DarthAffe/stable-diffusion.cpp
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leejet 2026-08-04 22:12:27 +08:00
parent 4aafb2981e
commit dc431105b3
3 changed files with 72 additions and 72 deletions
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28
src/model/diffusion/minimax_h3.hpp
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@ -397,7 +397,7 @@ namespace MiniMaxH3 {
config.attention_head_dim,
config.qk_norm_eps);
blocks["mlp"] = std::make_shared<MLP>(config.hidden_size,
config.ffn_hidden_size);
config.ffn_hidden_size);
blocks["adaln_proj"] = std::make_shared<AdaLayerNormModulation>(config.time_embed_dim,
config.hidden_size,
6,
@ -561,7 +561,7 @@ namespace MiniMaxH3 {
1);
ggml_tensor* angles = nullptr;
for (int axis = 0; axis < 3; ++axis) {
auto pos = ggml_ext_slice(ctx->ggml_ctx, position_ids, 0, axis, axis + 1);
auto pos = ggml_ext_slice(ctx->ggml_ctx, position_ids, 0, axis, axis + 1);
auto expanded_inv = ggml_repeat_4d(ctx->ggml_ctx,
inv,
inv->ne[0],
@ -569,7 +569,7 @@ namespace MiniMaxH3 {
1,
1);
auto a = ggml_mul(ctx->ggml_ctx, expanded_inv, pos);
angles = angles == nullptr ? a : ggml_concat(ctx->ggml_ctx, angles, a, 0);
angles = angles == nullptr ? a : ggml_concat(ctx->ggml_ctx, angles, a, 0);
}
auto c = ggml_reshape_4d(ctx->ggml_ctx, ggml_cos(ctx->ggml_ctx, angles), 1, angles->ne[0], angles->ne[1], 1);
auto s = ggml_reshape_4d(ctx->ggml_ctx, ggml_sin(ctx->ggml_ctx, angles), 1, angles->ne[0], angles->ne[1], 1);
@ -756,17 +756,17 @@ namespace MiniMaxH3 {
}
static PackedSequenceLayout build_layout(int64_t text_len,
int64_t latent_t,
int64_t latent_h,
int64_t latent_w,
int64_t audio_t,
const std::vector<sd::Tensor<float>>& condition_videos,
const std::vector<sd::Tensor<float>>& condition_audios,
const sd::Tensor<int32_t>& keyframe_indices,
const std::vector<MiniMaxH3ReferenceBlock>& reference_blocks,
const sd::Tensor<int32_t>& text_tags,
float video_t,
float audio_timestep) {
int64_t latent_t,
int64_t latent_h,
int64_t latent_w,
int64_t audio_t,
const std::vector<sd::Tensor<float>>& condition_videos,
const std::vector<sd::Tensor<float>>& condition_audios,
const sd::Tensor<int32_t>& keyframe_indices,
const std::vector<MiniMaxH3ReferenceBlock>& reference_blocks,
const sd::Tensor<int32_t>& text_tags,
float video_t,
float audio_timestep) {
PackedSequenceLayout layout;
float sqrt_area = std::sqrt(static_cast<float>(latent_h * latent_w));
auto h_axis = spatial_axis(latent_h, sqrt_area);

16
src/model/vae/minimax_h3_audio_vae.hpp
View File

@ -397,14 +397,14 @@ namespace MiniMaxH3 {
ggml_mul(ctx->ggml_ctx, latent, std),
mean);
auto dec_in = std::dynamic_pointer_cast<LTXV::Conv1D>(blocks["dec_in_proj"]);
auto decoder = std::dynamic_pointer_cast<BigVGAN>(blocks["decoder"]);
int64_t streams = latent->ne[2] * latent->ne[3];
latent = ggml_reshape_3d(ctx->ggml_ctx,
latent,
latent->ne[0],
latent->ne[1],
streams);
auto dec_in = std::dynamic_pointer_cast<LTXV::Conv1D>(blocks["dec_in_proj"]);
auto decoder = std::dynamic_pointer_cast<BigVGAN>(blocks["decoder"]);
int64_t streams = latent->ne[2] * latent->ne[3];
latent = ggml_reshape_3d(ctx->ggml_ctx,
latent,
latent->ne[0],
latent->ne[1],
streams);
ggml_tensor* waveform = nullptr;
for (int64_t stream = 0; stream < streams; ++stream) {
auto stream_latent = ggml_ext_slice(ctx->ggml_ctx, latent, 2, stream, stream + 1);

100
src/model/vae/minimax_h3_vae.hpp
View File

@ -86,14 +86,14 @@ namespace MiniMaxH3VAE {
frame->ne[1],
num_channels,
batch_size);
frame = GroupNorm::forward(ctx, frame);
frame = ggml_reshape_4d(ctx->ggml_ctx,
frame,
frame->ne[0],
frame->ne[1],
1,
num_channels * batch_size);
result = result == nullptr ? frame : ggml_concat(ctx->ggml_ctx, result, frame, 2);
frame = GroupNorm::forward(ctx, frame);
frame = ggml_reshape_4d(ctx->ggml_ctx,
frame,
frame->ne[0],
frame->ne[1],
1,
num_channels * batch_size);
result = result == nullptr ? frame : ggml_concat(ctx->ggml_ctx, result, frame, 2);
}
return result;
}
@ -257,49 +257,49 @@ namespace MiniMaxH3VAE {
ggml_tensor* forward(GGMLRunnerContext* ctx,
ggml_tensor* x,
ggml_tensor* pe) {
auto to_qkv = std::dynamic_pointer_cast<Linear>(blocks["to_qkv"]);
auto to_out = std::dynamic_pointer_cast<Linear>(blocks["to_out"]);
auto to_qkv = std::dynamic_pointer_cast<Linear>(blocks["to_qkv"]);
auto to_out = std::dynamic_pointer_cast<Linear>(blocks["to_out"]);
auto qkv_projection = to_qkv->forward(ctx, x);
int64_t sequence = x->ne[1];
int64_t batch_size = x->ne[2] * x->ne[3];
qkv_projection = ggml_reshape_4d(ctx->ggml_ctx,
qkv_projection,
3 * head_dim,
num_head,
sequence,
batch_size);
auto qkv = ggml_ext_chunk(ctx->ggml_ctx, qkv_projection, 3, 0);
auto q = ggml_reshape_4d(ctx->ggml_ctx,
qkv[0],
head_dim,
num_head,
sequence,
batch_size);
auto k = ggml_reshape_4d(ctx->ggml_ctx,
qkv[1],
head_dim,
num_head,
sequence,
batch_size);
auto v = ggml_reshape_4d(ctx->ggml_ctx,
qkv[2],
head_dim,
num_head,
sequence,
batch_size);
q = ggml_rms_norm(ctx->ggml_ctx, q, 1e-5f);
k = ggml_rms_norm(ctx->ggml_ctx, k, 1e-5f);
q = apply_partial_rope(ctx->ggml_ctx, q, pe);
k = apply_partial_rope(ctx->ggml_ctx, k, pe);
auto out = ggml_ext_attention_ext(ctx->ggml_ctx,
ctx->backend,
q,
k,
v,
num_head,
nullptr,
true,
ctx->flash_attn_enabled);
int64_t sequence = x->ne[1];
int64_t batch_size = x->ne[2] * x->ne[3];
qkv_projection = ggml_reshape_4d(ctx->ggml_ctx,
qkv_projection,
3 * head_dim,
num_head,
sequence,
batch_size);
auto qkv = ggml_ext_chunk(ctx->ggml_ctx, qkv_projection, 3, 0);
auto q = ggml_reshape_4d(ctx->ggml_ctx,
qkv[0],
head_dim,
num_head,
sequence,
batch_size);
auto k = ggml_reshape_4d(ctx->ggml_ctx,
qkv[1],
head_dim,
num_head,
sequence,
batch_size);
auto v = ggml_reshape_4d(ctx->ggml_ctx,
qkv[2],
head_dim,
num_head,
sequence,
batch_size);
q = ggml_rms_norm(ctx->ggml_ctx, q, 1e-5f);
k = ggml_rms_norm(ctx->ggml_ctx, k, 1e-5f);
q = apply_partial_rope(ctx->ggml_ctx, q, pe);
k = apply_partial_rope(ctx->ggml_ctx, k, pe);
auto out = ggml_ext_attention_ext(ctx->ggml_ctx,
ctx->backend,
q,
k,
v,
num_head,
nullptr,
true,
ctx->flash_attn_enabled);
return to_out->forward(ctx, out);
}
};