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allow tiling on qwen image

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Concedo 2025-10-04 20:43:36 +08:00
parent bb06956b2d
commit a98b63013e
2 changed files with 27 additions and 26 deletions
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48
otherarch/sdcpp/ggml_extend.hpp
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@ -479,12 +479,15 @@ __STATIC_INLINE__ void ggml_split_tensor_2d(struct ggml_tensor* input,
int64_t width = output->ne[0];
int64_t height = output->ne[1];
int64_t channels = output->ne[2];
int64_t ne3 = output->ne[3];
GGML_ASSERT(input->type == GGML_TYPE_F32 && output->type == GGML_TYPE_F32);
for (int iy = 0; iy < height; iy++) {
for (int ix = 0; ix < width; ix++) {
for (int k = 0; k < channels; k++) {
float value = ggml_tensor_get_f32(input, ix + x, iy + y, k);
ggml_tensor_set_f32(output, value, ix, iy, k);
for (int l = 0; l < ne3; l++) {
float value = ggml_tensor_get_f32(input, ix + x, iy + y, k, l);
ggml_tensor_set_f32(output, value, ix, iy, k, l);
}
}
}
}
@ -507,6 +510,7 @@ __STATIC_INLINE__ void ggml_merge_tensor_2d(struct ggml_tensor* input,
int64_t width = input->ne[0];
int64_t height = input->ne[1];
int64_t channels = input->ne[2];
int64_t ne3 = input->ne[3];
int64_t img_width = output->ne[0];
int64_t img_height = output->ne[1];
@ -515,24 +519,26 @@ __STATIC_INLINE__ void ggml_merge_tensor_2d(struct ggml_tensor* input,
for (int iy = y_skip; iy < height; iy++) {
for (int ix = x_skip; ix < width; ix++) {
for (int k = 0; k < channels; k++) {
float new_value = ggml_tensor_get_f32(input, ix, iy, k);
if (overlap_x > 0 || overlap_y > 0) { // blend colors in overlapped area
float old_value = ggml_tensor_get_f32(output, x + ix, y + iy, k);
for (int l = 0; l < ne3; l++) {
float new_value = ggml_tensor_get_f32(input, ix, iy, k, l);
if (overlap_x > 0 || overlap_y > 0) { // blend colors in overlapped area
float old_value = ggml_tensor_get_f32(output, x + ix, y + iy, k, l);
const float x_f_0 = (overlap_x > 0 && x > 0) ? (ix - x_skip) / float(overlap_x) : 1;
const float x_f_1 = (overlap_x > 0 && x < (img_width - width)) ? (width - ix) / float(overlap_x) : 1;
const float y_f_0 = (overlap_y > 0 && y > 0) ? (iy - y_skip) / float(overlap_y) : 1;
const float y_f_1 = (overlap_y > 0 && y < (img_height - height)) ? (height - iy) / float(overlap_y) : 1;
const float x_f_0 = (overlap_x > 0 && x > 0) ? (ix - x_skip) / float(overlap_x) : 1;
const float x_f_1 = (overlap_x > 0 && x < (img_width - width)) ? (width - ix) / float(overlap_x) : 1;
const float y_f_0 = (overlap_y > 0 && y > 0) ? (iy - y_skip) / float(overlap_y) : 1;
const float y_f_1 = (overlap_y > 0 && y < (img_height - height)) ? (height - iy) / float(overlap_y) : 1;
const float x_f = std::min(std::min(x_f_0, x_f_1), 1.f);
const float y_f = std::min(std::min(y_f_0, y_f_1), 1.f);
const float x_f = std::min(std::min(x_f_0, x_f_1), 1.f);
const float y_f = std::min(std::min(y_f_0, y_f_1), 1.f);
ggml_tensor_set_f32(
output,
old_value + new_value * ggml_smootherstep_f32(y_f) * ggml_smootherstep_f32(x_f),
x + ix, y + iy, k);
} else {
ggml_tensor_set_f32(output, new_value, x + ix, y + iy, k);
ggml_tensor_set_f32(
output,
old_value + new_value * ggml_smootherstep_f32(y_f) * ggml_smootherstep_f32(x_f),
x + ix, y + iy, k, l);
} else {
ggml_tensor_set_f32(output, new_value, x + ix, y + iy, k, l);
}
}
}
}
@ -848,8 +854,8 @@ __STATIC_INLINE__ void sd_tiling_non_square(ggml_tensor* input,
}
struct ggml_init_params params = {};
params.mem_size += input_tile_size_x * input_tile_size_y * input->ne[2] * sizeof(float); // input chunk
params.mem_size += output_tile_size_x * output_tile_size_y * output->ne[2] * sizeof(float); // output chunk
params.mem_size += input_tile_size_x * input_tile_size_y * input->ne[2] * input->ne[3] * sizeof(float); // input chunk
params.mem_size += output_tile_size_x * output_tile_size_y * output->ne[2] * output->ne[3] * sizeof(float); // output chunk
params.mem_size += 3 * ggml_tensor_overhead();
params.mem_buffer = NULL;
params.no_alloc = false;
@ -864,8 +870,8 @@ __STATIC_INLINE__ void sd_tiling_non_square(ggml_tensor* input,
}
// tiling
ggml_tensor* input_tile = ggml_new_tensor_4d(tiles_ctx, GGML_TYPE_F32, input_tile_size_x, input_tile_size_y, input->ne[2], 1);
ggml_tensor* output_tile = ggml_new_tensor_4d(tiles_ctx, GGML_TYPE_F32, output_tile_size_x, output_tile_size_y, output->ne[2], 1);
ggml_tensor* input_tile = ggml_new_tensor_4d(tiles_ctx, GGML_TYPE_F32, input_tile_size_x, input_tile_size_y, input->ne[2], input->ne[3]);
ggml_tensor* output_tile = ggml_new_tensor_4d(tiles_ctx, GGML_TYPE_F32, output_tile_size_x, output_tile_size_y, output->ne[2], output->ne[3]);
int num_tiles = num_tiles_x * num_tiles_y;
LOG_INFO("processing %i tiles", num_tiles);
pretty_progress(0, num_tiles, 0.0f);

5
otherarch/sdcpp/sdtype_adapter.cpp
View file

@ -748,11 +748,6 @@ sd_generation_outputs sdtype_generate(const sd_generation_inputs inputs)
std::vector<sd_image_t> wan_imgs;
std::vector<sd_image_t> photomaker_imgs;
if(is_qwenimg)
{
dotile = false; //qwen image has divide by zero issue when tiling
}
int nx, ny, nc;
int img2imgW = sd_params->width; //for img2img input
int img2imgH = sd_params->height;