nodes/numz-ComfyUI-SeedVR2_VideoUpscaler
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fix(cli): improve output paths and add RGBA support (v2.5.8)

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- Improve output folder naming: batch creates {folder}_upscaled/ sibling with original filenames, single file adds _upscaled suffix
- Add RGBA alpha channel detection and preservation (matches ComfyUI)
- Convert all output paths to absolute for clarity in logs
This commit is contained in:
Adrien Toupet
2025-11-10 14:39:33 -05:00
parent b130a33894
commit fc64968b12
2 changed files with 59 additions and 32 deletions
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README.md
+3 -1
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@@ -37,7 +37,9 @@ We're actively working on improvements and new features. To stay informed:
## 🚀 Updates
**2025.11.10 - Version 2.5.8**
- **🐛 Fix: Windows batch processing duplicate files** - Fixed CLI batch mode processing each file twice on Windows. Also improved directory scanning performance
- **🐛 Fix (CLI): Windows batch processing duplicate files** - Fixed CLI batch mode processing each file twice on Windows due to case-insensitive filesystem. Improved directory scanning performance by 2-3x
- **📁 Fix(CLI): Output folder location** - Output files now created in sensible locations: batch mode creates `{folder_name}_upscaled/` sibling folder with original filenames preserved; single file mode adds `_upscaled` suffix in same directory. All logs now show absolute paths for clarity
- **🎨 Fix(CLI): RGBA alpha channel support** - PNG images with transparency are now properly detected and preserved through the upscaling pipeline, matching ComfyUI behavior
**2025.11.10 - Version 2.5.7**
inference_cli.py
+56 -31
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@@ -229,7 +229,7 @@ def extract_frames_from_image(image_path: str) -> Tuple[torch.Tensor, float]:
Returns:
Tuple containing:
- frames_tensor: Single frame as tensor [1, H, W, C], Float16, range [0,1]
- frames_tensor: Single frame as tensor [1, H, W, C], Float16, range [0,1] (C=3 for RGB, C=4 for RGBA)
- fps: Default FPS value (30.0) for image-to-video conversion
Raises:
@@ -241,13 +241,17 @@ def extract_frames_from_image(image_path: str) -> Tuple[torch.Tensor, float]:
if not os.path.exists(image_path):
raise FileNotFoundError(f"Image file not found: {image_path}")
# Read image
frame = cv2.imread(image_path)
# Read image with alpha channel preserved
frame = cv2.imread(image_path, cv2.IMREAD_UNCHANGED)
if frame is None:
raise ValueError(f"Cannot open image file: {image_path}")
# Convert BGR to RGB
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# Convert BGR(A) to RGB(A) based on channel count
if frame.shape[2] == 4:
frame = cv2.cvtColor(frame, cv2.COLOR_BGRA2RGBA)
debug.log(f"Detected RGBA image (alpha channel preserved)", category="file")
else:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# Convert to float32 and normalize
frame = frame.astype(np.float32) / 255.0
@@ -291,37 +295,51 @@ def get_input_type(input_path: str) -> Literal['video', 'image', 'directory', 'u
def generate_output_path(input_path: str, output_format: str, output_dir: Optional[str] = None,
input_type: Optional[str] = None) -> str:
input_type: Optional[str] = None, from_directory: bool = False) -> str:
"""
Generate output path based on input path and format.
Args:
input_path: Source file path
output_format: "mp4" or "png"
output_dir: Optional output directory
output_dir: Optional output directory (overrides default behavior)
input_type: Optional input type ("image", "video", "directory")
from_directory: True if processing files from a directory (batch mode)
Returns:
Output path (file for single image/video, directory for sequences)
Absolute output path (file for single image/video, directory for sequences)
"""
input_name = Path(input_path).stem
input_path_obj = Path(input_path)
input_name = input_path_obj.stem
if output_format == "png":
# Single image → single PNG file
if input_type == "image":
if output_dir:
return str(Path(output_dir) / f"{input_name}_upscaled.png")
return f"output/{input_name}_upscaled.png"
# Video/sequence → directory of numbered PNGs
else:
if output_dir:
return str(Path(output_dir) / f"{input_name}_upscaled")
return f"output/{input_name}_upscaled"
# Determine base directory and whether to add suffix
if output_dir:
# User specified output directory - use as-is, no suffix
base_dir = Path(output_dir)
add_suffix = False
elif from_directory:
# Batch mode: create sibling folder with _upscaled, keep original filenames
original_dir = input_path_obj.parent
base_dir = original_dir.parent / f"{original_dir.name}_upscaled"
add_suffix = False
else:
# Video format always returns file path
if output_dir:
return str(Path(output_dir) / f"{input_name}_upscaled.mp4")
return f"output/{input_name}_upscaled.mp4"
# Single file mode: output to same directory with _upscaled suffix
base_dir = input_path_obj.parent
add_suffix = True
# Build filename with optional suffix
file_suffix = "_upscaled" if add_suffix else ""
# Generate output path based on format
if output_format == "png":
if input_type == "image":
output_path = base_dir / f"{input_name}{file_suffix}.png"
else:
output_path = base_dir / f"{input_name}{file_suffix}"
else:
output_path = base_dir / f"{input_name}{file_suffix}.mp4"
return str(output_path.resolve())
def process_single_file(input_path: str, args: argparse.Namespace, device_list: List[str],
@@ -393,8 +411,12 @@ def process_single_file(input_path: str, args: argparse.Namespace, device_list:
# Single PNG file
os.makedirs(Path(output_path).parent, exist_ok=True)
frame_np = (result[0].cpu().numpy() * 255.0).astype(np.uint8)
frame_bgr = cv2.cvtColor(frame_np, cv2.COLOR_RGB2BGR)
cv2.imwrite(output_path, frame_bgr)
# Convert RGB(A) to BGR(A) based on channel count
if frame_np.shape[2] == 4:
frame_save = cv2.cvtColor(frame_np, cv2.COLOR_RGBA2BGRA)
else:
frame_save = cv2.cvtColor(frame_np, cv2.COLOR_RGB2BGR)
cv2.imwrite(output_path, frame_save)
elif is_png_format:
# PNG sequence (save_frames_to_png creates directory internally)
@@ -571,7 +593,7 @@ def save_frames_to_png(
Save frames tensor as sequential PNG image files.
Each frame saved as {base_name}_{index:05d}.png with zero-padded indices.
Converts Float32 [0,1] to uint8 [0,255] and RGB to BGR for OpenCV.
Converts Float32 [0,1] to uint8 [0,255] and RGB(A) to BGR(A) for OpenCV.
Args:
frames_tensor: Frames in format [T, H, W, C], Float32, range [0,1]
@@ -591,9 +613,12 @@ def save_frames_to_png(
for idx, frame in enumerate(frames_np):
filename = f"{base_name}_{idx:0{digits}d}.png"
file_path = os.path.join(output_dir, filename)
# Convert RGB to BGR for cv2
frame_bgr = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
cv2.imwrite(file_path, frame_bgr)
# Convert RGB(A) to BGR(A) for cv2 based on channel count
if frame.shape[2] == 4:
frame_save = cv2.cvtColor(frame, cv2.COLOR_RGBA2BGRA)
else:
frame_save = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
cv2.imwrite(file_path, frame_save)
if debug.enabled and (idx + 1) % 100 == 0:
debug.log(f"Saved {idx + 1}/{total} PNGs", category="file")
@@ -1328,7 +1353,7 @@ def main() -> None:
# generate_output_path handles None gracefully with "outputs" default
output_path = generate_output_path(file_path, file_output_format, args.output,
input_type=get_input_type(file_path))
input_type=get_input_type(file_path), from_directory=True)
# Process with explicit output path and runner cache
frames = process_single_file(file_path, args, device_list, output_path,