Fix GGUF Model Invalid Shape Errors In ComfyUI

by Alex Johnson 47 views

It can be incredibly frustrating when you're working with AI models, especially in creative workflows like those in ComfyUI, and you hit a roadblock. One of the most perplexing issues is the "Inference failure: invalid shape error" when using GGUF models. This error, as seen in the logs provided, often pops up during the dequantization process, indicating a mismatch between the expected shape of the model's weights and the actual data it's trying to process. Let's dive deep into this common hiccup and explore how to get your ComfyUI setup running smoothly again.

Understanding the "Invalid Shape" Error with GGUF Models

The core of the problem lies in how GGUF (GPT-Generated Unified Format) models are quantized and loaded. Quantization is a technique used to reduce the size of a model and speed up inference by using lower-precision numbers (like 8-bit integers instead of 32-bit floats). When a GGUF model is loaded, especially for specific hardware like Apple Silicon (MPS) as indicated in your logs, the system needs to dequantize these weights back into a usable format for computations. The error message "shape '[7680, 2560]' is invalid for input of size 39321600" is a classic sign that something went wrong during this dequantization step. It means that the dequantization routine expected a certain arrangement of data (a shape of 7680 rows and 2560 columns) but received data that, when flattened, doesn't match that expected total size (39,321,600 elements). This can happen for several reasons, including:

  • Model Corruption or Incomplete Download: The GGUF file itself might be corrupted during download or transfer, leading to missing or malformed data. The log shows the model path as .../ComfyUI/models/SEEDVR2/seedvr2_ema_3b-Q8_0.gguf. If this file is not perfectly intact, issues like this are bound to arise.
  • Version Incompatibility: You've updated to v2.5.20, which is a significant clue. Newer versions of software, including ComfyUI and its custom nodes like SeedVR2_VideoUpscaler, might introduce changes in how they handle GGUF models or specific quantization types. It's possible that the version of the GGUF model you're using was quantized with a tool or setting that is no longer perfectly compatible with the latest dequantization logic in v2.5.20.
  • Backend/Hardware Specific Issues: The logs clearly indicate GPU: Apple Silicon (MPS). Sometimes, specific hardware backends have nuances in how they handle tensor operations and data dequantization. The way MPS processes certain quantized formats might differ slightly, leading to these shape mismatches.
  • Quantization Type Mismatch: GGUF models can be quantized using various methods (e.g., Q4_0, Q5_K_M, Q8_0). The error message specifies qtype: 8, suggesting it's trying to dequantize an 8-bit quantized model. If the dequantization code expects a specific variant of 8-bit quantization that doesn't perfectly align with how seedvr2_ema_3b-Q8_0.gguf was actually quantized, you'll see this error.
  • Software Bugs: While less common, there could be a bug in the specific version of ComfyUI, the SeedVR2 node, or the underlying PyTorch/GGUF libraries that handles the dequantization process incorrectly under certain conditions.

The traceback points directly to the gguf_dequant.py file and the dequantize function, specifically at result = blocks.reshape(oshape). This confirms that the error happens when trying to reshape the raw quantized data (blocks) into the expected output shape (oshape). The input size (39321600) and the target shape ([7680, 2560]) are indeed incompatible because 7680 * 2560 = 19,660,800, not 39,321,600. This is a critical mismatch. It's important to note that the log also mentions Data shape: torch.Size([7680, 2720]), which also results in 7680 * 2720 = 20,736,000. This suggests the raw data shape being processed is also not aligning with the expected output shape. This discrepancy is the root cause of the failure.

Step-by-Step Solutions to Resolve the Error

When faced with this RuntimeError: shape '[7680, 2560]' is invalid for input of size 39321600, don't panic! We can systematically troubleshoot this. The goal is to ensure the GGUF model is correctly formatted, compatible with your software versions, and properly loaded.

1. Verify the GGUF Model File Integrity

The first and often simplest solution is to rule out a corrupted download. Even if the download appears complete, file corruption can happen.

  • Re-download the Model: Go back to the source where you downloaded seedvr2_ema_3b-Q8_0.gguf. Download it again, preferably using a reliable internet connection. If possible, use a download manager that supports checksum verification.
  • Check File Size: Compare the size of the newly downloaded file with the original one (if you have a record of it) or with the expected size from the source. A significantly different size is a red flag.
  • Checksum Verification (if available): If the model provider offers MD5, SHA-1, or SHA-256 checksums, use a tool (like md5sum or shasum on macOS/Linux, or third-party tools on Windows) to calculate the checksum of your downloaded file and compare it to the provided one. If they don't match, the file is definitely corrupted.

2. Ensure Compatibility with ComfyUI and SeedVR2 Node Versions

Since you mentioned updating to v2.5.20, version compatibility is a prime suspect. The SeedVR2 node, like many custom nodes, evolves to support newer features and model formats. It's possible that the specific GGUF model you're using was optimized for an older version of the underlying libraries or the node itself.

  • Check SeedVR2 Node Documentation/Changelog: Visit the GitHub repository for ComfyUI-SeedVR2_VideoUpscaler. Look for any release notes or documentation related to v2.5.20 that mention GGUF handling or potential breaking changes. Sometimes, specific GGUF quantization types might be recommended or deprecated.
  • Check ComfyUI Version: Ensure your main ComfyUI installation is also up-to-date or at a version known to be stable with your custom nodes. Sometimes, core ComfyUI changes can affect how custom nodes interact with models.
  • Search for Known Issues: Look at the 'Issues' section of the SeedVR2 GitHub repository. Search for terms like "GGUF invalid shape", "dequantize error", or "MPS" to see if other users have encountered and solved this specific problem.

3. Re-quantize the Model (Advanced)

If you have the original model weights (e.g., in .safetensors or .pth format) and the tools to do so, you could try re-quantizing the model. This is a more advanced step but can resolve issues stemming from the original quantization process.

  • Use Compatible Quantization Tools: Ensure you are using a quantization tool that is known to be compatible with the version of the SeedVR2 node or the underlying GGUF library it uses. Look for specific recommendations within the SeedVR2 repository.
  • Experiment with Quantization Types: If the model was originally quantized as Q8_0, perhaps try a different 8-bit variant if available, or even a higher precision like Q4_K_M or F16 if your hardware supports it and the node permits.

4. Check GGUF Specifics and Backend Settings

Your logs show a clear indication of using Apple Silicon (MPS). While MPS is powerful, it sometimes has quirks.

  • GGUF Loader Configuration: Some GGUF loading mechanisms allow specifying parameters. While not evident in these logs, if there are advanced settings for the GGUF loader within the SeedVR2 node, check if any parameters related to dtype, device, or quantization can be adjusted.
  • MPS vs. CPU Fallback: As a diagnostic step, if possible, try running the process on the CPU to see if the error persists. This would strongly point towards an MPS-specific issue. However, given the model size, this might be impractical due to performance limitations.

5. Alternative Model Format or Source

If the seedvr2_ema_3b-Q8_0.gguf file continues to cause issues, consider if there are alternative versions available.

  • Different Quantization: Look for the same model in a different GGUF quantization format (e.g., Q5_K_M, Q4_K_M, or even F16 if storage and RAM permit). Sometimes, a specific quantization type might be problematic.
  • Different Model Source: If you downloaded it from a community hub like Hugging Face, check if there are other uploads of the same model or if the original creator provides a recommended format.
  • Non-GGUF Versions: If the SeedVR2 node supports other formats like .safetensors, and if you can find the model in that format, it might bypass the GGUF loading and dequantization issues altogether. However, GGUF is often preferred for its efficiency and broad compatibility.

Conclusion: Patience and Process

Encountering an "invalid shape" error during GGUF model inference in ComfyUI, especially with updates, is a common but solvable problem. The key is to approach it methodically. Start with the simplest checks like file integrity and version compatibility. If those don't resolve the issue, delve into more advanced troubleshooting like re-quantization or exploring alternative model versions. The traceback clearly shows a fundamental mismatch in expected data dimensions during the dequantization process, highlighting the need to ensure the GGUF file and the software handling it are perfectly aligned. With a bit of persistence, you’ll likely get your video upscaling workflow back on track.

For further assistance and more in-depth discussions on AI model troubleshooting, especially concerning performance and compatibility on macOS and Apple Silicon, consider checking out resources like:

  • The ComfyUI Official GitHub Repository: This is the central hub for ComfyUI development and issue tracking. You can find discussions, bug reports, and solutions related to various nodes and models. ComfyUI GitHub
  • The SeedVR2_VideoUpscaler GitHub Repository: As this specific issue appears to be within this custom node, their GitHub issues page is an invaluable resource for user-reported problems and developer insights. ComfyUI-SeedVR2_VideoUpscaler GitHub
  • Apple Developer Forums: For deep dives into MPS performance and potential backend-specific issues on macOS, the Apple Developer forums can offer insights from other developers working with similar hardware. Apple Developer Forums