DarthAffe/stable-diffusion.cpp
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refactor: unify ggml operation helpers around runner context (#2314)

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leejet authored and GitHub committed 2026-11-10 01:58:04 +08:00
1 parent 73fef08224
commit a3c49b13be
76 files changed
+1983 -1831
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src/model/diffusion/lens.hpp
+9 -9
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@@ -87,7 +87,7 @@ namespace Lens {
ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* timesteps) {
auto timestep_embedder = std::dynamic_pointer_cast<Qwen::TimestepEmbedding>(blocks["timestep_embedder"]);
auto timesteps_proj = ggml_ext_timestep_embedding(ctx->ggml_ctx, timesteps, 256, 10000, 1000.f);
auto timesteps_proj = ggml_ext_timestep_embedding(ctx, timesteps, 256, 10000, 1000.f);
return timestep_embedder->forward(ctx, timesteps_proj);
}
};
@@ -151,8 +151,8 @@ namespace Lens {
int64_t N = img->ne[2];
int64_t inner = dim_head * num_heads;
auto img_qkv_vec = split_qkv(ctx->ggml_ctx, img_qkv->forward(ctx, img));
auto txt_qkv_vec = split_qkv(ctx->ggml_ctx, txt_qkv->forward(ctx, txt));
auto img_qkv_vec = ggml_ext_split_qkv(ctx, img_qkv->forward(ctx, img));
auto txt_qkv_vec = ggml_ext_split_qkv(ctx, txt_qkv->forward(ctx, txt));
auto img_q = ggml_reshape_4d(ctx->ggml_ctx, img_qkv_vec[0], dim_head, num_heads, n_img, N);
auto img_k = ggml_reshape_4d(ctx->ggml_ctx, img_qkv_vec[1], dim_head, num_heads, n_img, N);
@@ -232,9 +232,9 @@ namespace Lens {
auto temb = ggml_silu(ctx->ggml_ctx, t_emb);
auto img_mod_params = img_mod_1->forward(ctx, temb);
auto img_mod_vec = ggml_ext_chunk(ctx->ggml_ctx, img_mod_params, 6, 0);
auto img_mod_vec = ggml_ext_chunk(ctx, img_mod_params, 6, 0);
auto txt_mod_params = txt_mod_1->forward(ctx, temb);
auto txt_mod_vec = ggml_ext_chunk(ctx->ggml_ctx, txt_mod_params, 6, 0);
auto txt_mod_vec = ggml_ext_chunk(ctx, txt_mod_params, 6, 0);
auto img_normed = img_norm1->forward(ctx, img);
auto img_modulated = Flux::modulate(ctx, img_normed, img_mod_vec[0], img_mod_vec[1]);
@@ -268,7 +268,7 @@ namespace Lens {
ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x, ggml_tensor* conditioning) {
auto linear = std::dynamic_pointer_cast<Linear>(blocks["linear"]);
auto mods = ggml_ext_chunk(ctx->ggml_ctx, linear->forward(ctx, ggml_silu(ctx->ggml_ctx, conditioning)), 2, 0);
auto mods = ggml_ext_chunk(ctx, linear->forward(ctx, ggml_silu(ctx->ggml_ctx, conditioning)), 2, 0);
auto scale = mods[0];
auto shift = mods[1];
x = ggml_norm(ctx->ggml_ctx, x, eps);
@@ -319,10 +319,10 @@ namespace Lens {
auto t_emb = time_text_embed->forward(ctx, timestep);
auto img = ggml_reshape_3d(ctx->ggml_ctx, x, W * H, C, N);
img = ggml_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx->ggml_ctx, img, 1, 0, 2, 3));
img = ggml_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx, img, 1, 0, 2, 3));
img = img_in->forward(ctx, img);
std::vector<ggml_tensor*> txt_chunks = ggml_ext_chunk(ctx->ggml_ctx, context, config.selected_layer_count, 0);
std::vector<ggml_tensor*> txt_chunks = ggml_ext_chunk(ctx, context, config.selected_layer_count, 0);
ggml_tensor* txt = nullptr;
for (int i = 0; i < config.selected_layer_count; ++i) {
auto txt_norm = std::dynamic_pointer_cast<RMSNorm>(blocks["txt_norm." + std::to_string(i)]);
@@ -346,7 +346,7 @@ namespace Lens {
img = norm_out->forward(ctx, img, t_emb);
img = proj_out->forward(ctx, img);
auto out = ggml_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx->ggml_ctx, img, 1, 0, 2, 3));
auto out = ggml_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx, img, 1, 0, 2, 3));
out = ggml_reshape_4d(ctx->ggml_ctx, out, W, H, config.patch_size * config.patch_size * config.out_channels, N);
return out;
}