DarthAffe/stable-diffusion.cpp
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feat: add verbose logging and log-level selection (#1941)

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leejet authored and GitHub committed 2026-09-06 23:14:03 +08:00
1 parent 462d675018
commit dbb611264e
69 files changed
+551 -484
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src/model/te/llm.hpp
+14 -14
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@@ -319,11 +319,11 @@ namespace LLM {
config.vision.deepstack_visual_indexes = {8, 16, 24};
}
}
LOG_DEBUG("llm: num_layers = %" PRId64 ", vocab_size = %" PRId64 ", hidden_size = %" PRId64 ", intermediate_size = %" PRId64,
config.num_layers,
config.vocab_size,
config.hidden_size,
config.intermediate_size);
LOG_VERBOSE("llm: num_layers = %" PRId64 ", vocab_size = %" PRId64 ", hidden_size = %" PRId64 ", intermediate_size = %" PRId64,
config.num_layers,
config.vocab_size,
config.hidden_size,
config.intermediate_size);
return config;
}
};
@@ -1887,9 +1887,9 @@ namespace LLM {
enable_vision = false;
}
if (enable_vision) {
LOG_DEBUG("enable llm vision");
LOG_VERBOSE("enable llm vision");
if (config.llama_cpp_style) {
LOG_DEBUG("llama.cpp style vision weight");
LOG_VERBOSE("llama.cpp style vision weight");
}
}
model = LLM(config, enable_vision, config.llama_cpp_style);
@@ -2375,7 +2375,7 @@ namespace LLM {
ss << "['" << item.first << "', " << item.second << "], ";
}
ss << "]";
LOG_DEBUG("parse '%s' to %s", text.c_str(), ss.str().c_str());
LOG_VERBOSE("parse '%s' to %s", text.c_str(), ss.str().c_str());
}
std::vector<int> tokens;
@@ -2426,7 +2426,7 @@ namespace LLM {
out = std::move(out_opt);
print_sd_tensor(out, false, "image_embed");
image_embed = out;
LOG_DEBUG("llm encode_image test done in %lldms", t1 - t0);
LOG_VERBOSE("llm encode_image test done in %lldms", t1 - t0);
}
std::string placeholder = "<|image_pad|>";
@@ -2466,7 +2466,7 @@ namespace LLM {
GGML_ASSERT(!out_opt.empty());
out = std::move(out_opt);
print_sd_tensor(out);
LOG_DEBUG("llm test done in %lldms", t1 - t0);
LOG_VERBOSE("llm test done in %lldms", t1 - t0);
} else if (test_vit) {
// auto image = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, 280, 280, 3);
// ggml_set_f32(image, 0.f);
@@ -2485,7 +2485,7 @@ namespace LLM {
// auto ref_out = load_tensor_from_file(ctx, "qwen2vl.bin");
// ggml_ext_tensor_diff(ref_out, out, 0.01f);
LOG_DEBUG("llm test done in %lldms", t1 - t0);
LOG_VERBOSE("llm test done in %lldms", t1 - t0);
} else if (test_mistral) {
std::pair<int, int> prompt_attn_range;
std::string text = "[SYSTEM_PROMPT]You are an AI that reasons about image descriptions. You give structured responses focusing on object relationships, object\nattribution and actions without speculation.[/SYSTEM_PROMPT][INST]";
@@ -2510,7 +2510,7 @@ namespace LLM {
GGML_ASSERT(!out_opt.empty());
out = std::move(out_opt);
print_sd_tensor(out);
LOG_DEBUG("llm test done in %lldms", t1 - t0);
LOG_VERBOSE("llm test done in %lldms", t1 - t0);
} else if (test_qwen3) {
std::pair<int, int> prompt_attn_range;
std::string text = "<|im_start|>user\n";
@@ -2535,7 +2535,7 @@ namespace LLM {
GGML_ASSERT(!out_opt.empty());
out = std::move(out_opt);
print_sd_tensor(out);
LOG_DEBUG("llm test done in %lldms", t1 - t0);
LOG_VERBOSE("llm test done in %lldms", t1 - t0);
} else {
std::pair<int, int> prompt_attn_range;
std::string text = "<|im_start|>system\nDescribe the image by detailing the color, shape, size, texture, quantity, text, spatial relationships of the objects and background:<|im_end|>\n<|im_start|>user\n";
@@ -2560,7 +2560,7 @@ namespace LLM {
GGML_ASSERT(!out_opt.empty());
out = std::move(out_opt);
print_sd_tensor(out);
LOG_DEBUG("llm test done in %lldms", t1 - t0);
LOG_VERBOSE("llm test done in %lldms", t1 - t0);
}
}