DarthAffe/stable-diffusion.cpp
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small tweak; still not getting ID faces

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bssrdf committed 2024-02-14 21:22:40 -05:00
1 parent 539a94adc7
commit 26c5cca043
3 files changed
+158 -5
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ggml_extend.hpp
+132
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@@ -77,6 +77,25 @@ __STATIC_INLINE__ ggml_fp16_t ggml_tensor_get_f16(const ggml_tensor* tensor, int
return *(ggml_fp16_t*)((char*)(tensor->data) + i * tensor->nb[3] + j * tensor->nb[2] + k * tensor->nb[1] + l * tensor->nb[0]);
}
static struct ggml_tensor * get_tensor_from_graph(struct ggml_cgraph * gf, const char *name){
struct ggml_tensor * res = NULL;
for(int i = 0; i < gf->n_nodes; i++) {
// printf("%d, %s \n", i, gf->nodes[i]->name);
if(strcmp(ggml_get_name(gf->nodes[i]), name) == 0) {
res = gf->nodes[i];
break;
}
}
for(int i = 0; i < gf->n_leafs; i++) {
// printf("%d, %s \n", i, gf->leafs[i]->name);
if(strcmp(ggml_get_name(gf->leafs[i]), name) == 0) {
res = gf->leafs[i];
break;
}
}
return res;
}
@@ -669,6 +688,87 @@ struct GGMLModule {
ggml_backend_metal_set_n_cb(backend, n_threads);
}
#endif
#if 0
struct ggml_tensor * imb = NULL;
for (int i = 0; i < gf->n_leafs; i++) {
struct ggml_tensor * t1 = gf->leafs[i];
if(strcmp(ggml_get_name(t1), "id_pixel_values_input") == 0) {
imb = t1;
int64_t stride = imb->ne[0];
float* out_data = new float[ggml_nelements(imb)];
ggml_backend_tensor_get(imb, out_data, 0, ggml_nbytes(imb));
for(int k = 0; k < 3; ++k){
float mi = 100.f, mx= -100.f;
for(int i = 0; i < stride; i++){
printf("[");
for(int j = 0; j < stride; j++){
float val = out_data[k*stride*stride+i*stride+j];
if(mi > val) mi = val;
if(mx < val) mx = val;
printf("%f, ", val);
}
printf("]\n");
}
printf("B. channel, min, max: %d, %f %f \n", k, mi, mx);
}
// printf("[");
// for(int i = 0; i < stride; i++){
// printf("%f, ", out_data[i]);
// }
// printf("]\n");
delete out_data;
}
}
for (int i = 0; i < gf->n_nodes; i++) {
struct ggml_tensor * t1 = gf->nodes[i];
// if(strcmp(ggml_get_name(t1), "embeddings_after_add") == 0) {
if(strcmp(ggml_get_name(t1), "pooled_output") == 0) {
imb = t1;
int64_t stride = imb->ne[0];
float* out_data = new float[ggml_nelements(imb)];
ggml_backend_tensor_get(imb, out_data, 0, ggml_nbytes(imb));
// for(int k = 0; k < 3; ++k){
// float mi = 100.f, mx= -100.f;
// for(int i = 0; i < stride; i++){
// printf("[");
// for(int j = 0; j < stride; j++){
// float val = out_data[k*stride*stride+i*stride+j];
// // if(mi > val) mi = val;
// // if(mx < val) mx = val;
// printf("%f, ", val);
// }
// printf("]\n");
// }
// // printf("B. channel, min, max: %d, %f %f \n", k, mi, mx);
// }
// for(int i = 0; i < 257; i++){
// printf("[");
// for(int j = 0; j < stride; j++){
// float val = out_data[i*stride+j];
// // if(mi > val) mi = val;
// // if(mx < val) mx = val;
// printf("%f, ", val);
// }
// printf("]\n");
// }
// printf("B. channel, min, max: %d, %f %f \n", k, mi, mx);
printf("[");
for(int i = 0; i < stride; i++){
printf("%f, ", out_data[i]);
}
printf("]\n");
delete out_data;
}
struct ggml_cgraph g1v = ggml_graph_view(gf, i, i + 1);
ggml_backend_graph_compute(backend, &g1v);
}
#endif
ggml_backend_graph_compute(backend, gf);
#ifdef GGML_PERF
@@ -679,6 +779,38 @@ struct GGMLModule {
if (output != NULL) {
ggml_backend_tensor_get_and_sync(backend, gf->nodes[gf->n_nodes - 1], output->data, 0, ggml_nbytes(output));
}
// struct ggml_tensor * imb = get_tensor_from_graph(gf, "id_pixel");
// struct ggml_tensor * imb = get_tensor_from_graph(gf, "id_pixel_values_input");
// if(imb != NULL){
// int64_t stride = imb->ne[0];
// float* out_data = new float[ggml_nelements(imb)];
// ggml_backend_tensor_get(imb, out_data, 0, ggml_nbytes(imb));
// for(int k = 0; k < 3; ++k){
// float mi = 100.f, mx= -100.f;
// for(int i = 0; i < stride; i++){
// printf("[");
// for(int j = 0; j < stride; j++){
// float val = out_data[k*stride*stride+i*stride+j];
// if(mi > val) mi = val;
// if(mx < val) mx = val;
// printf("%f, ", val);
// }
// printf("]\n");
// }
// printf("B. channel, min, max: %d, %f %f \n", k, mi, mx);
// }
// // printf("[");
// // for(int i = 0; i < stride; i++){
// // printf("%f, ", out_data[i]);
// // }
// // printf("]\n");
// delete out_data;
// }
// if (output != NULL)
// print_ggml_tensor(output, true, "output_get");
}