DarthAffe/stable-diffusion.cpp
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feat: Control Net support + Textual Inversion (embeddings) (#131)

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* add controlnet to pipeline

* add cli params

* control strength cli param

* cli param keep controlnet in cpu

* add Textual Inversion

* add canny preprocessor

* refactor: change ggml_type_sizef to ggml_row_size

* process hint once time

* ignore the embedding name case

---------

Co-authored-by: leejet <leejet714@gmail.com>
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Steward Garciaandleejet authored and GitHub committed 2024-01-29 22:38:51 +08:00
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unet.hpp
+57 -477
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@@ -3,468 +3,12 @@
#include "common.hpp"
#include "ggml_extend.hpp"
#include "model.h"
/*==================================================== UnetModel =====================================================*/
#define UNET_GRAPH_SIZE 10240
struct ResBlock {
// network hparams
int channels; // model_channels * (1, 1, 1, 2, 2, 4, 4, 4)
int emb_channels; // time_embed_dim
int out_channels; // mult * model_channels
// network params
// in_layers
struct ggml_tensor* in_layer_0_w; // [channels, ]
struct ggml_tensor* in_layer_0_b; // [channels, ]
// in_layer_1 is nn.SILU()
struct ggml_tensor* in_layer_2_w; // [out_channels, channels, 3, 3]
struct ggml_tensor* in_layer_2_b; // [out_channels, ]
// emb_layers
// emb_layer_0 is nn.SILU()
struct ggml_tensor* emb_layer_1_w; // [out_channels, emb_channels]
struct ggml_tensor* emb_layer_1_b; // [out_channels, ]
// out_layers
struct ggml_tensor* out_layer_0_w; // [out_channels, ]
struct ggml_tensor* out_layer_0_b; // [out_channels, ]
// out_layer_1 is nn.SILU()
// out_layer_2 is nn.Dropout(), p = 0 for inference
struct ggml_tensor* out_layer_3_w; // [out_channels, out_channels, 3, 3]
struct ggml_tensor* out_layer_3_b; // [out_channels, ]
// skip connection, only if out_channels != channels
struct ggml_tensor* skip_w; // [out_channels, channels, 1, 1]
struct ggml_tensor* skip_b; // [out_channels, ]
size_t calculate_mem_size(ggml_type wtype) {
double mem_size = 0;
mem_size += 2 * channels * ggml_type_sizef(GGML_TYPE_F32); // in_layer_0_w/b
mem_size += out_channels * channels * 3 * 3 * ggml_type_sizef(GGML_TYPE_F16); // in_layer_2_w
mem_size += 5 * out_channels * ggml_type_sizef(GGML_TYPE_F32); // in_layer_2_b/emb_layer_1_b/out_layer_0_w/out_layer_0_b/out_layer_3_b
mem_size += out_channels * emb_channels * ggml_type_sizef(wtype); // emb_layer_1_w
mem_size += out_channels * out_channels * 3 * 3 * ggml_type_sizef(GGML_TYPE_F16); // out_layer_3_w
if (out_channels != channels) {
mem_size += out_channels * channels * 1 * 1 * ggml_type_sizef(GGML_TYPE_F16); // skip_w
mem_size += out_channels * ggml_type_sizef(GGML_TYPE_F32); // skip_b
}
return static_cast<size_t>(mem_size);
}
void init_params(struct ggml_context* ctx, ggml_type wtype) {
in_layer_0_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, channels);
in_layer_0_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, channels);
in_layer_2_w = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, 3, 3, channels, out_channels);
in_layer_2_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, out_channels);
emb_layer_1_w = ggml_new_tensor_2d(ctx, wtype, emb_channels, out_channels);
emb_layer_1_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, out_channels);
out_layer_0_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, out_channels);
out_layer_0_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, out_channels);
out_layer_3_w = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, 3, 3, out_channels, out_channels);
out_layer_3_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, out_channels);
if (out_channels != channels) {
skip_w = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, 1, 1, channels, out_channels);
skip_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, out_channels);
}
}
void map_by_name(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
tensors[prefix + "in_layers.0.weight"] = in_layer_0_w;
tensors[prefix + "in_layers.0.bias"] = in_layer_0_b;
tensors[prefix + "in_layers.2.weight"] = in_layer_2_w;
tensors[prefix + "in_layers.2.bias"] = in_layer_2_b;
tensors[prefix + "emb_layers.1.weight"] = emb_layer_1_w;
tensors[prefix + "emb_layers.1.bias"] = emb_layer_1_b;
tensors[prefix + "out_layers.0.weight"] = out_layer_0_w;
tensors[prefix + "out_layers.0.bias"] = out_layer_0_b;
tensors[prefix + "out_layers.3.weight"] = out_layer_3_w;
tensors[prefix + "out_layers.3.bias"] = out_layer_3_b;
if (out_channels != channels) {
tensors[prefix + "skip_connection.weight"] = skip_w;
tensors[prefix + "skip_connection.bias"] = skip_b;
}
}
struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x, struct ggml_tensor* emb) {
// x: [N, channels, h, w]
// emb: [N, emb_channels]
// in_layers
auto h = ggml_nn_group_norm(ctx, x, in_layer_0_w, in_layer_0_b);
h = ggml_silu_inplace(ctx, h);
h = ggml_nn_conv_2d(ctx, h, in_layer_2_w, in_layer_2_b, 1, 1, 1, 1); // [N, out_channels, h, w]
// emb_layers
auto emb_out = ggml_silu(ctx, emb);
emb_out = ggml_nn_linear(ctx, emb_out, emb_layer_1_w, emb_layer_1_b); // [N, out_channels]
emb_out = ggml_reshape_4d(ctx, emb_out, 1, 1, emb_out->ne[0], emb_out->ne[1]); // [N, out_channels, 1, 1]
// out_layers
h = ggml_add(ctx, h, emb_out);
h = ggml_nn_group_norm(ctx, h, out_layer_0_w, out_layer_0_b);
h = ggml_silu_inplace(ctx, h);
// dropout, skip for inference
h = ggml_nn_conv_2d(ctx, h, out_layer_3_w, out_layer_3_b, 1, 1, 1, 1); // [N, out_channels, h, w]
// skip connection
if (out_channels != channels) {
x = ggml_nn_conv_2d(ctx, x, skip_w, skip_b); // [N, out_channels, h, w]
}
h = ggml_add(ctx, h, x);
return h; // [N, out_channels, h, w]
}
};
struct SpatialTransformer {
int in_channels; // mult * model_channels
int n_head; // num_heads
int d_head; // in_channels // n_heads
int depth = 1; // 1
int context_dim = 768; // hidden_size, 1024 for VERSION_2_x
// group norm
struct ggml_tensor* norm_w; // [in_channels,]
struct ggml_tensor* norm_b; // [in_channels,]
// proj_in
struct ggml_tensor* proj_in_w; // [in_channels, in_channels, 1, 1]
struct ggml_tensor* proj_in_b; // [in_channels,]
// transformer
struct Transformer {
// layer norm 1
struct ggml_tensor* norm1_w; // [in_channels, ]
struct ggml_tensor* norm1_b; // [in_channels, ]
// attn1
struct ggml_tensor* attn1_q_w; // [in_channels, in_channels]
struct ggml_tensor* attn1_k_w; // [in_channels, in_channels]
struct ggml_tensor* attn1_v_w; // [in_channels, in_channels]
struct ggml_tensor* attn1_out_w; // [in_channels, in_channels]
struct ggml_tensor* attn1_out_b; // [in_channels, ]
// layer norm 2
struct ggml_tensor* norm2_w; // [in_channels, ]
struct ggml_tensor* norm2_b; // [in_channels, ]
// attn2
struct ggml_tensor* attn2_q_w; // [in_channels, in_channels]
struct ggml_tensor* attn2_k_w; // [in_channels, context_dim]
struct ggml_tensor* attn2_v_w; // [in_channels, context_dim]
struct ggml_tensor* attn2_out_w; // [in_channels, in_channels]
struct ggml_tensor* attn2_out_b; // [in_channels, ]
// layer norm 3
struct ggml_tensor* norm3_w; // [in_channels, ]
struct ggml_tensor* norm3_b; // [in_channels, ]
// ff
struct ggml_tensor* ff_0_proj_w; // [in_channels * 4 * 2, in_channels]
struct ggml_tensor* ff_0_proj_b; // [in_channels * 4 * 2]
struct ggml_tensor* ff_2_w; // [in_channels, in_channels * 4]
struct ggml_tensor* ff_2_b; // [in_channels,]
};
std::vector<Transformer> transformers;
// proj_out
struct ggml_tensor* proj_out_w; // [in_channels, in_channels, 1, 1]
struct ggml_tensor* proj_out_b; // [in_channels,]
SpatialTransformer(int depth = 1)
: depth(depth) {
transformers.resize(depth);
}
int get_num_tensors() {
return depth * 20 + 7;
}
size_t calculate_mem_size(ggml_type wtype) {
double mem_size = 0;
mem_size += 2 * in_channels * ggml_type_sizef(GGML_TYPE_F32); // norm_w/norm_b
mem_size += 2 * in_channels * in_channels * 1 * 1 * ggml_type_sizef(GGML_TYPE_F16); // proj_in_w/proj_out_w
mem_size += 2 * in_channels * ggml_type_sizef(GGML_TYPE_F32); // proj_in_b/proj_out_b
// transformer
for (auto& transformer : transformers) {
mem_size += 6 * in_channels * ggml_type_sizef(GGML_TYPE_F32); // norm1-3_w/b
mem_size += 6 * in_channels * in_channels * ggml_type_sizef(wtype); // attn1_q/k/v/out_w attn2_q/out_w
mem_size += 2 * in_channels * context_dim * ggml_type_sizef(wtype); // attn2_k/v_w
mem_size += in_channels * 4 * 2 * in_channels * ggml_type_sizef(wtype); // ff_0_proj_w
mem_size += in_channels * 4 * 2 * ggml_type_sizef(GGML_TYPE_F32); // ff_0_proj_b
mem_size += in_channels * 4 * in_channels * ggml_type_sizef(wtype); // ff_2_w
mem_size += in_channels * ggml_type_sizef(GGML_TYPE_F32); // ff_2_b
}
return static_cast<size_t>(mem_size);
}
void init_params(struct ggml_context* ctx, ggml_allocr* alloc, ggml_type wtype) {
norm_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, in_channels);
norm_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, in_channels);
proj_in_w = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, 1, 1, in_channels, in_channels);
proj_in_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, in_channels);
proj_out_w = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, 1, 1, in_channels, in_channels);
proj_out_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, in_channels);
// transformer
for (auto& transformer : transformers) {
transformer.norm1_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, in_channels);
transformer.norm1_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, in_channels);
transformer.attn1_q_w = ggml_new_tensor_2d(ctx, wtype, in_channels, in_channels);
transformer.attn1_k_w = ggml_new_tensor_2d(ctx, wtype, in_channels, in_channels);
transformer.attn1_v_w = ggml_new_tensor_2d(ctx, wtype, in_channels, in_channels);
transformer.attn1_out_w = ggml_new_tensor_2d(ctx, wtype, in_channels, in_channels);
transformer.attn1_out_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, in_channels);
transformer.norm2_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, in_channels);
transformer.norm2_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, in_channels);
transformer.attn2_q_w = ggml_new_tensor_2d(ctx, wtype, in_channels, in_channels);
transformer.attn2_k_w = ggml_new_tensor_2d(ctx, wtype, context_dim, in_channels);
transformer.attn2_v_w = ggml_new_tensor_2d(ctx, wtype, context_dim, in_channels);
transformer.attn2_out_w = ggml_new_tensor_2d(ctx, wtype, in_channels, in_channels);
transformer.attn2_out_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, in_channels);
transformer.norm3_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, in_channels);
transformer.norm3_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, in_channels);
transformer.ff_0_proj_w = ggml_new_tensor_2d(ctx, wtype, in_channels, in_channels * 4 * 2);
transformer.ff_0_proj_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, in_channels * 4 * 2);
transformer.ff_2_w = ggml_new_tensor_2d(ctx, wtype, in_channels * 4, in_channels);
transformer.ff_2_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, in_channels);
}
}
void map_by_name(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
tensors[prefix + "norm.weight"] = norm_w;
tensors[prefix + "norm.bias"] = norm_b;
tensors[prefix + "proj_in.weight"] = proj_in_w;
tensors[prefix + "proj_in.bias"] = proj_in_b;
// transformer
for (int i = 0; i < transformers.size(); i++) {
auto& transformer = transformers[i];
std::string transformer_prefix = prefix + "transformer_blocks." + std::to_string(i) + ".";
tensors[transformer_prefix + "attn1.to_q.weight"] = transformer.attn1_q_w;
tensors[transformer_prefix + "attn1.to_k.weight"] = transformer.attn1_k_w;
tensors[transformer_prefix + "attn1.to_v.weight"] = transformer.attn1_v_w;
tensors[transformer_prefix + "attn1.to_out.0.weight"] = transformer.attn1_out_w;
tensors[transformer_prefix + "attn1.to_out.0.bias"] = transformer.attn1_out_b;
tensors[transformer_prefix + "ff.net.0.proj.weight"] = transformer.ff_0_proj_w;
tensors[transformer_prefix + "ff.net.0.proj.bias"] = transformer.ff_0_proj_b;
tensors[transformer_prefix + "ff.net.2.weight"] = transformer.ff_2_w;
tensors[transformer_prefix + "ff.net.2.bias"] = transformer.ff_2_b;
tensors[transformer_prefix + "attn2.to_q.weight"] = transformer.attn2_q_w;
tensors[transformer_prefix + "attn2.to_k.weight"] = transformer.attn2_k_w;
tensors[transformer_prefix + "attn2.to_v.weight"] = transformer.attn2_v_w;
tensors[transformer_prefix + "attn2.to_out.0.weight"] = transformer.attn2_out_w;
tensors[transformer_prefix + "attn2.to_out.0.bias"] = transformer.attn2_out_b;
tensors[transformer_prefix + "norm1.weight"] = transformer.norm1_w;
tensors[transformer_prefix + "norm1.bias"] = transformer.norm1_b;
tensors[transformer_prefix + "norm2.weight"] = transformer.norm2_w;
tensors[transformer_prefix + "norm2.bias"] = transformer.norm2_b;
tensors[transformer_prefix + "norm3.weight"] = transformer.norm3_w;
tensors[transformer_prefix + "norm3.bias"] = transformer.norm3_b;
}
tensors[prefix + "proj_out.weight"] = proj_out_w;
tensors[prefix + "proj_out.bias"] = proj_out_b;
}
struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x, struct ggml_tensor* context) {
// x: [N, in_channels, h, w]
// context: [N, max_position, hidden_size(aka context_dim)]
auto x_in = x;
x = ggml_nn_group_norm(ctx, x, norm_w, norm_b);
// proj_in
x = ggml_nn_conv_2d(ctx, x, proj_in_w, proj_in_b); // [N, in_channels, h, w]
// transformer
const int64_t n = x->ne[3];
const int64_t c = x->ne[2];
const int64_t h = x->ne[1];
const int64_t w = x->ne[0];
const int64_t max_position = context->ne[1];
x = ggml_cont(ctx, ggml_permute(ctx, x, 1, 2, 0, 3)); // [N, h, w, in_channels]
for (auto& transformer : transformers) {
auto r = x;
// layer norm 1
x = ggml_reshape_2d(ctx, x, c, w * h * n);
x = ggml_nn_layer_norm(ctx, x, transformer.norm1_w, transformer.norm1_b);
// self-attention
{
x = ggml_reshape_2d(ctx, x, c, h * w * n); // [N * h * w, in_channels]
struct ggml_tensor* q = ggml_mul_mat(ctx, transformer.attn1_q_w, x); // [N * h * w, in_channels]
#if !defined(SD_USE_FLASH_ATTENTION) || defined(SD_USE_CUBLAS) || defined(SD_USE_METAL)
q = ggml_scale_inplace(ctx, q, 1.0f / sqrt((float)d_head));
#endif
q = ggml_reshape_4d(ctx, q, d_head, n_head, h * w, n); // [N, h * w, n_head, d_head]
q = ggml_cont(ctx, ggml_permute(ctx, q, 0, 2, 1, 3)); // [N, n_head, h * w, d_head]
q = ggml_reshape_3d(ctx, q, d_head, h * w, n_head * n); // [N * n_head, h * w, d_head]
struct ggml_tensor* k = ggml_mul_mat(ctx, transformer.attn1_k_w, x); // [N * h * w, in_channels]
k = ggml_reshape_4d(ctx, k, d_head, n_head, h * w, n); // [N, h * w, n_head, d_head]
k = ggml_cont(ctx, ggml_permute(ctx, k, 0, 2, 1, 3)); // [N, n_head, h * w, d_head]
k = ggml_reshape_3d(ctx, k, d_head, h * w, n_head * n); // [N * n_head, h * w, d_head]
struct ggml_tensor* v = ggml_mul_mat(ctx, transformer.attn1_v_w, x); // [N * h * w, in_channels]
v = ggml_reshape_4d(ctx, v, d_head, n_head, h * w, n); // [N, h * w, n_head, d_head]
v = ggml_cont(ctx, ggml_permute(ctx, v, 1, 2, 0, 3)); // [N, n_head, d_head, h * w]
v = ggml_reshape_3d(ctx, v, h * w, d_head, n_head * n); // [N * n_head, d_head, h * w]
#if defined(SD_USE_FLASH_ATTENTION) && !defined(SD_USE_CUBLAS) && !defined(SD_USE_METAL)
struct ggml_tensor* kqv = ggml_flash_attn(ctx, q, k, v, false); // [N * n_head, h * w, d_head]
#else
struct ggml_tensor* kq = ggml_mul_mat(ctx, k, q); // [N * n_head, h * w, h * w]
// kq = ggml_diag_mask_inf_inplace(ctx, kq, 0);
kq = ggml_soft_max_inplace(ctx, kq);
struct ggml_tensor* kqv = ggml_mul_mat(ctx, v, kq); // [N * n_head, h * w, d_head]
#endif
kqv = ggml_reshape_4d(ctx, kqv, d_head, h * w, n_head, n);
kqv = ggml_cont(ctx, ggml_permute(ctx, kqv, 0, 2, 1, 3)); // [N, h * w, n_head, d_head]
// x = ggml_cpy(ctx, kqv, ggml_new_tensor_2d(ctx, GGML_TYPE_F32, d_head * n_head, h * w * n));
x = ggml_reshape_2d(ctx, kqv, d_head * n_head, h * w * n);
x = ggml_nn_linear(ctx, x, transformer.attn1_out_w, transformer.attn1_out_b);
x = ggml_reshape_4d(ctx, x, c, w, h, n);
}
x = ggml_add(ctx, x, r);
r = x;
// layer norm 2
x = ggml_nn_layer_norm(ctx, x, transformer.norm2_w, transformer.norm2_b);
// cross-attention
{
x = ggml_reshape_2d(ctx, x, c, h * w * n); // [N * h * w, in_channels]
context = ggml_reshape_2d(ctx, context, context->ne[0], context->ne[1] * context->ne[2]); // [N * max_position, hidden_size]
struct ggml_tensor* q = ggml_mul_mat(ctx, transformer.attn2_q_w, x); // [N * h * w, in_channels]
#if !defined(SD_USE_FLASH_ATTENTION) || defined(SD_USE_CUBLAS) || defined(SD_USE_METAL)
q = ggml_scale_inplace(ctx, q, 1.0f / sqrt((float)d_head));
#endif
q = ggml_reshape_4d(ctx, q, d_head, n_head, h * w, n); // [N, h * w, n_head, d_head]
q = ggml_cont(ctx, ggml_permute(ctx, q, 0, 2, 1, 3)); // [N, n_head, h * w, d_head]
q = ggml_reshape_3d(ctx, q, d_head, h * w, n_head * n); // [N * n_head, h * w, d_head]
struct ggml_tensor* k = ggml_mul_mat(ctx, transformer.attn2_k_w, context); // [N * max_position, in_channels]
k = ggml_reshape_4d(ctx, k, d_head, n_head, max_position, n); // [N, max_position, n_head, d_head]
k = ggml_cont(ctx, ggml_permute(ctx, k, 0, 2, 1, 3)); // [N, n_head, max_position, d_head]
k = ggml_reshape_3d(ctx, k, d_head, max_position, n_head * n); // [N * n_head, max_position, d_head]
struct ggml_tensor* v = ggml_mul_mat(ctx, transformer.attn2_v_w, context); // [N * max_position, in_channels]
v = ggml_reshape_4d(ctx, v, d_head, n_head, max_position, n); // [N, max_position, n_head, d_head]
v = ggml_cont(ctx, ggml_permute(ctx, v, 1, 2, 0, 3)); // [N, n_head, d_head, max_position]
v = ggml_reshape_3d(ctx, v, max_position, d_head, n_head * n); // [N * n_head, d_head, max_position]
#if defined(SD_USE_FLASH_ATTENTION) && !defined(SD_USE_CUBLAS) && !defined(SD_USE_METAL)
struct ggml_tensor* kqv = ggml_flash_attn(ctx, q, k, v, false); // [N * n_head, h * w, d_head]
#else
struct ggml_tensor* kq = ggml_mul_mat(ctx, k, q); // [N * n_head, h * w, max_position]
// kq = ggml_diag_mask_inf_inplace(ctx, kq, 0);
kq = ggml_soft_max_inplace(ctx, kq);
struct ggml_tensor* kqv = ggml_mul_mat(ctx, v, kq); // [N * n_head, h * w, d_head]
#endif
kqv = ggml_reshape_4d(ctx, kqv, d_head, h * w, n_head, n);
kqv = ggml_cont(ctx, ggml_permute(ctx, kqv, 0, 2, 1, 3));
// x = ggml_cpy(ctx, kqv, ggml_new_tensor_2d(ctx, GGML_TYPE_F32, d_head * n_head, h * w * n)); // [N * h * w, in_channels]
x = ggml_reshape_2d(ctx, kqv, d_head * n_head, h * w * n); // [N * h * w, in_channels]
x = ggml_nn_linear(ctx, x, transformer.attn2_out_w, transformer.attn2_out_b);
x = ggml_reshape_4d(ctx, x, c, w, h, n);
}
x = ggml_add(ctx, x, r);
r = x;
// layer norm 3
x = ggml_reshape_2d(ctx, x, c, h * w * n); // [N * h * w, in_channels]
x = ggml_nn_layer_norm(ctx, x, transformer.norm3_w, transformer.norm3_b);
// ff
{
// GEGLU
auto x_w = ggml_view_2d(ctx,
transformer.ff_0_proj_w,
transformer.ff_0_proj_w->ne[0],
transformer.ff_0_proj_w->ne[1] / 2,
transformer.ff_0_proj_w->nb[1],
0); // [in_channels * 4, in_channels]
auto x_b = ggml_view_1d(ctx,
transformer.ff_0_proj_b,
transformer.ff_0_proj_b->ne[0] / 2,
0); // [in_channels * 4, in_channels]
auto gate_w = ggml_view_2d(ctx,
transformer.ff_0_proj_w,
transformer.ff_0_proj_w->ne[0],
transformer.ff_0_proj_w->ne[1] / 2,
transformer.ff_0_proj_w->nb[1],
transformer.ff_0_proj_w->nb[1] * transformer.ff_0_proj_w->ne[1] / 2); // [in_channels * 4, ]
auto gate_b = ggml_view_1d(ctx,
transformer.ff_0_proj_b,
transformer.ff_0_proj_b->ne[0] / 2,
transformer.ff_0_proj_b->nb[0] * transformer.ff_0_proj_b->ne[0] / 2); // [in_channels * 4, ]
x = ggml_reshape_2d(ctx, x, c, w * h * n);
auto x_in = x;
x = ggml_nn_linear(ctx, x_in, x_w, x_b); // [N * h * w, in_channels * 4]
auto gate = ggml_nn_linear(ctx, x_in, gate_w, gate_b); // [N * h * w, in_channels * 4]
gate = ggml_gelu_inplace(ctx, gate);
x = ggml_mul(ctx, x, gate); // [N * h * w, in_channels * 4]
// fc
x = ggml_nn_linear(ctx, x, transformer.ff_2_w, transformer.ff_2_b); // [N * h * w, in_channels]
}
x = ggml_reshape_4d(ctx, x, c, w, h, n); // [N, h, w, in_channels]
// residual
x = ggml_add(ctx, x, r);
}
x = ggml_cont(ctx, ggml_permute(ctx, x, 2, 0, 1, 3)); // [N, in_channels, h, w]
// proj_out
x = ggml_nn_conv_2d(ctx, x, proj_out_w, proj_out_b); // [N, in_channels, h, w]
x = ggml_add(ctx, x, x_in);
return x;
}
};
// ldm.modules.diffusionmodules.openaimodel.UNetModel
struct UNetModel : public GGMLModule {
SDVersion version = VERSION_1_x;
@@ -636,21 +180,21 @@ struct UNetModel : public GGMLModule {
}
size_t calculate_mem_size() {
double mem_size = 0;
mem_size += time_embed_dim * model_channels * ggml_type_sizef(wtype); // time_embed_0_w
mem_size += time_embed_dim * ggml_type_sizef(GGML_TYPE_F32); // time_embed_0_b
mem_size += time_embed_dim * time_embed_dim * ggml_type_sizef(wtype); // time_embed_2_w
mem_size += time_embed_dim * ggml_type_sizef(GGML_TYPE_F32); // time_embed_2_b
size_t mem_size = 0;
mem_size += ggml_row_size(wtype, time_embed_dim * model_channels); // time_embed_0_w
mem_size += ggml_row_size(GGML_TYPE_F32, time_embed_dim); // time_embed_0_b
mem_size += ggml_row_size(wtype, time_embed_dim * time_embed_dim); // time_embed_2_w
mem_size += ggml_row_size(GGML_TYPE_F32, time_embed_dim); // time_embed_2_b
if (version == VERSION_XL) {
mem_size += time_embed_dim * adm_in_channels * ggml_type_sizef(wtype); // label_embed_0_w
mem_size += time_embed_dim * ggml_type_sizef(GGML_TYPE_F32); // label_embed_0_b
mem_size += time_embed_dim * time_embed_dim * ggml_type_sizef(wtype); // label_embed_2_w
mem_size += time_embed_dim * ggml_type_sizef(GGML_TYPE_F32); // label_embed_2_b
mem_size += ggml_row_size(wtype, time_embed_dim * adm_in_channels); // label_embed_0_w
mem_size += ggml_row_size(GGML_TYPE_F32, time_embed_dim); // label_embed_0_b
mem_size += ggml_row_size(wtype, time_embed_dim * time_embed_dim); // label_embed_2_w
mem_size += ggml_row_size(GGML_TYPE_F32, time_embed_dim); // label_embed_2_b
}
mem_size += model_channels * in_channels * 3 * 3 * ggml_type_sizef(GGML_TYPE_F16); // input_block_0_w
mem_size += model_channels * ggml_type_sizef(GGML_TYPE_F32); // input_block_0_b
mem_size += ggml_row_size(GGML_TYPE_F16, model_channels * in_channels * 3 * 3); // input_block_0_w
mem_size += ggml_row_size(GGML_TYPE_F32, model_channels); // input_block_0_b
// input_blocks
int ds = 1;
@@ -691,11 +235,11 @@ struct UNetModel : public GGMLModule {
}
// out
mem_size += 2 * model_channels * ggml_type_sizef(GGML_TYPE_F32); // out_0_w/b
mem_size += out_channels * model_channels * 3 * 3 * ggml_type_sizef(GGML_TYPE_F16); // out_2_w
mem_size += out_channels * ggml_type_sizef(GGML_TYPE_F32); // out_2_b
mem_size += 2 * ggml_row_size(GGML_TYPE_F32, model_channels); // out_0_w/b
mem_size += ggml_row_size(GGML_TYPE_F16, out_channels * model_channels * 3 * 3); // out_2_w
mem_size += ggml_row_size(GGML_TYPE_F32, out_channels); // out_2_b
return static_cast<size_t>(mem_size);
return mem_size;
}
size_t get_num_tensors() {
@@ -892,6 +436,8 @@ struct UNetModel : public GGMLModule {
struct ggml_tensor* x,
struct ggml_tensor* timesteps,
struct ggml_tensor* context,
std::vector<struct ggml_tensor*> control,
float control_net_strength,
struct ggml_tensor* t_emb = NULL,
struct ggml_tensor* y = NULL) {
// x: [N, in_channels, h, w]
@@ -949,12 +495,24 @@ struct UNetModel : public GGMLModule {
h = middle_block_1.forward(ctx0, h, context); // [N, 4*model_channels, h/8, w/8]
h = middle_block_2.forward(ctx0, h, emb); // [N, 4*model_channels, h/8, w/8]
if(control.size() > 0) {
auto cs = ggml_scale_inplace(ctx0, control[control.size() - 1], control_net_strength);
h = ggml_add(ctx0, h, cs); // middle control
}
int control_offset = control.size() - 2;
// output_blocks
for (int i = (int)len_mults - 1; i >= 0; i--) {
for (int j = 0; j < num_res_blocks + 1; j++) {
auto h_skip = hs.back();
hs.pop_back();
if(control.size() > 0) {
auto cs = ggml_scale_inplace(ctx0, control[control_offset], control_net_strength);
h_skip = ggml_add(ctx0, h_skip, cs); // control net condition
control_offset--;
}
h = ggml_concat(ctx0, h, h_skip);
h = output_res_blocks[i][j].forward(ctx0, h, emb);
@@ -983,8 +541,10 @@ struct UNetModel : public GGMLModule {
struct ggml_cgraph* build_graph(struct ggml_tensor* x,
struct ggml_tensor* timesteps,
struct ggml_tensor* context,
std::vector<struct ggml_tensor*> control,
struct ggml_tensor* t_emb = NULL,
struct ggml_tensor* y = NULL) {
struct ggml_tensor* y = NULL,
float control_net_strength = 1.0) {
// since we are using ggml-alloc, this buffer only needs enough space to hold the ggml_tensor and ggml_cgraph structs, but not the tensor data
static size_t buf_size = ggml_tensor_overhead() * UNET_GRAPH_SIZE + ggml_graph_overhead();
static std::vector<uint8_t> buf(buf_size);
@@ -1006,6 +566,7 @@ struct UNetModel : public GGMLModule {
struct ggml_tensor* context_t = NULL;
struct ggml_tensor* t_emb_t = NULL;
struct ggml_tensor* y_t = NULL;
std::vector<struct ggml_tensor*> control_t;
// it's performing a compute, check if backend isn't cpu
if (!ggml_backend_is_cpu(backend)) {
@@ -1049,7 +610,22 @@ struct UNetModel : public GGMLModule {
y_t = y;
}
struct ggml_tensor* out = forward(ctx0, x_t, timesteps_t, context_t, t_emb_t, y_t);
// offload all controls tensors to gpu
if(control.size() > 0 && !ggml_backend_is_cpu(backend) && control[0]->backend != GGML_BACKEND_GPU) {
for(int i = 0; i < control.size(); i++) {
ggml_tensor* cntl_t = ggml_dup_tensor(ctx0, control[i]);
control_t.push_back(cntl_t);
ggml_allocr_alloc(compute_allocr, cntl_t);
if(!ggml_allocr_is_measure(compute_allocr)) {
ggml_backend_tensor_copy(control[i], control_t[i]);
ggml_backend_synchronize(backend);
}
}
} else {
control_t = control;
}
struct ggml_tensor* out = forward(ctx0, x_t, timesteps_t, context_t, control_t, control_net_strength, t_emb_t, y_t);
ggml_build_forward_expand(gf, out);
ggml_free(ctx0);
@@ -1059,10 +635,12 @@ struct UNetModel : public GGMLModule {
void alloc_compute_buffer(struct ggml_tensor* x,
struct ggml_tensor* context,
std::vector<struct ggml_tensor*> control,
struct ggml_tensor* t_emb = NULL,
struct ggml_tensor* y = NULL) {
struct ggml_tensor* y = NULL,
float control_net_strength = 1.0) {
auto get_graph = [&]() -> struct ggml_cgraph* {
return build_graph(x, NULL, context, t_emb, y);
return build_graph(x, NULL, context, control, t_emb, y, control_net_strength);
};
GGMLModule::alloc_compute_buffer(get_graph);
}
@@ -1072,10 +650,12 @@ struct UNetModel : public GGMLModule {
struct ggml_tensor* x,
struct ggml_tensor* timesteps,
struct ggml_tensor* context,
std::vector<struct ggml_tensor*> control,
float control_net_strength,
struct ggml_tensor* t_emb = NULL,
struct ggml_tensor* y = NULL) {
auto get_graph = [&]() -> struct ggml_cgraph* {
return build_graph(x, timesteps, context, t_emb, y);
return build_graph(x, timesteps, context, control, t_emb, y, control_net_strength);
};
GGMLModule::compute(get_graph, n_threads, work_latent);