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Model Architecture Mismatch

Symptoms: Tensor dimension errors during generation, especially during VAE decode stage Common error messages:
  • Given groups=1, weight of size [64, 4, 3, 3], expected input[1, 16, 128, 128] to have 4 channels, but got 16 channels instead
  • Given groups=1, weight of size [4, 4, 1, 1], expected input[1, 16, 144, 112] to have 4 channels, but got 16 channels instead
  • Given groups=1, weight of size [320, 4, 3, 3], expected input[2, 16, 192, 128] to have 4 channels, but got 16 channels instead
  • The size of tensor a (49) must match the size of tensor b (16) at non-singleton dimension 1
  • Tensors must have same number of dimensions: got 2 and 3
  • mat1 and mat2 shapes cannot be multiplied (154x2048 and 768x320)
Root cause: Using models from different architecture families together

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Solutions

  1. Verify model family compatibility:
    • Flux models use 16-channel latent space with dual text encoder conditioning (CLIP-L + T5-XXL)
    • SD1.5 models use 4-channel latent space with single CLIP ViT-L/14 text encoder
    • SDXL models use 4-channel latent space with dual text encoders (CLIP ViT-L/14 + OpenCLIP ViT-bigG/14)
    • SD3 models use 16-channel latent space with triple text encoder conditioning (CLIP-L + OpenCLIP bigG + T5-XXL)
    • ControlNet models must match the architecture of the base checkpoint (SD1.5 ControlNets only work with SD1.5 checkpoints, SDXL ControlNets only work with SDXL checkpoints, etc.)
  2. Common mismatch scenarios and fixes: Flux + wrong VAE: 
    Problem: Using taesd or sdxl_vae.safetensors with Flux checkpoint
Fix: Use ae.safetensors (Flux VAE) from Hugging Face Flux releases
    Flux + incorrect CLIP configuration: 
    Problem: Using t5xxl_fp8_e4m3fn.safetensors in both CLIP slots of DualClipLoader
Fix: Use t5xxl_fp8_e4m3fn.safetensors in one slot and clip_l.safetensors in the other
    ControlNet architecture mismatch: 
    Problem: SD1.5 ControlNet with SDXL checkpoint (or vice versa)
Error: "mat1 and mat2 shapes cannot be multiplied (154x2048 and 768x320)"
Fix: Use ControlNet models designed for your checkpoint architecture
     - SD1.5 checkpoints require SD1.5 ControlNets
     - SDXL checkpoints require SDXL ControlNets
  3. Quick diagnostics: 
    # Check if error occurs at VAE decode stage
# Look for "expected input[X, Y, Z] to have N channels, but got M channels"
# Y value indicates channel count: 4 = SD models, 16 = Flux models
  4. Prevention strategies:
    • Keep all workflow models within the same architecture family
    • Download complete model packages from same source/release (often all in a Hugging Face repo)
    • When trying new models, start with the template workflows or official ComfyUI workflow examples before customizing

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Missing Models Error

Example error message: 
Prompt execution failed
Prompt outputs failed validation:
CheckpointLoaderSimple:
- Value not in list: ckpt_name: 'model-name.safetensors' not in []

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Solutions

  1. Download required models:
    • Use ComfyUI Manager to auto-download models
    • Verify models are in correct subfolders
  2. Check model paths:
    • Checkpoints: models/checkpoints/
    • VAE: models/vae/
    • LoRA: models/loras/
    • ControlNet: models/controlnet/
    • Embeddings: models/embeddings/
  3. Share models between UIs or use custom paths:

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Model Search Path Configuration

If you have models in custom locations, see the detailed guide for ComfyUI Model Sharing and Custom Model Directory Configuration to configure ComfyUI to find them.

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Model Loading Errors

Error message: “Error while deserializing header”

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Solutions

  1. Re-download the model - File may be corrupted during download
  2. Check available disk space - Ensure enough space for model loading (models can be 2-15GB+)
  3. Check file permissions - Ensure ComfyUI can read the model files
  4. Test with different model - Verify if issue is model-specific or system-wide

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Model Performance Issues

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Slow Model Loading

Symptoms: Long delays when switching models or starting generation Solutions:
  1. Use faster storage:
    • Move models to SSD if using HDD
    • Use NVMe SSD for best performance
  2. Adjust caching settings: 
    python main.py --cache-classic       # Use the old style (aggressive) caching. 
python main.py --cache-lru 10         # Increase size of LRU cache

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Memory Issues with Large Models

“RuntimeError: CUDA out of memory”: 
# Progressive memory reduction
python main.py --lowvram          # First try
python main.py --novram           # If lowvram insufficient  
python main.py --cpu              # Last resort
Model-specific memory optimization: 
# Force lower precision
python main.py --force-fp16

# Reduce attention memory usage
python main.py --use-pytorch-cross-attention
For additional model configuration and setup information, see the Models documentation.