upstream-archive/stable-diffusion.cpp
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removing image normalization step seems making ID fidelity much higher

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bssrdf
2024-02-22 21:52:01 -05:00
parent 8053aefdf7
commit dbe74df6a5
3 changed files with 44 additions and 21 deletions
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17
ggml_extend.hpp
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@@ -706,18 +706,6 @@ struct GGMLModule {
for (int i = 0; i < gf->n_leafs; i++) {
struct ggml_tensor * t1 = gf->leafs[i];
if(strcmp(ggml_get_name(t1), "class_tokens_mask_pos_input") == 0) {
imb = t1;
int64_t stride = imb->ne[0];
int* out_data = new int[ggml_nelements(imb)];
ggml_backend_tensor_get(imb, out_data, 0, ggml_nbytes(imb));
printf("[");
for(int i = 0; i < stride; i++){
printf("%d, ", out_data[i]);
}
printf("]\n");
delete out_data;
}
// if(strcmp(ggml_get_name(t1), "prompt_embeds_input") == 0) {
if(strcmp(ggml_get_name(t1), "id_pixel_values_input") == 0) {
@@ -796,8 +784,9 @@ struct GGMLModule {
delete out_data;
}
}
//#if 0
//#endif
#endif
#if 0
// struct ggml_tensor * imb = NULL;
for (int i = 0; i < gf->n_nodes; i++) {
struct ggml_cgraph g1v = ggml_graph_view(gf, i, i + 1);

6
model.cpp
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@@ -789,8 +789,8 @@ bool ModelLoader::init_from_safetensors_file(const std::string& file_path, const
ne[i] = shape[i].get<int64_t>();
}
TensorStorage tensor_storage(prefix + name, type, ne, n_dims, file_index, ST_HEADER_SIZE_LEN + header_size_ + begin);
// if(starts_with(prefix, "pmi"))
// printf(" %s:%s [%zu, %zu, %zu, %zu]\n", (prefix + name).c_str(), ggml_type_name(type), ne[0], ne[1],ne[2], ne[3]);
// if(starts_with(prefix, "scar"))
// printf(" %s:%s [%zu, %zu, %zu, %zu]\n", (prefix + name).c_str(), ggml_type_name(type), ne[0], ne[1],ne[2], ne[3]);
tensor_storage.reverse_ne();
size_t tensor_data_size = end - begin;
@@ -1127,7 +1127,7 @@ bool ModelLoader::parse_data_pkl(uint8_t* buffer,
if (reader.phase == PickleTensorReader::READ_DIMENS) {
reader.tensor_storage.reverse_ne();
reader.tensor_storage.file_index = file_index;
// if(strcmp(prefix.c_str(), "pm") == 0)
// if(strcmp(prefix.c_str(), "scarlett") == 0)
// printf(" got tensor %s \n ", reader.tensor_storage.name.c_str());
reader.tensor_storage.name = prefix + reader.tensor_storage.name;
tensor_storages.push_back(reader.tensor_storage);

42
stable-diffusion.cpp
View File

@@ -270,7 +270,7 @@ public:
if(stacked_id){
pmid_model.init_params(GGML_TYPE_F32);
pmid_model.map_by_name(tensors, "pmid.");
pmid_model.map_by_name(tensors, "pmid.");
}
}
@@ -648,6 +648,21 @@ public:
pmid_model.alloc_compute_buffer(work_ctx, init_img, prompts_embeds, class_tokens_mask);
pmid_model.compute(n_threads, init_img, prompts_embeds, class_tokens_mask, res);
pmid_model.free_compute_buffer();
// float original_mean = ggml_tensor_mean(res);
// for (int i2 = 0; i2 < res->ne[2]; i2++) {
// for (int i1 = 0; i1 < res->ne[1]; i1++) {
// if (class_tokens_mask[i1]){
// for (int i0 = 0; i0 < res->ne[0]; i0++) {
// float value = ggml_tensor_get_f32(res, i0, i1, i2);
// value *= 1.1f;
// ggml_tensor_set_f32(res, value, i0, i1, i2);
// }
// }
// }
// }
// float new_mean = ggml_tensor_mean(res);
// ggml_tensor_scale(res, (original_mean / new_mean));
// for(int j = 0; j < cond_stage_model.text_model.max_position_embeddings; j++){
// printf("[");
@@ -1530,9 +1545,28 @@ sd_image_t* txt2img(sd_ctx_t* sd_ctx,
float std[] = {0.26862954, 0.26130258, 0.27577711};
for(int i = 0; i < num_input_images; i++) {
sd_image_t* init_image = input_id_images[i];
sd_mul_images_to_tensor(init_image->data, init_img, i, mean, std);
// sd_mul_images_to_tensor(init_image->data, init_img, i, NULL, NULL);
}
// sd_mul_images_to_tensor(init_image->data, init_img, i, mean, std);
sd_mul_images_to_tensor(init_image->data, init_img, i, NULL, NULL);
}
// ModelLoader model_loader;
// std::string img_path("examples/scarlett.safetensors");
// if (!model_loader.init_from_file(img_path, "scarlett.")){
// LOG_ERROR("init model loader from file failed: '%s'", img_path.c_str());
// return NULL;
// }
// ggml_backend_t cpu_backend = ggml_backend_cpu_init();
// std::map<std::string, struct ggml_tensor*> tensors_need_to_load;
// std::set<std::string> ignore_tensors;
// tensors_need_to_load["scarlett.img"] = init_img;
// bool success = model_loader.load_tensors(tensors_need_to_load, cpu_backend, ignore_tensors);
// if (!success) {
// LOG_ERROR("load tensors from model loader failed");
// ggml_free(work_ctx);
// return NULL;
// }
// sd_image_t* cropped_images = (sd_image_t*)calloc(num_input_images, sizeof(sd_image_t));
// if (cropped_images == NULL) {
// ggml_free(work_ctx);