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Merged
leejet merged 5 commits into from
Apr 5, 2026
+192 −14
Merged

Inpaint improvements #1357

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d78db9b
inpaint: get max pixel max instead of single sample
stduhpf Mar 19, 2026
6d484cd
inpaint: masked diffusion for inpainting models with inflated mask
stduhpf Mar 19, 2026
7556e09
Merge branch 'master' into inpaint-imporvements
leejet Apr 5, 2026
53f8b3d
refactor tensor interpolate nearest-like reduction paths and generali…
leejet Apr 5, 2026
090b914
remove unused get_flat_index
leejet Apr 5, 2026
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9 changes: 7 additions & 2 deletions src/stable-diffusion.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -2846,7 +2846,8 @@ static std::optional<ImageGenerationLatents> prepare_image_generation_latents(sd
{request->width / request->vae_scale_factor,
request->height / request->vae_scale_factor,
1,
1});
1},
sd::ops::InterpolateMode::NearestMax);

sd::Tensor<float> init_latent;
sd::Tensor<float> control_latent;
Expand Down Expand Up @@ -2991,8 +2992,12 @@ static std::optional<ImageGenerationLatents> prepare_image_generation_latents(sd
latents.ref_latents = std::move(ref_latents);

if (sd_version_is_inpaint(sd_ctx->sd->version)) {
latents.denoise_mask = std::move(latent_mask);
latent_mask = sd::ops::max_pool_2d(latent_mask,
{3, 3},
{1, 1},
{1, 1});
}
latents.denoise_mask = std::move(latent_mask);

return latents;
}
Expand Down
197 changes: 185 additions & 12 deletions src/tensor.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -815,6 +815,9 @@ namespace sd {
namespace ops {
enum class InterpolateMode {
Nearest,
NearestMax,
NearestMin,
NearestAvg,
};

inline int64_t normalize_slice_bound(int64_t index, int64_t dim_size) {
Expand Down Expand Up @@ -1012,12 +1015,16 @@ namespace sd {
std::vector<int64_t> output_shape,
InterpolateMode mode = InterpolateMode::Nearest,
bool align_corners = false) {
if (mode != InterpolateMode::Nearest) {
tensor_throw_invalid_argument("Only nearest interpolate mode is implemented, got mode=" +
const bool is_nearest_like_mode = (mode == InterpolateMode::Nearest ||
mode == InterpolateMode::NearestMax ||
mode == InterpolateMode::NearestMin ||
mode == InterpolateMode::NearestAvg);
if (!is_nearest_like_mode) {
tensor_throw_invalid_argument("Only nearest-like interpolate modes are implemented, got mode=" +
std::to_string(static_cast<int>(mode)));
}
if (align_corners) {
tensor_throw_invalid_argument("align_corners is not supported for nearest interpolate: input_shape=" +
tensor_throw_invalid_argument("align_corners is not supported for nearest-like interpolate: input_shape=" +
tensor_shape_to_string(input.shape()) + ", output_shape=" +
tensor_shape_to_string(output_shape));
}
Expand All @@ -1044,14 +1051,102 @@ namespace sd {
}
}

bool has_downsampling = false;
for (int64_t i = 0; i < input.dim(); ++i) {
if (input.shape()[i] > output_shape[i]) {
has_downsampling = true;
break;
}
}

Tensor<T> output(std::move(output_shape));
for (int64_t flat = 0; flat < output.numel(); ++flat) {
std::vector<int64_t> output_coord = tensor_unravel_index(flat, output.shape());
std::vector<int64_t> input_coord(static_cast<size_t>(input.dim()), 0);
for (size_t i = 0; i < static_cast<size_t>(input.dim()); ++i) {
input_coord[i] = output_coord[i] * input.shape()[i] / output.shape()[i];
if (mode == InterpolateMode::Nearest || !has_downsampling) {
for (int64_t flat = 0; flat < output.numel(); ++flat) {
std::vector<int64_t> output_coord = tensor_unravel_index(flat, output.shape());
std::vector<int64_t> input_coord(static_cast<size_t>(input.dim()), 0);
for (size_t i = 0; i < static_cast<size_t>(input.dim()); ++i) {
input_coord[i] = output_coord[i] * input.shape()[i] / output.shape()[i];
}
output[flat] = input.index(input_coord);
}
output[flat] = input.index(input_coord);

return output;
}

auto init_reduction = [&]() -> T {
switch (mode) {
case InterpolateMode::NearestMax:
return std::numeric_limits<T>::lowest();
case InterpolateMode::NearestMin:
return std::numeric_limits<T>::max();
case InterpolateMode::NearestAvg:
return T(0);
case InterpolateMode::Nearest:
return T(0);
}

tensor_throw_invalid_argument("Unsupported interpolate mode: mode=" +
std::to_string(static_cast<int>(mode)));
};

auto reduce_value = [&](T& acc, const T& sample) {
switch (mode) {
case InterpolateMode::NearestMax:
acc = std::max(acc, sample);
break;
case InterpolateMode::NearestMin:
acc = std::min(acc, sample);
break;
case InterpolateMode::NearestAvg:
acc += sample;
break;
case InterpolateMode::Nearest:
break;
}
};

// Reduction modes only differ from nearest mode when downsampling.
for (int64_t flat_out = 0; flat_out < output.numel(); ++flat_out) {
std::vector<int64_t> output_coord = tensor_unravel_index(flat_out, output.shape());

std::vector<int64_t> input_start(output.dim(), 0);
std::vector<int64_t> input_end(output.dim(), 0);

for (size_t i = 0; i < static_cast<size_t>(output.dim()); ++i) {
const int64_t input_dim = input.shape()[i];
const int64_t output_dim = output.shape()[i];

input_start[i] = std::max(int64_t(0), static_cast<int64_t>(output_coord[i] * input_dim / output_dim));
input_end[i] = std::min(input_dim, ((output_coord[i] + 1) * input_dim + output_dim - 1) / output_dim);
}

T value = init_reduction();
bool done_window = false;
std::vector<int64_t> current_in_coord = input_start;

while (!done_window) {
reduce_value(value, input.index(current_in_coord));

for (int d = static_cast<int>(output.dim()) - 1; d >= 0; --d) {
if (++current_in_coord[d] < input_end[d]) {
break;
}
current_in_coord[d] = input_start[d];
if (d == 0) {
done_window = true;
}
}
}

if (mode == InterpolateMode::NearestAvg) {
int64_t window_size = 1;
for (size_t i = 0; i < static_cast<size_t>(output.dim()); ++i) {
window_size *= (input_end[i] - input_start[i]);
}
value /= static_cast<T>(window_size);
}

output[flat_out] = value;
}

return output;
Expand All @@ -1063,12 +1158,16 @@ namespace sd {
const std::optional<std::vector<double>>& scale_factor,
InterpolateMode mode = InterpolateMode::Nearest,
bool align_corners = false) {
if (mode != InterpolateMode::Nearest) {
tensor_throw_invalid_argument("Only nearest interpolate mode is implemented, got mode=" +
const bool is_nearest_like_mode = (mode == InterpolateMode::Nearest ||
mode == InterpolateMode::NearestMax ||
mode == InterpolateMode::NearestMin ||
mode == InterpolateMode::NearestAvg);
if (!is_nearest_like_mode) {
tensor_throw_invalid_argument("Only nearest-like interpolate modes are implemented, got mode=" +
std::to_string(static_cast<int>(mode)));
}
if (align_corners) {
tensor_throw_invalid_argument("align_corners is not supported for nearest interpolate: input_shape=" +
tensor_throw_invalid_argument("align_corners is not supported for nearest-like interpolate: input_shape=" +
tensor_shape_to_string(input.shape()));
}
if (size.has_value() == scale_factor.has_value()) {
Expand Down Expand Up @@ -1128,6 +1227,80 @@ namespace sd {
align_corners);
}

template <typename T>
inline Tensor<T> max_pool_2d(const Tensor<T>& input,
std::vector<int64_t> kernel_size,
std::vector<int64_t> stride,
std::vector<int64_t> padding) {
if (input.dim() < 2) {
tensor_throw_invalid_argument("Tensor max_pool_2d requires input_dim >= 2: input_dim=" +
std::to_string(input.dim()) + ", input_shape=" +
tensor_shape_to_string(input.shape()));
}
if (kernel_size.size() != 2 || stride.size() != 2 || padding.size() != 2) {
tensor_throw_invalid_argument("Tensor max_pool_2d requires kernel_size, stride, and padding to have length 2");
}
for (size_t i = 0; i < 2; ++i) {
if (kernel_size[i] <= 0) {
tensor_throw_invalid_argument("Tensor max_pool_2d kernel_size must be positive: kernel_size=" +
tensor_shape_to_string(kernel_size));
}
if (stride[i] <= 0) {
tensor_throw_invalid_argument("Tensor max_pool_2d stride must be positive: stride=" +
tensor_shape_to_string(stride));
}
if (padding[i] < 0) {
tensor_throw_invalid_argument("Tensor max_pool_2d padding must be non-negative: padding=" +
tensor_shape_to_string(padding));
}
}

const int64_t in_height = input.shape()[0];
const int64_t in_width = input.shape()[1];

const int64_t out_height = (in_height + 2 * padding[0] - kernel_size[0]) / stride[0] + 1;
const int64_t out_width = (in_width + 2 * padding[1] - kernel_size[1]) / stride[1] + 1;

if (out_height <= 0 || out_width <= 0) {
tensor_throw_invalid_argument("max_pool_2d results in invalid output dimensions: " +
std::to_string(out_height) + "x" + std::to_string(out_width));
}

std::vector<int64_t> output_shape = input.shape();
output_shape[0] = out_height;
output_shape[1] = out_width;

Tensor<T> output(std::move(output_shape));

for (int64_t flat_out = 0; flat_out < output.numel(); ++flat_out) {
std::vector<int64_t> output_coord = tensor_unravel_index(flat_out, output.shape());
std::vector<int64_t> input_coord = output_coord;

const int64_t oh = output_coord[0];
const int64_t ow = output_coord[1];

T max_val = std::numeric_limits<T>::lowest();
bool has_valid_input = false;

for (int64_t kh = 0; kh < kernel_size[0]; ++kh) {
for (int64_t kw = 0; kw < kernel_size[1]; ++kw) {
const int64_t ih = oh * stride[0] + kh - padding[0];
const int64_t iw = ow * stride[1] + kw - padding[1];

if (ih >= 0 && ih < in_height && iw >= 0 && iw < in_width) {
input_coord[0] = ih;
input_coord[1] = iw;
max_val = std::max(max_val, input.index(input_coord));
has_valid_input = true;
}
}
}

output[flat_out] = has_valid_input ? max_val : T(0);
}
return output;
}

template <typename T>
inline Tensor<T> concat(const Tensor<T>& lhs, const Tensor<T>& rhs, size_t dim) {
if (lhs.dim() != rhs.dim()) {
Expand Down
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