How to do Outpainting without size limits in A1111 Img2Img with ControlNet [Generative Fill w SD]!
TLDRIn this video, Keyboard Alchemist walks through the process of outpainting using ControlNet with Stable Diffusion, expanding an image beyond its borders. He explains how to extend images without size limits, shares tips on avoiding artifacts, and demonstrates techniques for vertical and horizontal outpainting. He also provides insights into managing VRAM constraints, adjusting parameters for optimal results, and performing inpainting to fix image details. By following his steps, viewers can significantly extend their images while maintaining quality, and even break size limits by tweaking configuration settings.
Takeaways
- 🖼️ Outpainting extends an image beyond its borders, allowing you to visualize more of a generated image.
- 🛠️ ControlNet is a useful tool for performing outpainting, providing consistent results without manual adjustments.
- 🔧 Start by adjusting the 'resize mode' to 'resize and fill' to expand the image size and allow for new content generation.
- ⚖️ Increase image dimensions gradually for better results, as large jumps can cause unwanted artifacts like elongated limbs.
- 💡 Adjust positive prompts and use small, incremental size increases to avoid artifacts and keep proper proportions.
- ⏳ If using limited VRAM, increase batch count instead of batch size to speed up the generation process and avoid slowdowns.
- 📏 Use ControlNet’s 'ending control step' to control how much of the process is influenced by ControlNet, with higher values reducing seams and artifacts.
- 📂 To break the max image size limit (2048x2048), adjust the 'ui-config.json' file to set higher max width and height values.
- 🚫 Don’t try to outpaint in both width and height simultaneously. Instead, extend one dimension at a time for optimal results.
- 🖌️ After outpainting, use inpainting to fix minor issues like distorted limbs or floating objects, and consider latent upscaling for a final high-res result.
Q & A
What is outpainting in Stable Diffusion?
-Outpainting refers to extending an image beyond its original borders by generating new content based on the existing image, allowing the creation of a larger scene.
What tools and models are required for outpainting using Stable Diffusion?
-You will need the ControlNet extension and the ControlNet inpainting model to perform outpainting in Stable Diffusion.
Why is it important to incrementally increase image size during outpainting?
-Incrementally increasing the image size avoids artifacts, such as elongated or distorted elements, as the AI needs context to correctly fill in the expanded areas without adding unwanted features.
How can you prevent artifacts when extending an image too much initially?
-To prevent artifacts, you should extend the image gradually, only increasing by a few hundred pixels at a time, allowing the AI to generate coherent content based on the original image.
What is the recommended method for generating multiple outpainted images?
-It is recommended to increase the batch count instead of the batch size when generating multiple images, especially if you have limited VRAM, to avoid slowing down the generation process.
Why does generating more images in one batch take longer with limited VRAM?
-When VRAM is maxed out during batch generation, the system offloads data to RAM, causing slower data transfer between VRAM and RAM, which significantly increases generation time.
What settings should be used for ControlNet during outpainting?
-Enable ControlNet, upload the original image as the control image, select the inpaint option, and choose 'inpaint only + Lama' as the preprocessor. Set the end control step value to 1 for the best results.
How can you bypass the default width and height limits in Stable Diffusion?
-You can modify the 'ui-config.json' file by changing the values for 'image to image/width/maximum' and 'image to image/height/maximum' to your desired maximum dimensions, such as 4096.
Why is it recommended to only outpaint in one direction at a time?
-Outpainting in one direction at a time ensures that Stable Diffusion focuses on generating coherent content in that direction, rather than simply resizing the image, which could lead to poor results.
What can you do if you encounter a CUDA out-of-memory error while generating large images?
-You can add the '--medvram' argument to your command line, which allows Stable Diffusion to better manage memory usage and avoid out-of-memory errors, though it may slow down the generation process.
Outlines
🎨 Introduction to Outpainting with Stable Diffusion
In this paragraph, the speaker introduces the concept of outpainting in Stable Diffusion, which allows users to extend images beyond their original borders. They explain that the goal of the tutorial is to guide viewers through the process of outpainting using ControlNet. The example used is a portrait of a young woman, and the tutorial will cover how to expand it from 512x768 to 2048x2048, breaking the limits of image dimensions.
🔧 Setting Up ControlNet and Preparing the Image
This section details the preparation steps, including ensuring that the ControlNet extension and inpainting model are installed. The speaker advises viewers to refer to a previous video for setup instructions. After loading the portrait into the PNG info section and copying over a positive prompt for the image, they provide an overview of the key settings, such as resizing the image and selecting the sampling method. They also recommend expanding images incrementally to avoid artifacts and explain common issues with extending too quickly.
🖼 Overcoming Artifacts in the Outpainting Process
Here, the speaker shares insights into how AI sometimes elongates parts of the image, such as forearms, when increasing the image height too much in one step. They explain their troubleshooting process, including adjusting the height and prompts to improve the final output. This section emphasizes adapting outpainting strategies to specific images and addresses the importance of context for the AI to properly render extensions like hands. The speaker found success by increasing the height directly to 1600 pixels.
⚡ Optimizing Batch Settings for Faster Image Generation
This paragraph focuses on optimizing the batch count versus batch size to generate images faster. The speaker describes the impact of limited VRAM on image generation and explains that increasing the batch count instead of the batch size can prevent memory issues and reduce generation time. They also discuss key settings like CFG scale and denoising strength while encouraging viewers to experiment with parameters to achieve desired results. ControlNet is used to ensure consistent results without manual intervention.
📏 Tweaking Control Steps to Improve Outpainting Quality
The speaker explains how adjusting the ending control step value can improve the quality of outpainted images by reducing visible seams and artifacts. They walk through a comparison of different values, noting that a value of 1 provides the best results. This section also touches on the importance of balancing control mode and resizing settings, and how to use ControlNet to process images effectively.
🔄 Outpainting in Horizontal and Vertical Directions
In this part, the speaker completes outpainting in the vertical direction and prepares to expand the image horizontally. They advise following the same procedure as the vertical expansion and highlight the importance of adjusting the positive prompt to suit the new background. After experimenting with different models, the speaker settled on a model that best suited their needs and prepared to extend the image to 2048 pixels wide.
📂 Editing the UI Configuration for Maximum Dimensions
The speaker introduces a 'secret' trick to increase the maximum width and height for outpainting beyond 2048 pixels by editing the 'ui-config.json' file in the Stable Diffusion installation folder. They demonstrate how to modify the maximum width and height values to 4096 pixels. This simple yet effective change allows users to surpass the default size limits, enabling larger outpainting projects.
🔄 Finishing the Outpainting Process and Inpainting Fixes
Here, the speaker summarizes the final steps of the outpainting process, expanding the image to 2560x1920 pixels. They provide tips for managing large images, especially if using a limited 8GB VRAM graphics card, and explain how to prevent memory issues by adding a command line argument. After addressing some artifacts with inpainting, they upscale the image to 3072x2304 pixels and make final tweaks, such as editing the woman’s dress and correcting other visual elements.
🎬 Conclusion and Call to Action
In the final paragraph, the speaker wraps up the tutorial by showcasing the final outpainted and inpainted image. They reflect on the fun and creative process of expanding the image and invite viewers to like and subscribe to support the channel. The speaker concludes by encouraging viewers to explore other videos for more inpainting techniques and offers gratitude for their engagement.
Mindmap
Keywords
💡Outpainting
💡ControlNet
💡Stable Diffusion
💡Resize and Fill
💡Sampling Method
💡Batch Count vs Batch Size
💡VRAM
💡CFG Scale
💡Denoising Strength
💡Latent Upscale
Highlights
Introduction to outpainting with Stable Diffusion and ControlNet.
Using outpainting to expand an image beyond its original borders.
Explanation of how ControlNet helps improve the outpainting process.
The process of expanding a 512x768 portrait to a 2048x2048 image.
How to break the maximum width and height limits in Stable Diffusion's img2img function.
Key settings: resize mode, sampling method, and batch size vs batch count.
Advice on increasing image size in small increments to avoid artifacts.
Common issues with AI-generated artifacts, like elongated forearms or unwanted background elements.
Using ControlNet's inpainting model to efficiently generate consistent outpainted images.
A tip on adjusting ControlNet's ending control step to improve image consistency.
Maximizing VRAM usage for faster image generation by adjusting batch count and batch size.
Steps for expanding an image horizontally after vertical outpainting is complete.
The trick to breaking width and height limits by editing the `ui-config.json` file.
How to deal with CUDA out-of-memory errors by using the `--medvram` argument.
Final steps of inpainting to fix minor image flaws and upscaling the completed image.