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https://github.com/leejet/stable-diffusion.cpp.git
synced 2026-06-23 14:46:39 +00:00
fix: avoid writable mmap for read-only weights (#1698)
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@ -261,8 +261,8 @@ bool parse_options(int argc, const char** argv, const std::vector<ArgOptions>& o
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invalid_arg = true;
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return;
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}
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if(option.concat && !option.target->empty()){
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if(option.concat > 0 && option.concat <= 0xff){
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if (option.concat && !option.target->empty()) {
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if (option.concat > 0 && option.concat <= 0xff) {
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*option.target += static_cast<char>(option.concat);
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}
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*option.target += argv_to_utf8(i, argv);
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@ -480,7 +480,7 @@ bool ModelManager::mmap_params(const std::vector<TensorState*>& states,
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return true;
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}
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auto mmap_store = model_loader_.mmap_tensors(mmap_candidates, {}, true);
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auto mmap_store = model_loader_.mmap_tensors(mmap_candidates, {}, writable_mmap_);
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if (mmap_store.empty()) {
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return true;
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}
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@ -69,6 +69,7 @@ private:
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uint64_t current_lora_epoch_ = 0;
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int n_threads_ = 0;
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bool enable_mmap_ = false;
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bool writable_mmap_ = false;
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void finish_compute_backend_usage(const std::vector<TensorState*>& states);
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void release_all();
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@ -110,6 +111,7 @@ public:
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model_loader_.set_n_threads(n_threads);
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}
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void set_enable_mmap(bool enable_mmap) { enable_mmap_ = enable_mmap; }
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void set_writable_mmap(bool writable_mmap) { writable_mmap_ = writable_mmap; }
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void set_common_ignore_tensors(std::set<std::string> ignore_tensors);
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void set_loras(std::vector<LoraSpec> loras, SDVersion version);
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@ -3,9 +3,9 @@
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#include <algorithm>
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#include <cmath>
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#include <cstdlib>
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#include <optional>
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#include <string>
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#include <utility>
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#include <optional>
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#include "core/util.h"
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@ -3,8 +3,8 @@
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#include <cstddef>
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#include <functional>
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#include <vector>
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#include <optional>
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#include <vector>
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#include "core/tensor.hpp"
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@ -532,7 +532,6 @@ public:
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if (wtype != GGML_TYPE_COUNT || tensor_type_rules.size() > 0) {
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model_loader.set_wtype_override(wtype, tensor_type_rules);
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}
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model_loader.process_model_files(enable_mmap, true);
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std::map<ggml_type, uint32_t> wtype_stat = model_loader.get_wtype_stat();
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std::map<ggml_type, uint32_t> conditioner_wtype_stat = model_loader.get_conditioner_wtype_stat();
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@ -586,9 +585,12 @@ public:
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apply_lora_immediately = false;
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}
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bool needs_writable_mmap = enable_mmap && apply_lora_immediately;
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model_manager->set_writable_mmap(needs_writable_mmap);
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if (enable_mmap && apply_lora_immediately) {
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LOG_WARN("in mode 'immediately', LoRAs will cause extra memory usage with mmap");
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}
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model_loader.process_model_files(enable_mmap, needs_writable_mmap);
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load_alphas_cumprod(model_loader);
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size_t text_encoder_params_mem_size = 0;
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@ -1943,24 +1945,24 @@ public:
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bool slg_uncond = sd::guidance::parse_skip_layer_guidance_uncond_arg(extra_sample_args);
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std::vector<float> guidance_schedule = sd::guidance::parse_guidance_schedule(extra_sample_args);
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if(!guidance_schedule.empty() && guidance_schedule.size() != sigmas.size() - 1) {
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if(guidance_schedule.size() > sigmas.size()) {
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if (!guidance_schedule.empty() && guidance_schedule.size() != sigmas.size() - 1) {
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if (guidance_schedule.size() > sigmas.size()) {
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LOG_WARN("guidance_schedule length (%zu) is greater than number of steps (%zu)", guidance_schedule.size(), sigmas.size() - 1);
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LOG_WARN("truncating guidance_schedule to match step count");
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guidance_schedule.resize(sigmas.size() - 1);
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} else {
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LOG_INFO("padding guidance_schedule with cfg_scale");
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while(guidance_schedule.size() < sigmas.size() - 1) {
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while (guidance_schedule.size() < sigmas.size() - 1) {
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guidance_schedule.push_back(cfg_scale);
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}
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}
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}
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if(!guidance_schedule.empty()) {
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if (!guidance_schedule.empty()) {
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std::string schedule_str = "[";
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for(size_t i = 0; i < guidance_schedule.size(); ++i) {
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for (size_t i = 0; i < guidance_schedule.size(); ++i) {
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schedule_str += std::to_string(guidance_schedule[i]);
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if(i < guidance_schedule.size() - 1) {
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if (i < guidance_schedule.size() - 1) {
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schedule_str += ", ";
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}
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}
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@ -2208,9 +2210,7 @@ public:
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guidance_input.pred_uncond = uncond_out.empty() ? nullptr : &uncond_out;
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guidance_input.pred_img_uncond = img_uncond_out.empty() ? nullptr : &img_uncond_out;
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sd::guidance::GuiderOutput guided = guidance_schedule.empty()?
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primary_guidance.forward(guidance_input, {}):
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primary_guidance.forward(guidance_input, {}, guidance_schedule[guidance_schedule.size() - 1 - step]);
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sd::guidance::GuiderOutput guided = guidance_schedule.empty() ? primary_guidance.forward(guidance_input, {}) : primary_guidance.forward(guidance_input, {}, guidance_schedule[guidance_schedule.size() - 1 - step]);
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if (guided.pred.empty()) {
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return {};
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}
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