upstream-archive/stable-diffusion.cpp
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sync: update ggml (#180)

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leejet
2024-02-25 21:11:01 +08:00
committed by GitHub
parent 193fb620b1
commit 730585d515
9 changed files with 197 additions and 182 deletions
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34
stable-diffusion.cpp
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@@ -363,9 +363,10 @@ public:
struct ggml_tensor* c = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, 1024, 2, 1, 1);
ggml_set_f32(c, 0.5);
std::vector<float> timesteps = {999.f}; // [N, ]
int64_t t0 = ggml_time_ms();
struct ggml_tensor* out = ggml_dup_tensor(work_ctx, x_t);
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();
@@ -456,9 +457,29 @@ public:
int64_t t0 = ggml_time_ms();
struct ggml_tensor* hidden_states = NULL; // [N, n_token, hidden_size]
struct ggml_tensor* pooled = NULL;
cond_stage_model->compute(n_threads, tokens, false, &hidden_states, work_ctx);
auto input_ids = vector_to_ggml_tensor_i32(work_ctx, tokens);
struct ggml_tensor* input_ids2 = NULL;
size_t max_token_idx = 0;
if (version == VERSION_XL) {
cond_stage_model->compute(n_threads, tokens, true, &pooled, work_ctx);
auto it = std::find(tokens.begin(), tokens.end(), EOS_TOKEN_ID);
if (it != tokens.end()) {
std::fill(std::next(it), tokens.end(), 0);
}
max_token_idx = std::min<size_t>(std::distance(tokens.begin(), it), tokens.size() - 1);
input_ids2 = vector_to_ggml_tensor_i32(work_ctx, tokens);
// for (int i = 0; i < tokens.size(); i++) {
// printf("%d ", tokens[i]);
// }
// printf("\n");
}
cond_stage_model->compute(n_threads, input_ids, input_ids2, max_token_idx, false, &hidden_states, work_ctx);
if (version == VERSION_XL) {
cond_stage_model->compute(n_threads, input_ids, input_ids2, max_token_idx, true, &pooled, work_ctx);
}
// if (pooled != NULL) {
// print_ggml_tensor(hidden_states);
@@ -675,7 +696,8 @@ public:
}
float t = denoiser->schedule->sigma_to_t(sigma);
std::vector<float> timesteps(x->ne[3], t); // [N, ]
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