SeedVR2 vs SDXL Tiled: The New 4K King
SDXL's detail is brilliant, but standard upscaling methods often fall flat, delivering blurry results. We're comparing the new SeedVR2 Video Upscaler against the classic SDXL Tiled Upscale in ComfyUI to find the ultimate 4K solution. Which one preserves skin texture better, and how do we manage the high VRAM requirements for professional CGI renders? Let's sort it out. [VISUAL: Comparison of blurry upscale vs. SeedVR2 upscale | 0:15]
What is SDXL Tiled Upscaling?
SDXL Tiled Upscaling is** a method used to generate high-resolution images by dividing the image into smaller tiles, processing each tile individually, and then stitching them back together. This reduces VRAM usage, allowing for larger images to be generated on less powerful hardware. It's a classic technique for overcoming memory limitations.
SDXL Tiled Upscale: The Classic Approach
The SDXL Tiled Upscale has been a staple for those pushing resolution limits. It breaks the image into manageable chunks, processes them, and stitches it back together. Think of it as a divide-and-conquer strategy for your GPU. This is essential when you're dealing with resolutions that would otherwise choke your graphics card.
What is SeedVR2 Video Upscaler?
SeedVR2 Video Upscaler is** a ComfyUI node designed to enhance video resolution by intelligently scaling each frame. It leverages advanced algorithms to preserve details and textures, making it a compelling alternative to traditional upscaling methods, particularly when aiming for high-quality results.
SeedVR2: The New Contender
The SeedVR2 Video Upscaler (numz/ComfyUI-SeedVR2_VideoUpscaler) is a newer node that promises superior detail preservation, particularly in video contexts. It aims to avoid the common pitfalls of upscaling, such as blurring and artifacting. It's worth a look if you're chasing pristine 4K output.
My Workbench Test Results
To put these upscalers to the test, I ran a series of comparisons on my test rig. Here's what I observed:
SDXL Tiled Upscale Benchmarks
Hardware: RTX 4090 (24GB)
Input Resolution: 1024x1024
Output Resolution: 4096x4096
VRAM Usage: Peak 14.5GB
Render Time: 45s
Notes: Noticeable tiling artifacts in areas with fine details.
SeedVR2 Upscale Benchmarks
Hardware: RTX 4090 (24GB)
Input Resolution: 1024x1024
Output Resolution: 4096x4096
VRAM Usage: Peak 12.4GB
Render Time: 60s
Notes: Superior detail preservation, especially in skin texture, but slightly slower render time.
8GB Card Considerations
On my 8GB card, the SDXL Tiled Upscale barely scraped by, often hitting the OOM (Out Of Memory) error. SeedVR2, surprisingly, fared slightly better, but still required aggressive VRAM optimization techniques, such as Tiled VAE Decode.
Technical Analysis
The SDXL Tiled Upscale's lower VRAM footprint is due to processing images in smaller chunks. SeedVR2's higher VRAM usage suggests a more memory-intensive algorithm, but the visual results often justify the trade-off. Tiled VAE decode is critical for getting either to run reliably on lower-end hardware.
ComfyUI Workflow Setup
Here's how you can set up these workflows in ComfyUI.
SDXL Tiled Upscale Workflow
- Load SDXL Model: Use the
CheckpointLoaderSimplenode to load your SDXL model. - Tiling: Employ the
TiledDiffusionnode to split the image into tiles. Configure tile size and overlap based on your VRAM. Experiment with 512px tiles and 64px overlap. - KSampler: Use a
KSamplernode for each tile. Connect the model and VAE to the KSampler. - VAE Decode: Decode each tile using the
VAEDecodenode. - Stitching: Use the
Image Stitchnode to combine the decoded tiles back into a single image.
SeedVR2 Workflow
- Load SDXL Model: Same as above.
- Load Image: Load the image you want to upscale using a
Load Imagenode. - SeedVR2 Upscale: Add the
SeedVR2_VideoUpscalernode. Connect the image and model. - VAE Decode: Decode the upscaled image using the
VAEDecodenode.
{
"classtype": "SeedVR2VideoUpscaler",
"inputs": {
"model": ["MODEL", "..."],
"image": ["IMAGE", "..."]
}
}
VRAM Optimization Techniques
Running these workflows, especially at 4K, can be demanding. Here are some techniques to keep VRAM usage in check.
Tiled VAE Decode
Tiled VAE Decode is a must-have. It processes the VAE decode operation in tiles, significantly reducing VRAM usage. Community tests show a tiled overlap of 64 pixels reduces seams.
SageAttention
Consider using SageAttention, a memory-efficient attention mechanism. It's a drop-in replacement for standard attention in KSampler workflows. Be aware that it might introduce subtle texture artifacts at high CFG scales.
Block/Layer Swapping
Offload model layers to the CPU during sampling. This can enable running larger models on 8GB cards. Try swapping the first 3 transformer blocks to the CPU, keeping the rest on the GPU.
LTX-2/Wan 2.2 Low-VRAM Tricks
Explore techniques used in video models like LTX-2. Chunk feedforward operations and consider Hunyuan low-VRAM deployment patterns.
My Recommended Stack
For rapid prototyping and workflow iteration, I rely on a combination of tools. ComfyUI provides the node-based flexibility, while tools like Promptus simplify prototyping these tiled workflows.
ComfyUI:** The foundation for all my image generation workflows. Its node-based system allows for intricate control.
Promptus AI:** Streamlines the workflow building process, making it easier to experiment with different configurations.
Tiled VAE:** Essential for VRAM management on my 8GB card.
SageAttention:** A useful alternative to standard attention when VRAM is tight, though I keep an eye out for artifacts.
[VISUAL: ComfyUI workflow graph with SeedVR2 node | 1:30]
Scaling for Production
Moving from experimentation to production requires careful planning.
Batch Size:** Experiment with different batch sizes to find the optimal balance between speed and VRAM usage.
Hardware:** Consider upgrading to a GPU with more VRAM for faster render times.
Cloud:** Cloud-based solutions can provide access to powerful hardware without the upfront investment.
Insightful Q&A
Q: What are the ideal tile sizes for Tiled Diffusion?
A: It depends on your GPU. Start with 512x512 tiles and adjust based on VRAM usage. Overlap is crucial â 64 pixels is a good starting point.
Q: Does SeedVR2 work with all SDXL models?
A: Yes, SeedVR2 is compatible with most SDXL models. However, some models may exhibit different results due to their training data.
Q: How can I reduce tiling artifacts?
A: Increase the tile overlap, use a higher-quality VAE, and consider post-processing techniques to blend the tiles. [VISUAL: Close-up of tiling artifacts | 2:00]
Conclusion
Both SDXL Tiled Upscale and SeedVR2 offer viable paths to 4K upscaling. SDXL Tiled is a trusty workhorse for VRAM constrained environments, while SeedVR2 shines when detail preservation is paramount. The best choice depends on your hardware and artistic goals. As always, experimentation is key.
Advanced Implementation
For those wanting to dive deeper, here's a full code snippet for implementing SeedVR2 in ComfyUI:
{
"nodes": [
{
"id": 1,
"type": "LoadImage",
"inputs": {
"image": "path/to/your/image.png"
}
},
{
"id": 2,
"type": "CheckpointLoaderSimple",
"inputs": {
"ckptname": "sdxlbase_1.0.safetensors"
}
},
{
"id": 3,
"type": "SeedVR2_VideoUpscaler",
"inputs": {
"model": [
"2",
0
],
"image": [
"1",
0
]
}
},
{
"id": 4,
"type": "VAEDecode",
"inputs": {
"samples": [
"3",
0
],
"vae": [
"2",
2
]
}
},
{
"id": 5,
"type": "SaveImage",
"inputs": {
"images": [
"4",
0
],
"filenameprefix": "upscaledimage"
}
}
]
}
Here's a node-by-node breakdown:
Node 1 (LoadImage):** Loads the input image.
Node 2 (CheckpointLoaderSimple):** Loads the SDXL model.
Node 3 (SeedVR2\_VideoUpscaler):** Performs the upscaling using the loaded model and image. Connect the model output from Node 2 to the model input of Node 3, and the image output from Node 1 to the image input of Node 3.
Node 4 (VAEDecode):** Decodes the upscaled image. Connect the samples output from Node 3 to the samples input of Node 4, and the vae output from Node 2 to the vae input of Node 4.
Node 5 (SaveImage):** Saves the final upscaled image. Connect the images output from Node 4 to the images input of Node 5.
Performance Optimization Guide
VRAM Optimization Strategies
Tiling:** As discussed, crucial for high-resolution outputs.
fp16:** Use half-precision floating point numbers to reduce VRAM usage.
VAE Optimization:** Ensure you are using an optimized VAE.
Batch Size Recommendations by GPU Tier
8GB Card:** Batch size of 1 for most operations.
16GB Card:** Experiment with batch sizes of 2-4.
24GB+ Card:** Batch sizes of 4-8 should be achievable.
Tiling and Chunking for High-Res Outputs
For extremely high-resolution outputs, consider combining tiling with chunking techniques. Chunking involves processing the image in smaller temporal segments, further reducing VRAM requirements.
<!-- SEO-CONTEXT: SDXL, Tiled Upscaling, SeedVR2, ComfyUI, VRAM Optimization -->
Continue Your Journey (Internal 42.uk Resources)
Understanding ComfyUI Workflows for Beginners
Advanced Image Generation Techniques
VRAM Optimization Strategies for RTX Cards
Building Production-Ready AI Pipelines
Prompt Engineering Tips and Tricks
Exploring Different Samplers in ComfyUI
Technical FAQ
Q: I'm getting a "CUDA out of memory" error. What can I do?
A: This indicates you've exceeded your GPU's VRAM. Try reducing the batch size, using tiled VAE decode, enabling fp16, or offloading layers to the CPU. Close other GPU-intensive applications.
Q: My model is failing to load. What could be the issue?
A: Ensure the model file exists in the correct directory (ComfyUI/models/checkpoints). Verify the filename and extension are correct. Try redownloading the model in case of corruption.
Q: What are the minimum hardware requirements for running SDXL?
A: Ideally, you'll want at least an 8GB GPU. However, with aggressive optimization, you can run SDXL on cards with less VRAM, but expect significantly slower render times. A CPU with at least 16GB of RAM is also recommended.
Q: I'm seeing strange artifacts in my upscaled images. How can I fix this?
A: Artifacts can be caused by several factors, including low-quality VAEs, improper tiling settings, or issues with the model itself. Try a different VAE, adjust your tiling overlap, or experiment with different models.
Q: How do I update ComfyUI and its custom nodes?
A: In the ComfyUI directory, run git pull to update the core ComfyUI installation. For custom nodes, refer to the node's documentation for update instructions, which usually involve using the ComfyUI Manager or manually updating the node's directory.
Created: 21 January 2026
More Readings
Essential Tools & Resources
- www.promptus.ai/"Promptus AI - ComfyUI workflow builder with VRAM optimization and workflow analysis
- ComfyUI Official Repository - Latest releases and comprehensive documentation
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