How to Use Stable Diffusion Outpainting [Step-by-Step Guide]
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Outpainting is the process of extending an existing image beyond its original borders or canvas size. Stable Diffusion, a powerful text-to-image AI model, can be leveraged for outpainting tasks, allowing users to expand their digital artwork seamlessly. This technology allows your favorite artwork or photos to extend seamlessly, revealing new, coherent details that blend perfectly with the original content. By using advanced algorithms and deep learning, Stable Diffusion Outpainting ensures every added pixel feels natural, enhancing visual storytelling and digital creativity. Below, we have crafted a detailed tutorial on how to use Stable Diffusion Outpainting. Read on!
What is Outpainting
Outpainting is a cutting-edge technique in the field of artificial intelligence and image processing that allows for the extension of an image beyond its original borders. This method utilizes advanced algorithms and deep learning models to generate new content that seamlessly blends with the existing parts of the image. The newly created areas are designed to match the style, color, and detail of the original image, making it appear as though the image was always larger than it initially was.
Outpainting is particularly useful in various applications such as enhancing artwork, filling in missing parts of photographs, and creating expansive, immersive visuals in digital media. It enables artists and designers to push creative boundaries by adding coherent extensions to their work, thereby providing a more comprehensive visual experience. This technology also finds applications in fields like virtual reality, game design, and advertising, where extended visuals can significantly enhance user engagement and experience.
Principles of Stable Diffusion Outpainting
Stable Diffusion Outpainting operates on several core principles to ensure the effective and seamless extension of images. The primary principle is contextual consistency, where the new pixels generated must blend perfectly with the original image, maintaining a coherent visual narrative. Realism is achieved through the use of dilated convolutions within the encoder-decoder structure of the generator network, which increases the local receptive fields of neurons and allows them to access more information, resulting in highly realistic extensions.
Additionally, the discriminator network employs local discriminators, each focusing on a specific region of the image. This localized approach ensures that the generated extensions are contextually appropriate and realistic for each specific area. The outputs from these local discriminators are then combined through a concatenation layer to produce a final, cohesive result. Scalability and efficiency are also crucial principles, enabling the technique to handle images of various sizes and resolutions without losing quality, and allowing for practical real-time applications and large-scale image processing tasks. These principles together ensure that Stable Diffusion Outpainting produces high-quality, seamless extensions that enhance the visual narrative and realism of original images.
Below, we are going to walk you through how to use the outpainting feature in Stable Diffusion AUTOMATIC1111 GUI to expand your images.
How to Outpaint with Stable Diffusion
Step 0. Setup
- Environment: Ensure you have a working setup of Stable Diffusion. Using the AUTOMATIC1111 GUI is highly recommended for its user-friendly interface.
- Load Model: Load your preferred Stable Diffusion model in the interface.
Step 1. Upload the image to AUTOMATIC1111
- If your image was generated by the AUTOMATIC1111 GUI, the generation parameters are stored in the PNG file's metadata. Go to the PNG Info tab in the AUTOMATIC1111 interface, drag and drop your image from local storage onto the canvas area in this tab, and the generation parameters will automatically appear on the right side. Click on Send to img2img to transfer the image along with its generation parameters to the img2img tab. The image and its prompt will now appear in the img2img sub-tab of the img2img tab.
- If your starting image was not created by AUTOMATIC1111, navigate directly to the img2img tab. Upload your image to the img2img canvas by dragging and dropping or using the upload button, and write a prompt that can accurately describe the image and its style. You can also use the Interrogate CLIP button to automatically generate a descriptive prompt for your image, and be sure to review the prompt to ensure it accurately describes the image.
Ensure that you have the prompt, image size, and other necessary settings populated in the img2img tab.
Step 2. Set parameters for Stable Diffusion Outpainting
If using a PNG image, the size should be set correctly automatically using the PNG Info, and you can use the ruler icon (Auto detect size from img2img) for assistance. For a custom image, set the shorter side to the native resolution of the model you use, and adjust the longer side accordingly to maintain the aspect ratio of the input image. Set the Denoising Strength to 0.6 as a starting point, and you can experiment with different values to achieve the most desired results. For other parameters, I leave them to default, and here is what I use:
- Seed: -1
- Sampling Method: DPM++ 2M Karras
- Sampling Steps: 30
- Batch size: 1
These settings should provide a good starting point for outpainting with Stable Diffusion. You can adjust the parameters based on your specific requirements and desired output.
Step 3. Choose the Outpainting script and generate
Now find the Script dropdown menu, where you can find two outpainting scripts (Outpainting mk2 and Poor man's outpainting), and choose the one you prefer to use. Here I use 'Poor man's outpainting'.
For 'Masked content', choose 'fill'. For outpainting direction, it is recommended to outpaint one direction at a time, and here I pick the 'down' direction. For other parameters and prompts, I leave them to default.
Once set, click on 'Generate' and the outpainting process shall begin.
Step 4. Refine and repeat
Repeat the process to expand the other sides of your image, and adjust the prompt if needed for desired elements for your extension. If you are not satisfied with the outpainting result, you can:
- Use the inpaint brush tool to refill the masked areas that you are not satisfied with.
- Tweak the denoising strength, lowering values will make less drastic changes while increasing values will allow mode modification to the inpainted areas.
After each outpainting iteration, review the result and make any necessary adjustments to the prompt, outpainting direction, or other parameters.
Step 5. Finalize and save the expanded image
Once you are satisfied with the outpainting result, click save to save the expanded image from Stable Diffusion.
Conclusion
Follow the steps above to outpaint your images using Stable Diffusion! If you wish to further enhance your outpainting works, check out Aiarty Image Enhancer, a generative AI image enhancement software to denoise, deblur, generate more details, and upscale images.
Upscale and Enhance Stable Diffusion Images Easily with AI
- One-stop AI image enhancer, denoiser, deblurer, and upscaler.
- Use deep learning tech to reconstruct images with improved quality.
- Upscale your AI artworks to stunning 16K/32K resolution.
- Deliver Hollywood-level resolution without losing quality.
- Friendly to users at all levels, and support both GPU/CPU processing.
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Kistent Waung is a quality-driven writer who has extensive knowledge of the multimedia market. She started her journey managing and supporting content at Digiarty Software, where she shared her expertise on a range of multimedia products spanning AI and codec trends, software reviews, and comparisons. She's now a writer for aiarty.com, with a focus on AI technology.
