DarthAffe/stable-diffusion.cpp
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fix: resolve MSVC narrowing conversion warnings (#1969)

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leejet 2026-09-13 23:45:54 +08:00 committed by GitHub
parent ca37fad89a
commit 5a5400bf0c
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4 changed files with 19 additions and 19 deletions
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2
src/model/diffusion/sensenova_u1.h
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@ -146,7 +146,7 @@ namespace SenseNovaU1 {
auto x = ggml_ext_timestep_embedding(ctx->ggml_ctx,
timesteps,
static_cast<int>(frequency_embedding_size),
10000.f,
10000,
1.f);
x = mlp_0->forward(ctx, x);
x = ggml_silu_inplace(ctx->ggml_ctx, x);

30
src/model/diffusion/wan.hpp
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@ -20,22 +20,22 @@ namespace WAN {
constexpr int WAN_GRAPH_SIZE = 10240;
struct WanConfig {
std::string model_type = "t2v";
std::tuple<int, int, int> patch_size = {1, 2, 2};
int64_t text_len = 512;
int64_t in_dim = 16;
int64_t dim = 2048;
int64_t ffn_dim = 8192;
int freq_dim = 256;
int64_t text_dim = 4096;
int64_t out_dim = 16;
int64_t num_heads = 16;
int num_layers = 32;
int vace_layers = 0;
int64_t vace_in_dim = 96;
std::map<int, int> vace_layers_mapping = {};
std::string model_type = "t2v";
std::tuple<int, int, int> patch_size = {1, 2, 2};
int64_t text_len = 512;
int64_t in_dim = 16;
int64_t dim = 2048;
int64_t ffn_dim = 8192;
int freq_dim = 256;
int64_t text_dim = 4096;
int64_t out_dim = 16;
int64_t num_heads = 16;
int num_layers = 32;
int vace_layers = 0;
int64_t vace_in_dim = 96;
std::map<int, int> vace_layers_mapping = {};
int64_t audio_dim = 1024;
int num_audio_token = 4; // excludes the learned padding token
int num_audio_token = 4; // excludes the learned padding token
std::vector<int> audio_inject_layers = {};
std::map<int, int> audio_inject_mapping = {}; // block index -> injector index
std::string adain_mode = "attn_norm";

2
src/model/vae/tae.hpp
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@ -564,7 +564,7 @@ public:
int64_t chunk_frames = 5 * decoder->t_upscale;
int64_t pad = (chunk_frames - (num_frames % chunk_frames)) % chunk_frames;
result = ggml_ext_pad_ext(ctx->ggml_ctx, ctx->backend, result, 0, 0, 0, 0, 0, 0, 0, pad, false, false);
result = ggml_ext_pad_ext(ctx->ggml_ctx, ctx->backend, result, 0, 0, 0, 0, 0, 0, 0, static_cast<int>(pad), false, false);
int64_t num_chunks = (num_frames + pad) / chunk_frames;
auto to_trim = decoder->t_upscale - 1;

4
src/runtime/denoiser.hpp
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@ -2789,9 +2789,9 @@ static sd::Tensor<float> sample_lms(denoise_cb_t model,
sd::Tensor<float> d_cur = (x - denoised) / sigma;
x += d_cur * lms_coeff[0];
if (max_order > 1) { // if max_order == 1, the history is not used (order always < 2)
int hist_size_p1 = hist.size() + 1;
int hist_size_p1 = static_cast<int>(hist.size()) + 1;
if (i) { // history does not exist at 1st step
int hist_max = hist.size() - 1;
int hist_max = static_cast<int>(hist.size()) - 1;
for (int c = 2; c <= order; c++)
x += hist[std::min(hist_max, hist_size_p1 - c + shift)] * lms_coeff[c - 1];
// max_order == 4 => hist[] index = 2, 1, 0