DarthAffe/stable-diffusion.cpp
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finalized pmid lora

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bssrdf committed 2024-02-13 15:15:52 -05:00
1 parent 44052e1478
commit c122507fa1
2 files changed
+115 -10
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pmid.hpp
+105 -8
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@@ -547,6 +547,7 @@ struct PhotoMakerIDEncoder : public GGMLModule {
struct PhotoMakerLoraModel : public GGMLModule { struct PhotoMakerLoraModel : public GGMLModule {
float multiplier = 1.0f; float multiplier = 1.0f;
std::map<std::string, struct ggml_tensor*> lora_tensors; std::map<std::string, struct ggml_tensor*> lora_tensors;
std::vector<std::string> lora_tensors_to_be_ignored;
std::string file_path; std::string file_path;
ModelLoader model_loader; ModelLoader model_loader;
bool load_failed = false; bool load_failed = false;
@@ -582,7 +583,7 @@ struct PhotoMakerLoraModel : public GGMLModule {
ggml_allocr* alloc = ggml_allocr_new_from_buffer(params_buffer); ggml_allocr* alloc = ggml_allocr_new_from_buffer(params_buffer);
auto on_new_tensor_cb = [&](const TensorStorage& tensor_storage, ggml_tensor** dst_tensor) -> bool { auto on_new_tensor_cb = [&](const TensorStorage& tensor_storage, ggml_tensor** dst_tensor) -> bool {
const std::string& name = tensor_storage.name; std::string name = tensor_storage.name;
// LOG_INFO("loading LoRA tesnor '%s'", name.c_str()); // LOG_INFO("loading LoRA tesnor '%s'", name.c_str());
if (!starts_with(name, "pmid.unet")){ if (!starts_with(name, "pmid.unet")){
// LOG_INFO("skipping LoRA tesnor '%s'", name.c_str()); // LOG_INFO("skipping LoRA tesnor '%s'", name.c_str());
@@ -595,7 +596,12 @@ struct PhotoMakerLoraModel : public GGMLModule {
ggml_allocr_alloc(alloc, real); ggml_allocr_alloc(alloc, real);
*dst_tensor = real; *dst_tensor = real;
lora_tensors_to_be_ignored.push_back(name);
size_t k_pos = name.find(".processor");
if(k_pos != std::string::npos)
name.replace(k_pos, strlen(".processor"), "");
// if(starts_with(name, "pmid.unet.down_blocks.2.attentions.1.transformer_blocks.9.attn2"))
// print_ggml_tensor(real, true, name.c_str());
lora_tensors[name] = real; lora_tensors[name] = real;
return true; return true;
}; };
@@ -607,6 +613,28 @@ struct PhotoMakerLoraModel : public GGMLModule {
return true; return true;
} }
std::pair<int,int> find_ij0(int n){
int i, j;
for(i = 0; i < 3; i++){
for(j = 0; j < 2; j++){
if((i*3+j+1) == n)
return {i,j};
}
}
return {-1, -1};
}
std::pair<int,int> find_ij(int n){
int i, j;
for(i = 0; i < 2; i++){
for(j = 0; j < 3; j++){
if((i*3+j) == n)
return {i,j};
}
}
return {-1, -1};
}
struct ggml_cgraph* build_graph(std::map<std::string, struct ggml_tensor*> model_tensors) { struct ggml_cgraph* build_graph(std::map<std::string, struct ggml_tensor*> model_tensors) {
// make a graph to compute all lora, expected lora and models tensors are in the same backend // make a graph to compute all lora, expected lora and models tensors are in the same backend
// 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 // 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
@@ -627,15 +655,81 @@ struct PhotoMakerLoraModel : public GGMLModule {
for (auto it : model_tensors) { for (auto it : model_tensors) {
std::string k_tensor = it.first; std::string k_tensor = it.first;
struct ggml_tensor* weight = model_tensors[it.first]; struct ggml_tensor* weight = model_tensors[it.first];
std::string full_name = k_tensor;
size_t k_pos = k_tensor.find(".weight"); // size_t k_pos = k_tensor.find(".weight");
size_t k_pos = k_tensor.find(".attn1");
if (k_pos == std::string::npos) {
k_pos = k_tensor.find(".attn2");
if (k_pos == std::string::npos) { if (k_pos == std::string::npos) {
continue; continue;
} }
}
if(ends_with(k_tensor, "bias"))
continue;
int block_kind = -1;
int block_id = -1;
if ((k_pos = k_tensor.find("input_blocks")) != std::string::npos) {
block_id = atoi(k_tensor.substr(k_pos+strlen("input_blocks")+1).c_str());
block_kind = 0; // input -> down block
}else if ((k_pos = k_tensor.find("output_blocks")) != std::string::npos) {
block_id = atoi(k_tensor.substr(k_pos+strlen("output_blocks")+1).c_str());
block_kind = 1; // output -> up block
}else{
k_pos = k_tensor.find("transformer_blocks");
block_id = atoi(k_tensor.substr(k_pos-4,1).c_str());
block_kind = 2; // middle block
}
std::string lora_up_name;
std::string lora_down_name;
std::string prefix = "pmid.unet";
if (block_kind == 0){
prefix = prefix + ".down_blocks";
k_pos = k_tensor.find(".weight");
k_tensor = k_tensor.substr(0, k_pos); k_tensor = k_tensor.substr(0, k_pos);
replace_all_chars(k_tensor, '.', '_'); k_pos = k_tensor.find("transformer_blocks");
std::string lora_up_name = "lora." + k_tensor + ".lora_up.weight"; k_tensor = k_tensor.substr(k_pos);
std::string lora_down_name = "lora." + k_tensor + ".lora_down.weight"; if(ends_with(k_tensor, "0")){
k_tensor = k_tensor.substr(0, k_tensor.length()-2);
}
std::pair<int, int> ij = find_ij0(block_id);
if(ij.first == -1)
continue;
prefix = prefix + "."+std::to_string(ij.first)+".attentions."+std::to_string(ij.second)+".";
lora_up_name = prefix + k_tensor + "_lora.up.weight";
lora_down_name = prefix + k_tensor + "_lora.down.weight";
}else if (block_kind == 1){
prefix = prefix + ".up_blocks";
k_pos = k_tensor.find(".weight");
k_tensor = k_tensor.substr(0, k_pos);
k_pos = k_tensor.find("transformer_blocks");
k_tensor = k_tensor.substr(k_pos);
if(ends_with(k_tensor, "0")){
k_tensor = k_tensor.substr(0, k_tensor.length()-2);
}
std::pair<int, int> ij = find_ij(block_id);
if(ij.first == -1)
continue;
prefix = prefix + "."+std::to_string(ij.first)+".attentions."+std::to_string(ij.second)+".";
lora_up_name = prefix + k_tensor + "_lora.up.weight";
lora_down_name = prefix + k_tensor + "_lora.down.weight";
}else{
prefix = prefix + ".mid_block" + ".attentions.0.";
k_pos = k_tensor.find(".weight");
k_tensor = k_tensor.substr(0, k_pos);
k_pos = k_tensor.find("transformer_blocks");
k_tensor = k_tensor.substr(k_pos);
if(ends_with(k_tensor, "0")){
k_tensor = k_tensor.substr(0, k_tensor.length()-2);
}
lora_up_name = prefix + k_tensor + "_lora.up.weight";
lora_down_name = prefix + k_tensor + "_lora.down.weight";
}
// LOG_INFO("unet transformer tensor: %s ", full_name.c_str());
// LOG_INFO("corresponding up tensor: %s ", lora_up_name.c_str());
// LOG_INFO("corresponding dn tensor: %s ", lora_down_name.c_str());
ggml_tensor* lora_up = NULL; ggml_tensor* lora_up = NULL;
ggml_tensor* lora_down = NULL; ggml_tensor* lora_down = NULL;
@@ -649,14 +743,17 @@ struct PhotoMakerLoraModel : public GGMLModule {
} }
if (lora_up == NULL || lora_down == NULL) { if (lora_up == NULL || lora_down == NULL) {
LOG_WARN("can not find: %s and %s k,id = (%d, %d)", lora_down_name.c_str(), lora_up_name.c_str(), block_kind, block_id);
continue; continue;
} }
ggml_tensor* lora_up_orig = lora_up;
applied_lora_tensors.insert(lora_up_name); applied_lora_tensors.insert(lora_up_name);
applied_lora_tensors.insert(lora_down_name); applied_lora_tensors.insert(lora_down_name);
// ggml_mul_mat requires tensor b transposed // ggml_mul_mat requires tensor b transposed
lora_up = ggml_cont(ctx0, ggml_transpose(ctx0, lora_up)); lora_down = ggml_cont(ctx0, ggml_transpose(ctx0, lora_down));
struct ggml_tensor* updown = ggml_mul_mat(ctx0, lora_down, lora_up); struct ggml_tensor* updown = ggml_mul_mat(ctx0, lora_down, lora_up);
updown = ggml_cont(ctx0, updown); updown = ggml_cont(ctx0, updown);
GGML_ASSERT(ggml_nelements(updown) == ggml_nelements(weight)); GGML_ASSERT(ggml_nelements(updown) == ggml_nelements(weight));
stable-diffusion.cpp
+10 -2
View File
@@ -302,8 +302,7 @@ public:
tensors_need_to_load.insert(pair); tensors_need_to_load.insert(pair);
} }
if (stacked_id) { if (stacked_id) {
for (auto& pair : pmid_lora.lora_tensors){ for (auto& name : pmid_lora.lora_tensors_to_be_ignored){
const std::string& name = pair.first;
ignore_tensors.insert(name); ignore_tensors.insert(name);
} }
} }
@@ -1458,6 +1457,15 @@ sd_image_t* txt2img(sd_ctx_t* sd_ctx,
sd_ctx->sd->apply_loras(lora_f2m); sd_ctx->sd->apply_loras(lora_f2m);
int64_t t1 = ggml_time_ms(); int64_t t1 = ggml_time_ms();
LOG_INFO("apply_loras completed, taking %.2fs", (t1 - t0) * 1.0f / 1000); LOG_INFO("apply_loras completed, taking %.2fs", (t1 - t0) * 1.0f / 1000);
if(sd_ctx->sd->stacked_id){
sd_ctx->sd->pmid_lora.apply(sd_ctx->sd->tensors, sd_ctx->sd->n_threads);
sd_ctx->sd->pmid_lora.free_compute_buffer();
if (sd_ctx->sd->free_params_immediately) {
sd_ctx->sd->pmid_lora.free_params_buffer();
}
}
struct ggml_init_params params; struct ggml_init_params params;
params.mem_size = static_cast<size_t>(10 * 1024 * 1024); // 10 MB params.mem_size = static_cast<size_t>(10 * 1024 * 1024); // 10 MB
params.mem_size += width * height * 3 * sizeof(float); params.mem_size += width * height * 3 * sizeof(float);