upstream-archive/stable-diffusion.cpp
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refactor: centralize circular RoPE and extend image model support (#2039)

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leejet
2026-09-24 01:45:14 +08:00
committed by GitHub
parent 500ef5fa7c
commit 88411ef1e0
17 changed files with 611 additions and 564 deletions
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src/model/diffusion/anima.hpp
+35 -32
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@@ -603,34 +603,37 @@ namespace Anima {
return std::pow(extrapolation_ratio, static_cast<float>(axis_dim) / static_cast<float>(axis_dim - 2));
}
static std::vector<float> gen_anima_image_pe_vec(int bs,
int h,
int w,
int patch_size,
int theta,
const std::vector<int>& axes_dim,
float h_extrapolation_ratio,
float w_extrapolation_ratio,
float t_extrapolation_ratio,
const std::vector<ggml_tensor*>& ref_latents) {
auto ids = Rope::gen_flux_ids(h,
w,
patch_size,
bs,
static_cast<int>(axes_dim.size()),
0,
{},
ref_latents,
Rope::RefIndexMode::FIXED,
1.0f,
false);
static Rope::Embedding gen_anima_image_pe_vec(int bs,
int h,
int w,
int patch_size,
int theta,
const std::vector<int>& axes_dim,
float h_extrapolation_ratio,
float w_extrapolation_ratio,
float t_extrapolation_ratio,
const std::vector<ggml_tensor*>& ref_latents) {
Rope::Embedding result;
result.batch_size = bs;
result.ids = Rope::gen_flux_ids(h,
w,
patch_size,
bs,
static_cast<int>(axes_dim.size()),
0,
{},
ref_latents,
Rope::RefIndexMode::FIXED,
1.0f,
false, &result.positions);
std::vector<float> axis_thetas = {
static_cast<float>(theta) * calc_ntk_factor(t_extrapolation_ratio, axes_dim[0]),
static_cast<float>(theta) * calc_ntk_factor(h_extrapolation_ratio, axes_dim[1]),
static_cast<float>(theta) * calc_ntk_factor(w_extrapolation_ratio, axes_dim[2]),
};
return Rope::embed_nd(ids, bs, axis_thetas, axes_dim);
result.values = Rope::embed_nd(result.ids, bs, axis_thetas, axes_dim, result.layout, &result.frequencies);
return result;
}
ggml_cgraph* build_graph(const sd::Tensor<float>& x_tensor,
@@ -657,16 +660,16 @@ namespace Anima {
int64_t h_pad = x->ne[1] + pad_h;
int64_t w_pad = x->ne[0] + pad_w;
image_pe_vec = gen_anima_image_pe_vec(1,
static_cast<int>(h_pad),
static_cast<int>(w_pad),
static_cast<int>(config.patch_size),
config.theta,
config.axes_dim,
4.0f,
4.0f,
1.0f,
ref_latents);
image_pe_vec = finish_rope_pe(gen_anima_image_pe_vec(1,
static_cast<int>(h_pad),
static_cast<int>(w_pad),
static_cast<int>(config.patch_size),
config.theta,
config.axes_dim,
4.0f,
4.0f,
1.0f,
ref_latents));
int64_t image_pos_len = static_cast<int64_t>(image_pe_vec.size()) / (2 * 2 * (config.head_dim / 2));
auto image_pe = ggml_new_tensor_4d(compute_ctx, GGML_TYPE_F32, 2, 2, config.head_dim / 2, image_pos_len);
set_backend_tensor_data(image_pe, image_pe_vec.data());