mirror of
https://github.com/leejet/stable-diffusion.cpp.git
synced 2026-09-21 13:38:01 -05:00
412 lines
16 KiB
C++
412 lines
16 KiB
C++
#include "backend_fit.h"
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#include <algorithm>
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#include <cctype>
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#include <cstddef>
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#include <cstdint>
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#include <fstream>
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#include <utility>
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#include <vector>
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#if defined(_WIN32)
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#ifndef NOMINMAX
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#define NOMINMAX
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#endif
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#include <windows.h>
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#elif defined(__APPLE__)
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#include <mach/mach.h>
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#endif
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#include "core/ggml_extend_backend.h"
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#include "core/util.h"
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#include "ggml-backend.h"
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namespace sd::backend_fit {
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static constexpr int64_t MiB = 1024ll * 1024;
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enum class ComponentKind {
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DIT,
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CONDITIONER,
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VAE,
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};
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struct Component {
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ComponentKind kind;
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const char* name;
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int64_t params_bytes = 0;
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int64_t reserve_bytes = 0;
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int64_t staging_bytes = 0;
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};
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struct Device {
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std::string name;
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std::string description;
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int64_t free_bytes = 0;
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int64_t budget_bytes = 0;
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};
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enum class ParamsLocation {
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MAIN_GPU,
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CPU,
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OTHER_GPU,
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DISK,
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};
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struct Decision {
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ParamsLocation params_location = ParamsLocation::DISK;
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size_t params_device = SIZE_MAX;
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};
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struct Plan {
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bool valid = false;
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size_t main_device = SIZE_MAX;
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std::vector<Decision> decisions;
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};
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static bool classify_tensor(const std::string& name, ComponentKind& out) {
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auto contains = [&](const char* s) { return name.find(s) != std::string::npos; };
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if (contains("model.diffusion_model.") || contains("unet.")) {
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out = ComponentKind::DIT;
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return true;
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}
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if (contains("first_stage_model.") ||
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name.rfind("vae.", 0) == 0 ||
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name.rfind("tae.", 0) == 0) {
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out = ComponentKind::VAE;
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return true;
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}
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if (contains("text_encoders") ||
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contains("cond_stage_model") ||
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contains("te.text_model.") ||
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contains("conditioner") ||
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name.rfind("text_encoder.", 0) == 0 ||
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name.rfind("text_embedding_projection.", 0) == 0 ||
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contains(".aggregate_embed.")) {
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out = ComponentKind::CONDITIONER;
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return true;
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}
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return false;
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}
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static std::vector<Component> estimate_components(ModelLoader& loader, ggml_type override_wtype) {
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int64_t bytes[3] = {0, 0, 0};
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int64_t largest_tensor[3] = {0, 0, 0};
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for (const auto& [name, stored_tensor] : loader.get_tensor_storage_map()) {
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TensorStorage ts = stored_tensor;
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ComponentKind kind;
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if (is_unused_tensor(ts.name) || !classify_tensor(ts.name, kind)) {
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continue;
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}
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if (ts.expected_type != GGML_TYPE_COUNT) {
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ts.type = ts.expected_type;
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} else if (override_wtype != GGML_TYPE_COUNT && loader.tensor_should_be_converted(ts, override_wtype)) {
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ts.type = override_wtype;
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}
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const int64_t tensor_bytes = (int64_t)ts.nbytes() + 64;
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bytes[int(kind)] += tensor_bytes;
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largest_tensor[int(kind)] = std::max(largest_tensor[int(kind)], tensor_bytes);
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}
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return {
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{ComponentKind::DIT, "DiT", bytes[int(ComponentKind::DIT)], 2048 * MiB, largest_tensor[int(ComponentKind::DIT)]},
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{ComponentKind::CONDITIONER, "Conditioner", bytes[int(ComponentKind::CONDITIONER)], 2048 * MiB, largest_tensor[int(ComponentKind::CONDITIONER)]},
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{ComponentKind::VAE, "VAE", bytes[int(ComponentKind::VAE)], 1024 * MiB, largest_tensor[int(ComponentKind::VAE)]},
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};
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}
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static std::string budget_key(std::string name) {
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std::transform(name.begin(), name.end(), name.begin(), [](unsigned char c) { return (char)std::tolower(c); });
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return name;
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}
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static std::vector<Device> enumerate_gpu_devices(const sd::ggml_graph_cut::MaxVramAssignment& budgets) {
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std::vector<Device> out;
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for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
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ggml_backend_dev_t dev = ggml_backend_dev_get(i);
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if (ggml_backend_dev_type(dev) != GGML_BACKEND_DEVICE_TYPE_GPU) {
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continue;
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}
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Device device;
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device.name = ggml_backend_dev_name(dev);
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device.description = ggml_backend_dev_description(dev);
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size_t free_bytes = 0, total_bytes = 0;
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ggml_backend_dev_memory(dev, &free_bytes, &total_bytes);
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device.free_bytes = (int64_t)free_bytes;
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float gib = budgets.default_gib;
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auto it = budgets.backend_gib.find(budget_key(device.name));
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if (it != budgets.backend_gib.end()) {
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gib = it->second;
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}
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if (gib > 0.f) {
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device.budget_bytes = (int64_t)std::min(gib * 1024.0 * MiB, (double)device.free_bytes);
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} else if (gib < 0.f) {
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device.budget_bytes = (int64_t)std::max<double>(device.free_bytes + gib * 1024.0 * MiB, 0);
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} else {
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device.budget_bytes = std::max<int64_t>(device.free_bytes - 512 * MiB, 0);
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}
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out.push_back(std::move(device));
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}
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return out;
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}
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static int64_t available_ram_bytes() {
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#if defined(_WIN32)
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MEMORYSTATUSEX status{};
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status.dwLength = sizeof(status);
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if (GlobalMemoryStatusEx(&status)) {
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return (int64_t)status.ullAvailPhys;
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}
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#elif defined(__linux__)
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std::ifstream meminfo("/proc/meminfo");
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std::string key, unit;
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int64_t kib = 0;
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while (meminfo >> key >> kib >> unit) {
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if (key == "MemAvailable:" && unit == "kB" && kib >= 0) {
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return kib * 1024;
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}
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}
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#elif defined(__APPLE__)
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const mach_port_t host = mach_host_self();
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vm_size_t page_size = 0;
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vm_statistics64_data_t stats{};
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mach_msg_type_number_t count = HOST_VM_INFO64_COUNT;
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const bool ok = host_page_size(host, &page_size) == KERN_SUCCESS &&
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host_statistics64(host, HOST_VM_INFO64, (host_info64_t)&stats, &count) == KERN_SUCCESS;
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mach_port_deallocate(mach_task_self(), host);
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if (ok) {
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return ((int64_t)stats.free_count + stats.inactive_count) * page_size;
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}
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#endif
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return -1;
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}
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static Plan compute_plan(const std::vector<Component>& components,
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const std::vector<Device>& devices,
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int64_t ram_budget_bytes) {
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Plan plan;
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for (size_t di = 0; di < devices.size(); ++di) {
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if (devices[di].budget_bytes > 0 &&
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(plan.main_device == SIZE_MAX || devices[di].budget_bytes > devices[plan.main_device].budget_bytes)) {
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plan.main_device = di;
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}
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}
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if (plan.main_device == SIZE_MAX) {
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return plan;
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}
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std::vector<size_t> order(components.size());
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for (size_t ci = 0; ci < components.size(); ++ci) {
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order[ci] = ci;
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}
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std::stable_sort(order.begin(), order.end(), [&](size_t a, size_t b) {
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return components[a].kind < components[b].kind;
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});
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std::vector<int64_t> remaining;
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for (const Device& device : devices) {
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remaining.push_back(std::max<int64_t>(device.budget_bytes, 0));
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}
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ram_budget_bytes = std::max<int64_t>(ram_budget_bytes, 0);
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plan.decisions.resize(components.size());
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for (size_t ci : order) {
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const Component& comp = components[ci];
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Decision& decision = plan.decisions[ci];
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if (comp.params_bytes == 0) {
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continue;
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}
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// Higher-priority offloaded weights need GPU cache space across graph runs.
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int64_t headroom = 0;
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for (size_t other = 0; other < components.size(); ++other) {
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if (components[other].params_bytes == 0) {
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continue;
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}
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const bool resident = other == ci || plan.decisions[other].params_location == ParamsLocation::MAIN_GPU;
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const int64_t cached_weights = components[other].kind < comp.kind
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? components[other].params_bytes
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: components[other].staging_bytes;
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headroom = std::max(headroom, components[other].reserve_bytes +
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(resident ? 0 : cached_weights));
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}
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int64_t& main_remaining = remaining[plan.main_device];
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if (headroom <= main_remaining && comp.params_bytes <= main_remaining - headroom) {
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decision.params_location = ParamsLocation::MAIN_GPU;
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decision.params_device = plan.main_device;
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main_remaining -= comp.params_bytes;
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continue;
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}
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if (comp.params_bytes <= ram_budget_bytes) {
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decision.params_location = ParamsLocation::CPU;
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ram_budget_bytes -= comp.params_bytes;
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continue;
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}
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size_t best = SIZE_MAX;
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for (size_t di = 0; di < devices.size(); ++di) {
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if (di != plan.main_device && comp.params_bytes <= remaining[di] &&
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(best == SIZE_MAX || remaining[di] > remaining[best])) {
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best = di;
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}
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}
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if (best != SIZE_MAX) {
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decision.params_location = ParamsLocation::OTHER_GPU;
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decision.params_device = best;
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remaining[best] -= comp.params_bytes;
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}
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}
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plan.valid = true;
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return plan;
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}
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static std::string params_backend_name(const Decision& decision, const std::vector<Device>& devices) {
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switch (decision.params_location) {
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case ParamsLocation::MAIN_GPU:
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case ParamsLocation::OTHER_GPU:
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return devices[decision.params_device].name;
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case ParamsLocation::CPU:
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return "cpu";
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case ParamsLocation::DISK:
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return "disk";
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}
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return "disk";
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}
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static void print_plan(const Plan& plan,
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const std::vector<Component>& components,
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const std::vector<Device>& devices,
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int64_t free_ram,
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int64_t ram_budget) {
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LOG_INFO("auto-fit plan (single-GPU compute on %s):", devices[plan.main_device].name.c_str());
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LOG_INFO(" devices:");
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for (const Device& device : devices) {
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LOG_INFO(" %-12s %-32s free %6lld MiB, budget %6lld MiB",
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device.name.c_str(), device.description.c_str(),
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(long long)(device.free_bytes / MiB), (long long)(device.budget_bytes / MiB));
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}
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if (free_ram < 0) {
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LOG_WARN("auto-fit: available RAM is unknown; skipping CPU parameter residency");
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} else {
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LOG_INFO(" RAM free %6lld MiB, params budget %6lld MiB",
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(long long)(free_ram / MiB), (long long)(ram_budget / MiB));
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}
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LOG_INFO(" main-GPU weight cache priority: diffusion > te > vae");
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LOG_INFO(" components (params: main GPU -> RAM -> other GPU -> disk):");
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for (size_t ci = 0; ci < components.size(); ++ci) {
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const Component& comp = components[ci];
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if (comp.params_bytes == 0) {
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continue;
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}
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const std::string params = params_backend_name(plan.decisions[ci], devices);
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LOG_INFO(" %-12s params %6lld MiB, compute reserve %5lld MiB -> compute %s, params %s",
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comp.name, (long long)(comp.params_bytes / MiB), (long long)(comp.reserve_bytes / MiB),
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devices[plan.main_device].name.c_str(), params.c_str());
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}
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}
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static void append_assignment(std::string& spec, const char* key, const std::string& value) {
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if (!spec.empty()) {
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spec += ",";
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}
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spec += key;
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spec += "=";
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spec += value;
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}
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static const char* module_key(ComponentKind kind) {
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switch (kind) {
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case ComponentKind::DIT:
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return "diffusion";
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case ComponentKind::CONDITIONER:
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return "te";
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case ComponentKind::VAE:
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return "vae";
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}
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return "";
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}
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bool derive_backend_specs(ModelLoader& loader,
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ggml_type override_wtype,
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sd::ggml_graph_cut::MaxVramAssignment& budgets,
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std::string& runtime_spec,
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std::string& params_spec) {
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std::string error;
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if (!budgets.canonicalize_backend_keys(&error)) {
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LOG_ERROR("%s", error.c_str());
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return false;
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}
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const auto components = estimate_components(loader, override_wtype);
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const auto devices = enumerate_gpu_devices(budgets);
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const int64_t free_ram = available_ram_bytes();
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const int64_t ram_budget = std::max<int64_t>(free_ram - std::max<int64_t>(2048 * MiB, free_ram / 10), 0);
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const auto plan = compute_plan(components, devices, ram_budget);
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runtime_spec.clear();
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params_spec.clear();
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if (!plan.valid) {
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if (devices.empty()) {
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LOG_WARN("auto-fit: no GPU devices; using the default backend");
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} else {
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LOG_WARN("auto-fit: no GPU memory budget available; using CPU");
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runtime_spec = "cpu";
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}
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return true;
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}
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print_plan(plan, components, devices, free_ram, ram_budget);
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for (size_t ci = 0; ci < components.size(); ++ci) {
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if (components[ci].params_bytes == 0) {
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continue;
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}
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const char* key = module_key(components[ci].kind);
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append_assignment(runtime_spec, key, devices[plan.main_device].name);
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if (plan.decisions[ci].params_location != ParamsLocation::MAIN_GPU) {
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append_assignment(params_spec, key, params_backend_name(plan.decisions[ci], devices));
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}
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}
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// Keep the planner's safety margin when the runner resolves its device limits.
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for (const Device& device : devices) {
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if (device.budget_bytes > 0) {
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budgets.backend_gib[budget_key(device.name)] = (float)(device.budget_bytes / (1024.0 * MiB));
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}
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}
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budgets.resolved_backend_bytes.clear();
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LOG_INFO("auto-fit: --backend \"%s\"%s%s%s",
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runtime_spec.empty() ? "(default)" : runtime_spec.c_str(),
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params_spec.empty() ? "" : " --params-backend \"",
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params_spec.c_str(), params_spec.empty() ? "" : "\"");
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return true;
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}
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bool prepare_vae_decode_retry_tiling(sd_tiling_params_t& tiling_params, bool prefer_temporal_tiling) {
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const char* retry_mode = nullptr;
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if (prefer_temporal_tiling && !tiling_params.temporal_tiling) {
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tiling_params.temporal_tiling = true;
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retry_mode = tiling_params.enabled ? "spatial+temporal" : "temporal";
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} else if (!tiling_params.enabled) {
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tiling_params.enabled = true;
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tiling_params.rel_size_x = 0.5f;
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tiling_params.rel_size_y = 0.5f;
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if (tiling_params.tile_size_x <= 0) {
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tiling_params.tile_size_x = 256;
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}
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if (tiling_params.tile_size_y <= 0) {
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tiling_params.tile_size_y = 256;
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}
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retry_mode = tiling_params.temporal_tiling ? "spatial+temporal" : "spatial";
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} else {
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return false;
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}
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LOG_WARN("VAE decode failed (likely out of memory); retrying with %s tiling",
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retry_mode);
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return true;
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}
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} // namespace sd::backend_fit
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