【Stable Diffusion】画像から画像を作成するimg2imgの使い方について解説

AIジェネ【AIイラスト生成の情報発信】
15 Jul 202307:21

TLDRThe video script introduces the 'img2img' method for generating images with specific features and poses from an existing image using 'stable diffusion web ui'. It guides users through the process of uploading a reference image, adjusting its size, and inputting positive and negative prompts to influence the generated image. The script emphasizes the importance of 'denoising strength' in matching the reference image and discusses various 'resize mode' options for altering the output size. The video concludes by suggesting 'openpose' for users interested in imitating poses alone and encourages viewers to explore AI generation further.

Takeaways

  • 🎨 Use 'img2img' to generate images from an existing image, preserving specific features such as poses and background.
  • 🔄 Switch from 'txt2img' to 'img2img' in the 'stable diffusion web ui' by clicking the respective tab in the upper left corner.
  • 📸 Upload the reference image to the 'img2img' tab to start the process of generating a new image with desired features.
  • 📐 Adjust the image size automatically or manually to match the uploaded image's dimensions for accurate feature replication.
  • 🌟 Include a detailed prompt with positive attributes (e.g., 'masterpiece', 'best quality', 'detailed eyes') and negative attributes (e.g., 'worst quality', 'low quality') for better image generation.
  • 🔄 Compare the reference image and the generated image to ensure the desired features have been captured.
  • 🔢 Adjust the 'denoising strength' value to strengthen or weaken the influence of the reference image on the generated image.
  • 🔄 Experiment with different 'denoising strength' settings (e.g., 0, 0.5, 1) to achieve the desired level of similarity with the reference image.
  • 📏 Use 'resize and fill' in 'resize mode' to generate an image with a different size than the reference image while maintaining the features.
  • 🚀 Explore 'openpose' for imitating poses without necessarily replicating other features of the reference image.
  • 📢 Stay informed on AI generation advancements by following channels like 'ai Gene' for more insights and tutorials.

Q & A

  • What is the main difference between 'txt2img' and 'img2img' methods?

    -'Txt2img' generates an image based on textual descriptions, while 'img2img' creates an image by referencing an existing image, focusing on replicating specific features such as poses and background characteristics.

  • How do you switch from 'txt2img' to 'img2img' in the stable diffusion web UI?

    -Click on 'img2img' in the upper left corner of the stable diffusion web UI to switch from the default 'txt2img' method to 'img2img'.

  • What type of image is used in the example provided in the script?

    -In the example, a real live-action image is used to generate an anime-style illustration.

  • How can you adjust the image size in 'img2img' mode?

    -After uploading the image, you can set the image size to match the uploaded image by clicking the automatic adjustment option, and then use the up and down arrow icons to reverse the horizontal and vertical size settings if needed.

  • What should you include in the prompt when using 'img2img' to ensure high-quality image generation?

    -Include a quality spell in the prompt, such as 'masterpiece, best quality, detailed eyes, blouse, girl', and a negative prompt like 'easy negative, worst quality, low quality, normal quality, lowres' to specify the desired features and avoid poor quality images.

  • What is the purpose of adjusting the 'denoising strength' value in 'img2img'?

    -Adjusting the 'denoising strength' value allows you to strengthen or weaken the characteristics of the reference image, making it easier to match with the reference or create a more distinct image.

  • What happens when you set the 'denoising strength' to 0, 0.5, and 1?

    -Setting 'denoising strength' to 0 results in an image identical to the reference image. A setting of 0.5 allows for an anime-style illustration while maintaining the reference image's characteristics. A setting of 1 may result in a completely different image, moving away from the reference image.

  • How can you generate an image with a different size than the reference image?

    -Select 'resize and fill' in 'resize mode' to generate an image with a different size while maintaining the reference image's features.

  • What are the four 'resize mode' options and how do they affect the generated image?

    -The four 'resize mode' options are 'just resize', 'crop and resize', 'resize and fill', and 'latent upscale'. 'Just resize' changes the size but keeps the proportions, 'crop and resize' may alter the image's natural length, 'resize and fill' adds to the image outside the reference range, and 'latent upscale' stretches the image, often horizontally.

  • What is the recommended 'denoising strength' setting for generating an image that is similar but not identical to the reference image?

    -A 'denoising strength' setting of about 0.6 is recommended for generating an image that is similar to the reference image while allowing for some differences.

  • What is 'openpose' used for in image generation?

    -'Openpose' is used if you want to imitate only poses and generate images without focusing on other features, offering a different approach to pose-based image generation.

  • How can viewers find more information on AI generation?

    -Viewers can find more information on AI generation by visiting 'ai Gene', which provides additional insights and resources on the topic.

Outlines

00:00

🎨 Using 'img2img' for Feature-Specific Image Generation

This paragraph introduces the 'img2img' method for generating images based on specific features and poses from an existing image. It explains that 'img2img' is an alternative to 'txt2img' when the desired outcome is not achieved with text prompts alone. The process is demonstrated through the 'stable diffusion web ui' platform, where users can upload a reference image and adjust settings like image size and denoising strength to influence the generated image's resemblance to the reference. The importance of inputting quality spells in the prompt is emphasized to avoid poor image quality. The paragraph concludes with a comparison of the reference and generated images, highlighting the effectiveness of 'img2img' in capturing the reference's background and pose.

05:04

📏 Image Sizing and 'Resize Mode' in 'img2img'

The second paragraph delves into the intricacies of image sizing when using 'img2img'. It outlines the different 'resize mode' options: 'just resize', 'crop and resize', 'resize and fill', and 'latent upscale'. Each method's impact on the image is explained, with 'resize and fill' recommended for maintaining the character and background features while generating a different-sized image. The paragraph also discusses the 'denoising strength' setting, which can be adjusted to strengthen or weaken the reference image's characteristics. The summary ends with a suggestion to explore 'openpose' for pose-specific image generation and an invitation to visit ai Gene for more information on AI generation. The paragraph concludes with a call to action for viewers to subscribe to the channel.

Mindmap

Keywords

💡txt2img

Txt2img refers to the process of generating an image from textual descriptions. It's a method where the user inputs a description or 'spell' into the system, and the AI creates an image based on that description. In the context of the video, it's mentioned as the default mode when starting the 'stable diffusion web ui', which contrasts with 'img2img', another method discussed in the video.

💡img2img

Img2img is a method for generating images from existing images, which allows users to create new images that retain specific features, poses, or backgrounds of a reference image. This method is particularly useful when one wants to generate images that are similar to a provided example, and it can be adjusted to vary the degree of similarity.

💡stable diffusion web ui

Stable diffusion web ui is the user interface for a machine learning model that facilitates image generation. It is the platform where users can input text or upload images to generate new images. The video script mentions this interface as the starting point for image generation tasks.

💡resize to

The 'resize to' feature allows users to adjust the size of the uploaded image to match the desired output size. It's important for maintaining the aspect ratio and quality of the image when generating new images based on a reference.

💡prompt

In the context of the video, a 'prompt' is the textual description or 'spell' that users input to guide the AI in generating an image. It includes positive descriptors that the user wants to see in the generated image, such as 'masterpiece', 'best quality', etc.

💡negative prompt

A 'negative prompt' consists of terms that the user wants the AI to avoid including in the generated image. It helps to refine the output by specifying what characteristics not to include, such as 'low quality' or 'lowres'.

💡denoising strength

The 'denoising strength' setting adjusts the influence of the reference image on the generated image. A lower value strengthens the reference image's features, while a higher value weakens it, allowing for more variation and less similarity to the reference image.

💡scale

The 'scale' setting determines the size of the generated image relative to the reference image. By setting the scale to a value higher than 1, the user can generate an image that is larger than the original reference image, which can help in clarifying details such as eyes or other parts.

💡resize mode

The 'resize mode' offers different methods for adjusting the size of the generated image. Options like 'just resize', 'crop and resize', 'resize and fill', and 'latent upscale' each produce different results in terms of image dimensions and aspect ratio.

💡openpose

Openpose is mentioned as an alternative method for users who want to imitate poses specifically without necessarily replicating other features of the reference image. It's a tool that focuses on pose detection and can be used for generating images that maintain the pose while changing other elements.

💡AI generation

AI generation refers to the process by which artificial intelligence systems create new content, such as images, based on input data. In the context of the video, AI generation is the core technology behind 'txt2img' and 'img2img' methods, enabling users to create custom images through the stable diffusion web ui.

Highlights

Using 'img2img' allows for generating images from an existing image, preserving desired features such as poses and background.

The 'stable diffusion web ui' defaults to 'txt2img' but can be switched to 'img2img' for referencing images.

To switch methods, click 'img2img' in the upper left corner of the 'stable diffusion web ui'.

Upload the reference image to generate features like poses and people in the new image.

Adjust the image size to match the uploaded image or set it manually to avoid mistakes.

Enter a quality spell in the prompt to generate high-quality images, avoiding poor quality outputs.

An example quality spell includes 'masterpiece, best quality, detailed eyes, blouse, girl'.

The 'denoising strength' setting influences how closely the generated image matches the reference image.

A 'denoising strength' of 0 results in an image identical to the reference image.

A 'denoising strength' of 0.5 allows for an anime-style illustration while maintaining the reference image's characteristics.

Setting 'denoising strength' to about 0.6 is recommended for a balanced generation.

If the generated image's size needs to be different from the reference, use 'resize and fill' in 'resize mode'.

Different resizing options like 'just resize', 'crop and resize', 'resize and fill', and 'latent upscale' yield varied results.

For images with blurred or distorted eyes, increasing the scale can improve clarity.

The 'img2img' method is useful for imitating specific features while 'openpose' focuses on pose imitation.

AI Gene provides additional information on AI generation for those interested.

The video content demonstrates the practical application of 'img2img' for generating images with desired features.

The comparison of generated images showcases the impact of 'denoising strength' and resizing methods.

The video serves as a tutorial on how to effectively use 'img2img' to create images with similar features to a reference image.

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