upstream-archive/stable-diffusion.cpp
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feat: support building with upstream ggml (#1999)

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leejet
2026-09-19 22:25:01 +08:00
committed by GitHub
parent 17860c0e45
commit 1330cebae8
16 changed files with 167 additions and 18 deletions
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src/core/util.cpp
+30 -1
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@@ -414,14 +414,43 @@ std::vector<std::string> split_string(const std::string& str, char delimiter) {
}
ggml_type sd_type_to_ggml_type(sd_type_t sdtype) {
if (sdtype == SD_TYPE_F8_E4M3 || sdtype == SD_TYPE_F8_E5M2) {
#ifndef SD_USE_UPSTREAM_GGML
return sdtype == SD_TYPE_F8_E4M3 ? GGML_TYPE_F8_E4M3 : GGML_TYPE_F8_E5M2;
#else
return GGML_TYPE_COUNT;
#endif
}
const int type_value = static_cast<int>(sdtype);
if (type_value < std::min<int>(SD_TYPE_COUNT, GGML_TYPE_COUNT)) {
if (type_value >= 0 && type_value < std::min<int>(SD_TYPE_COUNT, GGML_TYPE_COUNT)) {
return static_cast<ggml_type>(type_value);
} else {
return GGML_TYPE_COUNT;
}
}
bool validate_tensor_types(sd_type_t type, const char* tensor_type_rules) {
if (type != SD_TYPE_COUNT && sd_type_to_ggml_type(type) == GGML_TYPE_COUNT) {
LOG_ERROR("weight type %s is not supported by this ggml build", sd_type_name(type));
return false;
}
#ifdef SD_USE_UPSTREAM_GGML
for (const auto& rule : split_string(SAFE_STR(tensor_type_rules), ',')) {
const auto pos = rule.find('=');
if (pos != std::string::npos) {
const auto name = rule.substr(pos + 1);
if (name == "f8_e4m3" || name == "f8_e5m2") {
LOG_ERROR("FP8 is not supported by this ggml build (tensor type rule '%s')", rule.c_str());
return false;
}
}
}
#else
GGML_UNUSED(tensor_type_rules);
#endif
return true;
}
KeyValueArgs parse_key_value_args(const char* args, const char* context) {
KeyValueArgs pairs;