alphard/stable-diffusion.cpp
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sync: update ggml

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This commit is contained in:
leejet 2023-08-27 14:35:26 +08:00
parent d765b95ed1
commit c8f85a4e30
2 changed files with 16 additions and 11 deletions
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2
ggml

@ -1 +1 @@
Subproject commit 3a0b87bde946d2d5d4896f1e700272e54d07968a
Subproject commit eec8d3ca1258d1317a26fdbf561682a7dad4eb01

25
stable-diffusion.cpp
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@ -251,6 +251,11 @@ void image_vec_to_ggml(const std::vector<uint8_t>& vec,
}
}
struct ggml_tensor * ggml_group_norm_32(struct ggml_context * ctx,
struct ggml_tensor * a) {
return ggml_group_norm(ctx, a, 32);
}
/*================================================== CLIPTokenizer ===================================================*/
const std::string UNK_TOKEN = "<|endoftext|>";
@ -899,7 +904,7 @@ struct ResBlock {
// in_layers
// group norm 32
auto h = ggml_group_norm(ctx, x);
auto h = ggml_group_norm_32(ctx, x);
h = ggml_add(ctx,
ggml_mul(ctx,
ggml_repeat(ctx,
@ -929,7 +934,7 @@ struct ResBlock {
// out_layers
h = ggml_add(ctx, h, emb_out);
// group norm 32
h = ggml_group_norm_inplace(ctx, h);
h = ggml_group_norm_inplace(ctx, h, 32);
h = ggml_add(ctx,
ggml_mul(ctx, ggml_repeat(ctx, ggml_reshape_4d(ctx, out_layer_0_w, 1, 1, out_layer_0_w->ne[0], 1), h), h),
ggml_repeat(ctx, ggml_reshape_4d(ctx, out_layer_0_b, 1, 1, out_layer_0_b->ne[0], 1), h));
@ -1122,7 +1127,7 @@ struct SpatialTransformer {
auto x_in = x;
// group norm 32
x = ggml_group_norm(ctx, x);
x = ggml_group_norm_32(ctx, x);
x = ggml_add(ctx,
ggml_mul(ctx, ggml_repeat(ctx, ggml_reshape_4d(ctx, norm_w, 1, 1, norm_w->ne[0], 1), x), x),
ggml_repeat(ctx, ggml_reshape_4d(ctx, norm_b, 1, 1, norm_b->ne[0], 1), x));
@ -1424,7 +1429,7 @@ struct UpSample {
struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
// x: [N, channels, h, w]
x = ggml_upscale(ctx, x); // [N, channels, h*2, w*2]
x = ggml_upscale(ctx, x, 2); // [N, channels, h*2, w*2]
x = ggml_conv_2d(ctx, conv_w, x, 1, 1, 1, 1, 1, 1);
x = ggml_add(ctx,
@ -1815,7 +1820,7 @@ struct UNetModel {
// out
// group norm 32
h = ggml_group_norm(ctx, h);
h = ggml_group_norm_32(ctx, h);
h = ggml_add(ctx,
ggml_mul(ctx,
ggml_repeat(ctx,
@ -1919,7 +1924,7 @@ struct ResnetBlock {
// z: [N, in_channels, h, w]
// group norm 32
auto h = ggml_group_norm(ctx, z);
auto h = ggml_group_norm_32(ctx, z);
h = ggml_mul(ctx,
ggml_repeat(ctx,
ggml_reshape_4d(ctx, norm1_w, 1, 1, norm1_w->ne[0], 1),
@ -1941,7 +1946,7 @@ struct ResnetBlock {
h)); // [N, out_channels, h, w]
// group norm 32
h = ggml_group_norm(ctx, h);
h = ggml_group_norm_32(ctx, h);
h = ggml_add(ctx,
ggml_mul(ctx, ggml_repeat(ctx, ggml_reshape_4d(ctx, norm2_w, 1, 1, norm2_w->ne[0], 1), h), h),
ggml_repeat(ctx, ggml_reshape_4d(ctx, norm2_b, 1, 1, norm2_b->ne[0], 1), h));
@ -2028,7 +2033,7 @@ struct AttnBlock {
// x: [N, in_channels, h, w]
// group norm 32
auto h_ = ggml_group_norm(ctx, x);
auto h_ = ggml_group_norm_32(ctx, x);
h_ = ggml_add(ctx,
ggml_mul(ctx, ggml_repeat(ctx, ggml_reshape_4d(ctx, norm_w, 1, 1, norm_w->ne[0], 1), h_), h_),
ggml_repeat(ctx, ggml_reshape_4d(ctx, norm_b, 1, 1, norm_b->ne[0], 1), h_));
@ -2253,7 +2258,7 @@ struct Encoder {
h = mid.block_2.forward(ctx, h); // [N, block_in, h, w]
// group norm 32
h = ggml_group_norm(ctx, h);
h = ggml_group_norm_32(ctx, h);
h = ggml_add(ctx,
ggml_mul(ctx, ggml_repeat(ctx, ggml_reshape_4d(ctx, norm_out_w, 1, 1, norm_out_w->ne[0], 1), h), h),
ggml_repeat(ctx, ggml_reshape_4d(ctx, norm_out_b, 1, 1, norm_out_b->ne[0], 1), h));
@ -2435,7 +2440,7 @@ struct Decoder {
}
// group norm 32
h = ggml_group_norm(ctx, h);
h = ggml_group_norm_32(ctx, h);
h = ggml_add(ctx,
ggml_mul(ctx, ggml_repeat(ctx, ggml_reshape_4d(ctx, norm_out_w, 1, 1, norm_out_w->ne[0], 1), h), h),
ggml_repeat(ctx, ggml_reshape_4d(ctx, norm_out_b, 1, 1, norm_out_b->ne[0], 1), h));