DarthAffe/stable-diffusion.cpp
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fix: avoid Vulkan f16 repeat in LTX audio VAE (#1528)

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leejet 2026-05-19 23:15:26 +08:00 committed by GitHub
parent 0045a72b96
commit 5b0267e941
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2 changed files with 40 additions and 16 deletions
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2
src/ggml_graph_cut.cpp
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@ -16,7 +16,7 @@
namespace sd::ggml_graph_cut {
static constexpr double MAX_VRAM_BYTES_PER_GIB = 1024.0 * 1024.0 * 1024.0;
static constexpr double MAX_VRAM_BYTES_PER_GIB = 1024.0 * 1024.0 * 1024.0;
static std::string graph_cut_tensor_display_name(const ggml_tensor* tensor) {
if (tensor == nullptr) {

54
src/ltx_audio_vae.h
View File

@ -349,42 +349,66 @@ namespace LTXV {
return type == GGML_TYPE_BF16 ? GGML_TYPE_F16 : type;
}
static ggml_tensor* repeat_1d_value(ggml_context* ctx, ggml_tensor* x, int64_t count) {
GGML_ASSERT(x->ne[0] == 1);
ggml_tensor* target = ggml_new_tensor_4d(ctx, x->type, count, x->ne[1], x->ne[2], x->ne[3]);
return ggml_repeat(ctx, x, target);
static ggml_tensor* repeat_with_vulkan_f32_workaround(ggml_backend_t backend,
ggml_context* ctx,
ggml_tensor* x,
int64_t ne0,
int64_t ne1,
int64_t ne2,
int64_t ne3) {
if (x->type != GGML_TYPE_F32 &&
(x->type == GGML_TYPE_F16 || x->type == GGML_TYPE_BF16) &&
sd_backend_is(backend, "vulkan")) {
auto x_f32 = ggml_cast(ctx, x, GGML_TYPE_F32);
auto repeated = ggml_repeat_4d(ctx,
x_f32,
ne0,
ne1,
ne2,
ne3);
return ggml_cast(ctx, repeated, x->type);
}
return ggml_repeat_4d(ctx, x, ne0, ne1, ne2, ne3);
}
static ggml_tensor* replicate_pad_1d(ggml_context* ctx, ggml_tensor* x, int64_t left, int64_t right) {
static ggml_tensor* repeat_1d_value(GGMLRunnerContext* runner_ctx, ggml_tensor* x, int64_t count) {
auto ctx = runner_ctx->ggml_ctx;
GGML_ASSERT(x->ne[0] == 1);
return repeat_with_vulkan_f32_workaround(runner_ctx->backend, ctx, x, count, x->ne[1], x->ne[2], x->ne[3]);
}
static ggml_tensor* replicate_pad_1d(GGMLRunnerContext* runner_ctx, ggml_tensor* x, int64_t left, int64_t right) {
auto ctx = runner_ctx->ggml_ctx;
if (left > 0) {
auto first = ggml_ext_slice(ctx, x, 0, 0, 1);
x = ggml_concat(ctx, repeat_1d_value(ctx, first, left), x, 0);
x = ggml_concat(ctx, repeat_1d_value(runner_ctx, first, left), x, 0);
}
if (right > 0) {
auto last = ggml_ext_slice(ctx, x, 0, x->ne[0] - 1, x->ne[0]);
x = ggml_concat(ctx, x, repeat_1d_value(ctx, last, right), 0);
x = ggml_concat(ctx, x, repeat_1d_value(runner_ctx, last, right), 0);
}
return x;
}
static ggml_tensor* tile_depthwise_filter_1d(ggml_context* ctx, ggml_tensor* filter, int64_t channels) {
static ggml_tensor* tile_depthwise_filter_1d(GGMLRunnerContext* runner_ctx, ggml_tensor* filter, int64_t channels) {
auto ctx = runner_ctx->ggml_ctx;
ggml_tensor* base = filter;
if (ggml_n_dims(base) == 3) {
base = ggml_reshape_4d(ctx, base, base->ne[0], 1, 1, 1);
} else if (ggml_n_dims(base) == 1) {
base = ggml_reshape_4d(ctx, base, base->ne[0], 1, 1, 1);
}
ggml_tensor* target = ggml_new_tensor_4d(ctx, base->type, base->ne[0], 1, channels, 1);
return ggml_repeat(ctx, base, target);
return repeat_with_vulkan_f32_workaround(runner_ctx->backend, ctx, base, base->ne[0], 1, channels, 1);
}
static ggml_tensor* depthwise_conv1d(ggml_context* ctx,
static ggml_tensor* depthwise_conv1d(GGMLRunnerContext* runner_ctx,
ggml_tensor* x,
ggml_tensor* filter,
int stride,
int padding) {
auto ctx = runner_ctx->ggml_ctx;
GGML_ASSERT(x->ne[3] == 1);
auto tiled = tile_depthwise_filter_1d(ctx, filter, x->ne[1]);
auto tiled = tile_depthwise_filter_1d(runner_ctx, filter, x->ne[1]);
auto out = ggml_conv_1d_dw(ctx, tiled, x, stride, padding, 1);
return ggml_reshape_4d(ctx, out, out->ne[0], out->ne[1], 1, 1);
}
@ -654,7 +678,7 @@ namespace LTXV {
int up_pad_left = up_pad * up_ratio + (up_kernel_size - up_ratio) / 2;
int up_pad_right = up_pad * up_ratio + (up_kernel_size - up_ratio + 1) / 2;
x = replicate_pad_1d(ctx->ggml_ctx, x, up_pad, up_pad);
x = replicate_pad_1d(ctx, x, up_pad, up_pad);
x = depthwise_conv_transpose1d(ctx->ggml_ctx, x, up_filter, up_ratio);
x = ggml_ext_slice(ctx->ggml_ctx, x, 0, up_pad_left, x->ne[0] - up_pad_right);
@ -662,8 +686,8 @@ namespace LTXV {
int down_pad_left = down_kernel_size / 2 - (down_kernel_size % 2 == 0 ? 1 : 0);
int down_pad_right = down_kernel_size / 2;
x = replicate_pad_1d(ctx->ggml_ctx, x, down_pad_left, down_pad_right);
x = depthwise_conv1d(ctx->ggml_ctx, x, down_filter, down_ratio, 0);
x = replicate_pad_1d(ctx, x, down_pad_left, down_pad_right);
x = depthwise_conv1d(ctx, x, down_filter, down_ratio, 0);
return x;
}
};