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conditioner.hpp
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#ifndef __CONDITIONER_HPP__
#define __CONDITIONER_HPP__
#include "clip.hpp"
#include "t5.hpp"
struct SDCondition {
struct ggml_tensor* c_crossattn = NULL; // aka context
struct ggml_tensor* c_vector = NULL; // aka y
struct ggml_tensor* c_concat = NULL;
SDCondition() = default;
SDCondition(struct ggml_tensor* c_crossattn, struct ggml_tensor* c_vector, struct ggml_tensor* c_concat)
: c_crossattn(c_crossattn), c_vector(c_vector), c_concat(c_concat) {}
};
struct Conditioner {
virtual SDCondition get_learned_condition(ggml_context* work_ctx,
int n_threads,
const std::string& text,
int clip_skip,
int width,
int height,
int adm_in_channels = -1,
bool force_zero_embeddings = false) = 0;
virtual void alloc_params_buffer() = 0;
virtual void free_params_buffer() = 0;
virtual void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) = 0;
virtual size_t get_params_buffer_size() = 0;
virtual std::tuple<SDCondition, std::vector<bool>> get_learned_condition_with_trigger(ggml_context* work_ctx,
int n_threads,
const std::string& text,
int clip_skip,
int width,
int height,
int num_input_imgs,
int adm_in_channels = -1,
bool force_zero_embeddings = false) = 0;
virtual std::string remove_trigger_from_prompt(ggml_context* work_ctx,
const std::string& prompt) = 0;
};
// ldm.modules.encoders.modules.FrozenCLIPEmbedder
// Ref: https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/cad87bf4e3e0b0a759afa94e933527c3123d59bc/modules/sd_hijack_clip.py#L283
struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
SDVersion version = VERSION_SD1;
PMVersion pm_version = PM_VERSION_1;
CLIPTokenizer tokenizer;
std::shared_ptr<CLIPTextModelRunner> text_model;
std::shared_ptr<CLIPTextModelRunner> text_model2;
std::string trigger_word = "img"; // should be user settable
std::string embd_dir;
int32_t num_custom_embeddings = 0;
int32_t num_custom_embeddings_2 = 0;
std::vector<uint8_t> token_embed_custom;
std::vector<std::string> readed_embeddings;
FrozenCLIPEmbedderWithCustomWords(ggml_backend_t backend,
std::map<std::string, enum ggml_type>& tensor_types,
const std::string& embd_dir,
SDVersion version = VERSION_SD1,
PMVersion pv = PM_VERSION_1,
int clip_skip = -1)
: version(version), pm_version(pv), tokenizer(sd_version_is_sd2(version) ? 0 : 49407), embd_dir(embd_dir) {
if (clip_skip <= 0) {
clip_skip = 1;
if (sd_version_is_sd2(version) || sd_version_is_sdxl(version)) {
clip_skip = 2;
}
}
if (sd_version_is_sd1(version)) {
text_model = std::make_shared<CLIPTextModelRunner>(backend, tensor_types, "cond_stage_model.transformer.text_model", OPENAI_CLIP_VIT_L_14, clip_skip);
} else if (sd_version_is_sd2(version)) {
text_model = std::make_shared<CLIPTextModelRunner>(backend, tensor_types, "cond_stage_model.transformer.text_model", OPEN_CLIP_VIT_H_14, clip_skip);
} else if (sd_version_is_sdxl(version)) {
text_model = std::make_shared<CLIPTextModelRunner>(backend, tensor_types, "cond_stage_model.transformer.text_model", OPENAI_CLIP_VIT_L_14, clip_skip, false);
text_model2 = std::make_shared<CLIPTextModelRunner>(backend, tensor_types, "cond_stage_model.1.transformer.text_model", OPEN_CLIP_VIT_BIGG_14, clip_skip, false);
}
}
void set_clip_skip(int clip_skip) {
text_model->set_clip_skip(clip_skip);
if (sd_version_is_sdxl(version)) {
text_model2->set_clip_skip(clip_skip);
}
}
void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) {
text_model->get_param_tensors(tensors, "cond_stage_model.transformer.text_model");
if (sd_version_is_sdxl(version)) {
text_model2->get_param_tensors(tensors, "cond_stage_model.1.transformer.text_model");
}
}
void alloc_params_buffer() {
text_model->alloc_params_buffer();
if (sd_version_is_sdxl(version)) {
text_model2->alloc_params_buffer();
}
}
void free_params_buffer() {
text_model->free_params_buffer();
if (sd_version_is_sdxl(version)) {
text_model2->free_params_buffer();
}
}
size_t get_params_buffer_size() {
size_t buffer_size = text_model->get_params_buffer_size();
if (sd_version_is_sdxl(version)) {
buffer_size += text_model2->get_params_buffer_size();
}
return buffer_size;
}
bool load_embedding(std::string embd_name, std::string embd_path, std::vector<int32_t>& bpe_tokens) {
// the order matters
ModelLoader model_loader;
if (!model_loader.init_from_file(embd_path)) {
LOG_ERROR("embedding '%s' failed", embd_name.c_str());
return false;
}
if (std::find(readed_embeddings.begin(), readed_embeddings.end(), embd_name) != readed_embeddings.end()) {
LOG_DEBUG("embedding already read in: %s", embd_name.c_str());
return true;
}
struct ggml_init_params params;
params.mem_size = 10 * 1024 * 1024; // max for custom embeddings 10 MB
params.mem_buffer = NULL;
params.no_alloc = false;
struct ggml_context* embd_ctx = ggml_init(params);
struct ggml_tensor* embd = NULL;
struct ggml_tensor* embd2 = NULL;
auto on_load = [&](const TensorStorage& tensor_storage, ggml_tensor** dst_tensor) {
if (tensor_storage.ne[0] != text_model->model.hidden_size) {
if (text_model2) {
if (tensor_storage.ne[0] == text_model2->model.hidden_size) {
embd2 = ggml_new_tensor_2d(embd_ctx, tensor_storage.type, text_model2->model.hidden_size, tensor_storage.n_dims > 1 ? tensor_storage.ne[1] : 1);
*dst_tensor = embd2;
} else {
LOG_DEBUG("embedding wrong hidden size, got %i, expected %i or %i", tensor_storage.ne[0], text_model->model.hidden_size, text_model2->model.hidden_size);
return false;
}
} else {
LOG_DEBUG("embedding wrong hidden size, got %i, expected %i", tensor_storage.ne[0], text_model->model.hidden_size);
return false;
}
} else {
embd = ggml_new_tensor_2d(embd_ctx, tensor_storage.type, text_model->model.hidden_size, tensor_storage.n_dims > 1 ? tensor_storage.ne[1] : 1);
*dst_tensor = embd;
}
return true;
};
model_loader.load_tensors(on_load, NULL);
readed_embeddings.push_back(embd_name);
if (embd) {
int64_t hidden_size = text_model->model.hidden_size;
token_embed_custom.resize(token_embed_custom.size() + ggml_nbytes(embd));
memcpy((void*)(token_embed_custom.data() + num_custom_embeddings * hidden_size * ggml_type_size(embd->type)),
embd->data,
ggml_nbytes(embd));
for (int i = 0; i < embd->ne[1]; i++) {
bpe_tokens.push_back(text_model->model.vocab_size + num_custom_embeddings);
// LOG_DEBUG("new custom token: %i", text_model.vocab_size + num_custom_embeddings);
num_custom_embeddings++;
}
LOG_DEBUG("embedding '%s' applied, custom embeddings: %i", embd_name.c_str(), num_custom_embeddings);
}
if (embd2) {
int64_t hidden_size = text_model2->model.hidden_size;
token_embed_custom.resize(token_embed_custom.size() + ggml_nbytes(embd2));
memcpy((void*)(token_embed_custom.data() + num_custom_embeddings_2 * hidden_size * ggml_type_size(embd2->type)),
embd2->data,
ggml_nbytes(embd2));
for (int i = 0; i < embd2->ne[1]; i++) {
bpe_tokens.push_back(text_model2->model.vocab_size + num_custom_embeddings_2);
// LOG_DEBUG("new custom token: %i", text_model.vocab_size + num_custom_embeddings);
num_custom_embeddings_2++;
}
LOG_DEBUG("embedding '%s' applied, custom embeddings: %i (text model 2)", embd_name.c_str(), num_custom_embeddings_2);
}
return true;
}
std::tuple<std::vector<int>, std::vector<float>, std::vector<bool>>
tokenize_with_trigger_token(std::string text,
int num_input_imgs,
int32_t image_token,
bool padding = false) {
return tokenize_with_trigger_token(text, num_input_imgs, image_token,
text_model->model.n_token, padding);
}
std::vector<int> convert_token_to_id(std::string text) {
auto on_new_token_cb = [&](std::string& str, std::vector<int32_t>& bpe_tokens) -> bool {
size_t word_end = str.find(",");
std::string embd_name = word_end == std::string::npos ? str : str.substr(0, word_end);
embd_name = trim(embd_name);
std::string embd_path = get_full_path(embd_dir, embd_name + ".pt");
if (embd_path.size() == 0) {
embd_path = get_full_path(embd_dir, embd_name + ".ckpt");
}
if (embd_path.size() == 0) {
embd_path = get_full_path(embd_dir, embd_name + ".safetensors");
}
if (embd_path.size() > 0) {
if (load_embedding(embd_name, embd_path, bpe_tokens)) {
if (word_end != std::string::npos) {
str = str.substr(word_end);
} else {
str = "";
}
return true;
}
}
return false;
};
std::vector<int> curr_tokens = tokenizer.encode(text, on_new_token_cb);
return curr_tokens;
}
std::string decode(const std::vector<int>& tokens) {
return tokenizer.decode(tokens);
}
std::tuple<std::vector<int>, std::vector<float>, std::vector<bool>>
tokenize_with_trigger_token(std::string text,
int num_input_imgs,
int32_t image_token,
size_t max_length = 0,
bool padding = false) {
auto parsed_attention = parse_prompt_attention(text);
{
std::stringstream ss;
ss << "[";
for (const auto& item : parsed_attention) {
ss << "['" << item.first << "', " << item.second << "], ";
}
ss << "]";
LOG_DEBUG("parse '%s' to %s", text.c_str(), ss.str().c_str());
}
auto on_new_token_cb = [&](std::string& str, std::vector<int32_t>& bpe_tokens) -> bool {
size_t word_end = str.find(",");
std::string embd_name = word_end == std::string::npos ? str : str.substr(0, word_end);
embd_name = trim(embd_name);
std::string embd_path = get_full_path(embd_dir, embd_name + ".pt");
if (embd_path.size() == 0) {
embd_path = get_full_path(embd_dir, embd_name + ".ckpt");
}
if (embd_path.size() == 0) {
embd_path = get_full_path(embd_dir, embd_name + ".safetensors");
}
if (embd_path.size() > 0) {
if (load_embedding(embd_name, embd_path, bpe_tokens)) {
if (word_end != std::string::npos) {
str = str.substr(word_end);
} else {
str = "";
}
return true;
}
}
return false;
};
std::vector<int> tokens;
std::vector<float> weights;
std::vector<bool> class_token_mask;
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++) {
int token_id = curr_tokens[i];
if (token_id == image_token)
class_token_index.push_back(clean_index - 1);
else {
clean_input_ids.push_back(token_id);
clean_index++;
}
}
// GGML_ASSERT(class_token_index.size() == 1); // PhotoMaker currently does not support multiple
// trigger words in a single prompt.
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 < (pm_version == PM_VERSION_2 ? 2 * num_input_imgs : 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;
tokens.insert(tokens.end(), clean_input_ids.begin(), clean_input_ids.end());
weights.insert(weights.end(), clean_input_ids.size(), curr_weight);
}
// BUG!! double couting, pad_tokens will add BOS at the beginning
// tokens.insert(tokens.begin(), tokenizer.BOS_TOKEN_ID);
// weights.insert(weights.begin(), 1.0);
tokenizer.pad_tokens(tokens, weights, max_length, padding);
int offset = pm_version == PM_VERSION_2 ? 2 * num_input_imgs : num_input_imgs;
for (uint32_t i = 0; i < tokens.size(); i++) {
// if (class_idx + 1 <= i && i < class_idx + 1 + 2*num_input_imgs) // photomaker V2 has num_tokens(=2)*num_input_imgs
if (class_idx + 1 <= i && i < class_idx + 1 + offset) // photomaker V2 has num_tokens(=2)*num_input_imgs
// hardcode for now
class_token_mask.push_back(true);
else
class_token_mask.push_back(false);
}
// printf("[");
// for (int i = 0; i < tokens.size(); i++) {
// printf("%d, ", class_token_mask[i] ? 1 : 0);
// }
// printf("]\n");
// for (int i = 0; i < tokens.size(); i++) {
// std::cout << tokens[i] << ":" << weights[i] << ", ";
// }
// std::cout << std::endl;
return std::make_tuple(tokens, weights, class_token_mask);
}
std::pair<std::vector<int>, std::vector<float>> tokenize(std::string text,
bool padding = false) {
return tokenize(text, text_model->model.n_token, padding);
}
std::pair<std::vector<int>, std::vector<float>> tokenize(std::string text,
size_t max_length = 0,
bool padding = false) {
auto parsed_attention = parse_prompt_attention(text);
{
std::stringstream ss;
ss << "[";
for (const auto& item : parsed_attention) {
ss << "['" << item.first << "', " << item.second << "], ";
}
ss << "]";
LOG_DEBUG("parse '%s' to %s", text.c_str(), ss.str().c_str());
}
auto on_new_token_cb = [&](std::string& str, std::vector<int32_t>& bpe_tokens) -> bool {
size_t word_end = str.find(",");
std::string embd_name = word_end == std::string::npos ? str : str.substr(0, word_end);
embd_name = trim(embd_name);
std::string embd_path = get_full_path(embd_dir, embd_name + ".pt");
if (embd_path.size() == 0) {
embd_path = get_full_path(embd_dir, embd_name + ".ckpt");
}
if (embd_path.size() == 0) {
embd_path = get_full_path(embd_dir, embd_name + ".safetensors");
}
if (embd_path.size() > 0) {
if (load_embedding(embd_name, embd_path, bpe_tokens)) {
if (word_end != std::string::npos) {
str = str.substr(word_end);
} else {
str = "";
}
return true;
}
}
return false;
};
std::vector<int> tokens;
std::vector<float> weights;
for (const auto& item : parsed_attention) {
const std::string& curr_text = item.first;
float curr_weight = item.second;
std::vector<int> curr_tokens = tokenizer.encode(curr_text, on_new_token_cb);
tokens.insert(tokens.end(), curr_tokens.begin(), curr_tokens.end());
weights.insert(weights.end(), curr_tokens.size(), curr_weight);
}
tokenizer.pad_tokens(tokens, weights, max_length, padding);
// for (int i = 0; i < tokens.size(); i++) {
// std::cout << tokens[i] << ":" << weights[i] << ", ";
// }
// std::cout << std::endl;
return {tokens, weights};
}
SDCondition get_learned_condition_common(ggml_context* work_ctx,
int n_threads,
std::vector<int>& tokens,
std::vector<float>& weights,
int clip_skip,
int width,
int height,
int adm_in_channels = -1,
bool force_zero_embeddings = false) {
set_clip_skip(clip_skip);
int64_t t0 = ggml_time_ms();
struct ggml_tensor* hidden_states = NULL; // [N, n_token, hidden_size]
struct ggml_tensor* chunk_hidden_states = NULL; // [n_token, hidden_size] or [n_token, hidden_size + hidden_size2]
struct ggml_tensor* chunk_hidden_states1 = NULL; // [n_token, hidden_size]
struct ggml_tensor* chunk_hidden_states2 = NULL; // [n_token, hidden_size2]
struct ggml_tensor* pooled = NULL;
std::vector<float> hidden_states_vec;
size_t chunk_len = 77;
size_t chunk_count = tokens.size() / chunk_len;
for (int chunk_idx = 0; chunk_idx < chunk_count; chunk_idx++) {
std::vector<int> chunk_tokens(tokens.begin() + chunk_idx * chunk_len,
tokens.begin() + (chunk_idx + 1) * chunk_len);
std::vector<float> chunk_weights(weights.begin() + chunk_idx * chunk_len,
weights.begin() + (chunk_idx + 1) * chunk_len);
auto input_ids = vector_to_ggml_tensor_i32(work_ctx, chunk_tokens);
struct ggml_tensor* input_ids2 = NULL;
size_t max_token_idx = 0;
if (sd_version_is_sdxl(version)) {
auto it = std::find(chunk_tokens.begin(), chunk_tokens.end(), tokenizer.EOS_TOKEN_ID);
if (it != chunk_tokens.end()) {
std::fill(std::next(it), chunk_tokens.end(), 0);
}
max_token_idx = std::min<size_t>(std::distance(chunk_tokens.begin(), it), chunk_tokens.size() - 1);
input_ids2 = vector_to_ggml_tensor_i32(work_ctx, chunk_tokens);
// for (int i = 0; i < chunk_tokens.size(); i++) {
// printf("%d ", chunk_tokens[i]);
// }
// printf("\n");
}
{
text_model->compute(n_threads,
input_ids,
num_custom_embeddings,
token_embed_custom.data(),
max_token_idx,
false,
&chunk_hidden_states1,
work_ctx);
if (sd_version_is_sdxl(version)) {
text_model2->compute(n_threads,
input_ids2,
0,
NULL,
max_token_idx,
false,
&chunk_hidden_states2, work_ctx);
// concat
chunk_hidden_states = ggml_tensor_concat(work_ctx, chunk_hidden_states1, chunk_hidden_states2, 0);
if (chunk_idx == 0) {
text_model2->compute(n_threads,
input_ids2,
0,
NULL,
max_token_idx,
true,
&pooled,
work_ctx);
}
} else {
chunk_hidden_states = chunk_hidden_states1;
}
}
int64_t t1 = ggml_time_ms();
LOG_DEBUG("computing condition graph completed, taking %" PRId64 " ms", t1 - t0);
ggml_tensor* result = ggml_dup_tensor(work_ctx, chunk_hidden_states);
{
float original_mean = ggml_tensor_mean(chunk_hidden_states);
for (int i2 = 0; i2 < chunk_hidden_states->ne[2]; i2++) {
for (int i1 = 0; i1 < chunk_hidden_states->ne[1]; i1++) {
for (int i0 = 0; i0 < chunk_hidden_states->ne[0]; i0++) {
float value = ggml_tensor_get_f32(chunk_hidden_states, i0, i1, i2);
value *= chunk_weights[i1];
ggml_tensor_set_f32(result, value, i0, i1, i2);
}
}
}
float new_mean = ggml_tensor_mean(result);
ggml_tensor_scale(result, (original_mean / new_mean));
}
if (force_zero_embeddings) {
float* vec = (float*)result->data;
for (int i = 0; i < ggml_nelements(result); i++) {
vec[i] = 0;
}
}
hidden_states_vec.insert(hidden_states_vec.end(), (float*)result->data, ((float*)result->data) + ggml_nelements(result));
}
hidden_states = vector_to_ggml_tensor(work_ctx, hidden_states_vec);
hidden_states = ggml_reshape_2d(work_ctx,
hidden_states,
chunk_hidden_states->ne[0],
ggml_nelements(hidden_states) / chunk_hidden_states->ne[0]);
ggml_tensor* vec = NULL;
if (sd_version_is_sdxl(version)) {
int out_dim = 256;
vec = ggml_new_tensor_1d(work_ctx, GGML_TYPE_F32, adm_in_channels);
// [0:1280]
size_t offset = 0;
memcpy(vec->data, pooled->data, ggml_nbytes(pooled));
offset += ggml_nbytes(pooled);
// original_size_as_tuple
float orig_width = (float)width;
float orig_height = (float)height;
std::vector<float> timesteps = {orig_height, orig_width};
ggml_tensor* embed_view = ggml_view_2d(work_ctx, vec, out_dim, 2, ggml_type_size(GGML_TYPE_F32) * out_dim, offset);
offset += ggml_nbytes(embed_view);
set_timestep_embedding(timesteps, embed_view, out_dim);
// print_ggml_tensor(ggml_reshape_1d(work_ctx, embed_view, out_dim * 2));
// crop_coords_top_left
float crop_coord_top = 0.f;
float crop_coord_left = 0.f;
timesteps = {crop_coord_top, crop_coord_left};
embed_view = ggml_view_2d(work_ctx, vec, out_dim, 2, ggml_type_size(GGML_TYPE_F32) * out_dim, offset);
offset += ggml_nbytes(embed_view);
set_timestep_embedding(timesteps, embed_view, out_dim);
// print_ggml_tensor(ggml_reshape_1d(work_ctx, embed_view, out_dim * 2));
// target_size_as_tuple
float target_width = (float)width;
float target_height = (float)height;
timesteps = {target_height, target_width};
embed_view = ggml_view_2d(work_ctx, vec, out_dim, 2, ggml_type_size(GGML_TYPE_F32) * out_dim, offset);
offset += ggml_nbytes(embed_view);
set_timestep_embedding(timesteps, embed_view, out_dim);
// print_ggml_tensor(ggml_reshape_1d(work_ctx, embed_view, out_dim * 2));
GGML_ASSERT(offset == ggml_nbytes(vec));
}
// print_ggml_tensor(result);
return SDCondition(hidden_states, vec, NULL);
}
std::tuple<SDCondition, std::vector<bool>>
get_learned_condition_with_trigger(ggml_context* work_ctx,
int n_threads,
const std::string& text,
int clip_skip,
int width,
int height,
int num_input_imgs,
int adm_in_channels = -1,
bool force_zero_embeddings = false) {
auto image_tokens = convert_token_to_id(trigger_word);
// if(image_tokens.size() == 1){
// printf(" image token id is: %d \n", image_tokens[0]);
// }
GGML_ASSERT(image_tokens.size() == 1);
auto tokens_and_weights = tokenize_with_trigger_token(text,
num_input_imgs,
image_tokens[0],
true);
std::vector<int>& tokens = std::get<0>(tokens_and_weights);
std::vector<float>& weights = std::get<1>(tokens_and_weights);
std::vector<bool>& clsm = std::get<2>(tokens_and_weights);
// printf("tokens: \n");
// for(int i = 0; i < tokens.size(); ++i)
// printf("%d ", tokens[i]);
// printf("\n");
// printf("clsm: \n");
// for(int i = 0; i < clsm.size(); ++i)
// printf("%d ", clsm[i]?1:0);
// printf("\n");
auto cond = get_learned_condition_common(work_ctx, n_threads, tokens, weights, clip_skip, width, height, adm_in_channels, force_zero_embeddings);
return std::make_tuple(cond, clsm);
}
std::string remove_trigger_from_prompt(ggml_context* work_ctx,
const std::string& prompt) {
auto image_tokens = convert_token_to_id(trigger_word);
GGML_ASSERT(image_tokens.size() == 1);
auto tokens_and_weights = tokenize(prompt, false);
std::vector<int>& tokens = tokens_and_weights.first;
auto it = std::find(tokens.begin(), tokens.end(), image_tokens[0]);
GGML_ASSERT(it != tokens.end()); // prompt must have trigger word
tokens.erase(it);
return decode(tokens);
}
SDCondition get_learned_condition(ggml_context* work_ctx,
int n_threads,
const std::string& text,
int clip_skip,
int width,
int height,
int adm_in_channels = -1,
bool force_zero_embeddings = false) {
auto tokens_and_weights = tokenize(text, true);
std::vector<int>& tokens = tokens_and_weights.first;
std::vector<float>& weights = tokens_and_weights.second;
return get_learned_condition_common(work_ctx, n_threads, tokens, weights, clip_skip, width, height, adm_in_channels, force_zero_embeddings);
}
};
struct FrozenCLIPVisionEmbedder : public GGMLRunner {
CLIPVisionModelProjection vision_model;
FrozenCLIPVisionEmbedder(ggml_backend_t backend, std::map<std::string, enum ggml_type>& tensor_types)
: vision_model(OPEN_CLIP_VIT_H_14, true), GGMLRunner(backend) {
vision_model.init(params_ctx, tensor_types, "cond_stage_model.transformer");
}
std::string get_desc() {
return "clip_vision";
}
void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) {
vision_model.get_param_tensors(tensors, "cond_stage_model.transformer");
}
struct ggml_cgraph* build_graph(struct ggml_tensor* pixel_values) {
struct ggml_cgraph* gf = ggml_new_graph(compute_ctx);
pixel_values = to_backend(pixel_values);
struct ggml_tensor* hidden_states = vision_model.forward(compute_ctx, pixel_values);
ggml_build_forward_expand(gf, hidden_states);
return gf;
}
void compute(const int n_threads,
ggml_tensor* pixel_values,
ggml_tensor** output,
ggml_context* output_ctx) {
auto get_graph = [&]() -> struct ggml_cgraph* {
return build_graph(pixel_values);
};
GGMLRunner::compute(get_graph, n_threads, true, output, output_ctx);
}
};
struct SD3CLIPEmbedder : public Conditioner {
CLIPTokenizer clip_l_tokenizer;
CLIPTokenizer clip_g_tokenizer;
T5UniGramTokenizer t5_tokenizer;
std::shared_ptr<CLIPTextModelRunner> clip_l;
std::shared_ptr<CLIPTextModelRunner> clip_g;
std::shared_ptr<T5Runner> t5;
SD3CLIPEmbedder(ggml_backend_t backend,
std::map<std::string, enum ggml_type>& tensor_types,
int clip_skip = -1)
: clip_g_tokenizer(0) {
if (clip_skip <= 0) {
clip_skip = 2;
}
clip_l = std::make_shared<CLIPTextModelRunner>(backend, tensor_types, "text_encoders.clip_l.transformer.text_model", OPENAI_CLIP_VIT_L_14, clip_skip, false);
clip_g = std::make_shared<CLIPTextModelRunner>(backend, tensor_types, "text_encoders.clip_g.transformer.text_model", OPEN_CLIP_VIT_BIGG_14, clip_skip, false);
t5 = std::make_shared<T5Runner>(backend, tensor_types, "text_encoders.t5xxl.transformer");
}
void set_clip_skip(int clip_skip) {
clip_l->set_clip_skip(clip_skip);
clip_g->set_clip_skip(clip_skip);
}
void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) {
clip_l->get_param_tensors(tensors, "text_encoders.clip_l.transformer.text_model");
clip_g->get_param_tensors(tensors, "text_encoders.clip_g.transformer.text_model");
t5->get_param_tensors(tensors, "text_encoders.t5xxl.transformer");
}
void alloc_params_buffer() {
clip_l->alloc_params_buffer();
clip_g->alloc_params_buffer();
t5->alloc_params_buffer();
}
void free_params_buffer() {
clip_l->free_params_buffer();
clip_g->free_params_buffer();
t5->free_params_buffer();
}
size_t get_params_buffer_size() {
size_t buffer_size = clip_l->get_params_buffer_size();
buffer_size += clip_g->get_params_buffer_size();
buffer_size += t5->get_params_buffer_size();
return buffer_size;
}
std::vector<std::pair<std::vector<int>, std::vector<float>>> tokenize(std::string text,
size_t max_length = 0,
bool padding = false) {
auto parsed_attention = parse_prompt_attention(text);
{
std::stringstream ss;
ss << "[";
for (const auto& item : parsed_attention) {
ss << "['" << item.first << "', " << item.second << "], ";
}
ss << "]";
LOG_DEBUG("parse '%s' to %s", text.c_str(), ss.str().c_str());
}
auto on_new_token_cb = [&](std::string& str, std::vector<int32_t>& bpe_tokens) -> bool {
return false;
};
std::vector<int> clip_l_tokens;
std::vector<float> clip_l_weights;
std::vector<int> clip_g_tokens;
std::vector<float> clip_g_weights;
std::vector<int> t5_tokens;
std::vector<float> t5_weights;
for (const auto& item : parsed_attention) {
const std::string& curr_text = item.first;
float curr_weight = item.second;
std::vector<int> curr_tokens = clip_l_tokenizer.encode(curr_text, on_new_token_cb);
clip_l_tokens.insert(clip_l_tokens.end(), curr_tokens.begin(), curr_tokens.end());
clip_l_weights.insert(clip_l_weights.end(), curr_tokens.size(), curr_weight);
curr_tokens = clip_g_tokenizer.encode(curr_text, on_new_token_cb);
clip_g_tokens.insert(clip_g_tokens.end(), curr_tokens.begin(), curr_tokens.end());
clip_g_weights.insert(clip_g_weights.end(), curr_tokens.size(), curr_weight);
curr_tokens = t5_tokenizer.Encode(curr_text, true);
t5_tokens.insert(t5_tokens.end(), curr_tokens.begin(), curr_tokens.end());
t5_weights.insert(t5_weights.end(), curr_tokens.size(), curr_weight);
}
clip_l_tokenizer.pad_tokens(clip_l_tokens, clip_l_weights, max_length, padding);
clip_g_tokenizer.pad_tokens(clip_g_tokens, clip_g_weights, max_length, padding);
t5_tokenizer.pad_tokens(t5_tokens, t5_weights, max_length, padding);
// for (int i = 0; i < clip_l_tokens.size(); i++) {
// std::cout << clip_l_tokens[i] << ":" << clip_l_weights[i] << ", ";
// }
// std::cout << std::endl;
// for (int i = 0; i < clip_g_tokens.size(); i++) {
// std::cout << clip_g_tokens[i] << ":" << clip_g_weights[i] << ", ";
// }
// std::cout << std::endl;
// for (int i = 0; i < t5_tokens.size(); i++) {
// std::cout << t5_tokens[i] << ":" << t5_weights[i] << ", ";
// }
// std::cout << std::endl;
return {{clip_l_tokens, clip_l_weights}, {clip_g_tokens, clip_g_weights}, {t5_tokens, t5_weights}};
}
SDCondition get_learned_condition_common(ggml_context* work_ctx,
int n_threads,
std::vector<std::pair<std::vector<int>, std::vector<float>>> token_and_weights,
int clip_skip,
bool force_zero_embeddings = false) {
set_clip_skip(clip_skip);
auto& clip_l_tokens = token_and_weights[0].first;
auto& clip_l_weights = token_and_weights[0].second;
auto& clip_g_tokens = token_and_weights[1].first;
auto& clip_g_weights = token_and_weights[1].second;
auto& t5_tokens = token_and_weights[2].first;
auto& t5_weights = token_and_weights[2].second;
int64_t t0 = ggml_time_ms();
struct ggml_tensor* hidden_states = NULL; // [N, n_token*2, 4096]
struct ggml_tensor* chunk_hidden_states = NULL; // [n_token*2, 4096]
struct ggml_tensor* chunk_hidden_states_l = NULL; // [n_token, hidden_size_l]
struct ggml_tensor* chunk_hidden_states_g = NULL; // [n_token, hidden_size_g]
struct ggml_tensor* chunk_hidden_states_t5 = NULL; // [n_token, hidden_size_t5]
struct ggml_tensor* pooled = NULL;
struct ggml_tensor* pooled_l = NULL; // [768,]
struct ggml_tensor* pooled_g = NULL; // [1280,]
std::vector<float> hidden_states_vec;
size_t chunk_len = 77;
size_t chunk_count = clip_l_tokens.size() / chunk_len;
for (int chunk_idx = 0; chunk_idx < chunk_count; chunk_idx++) {
// clip_l
{
std::vector<int> chunk_tokens(clip_l_tokens.begin() + chunk_idx * chunk_len,
clip_l_tokens.begin() + (chunk_idx + 1) * chunk_len);
std::vector<float> chunk_weights(clip_l_weights.begin() + chunk_idx * chunk_len,
clip_l_weights.begin() + (chunk_idx + 1) * chunk_len);
auto input_ids = vector_to_ggml_tensor_i32(work_ctx, chunk_tokens);
size_t max_token_idx = 0;
clip_l->compute(n_threads,
input_ids,
0,
NULL,
max_token_idx,
false,
&chunk_hidden_states_l,
work_ctx);
{
auto tensor = chunk_hidden_states_l;
float original_mean = ggml_tensor_mean(tensor);
for (int i2 = 0; i2 < tensor->ne[2]; i2++) {
for (int i1 = 0; i1 < tensor->ne[1]; i1++) {
for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
float value = ggml_tensor_get_f32(tensor, i0, i1, i2);
value *= chunk_weights[i1];
ggml_tensor_set_f32(tensor, value, i0, i1, i2);
}
}
}
float new_mean = ggml_tensor_mean(tensor);
ggml_tensor_scale(tensor, (original_mean / new_mean));
}
if (chunk_idx == 0) {
auto it = std::find(chunk_tokens.begin(), chunk_tokens.end(), clip_l_tokenizer.EOS_TOKEN_ID);
max_token_idx = std::min<size_t>(std::distance(chunk_tokens.begin(), it), chunk_tokens.size() - 1);
clip_l->compute(n_threads,
input_ids,
0,
NULL,
max_token_idx,
true,
&pooled_l,
work_ctx);
}
}
// clip_g
{
std::vector<int> chunk_tokens(clip_g_tokens.begin() + chunk_idx * chunk_len,
clip_g_tokens.begin() + (chunk_idx + 1) * chunk_len);
std::vector<float> chunk_weights(clip_g_weights.begin() + chunk_idx * chunk_len,
clip_g_weights.begin() + (chunk_idx + 1) * chunk_len);
auto input_ids = vector_to_ggml_tensor_i32(work_ctx, chunk_tokens);
size_t max_token_idx = 0;
clip_g->compute(n_threads,
input_ids,
0,
NULL,
max_token_idx,
false,
&chunk_hidden_states_g,
work_ctx);
{
auto tensor = chunk_hidden_states_g;
float original_mean = ggml_tensor_mean(tensor);
for (int i2 = 0; i2 < tensor->ne[2]; i2++) {
for (int i1 = 0; i1 < tensor->ne[1]; i1++) {
for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
float value = ggml_tensor_get_f32(tensor, i0, i1, i2);
value *= chunk_weights[i1];
ggml_tensor_set_f32(tensor, value, i0, i1, i2);
}
}
}
float new_mean = ggml_tensor_mean(tensor);
ggml_tensor_scale(tensor, (original_mean / new_mean));
}
if (chunk_idx == 0) {
auto it = std::find(chunk_tokens.begin(), chunk_tokens.end(), clip_g_tokenizer.EOS_TOKEN_ID);
max_token_idx = std::min<size_t>(std::distance(chunk_tokens.begin(), it), chunk_tokens.size() - 1);
clip_g->compute(n_threads,
input_ids,
0,
NULL,
max_token_idx,
true,
&pooled_g,
work_ctx);
}
}
// t5
{
std::vector<int> chunk_tokens(t5_tokens.begin() + chunk_idx * chunk_len,
t5_tokens.begin() + (chunk_idx + 1) * chunk_len);
std::vector<float> chunk_weights(t5_weights.begin() + chunk_idx * chunk_len,
t5_weights.begin() + (chunk_idx + 1) * chunk_len);
auto input_ids = vector_to_ggml_tensor_i32(work_ctx, chunk_tokens);
t5->compute(n_threads,
input_ids,
&chunk_hidden_states_t5,
work_ctx);
{
auto tensor = chunk_hidden_states_t5;
float original_mean = ggml_tensor_mean(tensor);
for (int i2 = 0; i2 < tensor->ne[2]; i2++) {
for (int i1 = 0; i1 < tensor->ne[1]; i1++) {
for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
float value = ggml_tensor_get_f32(tensor, i0, i1, i2);
value *= chunk_weights[i1];
ggml_tensor_set_f32(tensor, value, i0, i1, i2);
}
}
}
float new_mean = ggml_tensor_mean(tensor);
ggml_tensor_scale(tensor, (original_mean / new_mean));
}
}
auto chunk_hidden_states_lg_pad = ggml_new_tensor_3d(work_ctx,
chunk_hidden_states_l->type,
4096,
chunk_hidden_states_l->ne[1],
chunk_hidden_states_l->ne[2]); // [n_token, 4096]
for (int i2 = 0; i2 < chunk_hidden_states_lg_pad->ne[2]; i2++) {
for (int i1 = 0; i1 < chunk_hidden_states_lg_pad->ne[1]; i1++) {
for (int i0 = 0; i0 < chunk_hidden_states_lg_pad->ne[0]; i0++) {
float value = 0.f;
if (i0 < chunk_hidden_states_l->ne[0]) {
value = ggml_tensor_get_f32(chunk_hidden_states_l, i0, i1, i2);
} else if (i0 < chunk_hidden_states_l->ne[0] + chunk_hidden_states_g->ne[0]) {
value = ggml_tensor_get_f32(chunk_hidden_states_g, i0 - chunk_hidden_states_l->ne[0], i1, i2);
}
ggml_tensor_set_f32(chunk_hidden_states_lg_pad, value, i0, i1, i2);
}
}
}
chunk_hidden_states = ggml_tensor_concat(work_ctx, chunk_hidden_states_lg_pad, chunk_hidden_states_t5, 1); // [n_token*2, 4096]
if (chunk_idx == 0) {
pooled = ggml_tensor_concat(work_ctx, pooled_l, pooled_g, 0); // [768 + 1280]
}
int64_t t1 = ggml_time_ms();
LOG_DEBUG("computing condition graph completed, taking %" PRId64 " ms", t1 - t0);
if (force_zero_embeddings) {
float* vec = (float*)chunk_hidden_states->data;
for (int i = 0; i < ggml_nelements(chunk_hidden_states); i++) {
vec[i] = 0;
}
}
hidden_states_vec.insert(hidden_states_vec.end(),
(float*)chunk_hidden_states->data,
((float*)chunk_hidden_states->data) + ggml_nelements(chunk_hidden_states));
}
hidden_states = vector_to_ggml_tensor(work_ctx, hidden_states_vec);
hidden_states = ggml_reshape_2d(work_ctx,
hidden_states,
chunk_hidden_states->ne[0],
ggml_nelements(hidden_states) / chunk_hidden_states->ne[0]);
return SDCondition(hidden_states, pooled, NULL);
}
SDCondition get_learned_condition(ggml_context* work_ctx,
int n_threads,
const std::string& text,
int clip_skip,
int width,
int height,
int adm_in_channels = -1,
bool force_zero_embeddings = false) {
auto tokens_and_weights = tokenize(text, 77, true);
return get_learned_condition_common(work_ctx, n_threads, tokens_and_weights, clip_skip, force_zero_embeddings);
}
std::tuple<SDCondition, std::vector<bool>> get_learned_condition_with_trigger(ggml_context* work_ctx,
int n_threads,
const std::string& text,
int clip_skip,
int width,
int height,
int num_input_imgs,
int adm_in_channels = -1,
bool force_zero_embeddings = false) {
GGML_ASSERT(0 && "Not implemented yet!");
}
std::string remove_trigger_from_prompt(ggml_context* work_ctx,
const std::string& prompt) {
GGML_ASSERT(0 && "Not implemented yet!");
}
};