DarthAffe/stable-diffusion.cpp
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leejet 2025-06-29 23:40:55 +08:00
parent b1cc40c35c
commit 45d0ebb30c
11 changed files with 92 additions and 99 deletions
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3
conditioner.hpp
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@ -1231,7 +1231,8 @@ struct PixArtCLIPEmbedder : public Conditioner {
std::map<std::string, enum ggml_type>& tensor_types, std::map<std::string, enum ggml_type>& tensor_types,
int clip_skip = -1, int clip_skip = -1,
bool use_mask = false, bool use_mask = false,
int mask_pad = 1) : use_mask(use_mask), mask_pad(mask_pad) { int mask_pad = 1)
: use_mask(use_mask), mask_pad(mask_pad) {
t5 = std::make_shared<T5Runner>(backend, tensor_types, "text_encoders.t5xxl.transformer"); t5 = std::make_shared<T5Runner>(backend, tensor_types, "text_encoders.t5xxl.transformer");
} }

26
denoiser.hpp
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@ -1031,7 +1031,8 @@ static void sample_k_diffusion(sample_method_t method,
(1.0f - (1.0f -
std::pow(sqrtf(beta_start) + std::pow(sqrtf(beta_start) +
(sqrtf(beta_end) - sqrtf(beta_start)) * (sqrtf(beta_end) - sqrtf(beta_start)) *
((float)i / (TIMESTEPS - 1)), 2)); ((float)i / (TIMESTEPS - 1)),
2));
compvis_sigmas[i] = compvis_sigmas[i] =
std::sqrt((1 - alphas_cumprod[i]) / std::sqrt((1 - alphas_cumprod[i]) /
alphas_cumprod[i]); alphas_cumprod[i]);
@ -1061,7 +1062,8 @@ static void sample_k_diffusion(sample_method_t method,
// - pred_prev_sample -> "x_t-1" // - pred_prev_sample -> "x_t-1"
int timestep = int timestep =
roundf(TIMESTEPS - roundf(TIMESTEPS -
i * ((float)TIMESTEPS / steps)) - 1; i * ((float)TIMESTEPS / steps)) -
1;
// 1. get previous step value (=t-1) // 1. get previous step value (=t-1)
int prev_timestep = timestep - TIMESTEPS / steps; int prev_timestep = timestep - TIMESTEPS / steps;
// The sigma here is chosen to cause the // The sigma here is chosen to cause the
@ -1088,8 +1090,7 @@ static void sample_k_diffusion(sample_method_t method,
vec_x[j] *= std::sqrt(sigma * sigma + 1) / vec_x[j] *= std::sqrt(sigma * sigma + 1) /
sigma; sigma;
} }
} } else {
else {
// For the subsequent steps after the first one, // For the subsequent steps after the first one,
// at this point x = latents or x = sample, and // at this point x = latents or x = sample, and
// needs to be prescaled with x <- sample / c_in // needs to be prescaled with x <- sample / c_in
@ -1127,8 +1128,7 @@ static void sample_k_diffusion(sample_method_t method,
float alpha_prod_t = alphas_cumprod[timestep]; float alpha_prod_t = alphas_cumprod[timestep];
// Note final_alpha_cumprod = alphas_cumprod[0] due to // Note final_alpha_cumprod = alphas_cumprod[0] due to
// trailing timestep spacing // trailing timestep spacing
float alpha_prod_t_prev = prev_timestep >= 0 ? float alpha_prod_t_prev = prev_timestep >= 0 ? alphas_cumprod[prev_timestep] : alphas_cumprod[0];
alphas_cumprod[prev_timestep] : alphas_cumprod[0];
float beta_prod_t = 1 - alpha_prod_t; float beta_prod_t = 1 - alpha_prod_t;
// 3. compute predicted original sample from predicted // 3. compute predicted original sample from predicted
// noise also called "predicted x_0" of formula (12) // noise also called "predicted x_0" of formula (12)
@ -1220,7 +1220,8 @@ static void sample_k_diffusion(sample_method_t method,
(1.0f - (1.0f -
std::pow(sqrtf(beta_start) + std::pow(sqrtf(beta_start) +
(sqrtf(beta_end) - sqrtf(beta_start)) * (sqrtf(beta_end) - sqrtf(beta_start)) *
((float)i / (TIMESTEPS - 1)), 2)); ((float)i / (TIMESTEPS - 1)),
2));
compvis_sigmas[i] = compvis_sigmas[i] =
std::sqrt((1 - alphas_cumprod[i]) / std::sqrt((1 - alphas_cumprod[i]) /
alphas_cumprod[i]); alphas_cumprod[i]);
@ -1238,10 +1239,7 @@ static void sample_k_diffusion(sample_method_t method,
(TIMESTEPS / original_steps) * (TIMESTEPS / original_steps) *
(int)floor(i * ((float)original_steps / steps)); (int)floor(i * ((float)original_steps / steps));
// 1. get previous step value // 1. get previous step value
int prev_timestep = i >= steps - 1 ? 0 : int prev_timestep = i >= steps - 1 ? 0 : TIMESTEPS - 1 - (TIMESTEPS / original_steps) * (int)floor((i + 1) * ((float)original_steps / steps));
TIMESTEPS - 1 - (TIMESTEPS / original_steps) *
(int)floor((i + 1) *
((float)original_steps / steps));
// Here timestep_s is tau_n' in Algorithm 4. The _s // Here timestep_s is tau_n' in Algorithm 4. The _s
// notation appears to be that from C. Lu, // notation appears to be that from C. Lu,
// "DPM-Solver: A Fast ODE Solver for Diffusion // "DPM-Solver: A Fast ODE Solver for Diffusion
@ -1260,8 +1258,7 @@ static void sample_k_diffusion(sample_method_t method,
vec_x[j] *= std::sqrt(sigma * sigma + 1) / vec_x[j] *= std::sqrt(sigma * sigma + 1) /
sigma; sigma;
} }
} } else {
else {
float* vec_x = (float*)x->data; float* vec_x = (float*)x->data;
for (int j = 0; j < ggml_nelements(x); j++) { for (int j = 0; j < ggml_nelements(x); j++) {
vec_x[j] *= std::sqrt(sigma * sigma + 1); vec_x[j] *= std::sqrt(sigma * sigma + 1);
@ -1297,8 +1294,7 @@ static void sample_k_diffusion(sample_method_t method,
float beta_prod_t = 1 - alpha_prod_t; float beta_prod_t = 1 - alpha_prod_t;
// Note final_alpha_cumprod = alphas_cumprod[0] since // Note final_alpha_cumprod = alphas_cumprod[0] since
// TCD is always "trailing" // TCD is always "trailing"
float alpha_prod_t_prev = prev_timestep >= 0 ? float alpha_prod_t_prev = prev_timestep >= 0 ? alphas_cumprod[prev_timestep] : alphas_cumprod[0];
alphas_cumprod[prev_timestep] : alphas_cumprod[0];
// The subscript _s are the only portion in this // The subscript _s are the only portion in this
// section (2) unique to TCD // section (2) unique to TCD
float alpha_prod_s = alphas_cumprod[timestep_s]; float alpha_prod_s = alphas_cumprod[timestep_s];

5
flux.hpp
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@ -512,7 +512,8 @@ namespace Flux {
LastLayer(int64_t hidden_size, LastLayer(int64_t hidden_size,
int64_t patch_size, int64_t patch_size,
int64_t out_channels, int64_t out_channels,
bool prune_mod = false) : prune_mod(prune_mod) { bool prune_mod = false)
: prune_mod(prune_mod) {
blocks["norm_final"] = std::shared_ptr<GGMLBlock>(new LayerNorm(hidden_size, 1e-06f, false)); blocks["norm_final"] = std::shared_ptr<GGMLBlock>(new LayerNorm(hidden_size, 1e-06f, false));
blocks["linear"] = std::shared_ptr<GGMLBlock>(new Linear(hidden_size, patch_size * patch_size * out_channels)); blocks["linear"] = std::shared_ptr<GGMLBlock>(new Linear(hidden_size, patch_size * patch_size * out_channels));
if (!prune_mod) { if (!prune_mod) {
@ -744,7 +745,6 @@ namespace Flux {
return ids; return ids;
} }
// Generate positional embeddings // Generate positional embeddings
std::vector<float> gen_pe(int h, int w, int patch_size, int bs, int context_len, std::vector<ggml_tensor*> ref_latents, int theta, const std::vector<int>& axes_dim) { std::vector<float> gen_pe(int h, int w, int patch_size, int bs, int context_len, std::vector<ggml_tensor*> ref_latents, int theta, const std::vector<int>& axes_dim) {
std::vector<std::vector<float>> ids = gen_ids(h, w, patch_size, bs, context_len, ref_latents); std::vector<std::vector<float>> ids = gen_ids(h, w, patch_size, bs, context_len, ref_latents);
@ -962,7 +962,6 @@ namespace Flux {
struct ggml_tensor* process_img(struct ggml_context* ctx, struct ggml_tensor* process_img(struct ggml_context* ctx,
struct ggml_tensor* x) { struct ggml_tensor* x) {
int64_t W = x->ne[0]; int64_t W = x->ne[0];
int64_t H = x->ne[1]; int64_t H = x->ne[1];
int64_t patch_size = 2; int64_t patch_size = 2;

1
lora.hpp
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@ -291,7 +291,6 @@ struct LoraModel : public GGMLRunner {
std::string hada_2_down_name = ""; std::string hada_2_down_name = "";
std::string hada_2_up_name = ""; std::string hada_2_up_name = "";
hada_1_down_name = fk + ".hada_w1_b"; hada_1_down_name = fk + ".hada_w1_b";
hada_1_up_name = fk + ".hada_w1_a"; hada_1_up_name = fk + ".hada_w1_a";
hada_1_mid_name = fk + ".hada_t1"; hada_1_mid_name = fk + ".hada_t1";

2
model.h
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@ -12,9 +12,9 @@
#include "ggml-backend.h" #include "ggml-backend.h"
#include "ggml.h" #include "ggml.h"
#include "gguf.h"
#include "json.hpp" #include "json.hpp"
#include "zip.h" #include "zip.h"
#include "gguf.h"
#define SD_MAX_DIMS 5 #define SD_MAX_DIMS 5

4
stable-diffusion.cpp
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@ -48,8 +48,7 @@ const char* sampling_methods_str[] = {
"iPNDM_v", "iPNDM_v",
"LCM", "LCM",
"DDIM \"trailing\"", "DDIM \"trailing\"",
"TCD" "TCD"};
};
/*================================================== Helper Functions ================================================*/ /*================================================== Helper Functions ================================================*/
@ -1973,7 +1972,6 @@ SD_API sd_image_t* img2vid(sd_ctx_t* sd_ctx,
return result_images; return result_images;
} }
sd_image_t* edit(sd_ctx_t* sd_ctx, sd_image_t* edit(sd_ctx_t* sd_ctx,
sd_image_t* ref_images, sd_image_t* ref_images,
int ref_images_count, int ref_images_count,