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/common/block.hpp
+6 -8
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@@ -32,7 +32,7 @@ public:
if (vae_downsample) {
auto conv = std::dynamic_pointer_cast<Conv2d>(blocks["conv"]);
x = ggml_ext_pad(ctx->ggml_ctx, x, 1, 1, 0, 0, ctx->circular_x_enabled, ctx->circular_y_enabled);
x = ggml_ext_pad(ctx, x, 1, 1, 0, 0, ctx->circular_x_enabled, ctx->circular_y_enabled);
x = conv->forward(ctx, x);
} else {
auto conv = std::dynamic_pointer_cast<Conv2d>(blocks["op"]);
@@ -220,7 +220,7 @@ public:
auto proj = std::dynamic_pointer_cast<Linear>(blocks["proj"]);
x = proj->forward(ctx, x);
x = ggml_ext_gelu(ctx->ggml_ctx, x, true);
x = ggml_ext_gelu(ctx, x, true);
return x;
}
};
@@ -250,7 +250,7 @@ public:
auto fc2 = std::dynamic_pointer_cast<Linear>(blocks["fc2"]);
x = fc1->forward(ctx, x);
x = ggml_ext_gelu(ctx->ggml_ctx, x, true);
x = ggml_ext_gelu(ctx, x, true);
x = fc2->forward(ctx, x);
return x;
}
@@ -293,9 +293,7 @@ public:
auto net_0 = std::dynamic_pointer_cast<UnaryBlock>(blocks["net.0"]);
auto net_2 = std::dynamic_pointer_cast<Linear>(blocks["net.2"]);
if (sd_backend_is(ctx->backend, "Vulkan") || sd_backend_is(ctx->backend, "ROCm")) {
net_2->set_force_prec_f32(true);
}
net_2->set_precision_flags(GGML_PRECISION_F32_VULKAN | GGML_PRECISION_F32_ROCM);
x = net_0->forward(ctx, x); // [ne3, ne2, ne1, inner_dim]
x = net_2->forward(ctx, x); // [ne3, ne2, ne1, dim_out]
@@ -597,7 +595,7 @@ public:
x = ggml_add(ctx->ggml_ctx, x, attn->forward(ctx, ln_1->forward(ctx, x)));
auto h = mlp_c_fc->forward(ctx, ln_2->forward(ctx, x));
h = ggml_ext_gelu_quick(ctx->ggml_ctx, h);
h = ggml_ext_gelu_quick(ctx, h);
h = mlp_c_proj->forward(ctx, h);
return ggml_add(ctx->ggml_ctx, x, h);
}
@@ -614,7 +612,7 @@ protected:
ggml_tensor* get_alpha(GGMLRunnerContext* ctx) {
// image_only_indicator is always tensor([0.]) and since mix_factor.shape is [1,]
// so learned_with_images is same as learned
auto mix_factor = ggml_ext_cast_f32(ctx->ggml_ctx, ctx->backend, params["mix_factor"]);
auto mix_factor = ggml_ext_cast_f32(ctx, params["mix_factor"]);
return ggml_sigmoid(ctx->ggml_ctx, mix_factor);
}