SDXS - New Image Generation model
TLDRThe video introduces the new SD XS 512 model, boasting an impressive inference rate of 100 FPS on a single GPU, significantly faster than its predecessors. It discusses the model's architecture, available on GitHub, and compares its performance with other versions. The presenter shares their workflow collection, demonstrating how to use the model for text-to-image and image-to-image tasks, and explains the installation process. They also touch on the use of control nets and the potential for future model releases.
Takeaways
- ๐ The SD XS 512 model is introduced, boasting an inference speed of 100 FPS on a single GPU, which is 30 times faster than SD 1.5 and 60 times faster than the previous model, SDXL.
- ๐ The model's architecture is described as 2.1, but it's noted that understanding it isn't straightforward. More details can be found on GitHub.
- ๐ Performance comparisons are available for the 2.1 base versus the 512 SXS, and SDXL versus SXS 1024, with future coverage anticipated for the upcoming release.
- ๐ Examples and workflow collections are provided, including text-to-image and image-to-image processes using the Zenai system, which showcases how to load 2.1 Luras with incomplete layers.
- ๐ฆ Installation instructions are given, requiring the download and renaming of three files and their placement into specific directories for the model to work properly.
- ๐ง The core workflow involves a UNet loader, CLIP loader, and VAE loader, with an aspect size custom node set for 512x512 SD settings.
- ๐จ The seed for generation can be fixed, and the empty latent goes into the K sampler. The model uses one step and one CFG for fast processing.
- ๐ผ๏ธ An image upscaling process is mentioned, and various prompts are explored, including automatic negative prompts, magic prompts, and text randomness controlled by a seed generator.
- ๐ The text-to-image workflow involves a custom wildcard setup, with a focus on negative and magic prompts and dynamic prompts custom node.
- ๐ญ The Zenai system comes with hundreds of styles for prompts, and the weight of the style can be adjusted for importance in the generation process.
- ๐ค The video discusses the trial and error of different prompts and settings, highlighting the subjective nature of achieving desired results and the need for tweaking values.
Q & A
What is the main claim of the SD XS 512 model?
-The main claim of the SD XS 512 model is its inference speed of 100 FPS, which is 30 times faster than SD 1.5 5 and 60 times faster than sdl on a single GPU.
What is the current status of the 1224 model?
-The 1224 model is currently in pre-release with version 0.9 available.
How can one access the performance comparisons between the different models?
-Performance comparisons can be found on the GitHub page mentioned in the transcript.
What is the role of the Zenai system in the workflow?
-The Zenai system is used to load 2.1 luras and incomplete layers, which are trained on SD XS due to the shared architecture.
How does the installation process of the new model work?
-To install the new model, one needs to download three files, rename them, and place them into the appropriate directories as shown in the transcript.
What are the core components of the basic workflow?
-The core components of the basic workflow include a unet loader, a clip loader, and a VA loader.
How does the negative prompt work in the text to image process?
-The negative prompt automatically generates a negative prompt for the user in the format of the model, which is currently not available for SDXS but has been tested with other models using clip H.
What is the purpose of the magic prompt in the workflow?
-The magic prompt adds elements to the prompt, helping to refine the image generation process by controlling the style and content of the output.
How can the style of the generated image be controlled?
-The style of the generated image can be controlled using the Zenai system, which comes with hundreds of styles that can be keyed into for the prompt.
What is the significance of the seed in the prompt generator?
-The seed allows for control over whether the same image is generated each time, ensuring consistency in the image generation process.
What challenges are there in making image to image work effectively with the new model?
-The challenges in making image to image work effectively include the need for a magic token for photo mode and the complexity of the prompt generator, which may require endless tweaking of values for desired results.
Outlines
๐ Introduction to the SD XS 512 Model
The video begins with an introduction to a new base model named SD XS 512, which is claimed to offer an inference speed of 100 FPS. This is a significant improvement over its predecessors, being 30 times faster than SD 1.5 5 and 60 times faster than sdx1 on a single GPU. The presenter mentions that there will also be a 1224 model released soon. The focus of the discussion is on the SD XS 512 model, its architecture, and its capabilities, with a brief mention of the 2.1 architecture that seems to be a part of it. The presenter encourages viewers to visit GitHub for more information and performance comparisons between different models.
๐ ๏ธ Workflow Collection and Installation Process
The second paragraph delves into the workflow collection, which includes basic text-to-image and image-to-image processes using the presenter's zenai system. This system showcases how to load 2.1 luras, which are incomplete layers that can be trained on SD XS due to their shared architecture. The presenter provides an overview of the installation process, which involves downloading and renaming three files and placing them into specific directories. The workflow collection is designed to be easy to navigate, with options to select different components under unit loader, clip, and vae. The presenter also discusses the compatibility of 2.1 Laur with the 512 base model and shares their experience trying it out.
๐จ Custom Prompts and Stylization Techniques
In the third paragraph, the focus shifts to custom prompts and stylization techniques. The presenter explains their unique setup, which includes a negative prompt display, a positive prompt display, and a custom node that uses dynamic prompts. They discuss the process of generating a negative prompt automatically and how it integrates with the model's functionality. The presenter also explores the use of a magic prompt to add elements to the prompt and control the generation process. The paragraph highlights the use of style triggers and the zenai system's hundreds of styles, which can be incorporated into the workflow to achieve desired visual outcomes.
๐ค Image-to-Image Transformations and Model Experimentation
The final paragraph discusses image-to-image transformations and the presenter's experiments with the model. They explore the use of different tokens and settings to refine the image generation process, including the adjustment of specific parameters to achieve sharper or more realistic images. The presenter shares their findings on how certain values and combinations can significantly alter the output, emphasizing the importance of tweaking and experimentation. They also touch upon the potential of the model for various artistic styles and the challenges they faced in getting the desired photo mode. The paragraph concludes with a brief demonstration of the image-to-image model and the presenter's anticipation for future improvements and discoveries in this area.
Mindmap
Keywords
๐กSD XS 512
๐กInference
๐กGitHub
๐กWorkflow
๐กZenai System
๐กArchitecture
๐กPrompt
๐กUpscale
๐กSeed
๐กStyle
๐กImage to Image
Highlights
Introduction of the new SD XS 512 model, a significant upgrade in the SD series.
The SD XS 512 boasts an impressive inference rate of 100 FPS on a single GPU, marking a 30x increase from SD 1.5.
A pre-release version of the SD XS 1224 model is available, indicating future advancements in the series.
The 2.1 architecture is mentioned as part of the SD XS 512 model, hinting at a complex and innovative design.
Performance comparisons between different models are available on GitHub for interested users.
The workflow collection includes various methods such as text-to-image and image-to-image, enhancing the user's creative possibilities.
The Zenai system is showcased, demonstrating its integration with the 2.1 Luras and compatibility with the SD XS architecture.
A detailed explanation of the installation process for the new model is provided, including the necessary files and steps.
The basic workflow is simplified and expanded upon from the default UI, offering more control and customization.
The use of a custom wild card setup for text-to-image and image-to-image workflows is introduced, increasing flexibility.
The implementation of a seed generator for consistent results and the ability to fix the seed for specific outcomes are discussed.
The Zenai system's style options are highlighted, showcasing the variety of styles available for use in prompts.
The video provides insights into the process of image refinement and the impact of different settings on the final output.
Experimentation with various prompts and styles is encouraged, as it can lead to unexpected and creative results.
The presenter shares their experience with the model, noting the differences between trained and untrained outputs.
A demonstration of the image-to-image process is given, revealing the current limitations and potential for future improvements.
The video concludes with an encouragement to explore the new model's capabilities and an offer to share more insights in future content.