padding settings for better image integration.
The advantage of using Krita for its model selection and live preview features.
Comparison of different AI models and their suitability for various art styles and subjects.
The potential for future improvements in AI models and their integration into Krita.
The recommendation to use models suited for photo restoration for better results.
Discussion on the potential of AI in creating reflective water effects in images.
The importance of supporting open-source projects and the creators behind them.
Encouragement for viewers to engage with and provide feedback on the discussed techniques.