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https://github.com/leejet/stable-diffusion.cpp.git
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4570715727
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4570715727 | ||
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53b415f787 |
@ -926,7 +926,8 @@ int main(int argc, const char* argv[]) {
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params.style_ratio,
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params.normalize_input,
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params.input_id_images_path.c_str(),
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params.skip_layers,
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params.skip_layers.data(),
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params.skip_layers.size(),
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params.slg_scale,
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params.skip_layer_start,
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params.skip_layer_end);
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@ -991,7 +992,12 @@ int main(int argc, const char* argv[]) {
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params.control_strength,
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params.style_ratio,
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params.normalize_input,
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params.input_id_images_path.c_str());
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params.input_id_images_path.c_str(),
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params.skip_layers.data(),
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params.skip_layers.size(),
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params.slg_scale,
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params.skip_layer_start,
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params.skip_layer_end);
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}
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}
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@ -661,6 +661,30 @@ __STATIC_INLINE__ std::vector<struct ggml_tensor*> split_qkv(struct ggml_context
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return {q, k, v};
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}
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// q: [N * n_head, n_token, d_head]
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// k: [N * n_head, n_k, d_head]
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// v: [N * n_head, d_head, n_k]
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// return: [N * n_head, n_token, d_head]
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__STATIC_INLINE__ struct ggml_tensor* ggml_nn_attention(struct ggml_context* ctx,
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struct ggml_tensor* q,
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struct ggml_tensor* k,
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struct ggml_tensor* v,
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bool mask = false) {
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#if defined(SD_USE_FLASH_ATTENTION) && !defined(SD_USE_CUBLAS) && !defined(SD_USE_METAL) && !defined(SD_USE_VULKAN) && !defined(SD_USE_SYCL)
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struct ggml_tensor* kqv = ggml_flash_attn(ctx, q, k, v, false); // [N * n_head, n_token, d_head]
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#else
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float d_head = (float)q->ne[0];
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struct ggml_tensor* kq = ggml_mul_mat(ctx, k, q); // [N * n_head, n_token, n_k]
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kq = ggml_scale_inplace(ctx, kq, 1.0f / sqrt(d_head));
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if (mask) {
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kq = ggml_diag_mask_inf_inplace(ctx, kq, 0);
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}
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kq = ggml_soft_max_inplace(ctx, kq);
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struct ggml_tensor* kqv = ggml_mul_mat(ctx, v, kq); // [N * n_head, n_token, d_head]
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#endif
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return kqv;
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}
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// q: [N, L_q, C] or [N*n_head, L_q, d_head]
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// k: [N, L_k, C] or [N*n_head, L_k, d_head]
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// v: [N, L_k, C] or [N, L_k, n_head, d_head]
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@ -780,7 +780,7 @@ public:
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int start_merge_step,
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SDCondition id_cond,
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std::vector<int> skip_layers = {},
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float slg_scale = 2.5,
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float slg_scale = 0,
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float skip_layer_start = 0.01,
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float skip_layer_end = 0.2) {
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size_t steps = sigmas.size() - 1;
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@ -1162,7 +1162,7 @@ sd_image_t* generate_image(sd_ctx_t* sd_ctx,
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bool normalize_input,
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std::string input_id_images_path,
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std::vector<int> skip_layers = {},
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float slg_scale = 2.5,
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float slg_scale = 0,
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float skip_layer_start = 0.01,
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float skip_layer_end = 0.2) {
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if (seed < 0) {
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@ -1453,10 +1453,12 @@ sd_image_t* txt2img(sd_ctx_t* sd_ctx,
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float style_ratio,
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bool normalize_input,
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const char* input_id_images_path_c_str,
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std::vector<int> skip_layers,
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float slg_scale,
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float skip_layer_start,
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float skip_layer_end) {
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int* skip_layers = NULL,
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size_t skip_layers_count = 0,
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float slg_scale = 0,
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float skip_layer_start = 0.01,
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float skip_layer_end = 0.2) {
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std::vector<int> skip_layers_vec(skip_layers, skip_layers + skip_layers_count);
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LOG_DEBUG("txt2img %dx%d", width, height);
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if (sd_ctx == NULL) {
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return NULL;
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@ -1525,7 +1527,7 @@ sd_image_t* txt2img(sd_ctx_t* sd_ctx,
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style_ratio,
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normalize_input,
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input_id_images_path_c_str,
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skip_layers,
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skip_layers_vec,
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slg_scale,
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skip_layer_start,
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skip_layer_end);
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@ -1556,10 +1558,12 @@ sd_image_t* img2img(sd_ctx_t* sd_ctx,
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float style_ratio,
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bool normalize_input,
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const char* input_id_images_path_c_str,
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std::vector<int> skip_layers,
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float slg_scale,
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float skip_layer_start,
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float skip_layer_end) {
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int* skip_layers = NULL,
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size_t skip_layers_count = 0,
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float slg_scale = 0,
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float skip_layer_start = 0.01,
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float skip_layer_end = 0.2) {
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std::vector<int> skip_layers_vec(skip_layers, skip_layers + skip_layers_count);
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LOG_DEBUG("img2img %dx%d", width, height);
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if (sd_ctx == NULL) {
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return NULL;
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@ -1634,7 +1638,7 @@ sd_image_t* img2img(sd_ctx_t* sd_ctx,
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style_ratio,
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normalize_input,
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input_id_images_path_c_str,
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skip_layers,
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skip_layers_vec,
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slg_scale,
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skip_layer_start,
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skip_layer_end);
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@ -166,10 +166,11 @@ SD_API sd_image_t* txt2img(sd_ctx_t* sd_ctx,
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float style_strength,
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bool normalize_input,
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const char* input_id_images_path,
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std::vector<int> skip_layers = {},
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float slg_scale = 2.5,
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float skip_layer_start = 0.01,
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float skip_layer_end = 0.2);
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int* skip_layers,
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size_t skip_layers_count,
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float slg_scale,
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float skip_layer_start,
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float skip_layer_end);
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SD_API sd_image_t* img2img(sd_ctx_t* sd_ctx,
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sd_image_t init_image,
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@ -190,10 +191,11 @@ SD_API sd_image_t* img2img(sd_ctx_t* sd_ctx,
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float style_strength,
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bool normalize_input,
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const char* input_id_images_path,
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std::vector<int> skip_layers = {},
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float slg_scale = 2.5,
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float skip_layer_start = 0.01,
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float skip_layer_end = 0.2);
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int* skip_layers,
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size_t skip_layers_count,
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float slg_scale,
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float skip_layer_start,
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float skip_layer_end);
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SD_API sd_image_t* img2vid(sd_ctx_t* sd_ctx,
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sd_image_t init_image,
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8
vae.hpp
8
vae.hpp
@ -99,12 +99,10 @@ public:
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k = ggml_cont(ctx, ggml_permute(ctx, k, 1, 2, 0, 3)); // [N, h, w, in_channels]
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k = ggml_reshape_3d(ctx, k, c, h * w, n); // [N, h * w, in_channels]
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auto v = v_proj->forward(ctx, h_); // [N, in_channels, h, w]
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v = ggml_cont(ctx, ggml_permute(ctx, v, 1, 2, 0, 3)); // [N, h, w, in_channels]
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v = ggml_reshape_3d(ctx, v, c, h * w, n); // [N, h * w, in_channels]
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auto v = v_proj->forward(ctx, h_); // [N, in_channels, h, w]
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v = ggml_reshape_3d(ctx, v, h * w, c, n); // [N, in_channels, h * w]
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// h_ = ggml_nn_attention(ctx, q, k, v, false); // [N, h * w, in_channels]
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h_ = ggml_nn_attention_ext(ctx, q, k, v, 1, nullptr, false, true, false);
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h_ = ggml_nn_attention(ctx, q, k, v, false); // [N, h * w, in_channels]
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h_ = ggml_cont(ctx, ggml_permute(ctx, h_, 1, 0, 2, 3)); // [N, in_channels, h * w]
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h_ = ggml_reshape_4d(ctx, h_, w, h, c, n); // [N, in_channels, h, w]
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