DarthAffe/stable-diffusion.cpp
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refactor: centralize runner weight staging and cleanup (#1644)

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leejet authored and GitHub committed 2026-06-13 13:19:13 +08:00
1 parent 3a54597776
commit 563137a592
43 files changed
+2335 -1729
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src/model/diffusion/control.hpp
+10 -7
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@@ -309,6 +309,7 @@ public:
struct ControlNet : public GGMLRunner {
SDVersion version = VERSION_SD1;
ControlNetBlock control_net;
std::string weight_prefix;
ggml_backend_buffer_t control_buffer = nullptr;
ggml_context* control_ctx = nullptr;
@@ -321,9 +322,10 @@ struct ControlNet : public GGMLRunner {
ControlNet(ggml_backend_t backend,
ggml_backend_t params_backend,
const String2TensorStorage& tensor_storage_map = {},
SDVersion version = VERSION_SD1)
: GGMLRunner(backend, params_backend), control_net(version) {
control_net.init(params_ctx, tensor_storage_map, "");
SDVersion version = VERSION_SD1,
const std::string& prefix = "")
: GGMLRunner(backend, params_backend), version(version), control_net(version), weight_prefix(prefix) {
control_net.init(params_ctx, tensor_storage_map, prefix);
}
~ControlNet() override {
@@ -374,8 +376,8 @@ struct ControlNet : public GGMLRunner {
return "control_net";
}
void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string prefix) {
control_net.get_param_tensors(tensors, prefix);
void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) {
control_net.get_param_tensors(tensors, weight_prefix);
}
ggml_cgraph* build_graph(const sd::Tensor<float>& x_tensor,
@@ -435,7 +437,7 @@ struct ControlNet : public GGMLRunner {
return build_graph(x, hint, timesteps, context, y);
};
auto compute_result = GGMLRunner::compute<float>(get_graph, n_threads, false);
auto compute_result = GGMLRunner::compute<float>(get_graph, n_threads, false, false, false);
if (!compute_result.has_value()) {
return std::nullopt;
}
@@ -472,7 +474,8 @@ struct ControlNet : public GGMLRunner {
return false;
}
bool success = model_loader.load_tensors(tensors, ignore_tensors, n_threads);
model_loader.set_n_threads(n_threads);
bool success = model_loader.load_tensors(tensors, ignore_tensors);
if (!success) {
LOG_ERROR("load control net tensors from model loader failed");