upstream-archive/stable-diffusion.cpp
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leejet
2024-01-29 23:05:18 +08:00
parent 36ec16ac99
commit 349439f239
14 changed files with 200 additions and 202 deletions
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stable-diffusion.cpp
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@@ -7,8 +7,8 @@
#include "util.h"
#include "clip.hpp"
#include "denoiser.hpp"
#include "control.hpp"
#include "denoiser.hpp"
#include "esrgan.hpp"
#include "lora.hpp"
#include "tae.hpp"
@@ -320,15 +320,15 @@ public:
LOG_DEBUG("finished loaded file");
ggml_free(ctx);
if(control_net_path.size() > 0) {
if (control_net_path.size() > 0) {
ggml_backend_t cn_backend = NULL;
if(control_net_cpu && !ggml_backend_is_cpu(backend)) {
if (control_net_cpu && !ggml_backend_is_cpu(backend)) {
LOG_DEBUG("ControlNet: Using CPU backend");
cn_backend = ggml_backend_cpu_init();
} else {
cn_backend = backend;
}
if(!control_net.load_from_file(control_net_path, cn_backend, GGML_TYPE_F16 /* just f16 controlnet models */)) {
if (!control_net.load_from_file(control_net_path, cn_backend, GGML_TYPE_F16 /* just f16 controlnet models */)) {
return false;
}
}
@@ -549,8 +549,8 @@ public:
struct ggml_tensor* noised_input = ggml_dup_tensor(work_ctx, x_t);
struct ggml_tensor* timesteps = ggml_new_tensor_1d(work_ctx, GGML_TYPE_F32, 1); // [N, ]
struct ggml_tensor* t_emb = new_timestep_embedding(work_ctx, NULL, timesteps, diffusion_model.model_channels); // [N, model_channels]
struct ggml_tensor* guided_hint = NULL;
if(control_hint != NULL) {
struct ggml_tensor* guided_hint = NULL;
if (control_hint != NULL) {
guided_hint = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, noised_input->ne[0], noised_input->ne[1], diffusion_model.model_channels, 1);
control_net.process_hint(guided_hint, n_threads, control_hint);
control_net.alloc_compute_buffer(noised_input, guided_hint, c, t_emb);
@@ -606,7 +606,7 @@ public:
ggml_tensor_scale(noised_input, c_in);
// cond
if(control_hint != NULL) {
if (control_hint != NULL) {
control_net.compute(n_threads, noised_input, guided_hint, c, t_emb);
}
diffusion_model.compute(out_cond, n_threads, noised_input, NULL, c, control_net.controls, control_strength, t_emb, c_vector);
@@ -614,7 +614,7 @@ public:
float* negative_data = NULL;
if (has_unconditioned) {
// uncond
if(control_hint != NULL) {
if (control_hint != NULL) {
control_net.compute(n_threads, noised_input, guided_hint, uc, t_emb);
}
@@ -1276,7 +1276,7 @@ sd_image_t* txt2img(sd_ctx_t* sd_ctx,
}
struct ggml_tensor* image_hint = NULL;
if(control_cond != NULL) {
if (control_cond != NULL) {
image_hint = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, width, height, 3, 1);
sd_image_to_tensor(control_cond->data, image_hint);
}
@@ -1451,7 +1451,7 @@ sd_image_t* img2img(sd_ctx_t* sd_ctx,
LOG_INFO("sampling using %s method", sampling_methods_str[sample_method]);
struct ggml_tensor* x_0 = sd_ctx->sd->sample(work_ctx, init_latent, noise, c, c_vector, uc,
uc_vector, NULL, cfg_scale, sample_method, sigma_sched, 1.0f);
uc_vector, NULL, cfg_scale, sample_method, sigma_sched, 1.0f);
// struct ggml_tensor *x_0 = load_tensor_from_file(ctx, "samples_ddim.bin");
// print_ggml_tensor(x_0);
int64_t t3 = ggml_time_ms();