DarthAffe/stable-diffusion.cpp
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refactor: unify runner lifecycles and weight residency (#1940)

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leejet authored and GitHub committed 2026-09-06 22:30:45 +08:00
1 parent 6c57cc3b38
commit 462d675018
78 files changed
+2963 -2051
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src/model/te/llm.hpp
+8 -20
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@@ -2079,9 +2079,7 @@ namespace LLM {
const ImageEmbeds& image_embeds,
std::set<int> out_layers,
bool return_all_hidden_states = false,
bool auto_free = true,
bool free_compute_buffer = true,
bool free_compute_params = true,
bool auto_runner_end = true,
const DeepStackImageEmbeds& deepstack_image_embeds = {},
const std::vector<ImageGrid>& image_grids = {}) {
auto get_graph = [&]() -> ggml_cgraph* {
@@ -2093,7 +2091,7 @@ namespace LLM {
out_layers,
return_all_hidden_states);
};
return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, auto_free, free_compute_buffer, free_compute_params),
return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, auto_runner_end),
input_ids.dim() + 1);
}
@@ -2173,13 +2171,11 @@ namespace LLM {
sd::Tensor<float> encode_image(const int n_threads,
const sd::Tensor<float>& image,
bool auto_free = false,
bool free_compute_buffer = false,
bool free_compute_params = false) {
bool auto_runner_end = false) {
auto get_graph = [&]() -> ggml_cgraph* {
return build_encode_image_graph(image);
};
return take_or_empty(GGMLRunner::compute<float>(get_graph, n_threads, auto_free, free_compute_buffer, free_compute_params));
return take_or_empty(GGMLRunner::compute<float>(get_graph, n_threads, auto_runner_end));
}
ggml_cgraph* build_encode_image_outputs_graph(const sd::Tensor<float>& image_tensor) {
@@ -2287,13 +2283,11 @@ namespace LLM {
std::vector<sd::Tensor<float>> encode_image_outputs(const int n_threads,
const sd::Tensor<float>& image,
bool auto_free = false,
bool free_compute_buffer = false,
bool free_compute_params = false) {
bool auto_runner_end = false) {
auto get_graph = [&]() -> ggml_cgraph* {
return build_encode_image_outputs_graph(image);
};
auto combined = take_or_empty(GGMLRunner::compute<float>(get_graph, n_threads, auto_free, free_compute_buffer, free_compute_params));
auto combined = take_or_empty(GGMLRunner::compute<float>(get_graph, n_threads, auto_runner_end));
if (combined.empty()) {
return {};
}
@@ -2312,20 +2306,14 @@ namespace LLM {
std::vector<sd::Tensor<float>> encode_video_block_outputs(const int n_threads,
const sd::Tensor<float>& frames,
bool auto_free = false,
bool free_compute_buffer = false,
bool free_compute_params = false) {
bool auto_runner_end = false) {
int grid_h = static_cast<int>(frames.shape()[1] / config.vision.patch_size);
int grid_w = static_cast<int>(frames.shape()[0] / config.vision.patch_size);
auto pixel_values = process_video_block_tensor(frames, config.vision);
auto get_graph = [&]() -> ggml_cgraph* {
return build_encode_video_block_outputs_graph(pixel_values, grid_h, grid_w);
};
auto combined = take_or_empty(GGMLRunner::compute<float>(get_graph,
n_threads,
auto_free,
free_compute_buffer,
free_compute_params));
auto combined = take_or_empty(GGMLRunner::compute<float>(get_graph, n_threads, auto_runner_end));
if (combined.empty()) {
return {};
}