-
Notifications
You must be signed in to change notification settings - Fork 606
Expand file tree
/
Copy pathupscaler.cpp
More file actions
170 lines (156 loc) · 6.33 KB
/
upscaler.cpp
File metadata and controls
170 lines (156 loc) · 6.33 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
#include "esrgan.hpp"
#include "ggml_extend.hpp"
#include "model.h"
#include "stable-diffusion.h"
#include "util.h"
struct UpscalerGGML {
ggml_backend_t backend = nullptr; // general backend
ggml_type model_data_type = GGML_TYPE_F16;
std::shared_ptr<ESRGAN> esrgan_upscaler;
std::string esrgan_path;
int n_threads;
bool direct = false;
int tile_size = 128;
UpscalerGGML(int n_threads,
bool direct = false,
int tile_size = 128)
: n_threads(n_threads),
direct(direct),
tile_size(tile_size) {
}
bool load_from_file(const std::string& esrgan_path,
bool offload_params_to_cpu,
int n_threads) {
ggml_log_set(ggml_log_callback_default, nullptr);
#ifdef SD_USE_CUDA
LOG_DEBUG("Using CUDA backend");
backend = ggml_backend_cuda_init(0);
#endif
#ifdef SD_USE_METAL
LOG_DEBUG("Using Metal backend");
backend = ggml_backend_metal_init();
#endif
#ifdef SD_USE_VULKAN
LOG_DEBUG("Using Vulkan backend");
backend = ggml_backend_vk_init(0);
#endif
#ifdef SD_USE_OPENCL
LOG_DEBUG("Using OpenCL backend");
backend = ggml_backend_opencl_init();
#endif
#ifdef SD_USE_SYCL
LOG_DEBUG("Using SYCL backend");
backend = ggml_backend_sycl_init(0);
#endif
ModelLoader model_loader;
if (!model_loader.init_from_file_and_convert_name(esrgan_path)) {
LOG_ERROR("init model loader from file failed: '%s'", esrgan_path.c_str());
}
model_loader.set_wtype_override(model_data_type);
if (!backend) {
LOG_DEBUG("Using CPU backend");
backend = ggml_backend_init_by_type(GGML_BACKEND_DEVICE_TYPE_CPU, nullptr);
}
LOG_INFO("Upscaler weight type: %s", ggml_type_name(model_data_type));
esrgan_upscaler = std::make_shared<ESRGAN>(backend, offload_params_to_cpu, tile_size, model_loader.get_tensor_storage_map());
if (direct) {
esrgan_upscaler->set_conv2d_direct_enabled(true);
}
if (!esrgan_upscaler->load_from_file(esrgan_path, n_threads)) {
return false;
}
return true;
}
sd::Tensor<float> upscale_tensor(const sd::Tensor<float>& input_tensor) {
sd::Tensor<float> upscaled;
if (tile_size <= 0 || (input_tensor.shape()[0] <= tile_size && input_tensor.shape()[1] <= tile_size)) {
upscaled = esrgan_upscaler->compute(n_threads, input_tensor);
} else {
auto on_processing = [&](const sd::Tensor<float>& input_tile) -> sd::Tensor<float> {
auto output_tile = esrgan_upscaler->compute(n_threads, input_tile);
if (output_tile.empty()) {
LOG_ERROR("esrgan compute failed while processing a tile");
return {};
}
return output_tile;
};
upscaled = process_tiles_2d(input_tensor,
static_cast<int>(input_tensor.shape()[0] * esrgan_upscaler->scale),
static_cast<int>(input_tensor.shape()[1] * esrgan_upscaler->scale),
esrgan_upscaler->scale,
tile_size,
tile_size,
0.25f,
false,
false,
on_processing);
}
esrgan_upscaler->free_compute_buffer();
if (upscaled.empty()) {
LOG_ERROR("esrgan compute failed");
return {};
}
return upscaled;
}
sd_image_t upscale(sd_image_t input_image, uint32_t upscale_factor) {
// upscale_factor, unused for RealESRGAN_x4plus_anime_6B.pth
sd_image_t upscaled_image = {0, 0, 0, nullptr};
int output_width = (int)input_image.width * esrgan_upscaler->scale;
int output_height = (int)input_image.height * esrgan_upscaler->scale;
LOG_INFO("upscaling from (%i x %i) to (%i x %i)",
input_image.width, input_image.height, output_width, output_height);
sd::Tensor<float> input_tensor = sd_image_to_tensor(input_image);
sd::Tensor<float> upscaled;
int64_t t0 = ggml_time_ms();
upscaled = upscale_tensor(input_tensor);
if (upscaled.empty()) {
return upscaled_image;
}
sd_image_t upscaled_data = tensor_to_sd_image(upscaled);
int64_t t3 = ggml_time_ms();
LOG_INFO("input_image_tensor upscaled, taking %.2fs", (t3 - t0) / 1000.0f);
upscaled_image = upscaled_data;
return upscaled_image;
}
};
struct upscaler_ctx_t {
UpscalerGGML* upscaler = nullptr;
};
upscaler_ctx_t* new_upscaler_ctx(const char* esrgan_path_c_str,
bool offload_params_to_cpu,
bool direct,
int n_threads,
int tile_size) {
upscaler_ctx_t* upscaler_ctx = (upscaler_ctx_t*)malloc(sizeof(upscaler_ctx_t));
if (upscaler_ctx == nullptr) {
return nullptr;
}
std::string esrgan_path(esrgan_path_c_str);
upscaler_ctx->upscaler = new UpscalerGGML(n_threads, direct, tile_size);
if (upscaler_ctx->upscaler == nullptr) {
return nullptr;
}
if (!upscaler_ctx->upscaler->load_from_file(esrgan_path, offload_params_to_cpu, n_threads)) {
delete upscaler_ctx->upscaler;
upscaler_ctx->upscaler = nullptr;
free(upscaler_ctx);
return nullptr;
}
return upscaler_ctx;
}
sd_image_t upscale(upscaler_ctx_t* upscaler_ctx, sd_image_t input_image, uint32_t upscale_factor) {
return upscaler_ctx->upscaler->upscale(input_image, upscale_factor);
}
int get_upscale_factor(upscaler_ctx_t* upscaler_ctx) {
if (upscaler_ctx == nullptr || upscaler_ctx->upscaler == nullptr || upscaler_ctx->upscaler->esrgan_upscaler == nullptr) {
return 1;
}
return upscaler_ctx->upscaler->esrgan_upscaler->scale;
}
void free_upscaler_ctx(upscaler_ctx_t* upscaler_ctx) {
if (upscaler_ctx->upscaler != nullptr) {
delete upscaler_ctx->upscaler;
upscaler_ctx->upscaler = nullptr;
}
free(upscaler_ctx);
}