MusePose: Black Video Output In Test_stage_2.py - How To Fix

Oct 14, 2025 by ADMIN 61 views

Hey everyone! Having trouble with MusePose and getting a black screen when running test_stage_2.py? You're not alone! This article breaks down a common issue where the generated video output is completely black, even though the script seems to run without errors. We'll explore the potential causes and dive into troubleshooting steps to get you back on track. Let's get started!

Understanding the Black Output Video Issue in MusePose

So, you've run the test_stage_2.py script for MusePose, everything seems to go smoothly – progress bars fill up, MP4 files are saved, and the logs look clean. But when you go to watch your masterpiece, all you see is a black screen. Frustrating, right? This issue often pops up when running MusePose on Windows systems with GPUs that have lower VRAM (like 4GB). The good news is that there are several things we can check and try to resolve this. The key is to systematically investigate each potential cause and apply the appropriate fix. We'll walk through some common culprits and their solutions, so you can get your MusePose videos looking awesome.

Initial Symptoms and Environment

The typical scenario involves running test_stage_2.py on a Windows machine with a GPU that has 4GB of VRAM. The script completes without any explicit errors or crashes, but the resulting MP4 video files show only black frames. This can happen even when using the assets directly from the repository. It's essential to confirm your environment to ensure the fixes we discuss are relevant. Specifically, note your OS, Python version, PyTorch, TorchVision, MMCV, MMDet, MMPose versions, and your GPU specs. This information can help narrow down the cause if it's related to specific software versions or hardware limitations. The configuration and setup play a crucial role in the successful execution of MusePose. Therefore, ensuring compatibility and proper installation is the first step in troubleshooting.

Troubleshooting Steps Already Taken

It's great that you've already tried a bunch of things! Let's recap what you've done so far. This will help us avoid retreading old ground and focus on new potential solutions:

Verified pose_align.py Output: You've confirmed that the aligned pose video generated by pose_align.py is valid. This is an important first step, as it rules out issues with the initial pose alignment process. If the pose alignment were failing, it could lead to problems down the line.
Smaller Parameters for Low VRAM: Smart move setting smaller parameters like -W 160 -H 160 -S 3 -O 1 --steps 12 --cfg 1.2 --skip 7 -L 30. This reduces the memory footprint, which is crucial for GPUs with limited VRAM. Running with lower resolution and fewer steps can often prevent out-of-memory errors, which might silently lead to black frames.
NaN Handling: Patching musepose/utils/util.py with torch.nan_to_num(...).clamp(0,1) to handle NaNs is a good preventative measure. NaN values can wreak havoc in numerical computations, so clamping the tensor values to a valid range (0 to 1) helps to ensure stable results. This is especially important when dealing with floating-point numbers and complex models.
Rescaling: Enabling rescale=True in all save_videos_grid() calls inside test_stage_2.py is another excellent step. Rescaling can help normalize the pixel values, ensuring they fall within the displayable range. Without proper scaling, pixel values might be outside the 0-255 range, leading to a black or distorted output.
Tensor Value Checks: Checking that tensors are within ${-1, 1}$ before saving is crucial. This is because many image and video formats expect pixel values to be in this range. If tensors have values outside this range, they might be clipped, resulting in a black image or other visual artifacts.
Runtime Warnings and CUDA Errors: Confirming that no runtime warnings or CUDA errors remain is essential for a stable run. These errors can sometimes indicate underlying issues with the computation, which might not immediately cause a crash but can lead to incorrect output.

Even with all these attempts, the output videos are still black. Don't worry, guys! We've got more tricks up our sleeves. Let's dive deeper.

Potential Causes and Solutions for Black Video Output

Since the typical fixes didn't work, let's explore some more specific potential causes. We'll consider issues related to data normalization, precision, resolution, and even potential bugs in the saving process.

1. Normalization Issues in `save_videos_grid()`

This is a big one. Even though you've enabled rescale=True, there might still be a normalization step missing or misconfigured within the save_videos_grid() function itself. Specifically, we need to ensure that the pixel values are correctly mapped to the 0-255 range for display. The function may assume a different range or might not handle certain edge cases properly. It's crucial to inspect the save_videos_grid() function closely to verify its normalization logic.

Solution:

Inspect save_videos_grid(): Open the save_videos_grid() function definition (likely in musepose/utils/util.py or a similar utility file). Examine how it normalizes the tensor values before saving them as a video. Look for potential issues in the scaling or mapping of values.
Explicit Normalization: Add an explicit normalization step before calling save_videos_grid(). This can act as a safeguard to ensure the values are in the expected range. For example, you could add the following code snippet before the save_videos_grid() call:
```
video_tensor = (video_tensor + 1) / 2  # Scale from [-1, 1] to [0, 1]
video_tensor = video_tensor.clamp(0, 1)  # Clamp values to [0, 1]
video_tensor = (video_tensor * 255).byte()  # Scale to [0, 255] and convert to byte
```
This code snippet first scales the tensor values from the typical ${-1, 1}$ range to ${0, 1}$ , then clamps them to ensure they fall within this range, and finally scales them to ${0, 255}$ and converts them to byte format, which is a common format for image and video data.
Debugging Prints: Add print statements within save_videos_grid() to check the minimum and maximum values of the tensors before and after normalization. This will help you understand whether the values are within the expected range at each step. You can use torch.min() and torch.max() to get the minimum and maximum values of the tensor.

2. Precision Issues with fp16 and Potential NaNs

The use of weight_dtype: fp16 in your configuration could be contributing to the problem. While fp16 (half-precision floating point) can reduce memory usage and speed up computations, it has a smaller dynamic range than fp32 (single-precision floating point). This means it's more susceptible to underflow and overflow, which can lead to NaN values or loss of precision. Even though you've tried patching NaNs, they might be cropping up again during later stages of the computation, especially if the clamping operation is not sufficient to prevent them.