yindo/rocm-stable-diffusion.cpp
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feat: add wtype stat (#899)

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This commit is contained in:
leejet
2025-10-17 23:40:32 +08:00
committed by GitHub
parent b25785bc10
commit db6f4791b4
3 changed files with 60 additions and 71 deletions
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59
stable-diffusion.cpp
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@@ -86,10 +86,6 @@ public:
ggml_backend_t clip_backend = NULL;
ggml_backend_t control_net_backend = NULL;
ggml_backend_t vae_backend = NULL;
ggml_type model_wtype = GGML_TYPE_COUNT;
ggml_type conditioner_wtype = GGML_TYPE_COUNT;
ggml_type diffusion_model_wtype = GGML_TYPE_COUNT;
ggml_type vae_wtype = GGML_TYPE_COUNT;
SDVersion version;
bool vae_decode_only = false;
@@ -294,37 +290,33 @@ public:
ggml_type wtype = (int)sd_ctx_params->wtype < std::min<int>(SD_TYPE_COUNT, GGML_TYPE_COUNT)
? (ggml_type)sd_ctx_params->wtype
: GGML_TYPE_COUNT;
if (wtype == GGML_TYPE_COUNT) {
model_wtype = model_loader.get_sd_wtype();
if (model_wtype == GGML_TYPE_COUNT) {
model_wtype = GGML_TYPE_F32;
LOG_WARN("can not get mode wtype frome weight, use f32");
}
conditioner_wtype = model_loader.get_conditioner_wtype();
if (conditioner_wtype == GGML_TYPE_COUNT) {
conditioner_wtype = wtype;
}
diffusion_model_wtype = model_loader.get_diffusion_model_wtype();
if (diffusion_model_wtype == GGML_TYPE_COUNT) {
diffusion_model_wtype = wtype;
}
vae_wtype = model_loader.get_vae_wtype();
if (vae_wtype == GGML_TYPE_COUNT) {
vae_wtype = wtype;
}
} else {
model_wtype = wtype;
conditioner_wtype = wtype;
diffusion_model_wtype = wtype;
vae_wtype = wtype;
if (wtype != GGML_TYPE_COUNT) {
model_loader.set_wtype_override(wtype);
}
LOG_INFO("Weight type: %s", ggml_type_name(model_wtype));
LOG_INFO("Conditioner weight type: %s", ggml_type_name(conditioner_wtype));
LOG_INFO("Diffusion model weight type: %s", ggml_type_name(diffusion_model_wtype));
LOG_INFO("VAE weight type: %s", ggml_type_name(vae_wtype));
std::map<ggml_type, uint32_t> wtype_stat = model_loader.get_wtype_stat();
std::map<ggml_type, uint32_t> conditioner_wtype_stat = model_loader.get_conditioner_wtype_stat();
std::map<ggml_type, uint32_t> diffusion_model_wtype_stat = model_loader.get_diffusion_model_wtype_stat();
std::map<ggml_type, uint32_t> vae_wtype_stat = model_loader.get_vae_wtype_stat();
auto wtype_stat_to_str = [](const std::map<ggml_type, uint32_t>& m, int key_width = 8, int value_width = 5) -> std::string {
std::ostringstream oss;
bool first = true;
for (const auto& [type, count] : m) {
if (!first)
oss << "|";
first = false;
oss << std::right << std::setw(key_width) << ggml_type_name(type)
<< ": "
<< std::left << std::setw(value_width) << count;
}
return oss.str();
};
LOG_INFO("Weight type stat: %s", wtype_stat_to_str(wtype_stat).c_str());
LOG_INFO("Conditioner weight type stat: %s", wtype_stat_to_str(conditioner_wtype_stat).c_str());
LOG_INFO("Diffusion model weight type stat: %s", wtype_stat_to_str(diffusion_model_wtype_stat).c_str());
LOG_INFO("VAE weight type stat: %s", wtype_stat_to_str(vae_wtype_stat).c_str());
LOG_DEBUG("ggml tensor size = %d bytes", (int)sizeof(ggml_tensor));
@@ -938,9 +930,6 @@ public:
}
void apply_loras(const std::unordered_map<std::string, float>& lora_state) {
if (lora_state.size() > 0 && model_wtype != GGML_TYPE_F16 && model_wtype != GGML_TYPE_F32) {
LOG_WARN("In quantized models when applying LoRA, the images have poor quality.");
}
std::unordered_map<std::string, float> lora_state_diff;
for (auto& kv : lora_state) {
const std::string& lora_name = kv.first;