alphard/stable-diffusion.cpp
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

chore: clear the msvc compilation warning

Browse Source
This commit is contained in:
leejet 2023-10-28 20:55:24 +08:00
parent 3001c23f7d
commit 3bf1665885
3 changed files with 35 additions and 35 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
rng.h
View File

@ -16,7 +16,7 @@ class STDDefaultRNG : public RNG {
public: public:
void manual_seed(uint64_t seed) { void manual_seed(uint64_t seed) {
generator.seed(seed); generator.seed((unsigned int)seed);
} }
std::vector<float> randn(uint32_t n) { std::vector<float> randn(uint32_t n) {
@ -24,7 +24,7 @@ class STDDefaultRNG : public RNG {
float mean = 0.0; float mean = 0.0;
float stddev = 1.0; float stddev = 1.0;
std::normal_distribution<float> distribution(mean, stddev); std::normal_distribution<float> distribution(mean, stddev);
for (int i = 0; i < n; i++) { for (uint32_t i = 0; i < n; i++) {
float random_number = distribution(generator); float random_number = distribution(generator);
result.push_back(random_number); result.push_back(random_number);
} }

18
rng_philox.h
View File

@ -16,8 +16,8 @@ class PhiloxRNG : public RNG {
private: private:
std::vector<uint32_t> philox_m = {0xD2511F53, 0xCD9E8D57}; std::vector<uint32_t> philox_m = {0xD2511F53, 0xCD9E8D57};
std::vector<uint32_t> philox_w = {0x9E3779B9, 0xBB67AE85}; std::vector<uint32_t> philox_w = {0x9E3779B9, 0xBB67AE85};
float two_pow32_inv = 2.3283064e-10; float two_pow32_inv = 2.3283064e-10f;
float two_pow32_inv_2pi = 2.3283064e-10 * 6.2831855; float two_pow32_inv_2pi = 2.3283064e-10f * 6.2831855f;
std::vector<uint32_t> uint32(uint64_t x) { std::vector<uint32_t> uint32(uint64_t x) {
std::vector<uint32_t> result(2); std::vector<uint32_t> result(2);
@ -27,10 +27,10 @@ class PhiloxRNG : public RNG {
} }
std::vector<std::vector<uint32_t>> uint32(const std::vector<uint64_t>& x) { std::vector<std::vector<uint32_t>> uint32(const std::vector<uint64_t>& x) {
int N = x.size(); uint32_t N = (uint32_t)x.size();
std::vector<std::vector<uint32_t>> result(2, std::vector<uint32_t>(N)); std::vector<std::vector<uint32_t>> result(2, std::vector<uint32_t>(N));
for (int i = 0; i < N; ++i) { for (uint32_t i = 0; i < N; ++i) {
result[0][i] = static_cast<uint32_t>(x[i] & 0xFFFFFFFF); result[0][i] = static_cast<uint32_t>(x[i] & 0xFFFFFFFF);
result[1][i] = static_cast<uint32_t>(x[i] >> 32); result[1][i] = static_cast<uint32_t>(x[i] >> 32);
} }
@ -41,7 +41,7 @@ class PhiloxRNG : public RNG {
// A single round of the Philox 4x32 random number generator. // A single round of the Philox 4x32 random number generator.
void philox4_round(std::vector<std::vector<uint32_t>>& counter, void philox4_round(std::vector<std::vector<uint32_t>>& counter,
const std::vector<std::vector<uint32_t>>& key) { const std::vector<std::vector<uint32_t>>& key) {
uint32_t N = counter[0].size(); uint32_t N = (uint32_t)counter[0].size();
for (uint32_t i = 0; i < N; i++) { for (uint32_t i = 0; i < N; i++) {
std::vector<uint32_t> v1 = uint32(static_cast<uint64_t>(counter[0][i]) * static_cast<uint64_t>(philox_m[0])); std::vector<uint32_t> v1 = uint32(static_cast<uint64_t>(counter[0][i]) * static_cast<uint64_t>(philox_m[0]));
std::vector<uint32_t> v2 = uint32(static_cast<uint64_t>(counter[2][i]) * static_cast<uint64_t>(philox_m[1])); std::vector<uint32_t> v2 = uint32(static_cast<uint64_t>(counter[2][i]) * static_cast<uint64_t>(philox_m[1]));
@ -63,7 +63,7 @@ class PhiloxRNG : public RNG {
std::vector<std::vector<uint32_t>> philox4_32(std::vector<std::vector<uint32_t>>& counter, std::vector<std::vector<uint32_t>> philox4_32(std::vector<std::vector<uint32_t>>& counter,
std::vector<std::vector<uint32_t>>& key, std::vector<std::vector<uint32_t>>& key,
int rounds = 10) { int rounds = 10) {
uint32_t N = counter[0].size(); uint32_t N = (uint32_t)counter[0].size();
for (int i = 0; i < rounds - 1; ++i) { for (int i = 0; i < rounds - 1; ++i) {
philox4_round(counter, key); philox4_round(counter, key);
@ -81,7 +81,7 @@ class PhiloxRNG : public RNG {
float u = x * two_pow32_inv + two_pow32_inv / 2; float u = x * two_pow32_inv + two_pow32_inv / 2;
float v = y * two_pow32_inv_2pi + two_pow32_inv_2pi / 2; float v = y * two_pow32_inv_2pi + two_pow32_inv_2pi / 2;
float s = sqrt(-2.0 * log(u)); float s = sqrt(-2.0f * log(u));
float r1 = s * sin(v); float r1 = s * sin(v);
return r1; return r1;
@ -115,8 +115,8 @@ class PhiloxRNG : public RNG {
std::vector<std::vector<uint32_t>> g = philox4_32(counter, key_uint32); std::vector<std::vector<uint32_t>> g = philox4_32(counter, key_uint32);
std::vector<float> result; std::vector<float> result;
for (int i = 0; i < n; ++i) { for (uint32_t i = 0; i < n; ++i) {
result.push_back(box_muller(g[0][i], g[1][i])); result.push_back(box_muller((float)g[0][i], (float)g[1][i]));
} }
return result; return result;
} }

48
stable-diffusion.cpp
View File

@ -18,7 +18,7 @@
#include "rng_philox.h" #include "rng_philox.h"
#include "stable-diffusion.h" #include "stable-diffusion.h"
#define EPS 1e-05 #define EPS 1e-05f
static SDLogLevel log_level = SDLogLevel::INFO; static SDLogLevel log_level = SDLogLevel::INFO;
@ -122,9 +122,9 @@ ggml_tensor* load_tensor_from_file(ggml_context* ctx, const std::string& file_pa
} }
void ggml_tensor_set_f32_randn(struct ggml_tensor* tensor, std::shared_ptr<RNG> rng) { void ggml_tensor_set_f32_randn(struct ggml_tensor* tensor, std::shared_ptr<RNG> rng) {
uint32_t n = ggml_nelements(tensor); uint32_t n = (uint32_t)ggml_nelements(tensor);
std::vector<float> random_numbers = rng->randn(n); std::vector<float> random_numbers = rng->randn(n);
for (int i = 0; i < n; i++) { for (uint32_t i = 0; i < n; i++) {
ggml_set_f32_1d(tensor, i, random_numbers[i]); ggml_set_f32_1d(tensor, i, random_numbers[i]);
} }
} }
@ -438,12 +438,12 @@ std::vector<std::pair<std::string, float>> parse_prompt_attention(const std::str
std::string weight = m[1]; std::string weight = m[1];
if (text == "(") { if (text == "(") {
round_brackets.push_back(res.size()); round_brackets.push_back((int)res.size());
} else if (text == "[") { } else if (text == "[") {
square_brackets.push_back(res.size()); square_brackets.push_back((int)res.size());
} else if (!weight.empty()) { } else if (!weight.empty()) {
if (!round_brackets.empty()) { if (!round_brackets.empty()) {
multiply_range(round_brackets.back(), std::stod(weight)); multiply_range(round_brackets.back(), std::stof(weight));
round_brackets.pop_back(); round_brackets.pop_back();
} }
} else if (text == ")" && !round_brackets.empty()) { } else if (text == ")" && !round_brackets.empty()) {
@ -2707,13 +2707,13 @@ struct DiscreteSchedule : SigmaSchedule {
if (n == 0) { if (n == 0) {
return result; return result;
} else if (n == 1) { } else if (n == 1) {
result.push_back(t_to_sigma(t_max)); result.push_back(t_to_sigma((float)t_max));
result.push_back(0); result.push_back(0);
return result; return result;
} }
float step = static_cast<float>(t_max) / static_cast<float>(n - 1); float step = static_cast<float>(t_max) / static_cast<float>(n - 1);
for (int i = 0; i < n; ++i) { for (uint32_t i = 0; i < n; ++i) {
float t = t_max - step * i; float t = t_max - step * i;
result.push_back(t_to_sigma(t)); result.push_back(t_to_sigma(t));
} }
@ -2726,17 +2726,17 @@ struct KarrasSchedule : SigmaSchedule {
std::vector<float> get_sigmas(uint32_t n) { std::vector<float> get_sigmas(uint32_t n) {
// These *COULD* be function arguments here, // These *COULD* be function arguments here,
// but does anybody ever bother to touch them? // but does anybody ever bother to touch them?
float sigma_min = 0.1; float sigma_min = 0.1f;
float sigma_max = 10.; float sigma_max = 10.f;
float rho = 7.; float rho = 7.f;
std::vector<float> result(n + 1); std::vector<float> result(n + 1);
float min_inv_rho = pow(sigma_min, (1. / rho)); float min_inv_rho = pow(sigma_min, (1.f / rho));
float max_inv_rho = pow(sigma_max, (1. / rho)); float max_inv_rho = pow(sigma_max, (1.f / rho));
for (int i = 0; i < n; i++) { for (uint32_t i = 0; i < n; i++) {
// Eq. (5) from Karras et al 2022 // Eq. (5) from Karras et al 2022
result[i] = pow(max_inv_rho + (float)i / ((float)n - 1.) * (min_inv_rho - max_inv_rho), rho); result[i] = pow(max_inv_rho + (float)i / ((float)n - 1.f) * (min_inv_rho - max_inv_rho), rho);
} }
result[n] = 0.; result[n] = 0.;
return result; return result;
@ -3748,7 +3748,7 @@ class StableDiffusionGGML {
} }
} else { } else {
// DPM-Solver-2 // DPM-Solver-2
float sigma_mid = exp(0.5 * (log(sigmas[i]) + log(sigmas[i + 1]))); float sigma_mid = exp(0.5f * (log(sigmas[i]) + log(sigmas[i + 1])));
float dt_1 = sigma_mid - sigmas[i]; float dt_1 = sigma_mid - sigmas[i];
float dt_2 = sigmas[i + 1] - sigmas[i]; float dt_2 = sigmas[i + 1] - sigmas[i];
@ -3811,7 +3811,7 @@ class StableDiffusionGGML {
float t = t_fn(sigmas[i]); float t = t_fn(sigmas[i]);
float t_next = t_fn(sigma_down); float t_next = t_fn(sigma_down);
float h = t_next - t; float h = t_next - t;
float s = t + 0.5 * h; float s = t + 0.5f * h;
float* vec_d = (float*)d->data; float* vec_d = (float*)d->data;
float* vec_x = (float*)x->data; float* vec_x = (float*)x->data;
@ -3820,7 +3820,7 @@ class StableDiffusionGGML {
// First half-step // First half-step
for (int j = 0; j < ggml_nelements(x); j++) { for (int j = 0; j < ggml_nelements(x); j++) {
vec_x2[j] = (sigma_fn(s) / sigma_fn(t)) * vec_x[j] - (exp(-h * 0.5) - 1) * vec_denoised[j]; vec_x2[j] = (sigma_fn(s) / sigma_fn(t)) * vec_x[j] - (exp(-h * 0.5f) - 1) * vec_denoised[j];
} }
denoise(x2, sigmas[i + 1], i + 1); denoise(x2, sigmas[i + 1], i + 1);
@ -3862,7 +3862,7 @@ class StableDiffusionGGML {
float t_next = t_fn(sigmas[i + 1]); float t_next = t_fn(sigmas[i + 1]);
float h = t_next - t; float h = t_next - t;
float a = sigmas[i + 1] / sigmas[i]; float a = sigmas[i + 1] / sigmas[i];
float b = exp(-h) - 1.; float b = exp(-h) - 1.f;
float* vec_x = (float*)x->data; float* vec_x = (float*)x->data;
float* vec_denoised = (float*)denoised->data; float* vec_denoised = (float*)denoised->data;
float* vec_old_denoised = (float*)old_denoised->data; float* vec_old_denoised = (float*)old_denoised->data;
@ -3876,7 +3876,7 @@ class StableDiffusionGGML {
float h_last = t - t_fn(sigmas[i - 1]); float h_last = t - t_fn(sigmas[i - 1]);
float r = h_last / h; float r = h_last / h;
for (int j = 0; j < ggml_nelements(x); j++) { for (int j = 0; j < ggml_nelements(x); j++) {
float denoised_d = (1. + 1. / (2. * r)) * vec_denoised[j] - (1. / (2. * r)) * vec_old_denoised[j]; float denoised_d = (1.f + 1.f / (2.f * r)) * vec_denoised[j] - (1.f / (2.f * r)) * vec_old_denoised[j];
vec_x[j] = a * vec_x[j] - b * denoised_d; vec_x[j] = a * vec_x[j] - b * denoised_d;
} }
} }
@ -3910,7 +3910,7 @@ class StableDiffusionGGML {
if (i == 0 || sigmas[i + 1] == 0) { if (i == 0 || sigmas[i + 1] == 0) {
// Simpler step for the edge cases // Simpler step for the edge cases
float b = exp(-h) - 1.; float b = exp(-h) - 1.f;
for (int j = 0; j < ggml_nelements(x); j++) { for (int j = 0; j < ggml_nelements(x); j++) {
vec_x[j] = a * vec_x[j] - b * vec_denoised[j]; vec_x[j] = a * vec_x[j] - b * vec_denoised[j];
} }
@ -3919,10 +3919,10 @@ class StableDiffusionGGML {
float h_min = std::min(h_last, h); float h_min = std::min(h_last, h);
float h_max = std::max(h_last, h); float h_max = std::max(h_last, h);
float r = h_max / h_min; float r = h_max / h_min;
float h_d = (h_max + h_min) / 2.; float h_d = (h_max + h_min) / 2.f;
float b = exp(-h_d) - 1.; float b = exp(-h_d) - 1.f;
for (int j = 0; j < ggml_nelements(x); j++) { for (int j = 0; j < ggml_nelements(x); j++) {
float denoised_d = (1. + 1. / (2. * r)) * vec_denoised[j] - (1. / (2. * r)) * vec_old_denoised[j]; float denoised_d = (1.f + 1.f / (2.f * r)) * vec_denoised[j] - (1.f / (2.f * r)) * vec_old_denoised[j];
vec_x[j] = a * vec_x[j] - b * denoised_d; vec_x[j] = a * vec_x[j] - b * denoised_d;
} }
} }