Z-Image Turbo ControlNet: ComfyUI Workflow & Speed Test
Takeaways
- 😀Z-Image Turbo ControlNet is a Union ControlNet model that supports various control conditions like pose, Canny edge, and depth. Developers can leverage the Z image API to enhance their image generation workflows.
- 🖼️ The model requires preprocessing images, which can be generated from any depth model or control node, such as Canny edge detection.
- 🔧 The workflow starts with the Z-Image Turbo Text-to-Image model, which can be downloaded from templates and used for generating images with control net features.
- ⚙️ A key step in the workflow involves resizing the control net input image using the 'Resize Image' node to avoid losing any image data.
- 📥 Make sure to update your ComfyUI to the latest version if you don't have the necessary control net model node.
- 🔄 To update ComfyUI, you can either use the manager's 'Update' function or directly run the 'update ComfyUI.bat' file in the portable folder.
- 🔌 The Z-Image Turbo model is combined with the 'Quen Image Diff Sync ControlNet' to process pre-processed images using control net nodes.
- 🔄 Control net conditions like pose, Canny edge, and depth can be applied through specific models, such as 'dw pose estimator' and 'Zoe Depth'.
- 🛠️ An all-in-one pre-processor simplifies selecting and using different control net conditions by enabling easy drop-down options.
- ⏱️ Time comparison: Z-Image Turbo without control net takes 40Z-Image Turbo ControlNet seconds for 10 steps, but adding control net increases the time significantly (up to 190 seconds for full workflow).
Q & A
What is the Z-Image Turbo ControlNet model?
-The Z-Image Turbo ControlNet model is a Union ControlNet model that supports multiple control conditions such as pose, Canny edge, and depth inputs to guide image generation.
What types of control conditions can be used with this Union ControlNet?
-You can use pose estimation, Canny edge detection, depth maps, and other line preprocess images as control conditions.
Why is a Resize Image node used before feeding the image into ControlNet?
-The Resize Image node resizes the input image without cropping it, ensuring no part of the reference image is lost while matching the required width and height for sampling.
What must users do if they cannot find or load the 'Model Patch with ControlNet' node in ComfyUI?
-They must update ComfyUI to the latest or nightly version using the ComfyUI Manager or by running the update_comfyui.bat file directly from the portable installation folder.
Where can users download the Z-Image Turbo ControlNet model?
-Users can download it from the Hugging Face page by navigating to the 'Files and versions' section and selecting the 'zimage turbo fun control net union' model.
What preprocessors are recommended for depth controlZ-Image Turbo ControlNet conditions?
-Depth Anything v2 and Zoe Depth are recommended for generating depth maps to feed into the ControlNet.
What is the function of the All-in-One Auxiliary Pre-Processor?
-It simplifies workflow setup by allowing users to select any preprocessing method—such as pose, depth, or Canny—directly from a dropdown menu.
Why is it important to disable the group of individual preprocessors when using the All-in-One pre-processor?
-Disabling the group prevents conflicts andJSON Code Correction ensures only the selected preprocessing method is used for the ControlNet input.
How does adding ControlNet affect generation time compared to using Z-Image Turbo alone?
-Generation time significantly increases. For example, a 1024 resolution image takes about 4 seconds without ControlNet, but around 16 seconds with ControlNet. At 2048 resolution, the time increases from 43 seconds to about 179 seconds.
Why does the inference time increase so much when ControlNet is added?
-ControlNet introduces additional processing steps, including preprocessing, conditioning, and model patch operations, which substantially increase computation time. However, these enhancements can be efficiently managed using the Z-Image-Turbo API.
What node receives the preprocessed image for conditioning?
-The 'QEn Image Diff Sync ControlNet' node accepts the preprocessed image input used to guide the generation.
What resolution settings were used during the speed tests mentioned in the script?
-The speed tests were conducted at resolutions of 1024 and 2048 pixels.
Outlines
- 00:00
🖼️ Introduction to Z-Image Turbo ControlNet Model
The video begins with an introduction to the Z-Image Turbo ControlNet model, a Union ControlNet model that allows for various control conditions like pose, edge (Canny), and depth. The presenter shares that they will be demonstrating the model's workflow, providing examples generated on their machine.
🛠️ Setting up the Z-Image Turbo Model
The presenter explains how to start working with the Z-Image Turbo model, emphasizing the importance of selecting the correct workflow template. They guide the audience on how to download the necessary models and prepare the setup for using the control net by uploading an image, resizing it, and passing it through the K-sampler node. They also mention the necessity of updating ComfyUI to the latest version to ensure compatibility.
🔄 Updating ComfyUI and Loading ControlNet
The process of updating ComfyUI is discussed in detail, including how to switch to the nightly version through the manager or manually update it by running the 'update ComfyUI.bat' file. This ensures that the user has the necessary nodes to load the ControlNet model and proceed with the workflow.
📂 Downloading the ControlNet Model and Preparing the Workflow
The presenter walks through downloading the Z-Image TurboZ-Image Turbo ControlNet ControlNet Union model from Hugging Face. They show how to navigate through the ComfyUI manager to get the appropriate model patch loader, which will then be used in the workflow for processing images through ControlNet. They also mention various control net conditions that can be applied, like Canny, depth, and pose estimations.
⚙️ Working with ControlNet Pre-Processors
In this section, the presenter talks about the different pre-processing options available within the Z-Image Turbo model. They highlight the ease of selecting any pre-processor from the all-in-one auxiliary pre-process node, and demonstrate how to disable and bypass certain nodes for smoother integration into the main workflow.
💻 Performance and Time Analysis with Z-Image Turbo ControlNet
The presenter shares insights into the performance of the Z-Image Turbo model. They provide benchmark data comparing the generation time of images with and without ControlNet. For example, generating images without ControlNet at 1024 pixels takes 4 seconds, while using ControlNet increases the time significantly. A similar trend is observed at 2048 pixels. This analysis helps users understand the impact of ControlNet on processing time.
📊 ControlNet Time Comparison Chart
The presenter provides a time comparison chart showing the difference in processing times for the Z-Image Turbo model with and without ControlNet. They compare both 1024px and 2048px image resolutions, highlighting that adding ControlNet increases the time required to generate images, with notable differences observed at the higher resolution.
🎬 Conclusion and Final Thoughts
The video wraps up with the presenter thanking the viewers for watching. They summarize the main points, including the performance differences and steps for using the Z-Image Turbo ControlNet model, and encourage viewers to try it out themselves. The video ends with a friendly goodbye and invitation to return for future videos.
Mindmap
Keywords
💡Z-Image Turbo Workflow Z-Image Turbo
Z-Image Turbo refers to a specific model used in generating images with high speed and efficiency. It’s part of a broader category of image generation models, optimized for fast and accurate results. In the video, it's mentioned as a text-to-image model used as the base for the workflow. This model can generate high-quality images with different control conditions in a short time, making it suitable for rapid experimentation and use in creative workflows.
💡ControlNet
ControlNet is a specialized deep learning model that allows more precise control over the image generation process. It works by taking additional input data like pose, edge, or depth maps, guiding the image generation based on these conditions. In the video, ControlNet is used to enhance the image generation by incorporating specific reference images, allowing for more complex and controlled outputs, especially when combined with the Z-Image Turbo model.
💡ComfyUI
ComfyUI is the user interface (UI) used in the video for interacting with models like Z-Image Turbo and ControlNet. It's a platform designed for running and managing workflows related to image generation and manipulation.JSON Code Correction In the script, it is emphasized that users need to ensure they are using the latest version of ComfyUI to be compatible with the ControlNet model, specifically mentioning the need to update the UI to the nightly version for full functionality.
💡Pre-processing
Pre-processing refers to the steps taken to prepare input data before it is fed into a model for generation. In the case of ControlNet, pre-processing includes preparing images by resizing, cropping, or applying specific transformations like edge detection or depth mapping. The video shows how resizing nodes are used to ensure images are properly formatted before passing them into the ControlNet for further processing.
💡Depth Model
A depth model is a type of neural network model that predicts the depth or 3D structure of an image. In the video, it is mentioned that any depth model can be used to generate depth maps, which are then used as input to the ControlNet for more sophisticated image generation. The inclusion of depth models allows for realistic visual effects like adding depth and perspective to otherwise flat images.
💡Canny Edge
The Canny edge detection algorithm is a technique used to identify and highlight the edges within an image. It’s useful in image processing as it helps the model understand the boundaries and shapes within a scene. In the script, Canny edge images are used as an input to ControlNet, allowing the model to generate images based on the contours and edges identified in the original image.
💡Pose Estimator
A pose estimator is a model that detects and estimates the pose or body posture of individuals in an image. This is especially useful for generating images with human figures, where the model needs to understand how the body is positioned. In the video, the DW pose estimator is used with ControlNet to guide the image generation based on specific human poses, enhancing the model's ability to create realistic and dynamic human figures.
💡K-Sampler
The K-Sampler is a component used within the workflow to sample images from a larger pool of possibilities generated by a model. In the context of the video, it works in conjunction with the resized image input, helping to ensure that the final output fits the desired parameters, including image dimensions and quality. The K-Sampler is linked to the ControlNet’s preprocessing step to fine-tune the generated images.
💡Inference Steps
Inference steps refer to the time and processes involved in generating an image from a model once all inputs are provided. The term 'inference' refers to the phase where the model processes the input data (text or images) and generates the final output. In the video, it’s noted that the Z-Image Turbo model takes around 40 seconds for the inference steps when generating an image at 1024 resolution, highlighting the model's speed and efficiency in producing results.
💡Resolution
Resolution refers to the size and detail of the generated image, usually represented by the number of pixels in width and height. Higher resolution images contain more detail but take longer to generate. In the video, different resolutions are tested: 1024 pixels takes less time to generate an image (around 16 seconds with ControlNet), while 2048 pixels significantly increases generation time (up to 179 seconds). The resolution is an important factor in determining both the quality and the processing time of the generated images.
Highlights
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Explanation of the Union ControlNet model with different control conditions.
Use of different preprocess images for control net, including depth, Canny edge, and pose.
Overview of the Z-Image Turbo Text-to-Image workflow and model templates.
Detailed setup of the ComfyUI workflow, including the addition of an image for the control net.
Process of resizing the input image without cropping using the resize image node.
Installation instructions for updating ComfyUI to the latest nightly version.
Step-by-step guide on how to load the ControlNet model with the necessary patches.
Using the pre-processed image in the Quen Image Diff Sync ControlNet node.
Introduction to multiple control net conditions, including depth, Canny, and pose estimators.
Implementation of the auxiliary pre-process node for simplified control net setup.
Demonstration of control net models with different conditions like depth, pose, and Canny edge.
Benchmark of inference timeZ-Image Turbo ControlNet for Z-Image Turbo model with and without control net.
Time comparison for generating images with Z-Image Turbo at different resolutions (1024px vs 2048px).
Performance impact of adding ControlNet and reference image for Z-Image Turbo model.
Wrap-up and conclusion with a thanks to the viewers and an invitation to return for future updates.