DarthAffe/stable-diffusion.cpp
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style: remove redundant struct qualifiers for consistent C/C++ type usage (#1349)

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leejet authored and GitHub committed 2026-03-16 22:17:22 +08:00
1 parent 997bb11fb6
commit 84cbd88df1
32 files changed
+1692 -1693
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src/qwen_image.hpp
+53 -53
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@@ -26,9 +26,9 @@ namespace Qwen {
blocks["linear_2"] = std::shared_ptr<GGMLBlock>(new Linear(time_embed_dim, out_dim, sample_proj_bias));
}
struct ggml_tensor* forward(GGMLRunnerContext* ctx,
struct ggml_tensor* sample,
struct ggml_tensor* condition = nullptr) {
ggml_tensor* forward(GGMLRunnerContext* ctx,
ggml_tensor* sample,
ggml_tensor* condition = nullptr) {
if (condition != nullptr) {
auto cond_proj = std::dynamic_pointer_cast<Linear>(blocks["cond_proj"]);
sample = ggml_add(ctx->ggml_ctx, sample, cond_proj->forward(ctx, condition));
@@ -49,8 +49,8 @@ namespace Qwen {
blocks["timestep_embedder"] = std::shared_ptr<GGMLBlock>(new TimestepEmbedding(256, embedding_dim));
}
struct ggml_tensor* forward(GGMLRunnerContext* ctx,
struct ggml_tensor* timesteps) {
ggml_tensor* forward(GGMLRunnerContext* ctx,
ggml_tensor* timesteps) {
// timesteps: [N,]
// return: [N, embedding_dim]
auto timestep_embedder = std::dynamic_pointer_cast<TimestepEmbedding>(blocks["timestep_embedder"]);
@@ -107,10 +107,10 @@ namespace Qwen {
}
std::pair<ggml_tensor*, ggml_tensor*> forward(GGMLRunnerContext* ctx,
struct ggml_tensor* img,
struct ggml_tensor* txt,
struct ggml_tensor* pe,
struct ggml_tensor* mask = nullptr) {
ggml_tensor* img,
ggml_tensor* txt,
ggml_tensor* pe,
ggml_tensor* mask = nullptr) {
// img: [N, n_img_token, hidden_size]
// txt: [N, n_txt_token, hidden_size]
// pe: [n_img_token + n_txt_token, d_head/2, 2, 2]
@@ -249,11 +249,11 @@ namespace Qwen {
}
virtual std::pair<ggml_tensor*, ggml_tensor*> forward(GGMLRunnerContext* ctx,
struct ggml_tensor* img,
struct ggml_tensor* txt,
struct ggml_tensor* t_emb,
struct ggml_tensor* pe,
struct ggml_tensor* modulate_index = nullptr) {
ggml_tensor* img,
ggml_tensor* txt,
ggml_tensor* t_emb,
ggml_tensor* pe,
ggml_tensor* modulate_index = nullptr) {
// img: [N, n_img_token, hidden_size]
// txt: [N, n_txt_token, hidden_size]
// pe: [n_img_token + n_txt_token, d_head/2, 2, 2]
@@ -325,9 +325,9 @@ namespace Qwen {
blocks["linear"] = std::shared_ptr<GGMLBlock>(new Linear(conditioning_embedding_dim, embedding_dim * 2, bias));
}
struct ggml_tensor* forward(GGMLRunnerContext* ctx,
struct ggml_tensor* x,
struct ggml_tensor* c) {
ggml_tensor* forward(GGMLRunnerContext* ctx,
ggml_tensor* x,
ggml_tensor* c) {
// x: [N, n_token, hidden_size]
// c: [N, hidden_size]
// return: [N, n_token, patch_size * patch_size * out_channels]
@@ -389,12 +389,12 @@ namespace Qwen {
blocks["proj_out"] = std::shared_ptr<GGMLBlock>(new Linear(inner_dim, params.patch_size * params.patch_size * params.out_channels));
}
struct ggml_tensor* forward_orig(GGMLRunnerContext* ctx,
struct ggml_tensor* x,
struct ggml_tensor* timestep,
struct ggml_tensor* context,
struct ggml_tensor* pe,
struct ggml_tensor* modulate_index = nullptr) {
ggml_tensor* forward_orig(GGMLRunnerContext* ctx,
ggml_tensor* x,
ggml_tensor* timestep,
ggml_tensor* context,
ggml_tensor* pe,
ggml_tensor* modulate_index = nullptr) {
auto time_text_embed = std::dynamic_pointer_cast<QwenTimestepProjEmbeddings>(blocks["time_text_embed"]);
auto txt_norm = std::dynamic_pointer_cast<RMSNorm>(blocks["txt_norm"]);
auto img_in = std::dynamic_pointer_cast<Linear>(blocks["img_in"]);
@@ -429,13 +429,13 @@ namespace Qwen {
return img;
}
struct ggml_tensor* forward(GGMLRunnerContext* ctx,
struct ggml_tensor* x,
struct ggml_tensor* timestep,
struct ggml_tensor* context,
struct ggml_tensor* pe,
std::vector<ggml_tensor*> ref_latents = {},
struct ggml_tensor* modulate_index = nullptr) {
ggml_tensor* forward(GGMLRunnerContext* ctx,
ggml_tensor* x,
ggml_tensor* timestep,
ggml_tensor* context,
ggml_tensor* pe,
std::vector<ggml_tensor*> ref_latents = {},
ggml_tensor* modulate_index = nullptr) {
// Forward pass of DiT.
// x: [N, C, H, W]
// timestep: [N,]
@@ -521,17 +521,17 @@ namespace Qwen {
return "qwen_image";
}
void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string prefix) {
qwen_image.get_param_tensors(tensors, prefix);
}
struct ggml_cgraph* build_graph(struct ggml_tensor* x,
struct ggml_tensor* timesteps,
struct ggml_tensor* context,
std::vector<ggml_tensor*> ref_latents = {},
bool increase_ref_index = false) {
ggml_cgraph* build_graph(ggml_tensor* x,
ggml_tensor* timesteps,
ggml_tensor* context,
std::vector<ggml_tensor*> ref_latents = {},
bool increase_ref_index = false) {
GGML_ASSERT(x->ne[3] == 1);
struct ggml_cgraph* gf = new_graph_custom(QWEN_IMAGE_GRAPH_SIZE);
ggml_cgraph* gf = new_graph_custom(QWEN_IMAGE_GRAPH_SIZE);
x = to_backend(x);
context = to_backend(context);
@@ -587,13 +587,13 @@ namespace Qwen {
auto runner_ctx = get_context();
struct ggml_tensor* out = qwen_image.forward(&runner_ctx,
x,
timesteps,
context,
pe,
ref_latents,
modulate_index);
ggml_tensor* out = qwen_image.forward(&runner_ctx,
x,
timesteps,
context,
pe,
ref_latents,
modulate_index);
ggml_build_forward_expand(gf, out);
@@ -601,17 +601,17 @@ namespace Qwen {
}
bool compute(int n_threads,
struct ggml_tensor* x,
struct ggml_tensor* timesteps,
struct ggml_tensor* context,
ggml_tensor* x,
ggml_tensor* timesteps,
ggml_tensor* context,
std::vector<ggml_tensor*> ref_latents = {},
bool increase_ref_index = false,
struct ggml_tensor** output = nullptr,
struct ggml_context* output_ctx = nullptr) {
ggml_tensor** output = nullptr,
ggml_context* output_ctx = nullptr) {
// x: [N, in_channels, h, w]
// timesteps: [N, ]
// context: [N, max_position, hidden_size]
auto get_graph = [&]() -> struct ggml_cgraph* {
auto get_graph = [&]() -> ggml_cgraph* {
return build_graph(x, timesteps, context, ref_latents, increase_ref_index);
};
@@ -619,12 +619,12 @@ namespace Qwen {
}
void test() {
struct ggml_init_params params;
ggml_init_params params;
params.mem_size = static_cast<size_t>(1024 * 1024) * 1024; // 1GB
params.mem_buffer = nullptr;
params.no_alloc = false;
struct ggml_context* work_ctx = ggml_init(params);
ggml_context* work_ctx = ggml_init(params);
GGML_ASSERT(work_ctx != nullptr);
{
@@ -641,7 +641,7 @@ namespace Qwen {
auto context = load_tensor_from_file(work_ctx, "./qwen_image_context.bin");
print_ggml_tensor(context);
struct ggml_tensor* out = nullptr;
ggml_tensor* out = nullptr;
int64_t t0 = ggml_time_ms();
compute(8, x, timesteps, context, {}, false, &out, work_ctx);