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

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leejet authored and GitHub committed 2024-02-25 21:11:01 +08:00
1 parent 193fb620b1
commit 730585d515
9 files changed
+197 -182
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control.hpp
+18 -18
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@@ -166,7 +166,6 @@ public:
struct ggml_tensor* resblock_forward(std::string name,
struct ggml_context* ctx,
struct ggml_allocr* allocr,
struct ggml_tensor* x,
struct ggml_tensor* emb) {
auto block = std::dynamic_pointer_cast<ResBlock>(blocks[name]);
@@ -175,7 +174,6 @@ public:
struct ggml_tensor* attention_layer_forward(std::string name,
struct ggml_context* ctx,
struct ggml_allocr* allocr,
struct ggml_tensor* x,
struct ggml_tensor* context) {
auto block = std::dynamic_pointer_cast<SpatialTransformer>(blocks[name]);
@@ -201,11 +199,10 @@ public:
}
std::vector<struct ggml_tensor*> forward(struct ggml_context* ctx,
struct ggml_allocr* allocr,
struct ggml_tensor* x,
struct ggml_tensor* hint,
struct ggml_tensor* guided_hint,
std::vector<float> timesteps,
struct ggml_tensor* timesteps,
struct ggml_tensor* context,
struct ggml_tensor* y = NULL) {
// x: [N, in_channels, h, w] or [N, in_channels/2, h, w]
@@ -231,7 +228,7 @@ public:
auto middle_block_out = std::dynamic_pointer_cast<Conv2d>(blocks["middle_block_out.0"]);
auto t_emb = new_timestep_embedding(ctx, allocr, timesteps, model_channels); // [N, model_channels]
auto t_emb = ggml_nn_timestep_embedding(ctx, timesteps, model_channels); // [N, model_channels]
auto emb = time_embed_0->forward(ctx, t_emb);
emb = ggml_silu_inplace(ctx, emb);
@@ -272,10 +269,10 @@ public:
for (int j = 0; j < num_res_blocks; j++) {
input_block_idx += 1;
std::string name = "input_blocks." + std::to_string(input_block_idx) + ".0";
h = resblock_forward(name, ctx, allocr, h, emb); // [N, mult*model_channels, h, w]
h = resblock_forward(name, ctx, h, emb); // [N, mult*model_channels, h, w]
if (std::find(attention_resolutions.begin(), attention_resolutions.end(), ds) != attention_resolutions.end()) {
std::string name = "input_blocks." + std::to_string(input_block_idx) + ".1";
h = attention_layer_forward(name, ctx, allocr, h, context); // [N, mult*model_channels, h, w]
h = attention_layer_forward(name, ctx, h, context); // [N, mult*model_channels, h, w]
}
auto zero_conv = std::dynamic_pointer_cast<Conv2d>(blocks["zero_convs." + std::to_string(input_block_idx) + ".0"]);
@@ -299,9 +296,9 @@ public:
// [N, 4*model_channels, h/8, w/8]
// middle_block
h = resblock_forward("middle_block.0", ctx, allocr, h, emb); // [N, 4*model_channels, h/8, w/8]
h = attention_layer_forward("middle_block.1", ctx, allocr, h, context); // [N, 4*model_channels, h/8, w/8]
h = resblock_forward("middle_block.2", ctx, allocr, h, emb); // [N, 4*model_channels, h/8, w/8]
h = resblock_forward("middle_block.0", ctx, h, emb); // [N, 4*model_channels, h/8, w/8]
h = attention_layer_forward("middle_block.1", ctx, h, context); // [N, 4*model_channels, h/8, w/8]
h = resblock_forward("middle_block.2", ctx, h, emb); // [N, 4*model_channels, h/8, w/8]
// out
outs.push_back(middle_block_out->forward(ctx, h));
@@ -386,18 +383,22 @@ struct ControlNet : public GGMLModule {
struct ggml_cgraph* build_graph(struct ggml_tensor* x,
struct ggml_tensor* hint,
std::vector<float> timesteps,
struct ggml_tensor* timesteps,
struct ggml_tensor* context,
struct ggml_tensor* y = NULL) {
struct ggml_cgraph* gf = ggml_new_graph_custom(compute_ctx, CONTROL_NET_GRAPH_SIZE, false);
x = to_backend(x);
hint = to_backend(hint);
context = to_backend(context);
y = to_backend(y);
x = to_backend(x);
if (guided_hint_cached) {
hint = NULL;
} else {
hint = to_backend(hint);
}
context = to_backend(context);
y = to_backend(y);
timesteps = to_backend(timesteps);
auto outs = control_net.forward(compute_ctx,
compute_allocr,
x,
hint,
guided_hint_cached ? guided_hint : NULL,
@@ -420,7 +421,7 @@ struct ControlNet : public GGMLModule {
void compute(int n_threads,
struct ggml_tensor* x,
struct ggml_tensor* hint,
std::vector<float> timesteps,
struct ggml_tensor* timesteps,
struct ggml_tensor* context,
struct ggml_tensor* y,
struct ggml_tensor** output = NULL,
@@ -434,7 +435,6 @@ struct ControlNet : public GGMLModule {
};
GGMLModule::compute(get_graph, n_threads, false, output, output_ctx);
guided_hint_cached = true;
}