This commit provides a more robust fix for the RuntimeWarning
(invalid value encountered in divide/cast) that could occur in
the `apply_mouth_area` function within
`modules/processors/frame/face_swapper.py`.
The previous check for `feathered_mask.max() == 0` was not
sufficient for all floating point edge cases.
The updated logic now:
- Checks if `feathered_mask.max()` is less than a small epsilon (1e-6).
- If true, it logs a warning and explicitly sets `feathered_mask`
to an all-zero `uint8` array of the correct shape.
- Otherwise, it proceeds with the normalization and casting to `uint8`.
This ensures that division by zero or by extremely small numbers is
prevented, and the `feathered_mask` is always in a valid state for
subsequent blending operations.
This commit implements changes based on the code review feedback
for the recent face swap enhancement features.
Key changes include:
1. **Error Handling for Color Transfer:**
* Wrapped the `apply_color_transfer` call in `swap_face` (within `face_swapper.py`) in a try-except block. If color transfer fails, an error is logged, and the system falls back to using the uncorrected swapped face ROI, preventing pipeline crashes.
2. **GaussianBlur Kernel Size Validation:**
* Added validation logic in `face_swapper.py` for `mouth_mask_blur_kernel_size` and `face_mask_blur_kernel_size`.
* A helper function `_validate_kernel_size` ensures that kernel dimensions are positive odd integers. If invalid values are provided via global settings, a warning is logged, and the functions fall back to safe default kernel sizes (e.g., (9,9) for mouth, (5,5) for face).
3. **Configurable GFPGAN Upscale Factor:**
* The `upscale` factor for `GFPGANer` in `face_enhancer.py` is now configurable via `getattr(modules.globals, 'gfpgan_upscale_factor', 2)`, allowing you to adjust this parameter.
4. **Clarification on Mouth Mask Blur Default:**
* Added a comment in `face_swapper.py` explaining that the new default `(9,9)` for `mouth_mask_blur_kernel_size` is a deliberate performance/quality trade-off and that this setting is configurable.
These changes improve the robustness, configurability, and clarity of the recently added features.
Here's a summary of the key changes:
1. **Upgraded Face Swapping Model:**
* I've updated the system to use a newer model (`inswapper_128.onnx`) which should provide a noticeable improvement in the base quality of the swapped faces.
* The model download logic in `modules/processors/frame/face_swapper.py` has been updated accordingly.
2. **Improved Face Enhancement (GFPGAN):**
* I've adjusted a parameter in `modules/processors/frame/face_enhancer.py` (`upscale` from `1` to `2`) which should result in enhanced faces having more detail and sharpness.
3. **Statistical Color Correction:**
* I've integrated a new color correction method into `modules/processors/frame/face_swapper.py`. This method uses statistical color transfer to better match skin tones and lighting conditions, significantly improving blending.
* This feature is controlled by a global setting.
4. **Optimized Mouth Masking Logic:**
* I've made some parameters in `modules/processors/frame/face_swapper.py` configurable with new, more performant defaults. These changes should reduce CPU load when mouth masking is enabled.
5. **Performance Considerations & Future Work:**
* While model inference is still the most computationally intensive part, these upgrades prioritize quality.
* The new color correction and mouth masking optimizations help to offset some of the CPU overhead.
* I recommend formally adding the new global variables to `modules/globals.py` and exposing them as command-line arguments for your use.
* Developing a comprehensive test suite would be beneficial to ensure robustness and track quality/performance over time.
These changes collectively address your request for improved face swap quality and provide options for optimizing performance.
This commit implements several improvements to the face swapping pipeline,
focusing on enhancing output quality and providing optimizations for performance.
Key changes include:
1. **Upgraded Face Swapping Model:**
* Switched from `inswapper_128_fp16.onnx` to the full-precision `inswapper_128.onnx` model. This is expected to provide higher fidelity face swaps.
* Updated the model download logic in `modules/processors/frame/face_swapper.py` accordingly.
2. **Optimized Face Enhancement (GFPGAN):**
* Modified `modules/processors/frame/face_enhancer.py` to set the `upscale` parameter for `GFPGANer` from `1` to `2`. This can improve the detail and perceived quality of faces processed by the enhancer.
3. **Improved Color Matching for Swapped Faces:**
* Implemented statistical color transfer in `modules/processors/frame/face_swapper.py`. This matches the color profile of the swapped face region to the original target face's region, leading to more seamless and natural blending.
* This feature is controlled by a new (assumed) global flag `statistical_color_correction`.
4. **Optimized Mouth Masking Logic:**
* Reduced default `forehead_extension_factor` in `create_face_mask` from `5.0` to `2.5` for slightly faster mask computation.
* Reduced default `mouth_mask_blur_kernel_size` in `create_lower_mouth_mask` from `(15, 15)` to `(9, 9)` to speed up this blur operation.
* These parameters are now fetched using `getattr` to allow future configuration via global variables (e.g., `modules.globals.forehead_extension_factor`).
5. **Performance Analysis & Other Considerations:**
* Identified model inference (swapper, enhancer) as primary GPU workloads.
* Noted that mouth masking (CPU-bound) and the new color correction add overhead. Making these features optional (which they are, via global flags like `mouth_mask` and `statistical_color_correction`) is important for you to balance quality and performance.
* Reviewed face detection usage and found it to be reasonably efficient for the modular pipeline structure.
These changes aim to significantly improve the visual quality of the face swaps and provide some performance tuning options.
Followed the `README` but ran into some errors running it locally. Made a few tweaks and got it working on my M3 PRO. Found this PR (Failing to run on Apple Silicon Mac M3) and thought improving the instructions might help others. Hope this helps!
great tool guys, thx a lot
- Add explicit checks for face detection results (source and target faces).
- Handle cases when face embeddings are not available, preventing AttributeError.
- Provide meaningful log messages for easier debugging in future scenarios.
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