DarthAffe/stable-diffusion.cpp
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refactor: route all runner params through model manager (#1649)

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leejet authored and GitHub committed 2026-06-14 02:05:23 +08:00
1 parent 9b0fceb41b
commit 8d4c7af95b
36 files changed
+596 -677
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src/conditioning/conditioner.hpp
+48 -98
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@@ -1,4 +1,4 @@
#ifndef __SD_CONDITIONING_CONDITIONER_HPP__
#ifndef __SD_CONDITIONING_CONDITIONER_HPP__
#define __SD_CONDITIONING_CONDITIONER_HPP__
#include <cmath>
@@ -118,7 +118,6 @@ public:
virtual void set_stream_layers_enabled(bool enabled) {}
virtual void set_flash_attention_enabled(bool enabled) = 0;
virtual void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) {}
virtual void set_weight_manager(const std::shared_ptr<RunnerWeightManager>& manager) {}
virtual void runner_done() {}
};
@@ -137,10 +136,10 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
std::map<std::string, std::pair<int, int>> embedding_pos_map;
FrozenCLIPEmbedderWithCustomWords(ggml_backend_t backend,
ggml_backend_t params_backend,
const String2TensorStorage& tensor_storage_map,
const std::map<std::string, std::string>& orig_embedding_map,
SDVersion version = VERSION_SD1)
SDVersion version = VERSION_SD1,
std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
: version(version), tokenizer(sd_version_is_sd2(version) ? 0 : 49407) {
for (const auto& kv : orig_embedding_map) {
std::string name = kv.first;
@@ -150,12 +149,12 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
}
bool force_clip_f32 = !embedding_map.empty();
if (sd_version_is_sd1(version)) {
text_model = std::make_shared<CLIPTextModelRunner>(backend, params_backend, tensor_storage_map, "cond_stage_model.transformer.text_model", OPENAI_CLIP_VIT_L_14, true, force_clip_f32);
text_model = std::make_shared<CLIPTextModelRunner>(backend, tensor_storage_map, "cond_stage_model.transformer.text_model", OPENAI_CLIP_VIT_L_14, true, force_clip_f32, weight_manager);
} else if (sd_version_is_sd2(version)) {
text_model = std::make_shared<CLIPTextModelRunner>(backend, params_backend, tensor_storage_map, "cond_stage_model.transformer.text_model", OPEN_CLIP_VIT_H_14, true, force_clip_f32);
text_model = std::make_shared<CLIPTextModelRunner>(backend, tensor_storage_map, "cond_stage_model.transformer.text_model", OPEN_CLIP_VIT_H_14, true, force_clip_f32, weight_manager);
} else if (sd_version_is_sdxl(version)) {
text_model = std::make_shared<CLIPTextModelRunner>(backend, params_backend, tensor_storage_map, "cond_stage_model.transformer.text_model", OPENAI_CLIP_VIT_L_14, false, force_clip_f32);
text_model2 = std::make_shared<CLIPTextModelRunner>(backend, params_backend, tensor_storage_map, "cond_stage_model.1.transformer.text_model", OPEN_CLIP_VIT_BIGG_14, false, force_clip_f32);
text_model = std::make_shared<CLIPTextModelRunner>(backend, tensor_storage_map, "cond_stage_model.transformer.text_model", OPENAI_CLIP_VIT_L_14, false, force_clip_f32, weight_manager);
text_model2 = std::make_shared<CLIPTextModelRunner>(backend, tensor_storage_map, "cond_stage_model.1.transformer.text_model", OPEN_CLIP_VIT_BIGG_14, false, force_clip_f32, weight_manager);
}
}
@@ -194,13 +193,6 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
}
}
void set_weight_manager(const std::shared_ptr<RunnerWeightManager>& manager) override {
text_model->set_weight_manager(manager);
if (sd_version_is_sdxl(version)) {
text_model2->set_weight_manager(manager);
}
}
void runner_done() override {
text_model->runner_done();
if (sd_version_is_sdxl(version)) {
@@ -522,9 +514,9 @@ struct FrozenCLIPVisionEmbedder : public GGMLRunner {
std::string weight_prefix = "cond_stage_model.transformer";
FrozenCLIPVisionEmbedder(ggml_backend_t backend,
ggml_backend_t params_backend,
const String2TensorStorage& tensor_storage_map = {})
: GGMLRunner(backend, params_backend) {
const String2TensorStorage& tensor_storage_map = {},
std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
: GGMLRunner(backend, weight_manager) {
bool proj_in = false;
for (const auto& [name, tensor_storage] : tensor_storage_map) {
if (!starts_with(name, weight_prefix)) {
@@ -580,8 +572,8 @@ struct SD3CLIPEmbedder : public Conditioner {
std::shared_ptr<T5Runner> t5;
SD3CLIPEmbedder(ggml_backend_t backend,
ggml_backend_t params_backend,
const String2TensorStorage& tensor_storage_map = {})
const String2TensorStorage& tensor_storage_map = {},
std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
: clip_g_tokenizer(0) {
bool use_clip_l = false;
bool use_clip_g = false;
@@ -600,13 +592,13 @@ struct SD3CLIPEmbedder : public Conditioner {
return;
}
if (use_clip_l) {
clip_l = std::make_shared<CLIPTextModelRunner>(backend, params_backend, tensor_storage_map, "text_encoders.clip_l.transformer.text_model", OPENAI_CLIP_VIT_L_14, false);
clip_l = std::make_shared<CLIPTextModelRunner>(backend, tensor_storage_map, "text_encoders.clip_l.transformer.text_model", OPENAI_CLIP_VIT_L_14, false, false, weight_manager);
}
if (use_clip_g) {
clip_g = std::make_shared<CLIPTextModelRunner>(backend, params_backend, tensor_storage_map, "text_encoders.clip_g.transformer.text_model", OPEN_CLIP_VIT_BIGG_14, false);
clip_g = std::make_shared<CLIPTextModelRunner>(backend, tensor_storage_map, "text_encoders.clip_g.transformer.text_model", OPEN_CLIP_VIT_BIGG_14, false, false, weight_manager);
}
if (use_t5) {
t5 = std::make_shared<T5Runner>(backend, params_backend, tensor_storage_map, "text_encoders.t5xxl.transformer");
t5 = std::make_shared<T5Runner>(backend, tensor_storage_map, "text_encoders.t5xxl.transformer", false, weight_manager);
}
}
@@ -670,18 +662,6 @@ struct SD3CLIPEmbedder : public Conditioner {
}
}
void set_weight_manager(const std::shared_ptr<RunnerWeightManager>& manager) override {
if (clip_l) {
clip_l->set_weight_manager(manager);
}
if (clip_g) {
clip_g->set_weight_manager(manager);
}
if (t5) {
t5->set_weight_manager(manager);
}
}
void runner_done() override {
if (clip_l) {
clip_l->runner_done();
@@ -961,8 +941,8 @@ struct FluxCLIPEmbedder : public Conditioner {
size_t chunk_len = 256;
FluxCLIPEmbedder(ggml_backend_t backend,
ggml_backend_t params_backend,
const String2TensorStorage& tensor_storage_map = {}) {
const String2TensorStorage& tensor_storage_map = {},
std::shared_ptr<RunnerWeightManager> weight_manager = nullptr) {
bool use_clip_l = false;
bool use_t5 = false;
for (auto pair : tensor_storage_map) {
@@ -979,12 +959,12 @@ struct FluxCLIPEmbedder : public Conditioner {
}
if (use_clip_l) {
clip_l = std::make_shared<CLIPTextModelRunner>(backend, params_backend, tensor_storage_map, "text_encoders.clip_l.transformer.text_model", OPENAI_CLIP_VIT_L_14, true);
clip_l = std::make_shared<CLIPTextModelRunner>(backend, tensor_storage_map, "text_encoders.clip_l.transformer.text_model", OPENAI_CLIP_VIT_L_14, true, false, weight_manager);
} else {
LOG_WARN("clip_l text encoder not found! Prompt adherence might be degraded.");
}
if (use_t5) {
t5 = std::make_shared<T5Runner>(backend, params_backend, tensor_storage_map, "text_encoders.t5xxl.transformer");
t5 = std::make_shared<T5Runner>(backend, tensor_storage_map, "text_encoders.t5xxl.transformer", false, weight_manager);
} else {
LOG_WARN("t5xxl text encoder not found! Prompt adherence might be degraded.");
}
@@ -1035,15 +1015,6 @@ struct FluxCLIPEmbedder : public Conditioner {
}
}
void set_weight_manager(const std::shared_ptr<RunnerWeightManager>& manager) override {
if (clip_l) {
clip_l->set_weight_manager(manager);
}
if (t5) {
t5->set_weight_manager(manager);
}
}
void runner_done() override {
if (clip_l) {
clip_l->runner_done();
@@ -1219,11 +1190,11 @@ struct T5CLIPEmbedder : public Conditioner {
bool is_umt5 = false;
T5CLIPEmbedder(ggml_backend_t backend,
ggml_backend_t params_backend,
const String2TensorStorage& tensor_storage_map = {},
bool use_mask = false,
int mask_pad = 0,
bool is_umt5 = false)
const String2TensorStorage& tensor_storage_map = {},
bool use_mask = false,
int mask_pad = 0,
bool is_umt5 = false,
std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
: use_mask(use_mask), mask_pad(mask_pad), t5_tokenizer(is_umt5) {
bool use_t5 = false;
for (auto pair : tensor_storage_map) {
@@ -1236,7 +1207,7 @@ struct T5CLIPEmbedder : public Conditioner {
LOG_WARN("IMPORTANT NOTICE: No text encoders provided, cannot process prompts!");
return;
} else {
t5 = std::make_shared<T5Runner>(backend, params_backend, tensor_storage_map, "text_encoders.t5xxl.transformer", is_umt5);
t5 = std::make_shared<T5Runner>(backend, tensor_storage_map, "text_encoders.t5xxl.transformer", is_umt5, weight_manager);
}
}
@@ -1270,12 +1241,6 @@ struct T5CLIPEmbedder : public Conditioner {
}
}
void set_weight_manager(const std::shared_ptr<RunnerWeightManager>& manager) override {
if (t5) {
t5->set_weight_manager(manager);
}
}
void runner_done() override {
if (t5) {
t5->runner_done();
@@ -1422,15 +1387,15 @@ struct AnimaConditioner : public Conditioner {
std::shared_ptr<LLM::LLMRunner> llm;
AnimaConditioner(ggml_backend_t backend,
ggml_backend_t params_backend,
const String2TensorStorage& tensor_storage_map = {}) {
const String2TensorStorage& tensor_storage_map = {},
std::shared_ptr<RunnerWeightManager> weight_manager = nullptr) {
qwen_tokenizer = std::make_shared<Qwen2Tokenizer>();
llm = std::make_shared<LLM::LLMRunner>(LLM::LLMArch::QWEN3,
backend,
params_backend,
tensor_storage_map,
"text_encoders.llm",
false);
false,
weight_manager);
}
void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
@@ -1453,10 +1418,6 @@ struct AnimaConditioner : public Conditioner {
llm->set_weight_adapter(adapter);
}
void set_weight_manager(const std::shared_ptr<RunnerWeightManager>& manager) override {
llm->set_weight_manager(manager);
}
void runner_done() override {
llm->runner_done();
}
@@ -1545,11 +1506,11 @@ struct LLMEmbedder : public Conditioner {
std::shared_ptr<LLM::LLMRunner> llm;
LLMEmbedder(ggml_backend_t backend,
ggml_backend_t params_backend,
const String2TensorStorage& tensor_storage_map = {},
SDVersion version = VERSION_QWEN_IMAGE,
const std::string prefix = "",
bool enable_vision = false)
const String2TensorStorage& tensor_storage_map = {},
SDVersion version = VERSION_QWEN_IMAGE,
const std::string prefix = "",
bool enable_vision = false,
std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
: version(version) {
LLM::LLMArch arch = LLM::LLMArch::QWEN2_5_VL;
if (version == VERSION_FLUX2) {
@@ -1576,10 +1537,10 @@ struct LLMEmbedder : public Conditioner {
}
llm = std::make_shared<LLM::LLMRunner>(arch,
backend,
params_backend,
tensor_storage_map,
"text_encoders.llm",
enable_vision);
enable_vision,
weight_manager);
}
void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
@@ -1604,12 +1565,6 @@ struct LLMEmbedder : public Conditioner {
}
}
void set_weight_manager(const std::shared_ptr<RunnerWeightManager>& manager) override {
if (llm) {
llm->set_weight_manager(manager);
}
}
void runner_done() override {
if (llm) {
llm->runner_done();
@@ -2106,10 +2061,10 @@ struct LTXAVTextProjectionRunner : public GGMLRunner {
LTXAVTextProjection model;
LTXAVTextProjectionRunner(ggml_backend_t backend,
ggml_backend_t params_backend,
const String2TensorStorage& tensor_storage_map = {},
const std::string& prefix = "")
: GGMLRunner(backend, params_backend),
const String2TensorStorage& tensor_storage_map = {},
const std::string& prefix = "",
std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
: GGMLRunner(backend, weight_manager),
model(tensor_storage_map.find(prefix + ".video_aggregate_embed.weight") != tensor_storage_map.end()) {
model.init(params_ctx, tensor_storage_map, prefix);
}
@@ -2154,22 +2109,22 @@ struct LTXAVEmbedder : public Conditioner {
bool dual_projection = false;
LTXAVEmbedder(ggml_backend_t backend,
ggml_backend_t params_backend,
const String2TensorStorage& tensor_storage_map = {},
const std::string& llm_prefix = "text_encoders.llm",
const std::string& projector_prefix = "text_embedding_projection") {
const String2TensorStorage& tensor_storage_map = {},
const std::string& llm_prefix = "text_encoders.llm",
const std::string& projector_prefix = "text_embedding_projection",
std::shared_ptr<RunnerWeightManager> weight_manager = nullptr) {
tokenizer = std::make_shared<GemmaTokenizer>();
llm = std::make_shared<LLM::LLMRunner>(LLM::LLMArch::GEMMA3_12B,
backend,
params_backend,
tensor_storage_map,
llm_prefix,
false);
false,
weight_manager);
dual_projection = tensor_storage_map.find(projector_prefix + ".video_aggregate_embed.weight") != tensor_storage_map.end();
projector = std::make_shared<LTXAVTextProjectionRunner>(backend,
params_backend,
tensor_storage_map,
projector_prefix);
projector_prefix,
weight_manager);
}
void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
@@ -2192,11 +2147,6 @@ struct LTXAVEmbedder : public Conditioner {
projector->set_weight_adapter(adapter);
}
void set_weight_manager(const std::shared_ptr<RunnerWeightManager>& manager) override {
llm->set_weight_manager(manager);
projector->set_weight_manager(manager);
}
void runner_done() override {
llm->runner_done();
projector->runner_done();