DarthAffe/stable-diffusion.cpp
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leejet committed 2024-01-29 23:05:18 +08:00
1 parent 36ec16ac99
commit 349439f239
14 files changed
+200 -202
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unet.hpp
+12 -12
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@@ -495,9 +495,9 @@ struct UNetModel : public GGMLModule {
h = middle_block_1.forward(ctx0, h, context); // [N, 4*model_channels, h/8, w/8]
h = middle_block_2.forward(ctx0, h, emb); // [N, 4*model_channels, h/8, w/8]
if(control.size() > 0) {
if (control.size() > 0) {
auto cs = ggml_scale_inplace(ctx0, control[control.size() - 1], control_net_strength);
h = ggml_add(ctx0, h, cs); // middle control
h = ggml_add(ctx0, h, cs); // middle control
}
int control_offset = control.size() - 2;
@@ -507,9 +507,9 @@ struct UNetModel : public GGMLModule {
auto h_skip = hs.back();
hs.pop_back();
if(control.size() > 0) {
auto cs = ggml_scale_inplace(ctx0, control[control_offset], control_net_strength);
h_skip = ggml_add(ctx0, h_skip, cs); // control net condition
if (control.size() > 0) {
auto cs = ggml_scale_inplace(ctx0, control[control_offset], control_net_strength);
h_skip = ggml_add(ctx0, h_skip, cs); // control net condition
control_offset--;
}
@@ -542,8 +542,8 @@ struct UNetModel : public GGMLModule {
struct ggml_tensor* timesteps,
struct ggml_tensor* context,
std::vector<struct ggml_tensor*> control,
struct ggml_tensor* t_emb = NULL,
struct ggml_tensor* y = NULL,
struct ggml_tensor* t_emb = NULL,
struct ggml_tensor* y = NULL,
float control_net_strength = 1.0) {
// since we are using ggml-alloc, this buffer only needs enough space to hold the ggml_tensor and ggml_cgraph structs, but not the tensor data
static size_t buf_size = ggml_tensor_overhead() * UNET_GRAPH_SIZE + ggml_graph_overhead();
@@ -611,12 +611,12 @@ struct UNetModel : public GGMLModule {
}
// offload all controls tensors to gpu
if(control.size() > 0 && !ggml_backend_is_cpu(backend) && control[0]->backend != GGML_BACKEND_GPU) {
for(int i = 0; i < control.size(); i++) {
if (control.size() > 0 && !ggml_backend_is_cpu(backend) && control[0]->backend != GGML_BACKEND_GPU) {
for (int i = 0; i < control.size(); i++) {
ggml_tensor* cntl_t = ggml_dup_tensor(ctx0, control[i]);
control_t.push_back(cntl_t);
ggml_allocr_alloc(compute_allocr, cntl_t);
if(!ggml_allocr_is_measure(compute_allocr)) {
if (!ggml_allocr_is_measure(compute_allocr)) {
ggml_backend_tensor_copy(control[i], control_t[i]);
ggml_backend_synchronize(backend);
}
@@ -636,8 +636,8 @@ struct UNetModel : public GGMLModule {
void alloc_compute_buffer(struct ggml_tensor* x,
struct ggml_tensor* context,
std::vector<struct ggml_tensor*> control,
struct ggml_tensor* t_emb = NULL,
struct ggml_tensor* y = NULL,
struct ggml_tensor* t_emb = NULL,
struct ggml_tensor* y = NULL,
float control_net_strength = 1.0) {
auto get_graph = [&]() -> struct ggml_cgraph* {
return build_graph(x, NULL, context, control, t_emb, y, control_net_strength);