This custom node for ComfyUI enables users to inject tailored prompts into specific blocks of the Stable Diffusion UNet, allowing for precise control over image generation. It leverages the understanding of content and subject matter embedded within the MID0 and MID1 blocks, as indicated in the B-Lora paper on Content Style separation.
- Enables the injection of distinct prompts into targeted UNet blocks for enhanced image specificity.
- Offers three variations of the node to accommodate diverse workflow needs and preferences.
- Allows for the adjustment of learning rates in specific blocks, enhancing focus on various aspects like content and style.
Context
The Prompt Injection Node is a specialized tool within ComfyUI designed to enhance the image generation process by allowing users to manipulate prompts at a granular level. Its primary purpose is to provide users with the ability to influence how different elements of an image are rendered by the Stable Diffusion model.
Key Features & Benefits
This tool stands out by offering the capability to inject unique prompts into specific blocks of the UNet, which means users can guide the model's output more effectively. The three node variations—single prompt, multiple prompts, and prompt dictionary—provide flexibility in how prompts are applied, catering to different artistic needs and styles.
Advanced Functionalities
The node's advanced functionality includes the ability to customize learning rates for individual blocks, which can be particularly useful for emphasizing certain aspects of the image, such as lighting or style. Additionally, there is potential for a "Mix of Experts" approach, enabling dynamic swapping of blocks based on the content of the prompts, which could significantly enhance creative possibilities.
Practical Benefits
By integrating this tool into their workflow, users can achieve greater control over the image generation process, resulting in higher quality outputs that align more closely with their artistic vision. The ability to fine-tune prompts and learning rates not only streamlines the workflow but also increases efficiency by allowing for rapid adjustments based on user preferences.
Credits/Acknowledgments
This tool is a modified and simplified version of an existing node from the repository at https://github.com/pamparamm/sd-perturbed-attention. It is inspired by insights and discussions from contributors @Mobioboros and @DataVoid.