HOW TO MAKE BEAUTIFUL STABLE DIFFUSION IMAGES | Negative Prompts

Binks
13 Jan 202305:02

TLDRIn this video, Binks discusses the significance of negative prompts in creating stable diffusion images. Using Protogen version 3.4's photorealism release, Binks demonstrates how to refine image generation by adjusting the prompt and sampling settings. Initially, a portrait of a blonde woman in a medieval style is generated without a negative prompt, resulting in an image with an unwanted canvas frame and other imperfections. By introducing a negative prompt that excludes elements like canvas frames and disfigurements, the AI produces a more accurate and desired image. Binks emphasizes the importance of crafting detailed prompts and experimenting with different settings to achieve the desired outcome. The video concludes with a stunning image generated through careful prompt tailoring and sampling settings, highlighting the artistry involved in working with AI to create visually appealing results.

Takeaways

  • 🎨 **Importance of Negative Prompts**: Negative prompts are crucial in Stable Diffusion to refine the generated images and avoid unwanted elements.
  • 🖼️ **Avoiding Canvas Frames**: Specifying 'canvas frame' in the negative prompt can prevent the AI from adding a frame that might not be desired.
  • 🧍‍♀️ **Subject Preservation**: The negative prompt helps ensure the subject of the image is not disfigured or mutated, maintaining a natural appearance.
  • 🔍 **Detail in Prompts**: Using detailed prompts and negative prompts can significantly influence the outcome, leading to more accurate results.
  • 📈 **Sampling Method and Steps**: The choice of sampling method (e.g., DPM++ SDE Keras) and the number of sampling steps can affect the photorealism of the generated image.
  • 🖥️ **Image Resolution**: Adjusting the image resolution (e.g., increasing height) can provide a more portrait-oriented look and better detail.
  • 🔧 **Config Scale Adjustment**: Tweaking the config scale can enhance the model's performance and the quality of the generated images.
  • 🧩 **Restoring Facial Features**: The 'restore faces' option can help maintain the integrity of faces in the generated images.
  • 🎭 **Stylized Portraits**: For a more stylized look, the prompt can be adjusted to include descriptors like 'medieval model shoot style'.
  • 📝 **Crafting Negative Prompts**: Crafting negative prompts with specific keywords can guide the AI to avoid certain outcomes and focus on the desired result.
  • 🔄 **Iterative Process**: Generating images is an iterative process that requires multiple attempts and adjustments to the prompts to achieve the best results.
  • 🎭 **Artistic Input**: There's a level of artistry involved in figuring out how to guide the AI to create images that meet the creator's vision.

Q & A

  • What is the main topic of the video?

    -The main topic of the video is the importance of using negative prompts in stable diffusion to improve the quality of generated images.

  • Who is the presenter of the video?

    -The presenter of the video is Binks.

  • What version of ProtoGen is Binks using in the video?

    -Binks is using ProtoGen version 3.4, specifically the photorealism release.

  • What is the significance of negative prompts in image generation?

    -Negative prompts are used to guide the AI away from generating unwanted elements or styles in the image, allowing for more control over the final result.

  • What is the default sampling method Binks prefers for ProtoGen?

    -Binks prefers the DPM++ SDE Keras sampling method for ProtoGen.

  • How does Binks adjust the sampling steps and image dimensions?

    -Binks increases the sampling steps to around 30 and changes the image height from 512 to 768 to achieve a more portrait-like look.

  • What does the 'restore faces' option do in the settings?

    -The 'restore faces' option is likely used to enhance or correct the facial features in the generated images.

  • Why does Binks include 'canvas frame' in the negative prompt?

    -Including 'canvas frame' in the negative prompt instructs the AI to avoid generating images with a canvas frame around them.

  • How does Binks tailor the original prompt for better results?

    -Binks tailors the original prompt by adding more detailed descriptions and keywords to guide the AI towards generating an image that matches the desired outcome more closely.

  • What does Binks suggest for users to do to improve their generated images?

    -Binks suggests that users should experiment by repeatedly clicking the generate button and adjusting their prompts and settings to understand how the system works and to achieve better results.

  • What is the role of artistry in using AI for image generation?

    -Artistry plays a role in understanding the system's capabilities and figuring out how to manipulate the prompts and settings to guide the AI in creating the desired image.

  • How can viewers get more information or ask questions about the video?

    -Viewers can leave questions in the comment section of the video and can also check the description for links to resources mentioned in the video.

Outlines

00:00

🎨 Understanding Negative Prompts in Stable Diffusion

In this video, Binks introduces viewers to the significance of negative prompts in the context of image generation using Stable Diffusion. Binks begins with a brief introduction and mentions the use of Protogen version 3.4, highlighting its photorealism capabilities. The video then delves into the process of using negative prompts to refine image generation, avoiding unwanted elements such as canvas frames and disfigurements. Binks demonstrates this by first generating an image with a simple prompt and then refining it using a detailed negative prompt. The result is a more accurate and stylized portrait of a blonde woman in a medieval setting, showcasing the power of tailored prompts and sampling settings in achieving desired outcomes. Binks encourages viewers to experiment with the AI and learn its intricacies to create stunning images.

Mindmap

Keywords

💡Stable Diffusion

Stable Diffusion refers to a type of artificial intelligence model used for generating images from textual descriptions. In the context of the video, it is the primary tool used to create images based on prompts provided by the user. The video discusses how to improve the quality of images generated by Stable Diffusion through the use of negative prompts.

💡Negative Prompts

Negative prompts are terms or phrases used in conjunction with a primary prompt to guide the AI model away from including certain elements or styles in the generated image. They are crucial for refining the output of the AI to match the user's vision more closely. In the video, Binks demonstrates how to use negative prompts to prevent unwanted features, such as a canvas frame or disfigured subjects, from appearing in the generated images.

💡Photorealism

Photorealism is a style of art where the subject is depicted with a high degree of resemblance to the real world. In the context of AI-generated images, photorealistic models aim to create images that closely mimic the look of actual photographs. The video mentions the use of a photorealism release of the Protogen model to achieve highly realistic results.

💡Protogen

Protogen is a term used in the video to refer to a specific version of an AI model designed for generating images. The video specifically mentions Protogen version 3.4, highlighting its photorealistic capabilities and how it can be used to produce high-quality images when paired with the right prompts and settings.

💡Sampling Method

The sampling method is a technique used within AI models to determine how the final image is generated from the initial input. Different sampling methods can yield different levels of detail and style in the output. In the video, Binks prefers the 'DPM++ SDE Keras' method for Protogen, adjusting the sampling steps to achieve the desired image quality.

💡Sampling Steps

Sampling steps refer to the number of iterations the AI goes through to generate an image. Increasing the number of sampling steps can lead to more refined and detailed images, but it also increases the computational time required. Binks suggests setting a higher number of sampling steps for better image quality in the video.

💡Config Scale

Config scale is a parameter within the AI model that can be adjusted to control the level of detail and the overall 'style' of the generated image. A higher config scale typically results in more detailed and varied outputs. In the video, Binks increases the config scale to enhance the image's detail.

💡Restore Faces

Restore Faces is an option within the AI model that is used to improve the quality and realism of faces in the generated images. It is a feature that helps the AI to better render human faces. Binks checks this option to ensure the generated portraits have realistic facial features.

💡Model Shoot Style

Model shoot style refers to a specific aesthetic or approach to photography that is typically used in fashion or commercial photography sessions. It implies a professional and stylized look. In the video, Binks tailors the prompt to include 'model shoot style' to guide the AI towards creating images with a more professional and polished appearance.

💡Tailored Prompt

A tailored prompt is a carefully crafted input that includes specific keywords and descriptions to guide the AI model in generating an image that closely matches the user's vision. It is an essential part of the process when using AI to create images, as it helps to refine the output. Binks demonstrates the creation of a tailored prompt in the video to achieve the desired image result.

💡Artistry

Artistry refers to the skill and creativity involved in producing art. In the context of using AI to generate images, artistry involves understanding how to interact with the AI model to produce the desired outcome. Binks emphasizes the role of artistry in working with AI, as it requires a combination of technical knowledge and creative input to achieve the best results.

Highlights

The importance of negative prompts in stable diffusion for image generation is discussed.

Protogen version 3.4, a photorealism release, is used for demonstration.

The use of darkstorm 2150 model on the Civic AI page is mentioned.

Examples of images generated with the model are shown on the protogen page.

The process of using a negative prompt from an example image is demonstrated.

A portrait of a blonde woman in a medieval style is generated as a starting point.

DPM plus plus sde Keras is identified as the preferred sampling method for protogen.

Increasing sampling steps and image dimensions can enhance portrait generation.

Config scale adjustments are shown to work well with the specific model.

The 'restore faces' option is used to improve the image generation.

Negative prompts are used to avoid unwanted elements like canvas frames and disfigurement.

Crafting negative prompts can help achieve desired image results.

A comparison between images generated with and without a negative prompt is shown.

Tailoring the prompt and negative prompt can lead to more accurate image generation.

Detailed prompts and tailored sampling settings are key to high-quality image generation.

The AI model requires some work and artistry to understand and use effectively.

The video provides tips on how to get the most out of the AI image generation system.

Encouragement to experiment with the generate button for different results is given.

The video concludes by emphasizing the creative process involved in AI image generation.

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