【Stable Diffusion】画像から画像を作成するimg2imgの使い方について解説
TLDRLearn how to use 'img2img' in Stable Diffusion to create images with desired features from an existing image. The video explains uploading a reference image, adjusting its size, and entering prompts for quality and style. It also discusses denoising strength for matching the reference, and 'resize and fill' for different image sizes. 'img2img' is ideal for imitating poses and other features, while 'openpose' focuses on poses alone.
Takeaways
- 🎨 Use 'img2img' to generate images with desired poses and features by referencing an existing image.
- 🔍 'txt2img' may not always produce satisfactory results when specific poses or features are required.
- 🌐 Start by accessing 'stable diffusion web ui' and switch from 'txt2img' to 'img2img' for image-based generation.
- 📸 Upload the reference image to capture features such as poses and background.
- 🖼️ Experiment with 'resize to' to adjust the image size, matching or modifying it as needed.
- 🎭 Include a detailed prompt with positive attributes (e.g., 'masterpiece', 'best quality') and negative attributes (e.g., 'worst quality', 'lowres') for better image generation.
- 🔄 Compare the reference image and the generated image to evaluate the similarity and quality.
- 🔢 Adjust 'denoising strength' to strengthen or weaken the influence of the reference image on the generated result.
- 📏 Use 'resize and fill' in 'resize mode' to generate images with different sizes than the reference image.
- 🚀 Explore 'openpose' for generating images that imitate only poses without other features.
- 🌐 Visit 'ai Gene' for more information on AI-generated content and stay updated.
Q & A
What is the main purpose of using 'img2img' in Stable Diffusion?
-The main purpose of using 'img2img' in Stable Diffusion is to generate images from an existing image, preserving specific features such as poses and background characteristics.
How does the 'txt2img' method differ from 'img2img'?
-The 'txt2img' method generates images based on textual descriptions provided in the prompt, while 'img2img' generates images based on an uploaded reference image, ensuring the new image retains certain features of the original.
What should you do first when using 'img2img'?
-When using 'img2img', the first step is to switch from the default 'txt2img' setting by clicking on 'img2img' in the upper left corner of the Stable Diffusion web UI.
How do you upload an image for reference in 'img2img'?
-To upload a reference image in 'img2img', scroll down and click on the 'img2img' tab, then upload the desired image.
What is the significance of adjusting the image size in 'img2img'?
-Adjusting the image size in 'img2img' helps to ensure that the generated image matches the size of the uploaded reference image, reducing the risk of errors when manually entering the size.
Why is including a prompt important when using 'img2img'?
-Including a prompt is crucial because it guides the generation process, specifying desired features like quality and details. Without a prompt, the generated image may be of poor quality.
How does the 'denoising strength' value affect the generated image?
-The 'denoising strength' value determines the influence of the reference image on the generated image. A lower number strengthens the reference image's features, while a higher number weakens it, allowing for more deviation.
What is the recommended 'denoising strength' setting for a slightly different image from the reference?
-A 'denoising strength' setting of about 0.6 is recommended for generating an image that is similar to the reference image but with some differences.
How can you generate an image with a different size from the reference image?
-To generate an image with a different size, select 'resize and fill' in the 'resize mode' to adjust the size while maintaining the reference image's features.
What are the four 'resize mode' options in 'img2img'?
-The four 'resize mode' options in 'img2img' are 'just resize', 'crop and resize', 'resize and fill', and 'latent upscale'.
What is the difference between 'img2img' and 'openpose'?
-While 'img2img' generates images with similar features including poses, 'openpose' is used specifically for imitating poses, and may not retain other features of the reference image.
Outlines
🎨 Using 'img2img' for Feature-Based Image Generation
This paragraph introduces the 'img2img' method for generating images based on specific poses and features from an existing image. It explains that while 'txt2img' can sometimes produce subpar results, 'img2img' allows for better control over the desired features. The video demonstrates how to use 'img2img' within the 'stable diffusion web ui' platform, starting from switching the default method to 'img2img', uploading a reference image, and adjusting the image size to match the uploaded image. It emphasizes the importance of including a quality spell in the prompt and negative prompt to generate high-quality images. The paragraph also discusses the 'denoising strength' setting, which influences how closely the generated image resembles the reference image, and the 'scale' setting, which can be adjusted to improve the clarity of details like eyes.
🔄 Exploring Resize Modes for Image Generation
The second paragraph delves into the different resize modes available for image generation, focusing on how they affect the output. It compares 'just resize', 'crop and resize', 'resize and fill', and 'latent upscale', explaining the results of each method. 'Just resize' and 'latent upscale' are noted to produce similar outcomes, while 'crop and resize' maintains the aspect ratio but may result in cropping. 'Resize and fill' is recommended for generating an image with additional content beyond the reference image. The paragraph concludes by suggesting the use of 'openpose' for those who wish to imitate poses without the other features of the reference image, and encourages viewers to explore AI generation further through provided resources. It also invites viewers to subscribe for more content.
Mindmap
Keywords
💡Stable Diffusion
💡img2img
💡pose
💡feature
💡resize to
💡prompt
💡negative prompt
💡denoising strength
💡scale
💡resize mode
💡openpose
💡AI Gene
Highlights
Stable Diffusion allows for image generation through 'img2img', a method that uses existing images to create new ones with similar features.
The 'txt2img' method sometimes fails to capture the desired pose or features, making 'img2img' a more reliable option for specific requirements.
To begin using 'img2img', start the 'stable diffusion web ui' and switch from 'txt2img' to 'img2img' mode.
Upload the reference image that you want to use for pose and feature replication in the 'img2img' tab.
Adjust the image size to match the uploaded image, using the 'resize to' feature for accuracy.
Enter a detailed prompt to ensure high-quality image generation, including terms like 'masterpiece', 'best quality', and specific features.
The 'negative prompt' helps to avoid unwanted features in the generated image, such as 'low quality' or 'lowres'.
The 'denoising strength' setting influences how closely the generated image resembles the reference, with lower values making it more similar.
Experiment with different 'denoising strength' values, such as 0, 0.5, and 1, to achieve the desired resemblance to the reference image.
Using a 'scale' value of 2 can help generate larger images with more defined features, like clearer eyes.
Compare different 'resize mode' options like 'just resize', 'crop and resize', 'resize and fill', and 'latent upscale' for optimal results.
The 'resize and fill' option is recommended for generating images with a different size than the reference image.
If you wish to imitate only poses and not other features, consider using the 'openpose' method.
The video provides a practical guide on using 'img2img' for generating images with similar features and poses.
The 'img2img' method is particularly useful for creating anime-style illustrations from real-life images.
The video offers insights into the technical aspects of image generation, such as adjusting image size and denoising strength for better results.
For a comprehensive understanding of AI image generation, consider exploring additional resources like 'ai Gene'.