DarthAffe/stable-diffusion.cpp
1
0 Fork 0
mirror of https://github.com/leejet/stable-diffusion.cpp.git synced 2026-11-11 19:36:33 +00:00
Code Issues Packages Projects Releases Wiki Activity

add style_ratio as a cli param; reworked encode with trigger for attention weights

Browse Source
This commit is contained in:
bssrdf committed 2024-02-19 12:49:41 -05:00
1 parent d38b85d87e
commit a6a676b646
7 files changed
+63 -43
Download Patch File Download Diff File Expand all files Collapse all files

No files matched your search

clip.hpp
+36 -28
View File
@@ -976,21 +976,21 @@ struct CLIPVisionModel {
// patch_embeddings_f16 = ggml_reshape_4d(ctx0, patch_embeddings_f16, ne[0], ne[1], ne[2], ne[3]);
struct ggml_tensor *patch_embeddings_f16 = ggml_cast(ctx0, patch_embeddings, GGML_TYPE_F16);
ggml_set_name(patch_embeddings_f16, "patch_embeddings_f16");
print_ggml_tensor(patch_embeddings_f16, true, "patch_embeddings_f16");
// print_ggml_tensor(patch_embeddings_f16, true, "patch_embeddings_f16");
struct ggml_tensor * inp = ggml_conv_2d(ctx0, patch_embeddings_f16, x, patch_size, patch_size, 0, 0, 1, 1);
ggml_set_name(inp, "inp_conv_2d");
print_ggml_tensor(inp, true, "inp_conv_2d");
// print_ggml_tensor(inp, true, "inp_conv_2d");
inp = ggml_reshape_3d(ctx0, inp, num_patches, hidden_size, batch_size);
ggml_set_name(inp, "inp_reshape_3d");
print_ggml_tensor(inp, true, "inp_reshape_3d");
// print_ggml_tensor(inp, true, "inp_reshape_3d");
// inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 0, 2, 3));
// print_ggml_tensor(ggml_permute(ctx0, inp, 2, 0, 1, 3), true, "inp_permute");
inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 2, 0, 1, 3));
print_ggml_tensor(inp, true, "inp_cont_perm+cont");
// print_ggml_tensor(inp, true, "inp_cont_perm+cont");
ggml_set_name(inp, "inp_cont");
// ggml_set_name(class_embedding, "class_embedding");
@@ -1000,18 +1000,18 @@ struct CLIPVisionModel {
// struct ggml_tensor * temp = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, 1, batch_size);
// ggml_tensor *class_embedding_rep = ggml_add(ctx0, cast_f32_class, class_embedding);
// ggml_set_name(class_embedding_rep, "add_class_embedding_to_zero");
print_ggml_tensor(class_embedding, true, "model.class_embedding");
// print_ggml_tensor(class_embedding, true, "model.class_embedding");
// print_ggml_tensor(class_embedding_rep, true, "class_embedding_rep_bef_repeat");
print_ggml_tensor(temp, true, "temp");
// print_ggml_tensor(temp, true, "temp");
ggml_tensor *class_embedding_rep = ggml_repeat(ctx0, class_embedding, temp);
ggml_set_name(class_embedding_rep, "class_embedding_rep");
print_ggml_tensor(class_embedding_rep, true, "class_embedding_rep");
// print_ggml_tensor(class_embedding_rep, true, "class_embedding_rep");
// class_embedding_rep = ggml_cast(ctx0, class_embedding_rep, inp->type);
// print_ggml_tensor(class_embedding_rep, true, "class_embedding_rep_aft_casting");
struct ggml_tensor *embeddings = ggml_concat(ctx0, class_embedding_rep, inp);
ggml_set_name(embeddings, "embeddings_after_concat");
print_ggml_tensor(embeddings, true, "embeddings_after_concat");
// print_ggml_tensor(embeddings, true, "embeddings_after_concat");
// print_ggml_tensor(ggml_permute(ctx0, embeddings, 0, 3, 1, 2), true, "embeddings_after_concat_permute");
embeddings = ggml_cont(ctx0, ggml_permute(ctx0, embeddings, 0, 2, 1, 3));
ggml_set_name(embeddings, "embeddings_after_permute");
@@ -1028,7 +1028,7 @@ struct CLIPVisionModel {
// ggml_add(ctx0, embeddings, ggml_repeat(ctx0, ggml_get_rows(ctx0, position_embeddings, positions), embeddings));
ggml_add(ctx0, embeddings, ggml_get_rows(ctx0, position_embeddings, positions));
ggml_set_name(embeddings, "embeddings_to_transformer");
print_ggml_tensor(embeddings, true, "embeddings_to_transformer");
// print_ggml_tensor(embeddings, true, "embeddings_to_transformer");
// pre-layernorm
embeddings = ggml_nn_layer_norm(ctx0, embeddings, pre_ln_w, pre_ln_w);
@@ -1039,12 +1039,12 @@ struct CLIPVisionModel {
embeddings = resblocks[i].forward(ctx0, embeddings); // [N, n_token, hidden_size]
}
ggml_set_name(embeddings, "embeddings_after_transformer");
print_ggml_tensor(embeddings, true, "embeddings_after_transformer");
// print_ggml_tensor(embeddings, true, "embeddings_after_transformer");
// get the output of cls token, e.g., 0th index
embeddings = ggml_get_rows(ctx0, ggml_reshape_2d(ctx0, embeddings, hidden_size, num_positions * batch_size), cls);
ggml_set_name(embeddings, "embeddings_pooled_after_encoder");
print_ggml_tensor(embeddings, true, "embeddings_pooled_after_encoder");
// print_ggml_tensor(embeddings, true, "embeddings_pooled_after_encoder");
// post-layernorm
embeddings = ggml_nn_layer_norm(ctx0, embeddings, post_ln_w, post_ln_b);
@@ -1291,11 +1291,13 @@ struct FrozenCLIPEmbedderWithCustomWords : public GGMLModule {
std::vector<int> tokens;
std::vector<float> weights;
std::vector<bool> class_token_mask;
std::vector<int> clean_input_ids;
std::vector<int> class_token_index;
int32_t class_idx = -1, tokens_acc = 0;
for (const auto& item : parsed_attention) {
std::vector<int> class_token_index;
std::vector<int> clean_input_ids;
const std::string& curr_text = item.first;
float curr_weight = item.second;
// printf(" %s: %f \n", curr_text.c_str(), curr_weight);
std::vector<int> curr_tokens = tokenizer.encode(curr_text, on_new_token_cb);
int32_t clean_index = 0;
for(uint32_t i = 0; i < curr_tokens.size(); i++){
@@ -1307,23 +1309,29 @@ struct FrozenCLIPEmbedderWithCustomWords : public GGMLModule {
clean_index++;
}
}
GGML_ASSERT(class_token_index.size() == 1); // PhotoMaker currently does not support multiple
//GGML_ASSERT(class_token_index.size() == 1); // PhotoMaker currently does not support multiple
// trigger words in a single prompt.
// Expand the class word token and corresponding mask
int class_token = clean_input_ids[class_token_index[0]];
std::vector<int> clean_input_ids_tmp;
for(uint32_t i = 0; i < class_token_index[0]; i++)
clean_input_ids_tmp.push_back(clean_input_ids[i]);
for(uint32_t i = 0; i < num_input_imgs; i++)
clean_input_ids_tmp.push_back(class_token);
for(uint32_t i = class_token_index[0]+1; i < clean_input_ids.size(); i++)
clean_input_ids_tmp.push_back(clean_input_ids[i]);
clean_input_ids.clear();
clean_input_ids = clean_input_ids_tmp;
if(class_token_index.size() == 1){
// Expand the class word token and corresponding mask
int class_token = clean_input_ids[class_token_index[0]];
class_idx = tokens_acc + class_token_index[0];
std::vector<int> clean_input_ids_tmp;
for(uint32_t i = 0; i < class_token_index[0]; i++)
clean_input_ids_tmp.push_back(clean_input_ids[i]);
for(uint32_t i = 0; i < num_input_imgs; i++)
clean_input_ids_tmp.push_back(class_token);
for(uint32_t i = class_token_index[0]+1; i < clean_input_ids.size(); i++)
clean_input_ids_tmp.push_back(clean_input_ids[i]);
clean_input_ids.clear();
clean_input_ids = clean_input_ids_tmp;
}
tokens_acc += clean_index;
// class_tokens_mask = [True if class_token_index <= i < class_token_index+num_id_images else False \
// for i in range(len(clean_input_ids))]
// printf("[");
// for(int i = 0; i < clean_input_ids.size(); ++i)
// printf("%d, ", clean_input_ids[i]);
// printf("]\n");
tokens.insert(tokens.end(), clean_input_ids.begin(), clean_input_ids.end());
weights.insert(weights.end(), clean_input_ids.size(), curr_weight);
}
@@ -1351,7 +1359,7 @@ struct FrozenCLIPEmbedderWithCustomWords : public GGMLModule {
}
for(uint32_t i = 0; i < tokens.size(); i++){
if(class_token_index[0]+1 <= i && i < class_token_index[0]+1+num_input_imgs)
if(class_idx+1 <= i && i < class_idx+1+num_input_imgs)
class_token_mask.push_back(true);
else
class_token_mask.push_back(false);