Removing Artifacts with SeedVR2: Complete Troubleshooting Guide 2025

You've set up SeedVR2, run your first upscale, and the result looks worse than the original. Tile seams cut across faces. Colors shifted to an ugly green tint. The model oversharpened everything until skin looks like plastic. Sound familiar?

Quick Answer: SeedVR2 artifacts are fixed by adjusting noise injection scales (0.1-0.3 for input, 0.05-0.15 for latent), using FP8 mixed-precision models to reduce 7B artifacts, increasing tile overlap to 20% of tile size, and applying LAB color correction. Most artifacts disappear with the right combination of these settings.

SeedVR2 from ByteDance is the best one-step video upscaler available in 2025, but it's sensitive to configuration. The wrong settings produce artifacts that make upscaled footage unusable. This guide covers every artifact type I've encountered and the exact fixes that work.

What Causes SeedVR2 Artifacts in the First Place?

Understanding why artifacts appear helps you prevent them. SeedVR2 uses diffusion-based upscaling with adversarial training, meaning the model learned to generate detail by competing against a discriminator network. This creates several artifact-prone scenarios.

The model processes video in tiles due to VRAM limitations. Each tile gets upscaled independently, then merged. If the merging isn't handled properly, you see visible seams where tiles meet. The model also uses adaptive window attention that adjusts based on resolution. At extreme resolutions, this attention mechanism can introduce inconsistencies.

The diffusion process itself can cause issues. SeedVR2 was trained primarily on photorealistic content. When processing AI-generated video, anime, or heavily stylized content, the model sometimes tries to add photorealistic texture where it doesn't belong.

Color shifts happen during VAE encoding and decoding. The variational autoencoder compresses frames to latent space, then decompresses after upscaling. Any mismatch in the VAE or incorrect color space handling produces shifted colors in output.

How Do You Fix Tile Seam Artifacts?

Tile seams are the most common SeedVR2 artifact. They appear as grid-like lines across your video where tile boundaries meet during processing. On faces, they're especially obvious and completely ruin the output.

The fix is simple but counterintuitive. Increase tile overlap to at least 20% of your tile size. If you're using tile_size 512, set tile_overlap to 100 or higher. For tile_size 384, use tile_overlap 75+.

Higher overlap means adjacent tiles share more pixels at their boundaries. The model processes these shared regions twice, then blends them together. More overlap equals smoother blending equals invisible seams.

The tradeoff is processing time. Doubling overlap roughly increases processing time by 20-30% because you're processing more overlapping regions. For production work, this tradeoff is always worth it. Visible seams are never acceptable.

If seams persist even with high overlap, check your VAE tiling settings. VAE tiling handles the encoding step separately from model tiling. Ensure VAE tile overlap matches or exceeds your model tile overlap. Mismatched VAE tiling creates seams even when model tiling is configured correctly.

For stubborn seam issues on specific content, try reducing tile_size instead of increasing overlap. Smaller tiles with moderate overlap often blend better than large tiles with maximum overlap, especially for content with fine detail patterns.

Why Does SeedVR2 Cause Color Shifts and How Do You Fix Them?

Color shifts manifest as overall tint changes, saturation problems, or contrast alterations. Your original video has natural colors, but the upscaled version looks greenish, washed out, or oversaturated.

The primary cause is VAE mismatch. SeedVR2 requires its specific VAE file. Using a generic SD1.5 VAE, SDXL VAE, or incorrect SeedVR2 VAE version produces color shifts. Verify you're using the correct seedvr2_vae.safetensors file from the official source.

If your VAE is correct but colors still shift, the issue is color space handling. SeedVR2 expects RGB input. YUV or other color space formats don't convert cleanly, causing shifts during processing.

The ComfyUI-SeedVR2_VideoUpscaler node includes five color correction methods:

LAB Color Correction (Recommended)

• Full perceptual color matching with detail preservation
• Best for most content types
• Handles skin tones accurately
• Use this as your default choice

Wavelet Color Correction

• Frequency-based approach preserves details
• Natural color reproduction
• Good for high-detail content where LAB produces slight softening

Wavelet Adaptive

• Combines frequency analysis with targeted saturation correction
• Best for content with extreme saturation variance
• Handles mixed lighting scenarios well

HSV Color Correction

• Statistical hue/saturation/value matching
• Fast processing
• Less accurate for complex color scenarios

AdaIN Color Correction

• Adaptive instance normalization approach
• Good for artistic content
• Can produce slightly unnatural results on photorealistic content

For most workflows, start with LAB. Switch to Wavelet if you notice any detail softening. The difference between methods is subtle on clean sources but significant on problematic content.

If color correction methods don't fully resolve shifts, the problem might be in your source encoding. Re-encode your source to standard h264 RGB before processing. Some codecs and containers introduce color space metadata that confuses the processing pipeline.

How Do You Reduce Oversharpening and Plastic Skin?

Oversharpening makes upscaled video look artificial. Skin appears plastic. Textures look hyper-detailed in an unnatural way. Edges have visible halos. This happens when SeedVR2's enhancement is too aggressive for your source material.

The official documentation notes that SeedVR2 "tends to overly generate details on inputs with very light degradations (e.g., 720p AIGC videos), leading to oversharpened results occasionally."

If your source is already clean (720p or higher, minimal compression), the model has less to enhance but still tries to add detail. The result is over-processing.

Fix 1: Reduce denoise_strength

Lower denoise_strength limits how much the model can reinterpret your content. For clean sources:

• Use 0.2-0.3 instead of the default 0.5
• Ultra-clean sources might need 0.15-0.2
• Test with 0.25 as a starting point for 720p AI-generated content

Fix 2: Add input noise

Counterintuitively, adding slight noise to clean inputs helps. The input_noise_scale parameter (0.0-1.0) adds controlled noise before processing. For oversharpening issues:

• Start with 0.1-0.15
• Increase to 0.2-0.3 if still oversharpened
• Don't exceed 0.4 or you'll introduce visible grain

The noise gives the model something to "clean up" instead of hallucinating excessive detail. It's like giving the model a job to do rather than letting it overwork clean content.

Fix 3: Use non-sharp model variants

SeedVR2 includes "sharp" model variants designed to enhance detail. If you're experiencing oversharpening, you might be using a sharp variant when you don't need it. Check your model filename:

• seedvr2_ema_7b_sharp_*.safetensors - Enhanced sharpening (avoid for clean sources)
• seedvr2_ema_7b_*.safetensors - Standard (use this for most work)

Switching from sharp to standard variant often resolves oversharpening without other setting changes.

Fix 4: Post-processing softening

If you can't fully resolve oversharpening in SeedVR2, apply subtle gaussian blur (0.3-0.5 radius) in post-processing. This softens the plastic look while preserving the upscaling benefits. Add 0.5-1% film grain afterward to restore natural texture.

What Are the Best Settings for the 7B Model Artifacts?

The SeedVR2 7B model produces better quality than the 3B model but introduces specific artifacts that require targeted fixes. Users report grid-like patterns, color banding, and inconsistent enhancement across frames.

The July 2025 update introduced FP8 mixed-precision variants specifically to address 7B artifacts. These models process most layers in FP8 precision but keep artifact-prone layers (specifically block 35) in FP16 precision.

Use this model for artifact-free 7B quality:

seedvr2_ema_7b_fp8_e4m3fn_mixed_block35_fp16.safetensors

This mixed-precision approach maintains 7B quality while eliminating the precision-related artifacts that plague pure FP8 or FP16 7B models. The file is available from HuggingFace.

If you're using the 7B model and experiencing artifacts, also check:

Temporal window settings for 7B:

• The 7B model needs higher temporal_window for consistency
• Use temporal_window 12+ instead of the 8 that works for 3B
• 7B processes more detail, requiring more temporal context to maintain consistency

VRAM considerations:

• 7B model needs significantly more VRAM than 3B
• If you're at VRAM limits, artifacts increase due to aggressive tiling
• Either upgrade to the FP8 mixed variant (lower VRAM) or reduce tile_size with compensating overlap

GGUF quantized alternatives:

• For extreme VRAM constraints, GGUF Q4_K_M variants exist
• Quality is "acceptable" per documentation, not ideal
• Use only when FP8 mixed won't fit in VRAM

For most users, the FP8 mixed block35 FP16 variant is the right choice. It fits in 12GB VRAM while eliminating 7B-specific artifacts. There's no reason to use pure FP8 or FP16 7B models anymore.

How Do You Fix Temporal Flickering in Upscaled Video?

Temporal flickering appears as frame-to-frame inconsistency where details shift, colors pulse, or textures morph across frames. SeedVR2 includes temporal attention to prevent this, but incorrect settings still produce flickering.

Cause 1: Temporal window too small

The temporal_window parameter determines how many surrounding frames the model considers. With temporal_window 4, each frame only sees 2 frames before and after. Fast motion or complex scenes need more context.

Increase temporal_window:

• Default: 8 (good for moderate motion)
• Complex scenes: 12
• Fast motion: 16
• Maximum quality: 24 (significant VRAM increase)

Each increase adds VRAM overhead. Temporal_window 16 requires approximately 4GB more VRAM than 8. Balance quality against available resources.

Cause 2: Denoise_strength too high

High denoise_strength (0.6+) gives the model freedom to hallucinate detail. Even with good temporal_window, aggressive hallucination can overwhelm temporal consistency.

Pair high denoise with high temporal_window:

• denoise 0.6 needs temporal_window 12+
• denoise 0.7 needs temporal_window 16+
• denoise 0.3-0.4 works fine with temporal_window 8

If you need aggressive enhancement, you must pay the VRAM cost for extended temporal context.

Cause 3: Batch processing without cleanup

When batch processing multiple videos, ComfyUI doesn't automatically clear VRAM between videos. Accumulated memory state causes degraded processing quality, often manifesting as flickering in later videos of the batch.

Add VAE Memory Cleanup node after each video in batch workflows, or restart ComfyUI between videos for critical work.

Cause 4: Edge frame effects

The first and last second of video lack full temporal context. With temporal_window 8, frame 1 can't see 4 frames before it because they don't exist. SeedVR2 pads with repeated frames, causing quality degradation at edges.

Fix edge flickering by adding 1-second padding to both ends before processing. Duplicate the first frame for 30 frames at the start, last frame for 30 frames at the end. Process the padded video, then trim padding afterward.

What Noise Settings Eliminate High-Resolution Artifacts?

At high resolutions (1080p to 4K upscaling, 4K to 8K), SeedVR2 can produce artifacts specific to extreme detail generation. These include micro-grid patterns, inconsistent texture generation, and detail hallucination in smooth areas.

The noise injection parameters specifically address these issues:

Input Noise Scale (input_noise_scale)

• Range: 0.0-1.0
• Purpose: Adds noise to input frames before processing
• Effect: Reduces artifacts at very high resolutions
• Recommended: 0.1-0.3 for 4K output, 0.2-0.4 for 8K output

Input noise prevents the model from over-focusing on low-level details in the source. Without input noise, clean sources at high resolution can trigger excessive detail hallucination. The noise provides "work" for the model, channeling its enhancement capability appropriately.

Latent Noise Scale (latent_noise_scale)

• Range: 0.0-1.0
• Purpose: Adds noise during the diffusion process
• Effect: Softens excessive detail, reduces micro-artifacts
• Recommended: 0.05-0.15

Latent noise operates differently. It's added during processing, not before. This softens the generation process, reducing the model's tendency to hallucinate fine detail. Use latent noise when input noise alone doesn't resolve issues.

Recommended combinations for high-resolution work:

Start with input noise only. Add latent noise if artifacts persist. Never use latent noise above 0.2 or output becomes visibly soft.

If you're regularly processing to 4K or higher and want automatic noise optimization, Apatero.com includes adaptive noise injection that adjusts based on output resolution and source characteristics.

Troubleshooting Specific Artifact Patterns

Different artifact patterns indicate different root causes. Here's a quick diagnostic guide:

Grid pattern across entire frame:

• Cause: Tile seams with insufficient overlap
• Fix: Increase tile_overlap to 20%+ of tile_size

Color tint (green, magenta, or yellow cast):

• Cause: Wrong VAE or color space mismatch
• Fix: Verify correct VAE, apply LAB color correction

Plastic/waxy skin texture:

• Cause: Oversharpening on clean source
• Fix: Reduce denoise_strength to 0.2-0.3, add input_noise 0.15

Flickering between frames:

• Cause: Insufficient temporal context
• Fix: Increase temporal_window to 12+

Halos around edges:

• Cause: Over-enhancement or sharp model variant
• Fix: Switch to standard model, reduce denoise_strength

Blocky artifacts in smooth areas:

• Cause: Source compression artifacts amplified
• Fix: Denoise source before upscaling, use latent_noise 0.1

Detail inconsistency frame-to-frame:

• Cause: High denoise with low temporal_window
• Fix: Match high denoise (0.6+) with temporal_window 16+

Blurry output despite upscaling:

• Cause: denoise_strength too low or latent_noise too high
• Fix: Increase denoise to 0.4-0.5, reduce latent_noise below 0.1

First/last second looks different:

• Cause: Edge frame temporal context padding
• Fix: Add 1-second padding before processing, trim after

For complex cases with multiple artifact types, address them in order: tile seams first, then color, then sharpening, then temporal. Fixing earlier issues often resolves later ones.

Production Settings That Minimize Artifacts

After extensive testing, these settings produce consistently artifact-free results across content types:

General purpose (most content):

model: seedvr2_ema_7b_fp8_e4m3fn_mixed_block35_fp16.safetensors
tile_size: 512
tile_overlap: 128
temporal_window: 12
denoise_strength: 0.4
input_noise_scale: 0.1
latent_noise_scale: 0.0
color_correction: LAB

AI-generated video (from WAN, AnimateDiff, etc.):

model: seedvr2_ema_7b_fp8_e4m3fn_mixed_block35_fp16.safetensors
tile_size: 512
tile_overlap: 100
temporal_window: 12
denoise_strength: 0.35
input_noise_scale: 0.15
latent_noise_scale: 0.0
color_correction: LAB

Heavily compressed source (YouTube downloads, etc.):

model: seedvr2_ema_7b_fp8_e4m3fn_mixed_block35_fp16.safetensors
tile_size: 384
tile_overlap: 96
temporal_window: 16
denoise_strength: 0.55
input_noise_scale: 0.2
latent_noise_scale: 0.1
color_correction: Wavelet

4K+ output:

model: seedvr2_ema_7b_fp8_e4m3fn_mixed_block35_fp16.safetensors
tile_size: 384
tile_overlap: 96
temporal_window: 12
denoise_strength: 0.4
input_noise_scale: 0.25
latent_noise_scale: 0.1
color_correction: LAB

These presets work for 90% of content. Adjust individual parameters based on specific issues rather than changing everything at once.

If you're processing video regularly and want these optimizations handled automatically, Apatero.com provides pre-configured SeedVR2 workflows with content-adaptive settings. The platform analyzes your source and applies appropriate artifact prevention without manual configuration.

Frequently Asked Questions

Why does SeedVR2 add artifacts when other upscalers don't?

SeedVR2 uses diffusion-based generation rather than simple interpolation. This produces superior quality but requires careful configuration. Traditional upscalers like ESRGAN don't generate new detail, so they can't introduce generation artifacts. SeedVR2's power comes with complexity.

Can I completely eliminate tile seams in SeedVR2?

Yes. Tile seams are 100% preventable with proper overlap settings. Set tile_overlap to at least 20% of tile_size, ensure VAE tiling matches model tiling, and seams become invisible. If seams persist after these changes, reduce tile_size rather than increasing overlap further.

Why does my upscaled video have different colors than the source?

Color shifts indicate VAE mismatch, color space issues, or missing color correction. Verify you're using the official SeedVR2 VAE, ensure input is RGB color space, and enable LAB color correction. Re-encoding source to standard h264 before processing eliminates most color space issues.

How much does fixing artifacts slow down processing?

Artifact prevention settings increase processing time by 15-40% depending on specifics. Higher tile_overlap adds 15-30%. Higher temporal_window adds 15-25%. This overhead is worthwhile because artifact-free output eliminates re-processing time. One clean pass beats multiple artifact-laden attempts.

Should I use the 3B or 7B model to avoid artifacts?

The 7B model produces better quality but historically had more artifacts. The FP8 mixed block35 FP16 variant of the 7B model eliminates these issues. Use the 7B FP8 mixed variant for best quality with minimal artifacts. Only use 3B if VRAM is severely constrained.

Why does adding noise help reduce artifacts?

Noise gives the model "work" to do. On clean sources, SeedVR2 has nothing to enhance but still tries to generate detail, causing over-processing. Light input noise (0.1-0.25) provides degradation for the model to fix, channeling its enhancement capability productively instead of hallucinating unwanted detail.

Can I fix artifacts in post-processing instead of preventing them?

Some artifacts can be mitigated in post. Light oversharpening responds to gaussian blur. Color shifts can be corrected with grading. However, tile seams, temporal flickering, and severe generation artifacts require re-processing with correct settings. Prevention is always more efficient than post-processing fixes.

Why do the first and last seconds of my video look different?

SeedVR2 uses temporal context from surrounding frames. At video edges, there aren't enough frames to fill the temporal window. The model pads with repeated frames, causing quality degradation. Add 1-second padding (duplicate first/last frames) before processing, then trim after upscaling.

What's the best model variant for artifact-free upscaling?

seedvr2_ema_7b_fp8_e4m3fn_mixed_block35_fp16.safetensors provides the best balance of quality and artifact prevention. It uses mixed precision to eliminate 7B-specific artifacts while maintaining full model capability. This variant fits in 12GB VRAM and should be your default choice.

How do I know if artifacts are from SeedVR2 or my source video?

Process a single frame as an image through a simple upscaler like ESRGAN. If artifacts appear in the single-frame upscale, they're in your source. If single-frame looks clean but video upscale has artifacts, they're from SeedVR2 temporal or tiling processing. Source artifacts must be fixed before video upscaling.

Conclusion

SeedVR2 artifacts aren't inevitable. They're configuration problems with specific solutions. Tile seams need overlap. Color shifts need proper VAE and correction. Oversharpening needs reduced enhancement or input noise. Temporal flickering needs extended context windows.

The FP8 mixed-precision 7B model eliminates most precision-related artifacts out of the box. Combined with proper tiling settings and appropriate denoise strength for your content, you'll get clean upscales consistently.

Start with the production presets in this guide and adjust based on your specific content. When artifacts appear, diagnose the pattern, apply the corresponding fix, and iterate. After a few projects, you'll intuitively know which settings your content needs.

For additional SeedVR2 techniques, check out my complete SeedVR2 upscaling guide covering basic workflows, VRAM optimization, and batch processing. If you're working with AI-generated video sources, the WAN 2.2 guide covers generating content that upscales cleanly with minimal artifacts.

For production workflows where you can't afford artifact troubleshooting time, Apatero.com provides SeedVR2 with pre-optimized artifact prevention settings and automatic content analysis. The platform handles the configuration complexity so you can focus on creating content rather than debugging upscaling issues.