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stable-diffusion.cpp
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stable-diffusion.cpp
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#include "ggml_extend.hpp"
#include "model.h"
#include "rng.hpp"
#include "rng_philox.hpp"
#include "stable-diffusion.h"
#include "util.h"
#include "clip.hpp"
#include "control.hpp"
#include "denoiser.hpp"
#include "esrgan.hpp"
#include "lora.hpp"
#include "pmid.hpp"
#include "tae.hpp"
#include "unet.hpp"
#include "vae.hpp"
#define STB_IMAGE_IMPLEMENTATION
#define STB_IMAGE_STATIC
#include "stb_image.h"
// #define STB_IMAGE_WRITE_IMPLEMENTATION
// #define STB_IMAGE_WRITE_STATIC
// #include "stb_image_write.h"
const char* model_version_to_str[] = {
"1.x",
"2.x",
"XL",
"SVD",
};
const char* sampling_methods_str[] = {
"Euler A",
"Euler",
"Heun",
"DPM2",
"DPM++ (2s)",
"DPM++ (2M)",
"modified DPM++ (2M)",
"LCM",
};
/*================================================== Helper Functions ================================================*/
void calculate_alphas_cumprod(float* alphas_cumprod,
float linear_start = 0.00085f,
float linear_end = 0.0120,
int timesteps = TIMESTEPS) {
float ls_sqrt = sqrtf(linear_start);
float le_sqrt = sqrtf(linear_end);
float amount = le_sqrt - ls_sqrt;
float product = 1.0f;
for (int i = 0; i < timesteps; i++) {
float beta = ls_sqrt + amount * ((float)i / (timesteps - 1));
product *= 1.0f - powf(beta, 2.0f);
alphas_cumprod[i] = product;
}
}
/*=============================================== StableDiffusionGGML ================================================*/
class StableDiffusionGGML {
public:
ggml_backend_t backend = NULL; // general backend
ggml_backend_t clip_backend = NULL;
ggml_backend_t control_net_backend = NULL;
ggml_backend_t vae_backend = NULL;
ggml_type model_data_type = GGML_TYPE_COUNT;
SDVersion version;
bool vae_decode_only = false;
bool free_params_immediately = false;
std::shared_ptr<RNG> rng = std::make_shared<STDDefaultRNG>();
int n_threads = -1;
float scale_factor = 0.18215f;
std::shared_ptr<FrozenCLIPEmbedderWithCustomWords> cond_stage_model;
std::shared_ptr<FrozenCLIPVisionEmbedder> clip_vision; // for svd
std::shared_ptr<UNetModel> diffusion_model;
std::shared_ptr<AutoEncoderKL> first_stage_model;
std::shared_ptr<TinyAutoEncoder> tae_first_stage;
std::shared_ptr<ControlNet> control_net;
std::shared_ptr<PhotoMakerIDEncoder> pmid_model;
std::shared_ptr<LoraModel> pmid_lora;
std::string taesd_path;
bool use_tiny_autoencoder = false;
bool vae_tiling = false;
bool stacked_id = false;
std::map<std::string, struct ggml_tensor*> tensors;
std::string lora_model_dir;
// lora_name => multiplier
std::unordered_map<std::string, float> curr_lora_state;
std::shared_ptr<Denoiser> denoiser = std::make_shared<CompVisDenoiser>();
std::string trigger_word = "img"; // should be user settable
StableDiffusionGGML() = default;
StableDiffusionGGML(int n_threads,
bool vae_decode_only,
bool free_params_immediately,
std::string lora_model_dir,
rng_type_t rng_type)
: n_threads(n_threads),
vae_decode_only(vae_decode_only),
free_params_immediately(free_params_immediately),
lora_model_dir(lora_model_dir) {
if (rng_type == STD_DEFAULT_RNG) {
rng = std::make_shared<STDDefaultRNG>();
} else if (rng_type == CUDA_RNG) {
rng = std::make_shared<PhiloxRNG>();
}
}
~StableDiffusionGGML() {
if (clip_backend != backend) {
ggml_backend_free(clip_backend);
}
if (control_net_backend != backend) {
ggml_backend_free(control_net_backend);
}
if (vae_backend != backend) {
ggml_backend_free(vae_backend);
}
ggml_backend_free(backend);
}
bool load_from_file(const std::string& model_path,
const std::string& vae_path,
const std::string control_net_path,
const std::string embeddings_path,
const std::string id_embeddings_path,
const std::string& taesd_path,
bool vae_tiling_,
ggml_type wtype,
schedule_t schedule,
bool clip_on_cpu,
bool control_net_cpu,
bool vae_on_cpu) {
use_tiny_autoencoder = taesd_path.size() > 0;
#ifdef SD_USE_CUBLAS
LOG_DEBUG("Using CUDA backend");
backend = ggml_backend_cuda_init(0);
#endif
#ifdef SD_USE_METAL
LOG_DEBUG("Using Metal backend");
ggml_backend_metal_log_set_callback(ggml_log_callback_default, nullptr);
backend = ggml_backend_metal_init();
#endif
if (!backend) {
LOG_DEBUG("Using CPU backend");
backend = ggml_backend_cpu_init();
}
#ifdef SD_USE_FLASH_ATTENTION
#if defined(SD_USE_CUBLAS) || defined(SD_USE_METAL)
LOG_WARN("Flash Attention not supported with GPU Backend");
#else
LOG_INFO("Flash Attention enabled");
#endif
#endif
LOG_INFO("loading model from '%s'", model_path.c_str());
ModelLoader model_loader;
vae_tiling = vae_tiling_;
if (!model_loader.init_from_file(model_path)) {
LOG_ERROR("init model loader from file failed: '%s'", model_path.c_str());
return false;
}
if (vae_path.size() > 0) {
LOG_INFO("loading vae from '%s'", vae_path.c_str());
if (!model_loader.init_from_file(vae_path, "vae.")) {
LOG_WARN("loading vae from '%s' failed", vae_path.c_str());
}
}
version = model_loader.get_sd_version();
if (version == VERSION_COUNT) {
LOG_ERROR("get sd version from file failed: '%s'", model_path.c_str());
return false;
}
LOG_INFO("Stable Diffusion %s ", model_version_to_str[version]);
if (wtype == GGML_TYPE_COUNT) {
model_data_type = model_loader.get_sd_wtype();
} else {
model_data_type = wtype;
}
LOG_INFO("Stable Diffusion weight type: %s", ggml_type_name(model_data_type));
LOG_DEBUG("ggml tensor size = %d bytes", (int)sizeof(ggml_tensor));
if (version == VERSION_XL) {
scale_factor = 0.13025f;
if (vae_path.size() == 0 && taesd_path.size() == 0) {
LOG_WARN(
"!!!It looks like you are using SDXL model. "
"If you find that the generated images are completely black, "
"try specifying SDXL VAE FP16 Fix with the --vae parameter. "
"You can find it here: https://huggingface.co/madebyollin/sdxl-vae-fp16-fix/blob/main/sdxl_vae.safetensors");
}
}
if (version == VERSION_SVD) {
clip_vision = std::make_shared<FrozenCLIPVisionEmbedder>(backend, model_data_type);
clip_vision->alloc_params_buffer();
clip_vision->get_param_tensors(tensors, "cond_stage_model.");
diffusion_model = std::make_shared<UNetModel>(backend, model_data_type, version);
diffusion_model->alloc_params_buffer();
diffusion_model->get_param_tensors(tensors, "model.diffusion_model");
first_stage_model = std::make_shared<AutoEncoderKL>(backend, model_data_type, vae_decode_only, true);
LOG_DEBUG("vae_decode_only %d", vae_decode_only);
first_stage_model->alloc_params_buffer();
first_stage_model->get_param_tensors(tensors, "first_stage_model");
} else {
clip_backend = backend;
if (clip_on_cpu && !ggml_backend_is_cpu(backend)) {
LOG_INFO("CLIP: Using CPU backend");
clip_backend = ggml_backend_cpu_init();
}
cond_stage_model = std::make_shared<FrozenCLIPEmbedderWithCustomWords>(clip_backend, model_data_type, version);
cond_stage_model->alloc_params_buffer();
cond_stage_model->get_param_tensors(tensors, "cond_stage_model.");
cond_stage_model->embd_dir = embeddings_path;
diffusion_model = std::make_shared<UNetModel>(backend, model_data_type, version);
diffusion_model->alloc_params_buffer();
diffusion_model->get_param_tensors(tensors, "model.diffusion_model");
ggml_type vae_type = model_data_type;
if (version == VERSION_XL) {
vae_type = GGML_TYPE_F32; // avoid nan, not work...
}
if (!use_tiny_autoencoder) {
if (vae_on_cpu && !ggml_backend_is_cpu(backend)) {
LOG_INFO("VAE Autoencoder: Using CPU backend");
vae_backend = ggml_backend_cpu_init();
} else {
vae_backend = backend;
}
first_stage_model = std::make_shared<AutoEncoderKL>(vae_backend, vae_type, vae_decode_only);
first_stage_model->alloc_params_buffer();
first_stage_model->get_param_tensors(tensors, "first_stage_model");
} else {
tae_first_stage = std::make_shared<TinyAutoEncoder>(backend, model_data_type, vae_decode_only);
}
// first_stage_model->get_param_tensors(tensors, "first_stage_model.");
if (control_net_path.size() > 0) {
ggml_backend_t controlnet_backend = NULL;
if (control_net_cpu && !ggml_backend_is_cpu(backend)) {
LOG_DEBUG("ControlNet: Using CPU backend");
controlnet_backend = ggml_backend_cpu_init();
} else {
controlnet_backend = backend;
}
control_net = std::make_shared<ControlNet>(controlnet_backend, model_data_type, version);
}
pmid_model = std::make_shared<PhotoMakerIDEncoder>(clip_backend, model_data_type, version);
if (id_embeddings_path.size() > 0) {
pmid_lora = std::make_shared<LoraModel>(backend, model_data_type, id_embeddings_path, "");
if (!pmid_lora->load_from_file(true)) {
LOG_WARN("load photomaker lora tensors from %s failed", id_embeddings_path.c_str());
return false;
}
LOG_INFO("loading stacked ID embedding (PHOTOMAKER) model file from '%s'", id_embeddings_path.c_str());
if (!model_loader.init_from_file(id_embeddings_path, "pmid.")) {
LOG_WARN("loading stacked ID embedding from '%s' failed", id_embeddings_path.c_str());
} else {
stacked_id = true;
}
}
if (stacked_id) {
if (!pmid_model->alloc_params_buffer()) {
LOG_ERROR(" pmid model params buffer allocation failed");
return false;
}
// LOG_INFO("pmid param memory buffer size = %.2fMB ",
// pmid_model->params_buffer_size / 1024.0 / 1024.0);
pmid_model->get_param_tensors(tensors, "pmid");
}
// if(stacked_id){
// pmid_model.init_params(GGML_TYPE_F32);
// pmid_model.map_by_name(tensors, "pmid.");
// }
LOG_DEBUG("loading vocab");
std::string merges_utf8_str = model_loader.load_merges();
if (merges_utf8_str.size() == 0) {
LOG_ERROR("get merges failed: '%s'", model_path.c_str());
return false;
}
cond_stage_model->tokenizer.load_from_merges(merges_utf8_str);
}
struct ggml_init_params params;
params.mem_size = static_cast<size_t>(10 * 1024) * 1024; // 10M
params.mem_buffer = NULL;
params.no_alloc = false;
// LOG_DEBUG("mem_size %u ", params.mem_size);
struct ggml_context* ctx = ggml_init(params); // for alphas_cumprod and is_using_v_parameterization check
GGML_ASSERT(ctx != NULL);
ggml_tensor* alphas_cumprod_tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, TIMESTEPS);
calculate_alphas_cumprod((float*)alphas_cumprod_tensor->data);
// load weights
LOG_DEBUG("loading weights");
int64_t t0 = ggml_time_ms();
std::set<std::string> ignore_tensors;
tensors["alphas_cumprod"] = alphas_cumprod_tensor;
if (use_tiny_autoencoder) {
ignore_tensors.insert("first_stage_model.");
}
if (stacked_id) {
ignore_tensors.insert("lora.");
}
if (vae_decode_only) {
ignore_tensors.insert("first_stage_model.encoder");
ignore_tensors.insert("first_stage_model.quant");
}
if (version == VERSION_SVD) {
ignore_tensors.insert("conditioner.embedders.3");
}
bool success = model_loader.load_tensors(tensors, backend, ignore_tensors);
if (!success) {
LOG_ERROR("load tensors from model loader failed");
ggml_free(ctx);
return false;
}
// LOG_DEBUG("model size = %.2fMB", total_size / 1024.0 / 1024.0);
if (version == VERSION_SVD) {
// diffusion_model->test();
// first_stage_model->test();
// return false;
} else {
size_t clip_params_mem_size = cond_stage_model->get_params_buffer_size();
size_t unet_params_mem_size = diffusion_model->get_params_buffer_size();
size_t vae_params_mem_size = 0;
if (!use_tiny_autoencoder) {
vae_params_mem_size = first_stage_model->get_params_buffer_size();
} else {
if (!tae_first_stage->load_from_file(taesd_path)) {
return false;
}
vae_params_mem_size = tae_first_stage->get_params_buffer_size();
}
size_t control_net_params_mem_size = 0;
if (control_net) {
if (!control_net->load_from_file(control_net_path)) {
return false;
}
control_net_params_mem_size = control_net->get_params_buffer_size();
}
size_t pmid_params_mem_size = 0;
if (stacked_id) {
pmid_params_mem_size = pmid_model->get_params_buffer_size();
}
size_t total_params_ram_size = 0;
size_t total_params_vram_size = 0;
if (ggml_backend_is_cpu(clip_backend)) {
total_params_ram_size += clip_params_mem_size + pmid_params_mem_size;
} else {
total_params_vram_size += clip_params_mem_size + pmid_params_mem_size;
}
if (ggml_backend_is_cpu(backend)) {
total_params_ram_size += unet_params_mem_size;
} else {
total_params_vram_size += unet_params_mem_size;
}
if (ggml_backend_is_cpu(vae_backend)) {
total_params_ram_size += vae_params_mem_size;
} else {
total_params_vram_size += vae_params_mem_size;
}
if (ggml_backend_is_cpu(control_net_backend)) {
total_params_ram_size += control_net_params_mem_size;
} else {
total_params_vram_size += control_net_params_mem_size;
}
size_t total_params_size = total_params_ram_size + total_params_vram_size;
LOG_INFO(
"total params memory size = %.2fMB (VRAM %.2fMB, RAM %.2fMB): "
"clip %.2fMB(%s), unet %.2fMB(%s), vae %.2fMB(%s), controlnet %.2fMB(%s), pmid %.2fMB(%s)",
total_params_size / 1024.0 / 1024.0,
total_params_vram_size / 1024.0 / 1024.0,
total_params_ram_size / 1024.0 / 1024.0,
clip_params_mem_size / 1024.0 / 1024.0,
ggml_backend_is_cpu(clip_backend) ? "RAM" : "VRAM",
unet_params_mem_size / 1024.0 / 1024.0,
ggml_backend_is_cpu(backend) ? "RAM" : "VRAM",
vae_params_mem_size / 1024.0 / 1024.0,
ggml_backend_is_cpu(vae_backend) ? "RAM" : "VRAM",
control_net_params_mem_size / 1024.0 / 1024.0,
ggml_backend_is_cpu(control_net_backend) ? "RAM" : "VRAM",
pmid_params_mem_size / 1024.0 / 1024.0,
ggml_backend_is_cpu(clip_backend) ? "RAM" : "VRAM");
}
int64_t t1 = ggml_time_ms();
LOG_INFO("loading model from '%s' completed, taking %.2fs", model_path.c_str(), (t1 - t0) * 1.0f / 1000);
// check is_using_v_parameterization_for_sd2
bool is_using_v_parameterization = false;
if (version == VERSION_2_x) {
if (is_using_v_parameterization_for_sd2(ctx)) {
is_using_v_parameterization = true;
}
} else if (version == VERSION_SVD) {
// TODO: V_PREDICTION_EDM
is_using_v_parameterization = true;
}
if (is_using_v_parameterization) {
denoiser = std::make_shared<CompVisVDenoiser>();
LOG_INFO("running in v-prediction mode");
} else {
LOG_INFO("running in eps-prediction mode");
}
if (schedule != DEFAULT) {
switch (schedule) {
case DISCRETE:
LOG_INFO("running with discrete schedule");
denoiser->schedule = std::make_shared<DiscreteSchedule>();
break;
case KARRAS:
LOG_INFO("running with Karras schedule");
denoiser->schedule = std::make_shared<KarrasSchedule>();
break;
case AYS:
LOG_INFO("Running with Align-Your-Steps schedule");
denoiser->schedule = std::make_shared<AYSSchedule>();
denoiser->schedule->version = version;
break;
case DEFAULT:
// Don't touch anything.
break;
default:
LOG_ERROR("Unknown schedule %i", schedule);
abort();
}
}
for (int i = 0; i < TIMESTEPS; i++) {
denoiser->schedule->alphas_cumprod[i] = ((float*)alphas_cumprod_tensor->data)[i];
denoiser->schedule->sigmas[i] = std::sqrt((1 - denoiser->schedule->alphas_cumprod[i]) / denoiser->schedule->alphas_cumprod[i]);
denoiser->schedule->log_sigmas[i] = std::log(denoiser->schedule->sigmas[i]);
}
LOG_DEBUG("finished loaded file");
ggml_free(ctx);
return true;
}
bool is_using_v_parameterization_for_sd2(ggml_context* work_ctx) {
struct ggml_tensor* x_t = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, 8, 8, 4, 1);
ggml_set_f32(x_t, 0.5);
struct ggml_tensor* c = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, 1024, 2, 1, 1);
ggml_set_f32(c, 0.5);
struct ggml_tensor* timesteps = ggml_new_tensor_1d(work_ctx, GGML_TYPE_F32, 1);
ggml_set_f32(timesteps, 999);
int64_t t0 = ggml_time_ms();
struct ggml_tensor* out = ggml_dup_tensor(work_ctx, x_t);
diffusion_model->compute(n_threads, x_t, timesteps, c, NULL, NULL, -1, {}, 0.f, &out);
diffusion_model->free_compute_buffer();
double result = 0.f;
{
float* vec_x = (float*)x_t->data;
float* vec_out = (float*)out->data;
int64_t n = ggml_nelements(out);
for (int i = 0; i < n; i++) {
result += ((double)vec_out[i] - (double)vec_x[i]);
}
result /= n;
}
int64_t t1 = ggml_time_ms();
LOG_DEBUG("check is_using_v_parameterization_for_sd2, taking %.2fs", (t1 - t0) * 1.0f / 1000);
return result < -1;
}
void apply_lora(const std::string& lora_name, float multiplier) {
int64_t t0 = ggml_time_ms();
std::string st_file_path = path_join(lora_model_dir, lora_name + ".safetensors");
std::string ckpt_file_path = path_join(lora_model_dir, lora_name + ".ckpt");
std::string file_path;
if (file_exists(st_file_path)) {
file_path = st_file_path;
} else if (file_exists(ckpt_file_path)) {
file_path = ckpt_file_path;
} else {
LOG_WARN("can not find %s or %s for lora %s", st_file_path.c_str(), ckpt_file_path.c_str(), lora_name.c_str());
return;
}
LoraModel lora(backend, model_data_type, file_path);
if (!lora.load_from_file()) {
LOG_WARN("load lora tensors from %s failed", file_path.c_str());
return;
}
lora.multiplier = multiplier;
lora.apply(tensors, n_threads);
lora.free_params_buffer();
int64_t t1 = ggml_time_ms();
LOG_INFO("lora '%s' applied, taking %.2fs", lora_name.c_str(), (t1 - t0) * 1.0f / 1000);
}
void apply_loras(const std::unordered_map<std::string, float>& lora_state) {
if (lora_state.size() > 0 && model_data_type != GGML_TYPE_F16 && model_data_type != GGML_TYPE_F32) {
LOG_WARN("In quantized models when applying LoRA, the images have poor quality.");
}
std::unordered_map<std::string, float> lora_state_diff;
for (auto& kv : lora_state) {
const std::string& lora_name = kv.first;
float multiplier = kv.second;
if (curr_lora_state.find(lora_name) != curr_lora_state.end()) {
float curr_multiplier = curr_lora_state[lora_name];
float multiplier_diff = multiplier - curr_multiplier;
if (multiplier_diff != 0.f) {
lora_state_diff[lora_name] = multiplier_diff;
}
} else {
lora_state_diff[lora_name] = multiplier;
}
}
LOG_INFO("Attempting to apply %lu LoRAs", lora_state.size());
for (auto& kv : lora_state_diff) {
apply_lora(kv.first, kv.second);
}
curr_lora_state = lora_state;
}
std::string remove_trigger_from_prompt(ggml_context* work_ctx,
const std::string& prompt) {
auto image_tokens = cond_stage_model->convert_token_to_id(trigger_word);
GGML_ASSERT(image_tokens.size() == 1);
auto tokens_and_weights = cond_stage_model->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 cond_stage_model->decode(tokens);
}
std::tuple<ggml_tensor*, ggml_tensor*, std::vector<bool>>
get_learned_condition_with_trigger(ggml_context* work_ctx,
const std::string& text,
int clip_skip,
int width,
int height,
int num_input_imgs,
bool force_zero_embeddings = false) {
auto image_tokens = cond_stage_model->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 = cond_stage_model->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, tokens, weights, clip_skip, width, height, force_zero_embeddings);
return std::make_tuple(cond.first, cond.second, clsm);
}
ggml_tensor* id_encoder(ggml_context* work_ctx,
ggml_tensor* init_img,
ggml_tensor* prompts_embeds,
std::vector<bool>& class_tokens_mask) {
ggml_tensor* res = NULL;
pmid_model->compute(n_threads, init_img, prompts_embeds, class_tokens_mask, &res, work_ctx);
return res;
}
std::pair<ggml_tensor*, ggml_tensor*> get_learned_condition(ggml_context* work_ctx,
const std::string& text,
int clip_skip,
int width,
int height,
bool force_zero_embeddings = false) {
auto tokens_and_weights = cond_stage_model->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, tokens, weights, clip_skip, width, height, force_zero_embeddings);
}
std::pair<ggml_tensor*, ggml_tensor*> get_learned_condition_common(ggml_context* work_ctx,
std::vector<int>& tokens,
std::vector<float>& weights,
int clip_skip,
int width,
int height,
bool force_zero_embeddings = false) {
cond_stage_model->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]
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 (version == VERSION_XL) {
auto it = std::find(chunk_tokens.begin(), chunk_tokens.end(), 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");
}
cond_stage_model->compute(n_threads, input_ids, input_ids2, max_token_idx, false, &chunk_hidden_states, work_ctx);
if (version == VERSION_XL && chunk_idx == 0) {
cond_stage_model->compute(n_threads, input_ids, input_ids2, max_token_idx, true, &pooled, work_ctx);
}
// if (pooled != NULL) {
// print_ggml_tensor(chunk_hidden_states);
// print_ggml_tensor(pooled);
// }
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 (version == VERSION_XL) {
int out_dim = 256;
vec = ggml_new_tensor_1d(work_ctx, GGML_TYPE_F32, diffusion_model->unet.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 {hidden_states, vec};
}
std::tuple<ggml_tensor*, ggml_tensor*, ggml_tensor*> get_svd_condition(ggml_context* work_ctx,
sd_image_t init_image,
int width,
int height,
int fps = 6,
int motion_bucket_id = 127,
float augmentation_level = 0.f,
bool force_zero_embeddings = false) {
// c_crossattn
int64_t t0 = ggml_time_ms();
struct ggml_tensor* c_crossattn = NULL;
{
if (force_zero_embeddings) {
c_crossattn = ggml_new_tensor_1d(work_ctx, GGML_TYPE_F32, clip_vision->vision_model.projection_dim);
ggml_set_f32(c_crossattn, 0.f);
} else {
sd_image_f32_t image = sd_image_t_to_sd_image_f32_t(init_image);
sd_image_f32_t resized_image = clip_preprocess(image, clip_vision->vision_model.image_size);
free(image.data);
image.data = NULL;
ggml_tensor* pixel_values = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, resized_image.width, resized_image.height, 3, 1);
sd_image_f32_to_tensor(resized_image.data, pixel_values, false);
free(resized_image.data);
resized_image.data = NULL;
// print_ggml_tensor(pixel_values);
clip_vision->compute(n_threads, pixel_values, &c_crossattn, work_ctx);
// print_ggml_tensor(c_crossattn);
}
}
// c_concat
struct ggml_tensor* c_concat = NULL;
{
if (force_zero_embeddings) {
c_concat = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, width / 8, height / 8, 4, 1);
ggml_set_f32(c_concat, 0.f);
} else {
ggml_tensor* init_img = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, width, height, 3, 1);
if (width != init_image.width || height != init_image.height) {
sd_image_f32_t image = sd_image_t_to_sd_image_f32_t(init_image);
sd_image_f32_t resized_image = resize_sd_image_f32_t(image, width, height);
free(image.data);
image.data = NULL;
sd_image_f32_to_tensor(resized_image.data, init_img, false);
free(resized_image.data);
resized_image.data = NULL;
} else {
sd_image_to_tensor(init_image.data, init_img);
}
if (augmentation_level > 0.f) {
struct ggml_tensor* noise = ggml_dup_tensor(work_ctx, init_img);
ggml_tensor_set_f32_randn(noise, rng);
// encode_pixels += torch.randn_like(pixels) * augmentation_level
ggml_tensor_scale(noise, augmentation_level);
ggml_tensor_add(init_img, noise);
}
print_ggml_tensor(init_img);
ggml_tensor* moments = encode_first_stage(work_ctx, init_img);
print_ggml_tensor(moments);
c_concat = get_first_stage_encoding(work_ctx, moments);
}
print_ggml_tensor(c_concat);
}
// y
struct ggml_tensor* y = NULL;
{
y = ggml_new_tensor_1d(work_ctx, GGML_TYPE_F32, diffusion_model->unet.adm_in_channels);
int out_dim = 256;
int fps_id = fps - 1;
std::vector<float> timesteps = {(float)fps_id, (float)motion_bucket_id, augmentation_level};
set_timestep_embedding(timesteps, y, out_dim);
print_ggml_tensor(y);
}
int64_t t1 = ggml_time_ms();
LOG_DEBUG("computing svd condition graph completed, taking %" PRId64 " ms", t1 - t0);
return {c_crossattn, c_concat, y};
}
ggml_tensor* sample(ggml_context* work_ctx,
ggml_tensor* x_t,
ggml_tensor* noise,
ggml_tensor* c,
ggml_tensor* c_concat,
ggml_tensor* c_vector,
ggml_tensor* uc,
ggml_tensor* uc_concat,
ggml_tensor* uc_vector,
ggml_tensor* control_hint,
float control_strength,
float min_cfg,
float cfg_scale,
sample_method_t method,
const std::vector<float>& sigmas,
int start_merge_step,
ggml_tensor* c_id,
ggml_tensor* c_vec_id) {
size_t steps = sigmas.size() - 1;
// x_t = load_tensor_from_file(work_ctx, "./rand0.bin");
// print_ggml_tensor(x_t);
struct ggml_tensor* x = ggml_dup_tensor(work_ctx, x_t);
copy_ggml_tensor(x, x_t);
struct ggml_tensor* noised_input = ggml_dup_tensor(work_ctx, x_t);
bool has_unconditioned = cfg_scale != 1.0 && uc != NULL;
if (noise == NULL) {
// x = x * sigmas[0]
ggml_tensor_scale(x, sigmas[0]);
} else {
// xi = x + noise * sigma_sched[0]
ggml_tensor_scale(noise, sigmas[0]);
ggml_tensor_add(x, noise);
}
// denoise wrapper
struct ggml_tensor* out_cond = ggml_dup_tensor(work_ctx, x);
struct ggml_tensor* out_uncond = NULL;
if (has_unconditioned) {
out_uncond = ggml_dup_tensor(work_ctx, x);
}
struct ggml_tensor* denoised = ggml_dup_tensor(work_ctx, x);
auto denoise = [&](ggml_tensor* input, float sigma, int step) {
if (step == 1) {
pretty_progress(0, (int)steps, 0);
}
int64_t t0 = ggml_time_us();
float c_skip = 1.0f;
float c_out = 1.0f;
float c_in = 1.0f;
std::vector<float> scaling = denoiser->get_scalings(sigma);
if (scaling.size() == 3) { // CompVisVDenoiser
c_skip = scaling[0];
c_out = scaling[1];
c_in = scaling[2];
} else { // CompVisDenoiser
c_out = scaling[0];
c_in = scaling[1];
}
float t = denoiser->schedule->sigma_to_t(sigma);
std::vector<float> timesteps_vec(x->ne[3], t); // [N, ]
auto timesteps = vector_to_ggml_tensor(work_ctx, timesteps_vec);
copy_ggml_tensor(noised_input, input);
// noised_input = noised_input * c_in
ggml_tensor_scale(noised_input, c_in);
std::vector<struct ggml_tensor*> controls;
if (control_hint != NULL) {
control_net->compute(n_threads, noised_input, control_hint, timesteps, c, c_vector);
controls = control_net->controls;
// print_ggml_tensor(controls[12]);
// GGML_ASSERT(0);
}
if (start_merge_step == -1 || step <= start_merge_step) {
// cond
diffusion_model->compute(n_threads,
noised_input,
timesteps,
c,
c_concat,
c_vector,
-1,
controls,
control_strength,
&out_cond);
} else {
diffusion_model->compute(n_threads,
noised_input,
timesteps,
c_id,
c_concat,
c_vec_id,
-1,
controls,
control_strength,
&out_cond);
}
float* negative_data = NULL;
if (has_unconditioned) {
// uncond
if (control_hint != NULL) {
control_net->compute(n_threads, noised_input, control_hint, timesteps, uc, uc_vector);
controls = control_net->controls;
}
diffusion_model->compute(n_threads,
noised_input,
timesteps,
uc,
uc_concat,
uc_vector,
-1,
controls,
control_strength,
&out_uncond);
negative_data = (float*)out_uncond->data;
}
float* vec_denoised = (float*)denoised->data;
float* vec_input = (float*)input->data;
float* positive_data = (float*)out_cond->data;
int ne_elements = (int)ggml_nelements(denoised);
for (int i = 0; i < ne_elements; i++) {
float latent_result = positive_data[i];
if (has_unconditioned) {
// out_uncond + cfg_scale * (out_cond - out_uncond)
int64_t ne3 = out_cond->ne[3];
if (min_cfg != cfg_scale && ne3 != 1) {
int64_t i3 = i / out_cond->ne[0] * out_cond->ne[1] * out_cond->ne[2];
float scale = min_cfg + (cfg_scale - min_cfg) * (i3 * 1.0f / ne3);
} else {
latent_result = negative_data[i] + cfg_scale * (positive_data[i] - negative_data[i]);
}
}
// v = latent_result, eps = latent_result
// denoised = (v * c_out + input * c_skip) or (input + eps * c_out)
vec_denoised[i] = latent_result * c_out + vec_input[i] * c_skip;
}
int64_t t1 = ggml_time_us();
if (step > 0) {
pretty_progress(step, (int)steps, (t1 - t0) / 1000000.f);
// LOG_INFO("step %d sampling completed taking %.2fs", step, (t1 - t0) * 1.0f / 1000000);
}
};
// sample_euler_ancestral
switch (method) {
case EULER_A: {
struct ggml_tensor* noise = ggml_dup_tensor(work_ctx, x);
struct ggml_tensor* d = ggml_dup_tensor(work_ctx, x);
for (int i = 0; i < steps; i++) {
float sigma = sigmas[i];
// denoise
denoise(x, sigma, i + 1);
// d = (x - denoised) / sigma