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feat: add configurable conditioning cache for all models (#2034)

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leejet
2026-09-23 22:25:42 +08:00
committed by GitHub
parent 241518b35d
commit e6281b6318
12 changed files with 172 additions and 51 deletions
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src/pipeline/diffusion_engine.cpp
+27 -3
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@@ -29,6 +29,7 @@
#include "stable-diffusion.h"
#include "conditioning/conditioner.hpp"
#include "conditioning/conditioning_cache.h"
#include "core/backend_fit.h"
#include "extensions/generation_extension.h"
#include "model/adapter/ip_adapter.hpp"
@@ -135,6 +136,7 @@ static_assert(std::atomic<sd_cancel_mode_t>::is_always_lock_free,
StableDiffusionGGML::StableDiffusionGGML()
: rng(std::make_shared<PhiloxRNG>()),
conditioning_cache_(std::make_unique<ConditioningCache>()),
denoiser(std::make_shared<CompVisDenoiser>()) {}
StableDiffusionGGML::~StableDiffusionGGML() = default;
@@ -204,6 +206,8 @@ void StableDiffusionGGML::end_runners() {
}
bool StableDiffusionGGML::reset_runners(const RunnerGroups& groups) {
conditioning_cache_->clear();
conditioning_loras_.clear();
end_runners();
clear_lora_adapters();
runtime_lora_models.clear();
@@ -915,6 +919,11 @@ bool StableDiffusionGGML::init(const sd_ctx_params_t* sd_ctx_params) {
return false;
}
}
if (sd_ctx_params->conditioning_cache_size < 0) {
LOG_ERROR("conditioning_cache_size must be non-negative");
return false;
}
conditioning_cache_->set_capacity(static_cast<size_t>(sd_ctx_params->conditioning_cache_size));
auto configuration = std::make_unique<ModelConfig>(*sd_ctx_params);
n_threads = sd_ctx_params->n_threads;
tensor_executor = std::make_unique<sd::ParallelExecutor>(n_threads > 0 ? n_threads : sd_get_num_physical_cores());
@@ -1762,13 +1771,22 @@ bool StableDiffusionGGML::apply_loras(const sd_lora_t* loras, uint32_t lora_coun
extension->collect_loras(all_loras);
}
conditioning_cache_allowed_ = all_loras.empty();
int64_t t0 = ggml_time_ms();
end_runners();
clear_lora_adapters();
if (!model_manager->prepare_lora_sources(all_loras))
if (!model_manager->prepare_lora_sources(all_loras)) {
conditioning_cache_->clear();
return false;
}
if (!std::equal(all_loras.begin(), all_loras.end(),
conditioning_loras_.begin(), conditioning_loras_.end(),
[](const ModelManager::LoraSpec& a, const ModelManager::LoraSpec& b) {
return a.file_id == b.file_id && a.file_revision == b.file_revision &&
a.multiplier == b.multiplier && a.is_high_noise == b.is_high_noise &&
a.tensor_name_prefix_filter == b.tensor_name_prefix_filter;
})) {
conditioning_cache_->clear();
}
runtime_lora_models.erase(std::remove_if(runtime_lora_models.begin(), runtime_lora_models.end(), [&](const RuntimeLora& entry) {
return std::none_of(all_loras.begin(), all_loras.end(), [&](const ModelManager::LoraSpec& spec) {
return entry.matches(spec);
@@ -1778,6 +1796,7 @@ bool StableDiffusionGGML::apply_loras(const sd_lora_t* loras, uint32_t lora_coun
const bool success = apply_lora_immediately ? apply_loras_immediately(all_loras)
: apply_loras_at_runtime(all_loras);
if (!success) {
conditioning_cache_->clear();
clear_lora_adapters();
runtime_lora_models.clear();
return false;
@@ -1787,9 +1806,14 @@ bool StableDiffusionGGML::apply_loras(const sd_lora_t* loras, uint32_t lora_coun
if (!all_loras.empty()) {
LOG_INFO("apply_loras completed, taking %.2fs", (t1 - t0) * 1.0f / 1000);
}
conditioning_loras_ = std::move(all_loras);
return true;
}
SDCondition StableDiffusionGGML::get_learned_condition(const ConditionerParams& params) {
return conditioning_cache_->get(*cond_stage_model, n_threads, params);
}
void StableDiffusionGGML::reset_generation_extensions() {
for (auto& extension : generation_extensions) {
extension->reset_runtime_condition();