How to do Outpainting without size limits in A1111 Img2Img with ControlNet [Generative Fill w SD]!
TLDRThis tutorial by Keyboard Alchemist demonstrates how to perform outpainting in Stable Diffusion using ControlNet. The video covers how to extend images beyond their original borders, turning a 512x768 portrait into a larger image of 2048x2048, and breaking the usual size limits. The process includes setting up ControlNet, adjusting image dimensions, managing VRAM, and fine-tuning with inpainting. Several tips are provided to avoid common issues, such as artifacts and image distortion. By following the tutorial, viewers can efficiently expand their images and achieve seamless, high-quality results.
Takeaways
- 🖼️ Outpainting allows you to extend an image beyond its borders, visualizing more of the picture using Stable Diffusion.
- 🧠 Using ControlNet for outpainting provides better control and consistent results with less manual editing.
- 📏 It's best to incrementally increase the image size by a few hundred pixels at a time to avoid artifacts.
- ⚠️ Increasing the height or width too drastically can lead to errors like elongated body parts or unwanted additions.
- 🖥️ Adjust your settings like batch count instead of batch size if you have limited VRAM to optimize generation time.
- 🧩 The ControlNet in-painting model helps ensure seamless blending between the original image and outpainted areas.
- 🛠️ You can break the default image size limits in Stable Diffusion by modifying the configuration file.
- 🕑 Outpainting in one direction (either height or width) before doing the other yields better results.
- 🎨 After outpainting, in-painting can be used to fix artifacts or adjust specific areas of the image.
- 🔍 When generating larger images, consider using latent upscaling and manage VRAM carefully to avoid memory errors.
Q & A
What is outpainting in Stable Diffusion?
-Outpainting is the process of extending an image beyond its original borders. It allows users to visualize and create additional content that wasn't present in the original image.
Why is ControlNet useful for outpainting?
-ControlNet helps guide the outpainting process by providing context from the original image, ensuring that the extended parts of the image are consistent with the content and style of the existing image.
What are the important settings for successful outpainting?
-Key settings include setting the resize mode to 'resize and fill,' choosing a suitable sampling method and step count (e.g., 50 steps), and adjusting the width and height in small increments to avoid artifacts.
How can you avoid artifacts when outpainting?
-To minimize artifacts, increase the size in small increments and avoid large jumps in dimensions. If artifacts appear, consider using inpainting to refine the affected areas.
What is the benefit of setting the batch count over the batch size?
-Setting the batch count to a higher value instead of the batch size helps manage VRAM usage more efficiently. Generating multiple batches of one image each is faster and less resource-intensive than generating all images in one large batch.
How can you bypass the maximum width and height limits in A1111?
-You can bypass the limits by modifying the 'ui-config.json' file in your Stable Diffusion installation folder. Increase the 'maximum' values for both width and height and save the changes. This allows you to input larger values directly into the fields.
What are the recommended values for CFG scale and denoising strength during outpainting?
-A CFG scale of around 7.5 and a denoising strength of 0.75 are recommended starting points. These can be adjusted based on the specific needs of your image and desired results.
How does the 'ControlNet Ending Step' value affect the image generation?
-The 'Ending Control Step' determines when ControlNet stops influencing the image generation process. Lower values can result in visible seams and artifacts, while higher values (closer to 1) help blend the extended areas seamlessly.
What should you do if you encounter a CUDA out of memory error?
-If you encounter this error, try adding the '--medvram' argument to your command line. This reduces VRAM usage, though it may slow down image generation.
What are some tips for improving outpainting results?
-Some tips include adjusting the positive prompts to suit the desired background, using smaller dimension increases, and testing different models to achieve the desired style and content in the outpainted areas.
Outlines
👋 Introduction to Outpainting with ControlNet
The speaker welcomes viewers to the tutorial on outpainting in Stable Diffusion, using ControlNet. They introduce the concept of outpainting—extending an image beyond its original borders—and explain how it can bring a visualized mental image to life. The tutorial will demonstrate how to expand a 512x768 portrait into a 2048x2048 image and break the size limitations of the tool.
🛠 Setting Up for Outpainting
This section provides an overview of the necessary setup for the outpainting process. It emphasizes the need for the ControlNet extension and inpainting model. The speaker briefly mentions their use of the MagicMix Version 5 model and the steps to prepare prompts, including copying positive prompts for the best results. They also touch on key settings like resize mode, sampling methods, and step parameters.
📏 Managing Image Dimensions and Artifacts
The speaker explains the importance of incrementally increasing image height for better results, using a comparison between different height increases. They highlight potential issues such as elongated forearms caused by improper height extension. They emphasize the need to adapt to the image's composition to avoid artifacts and suggest solutions, including resizing the height in more significant increments to give AI proper context.
⏳ Batch Count vs. Batch Size for Efficient Processing
This section offers valuable advice on using batch count instead of batch size for generating images efficiently. The speaker explains that increasing the batch size can slow down the process by using more VRAM, resulting in data offloading to RAM and causing delays. They recommend adjusting batch count to avoid VRAM limitations, especially for users with limited graphics card memory.
🎨 ControlNet and Image Expansion Settings
Here, the speaker details how to use ControlNet for outpainting. They guide viewers on uploading an image as a reference and selecting settings like the inpainting model and preprocessing method. Important settings such as the 'ending control step' are discussed, with recommendations to keep it at the default value of 1 for seamless results. The section concludes with generating the initial outpainted image.
👀 Hands and Final Adjustments for Vertical Expansion
The speaker focuses on generating multiple outpainted images and selecting the best one, particularly images with properly rendered hands. They describe their iterative process of generating images in batches and making final adjustments, such as increasing the height from 1600 to 1920 pixels while looking for ideal hand placements.
🌅 Outpainting the Image Horizontally
The tutorial shifts focus to outpainting the image horizontally. The speaker walks viewers through changing the width and modifying prompts to suit the background, such as adding 'blue sky' and 'sandy beach.' They mention experimenting with different models to achieve the desired background, and they skip the image generation process due to long render times.
💡 Breaking the Max Width Limit in Stable Diffusion
The speaker explains how to overcome the max width limit of 2048 pixels in Stable Diffusion. They walk viewers through the process of editing the UI configuration file to increase the maximum width and height to 4096 pixels. After adjusting the config file, the speaker demonstrates how to manually input new width values to continue outpainting.
🎛 Final Outpainting Tips and Upscaling
In this section, the speaker concludes the outpainting process, warning against changing both width and height simultaneously, as it results in simple resizing rather than outpainting. They suggest focusing on one direction at a time. The speaker also discusses fixing certain aspects of the image with inpainting, and attempts to upscale the image but encounters a memory error due to their graphics card limitations. The issue is resolved by adjusting the command line to accommodate limited VRAM.
🎉 Final Result and Conclusion
The speaker reveals the final outpainted and upscaled image, having resolved issues with artifacts, dress adjustments, and arm positioning. They conclude the tutorial, expressing enjoyment in the outpainting process, and encourage viewers to like and subscribe to support the channel. The tutorial ends with gratitude and a promise to see viewers in the next video.
Mindmap
Keywords
💡Outpainting
💡Stable Diffusion
💡ControlNet
💡Sampling steps
💡Resize and fill
💡Artifacts
💡CFG Scale
💡Inpainting
💡Batch count vs Batch size
💡VRAM
Highlights
Introduction to outpainting using Stable Diffusion and ControlNet.
Demonstrating how to expand a 512x768 portrait into a 2048x2048 image.
Tips to break the limits of width and height constraints to expand images even further.
Install ControlNet extension and ControlNet inpainting model for best results.
Using 'Resize and Fill' mode to expand images, leveraging edge pixels to generate new content.
Explanation of how increasing image dimensions in smaller increments leads to better results without artifacts.
Challenges when increasing image height leading to elongated features like arms, and tips to avoid these issues.
Use batch count instead of batch size to generate multiple images more efficiently and avoid VRAM limitations.
Setting key parameters like CFG scale, denoising strength, and controlling seed settings for consistent results.
Leveraging ControlNet for efficient outpainting by using the original image as a reference.
Experimenting with different ControlNet settings to reduce artifacts and smooth the seams between original and expanded areas.
Detailed steps for outpainting horizontally after vertical expansion.
Method for bypassing the default width/height limit by modifying the UI configuration file.
Inpainting used to fix artifacts in the final image, such as adjusting a dress and removing floating trees.
Using latent upscaling to enhance final image resolution, and resolving memory issues with limited VRAM.