SDXS - New Image Generation model
TLDRThe video introduces the new SD XS 512 model, boasting an impressive inference speed 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 creator also shares their workflow collection, including text-to-image and image-to-image processes, utilizing a Zenai system and various custom nodes. The video delves into the installation process, the use of unit text encoder and VAE, and the potential for style incorporation. It concludes with a demonstration of the model's image generation capabilities and a teaser for future updates.
Takeaways
- ๐ Introduction of a new base model called SD XS 512, promising fast inference speeds of 100 FPS on a single GPU.
- ๐ The SD XS 512 is 30 times faster than SD 1.5 and 60 times faster than sdl on a single GPU.
- ๐ The architecture of SD XS 512 is explained on GitHub, with performance comparisons available for different models.
- ๐ A pre-release version of SD XS 512 is available, with a full release anticipated in the future.
- ๐ Workflow collection includes basic text-to-image, image-to-image, and a zenai system for loading 2.1 luras with incomplete layers.
- ๐ง Installation instructions for the new model involve downloading and renaming three files and placing them into specific directories.
- ๐จ The core of the workflow consists of a unet loader, clip loader, and VA loader, with an aspect size custom node for 512x512 SD settings.
- ๐ The workflow involves a first pass, followed by a feedback loop using the same dimensions and settings for further refinement.
- ๐ The use of a magic prompt setup and a random line generator driven by the same seed for consistency in image generation.
- ๐ผ๏ธ The model's potential for image-to-image workflows is discussed, with experimentation and tweaking of values for desired outcomes.
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 expected performance of the upcoming 1224 model in comparison to the 512 model?
-The 1224 model is expected to offer further improvements in performance, although specific details are not provided in the transcript. It is mentioned that there will be a release of the 1224 model, with the current version being 0.9 which is a pre-release.
How does the architecture of the SD XS model differ from its predecessors?
-The SD XS model is said to have some elements of 2.1 in its architecture, but it's not a straightforward matter. More details about the architecture can be found on the GitHub page.
What is the purpose of the workflow collection in the context of the SD XS model?
-The workflow collection includes various configurations for text-to-image and image-to-image processes using the Zenai system. It demonstrates how to load 2.1 luras and incomplete layers, which are usable due to the shared architecture with SD XS.
How can one install the SD XS model?
-To install the SD XS model, one needs to download three files, rename them, and place them into specific directories as shown in the transcript. This will allow the user to select the appropriate model under the unit loader, clip, and vae sections.
What are the core components of the basic workflow in the SD XS model?
-At its core, the basic workflow in the SD XS model includes a unet loader, a clip loader, and a VA loader. These components are used in conjunction with custom nodes and settings to control the image generation process.
How does the prompt system work in the SD XS model?
-The prompt system in the SD XS model involves using a combination of negative prompts, magic prompts, and text inputs. These prompts are driven by a seed generator, which allows for control over the randomness and consistency of the generated images.
What is the significance of the Zenai system in the SD XS model?
-The Zenai system in the SD XS model is used for style control. It comes with hundreds of styles that can be keyed into for the prompt, allowing users to add specific artistic styles to the generated images.
What challenges were encountered with the image-to-image workflow in the SD XS model?
-The image-to-image workflow in the SD XS model seems to have some issues, particularly with missing layers and a lack of clarity on how to achieve photorealistic outputs. The speaker mentions that there might be a trick or a magic token needed to get the desired photo mode, which has not been discovered yet.
What are the potential applications of the SD XS model?
-The SD XS model, due to its speed and image generation capabilities, can be used for a variety of applications that require fast and high-quality image outputs. It might be a good option for users looking to generate images quickly and with a high level of control over the style and content.
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 the previous models, 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, but currently, only the 0.9 pre-release version of SD XS 512 is available. The architecture of the new model is discussed, with some references to it having elements of 2.1, but it's not straightforward. The presenter encourages viewers to visit GitHub for more information on the architecture and performance comparisons between different models. The video also touches on the workflow collection, which includes various text-to-image and image-to-image processes using the presenter's zenai system. The zenai model is mentioned as something that will be discussed later in the video.
๐ Workflow and Installation Details
The second paragraph delves into the specifics of the workflow and the installation process for the new model. The presenter explains that viewers will need to download three files, rename them, and place them into specific directories in order to use the model. The process is likened to using the unit loader, where different components such as the save tensor, clip, and vae are placed accordingly. The presenter also mentions a 2.1 768 model and its compatibility with the 512 base, sharing their successful experience with it. Links to the necessary files and more information are provided in the video description and an accompanying article.
๐จ Custom Prompts and Stylization
In this paragraph, the focus shifts to the intricacies of custom prompts and stylization using the new model. The presenter discusses a unique setup that includes a negative prompt display, a positive prompt, and a custom node with dynamic prompts. The process involves generating a negative prompt automatically and combining it with a magic prompt and controlled text. The use of a seed generator is highlighted, which allows for consistency across prompts. The presenter also talks about the zenai system's style options and how they can be used to influence the final output. The paragraph concludes with a discussion on the effectiveness of different prompts and the presenter's experimentation with various settings.
๐ผ๏ธ Image-to-Image Workflow and Results
The final paragraph discusses the image-to-image workflow and presents the results obtained from using the new model. The presenter shares their findings on how adjusting certain parameters can affect the output, noting that while making images softer is easy, achieving sharpness is more challenging. They demonstrate the differences in output based on varying levels of detail and coherence. The presenter also talks about their experience with the 'photo of a cat' prompt and how tweaking values can lead to endless adjustments based on personal taste. The video ends with a teaser for a future discussion on the magic prompt and an encouragement for viewers to experiment with the new model.
Mindmap
Keywords
๐กSD XS 512
๐กInference speed
๐กGitHub
๐กArchitecture
๐กWorkflow collection
๐กZenai system
๐กPrompt
๐กUpscale
๐กRandom seed
๐กStyle
๐กImage to image
Highlights
Introduction of the new SD XS 512 model with a significant claim of 100 FPS inference, which is 30 times faster than SD 1.5 and 60 times faster than sdl on a single GPU.
The pre-release version of SD XS 512 includes some 2.1 in its architecture, indicating a potential evolution in the model's design.
Performance comparisons are available on GitHub, allowing users to compare the 2.1 base versus the 512 sxs and then sdl versus sxs 1024.
The workflow collection includes basic text-to-image, image-to-image, and a zenai system showcasing how to load 2.1 luras with incomplete layers.
The installation process for the new model involves downloading three files, renaming them, and placing them into specific directories.
The core of the new workflow includes a unet loader, clip loader, and VA loader, with the addition of an aspect size custom node for 512x512 SD settings.
The use of a primitive for seed control in the generation process, allowing for fixed or variable seed manipulation.
The implementation of a one-step and one-CFG process in the model, resulting in faster image processing.
The exploration of different prompts, including automatic negative prompts, magic prompts, and random lines, all driven by the same seed generator.
Demonstration of the text-to-image process, showing how the model can generate images based on textual descriptions and style inputs.
The potential for using the model to create art styles that work well with its capabilities, especially for printing purposes.
The challenge of converting image-to-image prompts into photo mode and the ongoing search for a magic token to enhance this process.
The use of various tokens and settings to refine image output, highlighting the iterative nature of achieving desired results.
The inclusion of simple workflows in the pack for users to easily understand and utilize the new model's capabilities.
The demonstration of the model's ability to handle complex image manipulation tasks, such as transforming a cat image into a robot cat.
The acknowledgement of the model's speed and potential applications, encouraging users to experiment with it for their projects.