troubleshooting

name: troubleshooting description: Common ComfyUI errors and fixes — OOM, missing nodes, dtype mismatches, black images, and debugging strategies globs: - "**/*.json"

ComfyUI Troubleshooting Guide

Error Diagnosis Strategy

When a workflow fails, follow this systematic approach:

1. Get the error: Use get_history to retrieve the execution result with full traceback
2. Check logs: Use get_logs with keyword filters like "error", "warning", "traceback"
3. Identify the failing node: The history response includes the node_id and node_type that failed
4. Cross-reference inputs: Use get_node_info to verify the failing node's expected input schema
5. Check models: Use list_local_models to verify all referenced model files exist

Out of Memory (OOM)

Error Pattern

torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate X MiB.
GPU 0 has a total capacity of 24.00 GiB of which X MiB is free.

Or:

RuntimeError: CUDA error: out of memory

Root Cause

The GPU does not have enough VRAM to hold the model weights, intermediate tensors, and latent images simultaneously. Common triggers:

• High resolution images (2048x2048+)
• Multiple models loaded simultaneously
• FP32 precision models on limited VRAM
• Video generation (LTXV, AnimateDiff) with many frames
• Large batch sizes

Fixes (in order of preference)

1. Reduce resolution: Drop to the model's native resolution (512 for SD 1.5, 1024 for SDXL/Flux)
2. Use FP8/FP16 quantized models: FP8 Flux models use ~8GB vs ~24GB for FP16
   • Search for FP8 variants: search_models("flux fp8") or search_models("sdxl fp8")
3. Use --lowvram flag: ComfyUI CLI flag that offloads model parts to CPU during inference
4. Free VRAM between generations: ComfyUI should auto-manage, but restarting clears leaked memory
5. Use tiled VAE decoding: For high-resolution images, tile the VAE decode step
   • Node: VAEDecodeTiled instead of VAEDecode
   • Breaks the image into tiles, decodes each separately, and stitches them together
6. Reduce batch size: Set batch_size to 1 in EmptyLatentImage
7. Avoid multiple models: Don't load two full checkpoints simultaneously — use one checkpoint and LoRAs instead
8. For LTXV/video: Always use FP8 quantized video models on 24GB cards

VRAM Estimates

Device Mismatch

Error Pattern

RuntimeError: Expected all tensors to be on the same device, but found at least
two devices, cuda:0 and cpu!

Root Cause

A tensor on the CPU is being combined with a tensor on the GPU. This usually happens when:

• A custom node doesn't properly move tensors to the correct device
• Model offloading placed parts of the model on CPU
• A node produces CPU tensors while downstream expects GPU tensors

Fixes

1. Check if the error occurs with a specific custom node — update or replace that node
2. If using --lowvram or --cpu, some nodes may not support CPU offloading
3. Restart ComfyUI to reset device state
4. Check if a custom node has a newer version that fixes device handling

Missing Nodes

Error Pattern

Cannot find node class 'NodeClassName'

Or in the execution response:

"error": {"type": "node_not_found", "message": "Cannot find node class 'X'"}

Root Cause

The workflow references a node type that is not installed. This happens when:

• A custom node pack is not installed
• A custom node pack is installed but failed to load (import error)
• The node was renamed or removed in a pack update

Fixes

1. Search for the node pack:

   search_custom_nodes("NodeClassName")
   

2. Install via ComfyUI Manager or the registry
3. Check logs for import errors:

   get_logs(keyword="import")
   get_logs(keyword="error")
   

   Import errors often reveal missing Python dependencies
4. Install missing Python dependencies: If the custom node requires a pip package:

   pip install missing-package
   

5. Restart ComfyUI after installing any custom node — nodes are loaded at startup

NaN Tensor Errors

Error Pattern

RuntimeError: Input contains NaN

Or images come out as solid gray/noise with NaN warnings in logs.

Root Cause

Numerical instability during the diffusion process. Common triggers:

• CFG scale too high: Values above 15-20 can cause numerical overflow
• Corrupted model weights: Damaged download or incompatible merge
• FP16 overflow: Some operations overflow at half precision
• Incompatible LoRA: A LoRA trained for a different base model

Fixes

1. Lower CFG: Try CFG 7.0 for SD 1.5/SDXL, 1.0 for Flux
2. Use FP32 VAE: Some VAEs produce NaN in FP16. Switch to vae-ft-mse-840000-ema-pruned.safetensors (FP32)
3. Remove LoRAs: Test without LoRAs to isolate the cause
4. Re-download the model: Hash verification can detect corrupted files
5. Check LoRA compatibility: Ensure the LoRA matches the base model family

Dtype Mismatches

Error Pattern

RuntimeError: expected scalar type Float but found Half

Or:

RuntimeError: expected scalar type Half but found Float

Or:

RuntimeError: Input type (float) and bias type (c10::Half) should be the same

Root Cause

A model component expects one precision (FP32/FP16) but receives another. Most common with:

• VAE precision mismatch (FP16 model + FP32 VAE or vice versa)
• Mixed-precision LoRAs
• Custom nodes that force a specific dtype

Fixes

1. Use a separate VAE: Load an explicit FP32 VAE instead of the checkpoint's built-in VAE
   • Node: VAELoader with vae-ft-mse-840000-ema-pruned.safetensors
2. Match precision: If the model is FP16, use FP16-compatible nodes throughout
3. Force FP32 VAE decode: Some node packs offer VAEDecodeFP32 nodes
4. Check ComfyUI settings: --force-fp32 flag forces everything to FP32 (uses more VRAM)

CLIP Token Overflow

Error Pattern

No explicit error — the prompt is silently truncated at 77 tokens, and details mentioned late in the prompt are ignored.

Symptoms

• Later parts of long prompts have no effect on the image
• Adding more descriptive text doesn't change the output
• Removing early tokens suddenly makes later tokens work

Fixes

1. Use BREAK token: Split the prompt at natural boundaries:

   subject description, pose, clothing, setting
   BREAK
   lighting, style, quality, camera angle
   

2. Use CLIPTextEncodeSDXL: SDXL's dual-CLIP processes two 77-token chunks
3. Prioritize important tokens: Put the most important descriptors first
4. Use fewer filler words: Remove articles and prepositions where possible
5. Use embeddings: Condense complex concepts into single tokens with textual inversions

Black Images

Error Pattern

No error in the execution — the workflow "succeeds" but produces completely black or near-black images.

Root Causes and Fixes

Quick Diagnostic Checklist

1. Check denoise > 0 (should be 1.0 for txt2img)
2. Check cfg > 0 (should be 7.0 for SD 1.5, 1.0 for Flux)
3. Check steps > 0 (should be 20 for standard, 4 for turbo)
4. Verify the positive prompt is not empty
5. Try a different seed
6. Try a known-working sampler/scheduler combo: euler + normal

Connection Type Errors

Error Pattern

Output type 'IMAGE' doesn't match input type 'LATENT'

Or:

Required input 'model' of type 'MODEL' but got connection of type 'CLIP'

Root Cause

Connecting the wrong output slot of a node to an incompatible input. Often caused by using the wrong output index.

Fixes

1. Check output indices: Use get_node_info to verify the exact output order
   • CheckpointLoaderSimple outputs: 0=MODEL, 1=CLIP, 2=VAE
   • Getting index wrong: ["1", 0] gives MODEL, ["1", 1] gives CLIP
2. Verify connection format: ["nodeId", outputIndex] — node ID is a string, index is an integer
3. Check data type flow: Ensure the pipeline follows the correct type chain:

   MODEL → KSampler
   CLIP → CLIPTextEncode → CONDITIONING → KSampler
   LATENT → KSampler → LATENT → VAEDecode → IMAGE
   VAE → VAEDecode, VAEEncode
   

Model Loading Errors

Error Pattern

FileNotFoundError: [Errno 2] No such file or directory: 'models/checkpoints/model.safetensors'

Or:

SafetensorError: Error reading file: invalid header

Or:

RuntimeError: PytorchStreamReader failed reading zip archive

Root Causes

• File not found: Model file doesn't exist at the referenced path
• Corrupted download: Incomplete or damaged file
• Wrong format: File is not a valid safetensors/pickle/checkpoint format

Fixes

1. Verify the model exists: list_local_models(model_type="checkpoints")
2. Check the exact filename: Model names in workflows must match the filename exactly (case-sensitive)
3. Re-download: If hash mismatch or corruption:

   download_model(url="...", target_subfolder="checkpoints")
   

4. Check file size: A 1KB safetensors file is clearly corrupted — re-download
5. Verify subfolder: Models must be in the correct subfolder (checkpoints/, loras/, vae/, etc.)

Torch / CUDA Version Errors

Error Pattern

RuntimeError: CUDA error: no kernel image is available for execution on the device

Or:

ImportError: cannot import name 'xxx' from 'torch'

Or:

AssertionError: Torch not compiled with CUDA enabled

Root Cause

PyTorch and CUDA version incompatibility, usually after:

• Updating PyTorch without matching CUDA toolkit
• Installing a custom node that downgrades/changes PyTorch
• Using pip install that pulls a CPU-only PyTorch

Fixes

1. Check current versions:

   get_system_stats()  # Shows PyTorch version and CUDA version
   

2. Verify CUDA availability: In Python: torch.cuda.is_available()
3. Reinstall PyTorch with CUDA: Visit pytorch.org for the correct install command matching your CUDA version
4. Pin PyTorch version: After fixing, avoid running pip install commands that might change PyTorch
5. Use ComfyUI's bundled venv: ComfyUI Desktop ships with a pre-configured Python environment

ComfyUI Desktop vs CLI Differences

Key Differences

Common Issues

• Wrong port: MCP tools default to 8188 — if using Desktop, configure for port 8000
• Path confusion: Desktop separates user data from application files
• Custom node pip installs: Desktop's embedded Python may not be on PATH — install within the venv

Error-Specific Debugging Commands

Workflow Failed — Get Details

get_history()                           # Most recent execution
get_history(prompt_id="abc-123")        # Specific execution

The response includes:

• status.status_str: "success" or "error"
• status.messages: Timestamped execution messages
• outputs: Node outputs (images, etc.)
• Error traceback for failed nodes

Check Server Health

get_system_stats()    # GPU info, VRAM, Python/PyTorch versions
get_queue()           # Running and pending jobs
get_logs(max_lines=50, keyword="error")  # Recent error logs

Verify Node Availability

get_node_info(node_type="KSampler")              # Check specific node
get_node_info(node_type="ControlNetApply")        # Verify custom nodes loaded

Verify Models

list_local_models(model_type="checkpoints")       # Installed checkpoints
list_local_models(model_type="loras")             # Installed LoRAs
list_local_models(model_type="controlnet")        # Installed ControlNets

Quick Reference: Error to Fix