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fix: preserve alpha when upscaling RGBA images with ESRGAN (#2029)

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leejet 2026-09-23 02:23:10 +08:00 committed by GitHub
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2 changed files with 24 additions and 2 deletions
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2
docs/esrgan.md
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@ -2,6 +2,8 @@
You can use ESRGAN—such as the model [RealESRGAN_x4plus_anime_6B.pth](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth)—to upscale the generated images and improve their overall resolution and clarity. You can use ESRGAN—such as the model [RealESRGAN_x4plus_anime_6B.pth](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth)—to upscale the generated images and improve their overall resolution and clarity.
RGBA images, including Qwen Image 2.1 output, keep their alpha channel during model upscaling and hires fix. ESRGAN processes the RGB channels; the alpha channel is resized with bilinear interpolation and recombined with the upscaled image.
- Specify the model path using the `--upscale-model PATH` parameter. example: - Specify the model path using the `--upscale-model PATH` parameter. example:
```bash ```bash

24
src/upscaler.cpp
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@ -111,10 +111,22 @@ bool UpscalerGGML::load_from_file(const std::string& esrgan_path,
sd::Tensor<float> UpscalerGGML::upscale_tensor(const sd::Tensor<float>& input_tensor) { sd::Tensor<float> UpscalerGGML::upscale_tensor(const sd::Tensor<float>& input_tensor) {
sd::ParallelScope tensor_scope(&tensor_executor); sd::ParallelScope tensor_scope(&tensor_executor);
if (input_tensor.empty() || input_tensor.dim() != 4 ||
(input_tensor.shape()[2] != 3 && input_tensor.shape()[2] != 4)) {
LOG_ERROR("esrgan expects a 4D RGB or RGBA image tensor");
return {};
}
const bool has_alpha = input_tensor.shape()[2] == 4;
sd::Tensor<float> rgb;
if (has_alpha) {
rgb = sd::ops::slice(input_tensor, 2, 0, 3);
}
const sd::Tensor<float>& model_input = has_alpha ? rgb : input_tensor;
sd::Tensor<float> upscaled; sd::Tensor<float> upscaled;
const int scale = esrgan_upscaler->config.scale; const int scale = esrgan_upscaler->config.scale;
if (tile_size <= 0 || (input_tensor.shape()[0] <= tile_size && input_tensor.shape()[1] <= tile_size)) { if (tile_size <= 0 || (input_tensor.shape()[0] <= tile_size && input_tensor.shape()[1] <= tile_size)) {
upscaled = esrgan_upscaler->compute(n_threads, input_tensor); upscaled = esrgan_upscaler->compute(n_threads, model_input);
} else { } else {
auto on_processing = [&](const sd::Tensor<float>& input_tile) -> sd::Tensor<float> { auto on_processing = [&](const sd::Tensor<float>& input_tile) -> sd::Tensor<float> {
auto output_tile = esrgan_upscaler->compute(n_threads, input_tile); auto output_tile = esrgan_upscaler->compute(n_threads, input_tile);
@ -125,7 +137,7 @@ sd::Tensor<float> UpscalerGGML::upscale_tensor(const sd::Tensor<float>& input_te
return output_tile; return output_tile;
}; };
upscaled = process_tiles_2d(input_tensor, upscaled = process_tiles_2d(model_input,
static_cast<int>(input_tensor.shape()[0] * scale), static_cast<int>(input_tensor.shape()[0] * scale),
static_cast<int>(input_tensor.shape()[1] * scale), static_cast<int>(input_tensor.shape()[1] * scale),
scale, scale,
@ -141,6 +153,14 @@ sd::Tensor<float> UpscalerGGML::upscale_tensor(const sd::Tensor<float>& input_te
LOG_ERROR("esrgan compute failed"); LOG_ERROR("esrgan compute failed");
return {}; return {};
} }
if (has_alpha) {
auto alpha = sd::ops::slice(input_tensor, 2, 3, 4);
auto alpha_shape = alpha.shape();
alpha_shape[0] = upscaled.shape()[0];
alpha_shape[1] = upscaled.shape()[1];
alpha = sd::ops::interpolate(alpha, alpha_shape, sd::ops::InterpolateMode::Bilinear);
upscaled = sd::ops::concat(upscaled, alpha, 2);
}
return upscaled; return upscaled;
} }