feat: support safetensors export in convert mode (#1444)

CMakeLists.txt

@@ -156,10 +156,12 @@ endif()
 
 set(SD_LIB stable-diffusion)
 
-file(GLOB SD_LIB_SOURCES
+file(GLOB SD_LIB_SOURCES CONFIGURE_DEPENDS
     "src/*.h"
     "src/*.cpp"
     "src/*.hpp"
+    "src/model_io/*.h"
+    "src/model_io/*.cpp"
     "src/tokenizers/*.h"
     "src/tokenizers/*.cpp"
     "src/tokenizers/vocab/*.h"

README.md

@@ -77,9 +77,10 @@ API and command-line option may change frequently.***
   - OpenCL
   - SYCL
 - Supported weight formats
-  - Pytorch checkpoint (`.ckpt` or `.pth`)
+  - Pytorch checkpoint (`.ckpt` or `.pth` or `.pt`)
   - Safetensors (`.safetensors`)
   - GGUF (`.gguf`)
+- Convert mode supports converting model weights to `.gguf` or `.safetensors`
 - Supported platforms
     - Linux
     - Mac OS

examples/cli/README.md

@@ -14,6 +14,9 @@ CLI Options:
   --metadata-format <string>  metadata output format, one of [text, json] (default: text)
   --canny                     apply canny preprocessor (edge detection)
   --convert-name              convert tensor name (for convert mode)
+                              convert mode writes `.gguf` or `.safetensors` based on the output extension.
+                              `.safetensors` export currently supports f16, bf16, f32, and i32 tensor types only.
+                              i32 is passthrough only; no f32 <-> i32 conversion is performed
   -v, --verbose               print extra info
   --color                     colors the logging tags according to level
   --taesd-preview-only        prevents usage of taesd for decoding the final image. (for use with --preview tae)

format-code.sh

@@ -1,5 +1,5 @@
 for f in src/*.cpp src/*.h src/*.hpp src/tokenizers/*.h src/tokenizers/*.cpp src/tokenizers/vocab/*.h src/tokenizers/vocab/*.cpp \
-         examples/cli/*.cpp examples/cli/*.h examples/server/*.cpp \
+         src/model_io/*.h src/model_io/*.cpp examples/cli/*.cpp examples/cli/*.h examples/server/*.cpp \
          examples/common/*.hpp examples/common/*.h examples/common/*.cpp; do
   [[ "$f" == vocab* ]] && continue
   echo "formatting '$f'"

src/convert.cpp

@@ -0,0 +1,138 @@
+#include <cstring>
+#include <mutex>
+#include <regex>
+#include <vector>
+
+#include "model.h"
+#include "model_io/gguf_io.h"
+#include "model_io/safetensors_io.h"
+#include "util.h"
+
+#include "ggml-cpu.h"
+
+static ggml_type get_export_tensor_type(ModelLoader& model_loader,
+                                        const TensorStorage& tensor_storage,
+                                        ggml_type type,
+                                        const TensorTypeRules& tensor_type_rules) {
+    const std::string& name = tensor_storage.name;
+    ggml_type tensor_type   = tensor_storage.type;
+    ggml_type dst_type      = type;
+
+    for (const auto& tensor_type_rule : tensor_type_rules) {
+        std::regex pattern(tensor_type_rule.first);
+        if (std::regex_search(name, pattern)) {
+            dst_type = tensor_type_rule.second;
+            break;
+        }
+    }
+
+    if (model_loader.tensor_should_be_converted(tensor_storage, dst_type)) {
+        tensor_type = dst_type;
+    }
+
+    return tensor_type;
+}
+
+static bool load_tensors_for_export(ModelLoader& model_loader,
+                                    ggml_context* ggml_ctx,
+                                    ggml_type type,
+                                    const TensorTypeRules& tensor_type_rules,
+                                    std::vector<TensorWriteInfo>& tensors) {
+    std::mutex tensor_mutex;
+    auto on_new_tensor_cb = [&](const TensorStorage& tensor_storage, ggml_tensor** dst_tensor) -> bool {
+        const std::string& name = tensor_storage.name;
+        ggml_type tensor_type   = get_export_tensor_type(model_loader, tensor_storage, type, tensor_type_rules);
+
+        std::lock_guard<std::mutex> lock(tensor_mutex);
+        ggml_tensor* tensor = ggml_new_tensor(ggml_ctx, tensor_type, tensor_storage.n_dims, tensor_storage.ne);
+        if (tensor == nullptr) {
+            LOG_ERROR("ggml_new_tensor failed");
+            return false;
+        }
+        ggml_set_name(tensor, name.c_str());
+
+        if (!tensor->data) {
+            GGML_ASSERT(ggml_nelements(tensor) == 0);
+            // Avoid crashing writers by setting a dummy pointer for zero-sized tensors.
+            LOG_DEBUG("setting dummy pointer for zero-sized tensor %s", name.c_str());
+            tensor->data = ggml_get_mem_buffer(ggml_ctx);
+        }
+
+        TensorWriteInfo write_info;
+        write_info.tensor = tensor;
+        write_info.n_dims = tensor_storage.n_dims;
+        for (int i = 0; i < tensor_storage.n_dims; ++i) {
+            write_info.ne[i] = tensor_storage.ne[i];
+        }
+
+        *dst_tensor = tensor;
+        tensors.push_back(std::move(write_info));
+
+        return true;
+    };
+
+    bool success = model_loader.load_tensors(on_new_tensor_cb);
+    LOG_INFO("load tensors done");
+    return success;
+}
+
+bool convert(const char* input_path,
+             const char* vae_path,
+             const char* output_path,
+             sd_type_t output_type,
+             const char* tensor_type_rules,
+             bool convert_name) {
+    ModelLoader model_loader;
+
+    if (!model_loader.init_from_file(input_path)) {
+        LOG_ERROR("init model loader from file failed: '%s'", input_path);
+        return false;
+    }
+
+    if (vae_path != nullptr && strlen(vae_path) > 0) {
+        if (!model_loader.init_from_file(vae_path, "vae.")) {
+            LOG_ERROR("init model loader from file failed: '%s'", vae_path);
+            return false;
+        }
+    }
+    if (convert_name) {
+        model_loader.convert_tensors_name();
+    }
+
+    ggml_type type             = (ggml_type)output_type;
+    bool output_is_safetensors = ends_with(output_path, ".safetensors");
+    TensorTypeRules type_rules = parse_tensor_type_rules(tensor_type_rules);
+
+    auto backend    = ggml_backend_cpu_init();
+    size_t mem_size = 1 * 1024 * 1024;  // for padding
+    mem_size += model_loader.get_tensor_storage_map().size() * ggml_tensor_overhead();
+    mem_size += model_loader.get_params_mem_size(backend, type);
+    LOG_INFO("model tensors mem size: %.2fMB", mem_size / 1024.f / 1024.f);
+    ggml_context* ggml_ctx = ggml_init({mem_size, nullptr, false});
+
+    if (ggml_ctx == nullptr) {
+        LOG_ERROR("ggml_init failed for converter");
+        ggml_backend_free(backend);
+        return false;
+    }
+
+    std::vector<TensorWriteInfo> tensors;
+    bool success = load_tensors_for_export(model_loader, ggml_ctx, type, type_rules, tensors);
+    ggml_backend_free(backend);
+
+    std::string error;
+    if (success) {
+        if (output_is_safetensors) {
+            success = write_safetensors_file(output_path, tensors, &error);
+        } else {
+            success = write_gguf_file(output_path, tensors, &error);
+        }
+    }
+
+    if (!success && !error.empty()) {
+        LOG_ERROR("%s", error.c_str());
+    }
+
+    ggml_free(ggml_ctx);
+    return success;
+}

src/denoiser.hpp

@@ -977,7 +977,7 @@ static sd::Tensor<float> sample_dpmpp_2s_ancestral_flow(denoise_cb_t model,
                                                         float eta = 1.0f) {
     int steps = static_cast<int>(sigmas.size()) - 1;
     for (int i = 0; i < steps; i++) {
-        float sigma = sigmas[i];
+        float sigma    = sigmas[i];
         float sigma_to = sigmas[i + 1];
 
         bool opt_first_step = (1.0 - sigma < 1e-6);
@@ -1040,10 +1040,10 @@ static sd::Tensor<float> sample_dpmpp_2s_ancestral_flow(denoise_cb_t model,
                 // and sigma_s = sigma_fn(s) = 1.0f / (exp(s) + 1.0f)
 
                 float exp_s = std::sqrt(((1 - sigma) / sigma) * ((1 - sigma_down) / sigma_down));
-                sigma_s = 1.0f / (exp_s + 1.0f);
+                sigma_s     = 1.0f / (exp_s + 1.0f);
 
                 float sigma_s_i_ratio = sigma_s / sigma;
-                sd::Tensor<float> u = (x * sigma_s_i_ratio) + (denoised * (1.0f - sigma_s_i_ratio));
+                sd::Tensor<float> u   = (x * sigma_s_i_ratio) + (denoised * (1.0f - sigma_s_i_ratio));
 
                 auto denoised2_opt = model(u, sigma_s, i + 1);
                 if (denoised2_opt.empty()) {
@@ -1053,7 +1053,7 @@ static sd::Tensor<float> sample_dpmpp_2s_ancestral_flow(denoise_cb_t model,
             }
 
             float sigma_down_i_ratio = sigma_down / sigma;
-            x = (x * sigma_down_i_ratio) + (D_i * (1.0f - sigma_down_i_ratio));
+            x                        = (x * sigma_down_i_ratio) + (D_i * (1.0f - sigma_down_i_ratio));
 
             if (sigma_to > 0.0f && eta > 0.0f) {
                 x = alpha_scale * x + sd::Tensor<float>::randn_like(x, rng) * sigma_up;
@@ -1064,8 +1064,6 @@ static sd::Tensor<float> sample_dpmpp_2s_ancestral_flow(denoise_cb_t model,
     return x;
 }
 
-
-
 static sd::Tensor<float> sample_dpmpp_2m(denoise_cb_t model,
                                          sd::Tensor<float> x,
                                          const std::vector<float>& sigmas) {
@@ -1525,32 +1523,10 @@ static sd::Tensor<float> sample_ddim_trailing(denoise_cb_t model,
                                               const std::vector<float>& sigmas,
                                               std::shared_ptr<RNG> rng,
                                               float eta) {
-    float beta_start = 0.00085f;
-    float beta_end   = 0.0120f;
-    std::vector<double> alphas_cumprod(TIMESTEPS);
-    std::vector<double> compvis_sigmas(TIMESTEPS);
-    for (int i = 0; i < TIMESTEPS; i++) {
-        alphas_cumprod[i] =
-            (i == 0 ? 1.0f : alphas_cumprod[i - 1]) *
-            (1.0f -
-             std::pow(sqrtf(beta_start) +
-                          (sqrtf(beta_end) - sqrtf(beta_start)) *
-                              ((float)i / (TIMESTEPS - 1)),
-                      2));
-        compvis_sigmas[i] =
-            std::sqrt((1 - alphas_cumprod[i]) / alphas_cumprod[i]);
-    }
-
     int steps = static_cast<int>(sigmas.size()) - 1;
     for (int i = 0; i < steps; i++) {
-        int timestep      = static_cast<int>(roundf(TIMESTEPS - i * ((float)TIMESTEPS / steps))) - 1;
-        int prev_timestep = timestep - TIMESTEPS / steps;
-        float sigma       = static_cast<float>(compvis_sigmas[timestep]);
-        if (i == 0) {
-            x *= std::sqrt(sigma * sigma + 1) / sigma;
-        } else {
-            x *= std::sqrt(sigma * sigma + 1);
-        }
+        float sigma    = sigmas[i];
+        float sigma_to = sigmas[i + 1];
 
         auto model_output_opt = model(x, sigma, i + 1);
         if (model_output_opt.empty()) {
@@ -1559,8 +1535,8 @@ static sd::Tensor<float> sample_ddim_trailing(denoise_cb_t model,
         sd::Tensor<float> model_output = std::move(model_output_opt);
         model_output                   = (x - model_output) * (1.0f / sigma);
 
-        float alpha_prod_t      = static_cast<float>(alphas_cumprod[timestep]);
-        float alpha_prod_t_prev = static_cast<float>(prev_timestep >= 0 ? alphas_cumprod[prev_timestep] : alphas_cumprod[0]);
+        float alpha_prod_t      = 1.0f / (sigma * sigma + 1.0f);
+        float alpha_prod_t_prev = 1.0f / (sigma_to * sigma_to + 1.0f);
         float beta_prod_t       = 1.0f - alpha_prod_t;
 
         sd::Tensor<float> pred_original_sample = ((x / std::sqrt(sigma * sigma + 1)) -
@@ -1572,11 +1548,11 @@ static sd::Tensor<float> sample_ddim_trailing(denoise_cb_t model,
                          (1.0f - alpha_prod_t / alpha_prod_t_prev);
         float std_dev_t = eta * std::sqrt(variance);
 
-        x = std::sqrt(alpha_prod_t_prev) * pred_original_sample +
-            std::sqrt(1.0f - alpha_prod_t_prev - std::pow(std_dev_t, 2)) * model_output;
+        x = pred_original_sample +
+            std::sqrt((1.0f - alpha_prod_t_prev - std::pow(std_dev_t, 2)) / alpha_prod_t_prev) * model_output;
 
         if (eta > 0) {
-            x += std_dev_t * sd::Tensor<float>::randn_like(x, rng);
+            x += std_dev_t / std::sqrt(alpha_prod_t_prev) * sd::Tensor<float>::randn_like(x, rng);
         }
     }
     return x;
@@ -1603,19 +1579,26 @@ static sd::Tensor<float> sample_tcd(denoise_cb_t model,
             std::sqrt((1 - alphas_cumprod[i]) / alphas_cumprod[i]);
     }
 
-    int original_steps = 50;
-    int steps          = static_cast<int>(sigmas.size()) - 1;
-    for (int i = 0; i < steps; i++) {
-        int timestep      = TIMESTEPS - 1 - (TIMESTEPS / original_steps) * (int)floor(i * ((float)original_steps / steps));
-        int prev_timestep = i >= steps - 1 ? 0 : TIMESTEPS - 1 - (TIMESTEPS / original_steps) * (int)floor((i + 1) * ((float)original_steps / steps));
-        int timestep_s    = (int)floor((1 - eta) * prev_timestep);
-        float sigma       = static_cast<float>(compvis_sigmas[timestep]);
-
-        if (i == 0) {
-            x *= std::sqrt(sigma * sigma + 1) / sigma;
-        } else {
-            x *= std::sqrt(sigma * sigma + 1);
+    auto get_timestep_from_sigma = [&](float s) -> int {
+        auto it = std::lower_bound(compvis_sigmas.begin(), compvis_sigmas.end(), s);
+        if (it == compvis_sigmas.begin())
+            return 0;
+        if (it == compvis_sigmas.end())
+            return TIMESTEPS - 1;
+        int idx_high = static_cast<int>(std::distance(compvis_sigmas.begin(), it));
+        int idx_low  = idx_high - 1;
+        if (std::abs(compvis_sigmas[idx_high] - s) < std::abs(compvis_sigmas[idx_low] - s)) {
+            return idx_high;
         }
+        return idx_low;
+    };
+
+    int steps = static_cast<int>(sigmas.size()) - 1;
+    for (int i = 0; i < steps; i++) {
+        float sigma_to    = sigmas[i + 1];
+        int prev_timestep = get_timestep_from_sigma(sigma_to);
+        int timestep_s    = (int)floor((1 - eta) * prev_timestep);
+        float sigma       = sigmas[i];
 
         auto model_output_opt = model(x, sigma, i + 1);
         if (model_output_opt.empty()) {
@@ -1624,9 +1607,9 @@ static sd::Tensor<float> sample_tcd(denoise_cb_t model,
         sd::Tensor<float> model_output = std::move(model_output_opt);
         model_output                   = (x - model_output) * (1.0f / sigma);
 
-        float alpha_prod_t      = static_cast<float>(alphas_cumprod[timestep]);
+        float alpha_prod_t      = 1.0f / (sigma * sigma + 1.0f);
         float beta_prod_t       = 1.0f - alpha_prod_t;
-        float alpha_prod_t_prev = static_cast<float>(prev_timestep >= 0 ? alphas_cumprod[prev_timestep] : alphas_cumprod[0]);
+        float alpha_prod_t_prev = 1.0f / (sigma_to * sigma_to + 1.0f);
         float alpha_prod_s      = static_cast<float>(alphas_cumprod[timestep_s]);
         float beta_prod_s       = 1.0f - alpha_prod_s;
 
@@ -1634,12 +1617,12 @@ static sd::Tensor<float> sample_tcd(denoise_cb_t model,
                                                   std::sqrt(beta_prod_t) * model_output) *
                                                  (1.0f / std::sqrt(alpha_prod_t));
 
-        x = std::sqrt(alpha_prod_s) * pred_original_sample +
-            std::sqrt(beta_prod_s) * model_output;
+        x = std::sqrt(alpha_prod_s / alpha_prod_t_prev) * pred_original_sample +
+            std::sqrt(beta_prod_s / alpha_prod_t_prev) * model_output;
 
-        if (eta > 0 && i != steps - 1) {
+        if (eta > 0 && sigma_to > 0.0f) {
             x = std::sqrt(alpha_prod_t_prev / alpha_prod_s) * x +
-                std::sqrt(1.0f - alpha_prod_t_prev / alpha_prod_s) * sd::Tensor<float>::randn_like(x, rng);
+                std::sqrt(1.0f / alpha_prod_t_prev - 1.0f / alpha_prod_s) * sd::Tensor<float>::randn_like(x, rng);
         }
     }
     return x;

src/model.cpp

@@ -2,6 +2,7 @@
 #include <atomic>
 #include <chrono>
 #include <cstdarg>
+#include <cstdlib>
 #include <fstream>
 #include <functional>
 #include <mutex>
@@ -12,8 +13,11 @@
 #include <unordered_map>
 #include <vector>
 
-#include "gguf_reader.hpp"
 #include "model.h"
+#include "model_io/gguf_io.h"
+#include "model_io/safetensors_io.h"
+#include "model_io/torch_legacy_io.h"
+#include "model_io/torch_zip_io.h"
 #include "stable-diffusion.h"
 #include "util.h"
 
@@ -21,6 +25,7 @@
 #include "ggml-backend.h"
 #include "ggml-cpu.h"
 #include "ggml.h"
+#include "zip.h"
 
 #include "name_conversion.h"
 #include "stable-diffusion.h"
@@ -37,40 +42,6 @@
 #include "ggml-opencl.h"
 #endif
 
-#define ST_HEADER_SIZE_LEN 8
-
-uint64_t read_u64(uint8_t* buffer) {
-    // little endian
-    uint64_t value = 0;
-    value |= static_cast<int64_t>(buffer[7]) << 56;
-    value |= static_cast<int64_t>(buffer[6]) << 48;
-    value |= static_cast<int64_t>(buffer[5]) << 40;
-    value |= static_cast<int64_t>(buffer[4]) << 32;
-    value |= static_cast<int64_t>(buffer[3]) << 24;
-    value |= static_cast<int64_t>(buffer[2]) << 16;
-    value |= static_cast<int64_t>(buffer[1]) << 8;
-    value |= static_cast<int64_t>(buffer[0]);
-    return value;
-}
-
-int32_t read_int(uint8_t* buffer) {
-    // little endian
-    int value = 0;
-    value |= buffer[3] << 24;
-    value |= buffer[2] << 16;
-    value |= buffer[1] << 8;
-    value |= buffer[0];
-    return value;
-}
-
-uint16_t read_short(uint8_t* buffer) {
-    // little endian
-    uint16_t value = 0;
-    value |= buffer[1] << 8;
-    value |= buffer[0];
-    return value;
-}
-
 /*================================================= Preprocess ==================================================*/
 
 const char* unused_tensors[] = {
@@ -110,7 +81,7 @@ const char* unused_tensors[] = {
     "first_stage_model.bn.",
 };
 
-bool is_unused_tensor(std::string name) {
+bool is_unused_tensor(const std::string& name) {
     for (size_t i = 0; i < sizeof(unused_tensors) / sizeof(const char*); i++) {
         if (starts_with(name, unused_tensors[i])) {
             return true;
@@ -250,79 +221,6 @@ void ModelLoader::add_tensor_storage(const TensorStorage& tensor_storage) {
     tensor_storage_map[tensor_storage.name] = tensor_storage;
 }
 
-bool is_zip_file(const std::string& file_path) {
-    zip_t* zip = zip_open(file_path.c_str(), 0, 'r');
-    if (zip == nullptr) {
-        return false;
-    }
-    zip_close(zip);
-    return true;
-}
-
-bool is_gguf_file(const std::string& file_path) {
-    std::ifstream file(file_path, std::ios::binary);
-    if (!file.is_open()) {
-        return false;
-    }
-
-    char magic[4];
-
-    file.read(magic, sizeof(magic));
-    if (!file) {
-        return false;
-    }
-    for (uint32_t i = 0; i < sizeof(magic); i++) {
-        if (magic[i] != GGUF_MAGIC[i]) {
-            return false;
-        }
-    }
-
-    return true;
-}
-
-bool is_safetensors_file(const std::string& file_path) {
-    std::ifstream file(file_path, std::ios::binary);
-    if (!file.is_open()) {
-        return false;
-    }
-
-    // get file size
-    file.seekg(0, file.end);
-    size_t file_size_ = file.tellg();
-    file.seekg(0, file.beg);
-
-    // read header size
-    if (file_size_ <= ST_HEADER_SIZE_LEN) {
-        return false;
-    }
-
-    uint8_t header_size_buf[ST_HEADER_SIZE_LEN];
-    file.read((char*)header_size_buf, ST_HEADER_SIZE_LEN);
-    if (!file) {
-        return false;
-    }
-
-    size_t header_size_ = read_u64(header_size_buf);
-    if (header_size_ >= file_size_ || header_size_ <= 2) {
-        return false;
-    }
-
-    // read header
-    std::vector<char> header_buf;
-    header_buf.resize(header_size_ + 1);
-    header_buf[header_size_] = '\0';
-    file.read(header_buf.data(), header_size_);
-    if (!file) {
-        return false;
-    }
-    try {
-        nlohmann::json header_ = nlohmann::json::parse(header_buf.data());
-    } catch (const std::exception&) {
-        return false;
-    }
-    return true;
-}
-
 bool ModelLoader::init_from_file(const std::string& file_path, const std::string& prefix) {
     if (is_directory(file_path)) {
         LOG_INFO("load %s using diffusers format", file_path.c_str());
@@ -333,9 +231,12 @@ bool ModelLoader::init_from_file(const std::string& file_path, const std::string
     } else if (is_safetensors_file(file_path)) {
         LOG_INFO("load %s using safetensors format", file_path.c_str());
         return init_from_safetensors_file(file_path, prefix);
-    } else if (is_zip_file(file_path)) {
-        LOG_INFO("load %s using checkpoint format", file_path.c_str());
-        return init_from_ckpt_file(file_path, prefix);
+    } else if (is_torch_zip_file(file_path)) {
+        LOG_INFO("load %s using torch zip format", file_path.c_str());
+        return init_from_torch_zip_file(file_path, prefix);
+    } else if (init_from_torch_legacy_file(file_path, prefix)) {
+        LOG_INFO("load %s using torch legacy format", file_path.c_str());
+        return true;
     } else {
         if (file_exists(file_path)) {
             LOG_WARN("unknown format %s", file_path.c_str());
@@ -375,242 +276,121 @@ bool ModelLoader::init_from_file_and_convert_name(const std::string& file_path,
 
 bool ModelLoader::init_from_gguf_file(const std::string& file_path, const std::string& prefix) {
     LOG_DEBUG("init from '%s'", file_path.c_str());
+
+    std::vector<TensorStorage> tensor_storages;
+    std::string error;
+    if (!read_gguf_file(file_path, tensor_storages, &error)) {
+        LOG_ERROR("%s", error.c_str());
+        return false;
+    }
+
     file_paths_.push_back(file_path);
     size_t file_index = file_paths_.size() - 1;
 
-    gguf_context* ctx_gguf_ = nullptr;
-    ggml_context* ctx_meta_ = nullptr;
+    for (auto& tensor_storage : tensor_storages) {
+        // LOG_DEBUG("%s", tensor_storage.name.c_str());
 
-    ctx_gguf_ = gguf_init_from_file(file_path.c_str(), {true, &ctx_meta_});
-    if (!ctx_gguf_) {
-        LOG_ERROR("failed to open '%s' with gguf_init_from_file. Try to open it with GGUFReader.", file_path.c_str());
-        GGUFReader gguf_reader;
-        if (!gguf_reader.load(file_path)) {
-            LOG_ERROR("failed to open '%s' with GGUFReader.", file_path.c_str());
-            return false;
+        if (!starts_with(tensor_storage.name, prefix)) {
+            tensor_storage.name = prefix + tensor_storage.name;
         }
-
-        size_t data_offset = gguf_reader.data_offset();
-        for (const auto& gguf_tensor_info : gguf_reader.tensors()) {
-            std::string name = gguf_tensor_info.name;
-            if (!starts_with(name, prefix)) {
-                name = prefix + name;
-            }
-
-            TensorStorage tensor_storage(
-                name,
-                gguf_tensor_info.type,
-                gguf_tensor_info.shape.data(),
-                static_cast<int>(gguf_tensor_info.shape.size()),
-                file_index,
-                data_offset + gguf_tensor_info.offset);
-
-            // LOG_DEBUG("%s %s", name.c_str(), tensor_storage.to_string().c_str());
-
-            add_tensor_storage(tensor_storage);
-        }
-
-        return true;
-    }
-
-    int n_tensors = static_cast<int>(gguf_get_n_tensors(ctx_gguf_));
-
-    size_t total_size  = 0;
-    size_t data_offset = gguf_get_data_offset(ctx_gguf_);
-    for (int i = 0; i < n_tensors; i++) {
-        std::string name   = gguf_get_tensor_name(ctx_gguf_, i);
-        ggml_tensor* dummy = ggml_get_tensor(ctx_meta_, name.c_str());
-        size_t offset      = data_offset + gguf_get_tensor_offset(ctx_gguf_, i);
-
-        // LOG_DEBUG("%s", name.c_str());
-
-        if (!starts_with(name, prefix)) {
-            name = prefix + name;
-        }
-
-        TensorStorage tensor_storage(name, dummy->type, dummy->ne, ggml_n_dims(dummy), file_index, offset);
-
-        GGML_ASSERT(ggml_nbytes(dummy) == tensor_storage.nbytes());
+        tensor_storage.file_index = file_index;
 
         add_tensor_storage(tensor_storage);
     }
 
-    gguf_free(ctx_gguf_);
-    ggml_free(ctx_meta_);
-
     return true;
 }
 
 /*================================================= SafeTensorsModelLoader ==================================================*/
 
-ggml_type str_to_ggml_type(const std::string& dtype) {
-    ggml_type ttype = GGML_TYPE_COUNT;
-    if (dtype == "F16") {
-        ttype = GGML_TYPE_F16;
-    } else if (dtype == "BF16") {
-        ttype = GGML_TYPE_BF16;
-    } else if (dtype == "F32") {
-        ttype = GGML_TYPE_F32;
-    } else if (dtype == "F64") {
-        ttype = GGML_TYPE_F32;
-    } else if (dtype == "F8_E4M3") {
-        ttype = GGML_TYPE_F16;
-    } else if (dtype == "F8_E5M2") {
-        ttype = GGML_TYPE_F16;
-    } else if (dtype == "I64") {
-        ttype = GGML_TYPE_I32;
-    }
-    return ttype;
-}
-
-// https://huggingface.co/docs/safetensors/index
 bool ModelLoader::init_from_safetensors_file(const std::string& file_path, const std::string& prefix) {
     LOG_DEBUG("init from '%s', prefix = '%s'", file_path.c_str(), prefix.c_str());
+
+    std::vector<TensorStorage> tensor_storages;
+    std::string error;
+    if (!read_safetensors_file(file_path, tensor_storages, &error)) {
+        LOG_ERROR("%s", error.c_str());
+        return false;
+    }
+
     file_paths_.push_back(file_path);
     size_t file_index = file_paths_.size() - 1;
-    std::ifstream file(file_path, std::ios::binary);
-    if (!file.is_open()) {
-        LOG_ERROR("failed to open '%s'", file_path.c_str());
-        file_paths_.pop_back();
-        return false;
-    }
 
-    // get file size
-    file.seekg(0, file.end);
-    size_t file_size_ = file.tellg();
-    file.seekg(0, file.beg);
-
-    // read header size
-    if (file_size_ <= ST_HEADER_SIZE_LEN) {
-        LOG_ERROR("invalid safetensor file '%s'", file_path.c_str());
-        file_paths_.pop_back();
-        return false;
-    }
-
-    uint8_t header_size_buf[ST_HEADER_SIZE_LEN];
-    file.read((char*)header_size_buf, ST_HEADER_SIZE_LEN);
-    if (!file) {
-        LOG_ERROR("read safetensors header size failed: '%s'", file_path.c_str());
-        return false;
-    }
-
-    size_t header_size_ = read_u64(header_size_buf);
-    if (header_size_ >= file_size_) {
-        LOG_ERROR("invalid safetensor file '%s'", file_path.c_str());
-        file_paths_.pop_back();
-        return false;
-    }
-
-    // read header
-    std::vector<char> header_buf;
-    header_buf.resize(header_size_ + 1);
-    header_buf[header_size_] = '\0';
-    file.read(header_buf.data(), header_size_);
-    if (!file) {
-        LOG_ERROR("read safetensors header failed: '%s'", file_path.c_str());
-        file_paths_.pop_back();
-        return false;
-    }
-
-    nlohmann::json header_;
-    try {
-        header_ = nlohmann::json::parse(header_buf.data());
-    } catch (const std::exception&) {
-        LOG_ERROR("parsing safetensors header failed", file_path.c_str());
-        file_paths_.pop_back();
-        return false;
-    }
-
-    for (auto& item : header_.items()) {
-        std::string name           = item.key();
-        nlohmann::json tensor_info = item.value();
-        // LOG_DEBUG("%s %s\n", name.c_str(), tensor_info.dump().c_str());
-
-        if (name == "__metadata__") {
+    for (auto& tensor_storage : tensor_storages) {
+        if (is_unused_tensor(tensor_storage.name)) {
             continue;
         }
 
-        if (is_unused_tensor(name)) {
-            continue;
-        }
-
-        std::string dtype    = tensor_info["dtype"];
-        nlohmann::json shape = tensor_info["shape"];
-
-        if (dtype == "U8") {
-            continue;
-        }
-
-        size_t begin = tensor_info["data_offsets"][0].get<size_t>();
-        size_t end   = tensor_info["data_offsets"][1].get<size_t>();
-
-        ggml_type type = str_to_ggml_type(dtype);
-        if (type == GGML_TYPE_COUNT) {
-            LOG_ERROR("unsupported dtype '%s' (tensor '%s')", dtype.c_str(), name.c_str());
-            return false;
-        }
-
-        if (shape.size() > SD_MAX_DIMS) {
-            LOG_ERROR("invalid tensor '%s'", name.c_str());
-            return false;
-        }
-
-        int n_dims              = (int)shape.size();
-        int64_t ne[SD_MAX_DIMS] = {1, 1, 1, 1, 1};
-        for (int i = 0; i < n_dims; i++) {
-            ne[i] = shape[i].get<int64_t>();
-        }
-
-        if (n_dims == 5) {
-            n_dims = 4;
-            ne[0]  = ne[0] * ne[1];
-            ne[1]  = ne[2];
-            ne[2]  = ne[3];
-            ne[3]  = ne[4];
-        }
-
-        // ggml_n_dims returns 1 for scalars
-        if (n_dims == 0) {
-            n_dims = 1;
-        }
-
-        if (!starts_with(name, prefix)) {
-            name = prefix + name;
-        }
-
-        TensorStorage tensor_storage(name, type, ne, n_dims, file_index, ST_HEADER_SIZE_LEN + header_size_ + begin);
-        tensor_storage.reverse_ne();
-
-        size_t tensor_data_size = end - begin;
-
-        bool tensor_size_ok;
-        if (dtype == "F8_E4M3") {
-            tensor_storage.is_f8_e4m3 = true;
-            // f8 -> f16
-            tensor_size_ok = (tensor_storage.nbytes() == tensor_data_size * 2);
-        } else if (dtype == "F8_E5M2") {
-            tensor_storage.is_f8_e5m2 = true;
-            // f8 -> f16
-            tensor_size_ok = (tensor_storage.nbytes() == tensor_data_size * 2);
-        } else if (dtype == "F64") {
-            tensor_storage.is_f64 = true;
-            // f64 -> f32
-            tensor_size_ok = (tensor_storage.nbytes() * 2 == tensor_data_size);
-        } else if (dtype == "I64") {
-            tensor_storage.is_i64 = true;
-            // i64 -> i32
-            tensor_size_ok = (tensor_storage.nbytes() * 2 == tensor_data_size);
-        } else {
-            tensor_size_ok = (tensor_storage.nbytes() == tensor_data_size);
-        }
-        if (!tensor_size_ok) {
-            LOG_ERROR("size mismatch for tensor '%s' (%s)\n", name.c_str(), dtype.c_str());
-            return false;
+        if (!starts_with(tensor_storage.name, prefix)) {
+            tensor_storage.name = prefix + tensor_storage.name;
         }
+        tensor_storage.file_index = file_index;
 
         add_tensor_storage(tensor_storage);
 
-        // LOG_DEBUG("%s %s", tensor_storage.to_string().c_str(), dtype.c_str());
+        // LOG_DEBUG("%s", tensor_storage.to_string().c_str());
+    }
+
+    return true;
+}
+
+/*================================================= TorchLegacyModelLoader ==================================================*/
+
+bool ModelLoader::init_from_torch_legacy_file(const std::string& file_path, const std::string& prefix) {
+    LOG_DEBUG("init from torch legacy '%s'", file_path.c_str());
+
+    std::vector<TensorStorage> tensor_storages;
+    std::string error;
+    if (!read_torch_legacy_file(file_path, tensor_storages, &error)) {
+        if ((!error.empty()) && (ends_with(file_path, ".pt") || ends_with(file_path, ".pth"))) {
+            LOG_WARN("%s", error.c_str());
+        }
+        return false;
+    }
+
+    file_paths_.push_back(file_path);
+    size_t file_index = file_paths_.size() - 1;
+
+    for (auto& tensor_storage : tensor_storages) {
+        if (is_unused_tensor(tensor_storage.name)) {
+            continue;
+        }
+
+        if (!starts_with(tensor_storage.name, prefix)) {
+            tensor_storage.name = prefix + tensor_storage.name;
+        }
+        tensor_storage.file_index = file_index;
+
+        add_tensor_storage(tensor_storage);
+    }
+
+    return true;
+}
+
+/*================================================= TorchZipModelLoader ==================================================*/
+
+bool ModelLoader::init_from_torch_zip_file(const std::string& file_path, const std::string& prefix) {
+    LOG_DEBUG("init from '%s'", file_path.c_str());
+
+    std::vector<TensorStorage> tensor_storages;
+    std::string error;
+    if (!read_torch_zip_file(file_path, tensor_storages, &error)) {
+        LOG_ERROR("%s", error.c_str());
+        return false;
+    }
+
+    file_paths_.push_back(file_path);
+    size_t file_index = file_paths_.size() - 1;
+
+    for (auto& tensor_storage : tensor_storages) {
+        if (!starts_with(tensor_storage.name, prefix)) {
+            tensor_storage.name = prefix + tensor_storage.name;
+        }
+        tensor_storage.file_index = file_index;
+
+        add_tensor_storage(tensor_storage);
+
+        // LOG_DEBUG("%s", tensor_storage.to_string().c_str());
     }
 
     return true;
@@ -642,367 +422,6 @@ bool ModelLoader::init_from_diffusers_file(const std::string& file_path, const s
     return true;
 }
 
-/*================================================= CkptModelLoader ==================================================*/
-
-// $ python -m pickletools sd-v1-4/archive/data.pkl | head -n 100
-//     0: \x80 PROTO      2
-//     2: }    EMPTY_DICT
-//     3: q    BINPUT     0
-//     5: (    MARK
-//     6: X        BINUNICODE 'epoch'
-//    16: q        BINPUT     1
-//    18: K        BININT1    6
-//    20: X        BINUNICODE 'global_step'
-//    36: q        BINPUT     2
-//    38: J        BININT     470000
-//    43: X        BINUNICODE 'pytorch-lightning_version'
-//    73: q        BINPUT     3
-//    75: X        BINUNICODE '1.4.2'
-//    85: q        BINPUT     4
-//    87: X        BINUNICODE 'state_dict'
-//   102: q        BINPUT     5
-//   104: }        EMPTY_DICT
-//   105: q        BINPUT     6
-//   107: (        MARK
-//   108: X            BINUNICODE 'betas'
-//   118: q            BINPUT     7
-//   120: c            GLOBAL     'torch._utils _rebuild_tensor_v2'
-//   153: q            BINPUT     8
-//   155: (            MARK
-//   156: (                MARK
-//   157: X                    BINUNICODE 'storage'
-//   169: q                    BINPUT     9
-//   171: c                    GLOBAL     'torch FloatStorage'
-//   191: q                    BINPUT     10
-//   193: X                    BINUNICODE '0'
-//   199: q                    BINPUT     11
-//   201: X                    BINUNICODE 'cpu'
-//   209: q                    BINPUT     12
-//   211: M                    BININT2    1000
-//   214: t                    TUPLE      (MARK at 156)
-//   215: q                BINPUT     13
-//   217: Q                BINPERSID
-//   218: K                BININT1    0
-//   220: M                BININT2    1000
-//  ...............................
-//  3201: q            BINPUT     250
-//  3203: R            REDUCE
-//  3204: q            BINPUT     251
-//  3206: X            BINUNICODE 'model.diffusion_model.input_blocks.1.1.proj_in.weight'
-//  3264: q            BINPUT     252
-//  3266: h            BINGET     8
-//  3268: (            MARK
-//  3269: (                MARK
-//  3270: h                    BINGET     9
-//  3272: h                    BINGET     10
-//  3274: X                    BINUNICODE '30'
-//  3281: q                    BINPUT     253
-//  3283: h                    BINGET     12
-//  3285: J                    BININT     102400
-//  3290: t                    TUPLE      (MARK at 3269)
-//  3291: q                BINPUT     254
-//  3293: Q                BINPERSID
-//  3294: K                BININT1    0
-//  3296: (                MARK
-//  3297: M                    BININT2    320
-//  3300: M                    BININT2    320
-//  3303: K                    BININT1    1
-//  3305: K                    BININT1    1
-//  3307: t                    TUPLE      (MARK at 3296)
-//  3308: q                BINPUT     255
-//  3310: (                MARK
-//  3311: M                    BININT2    320
-//  3314: K                    BININT1    1
-//  3316: K                    BININT1    1
-//  3318: K                    BININT1    1
-//  3320: t                    TUPLE      (MARK at 3310)
-//  3321: r                LONG_BINPUT 256
-//  3326: \x89             NEWFALSE
-//  3327: h                BINGET     16
-//  3329: )                EMPTY_TUPLE
-//  3330: R                REDUCE
-//  3331: r                LONG_BINPUT 257
-//  3336: t                TUPLE      (MARK at 3268)
-//  3337: r            LONG_BINPUT 258
-//  3342: R            REDUCE
-//  3343: r            LONG_BINPUT 259
-//  3348: X            BINUNICODE 'model.diffusion_model.input_blocks.1.1.proj_in.bias'
-//  3404: r            LONG_BINPUT 260
-//  3409: h            BINGET     8
-//  3411: (            MARK
-//  3412: (                MARK
-//  3413: h                    BINGET     9
-//  3415: h                    BINGET     10
-//  3417: X                    BINUNICODE '31'
-
-struct PickleTensorReader {
-    enum ReadPhase {
-        READ_NAME,
-        READ_DATA,
-        CHECK_SIZE,
-        READ_DIMENS
-    };
-    ReadPhase phase   = READ_NAME;
-    size_t entry_size = 0;
-    int32_t nelements = 0;
-
-    TensorStorage tensor_storage;
-
-    static ggml_type global_type;  // all pickle_tensors data type
-    static bool read_global_type;
-
-    bool read_int_value(uint32_t value) {
-        if (phase == CHECK_SIZE) {
-            if (entry_size == value * ggml_type_size(tensor_storage.type)) {
-                nelements = value;
-                phase     = READ_DIMENS;
-                return true;
-            } else {
-                phase = READ_NAME;
-            }
-        } else if (phase == READ_DIMENS) {
-            if (tensor_storage.n_dims + 1 > SD_MAX_DIMS) {  // too many dimens
-                phase                 = READ_NAME;
-                tensor_storage.n_dims = 0;
-            }
-            if (nelements % value == 0) {
-                tensor_storage.ne[tensor_storage.n_dims] = value;
-                tensor_storage.n_dims++;
-            }
-        }
-        return false;
-    }
-
-    void read_global(const std::string& str) {
-        if (str == "FloatStorage") {
-            if (read_global_type) {
-                global_type      = GGML_TYPE_F32;
-                read_global_type = false;
-            }
-            tensor_storage.type = GGML_TYPE_F32;
-        } else if (str == "HalfStorage") {
-            if (read_global_type) {
-                global_type      = GGML_TYPE_F16;
-                read_global_type = false;
-            }
-            tensor_storage.type = GGML_TYPE_F16;
-        }
-    }
-
-    void read_string(const std::string& str, zip_t* zip, std::string dir) {
-        if (str == "storage") {
-            read_global_type = true;
-        } else if (str != "state_dict") {
-            if (phase == READ_DATA) {
-                std::string entry_name = dir + "data/" + std::string(str);
-
-                size_t i, n = zip_entries_total(zip);
-                for (i = 0; i < n; ++i) {
-                    zip_entry_openbyindex(zip, i);
-                    {
-                        std::string name = zip_entry_name(zip);
-                        if (name == entry_name) {
-                            tensor_storage.index_in_zip = (int)i;
-                            entry_size                  = zip_entry_size(zip);
-                            zip_entry_close(zip);
-                            break;
-                        }
-                    }
-                    zip_entry_close(zip);
-                }
-
-                phase = entry_size > 0 ? CHECK_SIZE : READ_NAME;
-            }
-            if (!read_global_type && phase == READ_NAME) {
-                tensor_storage.name = str;
-                phase               = READ_DATA;
-                tensor_storage.type = global_type;
-            }
-        }
-    }
-};
-
-ggml_type PickleTensorReader::global_type = GGML_TYPE_F32;  // all pickle_tensors data type
-bool PickleTensorReader::read_global_type = false;
-
-int find_char(uint8_t* buffer, int len, char c) {
-    for (int pos = 0; pos < len; pos++) {
-        if (buffer[pos] == c) {
-            return pos;
-        }
-    }
-    return -1;
-}
-
-#define MAX_STRING_BUFFER 512
-
-bool ModelLoader::parse_data_pkl(uint8_t* buffer,
-                                 size_t buffer_size,
-                                 zip_t* zip,
-                                 std::string dir,
-                                 size_t file_index,
-                                 const std::string prefix) {
-    uint8_t* buffer_end = buffer + buffer_size;
-    if (buffer[0] == 0x80) {  // proto
-        if (buffer[1] != 2) {
-            LOG_ERROR("Unsupported protocol\n");
-            return false;
-        }
-        buffer += 2;  // 0x80 and version
-        char string_buffer[MAX_STRING_BUFFER];
-        bool finish = false;
-        PickleTensorReader reader;
-        // read pickle binary file
-        while (!finish && buffer < buffer_end) {
-            uint8_t opcode = *buffer;
-            buffer++;
-            // https://github.com/python/cpython/blob/3.7/Lib/pickletools.py#L1048
-            // https://github.com/python/cpython/blob/main/Lib/pickle.py#L105
-            switch (opcode) {
-                case '}':  // EMPTY_DICT     = b'}'   # push empty dict
-                    break;
-                case ']':  // EMPTY_LIST     = b']'   # push empty list
-                    break;
-                // skip unused sections
-                case 'h':  // BINGET         = b'h'   #   "    "    "    "   "   "  ;   "    " 1-byte arg
-                case 'q':  // BINPUT         = b'q'   #   "     "    "   "   " ;   "    " 1-byte arg
-                case 'Q':  // BINPERSID      = b'Q'   #  "       "         "  ;  "  "   "     "  stack
-                    buffer++;
-                    break;
-                case 'r':  // LONG_BINPUT    = b'r'   #   "     "    "   "   " ;   "    " 4-byte arg
-                    buffer += 4;
-                    break;
-                case 0x95:  // FRAME            = b'\x95'  # indicate the beginning of a new frame
-                    buffer += 8;
-                    break;
-                case 0x94:  // MEMOIZE          = b'\x94'  # store top of the stack in memo
-                    break;
-                case '(':  // MARK           = b'('   # push special markobject on stack
-                    break;
-                case 'K':  // BININT1        = b'K'   # push 1-byte unsigned int
-                {
-                    uint8_t value = *buffer;
-                    if (reader.read_int_value(value)) {
-                        buffer++;
-                    }
-                    buffer++;
-                } break;
-                case 'M':  // BININT2        = b'M'   # push 2-byte unsigned int
-                {
-                    uint16_t value = read_short(buffer);
-                    if (reader.read_int_value(value)) {
-                        buffer++;
-                    }
-                    buffer += 2;
-                } break;
-                case 'J':  // BININT         = b'J'   # push four-byte signed int
-                {
-                    const int32_t value = read_int(buffer);
-                    if (reader.read_int_value(value)) {
-                        buffer++;  // skip tuple after read num_elements
-                    }
-                    buffer += 4;
-                } break;
-                case 'X':  // BINUNICODE     = b'X'   #   "     "       "  ; counted UTF-8 string argument
-                {
-                    const int32_t len = read_int(buffer);
-                    buffer += 4;
-                    memset(string_buffer, 0, MAX_STRING_BUFFER);
-                    if (len > MAX_STRING_BUFFER) {
-                        LOG_WARN("tensor name very large");
-                    }
-                    memcpy(string_buffer, buffer, len < MAX_STRING_BUFFER ? len : (MAX_STRING_BUFFER - 1));
-                    buffer += len;
-                    reader.read_string(string_buffer, zip, dir);
-                } break;
-                case 0x8C:  // SHORT_BINUNICODE = b'\x8c'  # push short string; UTF-8 length < 256 bytes
-                {
-                    const int8_t len = *buffer;
-                    buffer++;
-                    memset(string_buffer, 0, MAX_STRING_BUFFER);
-                    memcpy(string_buffer, buffer, len);
-                    buffer += len;
-                    // printf("String: '%s'\n", string_buffer);
-                } break;
-                case 'c':  // GLOBAL         = b'c'   # push self.find_class(modname, name); 2 string args
-                {
-                    int len = find_char(buffer, MAX_STRING_BUFFER, '\n');
-
-                    buffer += len + 1;
-                    len = find_char(buffer, MAX_STRING_BUFFER, '\n');
-
-                    memset(string_buffer, 0, MAX_STRING_BUFFER);
-                    memcpy(string_buffer, buffer, len);
-                    buffer += len + 1;
-                    reader.read_global(string_buffer);
-                } break;
-                case 0x86:  // TUPLE2         = b'\x86'  # build 2-tuple from two topmost stack items
-                case 0x85:  // TUPLE1         = b'\x85'  # build 1-tuple from stack top
-                case 't':   // TUPLE          = b't'   # build tuple from topmost stack items
-                    if (reader.phase == PickleTensorReader::READ_DIMENS) {
-                        reader.tensor_storage.reverse_ne();
-                        reader.tensor_storage.file_index = file_index;
-                        // if(strcmp(prefix.c_str(), "scarlett") == 0)
-                        // printf(" ZIP got tensor %s \n ", reader.tensor_storage.name.c_str());
-                        std::string name = reader.tensor_storage.name;
-                        if (!starts_with(name, prefix)) {
-                            name = prefix + name;
-                        }
-                        reader.tensor_storage.name = name;
-                        add_tensor_storage(reader.tensor_storage);
-
-                        // LOG_DEBUG("%s", reader.tensor_storage.name.c_str());
-                        // reset
-                        reader = PickleTensorReader();
-                    }
-                    break;
-                case '.':  // STOP           = b'.'   # every pickle ends with STOP
-                    finish = true;
-                    break;
-                default:
-                    break;
-            }
-        }
-    }
-    return true;
-}
-
-bool ModelLoader::init_from_ckpt_file(const std::string& file_path, const std::string& prefix) {
-    LOG_DEBUG("init from '%s'", file_path.c_str());
-    file_paths_.push_back(file_path);
-    size_t file_index = file_paths_.size() - 1;
-
-    zip_t* zip = zip_open(file_path.c_str(), 0, 'r');
-    if (zip == nullptr) {
-        LOG_ERROR("failed to open '%s'", file_path.c_str());
-        return false;
-    }
-    int n = (int)zip_entries_total(zip);
-    for (int i = 0; i < n; ++i) {
-        zip_entry_openbyindex(zip, i);
-        {
-            std::string name = zip_entry_name(zip);
-            size_t pos       = name.find("data.pkl");
-            if (pos != std::string::npos) {
-                std::string dir = name.substr(0, pos);
-                printf("ZIP %d, name = %s, dir = %s \n", i, name.c_str(), dir.c_str());
-                void* pkl_data = nullptr;
-                size_t pkl_size;
-                zip_entry_read(zip, &pkl_data, &pkl_size);
-
-                // LOG_DEBUG("%lld", pkl_size);
-
-                parse_data_pkl((uint8_t*)pkl_data, pkl_size, zip, dir, file_index, prefix);
-
-                free(pkl_data);
-            }
-        }
-        zip_entry_close(zip);
-    }
-    zip_close(zip);
-    return true;
-}
-
 SDVersion ModelLoader::get_sd_version() {
     TensorStorage token_embedding_weight, input_block_weight;
 
@@ -1268,8 +687,8 @@ std::map<ggml_type, uint32_t> ModelLoader::get_vae_wtype_stat() {
     return wtype_stat;
 }
 
-static std::vector<std::pair<std::string, ggml_type>> parse_tensor_type_rules(const std::string& tensor_type_rules) {
-    std::vector<std::pair<std::string, ggml_type>> result;
+TensorTypeRules parse_tensor_type_rules(const std::string& tensor_type_rules) {
+    TensorTypeRules result;
     for (const auto& item : split_string(tensor_type_rules, ',')) {
         if (item.size() == 0)
             continue;
@@ -1702,76 +1121,6 @@ bool ModelLoader::tensor_should_be_converted(const TensorStorage& tensor_storage
     return false;
 }
 
-bool ModelLoader::save_to_gguf_file(const std::string& file_path, ggml_type type, const std::string& tensor_type_rules_str) {
-    auto backend    = ggml_backend_cpu_init();
-    size_t mem_size = 1 * 1024 * 1024;  // for padding
-    mem_size += tensor_storage_map.size() * ggml_tensor_overhead();
-    mem_size += get_params_mem_size(backend, type);
-    LOG_INFO("model tensors mem size: %.2fMB", mem_size / 1024.f / 1024.f);
-    ggml_context* ggml_ctx = ggml_init({mem_size, nullptr, false});
-
-    gguf_context* gguf_ctx = gguf_init_empty();
-
-    auto tensor_type_rules = parse_tensor_type_rules(tensor_type_rules_str);
-
-    std::mutex tensor_mutex;
-    auto on_new_tensor_cb = [&](const TensorStorage& tensor_storage, ggml_tensor** dst_tensor) -> bool {
-        const std::string& name = tensor_storage.name;
-        ggml_type tensor_type   = tensor_storage.type;
-        ggml_type dst_type      = type;
-
-        for (const auto& tensor_type_rule : tensor_type_rules) {
-            std::regex pattern(tensor_type_rule.first);
-            if (std::regex_search(name, pattern)) {
-                dst_type = tensor_type_rule.second;
-                break;
-            }
-        }
-
-        if (tensor_should_be_converted(tensor_storage, dst_type)) {
-            tensor_type = dst_type;
-        }
-
-        std::lock_guard<std::mutex> lock(tensor_mutex);
-        ggml_tensor* tensor = ggml_new_tensor(ggml_ctx, tensor_type, tensor_storage.n_dims, tensor_storage.ne);
-        if (tensor == nullptr) {
-            LOG_ERROR("ggml_new_tensor failed");
-            return false;
-        }
-        ggml_set_name(tensor, name.c_str());
-
-        // LOG_DEBUG("%s %d %s %d[%d %d %d %d] %d[%d %d %d %d]", name.c_str(),
-        // ggml_nbytes(tensor), ggml_type_name(tensor_type),
-        // tensor_storage.n_dims,
-        // tensor_storage.ne[0], tensor_storage.ne[1], tensor_storage.ne[2], tensor_storage.ne[3],
-        // tensor->n_dims, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]);
-
-        if (!tensor->data) {
-            GGML_ASSERT(ggml_nelements(tensor) == 0);
-            // avoid crashing the gguf writer by setting a dummy pointer for zero-sized tensors
-            LOG_DEBUG("setting dummy pointer for zero-sized tensor %s", name.c_str());
-            tensor->data = ggml_get_mem_buffer(ggml_ctx);
-        }
-
-        *dst_tensor = tensor;
-
-        gguf_add_tensor(gguf_ctx, tensor);
-
-        return true;
-    };
-
-    bool success = load_tensors(on_new_tensor_cb);
-    ggml_backend_free(backend);
-    LOG_INFO("load tensors done");
-    LOG_INFO("trying to save tensors to %s", file_path.c_str());
-    if (success) {
-        gguf_write_to_file(gguf_ctx, file_path.c_str(), false);
-    }
-    ggml_free(ggml_ctx);
-    gguf_free(gguf_ctx);
-    return success;
-}
-
 int64_t ModelLoader::get_params_mem_size(ggml_backend_t backend, ggml_type type) {
     size_t alignment = 128;
     if (backend != nullptr) {
@@ -1791,29 +1140,3 @@ int64_t ModelLoader::get_params_mem_size(ggml_backend_t backend, ggml_type type)
 
     return mem_size;
 }
-
-bool convert(const char* input_path,
-             const char* vae_path,
-             const char* output_path,
-             sd_type_t output_type,
-             const char* tensor_type_rules,
-             bool convert_name) {
-    ModelLoader model_loader;
-
-    if (!model_loader.init_from_file(input_path)) {
-        LOG_ERROR("init model loader from file failed: '%s'", input_path);
-        return false;
-    }
-
-    if (vae_path != nullptr && strlen(vae_path) > 0) {
-        if (!model_loader.init_from_file(vae_path, "vae.")) {
-            LOG_ERROR("init model loader from file failed: '%s'", vae_path);
-            return false;
-        }
-    }
-    if (convert_name) {
-        model_loader.convert_tensors_name();
-    }
-    bool success = model_loader.save_to_gguf_file(output_path, (ggml_type)output_type, tensor_type_rules);
-    return success;
-}

src/model.h

@@ -5,20 +5,13 @@
 #include <map>
 #include <memory>
 #include <set>
-#include <sstream>
 #include <string>
-#include <tuple>
-#include <utility>
 #include <vector>
 
 #include "ggml-backend.h"
 #include "ggml.h"
-#include "gguf.h"
-#include "json.hpp"
+#include "model_io/tensor_storage.h"
 #include "ordered_map.hpp"
-#include "zip.h"
-
-#define SD_MAX_DIMS 5
 
 enum SDVersion {
     VERSION_SD1,
@@ -195,116 +188,10 @@ enum PMVersion {
     PM_VERSION_2,
 };
 
-struct TensorStorage {
-    std::string name;
-    ggml_type type          = GGML_TYPE_F32;
-    ggml_type expected_type = GGML_TYPE_COUNT;
-    bool is_f8_e4m3         = false;
-    bool is_f8_e5m2         = false;
-    bool is_f64             = false;
-    bool is_i64             = false;
-    int64_t ne[SD_MAX_DIMS] = {1, 1, 1, 1, 1};
-    int n_dims              = 0;
-
-    size_t file_index = 0;
-    int index_in_zip  = -1;  // >= means stored in a zip file
-    uint64_t offset   = 0;   // offset in file
-
-    TensorStorage() = default;
-
-    TensorStorage(std::string name, ggml_type type, const int64_t* ne, int n_dims, size_t file_index, size_t offset = 0)
-        : name(std::move(name)), type(type), n_dims(n_dims), file_index(file_index), offset(offset) {
-        for (int i = 0; i < n_dims; i++) {
-            this->ne[i] = ne[i];
-        }
-    }
-
-    int64_t nelements() const {
-        int64_t n = 1;
-        for (int i = 0; i < SD_MAX_DIMS; i++) {
-            n *= ne[i];
-        }
-        return n;
-    }
-
-    int64_t nbytes() const {
-        return nelements() * ggml_type_size(type) / ggml_blck_size(type);
-    }
-
-    int64_t nbytes_to_read() const {
-        if (is_f8_e4m3 || is_f8_e5m2) {
-            return nbytes() / 2;
-        } else if (is_f64 || is_i64) {
-            return nbytes() * 2;
-        } else {
-            return nbytes();
-        }
-    }
-
-    void unsqueeze() {
-        if (n_dims == 2) {
-            n_dims = 4;
-            ne[3]  = ne[1];
-            ne[2]  = ne[0];
-            ne[1]  = 1;
-            ne[0]  = 1;
-        }
-    }
-
-    std::vector<TensorStorage> chunk(size_t n) {
-        std::vector<TensorStorage> chunks;
-        uint64_t chunk_size = nbytes_to_read() / n;
-        // printf("%d/%d\n", chunk_size, nbytes_to_read());
-        reverse_ne();
-        for (size_t i = 0; i < n; i++) {
-            TensorStorage chunk_i = *this;
-            chunk_i.ne[0]         = ne[0] / n;
-            chunk_i.offset        = offset + i * chunk_size;
-            chunk_i.reverse_ne();
-            chunks.push_back(chunk_i);
-        }
-        reverse_ne();
-        return chunks;
-    }
-
-    void reverse_ne() {
-        int64_t new_ne[SD_MAX_DIMS] = {1, 1, 1, 1, 1};
-        for (int i = 0; i < n_dims; i++) {
-            new_ne[i] = ne[n_dims - 1 - i];
-        }
-        for (int i = 0; i < n_dims; i++) {
-            ne[i] = new_ne[i];
-        }
-    }
-
-    std::string to_string() const {
-        std::stringstream ss;
-        const char* type_name = ggml_type_name(type);
-        if (is_f8_e4m3) {
-            type_name = "f8_e4m3";
-        } else if (is_f8_e5m2) {
-            type_name = "f8_e5m2";
-        } else if (is_f64) {
-            type_name = "f64";
-        } else if (is_i64) {
-            type_name = "i64";
-        }
-        ss << name << " | " << type_name << " | ";
-        ss << n_dims << " [";
-        for (int i = 0; i < SD_MAX_DIMS; i++) {
-            ss << ne[i];
-            if (i != SD_MAX_DIMS - 1) {
-                ss << ", ";
-            }
-        }
-        ss << "]";
-        return ss.str();
-    }
-};
-
-typedef std::function<bool(const TensorStorage&, ggml_tensor**)> on_new_tensor_cb_t;
-
 typedef OrderedMap<std::string, TensorStorage> String2TensorStorage;
+using TensorTypeRules = std::vector<std::pair<std::string, ggml_type>>;
+
+TensorTypeRules parse_tensor_type_rules(const std::string& tensor_type_rules);
 
 class ModelLoader {
 protected:
@@ -314,16 +201,10 @@ protected:
 
     void add_tensor_storage(const TensorStorage& tensor_storage);
 
-    bool parse_data_pkl(uint8_t* buffer,
-                        size_t buffer_size,
-                        zip_t* zip,
-                        std::string dir,
-                        size_t file_index,
-                        const std::string prefix);
-
     bool init_from_gguf_file(const std::string& file_path, const std::string& prefix = "");
     bool init_from_safetensors_file(const std::string& file_path, const std::string& prefix = "");
-    bool init_from_ckpt_file(const std::string& file_path, const std::string& prefix = "");
+    bool init_from_torch_zip_file(const std::string& file_path, const std::string& prefix = "");
+    bool init_from_torch_legacy_file(const std::string& file_path, const std::string& prefix = "");
     bool init_from_diffusers_file(const std::string& file_path, const std::string& prefix = "");
 
 public:
@@ -353,7 +234,6 @@ public:
         return names;
     }
 
-    bool save_to_gguf_file(const std::string& file_path, ggml_type type, const std::string& tensor_type_rules);
     bool tensor_should_be_converted(const TensorStorage& tensor_storage, ggml_type type);
     int64_t get_params_mem_size(ggml_backend_t backend, ggml_type type = GGML_TYPE_COUNT);
     ~ModelLoader() = default;

src/model_io/binary_io.h

@@ -0,0 +1,57 @@
+#ifndef __SD_MODEL_IO_BINARY_IO_H__
+#define __SD_MODEL_IO_BINARY_IO_H__
+
+#include <cstdint>
+#include <ostream>
+
+namespace model_io {
+
+    inline int32_t read_int(const uint8_t* buffer) {
+        uint32_t value = 0;
+        value |= static_cast<uint32_t>(buffer[3]) << 24;
+        value |= static_cast<uint32_t>(buffer[2]) << 16;
+        value |= static_cast<uint32_t>(buffer[1]) << 8;
+        value |= static_cast<uint32_t>(buffer[0]);
+        return static_cast<int32_t>(value);
+    }
+
+    inline uint16_t read_short(const uint8_t* buffer) {
+        uint16_t value = 0;
+        value |= static_cast<uint16_t>(buffer[1]) << 8;
+        value |= static_cast<uint16_t>(buffer[0]);
+        return value;
+    }
+
+    inline uint64_t read_u64(const uint8_t* buffer) {
+        uint64_t value = 0;
+        value |= static_cast<uint64_t>(buffer[7]) << 56;
+        value |= static_cast<uint64_t>(buffer[6]) << 48;
+        value |= static_cast<uint64_t>(buffer[5]) << 40;
+        value |= static_cast<uint64_t>(buffer[4]) << 32;
+        value |= static_cast<uint64_t>(buffer[3]) << 24;
+        value |= static_cast<uint64_t>(buffer[2]) << 16;
+        value |= static_cast<uint64_t>(buffer[1]) << 8;
+        value |= static_cast<uint64_t>(buffer[0]);
+        return value;
+    }
+
+    inline void write_u64(std::ostream& stream, uint64_t value) {
+        uint8_t buffer[8];
+        for (int i = 0; i < 8; ++i) {
+            buffer[i] = static_cast<uint8_t>((value >> (8 * i)) & 0xFF);
+        }
+        stream.write((const char*)buffer, sizeof(buffer));
+    }
+
+    inline int find_char(const uint8_t* buffer, int len, char c) {
+        for (int pos = 0; pos < len; pos++) {
+            if (buffer[pos] == (uint8_t)c) {
+                return pos;
+            }
+        }
+        return -1;
+    }
+
+}  // namespace model_io
+
+#endif  // __SD_MODEL_IO_BINARY_IO_H__

src/model_io/gguf_io.cpp

@@ -0,0 +1,123 @@
+#include "gguf_io.h"
+
+#include <cstdint>
+#include <fstream>
+#include <string>
+#include <vector>
+
+#include "gguf.h"
+#include "gguf_reader_ext.h"
+#include "util.h"
+
+static void set_error(std::string* error, const std::string& message) {
+    if (error != nullptr) {
+        *error = message;
+    }
+}
+
+bool is_gguf_file(const std::string& file_path) {
+    std::ifstream file(file_path, std::ios::binary);
+    if (!file.is_open()) {
+        return false;
+    }
+
+    char magic[4];
+
+    file.read(magic, sizeof(magic));
+    if (!file) {
+        return false;
+    }
+    for (uint32_t i = 0; i < sizeof(magic); i++) {
+        if (magic[i] != GGUF_MAGIC[i]) {
+            return false;
+        }
+    }
+
+    return true;
+}
+
+bool read_gguf_file(const std::string& file_path,
+                    std::vector<TensorStorage>& tensor_storages,
+                    std::string* error) {
+    tensor_storages.clear();
+
+    gguf_context* ctx_gguf_ = nullptr;
+    ggml_context* ctx_meta_ = nullptr;
+
+    ctx_gguf_ = gguf_init_from_file(file_path.c_str(), {true, &ctx_meta_});
+    if (!ctx_gguf_) {
+        GGUFReader gguf_reader;
+        if (!gguf_reader.load(file_path)) {
+            set_error(error, "failed to open '" + file_path + "' with GGUFReader");
+            return false;
+        }
+
+        size_t data_offset = gguf_reader.data_offset();
+        for (const auto& gguf_tensor_info : gguf_reader.tensors()) {
+            TensorStorage tensor_storage(
+                gguf_tensor_info.name,
+                gguf_tensor_info.type,
+                gguf_tensor_info.shape.data(),
+                static_cast<int>(gguf_tensor_info.shape.size()),
+                0,
+                data_offset + gguf_tensor_info.offset);
+
+            tensor_storages.push_back(tensor_storage);
+        }
+
+        return true;
+    }
+
+    int n_tensors = static_cast<int>(gguf_get_n_tensors(ctx_gguf_));
+
+    size_t data_offset = gguf_get_data_offset(ctx_gguf_);
+    for (int i = 0; i < n_tensors; i++) {
+        std::string name   = gguf_get_tensor_name(ctx_gguf_, i);
+        ggml_tensor* dummy = ggml_get_tensor(ctx_meta_, name.c_str());
+        size_t offset      = data_offset + gguf_get_tensor_offset(ctx_gguf_, i);
+
+        TensorStorage tensor_storage(name, dummy->type, dummy->ne, ggml_n_dims(dummy), 0, offset);
+
+        if (ggml_nbytes(dummy) != tensor_storage.nbytes()) {
+            gguf_free(ctx_gguf_);
+            ggml_free(ctx_meta_);
+            set_error(error, "size mismatch for tensor '" + name + "'");
+            return false;
+        }
+
+        tensor_storages.push_back(tensor_storage);
+    }
+
+    gguf_free(ctx_gguf_);
+    ggml_free(ctx_meta_);
+
+    return true;
+}
+
+bool write_gguf_file(const std::string& file_path,
+                     const std::vector<TensorWriteInfo>& tensors,
+                     std::string* error) {
+    gguf_context* gguf_ctx = gguf_init_empty();
+    if (gguf_ctx == nullptr) {
+        set_error(error, "gguf_init_empty failed");
+        return false;
+    }
+
+    for (const TensorWriteInfo& write_tensor : tensors) {
+        ggml_tensor* tensor = write_tensor.tensor;
+        if (tensor == nullptr) {
+            set_error(error, "null tensor cannot be written to GGUF");
+            gguf_free(gguf_ctx);
+            return false;
+        }
+        gguf_add_tensor(gguf_ctx, tensor);
+    }
+
+    LOG_INFO("trying to save tensors to %s", file_path.c_str());
+    bool success = gguf_write_to_file(gguf_ctx, file_path.c_str(), false);
+    if (!success) {
+        set_error(error, "failed to write GGUF file '" + file_path + "'");
+    }
+    gguf_free(gguf_ctx);
+    return success;
+}

src/model_io/gguf_io.h

@@ -0,0 +1,17 @@
+#ifndef __SD_MODEL_IO_GGUF_IO_H__
+#define __SD_MODEL_IO_GGUF_IO_H__
+
+#include <string>
+#include <vector>
+
+#include "tensor_storage.h"
+
+bool is_gguf_file(const std::string& file_path);
+bool read_gguf_file(const std::string& file_path,
+                    std::vector<TensorStorage>& tensor_storages,
+                    std::string* error = nullptr);
+bool write_gguf_file(const std::string& file_path,
+                     const std::vector<TensorWriteInfo>& tensors,
+                     std::string* error = nullptr);
+
+#endif  // __SD_MODEL_IO_GGUF_IO_H__

src/model_io/gguf_reader_ext.h

@@ -1,5 +1,5 @@
-#ifndef __GGUF_READER_HPP__
-#define __GGUF_READER_HPP__
+#ifndef __SD_MODEL_IO_GGUF_READER_EXT_H__
+#define __SD_MODEL_IO_GGUF_READER_EXT_H__
 
 #include <cstdint>
 #include <fstream>
@@ -231,4 +231,4 @@ public:
     size_t data_offset() const { return data_offset_; }
 };
 
-#endif  // __GGUF_READER_HPP__
+#endif  // __SD_MODEL_IO_GGUF_READER_EXT_H__

src/model_io/pickle_io.h

@@ -0,0 +1,21 @@
+#ifndef __SD_MODEL_IO_PICKLE_IO_H__
+#define __SD_MODEL_IO_PICKLE_IO_H__
+
+#include <cstddef>
+#include <cstdint>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+#include "tensor_storage.h"
+
+bool skip_pickle_object(const uint8_t* buffer, size_t buffer_size, size_t* object_size);
+bool pickle_object_is_torch_magic_number(const uint8_t* buffer, size_t buffer_size);
+bool parse_pickle_uint32_object(const uint8_t* buffer, size_t buffer_size, uint32_t* value);
+bool parse_torch_state_dict_pickle(const uint8_t* buffer,
+                                   size_t buffer_size,
+                                   std::vector<TensorStorage>& tensor_storages,
+                                   std::unordered_map<std::string, uint64_t>& storage_nbytes,
+                                   std::string* error = nullptr);
+
+#endif  // __SD_MODEL_IO_PICKLE_IO_H__

src/model_io/safetensors_io.cpp

@@ -0,0 +1,316 @@
+#include "safetensors_io.h"
+
+#include <cstdint>
+#include <exception>
+#include <fstream>
+#include <string>
+#include <vector>
+
+#include "binary_io.h"
+#include "json.hpp"
+#include "util.h"
+
+static constexpr size_t ST_HEADER_SIZE_LEN = 8;
+
+static void set_error(std::string* error, const std::string& message) {
+    if (error != nullptr) {
+        *error = message;
+    }
+}
+
+bool is_safetensors_file(const std::string& file_path) {
+    std::ifstream file(file_path, std::ios::binary);
+    if (!file.is_open()) {
+        return false;
+    }
+
+    // get file size
+    file.seekg(0, file.end);
+    size_t file_size_ = file.tellg();
+    file.seekg(0, file.beg);
+
+    // read header size
+    if (file_size_ <= ST_HEADER_SIZE_LEN) {
+        return false;
+    }
+
+    uint8_t header_size_buf[ST_HEADER_SIZE_LEN];
+    file.read((char*)header_size_buf, ST_HEADER_SIZE_LEN);
+    if (!file) {
+        return false;
+    }
+
+    size_t header_size_ = model_io::read_u64(header_size_buf);
+    if (header_size_ >= file_size_ || header_size_ <= 2) {
+        return false;
+    }
+
+    // read header
+    std::vector<char> header_buf;
+    header_buf.resize(header_size_ + 1);
+    header_buf[header_size_] = '\0';
+    file.read(header_buf.data(), header_size_);
+    if (!file) {
+        return false;
+    }
+    try {
+        nlohmann::json header_ = nlohmann::json::parse(header_buf.data());
+    } catch (const std::exception&) {
+        return false;
+    }
+    return true;
+}
+
+static ggml_type safetensors_dtype_to_ggml_type(const std::string& dtype) {
+    ggml_type ttype = GGML_TYPE_COUNT;
+    if (dtype == "F16") {
+        ttype = GGML_TYPE_F16;
+    } else if (dtype == "BF16") {
+        ttype = GGML_TYPE_BF16;
+    } else if (dtype == "F32") {
+        ttype = GGML_TYPE_F32;
+    } else if (dtype == "F64") {
+        ttype = GGML_TYPE_F32;
+    } else if (dtype == "F8_E4M3") {
+        ttype = GGML_TYPE_F16;
+    } else if (dtype == "F8_E5M2") {
+        ttype = GGML_TYPE_F16;
+    } else if (dtype == "I32") {
+        ttype = GGML_TYPE_I32;
+    } else if (dtype == "I64") {
+        ttype = GGML_TYPE_I32;
+    }
+    return ttype;
+}
+
+// https://huggingface.co/docs/safetensors/index
+bool read_safetensors_file(const std::string& file_path,
+                           std::vector<TensorStorage>& tensor_storages,
+                           std::string* error) {
+    std::ifstream file(file_path, std::ios::binary);
+    if (!file.is_open()) {
+        set_error(error, "failed to open '" + file_path + "'");
+        return false;
+    }
+
+    // get file size
+    file.seekg(0, file.end);
+    size_t file_size_ = file.tellg();
+    file.seekg(0, file.beg);
+
+    // read header size
+    if (file_size_ <= ST_HEADER_SIZE_LEN) {
+        set_error(error, "invalid safetensor file '" + file_path + "'");
+        return false;
+    }
+
+    uint8_t header_size_buf[ST_HEADER_SIZE_LEN];
+    file.read((char*)header_size_buf, ST_HEADER_SIZE_LEN);
+    if (!file) {
+        set_error(error, "read safetensors header size failed: '" + file_path + "'");
+        return false;
+    }
+
+    size_t header_size_ = model_io::read_u64(header_size_buf);
+    if (header_size_ >= file_size_) {
+        set_error(error, "invalid safetensor file '" + file_path + "'");
+        return false;
+    }
+
+    // read header
+    std::vector<char> header_buf;
+    header_buf.resize(header_size_ + 1);
+    header_buf[header_size_] = '\0';
+    file.read(header_buf.data(), header_size_);
+    if (!file) {
+        set_error(error, "read safetensors header failed: '" + file_path + "'");
+        return false;
+    }
+
+    nlohmann::json header_;
+    try {
+        header_ = nlohmann::json::parse(header_buf.data());
+    } catch (const std::exception&) {
+        set_error(error, "parsing safetensors header failed: '" + file_path + "'");
+        return false;
+    }
+
+    tensor_storages.clear();
+    for (auto& item : header_.items()) {
+        std::string name           = item.key();
+        nlohmann::json tensor_info = item.value();
+        // LOG_DEBUG("%s %s\n", name.c_str(), tensor_info.dump().c_str());
+
+        if (name == "__metadata__") {
+            continue;
+        }
+
+        std::string dtype    = tensor_info["dtype"];
+        nlohmann::json shape = tensor_info["shape"];
+
+        if (dtype == "U8") {
+            continue;
+        }
+
+        size_t begin = tensor_info["data_offsets"][0].get<size_t>();
+        size_t end   = tensor_info["data_offsets"][1].get<size_t>();
+
+        ggml_type type = safetensors_dtype_to_ggml_type(dtype);
+        if (type == GGML_TYPE_COUNT) {
+            set_error(error, "unsupported dtype '" + dtype + "' (tensor '" + name + "')");
+            return false;
+        }
+
+        if (shape.size() > SD_MAX_DIMS) {
+            set_error(error, "invalid tensor '" + name + "'");
+            return false;
+        }
+
+        int n_dims              = (int)shape.size();
+        int64_t ne[SD_MAX_DIMS] = {1, 1, 1, 1, 1};
+        for (int i = 0; i < n_dims; i++) {
+            ne[i] = shape[i].get<int64_t>();
+        }
+
+        if (n_dims == 5) {
+            n_dims = 4;
+            ne[0]  = ne[0] * ne[1];
+            ne[1]  = ne[2];
+            ne[2]  = ne[3];
+            ne[3]  = ne[4];
+        }
+
+        // ggml_n_dims returns 1 for scalars
+        if (n_dims == 0) {
+            n_dims = 1;
+        }
+
+        TensorStorage tensor_storage(name, type, ne, n_dims, 0, ST_HEADER_SIZE_LEN + header_size_ + begin);
+        tensor_storage.reverse_ne();
+
+        size_t tensor_data_size = end - begin;
+
+        bool tensor_size_ok;
+        if (dtype == "F8_E4M3") {
+            tensor_storage.is_f8_e4m3 = true;
+            // f8 -> f16
+            tensor_size_ok = (tensor_storage.nbytes() == tensor_data_size * 2);
+        } else if (dtype == "F8_E5M2") {
+            tensor_storage.is_f8_e5m2 = true;
+            // f8 -> f16
+            tensor_size_ok = (tensor_storage.nbytes() == tensor_data_size * 2);
+        } else if (dtype == "F64") {
+            tensor_storage.is_f64 = true;
+            // f64 -> f32
+            tensor_size_ok = (tensor_storage.nbytes() * 2 == tensor_data_size);
+        } else if (dtype == "I64") {
+            tensor_storage.is_i64 = true;
+            // i64 -> i32
+            tensor_size_ok = (tensor_storage.nbytes() * 2 == tensor_data_size);
+        } else {
+            tensor_size_ok = (tensor_storage.nbytes() == tensor_data_size);
+        }
+        if (!tensor_size_ok) {
+            set_error(error, "size mismatch for tensor '" + name + "' (" + dtype + ")");
+            return false;
+        }
+
+        tensor_storages.push_back(tensor_storage);
+
+        // LOG_DEBUG("%s %s", tensor_storage.to_string().c_str(), dtype.c_str());
+    }
+
+    return true;
+}
+
+static bool ggml_type_to_safetensors_dtype(ggml_type type, std::string* dtype) {
+    switch (type) {
+        case GGML_TYPE_F16:
+            *dtype = "F16";
+            return true;
+        case GGML_TYPE_BF16:
+            *dtype = "BF16";
+            return true;
+        case GGML_TYPE_F32:
+            *dtype = "F32";
+            return true;
+        case GGML_TYPE_I32:
+            *dtype = "I32";
+            return true;
+        default:
+            return false;
+    }
+}
+
+bool write_safetensors_file(const std::string& file_path,
+                            const std::vector<TensorWriteInfo>& tensors,
+                            std::string* error) {
+    nlohmann::ordered_json header = nlohmann::ordered_json::object();
+
+    uint64_t data_offset = 0;
+    for (const TensorWriteInfo& write_tensor : tensors) {
+        ggml_tensor* tensor = write_tensor.tensor;
+        if (tensor == nullptr) {
+            set_error(error, "null tensor cannot be written to safetensors");
+            return false;
+        }
+
+        const std::string name = ggml_get_name(tensor);
+        std::string dtype;
+        if (!ggml_type_to_safetensors_dtype(tensor->type, &dtype)) {
+            set_error(error,
+                      "unsupported safetensors dtype '" + std::string(ggml_type_name(tensor->type)) +
+                          "' for tensor '" + name + "'");
+            return false;
+        }
+
+        const uint64_t tensor_nbytes = ggml_nbytes(tensor);
+
+        nlohmann::ordered_json json_tensor_info = nlohmann::ordered_json::object();
+        json_tensor_info["dtype"]               = dtype;
+
+        nlohmann::ordered_json shape = nlohmann::ordered_json::array();
+        for (int i = 0; i < write_tensor.n_dims; ++i) {
+            shape.push_back(write_tensor.ne[write_tensor.n_dims - 1 - i]);
+        }
+        json_tensor_info["shape"] = shape;
+
+        nlohmann::ordered_json data_offsets = nlohmann::ordered_json::array();
+        data_offsets.push_back(data_offset);
+        data_offsets.push_back(data_offset + tensor_nbytes);
+        json_tensor_info["data_offsets"] = data_offsets;
+
+        header[name] = json_tensor_info;
+        data_offset += tensor_nbytes;
+    }
+
+    const std::string header_str = header.dump();
+
+    std::ofstream file(file_path, std::ios::binary);
+    if (!file.is_open()) {
+        set_error(error, "failed to open '" + file_path + "' for writing");
+        return false;
+    }
+
+    LOG_INFO("trying to save tensors to %s", file_path.c_str());
+    model_io::write_u64(file, header_str.size());
+    file.write(header_str.data(), header_str.size());
+    if (!file) {
+        set_error(error, "failed to write safetensors header to '" + file_path + "'");
+        return false;
+    }
+
+    for (const TensorWriteInfo& write_tensor : tensors) {
+        ggml_tensor* tensor        = write_tensor.tensor;
+        const std::string name     = ggml_get_name(tensor);
+        const size_t tensor_nbytes = ggml_nbytes(tensor);
+        file.write((const char*)tensor->data, tensor_nbytes);
+        if (!file) {
+            set_error(error,
+                      "failed to write tensor '" + name + "' to '" + file_path + "'");
+            return false;
+        }
+    }
+
+    return true;
+}

src/model_io/safetensors_io.h

@@ -0,0 +1,17 @@
+#ifndef __SD_MODEL_IO_SAFETENSORS_IO_H__
+#define __SD_MODEL_IO_SAFETENSORS_IO_H__
+
+#include <string>
+#include <vector>
+
+#include "tensor_storage.h"
+
+bool is_safetensors_file(const std::string& file_path);
+bool read_safetensors_file(const std::string& file_path,
+                           std::vector<TensorStorage>& tensor_storages,
+                           std::string* error = nullptr);
+bool write_safetensors_file(const std::string& file_path,
+                            const std::vector<TensorWriteInfo>& tensors,
+                            std::string* error = nullptr);
+
+#endif  // __SD_MODEL_IO_SAFETENSORS_IO_H__

src/model_io/tensor_storage.h

@@ -0,0 +1,132 @@
+#ifndef __SD_TENSOR_STORAGE_H__
+#define __SD_TENSOR_STORAGE_H__
+
+#include <cstddef>
+#include <cstdint>
+#include <functional>
+#include <sstream>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "ggml.h"
+
+#define SD_MAX_DIMS 5
+
+struct TensorStorage {
+    std::string name;
+    ggml_type type          = GGML_TYPE_F32;
+    ggml_type expected_type = GGML_TYPE_COUNT;
+    bool is_f8_e4m3         = false;
+    bool is_f8_e5m2         = false;
+    bool is_f64             = false;
+    bool is_i64             = false;
+    int64_t ne[SD_MAX_DIMS] = {1, 1, 1, 1, 1};
+    int n_dims              = 0;
+
+    std::string storage_key;
+    size_t file_index = 0;
+    int index_in_zip  = -1;  // >= means stored in a zip file
+    uint64_t offset   = 0;   // offset in file
+
+    TensorStorage() = default;
+
+    TensorStorage(std::string name, ggml_type type, const int64_t* ne, int n_dims, size_t file_index, size_t offset = 0)
+        : name(std::move(name)), type(type), n_dims(n_dims), file_index(file_index), offset(offset) {
+        for (int i = 0; i < n_dims; i++) {
+            this->ne[i] = ne[i];
+        }
+    }
+
+    int64_t nelements() const {
+        int64_t n = 1;
+        for (int i = 0; i < SD_MAX_DIMS; i++) {
+            n *= ne[i];
+        }
+        return n;
+    }
+
+    int64_t nbytes() const {
+        return nelements() * ggml_type_size(type) / ggml_blck_size(type);
+    }
+
+    int64_t nbytes_to_read() const {
+        if (is_f8_e4m3 || is_f8_e5m2) {
+            return nbytes() / 2;
+        } else if (is_f64 || is_i64) {
+            return nbytes() * 2;
+        } else {
+            return nbytes();
+        }
+    }
+
+    void unsqueeze() {
+        if (n_dims == 2) {
+            n_dims = 4;
+            ne[3]  = ne[1];
+            ne[2]  = ne[0];
+            ne[1]  = 1;
+            ne[0]  = 1;
+        }
+    }
+
+    std::vector<TensorStorage> chunk(size_t n) {
+        std::vector<TensorStorage> chunks;
+        uint64_t chunk_size = nbytes_to_read() / n;
+        // printf("%d/%d\n", chunk_size, nbytes_to_read());
+        reverse_ne();
+        for (size_t i = 0; i < n; i++) {
+            TensorStorage chunk_i = *this;
+            chunk_i.ne[0]         = ne[0] / n;
+            chunk_i.offset        = offset + i * chunk_size;
+            chunk_i.reverse_ne();
+            chunks.push_back(chunk_i);
+        }
+        reverse_ne();
+        return chunks;
+    }
+
+    void reverse_ne() {
+        int64_t new_ne[SD_MAX_DIMS] = {1, 1, 1, 1, 1};
+        for (int i = 0; i < n_dims; i++) {
+            new_ne[i] = ne[n_dims - 1 - i];
+        }
+        for (int i = 0; i < n_dims; i++) {
+            ne[i] = new_ne[i];
+        }
+    }
+
+    std::string to_string() const {
+        std::stringstream ss;
+        const char* type_name = ggml_type_name(type);
+        if (is_f8_e4m3) {
+            type_name = "f8_e4m3";
+        } else if (is_f8_e5m2) {
+            type_name = "f8_e5m2";
+        } else if (is_f64) {
+            type_name = "f64";
+        } else if (is_i64) {
+            type_name = "i64";
+        }
+        ss << name << " | " << type_name << " | ";
+        ss << n_dims << " [";
+        for (int i = 0; i < SD_MAX_DIMS; i++) {
+            ss << ne[i];
+            if (i != SD_MAX_DIMS - 1) {
+                ss << ", ";
+            }
+        }
+        ss << "]";
+        return ss.str();
+    }
+};
+
+struct TensorWriteInfo {
+    int64_t ne[SD_MAX_DIMS] = {1, 1, 1, 1, 1};
+    int n_dims              = 0;
+    ggml_tensor* tensor     = nullptr;
+};
+
+typedef std::function<bool(const TensorStorage&, ggml_tensor**)> on_new_tensor_cb_t;
+
+#endif  // __SD_TENSOR_STORAGE_H__

src/model_io/torch_legacy_io.cpp

@@ -0,0 +1,252 @@
+#include "torch_legacy_io.h"
+
+#include <algorithm>
+#include <cstdint>
+#include <fstream>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+#include "pickle_io.h"
+#include "util.h"
+
+// torch.save format background:
+//
+//   - Before PyTorch 1.6.0, torch.save used this legacy non-zip format by
+//     default.
+//   - Since PyTorch 1.6.0, torch.save defaults to an uncompressed ZIP64 archive
+//     containing data.pkl, data/, version, and, since PyTorch 2.1.0, byteorder.
+//   - The old format can still be produced explicitly with:
+//       torch.save(obj, path, _use_new_zipfile_serialization=False)
+//
+// Whether obj is a state_dict or a whole nn.Module does not change the outer
+// container format selected by torch.save. It changes the pickled object inside:
+//
+//   - state_dict: usually an OrderedDict[str, Tensor]. pickle_io.cpp supports a
+//     restricted subset of this layout because tensor metadata and raw storages
+//     can be recovered without executing pickle callables.
+//   - whole module/checkpoint object: arbitrary Python object graph. This may
+//     require importing user classes and executing pickle GLOBAL/REDUCE rebuild
+//     logic, so it is intentionally not supported here.
+//
+// Legacy non-zip PyTorch files are not a single pickle object:
+//
+//   1. pickle object: PyTorch legacy magic number
+//   2. pickle object: legacy protocol version, expected to be 1001
+//   3. pickle object: sys_info metadata, ignored by this reader
+//   4. pickle object: state_dict metadata, parsed by pickle_io.cpp
+//   5. pickle object: serialized storage key list, skipped here
+//   6. raw storage data payloads
+//      - PyTorch writes storages after the pickles, ordered by storage key
+//      - each storage has an 8-byte legacy storage header followed by raw bytes
+static constexpr size_t LEGACY_STORAGE_HEADER_SIZE = 8;
+
+static void set_error(std::string* error, const std::string& message) {
+    if (error != nullptr) {
+        *error = message;
+    }
+}
+
+static std::string bytes_to_hex(const std::vector<uint8_t>& bytes) {
+    static const char* hex = "0123456789ABCDEF";
+    std::string result;
+    result.reserve(bytes.size() * 3);
+    for (size_t i = 0; i < bytes.size(); ++i) {
+        if (i > 0) {
+            result.push_back('-');
+        }
+        result.push_back(hex[(bytes[i] >> 4) & 0x0F]);
+        result.push_back(hex[bytes[i] & 0x0F]);
+    }
+    return result;
+}
+
+static bool is_probably_tar_file(const std::vector<uint8_t>& header) {
+    return header.size() >= 262 &&
+           header[257] == 'u' &&
+           header[258] == 's' &&
+           header[259] == 't' &&
+           header[260] == 'a' &&
+           header[261] == 'r';
+}
+
+static std::string torch_legacy_diagnostics(const std::string& file_path, const std::vector<uint8_t>& buffer) {
+    if (!ends_with(file_path, ".pt") && !ends_with(file_path, ".pth")) {
+        return "";
+    }
+    if (buffer.empty()) {
+        return "unsupported PyTorch file '" + file_path + "': empty file";
+    }
+
+    size_t short_len = std::min<size_t>(buffer.size(), 32);
+    std::vector<uint8_t> short_header(buffer.begin(), buffer.begin() + short_len);
+    const bool raw_pickle = buffer[0] == 0x80;
+    const bool tar_file   = is_probably_tar_file(buffer);
+
+    std::string message = "unsupported PyTorch file '" + file_path + "': first bytes " +
+                          bytes_to_hex(short_header) +
+                          ", raw_pickle=" + (raw_pickle ? "true" : "false") +
+                          ", tar=" + (tar_file ? "true" : "false");
+    if (raw_pickle) {
+        message += "; raw pickle did not match the restricted state_dict layouts currently supported";
+    } else if (tar_file) {
+        message += "; legacy tar PyTorch checkpoints are not supported yet";
+    }
+    return message;
+}
+
+bool read_torch_legacy_file(const std::string& file_path,
+                            std::vector<TensorStorage>& tensor_storages,
+                            std::string* error) {
+    std::ifstream file(file_path, std::ios::binary);
+    if (!file.is_open()) {
+        set_error(error, "failed to open '" + file_path + "'");
+        return false;
+    }
+
+    file.seekg(0, file.end);
+    size_t file_size = (size_t)file.tellg();
+    file.seekg(0, file.beg);
+    if (file_size == 0) {
+        set_error(error, "empty file '" + file_path + "'");
+        return false;
+    }
+
+    std::vector<uint8_t> buffer(file_size);
+    file.read((char*)buffer.data(), file_size);
+    if (!file) {
+        set_error(error, "failed to read '" + file_path + "'");
+        return false;
+    }
+
+    auto finalize_tensor_offsets = [&](size_t storage_data_offset,
+                                       const std::unordered_map<std::string, uint64_t>& legacy_storage_map) -> bool {
+        if (storage_data_offset > file_size) {
+            return false;
+        }
+
+        std::vector<std::string> storage_keys;
+        storage_keys.reserve(legacy_storage_map.size());
+        for (const auto& [storage_key, _] : legacy_storage_map) {
+            storage_keys.push_back(storage_key);
+        }
+        std::sort(storage_keys.begin(), storage_keys.end());
+
+        std::unordered_map<std::string, uint64_t> storage_offsets;
+        uint64_t current_offset = storage_data_offset;
+        for (const auto& storage_key : storage_keys) {
+            auto it = legacy_storage_map.find(storage_key);
+            if (it == legacy_storage_map.end()) {
+                return false;
+            }
+            if (current_offset + LEGACY_STORAGE_HEADER_SIZE + it->second > file_size) {
+                return false;
+            }
+            storage_offsets[storage_key] = current_offset + LEGACY_STORAGE_HEADER_SIZE;
+            current_offset += LEGACY_STORAGE_HEADER_SIZE + it->second;
+        }
+
+        for (auto& tensor_storage : tensor_storages) {
+            if (tensor_storage.storage_key.empty()) {
+                continue;
+            }
+
+            auto it_offset = storage_offsets.find(tensor_storage.storage_key);
+            auto it_size   = legacy_storage_map.find(tensor_storage.storage_key);
+            if (it_offset == storage_offsets.end() || it_size == legacy_storage_map.end()) {
+                return false;
+            }
+
+            uint64_t base_offset    = it_offset->second;
+            uint64_t storage_nbytes = it_size->second;
+            uint64_t tensor_nbytes  = tensor_storage.nbytes_to_read();
+            if (tensor_storage.offset + tensor_nbytes > storage_nbytes) {
+                return false;
+            }
+
+            tensor_storage.offset = base_offset + tensor_storage.offset;
+            tensor_storage.storage_key.clear();
+        }
+
+        return true;
+    };
+
+    auto parse_state_dict_at = [&](size_t state_dict_offset, size_t state_dict_size, size_t* storage_data_offset) -> bool {
+        tensor_storages.clear();
+        std::unordered_map<std::string, uint64_t> legacy_storage_map;
+        if (!parse_torch_state_dict_pickle(buffer.data() + state_dict_offset,
+                                           state_dict_size,
+                                           tensor_storages,
+                                           legacy_storage_map,
+                                           error)) {
+            return false;
+        }
+
+        size_t offset_after_state_dict = state_dict_offset + state_dict_size;
+        size_t storage_keys_size       = 0;
+        if (!skip_pickle_object(buffer.data() + offset_after_state_dict,
+                                buffer.size() - offset_after_state_dict,
+                                &storage_keys_size)) {
+            return false;
+        }
+
+        *storage_data_offset = offset_after_state_dict + storage_keys_size;
+        return finalize_tensor_offsets(*storage_data_offset, legacy_storage_map);
+    };
+
+    size_t object_size_1 = 0;
+    size_t offset        = 0;
+
+    if (skip_pickle_object(buffer.data(), buffer.size(), &object_size_1) &&
+        pickle_object_is_torch_magic_number(buffer.data(), object_size_1)) {
+        offset += object_size_1;
+
+        size_t object_size_2 = 0;
+        if (!skip_pickle_object(buffer.data() + offset, buffer.size() - offset, &object_size_2)) {
+            set_error(error, torch_legacy_diagnostics(file_path, buffer));
+            return false;
+        }
+        uint32_t protocol_version = 0;
+        if (!parse_pickle_uint32_object(buffer.data() + offset, object_size_2, &protocol_version) || protocol_version != 1001) {
+            set_error(error, torch_legacy_diagnostics(file_path, buffer));
+            return false;
+        }
+        offset += object_size_2;
+
+        size_t object_size_3 = 0;
+        if (!skip_pickle_object(buffer.data() + offset, buffer.size() - offset, &object_size_3)) {
+            set_error(error, torch_legacy_diagnostics(file_path, buffer));
+            return false;
+        }
+        offset += object_size_3;
+
+        size_t state_dict_size = 0;
+        if (!skip_pickle_object(buffer.data() + offset, buffer.size() - offset, &state_dict_size)) {
+            set_error(error, torch_legacy_diagnostics(file_path, buffer));
+            return false;
+        }
+
+        size_t storage_data_offset = 0;
+        if (parse_state_dict_at(offset, state_dict_size, &storage_data_offset)) {
+            return true;
+        }
+
+        if (error != nullptr && error->empty()) {
+            set_error(error, torch_legacy_diagnostics(file_path, buffer));
+        }
+        return false;
+    }
+
+    size_t state_dict_size = 0;
+    if (skip_pickle_object(buffer.data(), buffer.size(), &state_dict_size)) {
+        size_t storage_data_offset = 0;
+        if (parse_state_dict_at(0, state_dict_size, &storage_data_offset)) {
+            return true;
+        }
+    }
+
+    if (error != nullptr && error->empty()) {
+        set_error(error, torch_legacy_diagnostics(file_path, buffer));
+    }
+    return false;
+}

src/model_io/torch_legacy_io.h

@@ -0,0 +1,13 @@
+#ifndef __SD_MODEL_IO_TORCH_LEGACY_IO_H__
+#define __SD_MODEL_IO_TORCH_LEGACY_IO_H__
+
+#include <string>
+#include <vector>
+
+#include "tensor_storage.h"
+
+bool read_torch_legacy_file(const std::string& file_path,
+                            std::vector<TensorStorage>& tensor_storages,
+                            std::string* error = nullptr);
+
+#endif  // __SD_MODEL_IO_TORCH_LEGACY_IO_H__

src/model_io/torch_zip_io.cpp

@@ -0,0 +1,140 @@
+#include "torch_zip_io.h"
+
+#include <cstdint>
+#include <cstdlib>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+#include "pickle_io.h"
+
+#include "zip.h"
+
+static void set_error(std::string* error, const std::string& message) {
+    if (error != nullptr) {
+        *error = message;
+    }
+}
+
+bool is_torch_zip_file(const std::string& file_path) {
+    zip_t* zip = zip_open(file_path.c_str(), 0, 'r');
+    if (zip == nullptr) {
+        return false;
+    }
+    zip_close(zip);
+    return true;
+}
+
+static bool find_zip_entry(zip_t* zip, const std::string& entry_name, int* index, uint64_t* size) {
+    size_t n = zip_entries_total(zip);
+    for (size_t i = 0; i < n; ++i) {
+        zip_entry_openbyindex(zip, i);
+        std::string name = zip_entry_name(zip);
+        if (name == entry_name) {
+            *index = (int)i;
+            *size  = zip_entry_size(zip);
+            zip_entry_close(zip);
+            return true;
+        }
+        zip_entry_close(zip);
+    }
+    return false;
+}
+
+static bool parse_zip_data_pkl(const uint8_t* buffer,
+                               size_t buffer_size,
+                               zip_t* zip,
+                               const std::string& dir,
+                               std::vector<TensorStorage>& tensor_storages,
+                               std::string* error) {
+    std::vector<TensorStorage> parsed_tensors;
+    std::unordered_map<std::string, uint64_t> storage_nbytes;
+    if (!parse_torch_state_dict_pickle(buffer, buffer_size, parsed_tensors, storage_nbytes, error)) {
+        if (error != nullptr && error->empty()) {
+            *error = "failed to parse torch zip pickle metadata";
+        }
+        return false;
+    }
+
+    for (auto& tensor_storage : parsed_tensors) {
+        if (tensor_storage.storage_key.empty()) {
+            set_error(error, "tensor '" + tensor_storage.name + "' has no storage key");
+            return false;
+        }
+
+        const std::string entry_name = dir + "data/" + tensor_storage.storage_key;
+        int zip_index                = -1;
+        uint64_t entry_size          = 0;
+        if (!find_zip_entry(zip, entry_name, &zip_index, &entry_size)) {
+            set_error(error, "storage entry '" + entry_name + "' was not found");
+            return false;
+        }
+
+        auto it_storage_size = storage_nbytes.find(tensor_storage.storage_key);
+        if (it_storage_size != storage_nbytes.end() && entry_size < it_storage_size->second) {
+            set_error(error, "storage entry '" + entry_name + "' is smaller than pickle metadata");
+            return false;
+        }
+
+        uint64_t tensor_nbytes = tensor_storage.nbytes_to_read();
+        if (tensor_storage.offset + tensor_nbytes > entry_size) {
+            set_error(error, "tensor '" + tensor_storage.name + "' exceeds storage entry '" + entry_name + "'");
+            return false;
+        }
+
+        tensor_storage.index_in_zip = zip_index;
+        tensor_storage.storage_key.clear();
+        tensor_storages.push_back(tensor_storage);
+    }
+
+    return true;
+}
+
+bool read_torch_zip_file(const std::string& file_path,
+                         std::vector<TensorStorage>& tensor_storages,
+                         std::string* error) {
+    zip_t* zip = zip_open(file_path.c_str(), 0, 'r');
+    if (zip == nullptr) {
+        set_error(error, "failed to open '" + file_path + "'");
+        return false;
+    }
+
+    tensor_storages.clear();
+    bool success        = true;
+    bool found_data_pkl = false;
+    int n               = (int)zip_entries_total(zip);
+    for (int i = 0; i < n; ++i) {
+        zip_entry_openbyindex(zip, i);
+        std::string name = zip_entry_name(zip);
+        size_t pos       = name.find("data.pkl");
+        if (pos != std::string::npos) {
+            found_data_pkl  = true;
+            std::string dir = name.substr(0, pos);
+            void* pkl_data  = nullptr;
+            size_t pkl_size = 0;
+            zip_entry_read(zip, &pkl_data, &pkl_size);
+
+            if (pkl_data == nullptr || pkl_size == 0) {
+                set_error(error, "failed to read '" + name + "' from '" + file_path + "'");
+                success = false;
+            } else if (!parse_zip_data_pkl((const uint8_t*)pkl_data, pkl_size, zip, dir, tensor_storages, error)) {
+                success = false;
+            }
+
+            free(pkl_data);
+        }
+        zip_entry_close(zip);
+
+        if (!success) {
+            break;
+        }
+    }
+
+    if (success && !found_data_pkl) {
+        set_error(error, "data.pkl was not found in '" + file_path + "'");
+        success = false;
+    }
+
+    zip_close(zip);
+    return success;
+}

src/model_io/torch_zip_io.h

@@ -0,0 +1,14 @@
+#ifndef __SD_MODEL_IO_TORCH_ZIP_IO_H__
+#define __SD_MODEL_IO_TORCH_ZIP_IO_H__
+
+#include <string>
+#include <vector>
+
+#include "tensor_storage.h"
+
+bool is_torch_zip_file(const std::string& file_path);
+bool read_torch_zip_file(const std::string& file_path,
+                         std::vector<TensorStorage>& tensor_storages,
+                         std::string* error = nullptr);
+
+#endif  // __SD_MODEL_IO_TORCH_ZIP_IO_H__

src/stable-diffusion.cpp

@@ -2457,8 +2457,10 @@ enum scheduler_t sd_get_default_scheduler(const sd_ctx_t* sd_ctx, enum sample_me
             return EXPONENTIAL_SCHEDULER;
         }
     }
-    if (sample_method == LCM_SAMPLE_METHOD) {
+    if (sample_method == LCM_SAMPLE_METHOD || sample_method == TCD_SAMPLE_METHOD) {
         return LCM_SCHEDULER;
+    } else if (sample_method == DDIM_TRAILING_SAMPLE_METHOD) {
+        return SIMPLE_SCHEDULER;
     }
     return DISCRETE_SCHEDULER;
 }