What You Will Learn

• Build and run ComfyUI upscaling workflows using tile diffusion
• Choose and configure models (SDXL, Flux, Flux Mania, SD 1.5)
• Tune denoise, tile size, overlap and operation node to minimize artifacts
• Optimize pipelines for low-VRAM hardware and cloud execution
• Troubleshoot common issues and compare UP1 (diffusion) vs UP2 outputs

Study Guide

Introduction: Why Mastering AI Image Upscaling in ComfyUI Matters

Upscaling AI-generated images isn’t just about making pictures bigger,it’s about unlocking detail, maximizing quality, and pushing the limits of your workflow, even if your hardware isn’t top-tier. If you’ve ever struggled with artifacts like weird bands, unwanted objects, or images that lose their magic at higher resolutions, this guide is for you.
By the end of this course, you’ll know how to: Avoid common upscaling pitfalls, integrate advanced features like tile diffusion, tailor workflows for your hardware, and choose the best models and settings for your creative goals. Whether you’re a digital artist, researcher, or just curious about the guts of AI image enhancement, you’ll walk away with practical knowledge,and the confidence to create images that stand out.

Understanding the Landscape: What Is AI Image Upscaling?

AI image upscaling uses deep learning models to increase the resolution of images, recreating finer details and textures that standard software often can’t. This isn’t just for making images look bigger; it’s about making them look better,clearer, sharper, and more lifelike. In the context of ComfyUI, upscaling is powered by a blend of models (like SDXL, Flux, Flux Mania), nodes, and custom workflows that you can adapt to your needs.

Let’s start with two real-world scenarios:
Example 1: An artist generates a beautiful 1024x1024 portrait, but wants a 4K print for a gallery. Standard upscaling adds blur and artifacts. With ComfyUI’s advanced workflow, they upscale to 4096x4096, preserving brush strokes and facial features.
Example 2: A social media manager needs to convert a batch of AI-generated product photos into marketing banners, but their GPU’s low VRAM keeps crashing big jobs. Using tile diffusion and workflow tweaks, they process images smoothly, even on modest hardware.

The Problem: Artifacts and Upscaling Limitations

Most upscaling tutorials skim over a persistent issue: artifacts. These are unwanted visual elements,vertical bands, bars, or weird “extra” objects (like an extra eye),that creep in, especially with certain models like Flux. They’re most obvious when pushing images beyond their native resolution. This course is laser-focused on solving those problems, not just with band-aid fixes, but by overhauling the entire upscaling approach.

Example 1: You upscale a 1024x1024 image to 4096x4096 using an old Flux workflow and notice faint vertical bars across the background. The new tile diffusion node eliminates these, giving you clean results.
Example 2: Using Flux Mania, you spot an extra eye on a generated face,an artifact from how tiles are assembled. The right settings and prompt tweaks can fix this.

ComfyUI: A Node-Based Playground for AI Image Processing

ComfyUI turns image processing into a visual experience. It’s a node-based system,think of it like building a Lego set, but each brick is a function: loading models, scaling images, applying upscaling, and saving outputs. You connect nodes into workflows that can be reused, shared, and customized.

Example 1: Drag in an “image load” node, connect it to a “scale down” node, then to the upscaler, and finally to an “image save” node. Instantly, you have a working upscaling pipeline.
Example 2: Add a “text-to-image” node at the front of the workflow and generate new artwork, upscaling in the same process,no manual steps between generation and enhancement.

Core Concepts and Key Models

To use upscaling effectively in ComfyUI, get familiar with the main models and nodes:

• SDXL: A high-fidelity image generation model, great for both realism and illustration styles.
• Flux / Flux Mania: Alternative diffusion models, with Flux Mania excelling in realism but sometimes adding imperfections. Flux is preferred for clean illustrations.
• Stable Diffusion 1.5: Lightweight, suitable for hardware with lower VRAM.
• Upscalers (e.g., scax): Specialized models that enhance image resolution,often 4x, but scalable with workflow adjustments.
• Custom Nodes: “easy use,” “RG3,” “tile diffusion,” and (for Flux) “Guff.” These nodes extend ComfyUI’s capabilities beyond its defaults.

Example 1: For a photo-realistic portrait, use SDXL or Flux Mania with the tile diffusion node to upscale without artifacts.
Example 2: For a comic-style illustration, use Flux and select a lower Denoise value to preserve line work.

Setting Up: Installing Models, Nodes, and Workflows

Before you dive into upscaling, you need the right tools and models in the right places. Here’s how you do it:

• Workflows: Download from the community Discord server (link provided in the tutorial).
• Custom Nodes: Use the ComfyUI Manager or manual installation to add “easy use,” “RG3,” “tile diffusion,” and (for Flux) “Guff.”
• Model Placement:
  • “checkpoints” folder: For main models (SDXL, SD 1.5, etc.)
  • “upscale models” folder: For upscalers like scax
  • “diffusion models” folder: For Flux and Flux Mania
  • “clip” folder: For CLIP models

Example 1: You want to use the Flux Mania model. Download it and place it in the “diffusion models” folder, then add the “Guff” node via the Manager.
Example 2: To use the scax upscaler, download the model and add it to “upscale models,” ensuring your workflow references the correct path.

Tip: Always double-check folder names,misplacing a model will prevent ComfyUI from seeing it.

Workflow Foundations: Image-to-Image and Text-to-Image+Upscale

There are two main workflow types:

• Image-to-Image Upscaling: Start with an existing image, upscale it, and save the result.
• Text-to-Image plus Upscaler: Generate a new image from a prompt, then upscale it,either in one go, or in two steps for more control.

Example 1: You have a 1024x1024 landscape. Drag it into the image-to-image workflow, upscale, and you’re done.
Example 2: You want to experiment with different prompts. Use the text-to-image workflow, preview the results, and only enable upscaling when you’re happy with the base image.

Best Practice: Use the “fast group mutter” node in ComfyUI to enable/disable the upscaler group. This saves time,no need to upscale every single generation if you’re still tweaking your prompt.

The Tile Diffusion Node: The Secret Weapon Against Banding

One of the most frustrating issues in upscaling is the appearance of vertical bands or bars, especially when using models like Flux. The tile diffusion node is the breakthrough solution. Instead of processing the image as one huge block, it splits it into smaller tiles, runs the diffusion/upscaling process on each, and stitches them together seamlessly.

Why does this matter? Large images are hard for some models and hardware to handle. By working on smaller tiles, you not only avoid artifacts but also sidestep VRAM bottlenecks.

Example 1: You upscale a 2048x2048 portrait with tile diffusion. The result is artifact-free, with smooth transitions between tiles.
Example 2: You try the same image without tile diffusion and see faint vertical lines. Switching to the tile diffusion node instantly fixes the issue.

Tip: Adjust the tile size and overlap for your hardware. Smaller tiles and larger overlaps mean fewer artifacts but use more VRAM and take longer.

Key Nodes and Their Roles in Upscaling

Let’s break down the most important nodes in these workflows and how to use them:

• Image Scale Down Node: Shrinks large images to a manageable size before upscaling,crucial for VRAM-limited setups. Only reduces size; doesn’t enlarge.
• Image Resize or Scale Image to Total Pixels Node: Use these if your input image is too small and needs to be enlarged before upscaling.
• Operation Node: Controls the upscaling factor. For example, a value of 1 uses the full 4x power of the upscaler; 0.5 will upscale by 2x instead.
• Denoise Value (in K Sampler): Dictates how much the upscaling process “reimagines” the image. Lower values preserve the original; higher values add creativity (and sometimes artifacts).

Example 1: You have a 4096x4096 image but only 6GB VRAM. Use “image scale down” to shrink it to 1024x1024, then upscale back up in tiles.
Example 2: Your input image is only 512x512. Use “image resize” to boost it to 1024x1024 before sending it through the upscaler.

Best Practice: Always use multiples of 64 for image dimensions. This matches model expectations and reduces processing errors.

Choosing the Right Models for Your Project

Different models excel in different areas:

• SDXL (Juggernaut, etc.): Great for detailed, versatile upscaling. Use with denoise values between 0.2 and 0.4 for best results,lower for fidelity, higher for creative changes.
• Flux Mania: Preferred for realism, but can introduce slight “imperfections” or artifacts. If you see issues like extra eyes, adjust prompt, tile size, or denoise.
• Flux: Best for clean illustrations; less prone to introducing realism-based artifacts.
• SD 1.5: If you’re on a tight VRAM budget, this lighter model is your friend.

Example 1: For a hyper-realistic portrait, Flux Mania with a denoise of 0.8 brings out photoreal details.
Example 2: For a manga-style illustration, Flux with a denoise of 0.4 keeps lines crisp and colors clean.

Tip: If you’re using a Mac, Flux Mania (an FP8 model) may not work. Use Flux Dev or SDXL instead.

Fine-Tuning: Denoise and Its Impact on Results

The denoise parameter is one of the most powerful,and misunderstood,controls in upscaling:

• Lower denoise (e.g., 0.2): Keeps the upscaled image very similar to the original. Essential for preserving likeness or specific details.
• Medium denoise (0.4–0.6): Adds moderate detail and texture. Good for images that need a touch of enhancement.
• High denoise (0.8+): Makes the model more creative. Can add new details but sometimes introduces oddities (like extra eyes or misplaced features).

Example 1: Upscaling a product photo? Set denoise to 0.2 to preserve branding and color accuracy.
Example 2: Want your landscape to look more painterly? Try denoise at 0.6 for subtle texture and mood changes.

Best Practice: For SDXL Juggernaut, 0.2–0.4 is the sweet spot. For Flux Mania, start at 0.8, but don’t be afraid to experiment. If you see strange artifacts, dial it back.

Managing Hardware Constraints: Working with Low VRAM

Not everyone has a top-end GPU. The tutorial gives several strategies for maximizing results even on modest setups:

• Use smaller model versions (e.g., Q4 for GUF models, smaller CLIP versions).
• Reduce input image size with the “image scale down” node.
• Decrease tile width and height in the tile diffusion node,smaller tiles are easier to process.
• Lower batch size to reduce VRAM demands.
• As a last resort, use a cloud service like RunningHub to access more powerful hardware remotely.

Example 1: On a 4GB GPU, set tile size to 512x512 and batch size to 1. Upscale a 1024x1024 image without crashes.
Example 2: Upload your workflow to RunningHub and run it in the cloud, bypassing local hardware limits altogether.

Tip: Always monitor VRAM usage and adjust settings before running a large batch.

Workflow Efficiency and Customization

ComfyUI’s modular approach lets you tweak workflows for speed and quality:

• Enable or disable the upscaler step with the “fast group mutter” node,ideal for repeated text-to-image generations where you only want to upscale the final pick.
• Adjust tile overlap for smoother transitions between tiles. Higher overlap reduces visible seams but increases processing time.
• Use the “flux resolution calculator” node to auto-select optimal sizes for Flux models, saving trial and error.

Example 1: You generate ten different images with text-to-image, disable upscaling until you find the best one, then turn it on for the final output.
Example 2: You notice a faint grid in the upscaled image. Increase the tile overlap from 64 to 128 pixels and rerun for seamless results.

Best Practice: Take advantage of ComfyUI’s “image compare” node to preview differences between original and upscaled outputs side by side.

Multiple Upscale Outputs: Understanding UP1 and UP2

The presented workflows typically produce two upscaled images:

• UP1: Uses diffusion (often via the K sampler). Provides the best quality, detail, and realism. Usually preferred.
• UP2: A “quick” upscaler with no diffusion,just resizes the image. Faster but can be overly sharp or less detailed.

Files are saved with clear prefixes (UP1, UP2) so you can easily compare and choose.

Example 1: You look at both UP1 and UP2. UP1 has smooth, natural skin texture; UP2 is sharper but looks artificial. You choose UP1 for your project.
Example 2: For a quick social media post, UP2’s speed is perfect. For a print project, UP1’s quality wins out.

Tip: Use UP1 for any project where quality matters. Use UP2 for speed or where detail isn’t critical.

Troubleshooting Artifacts and Imperfections

Even with the best workflows, you may encounter issues,especially with advanced models like Flux Mania. Common problems include “extra eyes” or minor distortions caused by how tiles are generated and stitched.

Solutions:

• Improve your prompt,sometimes better guidance reduces weird outputs.
• Change the random seed for a new generation.
• Increase tile dimensions (e.g., 1024p), though this slows processing.
• Reduce the denoise value to keep results closer to the original.
• If minor, fix artifacts in post-processing (e.g., Photoshop).

Example 1: You see an extra eye in a portrait. Lower denoise from 0.8 to 0.6 and rerun,problem solved.
Example 2: For a persistent artifact, try a new seed or write a more specific prompt (“single person, closed eyes”).

Best Practice: Don’t be afraid to iterate. The first result isn’t always the best,ComfyUI makes it easy to tweak and rerun.

Organizing and Managing Outputs

Clarity is key when working with multiple upscaled images. The workflows discussed save outputs with clear prefixes:

• “UP1”: First, diffusion-based upscale (best quality)
• “UP2”: Simple model upscale (larger or sharper, but not always better)
• “img”: The original generated image, especially in text-to-image workflows

Example 1: After running a batch, you quickly spot UP1 files for portfolio use and archive UP2 for reference.
Example 2: You compare “img” and “UP1” in ComfyUI’s image compare node to check improvement.

Tip: Use consistent naming conventions for easy browsing and retrieval later.

Practical Applications and Real-World Use Cases

This workflow isn’t just for AI artists. Here are some scenarios where mastering upscaling in ComfyUI pays off:

• Creating print-ready posters or fine art from AI-generated images
• Enhancing low-res renders for animation or comics
• Upscaling product photos for e-commerce (with color and detail fidelity)
• Batch-processing images for research or advertising

Example 1: A designer takes a fuzzy 512x512 AI logo and upscales it to 2048x2048 for a large-format banner, preserving vector-like sharpness.
Example 2: An illustrator generates a comic panel at low res, then uses tile diffusion and Flux to produce a print-quality page.

Advanced Customization: Pushing Beyond the Basics

Once you’ve mastered the fundamentals, you can experiment with:

• Custom node chaining: Combine upscaling with other processing steps (e.g., color grading, style transfer).
• Batch automation: Process entire folders of images by scripting input/output nodes.
• Workflow sharing: Export your favorite setups and share on the ComfyUI Discord for feedback and improvement.

Example 1: You create a workflow that adds a vignette after upscaling, all in one pass.
Example 2: You batch upscale 50 images overnight, adjusting tile size dynamically based on VRAM usage.

Tip: Join the Discord server for a constant stream of tips, troubleshooting, and community-created workflows.

Common Questions and Troubleshooting

Q: Where do I get the workflows and custom nodes?
A: Download workflows from the author’s Discord server (link in the video description). Install custom nodes via ComfyUI’s Manager or manually from their repositories.

Q: What’s the best model for my hardware?
A: For high VRAM, use SDXL or Flux Mania. For mid/low VRAM, use Flux Dev or SD 1.5. Adjust tile size and batch size as needed.

Q: Why do I see artifacts?
A: Check your tile size, overlap, and denoise settings. Some models (like Flux Mania) are more prone to artifacts at high denoise or low tile overlap.

Q: Can I run this on a Mac?
A: FP8 models like Flux Mania may not work. Use Flux Dev or SDXL instead.

Q: How do I compare outputs?
A: Use the image compare node or simply view UP1 and UP2 side by side in your file explorer or image viewer.

Best Practices for Consistent, High-Quality Upscaling

• Always use multiples of 64 for dimensions to avoid model errors.
• Start with lower denoise values and increase only if you want more creative changes.
• Use tile diffusion for large images to minimize artifacts.
• Adjust tile overlap and batch size to match your hardware limits.
• Preview your results,small tweaks make a big difference.
• Keep your models and nodes organized for easy swapping in workflows.

Example 1: You consistently get smooth, artifact-free upscales by sticking to 1024x1024 tiles with 128 overlap on your 8GB GPU.
Example 2: You avoid crashes by scaling down oversized inputs before upscaling, especially on a 4GB GPU.

Iterative Workflow: Why It’s Smarter Not to Upscale Everything

One of the biggest time-savers is the iterative approach in text-to-image plus upscaler workflows. Instead of upscaling every single generation, you generate images first, review them, and only upscale the best. This saves VRAM, processing time, and frustration.

Example 1: You generate 20 variations of a character, select your favorite, and only then enable the upscaler group to create the final high-res version.
Example 2: You tweak your prompt five times, but only upscale the last, perfect image.

Tip: Use the “fast group mutter” node to toggle the upscaler group on and off instantly.

Experiment, Learn, and Connect

The real magic of ComfyUI is in experimentation. No two projects are the same, and every combination of prompt, model, and node gives you something new. Don’t just stick to the default settings,push the boundaries, use community workflows, and share your results for feedback.

Stay connected: The Discord server is not only for downloads, but a hub for troubleshooting, tips, and sharing discoveries. If you run into issues, chances are someone else has solved them,or wants to help you figure them out.

Example 1: You post a before-and-after of your upscaled image and get feedback on how to further reduce artifacts.
Example 2: You help a new user troubleshoot a VRAM error by sharing your tile size and overlap settings.

Conclusion: Bringing It All Together

Mastering AI image upscaling in ComfyUI is about more than pressing a button. It’s about understanding the strengths and quirks of each model, knowing how to optimize nodes like tile diffusion, and tuning every parameter for your hardware and creative vision. You’ve learned how to avoid common pitfalls like banding and artifacts, adapt workflows for any GPU, and choose between speed and quality with UP1/UP2 outputs.

The key takeaways:

• Use tile diffusion to solve banding and scaling issues,essential for clean, high-res outputs.
• Always match your workflow to your hardware,scale down, adjust tiles, and batch size as needed.
• Choose models based on your needs,realism, illustration, or hardware constraints.
• Iterate, compare, and only upscale what matters,it’s about quality, not just quantity.
• Don’t hesitate to experiment and leverage the community for continuous learning.

Apply these skills and you’ll consistently produce stunning, professional-grade AI images,no matter your hardware setup or artistic style. The path to mastering upscaling is iterative: tweak, test, and keep pushing for better results. And when you hit a wall, remember: the answer is almost always just a node, prompt, or setting away.

Frequently Asked Questions

This FAQ section provides clear, actionable answers to common and advanced questions about upscaling AI-generated images using ComfyUI, as covered in the tutorial episode focused on updated workflows with SDXL and Flux models. The goal is to clarify concepts, troubleshoot typical challenges, and help users,from beginners to experienced professionals,achieve high-quality image upscaling results regardless of hardware limitations.

What is the main goal of this tutorial series episode?

The main goal of this episode is to demonstrate how to effectively upscale AI-generated images using ComfyUI, with a focus on overcoming banding or vertical bars that can appear with certain models like Flux.
The tutorial presents improved workflows using both SDXL and Flux models, along with variations for different needs and hardware capabilities. The episode aims to help users achieve higher-quality upscales while minimizing common artefacts, making the process more accessible and consistent.

What are the primary models and tools used for upscaling in this tutorial?

The tutorial primarily focuses on using ComfyUI with various models and custom nodes.
The main models discussed are SDXL (with emphasis on the Juggernaut model) and Flux (including Flux Dev and Flux Mania). Essential custom nodes required for the presented workflows include easy use, RG3, and tile diffusion. Some workflows also use the Guff node and the Flux resolution calculator. These tools collectively enable flexible, high-quality upscaling workflows.

How does the tile diffusion node help with upscaling large images?

The tile diffusion node is crucial for upscaling large images, especially with the Flux model, because it helps prevent banding issues.
It works by breaking the image into smaller "tiles" and generating each section independently before seamlessly combining them. This method allows for processing larger images without visual artefacts and makes upscaling possible on machines with less VRAM. For example, using tile diffusion, a user can upscale a 2048x2048 image in manageable chunks rather than overwhelming their graphics card.

What is the difference between Upscale 1 and Upscale 2 images in the presented workflows?

Upscale 1 is the primary upscaled image resulting from the diffusion process in the K sampler, while Upscale 2 is a further simple upscale of Upscale 1 without another diffusion pass.
Upscale 1 typically offers better quality and detail because it adds information through the AI model's diffusion process. Upscale 2 simply increases the image size and may introduce sharpness, but often lacks the refined quality of Upscale 1. For most use cases, Upscale 1 is preferred unless an even larger image is needed for specific purposes, like large-format printing.

How can users with lower VRAM graphics cards still perform upscaling?

Users with lower VRAM can adjust several workflow settings to fit their hardware limitations.
This includes using smaller models (such as Q4 versions of Flux or SD 1.5), reducing input image size before upscaling with the "image scale down" node, lowering tile width and height in the tile diffusion node, decreasing tile overlap, and reducing batch size to one or two. Alternatively, users may consider a cloud-based service like RunningHub. These adaptations allow users with basic graphics hardware to still achieve quality upscales.

What are the advantages and disadvantages of using the Flux Mania model for upscaling?

The Flux Mania model is noted for realistic upscales and is especially favored for photographic results.
It introduces "imperfections" that can boost realism, which is useful for photos but less ideal for clean illustrations. One limitation is that the FP8 version of Flux Mania does not run on Mac systems, so Mac users need to use Flux Dev. If a project requires both realism and compatibility, users should consider these trade-offs.

How can users troubleshoot issues like extra eyes appearing in upscaled images?

Artefacts like extra eyes often result from the tiling process or high Denoise values.
To address this, users can: improve the prompt, generate with a different seed, increase tile width and height (though this increases processing time), or reduce the Denoise value to keep the result closer to the original. For instance, lowering Denoise from 0.5 to 0.3 can yield a more faithful upscale with fewer unwanted artefacts.

What is the recommended workflow for generating and then upscaling images with ComfyUI?

The recommended approach is to generate the desired image first, then enable the upscaler section of the workflow.
This is typically managed by a "fast group muter" node. Users generate images using the text-to-image workflow, select a satisfactory result (often fixing the seed for reproducibility), and only then activate the upscaler. This method saves time and processing power by upscaling only selected images, not every generated output.

What is the main problem the updated upscaling workflows aim to solve, and which models are discussed?

The main problem addressed is banding or vertical bars that often appear during upscaling, especially with the Flux model.
The tutorial explores solutions using both Flux and SDXL models, showing how updated workflows and specific nodes can minimize or eliminate these artefacts for cleaner upscales.

What are the two types of workflows presented for upscaling integration?

The tutorial covers two main workflow types: image-to-image for simple upscaling and text-to-image plus upscaler for generating and upscaling together.
Image-to-image is used for upscaling existing images, while the text-to-image plus upscaler workflow lets users generate new images and upscale in one pipeline, providing flexibility for different creative needs.

Where can users find and download the workflows discussed in the tutorial for free?

Workflows are available for free download from the author's Discord server.
After joining the Discord, users can access shared workflow files, instructions, and community support to help set up and customize their own upscaling pipelines.

Which custom nodes are required for the discussed upscaling workflows, and how are they installed?

The required custom nodes are easy use, RG3, and tile diffusion.
These can be installed via the ComfyUI Manager, which automates the process, or manually by downloading from their respective repositories and copying them into the custom nodes directory. Installation ensures all workflow components function as intended.

What is the role of the image scale down node, and when should it be replaced by another node?

The image scale down node reduces images that are too large (typically over 1024 pixels) before upscaling.
If the original image is already smaller or needs resizing to a specific target, nodes like image resize or scale image to total pixels may be more suitable. This step is key for optimizing VRAM usage and workflow compatibility.

What is the recommended Denoise value range for the SDXL model, and how does it affect results?

For SDXL, a Denoise value between 0.2 and 0.4 is recommended.
Lower values keep the upscaled image closer to the original, while higher values introduce more creativity and variability. For business use,where accuracy is often essential,lower values are typically preferred to avoid drifting too far from the original image.

Why might Mac users be unable to run the Flux Mania model, and what alternative is suggested?

Mac users often can't run Flux Mania because it's an FP8 model, which is incompatible with Mac hardware.
The recommended alternative is the Flux Dev model, which provides similar upscaling capabilities but is compatible with Macs.

In the text-to-image workflow with upscaler, how does the user control when upscaling begins?

Upscaling is triggered by enabling the upscaler group of nodes using a "fast group mutter" node.
This allows users to first focus on generating an image, and only after they're satisfied, manually enable the upscaling process. This avoids unnecessary processing and speeds up the creative workflow.

How do SDXL and Flux models compare for AI image upscaling in terms of performance, image quality, and style?

SDXL is known for versatility and high-quality results, especially for illustrations and general-purpose upscaling, while Flux excels at realism, particularly with photos.
SDXL tends to be more compatible with a variety of hardware, and its denoise settings offer precise control. Flux, especially Flux Mania, introduces subtle imperfections for photorealism, but may require more VRAM and isn’t always ideal for clean illustrations. When performance and compatibility matter, SDXL is often the safer choice; for realism, Flux is preferred.

Why is the tile diffusion node important in ComfyUI upscaling workflows?

Tile diffusion enables upscaling of large images by processing them in manageable sections, avoiding artefacts like banding.
This approach allows users with limited hardware to tackle big projects and reduces common issues found in traditional upscaling, such as visible seams or distortions. For example, marketing teams upscaling product photos can benefit from tile diffusion to produce crisp, artefact-free visuals for print or display.

What is the benefit of the iterative process in the text-to-image plus upscaler workflow?

Generating the base image first, then selectively upscaling, optimizes workflow speed and resource use.
Users can iterate and refine their images before committing to upscaling, preventing wasted time and VRAM. This is especially useful in professional settings where multiple variations may be tested before finalizing a high-resolution version.

What adjustments are recommended for users with limited hardware or low VRAM?

Recommended adjustments include using smaller or quantized models (e.g., Q4 or SD 1.5), reducing input image size, lowering tile size and overlap, and reducing batch size.
Cloud-based options like RunningHub are also suggested. This ensures users with everyday computers can still achieve impressive upscaling without crashes or slowdowns.

What is Denoise in AI image upscaling, and how does its value influence results?

Denoise controls how much the upscaling process alters the original image.
A low value keeps the output very close to the input, ideal for preserving detail. A higher value lets the AI add creative changes, which can be useful for artistic effects but may introduce unwanted artefacts. For business applications like upscaling product shots, a conservative Denoise setting (0.2–0.4) is usually best.

What are some common artefacts or issues encountered during AI upscaling, and how can they be resolved?

Common artefacts include banding, extra facial features, seam lines, and over-sharpening.
Solutions include using tile diffusion to avoid banding, adjusting Denoise to prevent overprocessing, refining prompts to reduce unwanted details, resizing tiles, and selecting a different seed for generation. For example, reducing Denoise can help when extra eyes or limbs appear in portraits.

What are practical business use cases for AI image upscaling using ComfyUI?

Common use cases include improving resolution for print marketing materials, enhancing product images for e-commerce, and preparing large-format visuals for presentations or billboards.
For example, a design agency might use ComfyUI to upscale a low-res product shot to poster size, preserving detail and clarity for high-impact advertising.

How do prompt quality and seed selection affect upscaled image results?

Prompt quality guides the model’s understanding of desired features, while seed selection ensures reproducibility.
A well-crafted prompt can reduce unwanted artefacts during upscaling. Using a fixed seed allows users to recreate specific images with minor tweaks, making it easier to iterate and achieve consistent results, which is vital for branding or campaign work.

How can users keep their custom nodes up to date in ComfyUI?

Custom nodes are updated using the ComfyUI Manager or by manually downloading the latest versions from their repositories.
Regular updates ensure compatibility with new workflows and prevent errors or deprecated features from affecting results. Staying current is especially important for business users who rely on stable, predictable outputs.

Are there differences in running these upscaling workflows on Mac, Windows, or Linux?

Yes, some models (like Flux Mania in FP8) are incompatible with Macs, and installation steps can vary by OS.
Windows and Linux users typically have broader compatibility, while Mac users may need to use alternatives like Flux Dev. Always check node and model requirements before starting a workflow.

How can users customize upscaling workflows for specific needs or styles?

Workflows can be customized by swapping models, adjusting Denoise, changing tile sizes, or integrating new nodes.
For instance, users working on detailed illustrations might prioritize SDXL and fine-tune Denoise for accuracy, while photographers could opt for Flux Mania with larger tiles for realism. The modular structure of ComfyUI encourages experimentation and adaptation.

What does the image compare node do, and how is it used in upscaling workflows?

The image compare node allows users to view two images side-by-side within ComfyUI.
This is particularly useful for evaluating the quality of upscales, comparing original and upscaled versions, and presenting results to clients or stakeholders for feedback and approval.

How do tile size and overlap settings affect upscaling results and performance?

Smaller tile sizes reduce VRAM requirements but can increase seams or artefacts, while larger tiles offer better quality but use more memory.
Tile overlap helps blend edges between tiles, reducing visible seams. Business users with limited hardware may need to balance these settings for optimal output without crashes.

Can multiple images be upscaled at once, and what are the considerations?

Batch upscaling is possible by increasing batch size, but it requires more VRAM and can slow down processing.
For large projects, like updating an e-commerce catalog, it may be more efficient to process smaller batches to avoid system overload.

What cloud-based alternatives exist for AI image upscaling if local hardware is insufficient?

Cloud services like RunningHub allow users to run ComfyUI workflows remotely, leveraging powerful GPUs without local hardware constraints.
This is ideal for businesses or individuals needing high-resolution outputs without investing in expensive graphics cards.

Is it better to manually adjust upscaling settings or use pre-built workflows?

Pre-built workflows save time and ensure reliability, especially for new users, while manual adjustment offers greater control for experienced practitioners.
Most business users start with proven templates and gradually customize settings as they gain confidence and require more nuanced outputs.

What are best practices for achieving high-quality upscaled images in ComfyUI?

Use appropriate models for your style and hardware, keep nodes updated, set Denoise conservatively for accuracy, fine-tune tile settings, and review results using the image compare node.
Testing different seeds and refining prompts also contribute to consistently sharp, artefact-free outputs.

If a workflow fails or produces errors, what troubleshooting steps should be taken?

Check for outdated or missing custom nodes, verify model compatibility with hardware, reduce image or tile sizes, and consult the workflow documentation or community resources.
Often, simply updating nodes or lowering batch size resolves common errors, making the workflow accessible even on modest systems.

Are there trends or emerging techniques in AI image upscaling that users should be aware of?

Techniques like tile diffusion, adaptive denoising, and quantized models for lower VRAM are gaining traction.
Staying informed about new nodes and workflow templates helps users consistently produce superior results and stay competitive in creative and business environments.