Fix ComfyUI NaN Errors and Black Image Output - Complete Guide

You've been working on a ComfyUI workflow for hours, finally dialing in the perfect settings. You hit Queue Prompt and watch the generation complete successfully, only to find that the output is completely black - a classic ComfyUI NaN error symptom. Or perhaps you're getting partially corrupted images with large patches of solid color, random noise, or strange artifacts that look nothing like your prompt. When you check the console, you see warnings about "NaN" values scattered throughout the output. These ComfyUI NaN error messages indicate numerical errors that are silently corrupting your generations, and understanding why ComfyUI NaN error problems happen is the key to eliminating them permanently.

NaN stands for "Not a Number" and represents the result of undefined mathematical operations in computing. In the context of neural network inference, ComfyUI NaN error issues typically arise when calculations produce results that exceed the representable range of the number format being used, or when operations like division by zero occur during the forward pass. Once a single NaN appears in the computation chain, it propagates through every subsequent operation, eventually corrupting the entire output. The insidious nature of ComfyUI NaN error problems is that they often occur silently during generation without raising explicit errors, only manifesting as ruined output at the very end of the process.

Understanding the Technical Roots of ComfyUI NaN Error

To effectively troubleshoot ComfyUI NaN error problems, you need to understand the numerical precision systems used in modern AI inference. Understanding why ComfyUI NaN error occurs is the first step to fixing it. ComfyUI and the underlying PyTorch framework support multiple floating-point formats, each with different tradeoffs between memory usage, computational speed, and numerical precision. FP32 (32-bit floating point) offers the highest precision with approximately 7 decimal digits of accuracy and an enormous dynamic range capable of representing values from roughly 1.4 x 10^-45 to 3.4 x 10^38. FP16 (16-bit floating point) halves memory usage but reduces precision to about 3 decimal digits and shrinks the dynamic range dramatically to approximately 5.96 x 10^-8 to 65,504. BF16 (Brain Float 16) maintains the same dynamic range as FP32 but with reduced precision similar to FP16.

The reduced dynamic range of FP16 is particularly problematic for certain neural network operations. When intermediate values grow very large (overflow) or very small (underflow), they exceed the representable range and become NaN or infinity. The VAE (Variational Autoencoder) decoder is especially vulnerable to these issues because it performs operations that can amplify small numerical errors into catastrophic failures. The VAE's job is to decode the compressed latent representation back into a full-resolution image, and this expansion process involves multiple layers of upsampling and convolution that can push values outside safe numerical ranges.

Why the VAE Is the Primary Culprit

The VAE decoder operates on a fundamentally different scale than the main diffusion model. While the U-Net processes data in a carefully normalized latent space where values typically stay within a bounded range, the VAE must decode these latents back to pixel space with values ranging from 0 to 255 (or -1 to 1 in normalized form). This transformation involves significant numerical scaling that can trigger overflow conditions in FP16.

Consider the actual computation happening in a VAE decoder layer. The decoder performs convolution operations followed by activation functions like SiLU (Sigmoid Linear Unit) or ReLU. When the convolution output contains values that are large in magnitude, the activation function can push them even higher. In FP16, values exceeding 65,504 become infinity, and subsequent operations on infinity produce NaN. The problem compounds as you move through decoder layers, with each layer potentially amplifying numerical instabilities introduced by previous layers.

Stable Diffusion 1.5 models using the original VAE are particularly susceptible to this issue because the VAE was trained in FP32 and doesn't account for FP16 limitations. SDXL's VAE includes improvements that make it more numerically stable, but it can still produce NaN errors under certain conditions, especially when generation parameters push the latent values to extremes.

Diagnosing ComfyUI NaN Error in Your Workflow

Before applying fixes, you need to identify where ComfyUI NaN error values are originating. ComfyUI provides console output that can reveal ComfyUI NaN error warnings if you know what to look for. When you run a generation, watch the terminal for messages like "NaN detected in latents" or "tensor contains NaN values." These warnings indicate the specific stage where numerical corruption is occurring.

You can add diagnostic nodes to your workflow to catch NaN values before they propagate. The following Python code shows how to create a simple NaN detection node that you can insert between any two nodes in your workflow:

class NaNDetector:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "latent": ("LATENT",),
                "label": ("STRING", {"default": "checkpoint"}),
            },
        }

    RETURN_TYPES = ("LATENT",)
    FUNCTION = "check_nan"
    CATEGORY = "debug"

    def check_nan(self, latent, label):
        samples = latent["samples"]
        nan_count = torch.isnan(samples).sum().item()
        inf_count = torch.isinf(samples).sum().item()

        if nan_count > 0 or inf_count > 0:
            print(f"WARNING [{label}]: Found {nan_count} NaN and {inf_count} Inf values")
            print(f"  Shape: {samples.shape}")
            print(f"  Min: {samples.min().item()}, Max: {samples.max().item()}")
        else:
            print(f"OK [{label}]: No NaN/Inf values detected")
            print(f"  Range: [{samples.min().item():.4f}, {samples.max().item():.4f}]")

        return (latent,)

Place this node after your KSampler and before your VAE Decode to determine whether the latents themselves contain NaN or whether the VAE decoder is producing them. If the latents are clean but the decoded image is corrupted, the VAE is the problem. If the latents already contain NaN, the issue is in your sampling process.

Interpreting Console Output

When NaN errors occur, the console output provides crucial diagnostic information. A typical NaN error traceback looks like this:

RuntimeWarning: invalid value encountered in multiply
...
UserWarning: Detected NaN in VAE decoder output

Pay attention to which operation triggered the warning. "Invalid value encountered in multiply" suggests that one of the multiplicands was already NaN or that the result overflowed. Warnings specifically mentioning the VAE decoder confirm that the VAE precision is the issue. If warnings mention the sampler or U-Net, the problem lies earlier in the pipeline.

Comprehensive Fixes for VAE-Related ComfyUI NaN Error

The most reliable fix for VAE-related ComfyUI NaN error is to run the VAE decoder in FP32 precision while keeping the rest of your workflow in FP16. This is the primary solution for ComfyUI NaN error problems. This approach gives you the memory efficiency of FP16 for the computationally intensive sampling process while ensuring numerical stability during the final decode step.

Method 1: Using a Dedicated FP32 VAE Loader

ComfyUI provides VAE loader nodes that allow explicit precision specification. Instead of using the VAE embedded in your checkpoint, load a separate VAE file and specify FP32 precision:

1. Add a "Load VAE" node to your workflow
2. Select your VAE file (for SD 1.5, use vae-ft-mse-840000-ema-pruned.safetensors; for SDXL, use sdxl_vae.safetensors)
3. Set the precision/dtype parameter to "fp32" if available, or use a VAE that forces FP32

The standalone VAE decode with explicit precision looks like this in a workflow:

[Load Checkpoint] -> [KSampler] -> [VAE Decode FP32] -> [Save Image]
       |                              ^
       v                              |
[Load VAE (fp32)] -------------------+

Method 2: Command-Line Precision Override

You can force FP32 VAE decoding globally by launching ComfyUI with specific command-line arguments:

python main.py --force-fp32-vae

This flag ensures that regardless of how VAE nodes are configured in your workflow, the VAE will always decode in FP32. The memory overhead is minimal (approximately 160MB additional VRAM) because only the VAE weights and its computation use FP32, not the much larger diffusion model.

For systems where even this overhead is problematic, you can use:

python main.py --vae-precision full

Some ComfyUI installations and forks use different flag names, so consult your specific version's documentation if these don't work.

Method 3: Tiled VAE Decoding

If FP32 VAE still causes out-of-memory errors on your system, tiled VAE decoding can help by processing the image in smaller chunks. Tiled decoding reduces peak memory usage and can also improve numerical stability by operating on smaller tensors:

# In ComfyUI, use the "VAE Decode Tiled" node with these settings:
tile_size = 512  # Smaller tiles use less memory
overlap = 64     # Overlap prevents seam artifacts

Tiled decoding is slower than full-image decoding but enables generation on memory-constrained systems while avoiding the numerical issues that can arise when processing very large tensors.

Fixing Model and Sampling ComfyUI NaN Error

When ComfyUI NaN error originates in the sampling process rather than VAE decoding, the causes and fixes are different. Sampling ComfyUI NaN error typically results from extreme generation parameters, corrupted model weights, or incompatible LoRA combinations.

Parameter-Induced NaN Errors

CFG (Classifier-Free Guidance) scale is a common culprit. The CFG scale amplifies the difference between conditioned and unconditioned predictions, and very high values can push this difference to numerical extremes. Most models work well with CFG between 5 and 12. Values above 15-20 risk numerical issues, and values above 30 are likely to cause NaN on at least some prompts.

If you're experiencing NaN only on certain prompts, try reducing CFG scale to 7 and testing again. If this fixes the issue, gradually increase until you find the threshold where problems begin. Different models and different prompts have different sensitivities because they produce different internal value distributions.

Step count interacts with scheduler choice to affect numerical stability. Some schedulers like DPM++ 2M Karras are more numerically stable than others. Euler and UniPC also tend to be stable choices. If you're using an unusual scheduler and experiencing NaN, try switching to one of these standard options.

Corrupted Model Files

Model files can become corrupted during download or storage, leading to weight values that cause numerical errors during inference. Signs of corruption include:

• NaN errors with a specific model that other models don't produce
• Different errors or outputs compared to others using the same model
• File size that doesn't match the expected size for that model

Verify your model file integrity by comparing checksums if available. CivitAI and HuggingFace both provide hash values for verification. On Linux/Mac:

sha256sum model.safetensors

On Windows (PowerShell):

Get-FileHash model.safetensors -Algorithm SHA256

If the hash doesn't match, delete the file and download again from a reliable source. Use download managers that support resume and verification for large files.

LoRA Conflicts and Corruption

Multiple LoRAs can interact in ways that produce extreme weight values. Each LoRA modifies specific weights in the model, and when multiple LoRAs modify the same weights in opposite directions or with large magnitudes, the result can exceed numerical ranges.

Test LoRAs individually to identify problematic ones:

1. Start with base model only - confirm no NaN
2. Add LoRAs one at a time
3. When NaN appears, the last added LoRA is involved
4. Test that LoRA alone to determine if it's individually broken or just incompatible

Reduce LoRA strength as a workaround. If a LoRA at strength 1.0 causes NaN, try 0.7 or 0.5. This scales down its effect on model weights and may keep values in safe ranges.

LoRA files themselves can be corrupted just like checkpoint files. If a specific LoRA causes NaN regardless of other settings, try redownloading it.

Advanced Diagnostic Techniques

For persistent NaN errors that don't respond to standard fixes, deeper investigation is required.

Memory Pressure Analysis

Extreme VRAM pressure can cause computation errors when memory management fails. Monitor VRAM usage during generation to identify if you're hitting limits:

import torch
print(f"Allocated: {torch.cuda.memory_allocated() / 1024**3:.2f} GB")
print(f"Reserved: {torch.cuda.memory_reserved() / 1024**3:.2f} GB")

If you're consistently above 90% of available VRAM, memory pressure may be corrupting computations. Reduce resolution, batch size, or enable more aggressive memory optimizations.

Gradient Analysis for Training

If you're experiencing NaN during training or fine-tuning rather than inference, gradient explosion is the likely cause. Gradient values can grow exponentially during backpropagation, eventually overflowing.

Implement gradient clipping to prevent explosion:

torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)

Also consider lowering your learning rate. High learning rates cause larger weight updates, which can push the model into numerically unstable regions.

Systematic Isolation

For complex workflows with multiple models, LoRAs, ControlNets, and custom nodes, use binary search to isolate the problem:

1. Disable half of your workflow components
2. Test for NaN
3. If NaN persists, the problem is in the enabled half
4. If NaN disappears, the problem is in the disabled half
5. Repeat, halving the suspect portion each time

This approach quickly identifies the problematic component even in workflows with dozens of nodes.

ComfyUI NaN Error Prevention Strategies

Once you've resolved your immediate ComfyUI NaN error issues, implement practices that prevent recurrence of ComfyUI NaN error problems.

Standard Precision Configuration

Adopt this as your default configuration for maximum stability:

• Main model (U-Net): FP16 or BF16
• Text encoders: FP16 or BF16
• VAE: FP32
• ControlNet: FP16 or BF16

BF16 has larger dynamic range than FP16 and can prevent some overflow issues, but requires Ampere or newer NVIDIA GPU (RTX 30 series and later).

Safe Parameter Ranges

Establish tested parameter ranges for your commonly used models:

• CFG Scale: 5-12 for most models
• Steps: 20-50 for most schedulers
• Resolution: Match model's native resolution (512x512 for SD 1.5, 1024x1024 for SDXL)

Document any model-specific requirements. Some models or LoRAs may have narrower safe ranges.

Regular Model Verification

Periodically verify model file integrity, especially after storage issues or system crashes. Maintain checksums for your important models and verify after any incident that might have affected files.

Frequently Asked Questions

Why do I get black images without any error messages from ComfyUI NaN error?

ComfyUI NaN error doesn't always produce explicit error messages in ComfyUI. The computation completes normally from PyTorch's perspective, but the values are corrupted. Enable verbose logging or add NaN detection nodes to your workflow to catch these silent ComfyUI NaN error failures. Some installations require the --verbose flag to show ComfyUI NaN error warnings.

Will FP32 VAE fix all black image issues?

FP32 VAE fixes the most common cause of black images, which is VAE decoder overflow. However, black images can also result from completely incorrect model loading (wrong architecture), severe LoRA conflicts, or other workflow errors. If FP32 VAE doesn't help, the cause is elsewhere in your pipeline.

How much extra VRAM does FP32 VAE require?

The VAE is relatively small compared to the main diffusion model. Running it in FP32 instead of FP16 adds approximately 160MB of VRAM usage. This is almost always an acceptable tradeoff for the stability it provides.

Can negative prompts prevent NaN errors?

No. Negative prompts affect what the model generates but don't change the numerical precision of computations. A negative prompt that reduces a certain concept might indirectly reduce some extreme values, but this is not a reliable fix and doesn't address the underlying precision issue.

Why does NaN happen with some prompts but not others?

Different prompts activate different patterns in the model, producing different internal value distributions. Some prompts may push certain neurons to produce very large activations that overflow in FP16, while other prompts keep values in safe ranges. This is why NaN errors can seem random when they're actually deterministic based on the specific computation being performed.

Is NaN more common with SDXL or SD 1.5?

SD 1.5's original VAE is more prone to NaN issues than SDXL's VAE, which was designed with better numerical stability. However, SDXL can still produce NaN errors under extreme conditions. The higher resolution of SDXL means larger tensors, which can expose numerical issues that wouldn't appear at 512x512.

Can I use INT8 quantized VAE to save memory?

INT8 VAE quantization is not recommended because the VAE decoder's numerical precision requirements are too stringent. While INT8 U-Net works well, INT8 VAE decoding typically produces visible artifacts or outright failures. Stick with FP32 VAE if stability is your goal.

How do I know if my GPU is defective?

If you experience ComfyUI NaN error across multiple different models, different ComfyUI installations, and the errors are inconsistent (happening at random points rather than specific workflows), GPU hardware issues are possible. Test with GPU diagnostics like nvidia-smi -q -d PERFORMANCE and watch for throttling or errors. Also test other CUDA applications to see if ComfyUI NaN error issues persist.

Conclusion

ComfyUI NaN error causing black or corrupted images is almost always solvable once you understand its origins. The VAE decoder running in FP16 is the most common cause of ComfyUI NaN error, and the fix is straightforward: use FP32 precision for VAE decoding. This single change resolves the majority of ComfyUI NaN error black image issues with minimal performance impact.

For ComfyUI NaN error originating in the sampling process, investigate your generation parameters, model file integrity, and LoRA combinations. Extreme CFG values and corrupted files are common culprits for ComfyUI NaN error. Systematic isolation techniques help you identify the specific cause of ComfyUI NaN error quickly.

Prevention is better than troubleshooting. Adopt stable default configurations with FP32 VAE, safe parameter ranges, and regular model verification. These practices eliminate most ComfyUI NaN error occurrences before they happen, letting you focus on creative work rather than technical debugging.

Once you understand the numerical precision constraints of AI image generation, ComfyUI NaN error transforms from a mysterious blocker into a quickly solvable technical issue. The knowledge to fix ComfyUI NaN error also helps you make better decisions about precision, memory, and performance tradeoffs throughout your ComfyUI workflows.

Advanced ComfyUI NaN Error Prevention and Management

Beyond fixing immediate issues, systematic approaches prevent NaN errors from disrupting your work.

Building NaN-Resistant Workflows

Design workflows that minimize NaN risk from the start:

Precision Configuration: Establish standard precision settings for different workflow types:

Parameter Bounds: Establish tested parameter ranges for your common models:

• CFG Scale: 5-12 for most use cases
• Steps: 20-40 for standard quality
• Resolution: Within model's trained resolution range

Document these ranges and stick to them unless experimenting deliberately.

Model Verification: When adding new models to your workflow:

1. Test with known-good prompts
2. Verify file integrity with checksums
3. Check compatibility with your precision settings
4. Test at edge cases (high CFG, extreme prompts)

Workflow Templates with Built-in Protection

Create reusable workflow templates that include NaN protection:

Standard Generation Template:

• Checkpoint loader with FP16
• Separate VAE loader with FP32
• CFG limited to safe range
• NaN detection node before VAE decode

High-Resolution Template:

• Tiled VAE decoding enabled
• Lower CFG default (7)
• Resolution matched to model

Multi-LoRA Template:

• Individual LoRA strength limits
• Total combined strength monitoring
• Fallback without LoRAs for testing

For building efficient workflow templates, our essential nodes guide covers the fundamental nodes and their proper configuration.

Automated Quality Checking

Implement automatic checks in your workflows:

Pre-Generation Checks:

• Verify all models loaded correctly
• Check VRAM usage is within limits
• Validate parameter ranges

Post-Generation Checks:

• Detect NaN in output latents
• Check for all-black or all-white outputs
• Verify output image statistics

Alerting: Configure notifications for failed generations so batch processes don't waste time on broken outputs.

Understanding Model-Specific NaN Behavior

Different models have different NaN susceptibilities.

Stable Diffusion 1.5 Models

Common Issues:

• Original VAE highly prone to FP16 NaN
• Some fine-tuned models have edge-case issues
• Old LoRAs may have problematic weight ranges

Recommended Settings:

• Always use FP32 VAE or fixed VAE (ft-mse)
• Test fine-tunes before committing to workflows
• Update old LoRAs to newer versions if available

SDXL Models

Common Issues:

• Better VAE than SD1.5 but not immune
• Large resolution generations more prone
• Some community checkpoints less stable

Recommended Settings:

• FP32 VAE still recommended
• Use official or well-tested VAE
• Monitor at resolutions above 1024x1024

Flux Models

Common Issues:

• Large model size creates memory pressure
• Some precision modes less stable
• Quantized versions vary in stability

Recommended Settings:

• FP16 or BF16 for main model
• Full precision VAE
• Monitor memory during generation

For optimizing Flux performance while maintaining stability, our performance optimization guide covers Flux-specific techniques.

Debugging Complex NaN Scenarios

When standard fixes don't work, deeper debugging is required.

Systematic Isolation Process

For complex workflows with multiple potential NaN sources:

Step 1: Simplify to Minimal Workflow Remove all non-essential nodes. Keep only:

• Checkpoint loader
• Basic sampler
• VAE decode
• Output

If this works, the issue is in removed components.

Step 2: Binary Addition Add back components in halves:

• First half works? Issue in second half
• First half fails? Issue in first half
• Continue halving until isolated

Step 3: Individual Component Testing Once isolated to specific component:

• Test with different settings
• Try alternative nodes
• Verify model files
• Check for known issues

Console Output Analysis

Extract maximum information from console output:

Enable Verbose Mode:

python main.py --verbose

Look For:

• NaN warning timestamps (when in generation?)
• Memory allocation patterns
• Model loading issues
• Warning clustering (many warnings at once?)

Capture Full Output:

python main.py 2>&1 | tee generation.log

Review logs for patterns across multiple generations.

Memory Analysis Tools

When memory pressure might cause NaN:

Monitor VRAM:

import torch
print(f"Allocated: {torch.cuda.memory_allocated() / 1e9:.2f} GB")
print(f"Reserved: {torch.cuda.memory_reserved() / 1e9:.2f} GB")

Track Memory Timeline: Log memory at workflow stages to identify peaks and potential pressure points.

Integration with Professional Workflows

For production use, NaN prevention is critical.

Quality Assurance Pipeline

Implement QA stages in production workflows:

Pre-Flight Checks:

• Model integrity verification
• Configuration validation
• Resource availability confirmation

Generation Monitoring:

• Real-time NaN detection
• Progress tracking
• Automatic retry on failures

Post-Generation QA:

• Output validation
• Quality metrics
• Logging for analysis

Batch Processing Considerations

High-volume processing requires robust NaN handling:

Failure Handling:

• Isolate and log failed generations
• Continue processing queue
• Retry strategy for transient failures

Resource Management:

• Clear memory between batches
• Monitor for degradation over time
• Restart if memory fragmentation detected

Reporting:

• Track NaN rate per model/workflow
• Identify problematic configurations
• Continuous improvement based on data

Team Environment Standardization

For teams using ComfyUI:

Standard Configurations:

• Approved precision settings
• Tested parameter ranges
• Verified model files

Documentation:

• NaN troubleshooting procedures
• Known issues per model
• Escalation paths

Training:

• Team understands NaN causes
• Everyone can apply basic fixes
• Specialists for complex issues

For users who want professional-quality output without managing NaN complexity, Apatero.com provides managed environments with optimized precision settings that eliminate these issues.

Future-Proofing Your Setup

As models and tools evolve, stay ahead of NaN issues.

Staying Updated

Follow Developments:

• ComfyUI release notes
• Model-specific communities
• Research on numerical precision

Test Updates:

• Evaluate new versions before production
• Check for precision-related changes
• Verify existing workflows still work

Hardware Considerations

Hardware affects NaN susceptibility:

GPU Architecture:

• Newer GPUs have better precision handling
• Tensor Core optimization varies
• Memory bandwidth affects stability

Future Hardware:

• Blackwell GPUs have improved precision support
• New FP4 and enhanced FP8 capabilities
• Better memory management

Consider hardware capabilities when building workflows for longevity.

Conclusion

NaN errors causing black or corrupted images in ComfyUI are almost always solvable once you understand their origins. The VAE decoder running in FP16 is the most common cause, and the fix is straightforward: use FP32 precision for VAE decoding. This single change resolves the majority of black image issues with minimal performance impact.

For NaN errors originating in the sampling process, investigate your generation parameters, model file integrity, and LoRA combinations. Extreme CFG values and corrupted files are common culprits. Systematic isolation techniques help you identify the specific cause quickly.

Prevention is better than troubleshooting. Adopt stable default configurations with FP32 VAE, safe parameter ranges, and regular model verification. These practices eliminate most NaN errors before they occur, letting you focus on creative work rather than technical debugging.

Once you understand the numerical precision constraints of AI image generation, NaN errors transform from mysterious blockers into quickly solvable technical issues. The knowledge to fix them also helps you make better decisions about precision, memory, and performance tradeoffs throughout your ComfyUI workflows.