INSANE Photorealism with Z Image Turbo + 2-Step Upscale
Takeaways
- 😀Z Image Table generates realistic images in seconds with the help of BF16 model, creating jaw-dropping photorealistic details using the Z-Image-Turbo API.
- 💻 To start, download the necessary models, including the diffusion model, text encoder, and VAE model.
- 🔧 Install the custom nodes for the workflow, including the RG3 custom node and Comfy UI CVR upscaler.
- ⚙️ Run the update file to ensure you have the most updated native nodes in Comfy UI before starting the workflow.
- 🎨 Set up the main workflow by adding the 'Load Diffusion Model' node, 'Load CLIP' node, and 'Load VAE' node.
- 🔌 Connect the model output to the K Sampler and the text to code through CLIP to generate images based on input prompts.
- 🖼️ Set the image resolution, width, and height according to your needs, and configure the batch size to 1 for a single image.
- 🚀 After the image is generated, connect the VAE node to decode the latent representation into a visible image.
- ⚡ The initial image may lack fine details, but the two-step upscale method enhances it significantly, boosting resolution and sharpness.
- 🔍 Use Seed VR and image comparer nodes to compare the before and after results, ensuring the upscale is done without oversharpenJSON error correctioning.
- 💾 Save the final, enhanced image and explore the complete tutorial for the full process, including Z Image Table tutorial how to unlock extra assets and workflows.
Q & A
What is the main focus of the video tutorial?
-The tutorial focuses on how to use the Z image table model for generating realistic images in seconds, and a two-step upscaling method to enhance the image detail into photorealistic quality.
What are the necessary models to download for this workflow?
-The required models include the main diffusion model (BF16), the text encoder model (Quen 3), and the VAE model, which are all needed for proper functionality within Comfy UI.
How do you update Comfy UI before starting the workflow?
-To update Comfy UI, you need to go to your directory, find the 'update_y_ui' file, and run it to ensure you have the most up-to-date native nodes.
Which custom nodes are needed for this workflow?
-The custom nodes required for this workflow are the RG3 custom node, the 'CG use everywhere' node, and the Comfy UI CVR upscaler.
What is the role of the 'load diffusion model' node in the workflow?
-The 'load diffusion model' node is used to detect and load the Z image table BF16 model that was downloaded earlier, which is essential for generating the initial image.
What does the 'clip text and code' node do?
-Z Image Turbo TutorialThe 'clip text and code' node translates the text prompt into a language that large AI models can understand, allowing the AI to generate an image based on the description provided.
How do you address errors in the workflow, such as missing connections?
-If you encounter an error, such as a missing connection, check that all nodes are correctly linked. For example, ensure the 'clip text and code' node is properly connected to both the positive and negative prompts in the K sampler.
What does the upscaling method in this tutorial achieve?
-The upscaling method enhances the resolution of the generated image, transforming a low-quality image into a sharper, clearer version with higher photorealistic detail using a two-step process.
What are the settings used in the two-step upscale method?
-The two-step upscale method uses the 'latent upscale' node with a scaling factor of 1.5, and then applies the 'Seed VR' upscaling method to sharpen the image while maintaining a natural look without oversharpening. For advanced users, the Z image API offers additional tools to enhance this process.
Why is the 'Seed VR' method important in the upscaling process?
-The 'Seed VR' method is used to further enhance image details and clarity while ensuring that the image remains natural, avoiding common issues like oversharpening that can occur with other upscaling methods.
Outlines
- 00:00
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This paragraph covers the initial steps for installing and setting up Z Image Table within the Comfy UI environment. It begins with downloading essential models, such as the BF16 diffusion model, text encoder (quen 3), and VAE model. Detailed instructions are provided for saving and locating the models in the correct directories within the 'diffusion models' and 'text encoders' folders. The process also includes running the update script to ensure all the latest native nodes are available in Comfy UI. The user is then guided to install the necessary custom nodes (RG3 custom node, CG use everywhere node, and Comfy UI CVR upscaler) and restart the UI to ensure proper configuration.
- 05:01
🖼️ Building the Workflow: Text-to-Image Model
This paragraph walks through the creation of a basic text-to-image generation workflow within Comfy UI. The user is instructed to add various nodes such as the 'load diffusion model,' 'load clip,' 'load VAE,' and 'K sampler' to the canvas. The process involves configuring nodes to handle text input, model outputs, and image sampling settings. The user is shown how to connect these nodes and make adjustments to parameters like batch size and resolution for the generated image. The first test run reveals an issue (missing negative prompt), which isSetting up Z image table corrected before running the model again. The paragraph emphasizes the speed of Z Image Table, generating the image within 30-40 seconds.
- 10:01
🔍 Enhancing Image Quality with Upscaling
In this paragraph, the focus shifts to improving the resolution and detail of the generated image. The user is guided to apply a two-step upscale process, starting by copying and pasting the K sampler node. The 'latent upscale' node is introduced, and the user is shown how to adjust its settings for resolution enhancement. A key step is adjusting the denoising value to preserve image quality. The workflow is further refined by using the 'Seed VR' upscale method, which sharpens details without over-sharpening, ensuring a more natural result. A comparison between the low-quality and enhanced images demonstrates the effectiveness of the upscale process.
💾 Saving and Final Thoughts on Z Image Table
The final paragraph discusses the concluding steps in the workflow. After completing the upscaling process, the user is reminded to save the final image using the save node. The workflow is organized into two groups: 'Group One: Text-to-Image' and 'Group Two: Latent Upscale,' with the addition of a third group for Seed VR upscaling. The tutorial closes with a reminder to check the video description for the Z Image Table link and additional resources. The creator invites viewers to leave a like, join the creators' resources, and check out a related video for a one-step upscaling workflow. The video concludes with a thank-you message and an invitation to the next tutorial.
Mindmap
Keywords
💡Z Image Turbo
Z Image Turbo refers to an AI model used to generate realistic images at high speed. It utilizes cutting-edge technology to produce photorealistic results in seconds. In the video, it is highlighted as a breakthrough in generating realistic images with remarkable efficiency. The speaker tests and demonstrates its power in comparison to other models, showcasing its ability to generate high-quality outputs quickly.
💡Comfy UI
Comfy UI is the user interface where the AI models and workflow are integrated and operated. It serves as the platform for running the Z Image Turbo model, allowing users to load different models (such as the diffusion and VAE models) and configure the settings for image generation. The video guides users on how to install and navigate Comfy UI for setting up their workflows.
💡Diffusion Model
A diffusion model is an AI model that generates images by reversing a noise process. Starting from random noise, it iteratively refines it to create coherent images based on a given prompt. In the video, the speaker shows how to download and use a diffusion model for the Z Image Turbo, particularly a BF16 model, which is optimized for betterZ Image Turbo guide performance and quicker results.
💡Text Encoder (Quen 3 Language Model)
A text encoder is a model that converts text descriptions into a form that can be understood by other AI models, particularly for generating images from text prompts. The 'Quen 3' language model is a specific version used in the workflow described in the video. It enables the AI to interpret the user's textual input and translate it into a detailed visual output, contributing to the overall accuracy and realism of the generated images.
💡VAE Model
The VAE (Variational Autoencoder) model is used in AI workflows to decode latent representations into images. It helps improve the quality of generated images by refining their details. In the video, the VAE model is part of the image generation process in Comfy UI, ensuring the final output is more realistic by decoding the latent space into a visual result.
💡K Sampler
The K Sampler is a node used in the Comfy UI workflow to sample and generate the final image based on the input model and parameters. It determines how the AI interprets the latent image data and transforms it into a visual form. In the video, the speaker shows how to use the K Sampler in conjunction with other nodes, ensuring that the image generation process runs smoothly and efficiently.
💡Latent Image
A latent image refers to the intermediate form of an image that exists in the 'latent space' of an AI model. It is not a fully visible image but a representation that needs to be decoded into something recognizable. In the video, latent images are used as a part of the workflow where initial low-quality images are upscaled and refined to produce high-quality outputs.
💡Upscaling
Upscaling is the process of increasing the resolution of an image to make it clearer and more detailed. The video outlines a two-step upscaling method that improves the quality of images generated by the Z Image Turbo model. The first step involves a basic upscale, followed by further enhancements using Seed VR, ensuring that the final image looks sharp and photorealistic while minimizing artificial sharpening.
💡Seed VR
Seed VR is a custom node used in the video’s upscaling process to refine image details and enhance the photorealism of generated images. It works by comparing a low-quality generated image with an upscaled version, minimizing oversharpening and improving the natural look of the final output. The video demonstrates how to use Seed VR to maintain the integrity of the image while enhancing details.
💡CFG (Classifier-Free Guidance)
CFG, or Classifier-Free Guidance, is a parameter used in diffusion models to control the strength of the guidance that steers the image generation towards the desired result. In the video, the CFG value is set to 1, meaning the AI is instructed to follow the input prompt without being overly constrained by the classifier, allowing for more creative freedom in the output.
Highlights
AI models are launching every week, but Z image table's photorealism generates results in seconds.
The video covers how to install and run Z image table inside Comfy UI for photorealistic images.
The required models for this workflow include a BF16 diffusion model, text encoder, and VAE model.
The Z image table model generates realistic images in seconds, and it's capable of running without a GGUF model.
The two-step upscale method enhances image detail from good to jaw-dropping photorealistic quality.
The installation process involves saving models into specific directories and running an update script in Comfy UI.
Custom nodes like RG3 custom node and Comfy UI CVR upscaler are essential to the workflow.
The text encoder node translates prompts into a language models can understand, making AI image generation more precise.
After connecting the nodes, the first image is generated within 30 to 40 seconds, showcasing impressive realism.
The first generated image is highly realistic, but zooming in shows low detail, prompting the need for upscaling.
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The denoising value in the upscale node is set to 0.45 to avoid over-altering the image during upscaling.
To further enhance the image, a two-step upscale method using Seed VR results in sharper, more natural details.
The Seed VR method prevents oversharpening, creating a more natural look compared to other upscaling models.
The final image can be saved after comparing the pre- and post-upscale results using the image comparer node.