Underused Midjourney v5 Prompt Commands :: How to use Text Weight and Image Weight
TLDRIn this video, the host delves into the underused yet powerful features of Midjourney v5's prompt commands, focusing on 'Image Weight' and 'Text Weight'. The script explains how these weights can be utilized to control the output of generated images by assigning more tokens to specific keywords. It also covers the importance of prompt structure, the correct formatting for weights, and the limitations of the system. The video showcases examples of how text and image weights can be combined to achieve desired results, and discusses the use of reference images to influence the style of the output. Additionally, it explores the challenges and workarounds associated with negative prompts. The host, Tim, invites viewers to like, subscribe, and engage with the content, offering a comprehensive guide to mastering the art of Midjourney prompts.
Takeaways
- ๐ **Understanding Weights**: In Midjourney, weights are used to control the emphasis on different parts of a prompt, with higher weights given to keywords that are more important to the final image.
- ๐ **Midjourney's Prompt Scanning**: The system scans prompts for keywords, assigns tokens to them, and uses these tokens to generate images, with more emphasis on words at the beginning of the prompt.
- ๐ข **Text Weight Syntax**: To apply text weight, use a double colon followed by a number (e.g., `::1` for equal importance or `::2` for double the importance) without spaces around the colons but with a space after the number.
- ๐ **Image Weight (IW)**: Image weight in Midjourney is used with reference images to tell the system how much to rely on the reference image for the final output, with a score between 0.5 and 2.
- ๐จ **Combining Text and Image Weights**: You can use both text and image weights together to fine-tune the composition and style of the generated image.
- โ๏ธ **Balance in Weights**: It's recommended to keep text weights between one and ten for simplicity, but higher values can be used for more precise control.
- ๐ง **Formatting Matters**: Incorrect formatting, such as adding spaces where they shouldn't be, can cause Midjourney to ignore the weights.
- ๐ค **Iterative Process**: Achieving the desired image might require several attempts and adjustments to the weights and prompt order.
- ๐งฉ **Photobashing**: When direct text prompts fail, techniques like photobashing (combining images) can be used to achieve the desired outcome.
- ๐ซ **Negative Prompts**: Using negative prompts (with a negative number after the double colon) can help remove unwanted elements from the image, although it can be tricky due to the system's tendency to include certain features.
- ๐ **Experimentation**: The process of generating images with Midjourney often involves trial and error, and playing around with different weights and prompts to get the best results.
- ๐บ **Reference Images**: Reference images can be used to inspire the style or composition of the generated image, but the output is not a direct copy of the reference.
Q & A
What are the two powerful techniques discussed in the video for controlling the output in mid-journey prompts?
-The two powerful techniques discussed are Image Weight and Text Weight.
How does the mid-journey prompt system work when scanning for keywords?
-Mid-journey scans the prompt for keywords, assigns tokens to those keywords, and uses those tokens to assemble the image based on its database.
How many tokens are typically assigned per prompt according to the video?
-It is mentioned that approximately 75 tokens are assigned per prompt, although the exact number may have increased as the language model has been refined.
What is the significance of the order of keywords in a mid-journey prompt?
-The order of keywords matters because the system places more emphasis on the words at the beginning of the prompt than those towards the end.
How can Text Weight be used to influence the importance of a keyword in a mid-journey prompt?
-Text Weight is used by replacing a comma with a double colon followed by a number to indicate the relative importance of a keyword. For example, using '::1' after a keyword would give it a weight of one, while '::2' would double its importance.
What is the correct format for using Text Weight in a mid-journey prompt?
-The correct format is to have no space between the keyword and the double colon, a space after the double colon and the number, and no comma following the number before the next keyword.
How does using Image Weight in mid-journey work?
-Image Weight is used by adding '--IW' followed by a number between 0.5 and 2 before the prompt, which tells mid-journey how much to rely on the reference image for the final output.
What is the difference between image referencing and stable diffusion's posed image?
-Image referencing in mid-journey creates an output inspired by the reference image, rather than a direct one-to-one replication. Stable diffusion's posed image would be a more direct replication.
How can negative prompts be used in mid-journey to remove certain elements from the generated image?
-Negative prompts are used by adding a keyword followed by a double colon and a negative number, which instructs mid-journey to exclude those elements from the image.
What is the role of photobashing in the process of refining the generated images?
-Photobashing is used when text prompts alone are not sufficient to achieve the desired result, such as removing a hat from an image. It involves manually editing the image to achieve the desired outcome.
What is the importance of experimenting with different weights and prompts in mid-journey?
-Experimenting with different weights and prompts allows for greater control over the composition and elements of the generated images, helping to achieve a more accurate and desired output.
What advice does the video give for those who are new to using weights in mid-journey prompts?
-The video advises starting with numbers between one and ten for Text Weight and experimenting with different Image Weight values to understand their effects on the generated images.
Outlines
๐ค Understanding Mid-Journey Prompting Techniques
The video begins with an introduction to two underutilized yet powerful techniques in mid-journey prompting: Image Weight and Text Weight. The speaker aims to clarify the confusion around these techniques by explaining how they function. Weights are used to control the output of the image by assigning more tokens to specific keywords within the prompt. The video also discusses the limitations of these techniques and offers strategies to overcome them. It's mentioned that Mid-Journey assigns around 75 tokens per prompt, with more emphasis on the beginning of the prompt. Text weights are applied by using a double colon followed by a number to indicate the importance of a keyword. The video provides examples of how different weights affect the composition of the generated images.
๐ผ๏ธ Image Weighting and Reference Images in Mid-Journey
The second paragraph delves into the use of reference images in Mid-Journey and how image weights can be applied to them using the '--IW' flag followed by a number between 0.5 and 2. This indicates the level of reliance on the reference image for the final output. The video contrasts image referencing with stable diffusion's posed image and demonstrates how to achieve a balance between the reference image and the desired artistic style. Examples are given using Scarlett Johansson as Black Widow and Clint Eastwood as a character in a fictional project. The speaker also shares a trick of photobashing to combine elements from different images when Mid-Journey struggles to generate the desired output.
๐ซ Experimenting with Negative Prompts in Mid-Journey
The final paragraph discusses the concept of negative prompting in Mid-Journey, which involves adding a keyword followed by a negative number to indicate elements that should be removed or de-emphasized in the output. The video shows how tricky it can be to achieve desired results with negative prompts, as the AI tends to include elements that are present in the reference image. Despite several attempts, the AI persistently includes a hat in the image, even when instructed to remove it. As a workaround, the speaker uses photobashing to manually edit out the unwanted element and suggests that this edited image could be used as a new reference for further iterations.
Mindmap
Keywords
Midjourney
Text Weight
Image Weight
Prompt
Tokens
Compositional Balance
Negative Prompt
Photobashing
Reference Image
Illustrator Style
Dynamics
Highlights
Explains the concept of Image Weight and Text Weight in mid-journey prompts
Discusses the underutilization of these powerful techniques due to confusion
Describes how mid-journey assigns tokens to keywords in a prompt
Mentions the emphasis on words at the start of a prompt over the end
Demonstrates how to use text weights with colons to control the importance of keywords
Advises on keeping text weight numbers between one and ten for simplicity
Clarifies the correct format for applying text weights without spaces or commas
Provides an example of how image composition changes with different text weights
Explains the use of reference images and image weights in mid-journey
Shows how adjusting image weight can influence the style and composition of the output
Discusses the limitations of negative prompting and the workarounds using photobashing
Illustrates the process of combining text prompts with image references for desired results
Shares a personal project example using mid-journey prompts and image weights
Provides a detailed walkthrough of experimenting with weights to achieve a specific visual style
Encourages viewers to like and subscribe for channel growth
Offers practical tips for fine-tuning prompts to get closer to the desired image
Concludes with an invitation for viewers to share questions, comments, or suggestions